SOP for pathway inference in Integrated Microbial Genomes (IMG).
Anderson, Iain; Chen, Amy; Markowitz, Victor; Kyrpides, Nikos; Ivanova, Natalia
2011-12-31
One of the most important aspects of genomic analysis is the prediction of which pathways, both metabolic and non-metabolic, are present in an organism. In IMG, this is carried out by the assignment of IMG terms, which are organized into IMG pathways. Based on manual and automatic assignment of IMG terms, the presence or absence of IMG pathways is automatically inferred. The three categories of pathway assertion are asserted (likely present), not asserted (likely absent), and unknown. In the unknown category, at least one term necessary for the pathway is missing, but an ortholog in another organism has the corresponding term assigned to it. Automatic pathway inference is an important initial step in genome analysis.
Inference of gene pathways using mixture Bayesian networks
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Ko Younhee
2009-05-01
Full Text Available Abstract Background Inference of gene networks typically relies on measurements across a wide range of conditions or treatments. Although one network structure is predicted, the relationship between genes could vary across conditions. A comprehensive approach to infer general and condition-dependent gene networks was evaluated. This approach integrated Bayesian network and Gaussian mixture models to describe continuous microarray gene expression measurements, and three gene networks were predicted. Results The first reconstructions of a circadian rhythm pathway in honey bees and an adherens junction pathway in mouse embryos were obtained. In addition, general and condition-specific gene relationships, some unexpected, were detected in these two pathways and in a yeast cell-cycle pathway. The mixture Bayesian network approach identified all (honey bee circadian rhythm and mouse adherens junction pathways or the vast majority (yeast cell-cycle pathway of the gene relationships reported in empirical studies. Findings across the three pathways and data sets indicate that the mixture Bayesian network approach is well-suited to infer gene pathways based on microarray data. Furthermore, the interpretation of model estimates provided a broader understanding of the relationships between genes. The mixture models offered a comprehensive description of the relationships among genes in complex biological processes or across a wide range of conditions. The mixture parameter estimates and corresponding odds that the gene network inferred for a sample pertained to each mixture component allowed the uncovering of both general and condition-dependent gene relationships and patterns of expression. Conclusion This study demonstrated the two main benefits of learning gene pathways using mixture Bayesian networks. First, the identification of the optimal number of mixture components supported by the data offered a robust approach to infer gene relationships and
Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics
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Lecca Paola
2012-05-01
Full Text Available Abstract Background The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientific community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. Results The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specifically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. Conclusions Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identification of the microscopic pharmacokinetics and
Inference of asynchronous Boolean network from biological pathways.
Das, Haimabati; Layek, Ritwik Kumar
2015-01-01
Gene regulation is a complex process with multiple levels of interactions. In order to describe this complex dynamical system with tractable parameterization, the choice of the dynamical system model is of paramount importance. The right abstraction of the modeling scheme can reduce the complexity in the inference and intervention design, both computationally and experimentally. This article proposes an asynchronous Boolean network framework to capture the transcriptional regulation as well as the protein-protein interactions in a genetic regulatory system. The inference of asynchronous Boolean network from biological pathways information and experimental evidence are explained using an algorithm. The suitability of this paradigm for the variability of several reaction rates is also discussed. This methodology and model selection open up new research challenges in understanding gene-protein interactive system in a coherent way and can be beneficial for designing effective therapeutic intervention strategy.
Martínez-Lavanchy, P M; Chen, Z; Lünsmann, V; Marin-Cevada, V; Vilchez-Vargas, R; Pieper, D H; Reiche, N; Kappelmeyer, U; Imparato, V; Junca, H; Nijenhuis, I; Müller, J A; Kuschk, P; Heipieper, H J
2015-09-01
In the present study, microbial toluene degradation in controlled constructed wetland model systems, planted fixed-bed reactors (PFRs), was queried with DNA-based methods in combination with stable isotope fractionation analysis and characterization of toluene-degrading microbial isolates. Two PFR replicates were operated with toluene as the sole external carbon and electron source for 2 years. The bulk redox conditions in these systems were hypoxic to anoxic. The autochthonous bacterial communities, as analyzed by Illumina sequencing of 16S rRNA gene amplicons, were mainly comprised of the families Xanthomonadaceae, Comamonadaceae, and Burkholderiaceae, plus Rhodospirillaceae in one of the PFR replicates. DNA microarray analyses of the catabolic potentials for aromatic compound degradation suggested the presence of the ring monooxygenation pathway in both systems, as well as the anaerobic toluene pathway in the PFR replicate with a high abundance of Rhodospirillaceae. The presence of catabolic genes encoding the ring monooxygenation pathway was verified by quantitative PCR analysis, utilizing the obtained toluene-degrading isolates as references. Stable isotope fractionation analysis showed low-level of carbon fractionation and only minimal hydrogen fractionation in both PFRs, which matches the fractionation signatures of monooxygenation and dioxygenation. In combination with the results of the DNA-based analyses, this suggests that toluene degradation occurs predominantly via ring monooxygenation in the PFRs. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
Chiruvella, Kishore K; Sebastian, Robin; Sharma, Sheetal; Karande, Anjali A; Choudhary, Bibha; Raghavan, Sathees C
2012-03-30
Repair of DNA double-strand breaks (DSBs) is crucial for maintaining genomic integrity during the successful development of a fertilized egg into a whole organism. To date, the mechanism of DSB repair in postimplantation embryos has been largely unknown. In the present study, using a cell-free repair system derived from the different embryonic stages of mice, we find that canonical nonhomologous end joining (NHEJ), one of the major DSB repair pathways in mammals, is predominant at 14.5 day of embryonic development. Interestingly, all four types of DSBs tested were repaired by ligase IV/XRCC4 and Ku-dependent classical NHEJ. Characterization of end-joined junctions and expression studies further showed evidences for canonical NHEJ. Strikingly, in contrast to the above, we observed noncanonical end joining accompanied by DSB resection, dependent on microhomology and ligase III in 18.5-day embryos. Interestingly, we observed an elevated expression of CtIP, MRE11, and NBS1 at this stage, suggesting that it could act as a switch between classical end joining and microhomology-mediated end joining at later stages of embryonic development. Thus, our results establish for the first time the existence of both canonical and alternative NHEJ pathways during the postimplantation stages of mammalian embryonic development. Copyright Â© 2012 Elsevier Ltd. All rights reserved.
Clostridium difficile flagella predominantly activate TLR5-linked NF-κB pathway in epithelial cells.
Batah, Jameel; Denève-Larrazet, Cécile; Jolivot, Pierre-Alain; Kuehne, Sarah; Collignon, Anne; Marvaud, Jean-Christophe; Kansau, Imad
2016-04-01
Clostridium difficile has become the most common enteropathogen responsible for intestinal nosocomial post-antibiotic infections. This has coincided with the appearance of serious cases related to the emergence of hypervirulent strains. The toxins are the main virulence factors and elicit an inflammatory response during C. difficile infection. However, other bacterial components appear to be involved in the inflammatory process. In some pathogens, flagella play a role in pathogenesis through abnormal stimulation of the TLR5-mediated host immune response. To date, few studies have addressed this role for C. difficile flagella. In the current study, we confirm in two different epithelial cell models that C. difficile thanks to its FliC flagellin interacts with TLR5. In addition, thanks to inhibition and transcriptomic studies we demonstrate that the interaction of flagellin and TLR5 predominantly activates the NF-κB and, in a lesser degree, the MAPK pathways, via TLR5, leading to up-regulation of pro-inflammatory gene expression and synthesis of pro-inflammatory mediators. These results suggest a role for C. difficile flagella in contributing to inflammatory response in host intestinal cells.
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Purushotham Arnie
2011-10-01
Full Text Available Abstract Background Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression with independent and highly curated structural interaction data (e.g protein interaction networks or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study. Results We present an algorithm called DART (Denoising Algorithm based on Relevance network Topology which filters out noise before estimating pathway activity. Using simulated and real multidimensional cancer genomic data and by comparing DART to other algorithms which do not assess the relevance of the prior pathway information, we here demonstrate that substantial improvement in pathway activity predictions can be made if prior pathway information is denoised before predictions are made. We also show that genes encoding hubs in expression correlation networks represent more reliable markers of pathway activity. Using the Netpath resource of signalling pathways in the context of breast cancer gene expression data we further demonstrate that DART leads to more robust inferences about pathway activity correlations. Finally, we show that DART identifies a hypothesized association between oestrogen signalling and mammographic density in ER+ breast cancer. Conclusions Evaluating the consistency of prior information of pathway databases in molecular tumour profiles may substantially improve the subsequent inference of pathway activity in clinical tumour specimens. This de-noising strategy should be incorporated in approaches which attempt to infer pathway activity from prior pathway models.
DEFF Research Database (Denmark)
Møller, Jesper
.1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...
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Wu Ling-Yun
2010-05-01
Full Text Available Abstract Background Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow. Results In this study, we propose new concepts of dissipation, saturation and direction to decipher the information flow behaviours in the pathways and thereby infer the biological pathways from a given source to its target. This model takes into account explicitly the common features of the information transmission and provides a general framework to model the biological pathways. It can incorporate different types of bio-molecular interactions to infer the signal transduction pathways and interpret the expression quantitative trait loci (eQTL associations. The model is formulated as a linear programming problem and thus is solved efficiently. Experiments on the real data of yeast indicate that the reproduced pathways are highly consistent with the current knowledge. Conclusions Our model explicitly treats the biological pathways as information flows with dissipation, saturation and direction. The effective applications suggest that the three new concepts may be valid to describe the organization rules of biological pathways. The deduced linear programming should be a promising tool to infer the various biological pathways from the high-throughput data.
Accurate and reliable cancer classification based on probabilistic inference of pathway activity.
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Junjie Su
Full Text Available With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers, for building the classifier. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes, and use the pathway activities for classification. It has been shown that pathway-based classifiers typically yield more reliable results compared to traditional gene-based classifiers. In this paper, we propose a new classification method based on probabilistic inference of pathway activities. For a given sample, we compute the log-likelihood ratio between different disease phenotypes based on the expression level of each gene. The activity of a given pathway is then inferred by combining the log-likelihood ratios of the constituent genes. We apply the proposed method to the classification of breast cancer metastasis, and show that it achieves higher accuracy and identifies more reproducible pathway markers compared to several existing pathway activity inference methods.
Kelly, K J; Liu, Yunlong; Zhang, Jizhong; Goswami, Chirayu; Lin, Hai; Dominguez, Jesus H
2013-08-15
Despite advances in the treatment of diabetic nephropathy (DN), currently available therapies have not prevented the epidemic of progressive chronic kidney disease (CKD). The morbidity of CKD, and the inexorable increase in the prevalence of end-stage renal disease, demands more effective approaches to prevent and treat progressive CKD. We undertook next-generation sequencing in a rat model of diabetic nephropathy to study in depth the pathogenic alterations involved in DN with progressive CKD. We employed the obese, diabetic ZS rat, a model that develops diabetic nephropathy, characterized by progressive CKD, inflammation, and fibrosis, the hallmarks of human disease. We then used RNA-seq to examine the combined effects of renal cells and infiltrating inflammatory cells acting as a pathophysiological unit. The comprehensive systems biology analysis of progressive CKD revealed multiple interactions of altered genes that were integrated into morbid networks. These pathological gene assemblies lead to renal inflammation and promote apoptosis and cell cycle arrest in progressive CKD. Moreover, in what is clearly a major therapeutic challenge, multiple and redundant pathways were found to be linked to renal fibrosis, a major cause of kidney loss. We conclude that systems biology applied to progressive CKD in DN can be used to develop novel therapeutic strategies directed to restore critical anomalies in affected gene networks.
Genes influencing milk production traits predominantly affect one of four biological pathways
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Goddard Michael
2008-01-01
Full Text Available Abstract In this study we introduce a method that accounts for false positive and false negative results in attempting to estimate the true proportion of quantitative trait loci that affect two different traits. This method was applied to data from a genome scan that was used to detect QTL for three independent milk production traits, Australian Selection Index (ASI, protein percentage (P% and fat percentage corrected for protein percentage (F% – P%. These four different scenarios are attributed to four biological pathways: QTL that (1 increase or decrease total mammary gland production (affecting ASI only; (2 increase or decrease lactose synthesis resulting in the volume of milk being changed but without a change in protein or fat yield (affecting P% only; (3 increase or decrease protein synthesis while milk volume remains relatively constant (affecting ASI and P% in the same direction; (4 increase or decrease fat synthesis while the volume of milk remains relatively constant (affecting F% – P% only. The results indicate that of the positions that detected a gene, most affected one trait and not the others, though a small proportion (2.8% affected ASI and P% in the same direction.
DEFF Research Database (Denmark)
Møller, Jesper
2010-01-01
Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...
Yu, Feng-Yan; Huang, Shao-Gang; Zhang, Hai-Yan; Ye, Hua; Chi, Hong-Gang; Zou, Ying; Lv, Ru-Xi; Zheng, Xue-Bao
2016-03-28
To study differences in the visceral sensitivity of the colonic mucosa between patients with diarrhea-predominant irritable bowel syndrome (IBS-D) and those with ulcerative colitis (UC) in remission and to relate these differences with changes in the 5-hydroxytryptophan (5-HT) signaling pathway. Gastrointestinal symptoms were used to determine the clinical symptom scores and rectal visceral sensitivity of patients with IBS-D and patients with UC in remission. Blood levels of 5-HT and 5-hydroxyindoleacetic acid (5-HIAA) were measured using an HPLC-electrochemical detection system. The levels of 5-HT 3 receptor (3R), 4R, and 7R mRNAs in colonic biopsy samples were detected using reverse transcription-polymerase chain reaction. The protein expression of TPH1 was analyzed by Western blot and immunohistochemistry. Abdominal pain or discomfort, stool frequency, and the scores of these symptoms in combination with gastrointestinal symptoms were higher in the IBS-D and UC groups than in the control groups. However, no significant differences were observed between the IBS-D and UC remission groups. With respect to rectal visceral sensitivity, the UC remission and IBS-D groups showed a decrease in the initial perception threshold, defecating threshold and pain threshold. However, these groups exhibited significantly increased anorectal relaxation pressure. Tests examining the main indicators of the 5-HT signaling pathway showed that the plasma 5-HT levels, 5-HIAA concentrations, TPH1 expression in the colonic mucosa, and 5-HT3R and 5-HT5R expression were increased in both the IBS-D and the UC remission groups; no increases were observed with respect to 5-HT7R expression. The IBS-D and UC groups showed similar clinical symptom scores, visceral sensitivity, and levels of serotonin signaling pathway indicators in the plasma and colonic mucosa. However, the pain threshold and 5-HT7R expression in the colonic mucosa were significantly different between these groups. The results
Yu, Feng-Yan; Huang, Shao-Gang; Zhang, Hai-Yan; Ye, Hua; Chi, Hong-Gang; Zou, Ying; Lv, Ru-Xi; Zheng, Xue-Bao
2016-01-01
AIM: To study differences in the visceral sensitivity of the colonic mucosa between patients with diarrhea-predominant irritable bowel syndrome (IBS-D) and those with ulcerative colitis (UC) in remission and to relate these differences with changes in the 5-hydroxytryptophan (5-HT) signaling pathway. METHODS: Gastrointestinal symptoms were used to determine the clinical symptom scores and rectal visceral sensitivity of patients with IBS-D and patients with UC in remission. Blood levels of 5-HT and 5-hydroxyindoleacetic acid (5-HIAA) were measured using an HPLC-electrochemical detection system. The levels of 5-HT 3 receptor (3R), 4R, and 7R mRNAs in colonic biopsy samples were detected using reverse transcription-polymerase chain reaction. The protein expression of TPH1 was analyzed by Western blot and immunohistochemistry. RESULTS: Abdominal pain or discomfort, stool frequency, and the scores of these symptoms in combination with gastrointestinal symptoms were higher in the IBS-D and UC groups than in the control groups. However, no significant differences were observed between the IBS-D and UC remission groups. With respect to rectal visceral sensitivity, the UC remission and IBS-D groups showed a decrease in the initial perception threshold, defecating threshold and pain threshold. However, these groups exhibited significantly increased anorectal relaxation pressure. Tests examining the main indicators of the 5-HT signaling pathway showed that the plasma 5-HT levels, 5-HIAA concentrations, TPH1 expression in the colonic mucosa, and 5-HT3R and 5-HT5R expression were increased in both the IBS-D and the UC remission groups; no increases were observed with respect to 5-HT7R expression. CONCLUSION: The IBS-D and UC groups showed similar clinical symptom scores, visceral sensitivity, and levels of serotonin signaling pathway indicators in the plasma and colonic mucosa. However, the pain threshold and 5-HT7R expression in the colonic mucosa were significantly different
Large scale statistical inference of signaling pathways from RNAi and microarray data
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Poustka Annemarie
2007-10-01
Full Text Available Abstract Background The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. Results In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. Conclusion Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-α pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.
Tian, Tianhai; Song, Jiangning
2017-01-01
The progress in proteomics technologies has led to a rapid accumulation of large-scale proteomic datasets in recent years, which provides an unprecedented opportunity and valuable resources to understand how living organisms perform necessary functions at systems levels. This work presents a computational method for designing mathematical models based on proteomic datasets. Using the mitogen-activated protein (MAP) kinase pathway as the test system, we first develop a mathematical model including the cytosolic and nuclear subsystems. A key step of modeling is to apply a genetic algorithm to infer unknown model parameters. Then the robustness property of mathematical models is used as a criterion to select appropriate rate constants from the estimated candidates. Moreover, quantitative information such as the absolute protein concentrations is used to further refine the mathematical model. The successful application of this inference method to the MAP kinase pathway suggests that it is a useful and powerful approach for developing accurate mathematical models to gain important insights into the regulatory mechanisms of cell signaling pathways.
Kang, Mingon; Zhang, Chunling; Chun, Hyung-Wook; Ding, Chris; Liu, Chunyu; Gao, Jean
2015-03-01
Epistasis is the interactions among multiple genetic variants. It has emerged to explain the 'missing heritability' that a marginal genetic effect does not account for by genome-wide association studies, and also to understand the hierarchical relationships between genes in the genetic pathways. The Fisher's geometric model is common in detecting the epistatic effects. However, despite the substantial successes of many studies with the model, it often fails to discover the functional dependence between genes in an epistasis study, which is an important role in inferring hierarchical relationships of genes in the biological pathway. We justify the imperfectness of Fisher's model in the simulation study and its application to the biological data. Then, we propose a novel generic epistasis model that provides a flexible solution for various biological putative epistatic models in practice. The proposed method enables one to efficiently characterize the functional dependence between genes. Moreover, we suggest a statistical strategy for determining a recessive or dominant link among epistatic expression quantitative trait locus to enable the ability to infer the hierarchical relationships. The proposed method is assessed by simulation experiments of various settings and is applied to human brain data regarding schizophrenia. The MATLAB source codes are publicly available at: http://biomecis.uta.edu/epistasis. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Pey, Jon; Valgepea, Kaspar; Rubio, Angel; Beasley, John E; Planes, Francisco J
2013-12-08
The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.
Jiang, Xian; Yan, Xiaoxiao; Ren, Wangyu; Jia, Yufeng; Chen, Jianian; Sun, Dongmei; Xu, Lin; Tang, Yawen
2016-11-16
For direct formic acid fuel cells (DFAFCs), the dehydrogenation pathway is a desired reaction pathway, to boost the overall cell efficiency. Elaborate composition tuning and nanostructure engineering provide two promising strategies to design efficient electrocatalysts for DFAFCs. Herein, we present a facile synthesis of porous AgPt bimetallic nanooctahedra with enriched Pt surface (denoted as AgPt@Pt nanooctahedra) by a selective etching strategy. The smart integration of geometric and electronic effect confers a substantial enhancement of desired dehydrogenation pathway as well as electro-oxidation activity for the formic acid oxidation reaction (FAOR). We anticipate that the obtained nanocatalyst may hold great promises in fuel cell devices, and furthermore, the facile synthetic strategy demonstrated here can be extendable for the fabrication of other multicomponent nanoalloys with desirable morphologies and enhanced electrocatalytic performances.
Muñoz-Martínez, Francisco; García-Fontana, Cristina; Rico-Jiménez, Miriam; Alfonso, Carlos; Krell, Tino
2012-01-01
Chemosensory pathways correspond to major signal transduction mechanisms and can be classified into the functional families flagellum-mediated taxis, type four pili-mediated taxis or pathways with alternative cellular functions (ACF). CheR methyltransferases are core enzymes in all of these families. CheR proteins fused to tetratricopeptide repeat (TPR) domains have been reported and we present an analysis of this uncharacterized family. We show that CheR-TPRs are widely distributed in GRAM-negative but almost absent from GRAM-positive bacteria. Most strains contain a single CheR-TPR and its abundance does not correlate with the number of chemoreceptors. The TPR domain fused to CheR is comparatively short and frequently composed of 2 repeats. The majority of CheR-TPR genes were found in gene clusters that harbor multidomain response regulators in which the REC domain is fused to different output domains like HK, GGDEF, EAL, HPT, AAA, PAS, GAF, additional REC, HTH, phosphatase or combinations thereof. The response regulator architectures coincide with those reported for the ACF family of pathways. Since the presence of multidomain response regulators is a distinctive feature of this pathway family, we conclude that CheR-TPR proteins form part of ACF type pathways. The diversity of response regulator output domains suggests that the ACF pathways form a superfamily which regroups many different regulatory mechanisms, in which all CheR-TPR proteins appear to participate. In the second part we characterize WspC of Pseudomonas putida, a representative example of CheR-TPR. The affinities of WspC-Pp for S-adenosylmethionine and S-adenosylhomocysteine were comparable to those of prototypal CheR, indicating that WspC-Pp activity is in analogy to prototypal CheRs controlled by product feed-back inhibition. The removal of the TPR domain did not impact significantly on the binding constants and consequently not on the product feed-back inhibition. WspC-Pp was found to be
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Irene Papatheodorou
Full Text Available A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.
Papatheodorou, Irene; Ziehm, Matthias; Wieser, Daniela; Alic, Nazif; Partridge, Linda; Thornton, Janet M
2012-01-01
A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.
Hi-Jack: a novel computational framework for pathway-based inference of host–pathogen interactions
Kleftogiannis, Dimitrios A.
2015-03-09
Motivation: Pathogens infect their host and hijack the host machinery to produce more progeny pathogens. Obligate intracellular pathogens, in particular, require resources of the host to replicate. Therefore, infections by these pathogens lead to alterations in the metabolism of the host, shifting in favor of pathogen protein production. Some computational identification of mechanisms of host-pathogen interactions have been proposed, but it seems the problem has yet to be approached from the metabolite-hijacking angle. Results: We propose a novel computational framework, Hi-Jack, for inferring pathway-based interactions between a host and a pathogen that relies on the idea of metabolite hijacking. Hi-Jack searches metabolic network data from hosts and pathogens, and identifies candidate reactions where hijacking occurs. A novel scoring function ranks candidate hijacked reactions and identifies pathways in the host that interact with pathways in the pathogen, as well as the associated frequent hijacked metabolites. We also describe host-pathogen interaction principles that can be used in the future for subsequent studies. Our case study on Mycobacterium tuberculosis (Mtb) revealed pathways in human-e.g. carbohydrate metabolism, lipids metabolism and pathways related to amino acids metabolism-that are likely to be hijacked by the pathogen. In addition, we report interesting potential pathway interconnections between human and Mtb such as linkage of human fatty acid biosynthesis with Mtb biosynthesis of unsaturated fatty acids, or linkage of human pentose phosphate pathway with lipopolysaccharide biosynthesis in Mtb. © The Author 2015. Published by Oxford University Press. All rights reserved.
Kalafut, Bennett; Visscher, Koen
2008-10-01
Optical tweezers experiments allow us to probe the role of force and mechanical work in a variety of biochemical processes. However, observable states do not usually correspond in a one-to-one fashion with the internal state of an enzyme or enzyme-substrate complex. Different kinetic pathways yield different distributions for the dwells in the observable states. Furthermore, the dwell-time distribution will be dependent upon force, and upon where in the biochemical pathway force acts. I will present a maximum-likelihood method for identifying rate constants and the locations of force-dependent transitions in transcription initiation by T7 RNA Polymerase. This method is generalizable to systems with more complicated kinetic pathways in which there are two observable states (e.g. bound and unbound) and an irreversible final transition.
Bayesian inference of the sites of perturbations in metabolic pathways via Markov chain Monte Carlo
Jayawardhana, Bayu; Kell, Douglas B.; Rattray, Magnus
2008-01-01
Motivation: Genetic modifications or pharmaceutical interventions can influence multiple sites in metabolic pathways, and often these are ‘distant’ from the primary effect. In this regard, the ability to identify target and off-target effects of a specific compound or gene therapy is both a major ch
Barnawi, Rayanah; Al-Khaldi, Samiyah; Majed Sleiman, Ghida; Sarkar, Abdullah; Al-Dhfyan, Abdullah; Al-Mohanna, Falah; Ghebeh, Hazem; Al-Alwan, Monther
2016-12-01
An emerging dogma shows that tumors are initiated and maintained by a subpopulation of cancer cells that hijack some stem cell features and thus referred to as "cancer stem cells" (CSCs). The exact mechanism that regulates the maintenance of CSC pool remains largely unknown. Fascin is an actin-bundling protein that we have previously demonstrated to be a major regulator of breast cancer chemoresistance and metastasis, two cardinal features of CSCs. Here, we manipulated fascin expression in breast cancer cell lines and used several in vitro and in vivo approaches to examine the relationship between fascin expression and breast CSCs. Fascin knockdown significantly reduced stem cell-like phenotype (CD44(hi) /CD24(lo) and ALDH(+) ) and reversal of epithelial to mesenchymal transition. Interestingly, expression of the embryonic stem cell transcriptional factors (Oct4, Nanog, Sox2, and Klf4) was significantly reduced when fascin expression was down-regulated. Functionally, fascin-knockdown cells were less competent in forming colonies and tumorspheres, consistent with lower basal self-renewal activity and higher susceptibility to chemotherapy. Fascin effect on CSC chemoresistance and self-renewability was associated with Notch signaling. Activation of Notch induced the relevant downstream targets predominantly in the fascin-positive cells. Limiting-dilution xenotransplantation assay showed higher frequency of tumor-initiating cells in the fascin-positive group. Collectively, our data demonstrated fascin as a critical regulator of breast CSC pool at least partially via activation of the Notch self-renewal signaling pathway and modification of the expression embryonic transcriptional factors. Targeting fascin may halt CSCs and thus presents a novel therapeutic approach for effective treatment of breast cancer. Stem Cells 2016;34:2799-2813 Video Highlight: https://youtu.be/GxS4fJ_Ow-o.
Directory of Open Access Journals (Sweden)
Christopher Y Park
Full Text Available Biomolecular pathways are built from diverse types of pairwise interactions, ranging from physical protein-protein interactions and modifications to indirect regulatory relationships. One goal of systems biology is to bridge three aspects of this complexity: the growing body of high-throughput data assaying these interactions; the specific interactions in which individual genes participate; and the genome-wide patterns of interactions in a system of interest. Here, we describe methodology for simultaneously predicting specific types of biomolecular interactions using high-throughput genomic data. This results in a comprehensive compendium of whole-genome networks for yeast, derived from ∼3,500 experimental conditions and describing 30 interaction types, which range from general (e.g. physical or regulatory to specific (e.g. phosphorylation or transcriptional regulation. We used these networks to investigate molecular pathways in carbon metabolism and cellular transport, proposing a novel connection between glycogen breakdown and glucose utilization supported by recent publications. Additionally, 14 specific predicted interactions in DNA topological change and protein biosynthesis were experimentally validated. We analyzed the systems-level network features within all interactomes, verifying the presence of small-world properties and enrichment for recurring network motifs. This compendium of physical, synthetic, regulatory, and functional interaction networks has been made publicly available through an interactive web interface for investigators to utilize in future research at http://function.princeton.edu/bioweaver/.
Hata, Maki; Takakura, Shinichi; Matsushima, Nobuo; Hashimoto, Takeshi; Utsugi, Mitsuru
2016-10-01
At Naka-dake cone, Aso caldera, Japan, volcanic activity is raised cyclically, an example of which was a phreatomagmatic eruption in September 2015. Using a three-dimensional model of electrical resistivity, we identify a magma pathway from a series of northward dipping conductive anomalies in the upper crust beneath the caldera. Our resistivity model was created from magnetotelluric measurements conducted in November-December 2015; thus, it provides the latest information about magma reservoir geometry beneath the caldera. The center of the conductive anomalies shifts from the north of Naka-dake at depths >10 km toward Naka-dake, along with a decrease in anomaly depths. The melt fraction is estimated at 13-15% at 2 km depth. Moreover, these anomalies are spatially correlated with the locations of earthquake clusters, which are distributed within resistive blocks on the conductive anomalies in the northwest of Naka-dake but distributed at the resistive sides of resistivity boundaries in the northeast.
Air-ice carbon pathways inferred from a sea ice tank experiment
Directory of Open Access Journals (Sweden)
Marie Kotovitch
2016-06-01
Full Text Available Abstract Given rapid sea ice changes in the Arctic Ocean in the context of climate warming, better constraints on the role of sea ice in CO2 cycling are needed to assess the capacity of polar oceans to buffer the rise of atmospheric CO2 concentration. Air-ice CO2 fluxes were measured continuously using automated chambers from the initial freezing of a sea ice cover until its decay during the INTERICE V experiment at the Hamburg Ship Model Basin. Cooling seawater prior to sea ice formation acted as a sink for atmospheric CO2, but as soon as the first ice crystals started to form, sea ice turned to a source of CO2, which lasted throughout the whole ice growth phase. Once ice decay was initiated by warming the atmosphere, the sea ice shifted back again to a sink of CO2. Direct measurements of outward ice-atmosphere CO2 fluxes were consistent with the depletion of dissolved inorganic carbon in the upper half of sea ice. Combining measured air-ice CO2 fluxes with the partial pressure of CO2 in sea ice, we determined strongly different gas transfer coefficients of CO2 at the air-ice interface between the growth and the decay phases (from 2.5 to 0.4 mol m−2 d−1 atm−1. A 1D sea ice carbon cycle model including gas physics and carbon biogeochemistry was used in various configurations in order to interpret the observations. All model simulations correctly predicted the sign of the air-ice flux. By contrast, the amplitude of the flux was much more variable between the different simulations. In none of the simulations was the dissolved gas pathway strong enough to explain the large fluxes during ice growth. This pathway weakness is due to an intrinsic limitation of ice-air fluxes of dissolved CO2 by the slow transport of dissolved inorganic carbon in the ice. The best means we found to explain the high air-ice carbon fluxes during ice growth is an intense yet uncertain gas bubble efflux, requiring sufficient bubble nucleation and upwards rise. We
Directory of Open Access Journals (Sweden)
Ignat Drozdov
Full Text Available Small intestinal (SI neuroendocrine tumors (NET are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including 'Nervous system development', 'Immune response', and 'Cell-cycle'. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations. All were up-regulated (p<0.035 with the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 10(-5 M significantly stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP activator, 10(-5 M stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D(2 and Serotonin [5-HT(2] receptor agonist, 10(-6 M stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8-2-fold for isoproterenol and forskolin. Gene network inference and graph topology analysis in SI NETs suggests that SI NETs express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a model NET cell system, confirmed that transcriptional
Energy Technology Data Exchange (ETDEWEB)
Noda, Taichi [Department of Biology, School of Medicine, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521 (Japan); Department of Dermatology, School of Medicine, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521 (Japan); Takahashi, Akihisa [Department of Biology, School of Medicine, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521 (Japan); Kondo, Natsuko [Particle Radiation Oncology Research Center, Research Reactor Institute, Kyoto University, Kumatori-cho, Sennan-gun, Osaka 590-0494 (Japan); Mori, Eiichiro [Department of Biology, School of Medicine, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521 (Japan); Okamoto, Noritomo [Department of Otorhinolaryngology, School of Medicine, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521 (Japan); Nakagawa, Yosuke [Department of Oral and Maxillofacial Surgery, School of Medicine, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521 (Japan); Ohnishi, Ken [Department of Biology, Ibaraki Prefectual University of Health Sciences, 4669-2 Ami, Ami-mati, Inasiki-gun, Ibaraki 300-0394 (Japan); Zdzienicka, Malgorzata Z. [Department of Molecular Cell Genetics, Collegium Medicum in Bydgoszcz, Nicolaus-Copernicus-University in Torun, ul. Sklodowskiej-Curie 9, 85-094 Bydgoszcz (Poland); Thompson, Larry H. [Biosciences and Biotechnology Division, L452, Lawrence Livermore National Laboratory, P.O. Box 808, Livermore, CA 94551-0808 (United States); Helleday, Thomas [Gray Institute for Radiation Oncology and Biology, University of Oxford, Old Road Campus Research Building, Off Roosevelt Drive, Oxford, OX3 7DQ (United Kingdom); Department of Genetics, Microbiology and Toxicology Stockholm University, SE-106 91 Stockholm (Sweden); Asada, Hideo [Department of Dermatology, School of Medicine, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521 (Japan); and others
2011-01-07
The role of the Fanconi anemia (FA) repair pathway for DNA damage induced by formaldehyde was examined in the work described here. The following cell types were used: mouse embryonic fibroblast cell lines FANCA{sup -/-}, FANCC{sup -/-}, FANCA{sup -/-}C{sup -/-}, FANCD2{sup -/-} and their parental cells, the Chinese hamster cell lines FANCD1 mutant (mt), FANCGmt, their revertant cells, and the corresponding wild-type (wt) cells. Cell survival rates were determined with colony formation assays after formaldehyde treatment. DNA double strand breaks (DSBs) were detected with an immunocytochemical {gamma}H2AX-staining assay. Although the sensitivity of FANCA{sup -/-}, FANCC{sup -/-} and FANCA{sup -/-}C{sup -/-} cells to formaldehyde was comparable to that of proficient cells, FANCD1mt, FANCGmt and FANCD2{sup -/-} cells were more sensitive to formaldehyde than the corresponding proficient cells. It was found that homologous recombination (HR) repair was induced by formaldehyde. In addition, {gamma}H2AX foci in FANCD1mt cells persisted for longer times than in FANCD1wt cells. These findings suggest that formaldehyde-induced DSBs are repaired by HR through the FA repair pathway which is independent of the FA nuclear core complex. -- Research highlights: {yields} We examined to clarify the repair pathways of formaldehyde-induced DNA damage. Formaldehyde induces DNA double strand breaks (DSBs). {yields} DSBs are repaired through the Fanconi anemia (FA) repair pathway. {yields} This pathway is independent of the FA nuclear core complex. {yields} We also found that homologous recombination repair was induced by formaldehyde.
Gröschel, Stefan; Sanders, Mathijs A; Hoogenboezem, Remco; Zeilemaker, Annelieke; Havermans, Marije; Erpelinck, Claudia; Bindels, Eric M J; Beverloo, H Berna; Döhner, Hartmut; Löwenberg, Bob; Döhner, Konstanze; Delwel, Ruud; Valk, Peter J M
2015-01-01
Myeloid malignancies bearing chromosomal inv(3)/t(3;3) abnormalities are among the most therapy-resistant leukemias. Deregulated expression of EVI1 is the molecular hallmark of this disease; however, the genome-wide spectrum of cooperating mutations in this disease subset has not been systematically elucidated. Here, we show that 98% of inv(3)/t(3;3) myeloid malignancies harbor mutations in genes activating RAS/receptor tyrosine kinase (RTK) signaling pathways. In addition, hemizygous mutations in GATA2, as well as heterozygous alterations in RUNX1, SF3B1, and genes encoding epigenetic modifiers, frequently co-occur with the inv(3)/t(3;3) aberration. Notably, neither mutational patterns nor gene expression profiles differ across inv(3)/t(3;3) acute myeloid leukemia, chronic myeloid leukemia, and myelodysplastic syndrome cases, suggesting recognition of inv(3)/t(3;3) myeloid malignancies as a single disease entity irrespective of blast count. The high incidence of activating RAS/RTK signaling mutations may provide a target for a rational treatment strategy in this high-risk patient group.
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Christelle Wauthier
2015-11-01
Full Text Available A summit and upper flank eruption occurred at Nyamulagira volcano, Democratic Republic of Congo, from 2–27 January 2010. Eruptions at Nyamulagira during 1996–2010 occurred from eruptive fissures on the upper flanks or within the summit caldera and were distributed along the ~N155E rift zone, whereas the 2011–2012 eruption occurred ~12 km ENE of the summit. 3D numerical modeling of Interferometric Synthetic Aperture Radar (InSAR geodetic measurements of the co-eruptive deformation in 2010 reveals that magma stored in a shallow (~3.5 km below the summit reservoir intruded as two subvertical dikes beneath the summit and southeastern flank of the volcano. The northern dike is connected to an ~N45E-trending intra-caldera eruptive fissure, extending to an ~2.5 km maximum depth. The southern dike is connected to an ~N175E-trending flank fissure extending to the depth of the inferred reservoir at ~3.5 km. The inferred reservoir location is coincident with the reservoir that was active during previous eruptions in 1938–1940 and 2006. The volumetric ratio of total emitted magma (intruded in dikes + erupted to the contraction of the reservoir (rv is 9.3, consistent with pressure recovery by gas exsolution in the small, shallow modeled magma reservoir. We derive a modified analytical expression for rv, accounting for changes in reservoir volume induced by gas exsolution, as well as eruptive volume. By using the precise magma composition, we estimate a magma compressibility of 1.9–3.2 × 109 Pa−1 and rv of 6.5–10.1. From a normal-stress change analysis, we infer that intrusions in 2010 could have encouraged the ascent of magma from a deeper reservoir along an ~N45E orientation, corresponding to the strike of the rift transfer zone structures and possibly resulting in the 2011–2012 intrusion. The intrusion of magma to greater distances from the summit may be enhanced along the N45E orientation, as it is more favorable to the regional rift
Barone, Lavinia; Fossati, Andrea; Guiducci, Valentina
2011-09-01
We report the outcome of an investigation on how specific attachment states of mind and corresponding risk factors related to different DSM Axis I comorbidities in subjects with BPD. Mental representations of attachment in four BPD sub-groups (BPD and Anxiety/Mood Disorders, BPD and Substance Use and Abuse Disorders, BPD and Alcohol Use and Abuse Disorders, and BPD and Eating Disorders) were assessed in 140 BPD subjects using the Adult Attachment Interview (AAI). In addition to the global attachment picture in which Insecure organized (Dismissing 51% and Enmeshed 35%) and Insecure disorganized categories (40%) were overrepresented, significant differences in attachment category were found between the four BPD sub-groups. Axis I comorbidities corresponded with attachment features on the internalizing/externalizing functioning dimension of the disorder. Furthermore, specific constellations of inferred developmental antecedents and attachment states of mind corresponded differentially with the BPD sub-groups. Implications for developmental research and clinical nosology are discussed.
Mahdavi, Vahideh; Ghanati, Faezeh; Ghassempour, Alireza
2016-02-01
Diazinon insecticide is widely applied in rice (Oryza sativa L.) fields in Iran. However, concerns are now being raised about its potential adverse impacts on rice. In this study, a time-course metabolic change in rice plants was investigated after diazinon treatment using gas chromatography-mass spectrometry (GC-MS) and subsequently three different methods, MetaboAnalyst, MetaboNetwork, and analysis of reporter reactions, as a potential multivariate method were used to find the underlying changes in metabolism with stronger evidence in order to link differentially expressed metabolites to biological pathways. Results clearly showed the similarity of acetylcholinesterase (AChE) of rice plants to that of animals in terms of its inhibitability by diazinon and emphasized that subsequent accumulation of AChE mainly affects the metabolism of osmolites and tricarboxylic acid intermediates subsequent accumulation of ACh mainly affects the metabolism of osmolites and TCA intermediates.
Institute of Scientific and Technical Information of China (English)
吴美容; 张瑞; 周俊; 谢欣欣; 雍晓雨; 闫志英; 葛明民; 郑涛
2014-01-01
Methanogens are strictly anaerobic archaea, which not only take part in the methanogenesis procedure but also limit this process. Temperature plays a key role in the anaerobic fermentation. Temperature could not only directly alter the community structure and function of methanogenic archaea,but also affect the supply of substrates for methanogens,which in turn indirectly regulates the pathways of methanogenic archaea.There are three pathway for methanogenesis, and they are started from acetic acid, H2/CO2 and C-1 compound respetively. Acetoclastic methanogenesis accounts for about two-thirds of the total methane production globally, while hydrogenotrophic methanogenesis accounts for about one third. Methanol- and methyl amine-derived methanogensis is restricted in ocean and saline water. Acetoclastic methanogenesis is the predominant methanogenesis at a low temperature, and methane is produced by acetoclastic and hydrogenotrophic methanogenesis at a medium temperature, while methane is exclusively produced by hydrogenotrophic methanogenesis at a high or ultra-high temperature.%产甲烷菌是严格厌氧的古菌，由其完成的产甲烷过程通常是厌氧微生物生化代谢中最重要的限速步骤。温度作为影响产甲烷菌的产甲烷速率重要因素，其变化会改变生物环境中的产甲烷的代谢途径和优势菌群分布。目前已知甲烷生物合成有3条途径：乙酸代谢途径、CO2还原途径和甲基营养型途径。理论上乙酸途径生成的甲烷约占甲烷生成总量的2/3，CO2还原产甲烷途径则约占1/3，甲基营养型途径只在少数情况下考虑其影响，例如盐湖。在低温条件下产甲烷菌以利用乙酸代谢为主；在中温条件下，产甲烷途径以乙酸代谢和H2/CO2还原一定比例存在；在高温和超高温条件下，以只利用CO2还原途径的菌群为主。
King, Gary; Rosen, Ori; Tanner, Martin A.
2004-09-01
This collection of essays brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half-decade has witnessed an explosion of research in ecological inference--the process of trying to infer individual behavior from aggregate data. Although uncertainties and information lost in aggregation make ecological inference one of the most problematic types of research to rely on, these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, by business in marketing research, and by governments in policy analysis.
Directory of Open Access Journals (Sweden)
Richard Shoemaker
2014-04-01
Full Text Available Establishing causality has been a problem throughout history of philosophy of science. This paper discusses the philosophy of causal inference along the different school of thoughts and methods: Rationalism, Empiricism, Inductive method, Hypothetical deductive method with pros and cons. The article it starting from the Problem of Hume, also close to the positions of Russell, Carnap, Popper and Kuhn to better understand the modern interpretation and implications of causal inference in epidemiological research.
Hierarchies of Predominantly Connected Communities
Hamann, Michael; Wagner, Dorothea
2013-01-01
We consider communities whose vertices are predominantly connected, i.e., the vertices in each community are stronger connected to other community members of the same community than to vertices outside the community. Flake et al. introduced a hierarchical clustering algorithm that finds such predominantly connected communities of different coarseness depending on an input parameter. We present a simple and efficient method for constructing a clustering hierarchy according to Flake et al. that supersedes the necessity of choosing feasible parameter values and guarantees the completeness of the resulting hierarchy, i.e., the hierarchy contains all clusterings that can be constructed by the original algorithm for any parameter value. However, predominantly connected communities are not organized in a single hierarchy. Thus, we develop a framework that, after precomputing at most $2(n-1)$ maximum flows, admits a linear time construction of a clustering $\\C(S)$ of predominantly connected communities that contains ...
Energy Technology Data Exchange (ETDEWEB)
Sunendra, Joshi R.; Kukkadapu, Ravi K.; Burdige, David J.; Bowden, Mark E.; Sparks, Donald L.; Jaisi, Deb P.
2015-05-19
The Chesapeake Bay, the largest and most productive estuary in the US, suffers from varying degrees of water quality issues fueled by both point and non–point source nutrient sources. Restoration of the bay is complicated by the multitude of nutrient sources, their variable inputs and hydrological conditions, and complex interacting factors including climate forcing. These complexities not only restrict formulation of effective restoration plans but also open up debates on accountability issues with nutrient loading. A detailed understanding of sediment phosphorus (P) dynamics enables one to identify the exchange of dissolved constituents across the sediment- water interface and aid to better constrain mechanisms and processes controlling the coupling between the sediments and the overlying waters. Here we used phosphate oxygen isotope ratios (δ18Op) in concert with sediment chemistry, XRD, and Mössbauer spectroscopy on the sediment retrieved from an organic rich, sulfidic site in the meso-haline portion of the mid-bay to identify sources and pathway of sedimentary P cycling and to infer potential feedback effect on bottom water hypoxia and surface water eutrophication. Isotope data indicate that the regeneration of inorganic P from organic matter degradation (remineralization) is the predominant, if not sole, pathway for authigenic P precipitation in the mid-bay sediments. We interpret that the excess inorganic P generated by remineralization should have overwhelmed any bottom-water and/or pore-water P derived from other sources or biogeochemical processes and exceeded saturation with respect to authigenic P precipitation. It is the first research that identifies the predominance of remineralization pathway against remobilization (coupled Fe-P cycling) pathway in the Chesapeake Bay. Therefore, these results are expected to have significant implications for the current understanding of P cycling and benthic-pelagic coupling in the bay, particularly on the
Joshi, Sunendra R; Kukkadapu, Ravi K; Burdige, David J; Bowden, Mark E; Sparks, Donald L; Jaisi, Deb P
2015-05-19
Chesapeake Bay, the largest and most productive estuary in the U.S., suffers from varying degrees of water quality issues fueled by both point and nonpoint nutrient sources. Restoration of the Bay is complicated by the multitude of nutrient sources, their variable inputs, and complex interaction between imported and regenerated nutrients. These complexities not only restrict formulation of effective restoration plans but also open up debates on accountability issues with nutrient loading. A detailed understanding of sediment phosphorus (P) dynamics provides information useful in identifying the exchange of dissolved constituents across the sediment-water interface as well as helps to better constrain the mechanisms and processes controlling the coupling between sediments and the overlying waters. Here we used phosphate oxygen isotope ratios (δ(18)O(P)) in concert with sediment chemistry, X-ray diffraction, and Mössbauer spectroscopy on sediments retrieved from an organic rich, sulfidic site in the mesohaline portion of the mid-Bay to identify sources and pathway of sedimentary P cycling and to infer potential feedbacks on bottom water hypoxia and surface water eutrophication. Authigenic phosphate isotope data suggest that the regeneration of inorganic P from organic matter degradation (remineralization) is the predominant, if not sole, pathway for authigenic P precipitation in the mid-Bay sediments. This indicates that the excess inorganic P generated by remineralization should have overwhelmed any pore water and/or bottom water because only a fraction of this precipitates as authigenic P. This is the first research that identifies the predominance of remineralization pathway and recycling of P within the Chesapeake Bay. Therefore, these results have significant implications on the current understanding of sediment P cycling and P exchange across the sediment-water interface in the Bay, particularly in terms of the sources and pathways of P that sustain hypoxia
Rohatgi, Vijay K
2003-01-01
Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Contents: 1. Introduction. 2. Probability Model. 3. Probability Distributions. 4. Introduction to Statistical Inference. 5. More on Mathematical Expectation. 6. Some Discrete Models. 7. Some Continuous Models. 8. Functions of Random Variables and Random Vectors. 9. Large-Sample Theory. 10. General Meth
Marine Plastic Pollution in Waters around Australia: Characteristics, Concentrations, and Pathways
Julia Reisser; Jeremy Shaw; Chris Wilcox; Britta Denise Hardesty; Maira Proietti; Michele Thums; Charitha Pattiaratchi
2013-01-01
Plastics represent the vast majority of human-made debris present in the oceans. However, their characteristics, accumulation zones, and transport pathways remain poorly assessed. We characterised and estimated the concentration of marine plastics in waters around Australia using surface net tows, and inferred their potential pathways using particle-tracking models and real drifter trajectories. The 839 marine plastics recorded were predominantly small fragments ("microplastics", median lengt...
Recessively transmitted predominantly motor neuropathies.
Parman, Yeşim; Battaloğlu, Esra
2013-01-01
Recessively transmitted predominantly motor neuropathies are rare and show a severe phenotype. They are frequently observed in populations with a high rate of consanguineous marriages. At least 15 genes and six loci have been found to be associated with autosomal recessive CMT (AR-CMT) and X-linked CMT (AR-CMTX) and also distal hereditary motor neuronopathy (AR-dHMN). These disorders are genetically heterogeneous but the clinical phenotype is relatively homogeneous. Distal muscle weakness and atrophy predominating in the lower extremities, diminished or absent deep tendon reflexes, distal sensory loss, and pes cavus are the main clinical features of this disorder with occasional cranial nerve involvement. Although genetic diagnosis of some of subtypes of AR-CMT are now available, rapid advances in the molecular genetics and cell biology show a great complexity. Animal models for the most common subtypes of human AR-CMT disease provide clues for understanding the pathogenesis of CMT and also help to reveal possible treatment strategies of inherited neuropathies. This chapter highlights the clinical features and the recent genetic and biological findings in these disorders based on the current classification.
Predominant discourses in Swedish nursing.
Dahlborg-Lyckhage, Elisabeth; Pilhammar-Anderson, Ewa
2009-05-01
The aim of this study was to elucidate the predominant discourse in the field of Swedish nursing in 2000, 25 years after nursing was introduced as an academic discipline in Sweden. The method used was content analysis and deconstructive analysis of discourses. Laws, statutes, regulations, and examination requirements, including official reports, recruitment campaigns, and media coverage, were analyzed. The findings uncovered competing discourses striving to gain hegemony. In the public sector, official requirements competed against the media fixation on gender stereotypes and the realities of local recruitment campaigns. Media has a major role in disseminating prevailing conceptions and conventions pertaining to the nursing profession. As a result, decision makers, students, patients, and family members could get lower expectations of the professional competence of nursing practitioners than would otherwise have been the case in the absence of media exposure.
Patrick, Ellis; Buckley, Michael; Müller, Samuel; Lin, David M; Yang, Jean Y H
2015-09-01
In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature. This framework is implemented as an R function, pMim, in the package sydSeq available from http://www.ellispatrick.com/r-packages. jean.yang@sydney.edu.au Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Directory of Open Access Journals (Sweden)
M. P. McCartney
1999-01-01
Full Text Available Dambos, seasonally saturated wetlands, are widespread in headwater catchments in sub-Saharan Africa. It is widely believed that they play an important role in regional hydrology but, despite research conducted over the last 25 years, their hydrological functions remain poorly understood. To improve conceptualisation of hydrological flow paths and investigate the water balance of a small Zimbabwean catchment containing a single dambo, measurements of alkalinity and chloride in different water types within the catchment have been used as chemical markers. The temporal variation in alkalinity is consistent with the premise that all stream water, including the prolonged dry season recession, is derived predominantly from shallow sources. The proposition that dry season recession flows are maintained by water travelling at depth within the underlying saprolite is not substantiated. There is evidence that a low permeability clay lens, commonly present in many dambos, acts as a barrier for vertical water exchange. However, the highly heterogeneous chemical composition of different waters precludes quantitative hydrograph split-ting using end member mixing analysis. Calculation of the chloride mass-balance confirms that, after rainfall, evaporation is the largest component of the catchment water budget. The study provides improved understanding of the hydrological functioning of dambos. Such understanding is essential for the development and implementation of sustainable management strategies for this landform.
Gomi, Nichina; Yoshida, Shuji; Matsumoto, Kazutsugu; Okudomi, Masayuki; Konno, Hiroki; Hisabori, Toru; Sugano, Yasushi
2011-11-01
We examined the degradation of amaranth, a representative azo dye, by Bjerkandera adusta Dec 1. The degradation products were analyzed by high performance liquid chromatography (HPLC), visible absorbance, and electrospray ionization time-of-flight mass spectroscopy (ESI-TOF-MS). At the primary culture stage (3 days), the probable reaction intermediates were 1-aminonaphthalene-2,3,6-triol, 4-(hydroxyamino) naphthalene-1-ol, and 2-hydroxy-3-[2-(4-sulfophenyl) hydrazinyl] benzenesulfonic acid. After 10 days, the reaction products detected were 4-nitrophenol, phenol, 2-hydroxy-3-nitrobenzenesulfonic acid, 4-nitrobenzene sulfonic acid, and 3,4'-disulfonyl azo benzene, suggesting that no aromatic amines were created. Manganese-dependent peroxidase activity increased sharply after 3 days culture. Based on these results, we herein propose, for the first time, a degradation pathway for amaranth. Our results suggest that Dec 1 degrades amaranth via the combined activities of peroxidase and hydrolase and reductase action.
HOLLEMA, H; VISSER, L; POPPEMA, S
1991-01-01
In this study, we compared small lymphocytic lymphomas with predominant lymphadenopathy with those with predominant splenomegaly and found differences in morphology and immunophenotype as well as clinical features. Cases with lymphadenopathy were characterized by widespread disease, CLL type
DEFF Research Database (Denmark)
Andersen, Jesper
2009-01-01
Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set ...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....
Insights on predominant edible bamboo shoot proteins
African Journals Online (AJOL)
hp pc
nutritive value and health enhancing properties; making it a suitable candidate for food security. Quantitative ... data that edible bamboo species as healthy food and a rich source of protein. ..... loci impedes accurate phylogenetic inference of bamboo species ... (P < 0.05) they failed the FDR test at cut-off value ≤ 1%.
Energy Technology Data Exchange (ETDEWEB)
Petrov, S.
1996-10-01
Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.
Nagao, Makoto
1990-01-01
Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig
Nodular lymphocyte-predominant Hodgkin lymphoma.
Savage, Kerry J; Mottok, Anja; Fanale, Michelle
2016-07-01
Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare subtype of Hodgkin lymphoma with distinct clinicopathologic features. It is typified by the presence of lymphocyte predominant (LP) cells, which are CD20(+) but CD15(-) and CD30(-) and are found scattered amongst small B lymphocytes arranged in a nodular pattern. Despite frequent and often late or multiple relapses, the prognosis of NLPHL is very favorable. There is an inherent risk of secondary aggressive non-Hodgkin lymphoma (NHL) and studies support that risk is highest in those with splenic involvement at presentation. Given disease rarity, the optimal management is unclear and opinions differ as to whether treatment paradigms should be similar to or differ from those for classical Hodgkin lymphoma (CHL). This review provides an overview of the existing literature describing pathological subtypes, outcome and treatment approaches for NLPHL.
Porphyromonas gingivalis: predominant pathogen in chronic periodontitis
Ramos Perfecto, Donald; Dpto. de CC. Básicas. Laboratorio de Microbiología Facultad de Odontología Universidad Nacional Mayor de San Marcos.; Moromi Nakata, Hilda; Dpto. de CC. Básicas. Laboratorio de Microbiología Facultad de Odontología Universidad Nacional Mayor de San Marcos.; Martínez Cadillo, Elba; Dpto. de CC. Básicas. Laboratorio de Microbiología Facultad de Odontología Universidad Nacional Mayor de San Marcos.
2014-01-01
Porphyromonas gingivalis is a gram negative bacillus predominant in chronic periodontitis, multiple virulence factors make it extremely aggressive. In the gingival sulcus find the conditions for growth,interacting with the host produces a slow but steady destruction of periodontal tissue. Its dominance has been considered a risk factor for systemic inflammatory diseases such as myocardial infarction. Although its susceptibility to a variety of drugs makes possible its handling with antimicrob...
Scaling Property in the Alpha Predominant EEG
Lin, D C; Kwan, H; Lin, Der Chyan; Sharif, Asif; Kwan, Hon
2004-01-01
The $\\alpha$ predominant electroencephalographic (EEG) recording of the human brain during eyes open and closed is studied using the zero-crossing time statistics. A model is presented to demonstrate and compare the key characteristics of the brain state. We found the zero-crossing time statistic is more accurate than the power spectral analysis and the detrend fluctuation analysis. Our results indicate different EEG fractal scaling in eyes closed and open for individuals capable of strong $\\alpha$ rhythm.
Probability and Statistical Inference
Prosper, Harrison B.
2006-01-01
These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.
Introductory statistical inference
Mukhopadhyay, Nitis
2014-01-01
This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of dist
Meta-learning framework applied in bioinformatics inference system design.
Arredondo, Tomás; Ormazábal, Wladimir
2015-01-01
This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.
Energy Technology Data Exchange (ETDEWEB)
Hanson, K.M.; Cunningham, G.S.
1996-04-01
The authors are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model.
Knuth, Kevin H
2010-01-01
We present a foundation for inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying finite lattices of logical statements in a way that satisfies general lattice symmetries. With other applications in mind, our derivations assume minimal symmetries, relying on neither complementarity nor continuity or differentiability. Each relevant symmetry corresponds to an axiom of quantification, and these axioms are used to derive a unique set of rules governing quantification of the lattice. These rules form the familiar probability calculus. We also derive a unique quantification of divergence and information. Taken together these results form a simple and clear foundation for the quantification of inference.
Making Type Inference Practical
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Oxhøj, Nicholas; Palsberg, Jens
1992-01-01
We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding. The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as List, in a useful way. Abo....... Experiments indicate that the implementation type checks as much as 100 lines pr. second. This results in a mature product, on which a number of tools can be based, for example a safety tool, an image compression tool, a code optimization tool, and an annotation tool. This may make type inference for object...
[Left predominance of varices: myth or reality?].
Cornu-Thénard, A; Maraval, M; Boivin, P; Parpex, P
1986-01-01
The study of 843 legs operated for major varices shows that they are equally distributed between the two lower limbs (48.6% on the right, 51.4% on the left). There is little sex-determined variation in this distribution (410 women - 184 men), the main difference being that found in men: +4.6% on the left. Other studies carried out in Europe come to much the same conclusion. Two of these studies do, however, note a much clearer predominance of left-leg varices in men (+10%). For some studies, the lack of information about the type of varices being considered has proved troublesome (for example the many isolated telangiectasis and varices) and means that it is impossible to come to any exact conclusion. Clinical quantification is therefore desirable: at least it takes into account the diameter of the varices studied.
Vertigo as a Predominant Manifestation of Neurosarcoidosis
Directory of Open Access Journals (Sweden)
Tasnim F. Imran
2015-01-01
Full Text Available Sarcoidosis is a granulomatous disease of unknown etiology that affects multiple organ systems. Neurological manifestations of sarcoidosis are less common and can include cranial neuropathies and intracranial lesions. We report the case of a 21-year-old man who presented with vertigo and uveitis. Extensive workup including brain imaging revealed enhancing focal lesions. A lacrimal gland biopsy confirmed the diagnosis of sarcoidosis. The patient was initially treated with prednisone, which did not adequately control his symptoms, and then was switched to methotrexate with moderate symptomatic improvement. Our patient had an atypical presentation with vertigo as the predominant manifestation of sarcoidosis. Patients with neurosarcoidosis typically present with systemic involvement of sarcoidosis followed by neurologic involvement. Vertigo is rarely reported as an initial manifestation. This case highlights the importance of consideration of neurosarcoidosis as an entity even in patients that may not have a typical presentation or systemic involvement of disease.
Watson, Jane
2007-01-01
Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…
Predominant palmoplantar lichen planus: A diagnostic challenge
Directory of Open Access Journals (Sweden)
Rameshwar Gutte
2014-01-01
Full Text Available Background: Palmoplantar lesions in lichen planus (LP are uncommon. In such cases, diagnosis is usually missed. This study was conducted to document various clinical and histopathological features of palmoplantar LP. Materials And Methods: A total of 18 patients from our outpatient department with lesions of LP, either predominantly or exclusively on palms and/or soles were studied. Patients with history of drug intake in recent past and patients with classical acute widespread LP with a few lesions on palms or soles were excluded. In each patient, diagnosis was made on clinicopathological correlation. Various clinical and histopathological features were analyzed. Results: Average age of onset was 38 years. Male: female ratio was 1:0.6 and average disease duration was 11 months. Exclusive palm or sole involvement was seen in 4/18 patients. Itching was the most common symptom. Clinically the most common variant was hypertrophic. Histologically presence of parakeratosis, spongiosis, lack of melanophages, and lack of hypergranulosis in some cases was seen in addition to classical features of LP. In 3 out of 4 patients with exclusive palmoplantar involvement diagnosis of LP was missed clinically. Conclusion: Involvement of palms and soles in LP poses a diagnostic challenge due to variable presentations. Histopathology is of vital importance for correct diagnosis and treatment.
Predominance of sperm motion in corners
Nosrati, Reza; Graham, Percival J.; Liu, Qiaozhi; Sinton, David
2016-05-01
Sperm migration through the female tract is crucial to fertilization, but the role of the complex and confined structure of the fallopian tube in sperm guidance remains unknown. Here, by confocal imaging microchannels head-on, we distinguish corner- vs. wall- vs. bulk-swimming bull sperm in confined geometries. Corner-swimming dominates with local areal concentrations as high as 200-fold that of the bulk. The relative degree of corner-swimming is strongest in small channels, decreases with increasing channel size, and plateaus for channels above 200 μm. Corner-swimming remains predominant across the physiologically-relevant range of viscosity and pH. Together, boundary-following sperm account for over 95% of the sperm distribution in small rectangular channels, which is similar to the percentage of wall swimmers in circular channels of similar size. We also demonstrate that wall-swimming sperm travel closer to walls in smaller channels (~100 μm), where the opposite wall is within the hydrodynamic interaction length-scale. The corner accumulation effect is more than the superposition of the influence of two walls, and over 5-fold stronger than that of a single wall. These findings suggest that folds and corners are dominant in sperm migration in the narrow (sub-mm) lumen of the fallopian tube and microchannel-based sperm selection devices.
Predominant bacteria diversity in Chinese traditional sourdough.
Zhang, Guohua; He, Guoqing
2013-08-01
The purpose of this study was to identify the major bacteria in Chinese traditional sourdough (CTS). Five CTS samples (Hn-87, Sx-91, Gs-107, Hf-112, and Hr-122) were collected from different Chinese steamed breads shops or private households. The total bacterial DNA was extracted from sourdough samples and sequenced using Illumina Hiseq 2000 system. Illumina tags were assigned to BLASTN server based on 16S rRNA libraries to reveal a genetic profile. Phylogenetic analysis revealed that the bacteria in traditional sourdough samples were dominated by the genera Leuconostoc and Lactobacillus. Beta diversity analysis, principal component analysis, and cluster analysis compared the bacterial differences in traditional sourdough samples. The results showed that Leuconostoc, Lactobacillus, and Weissella were the predominant genera among the 5 samples. This differentiated the sourdoughs into 3 typologies, namely, 1) Gs-107 and Sx-91, 2) Hr-122 and Hn-87, and 3) Hf-112. This study identified 3 unique major bacteria genus in CTS bread ecosystems.
Causal inference in econometrics
Kreinovich, Vladik; Sriboonchitta, Songsak
2016-01-01
This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
Directory of Open Access Journals (Sweden)
João Paulo Monteiro
2001-12-01
Full Text Available Russell's The Problems of Philosophy tries to establish a new theory of induction, at the same time that Hume is there accused of an irrational/ scepticism about induction". But a careful analysis of the theory of knowledge explicitly acknowledged by Hume reveals that, contrary to the standard interpretation in the XXth century, possibly influenced by Russell, Hume deals exclusively with causal inference (which he never classifies as "causal induction", although now we are entitled to do so, never with inductive inference in general, mainly generalizations about sensible qualities of objects ( whether, e.g., "all crows are black" or not is not among Hume's concerns. Russell's theories are thus only false alternatives to Hume's, in (1912 or in his (1948.
Stochastic processes inference theory
Rao, Malempati M
2014-01-01
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Energy Technology Data Exchange (ETDEWEB)
KENNETH M. HANSON; JANE M. BOOKER
2000-09-08
The authors an uncertainty analysis of data taken using the Rossi technique, in which the horizontal oscilloscope sweep is driven sinusoidally in time ,while the vertical axis follows the signal amplitude. The analysis is done within a Bayesian framework. Complete inferences are obtained by tilting the Markov chain Monte Carlo technique, which produces random samples from the posterior probability distribution expressed in terms of the parameters.
Inferring Microbial Fitness Landscapes
2016-02-25
experiments on evolving microbial populations. Although these experiments have produced examples of remarkable phenomena – e.g. the emergence of mutator...what specific mutations, avian influenza viruses will adapt to novel human hosts; or how readily infectious bacteria will escape antibiotics or the...infer from data the determinants of microbial evolution with sufficient resolution that we can quantify 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND
Continuous Integrated Invariant Inference Project
National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...
Probabilistic Inferences in Bayesian Networks
Ding, Jianguo
2010-01-01
This chapter summarizes the popular inferences methods in Bayesian networks. The results demonstrates that the evidence can propagated across the Bayesian networks by any links, whatever it is forward or backward or intercausal style. The belief updating of Bayesian networks can be obtained by various available inference techniques. Theoretically, exact inferences in Bayesian networks is feasible and manageable. However, the computing and inference is NP-hard. That means, in applications, in ...
Multimodel inference and adaptive management
Rehme, S.E.; Powell, L.A.; Allen, C.R.
2011-01-01
Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.
Nanotechnology and statistical inference
Vesely, Sara; Vesely, Leonardo; Vesely, Alessandro
2017-08-01
We discuss some problems that arise when applying statistical inference to data with the aim of disclosing new func-tionalities. A predictive model analyzes the data taken from experiments on a specific material to assess the likelihood that another product, with similar structure and properties, will exhibit the same functionality. It doesn't have much predictive power if vari-ability occurs as a consequence of a specific, non-linear behavior. We exemplify our discussion on some experiments with biased dice.
Directory of Open Access Journals (Sweden)
Kevin H. Knuth
2012-06-01
Full Text Available We present a simple and clear foundation for finite inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying lattices of logical statements in a way that satisfies general lattice symmetries. With other applications such as measure theory in mind, our derivations assume minimal symmetries, relying on neither negation nor continuity nor differentiability. Each relevant symmetry corresponds to an axiom of quantification, and these axioms are used to derive a unique set of quantifying rules that form the familiar probability calculus. We also derive a unique quantification of divergence, entropy and information.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
DEFF Research Database (Denmark)
Andersen, Jesper; Lawall, Julia
2010-01-01
A key issue in maintaining Linux device drivers is the need to keep them up to date with respect to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spdiff, that identifies common changes...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...
Wu, Yonghua; Wang, Haifeng; Hadly, Elizabeth A.
2017-01-01
Nocturnality is a key evolutionary innovation of mammals that enables mammals to occupy relatively empty nocturnal niches. Invasion of ancestral mammals into nocturnality has long been inferred from the phylogenetic relationships of crown Mammalia, which is primarily nocturnal, and crown Reptilia, which is primarily diurnal, although molecular evidence for this is lacking. Here we used phylogenetic analyses of the vision genes involved in the phototransduction pathway to predict the diel activity patterns of ancestral mammals and reptiles. Our results demonstrated that the common ancestor of the extant Mammalia was dominated by positive selection for dim-light vision, supporting the predominate nocturnality of the ancestral mammals. Further analyses showed that the nocturnality of the ancestral mammals was probably derived from the predominate diurnality of the ancestral amniotes, which featured strong positive selection for bright-light vision. Like the ancestral amniotes, the common ancestor of the extant reptiles and various taxa in Squamata, one of the main competitors of the temporal niches of the ancestral mammals, were found to be predominate diurnality as well. Despite this relatively apparent temporal niche partitioning between ancestral mammals and the relevant reptiles, our results suggested partial overlap of their temporal niches during crepuscular periods. PMID:28425474
Statistical inferences in phylogeography
DEFF Research Database (Denmark)
Nielsen, Rasmus; Beaumont, Mark A
2009-01-01
In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process ...... may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods....... can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis...... is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods...
Moment inference from tomograms
Day-Lewis, F. D.; Chen, Y.; Singha, K.
2007-01-01
Time-lapse geophysical tomography can provide valuable qualitative insights into hydrologic transport phenomena associated with aquifer dynamics, tracer experiments, and engineered remediation. Increasingly, tomograms are used to infer the spatial and/or temporal moments of solute plumes; these moments provide quantitative information about transport processes (e.g., advection, dispersion, and rate-limited mass transfer) and controlling parameters (e.g., permeability, dispersivity, and rate coefficients). The reliability of moments calculated from tomograms is, however, poorly understood because classic approaches to image appraisal (e.g., the model resolution matrix) are not directly applicable to moment inference. Here, we present a semi-analytical approach to construct a moment resolution matrix based on (1) the classic model resolution matrix and (2) image reconstruction from orthogonal moments. Numerical results for radar and electrical-resistivity imaging of solute plumes demonstrate that moment values calculated from tomograms depend strongly on plume location within the tomogram, survey geometry, regularization criteria, and measurement error. Copyright 2007 by the American Geophysical Union.
Inferring attitudes from mindwandering.
Critcher, Clayton R; Gilovich, Thomas
2010-09-01
Self-perception theory posits that people understand their own attitudes and preferences much as they understand others', by interpreting the meaning of their behavior in light of the context in which it occurs. Four studies tested whether people also rely on unobservable "behavior," their mindwandering, when making such inferences. It is proposed here that people rely on the content of their mindwandering to decide whether it reflects boredom with an ongoing task or a reverie's irresistible pull. Having the mind wander to positive events, to concurrent as opposed to past activities, and to many events rather than just one tends to be attributed to boredom and therefore leads to perceived dissatisfaction with an ongoing task. Participants appeared to rely spontaneously on the content of their wandering minds as a cue to their attitudes, but not when an alternative cause for their mindwandering was made salient.
Bayesian inference in geomagnetism
Backus, George E.
1988-01-01
The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear Gaussian BI. The problem of estimating the radial magnetic field B(r) at the earth core-mantle boundary from surface and satellite measurements is then analyzed in detail, with specific attention to the selection of lambda in the studies of Gubbins (1983) and Gubbins and Bloxham (1985). It is argued that the selection method is inappropriate and leads to lambda values much larger than those that would result if a reasonable bound on the heat flow at the CMB were assumed.
Inferring the eccentricity distribution
Hogg, David W; Bovy, Jo
2010-01-01
Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual-star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision--other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, parallaxes, or photometr...
Inferring deterministic causal relations
Daniusis, Povilas; Mooij, Joris; Zscheischler, Jakob; Steudel, Bastian; Zhang, Kun; Schoelkopf, Bernhard
2012-01-01
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.
Admissibility of logical inference rules
Rybakov, VV
1997-01-01
The aim of this book is to present the fundamental theoretical results concerning inference rules in deductive formal systems. Primary attention is focused on: admissible or permissible inference rules the derivability of the admissible inference rules the structural completeness of logics the bases for admissible and valid inference rules. There is particular emphasis on propositional non-standard logics (primary, superintuitionistic and modal logics) but general logical consequence relations and classical first-order theories are also considered. The book is basically self-contained and
Bessonov, Kyrylo; Van Steen, Kristel
2016-12-01
Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs, based on Conditional Inference Forests (CIFs) as proposed by Strobl et al. Our framework consists of using ensembles of Conditional Inference Trees (CITs) and selecting an appropriate aggregation scheme for variant selection prior to network construction. We show on synthetic microarray data that taking the original implementation of CIFs with conditional permutation scheme (CIFcond ) may lead to improved performance compared to Breiman's implementation of Random Forests (RF). Among all newly introduced CIF-based methods and five network scenarios obtained from the DREAM4 challenge, CIFcond performed best. Networks derived from well-tuned CIFs, obtained by simply averaging P-values over tree ensembles (CIFmean ) are particularly attractive, because they combine adequate performance with computational efficiency. Moreover, thresholds for variable selection are based on significance levels for P-values and, hence, do not need to be tuned. From a practical point of view, our extensive simulations show the potential advantages of CIFmean -based methods. Although more work is needed to improve on speed, especially when fully exploiting the advantages of CITs in the context of heterogeneous and correlated data, we have shown that CIF methodology can be flexibly inserted in a framework to infer biological interactions. Notably, we confirmed biologically relevant interaction between IL2RA and FOXP1, linked to the IL-2 signaling pathway and to type 1 diabetes.
An Inference Language for Imaging
DEFF Research Database (Denmark)
Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen
2014-01-01
We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framework...
Interactive Instruction in Bayesian Inference
DEFF Research Database (Denmark)
Khan, Azam; Breslav, Simon; Hornbæk, Kasper
2017-01-01
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...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....
Causal Inference and Developmental Psychology
Foster, E. Michael
2010-01-01
Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…
Causal Inference and Developmental Psychology
Foster, E. Michael
2010-01-01
Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…
Harik, Georges
2010-01-01
We introduce a framework for representing a variety of interesting problems as inference over the execution of probabilistic model programs. We represent a "solution" to such a problem as a guide program which runs alongside the model program and influences the model program's random choices, leading the model program to sample from a different distribution than from its priors. Ideally the guide program influences the model program to sample from the posteriors given the evidence. We show how the KL- divergence between the true posterior distribution and the distribution induced by the guided model program can be efficiently estimated (up to an additive constant) by sampling multiple executions of the guided model program. In addition, we show how to use the guide program as a proposal distribution in importance sampling to statistically prove lower bounds on the probability of the evidence and on the probability of a hypothesis and the evidence. We can use the quotient of these two bounds as an estimate of ...
Energy Technology Data Exchange (ETDEWEB)
Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Ahn, Sungsoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Shin, Jinwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)
2017-05-25
Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where meanfield (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.
Statistical Inference and String Theory
Heckman, Jonathan J
2013-01-01
In this note we expose some surprising connections between string theory and statistical inference. We consider a large collective of agents sweeping out a family of nearby statistical models for an M-dimensional manifold of statistical fitting parameters. When the agents making nearby inferences align along a d-dimensional grid, we find that the pooled probability that the collective reaches a correct inference is the partition function of a non-linear sigma model in d dimensions. Stability under perturbations to the original inference scheme requires the agents of the collective to distribute along two dimensions. Conformal invariance of the sigma model corresponds to the condition of a stable inference scheme, directly leading to the Einstein field equations for classical gravity. By summing over all possible arrangements of the agents in the collective, we reach a string theory. We also use this perspective to quantify how much an observer can hope to learn about the internal geometry of a superstring com...
Waveform simulation of predominant periods in Osaka basin
Petukhin, A.; Tsurugi, M.
2016-12-01
Predominant period of strong ground motions is an important parameter in earthquake engineering practice. Resonance at predominant period may result in collapse of building. Usually, predominant periods are associated with the soil resonances. However, considering that strong ground motions are composed from source, path and site effects, predominant periods are affected by source and propagation path too. From another side, 3D basin interferences may amplify quite different periods, depending on site location relatively to the basin edges and independently on the soil depth. Moreover, constructive or destructive interference of waves from different asperities of a large source may enhance or diminish amplitudes at a particular predominant period respectively. In this study, to demonstrate variations of predominant periods due to complicated effects above, we simulated wavefield snapshots and waveforms at a few representative sites of Osaka basin, Japan. Seismic source is located in Nankai trough, hosting anticipated M9 earthquake. 3D velocity structure is combined from JIVSM velocity structure (Koketsu et al., 2012) and Osaka basin structure of Iwaki and Iwata, 2011. 3D-FDM method is used to simulate waveforms. Simulation results confirm some previous results that due to elongated elliptical shape of Osaka basin, interference effects are strong and peak amplitudes has characteristic stripped pattern elongated in parallel to the long axis of basin. We demonstrate that predominant periods have similar pattern and value of predominant period may strongly depend on the location of site and azimuthal orientation of waveform component.
75 FR 38797 - Predominantly Black Institutions Formula Grant Program
2010-07-06
... [Federal Register Volume 75, Number 128 (Tuesday, July 6, 2010)] [Notices] [Pages 38797-38798] [FR Doc No: 2010-16376] DEPARTMENT OF EDUCATION [CFDA No. 84.031P] Predominantly Black Institutions... applications for new awards for FY 2010 for the Predominantly Black Institutions Formula Grant Program...
Hao, Li-Ping; Lü, Fan; He, Pin-Jing; Li, Lei; Shao, Li-Ming
2011-01-15
To quantify the contribution of syntrophic acetate oxidation to thermophilic anaerobic methanogenesis under the stressed condition induced by acidification, the methanogenic conversion process of 100 mmol/L acetate was monitored simultaneously by using isotopic tracing and selective inhibition techniques, supplemented with the analysis of unculturable microorganisms. Both quantitative methods demonstrated that, in the presence of aceticlastic and hydrogenotrophic methanogens, a large percentage of methane (up to 89%) was initially derived from CO(2) reduction, indicating the predominant contribution of the syntrophic acetate oxidation pathway to acetate degradation at high acid concentrations. A temporal decrease of the fraction of hydrogenotrophic methanogenesis from more than 60% to less than 40% reflected the gradual prevalence of the aceticlastic methanogenesis pathway along with the reduction of acetate. This apparent discrimination of acetate methanization pathways highlighted the importance of the syntrophic acetate-oxidizing bacteria to initialize methanogenesis from high organic loadings.
Reinforcement and inference in cross-situational word learning
Directory of Open Access Journals (Sweden)
Paulo F.C. Tilles
2013-11-01
Full Text Available Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.
Optimization methods for logical inference
Chandru, Vijay
2011-01-01
Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in
Statistical inference via fiducial methods
Salomé, Diemer
1998-01-01
In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary
On principles of inductive inference
Kostecki, Ryszard Paweł
2011-01-01
We propose an intersubjective epistemic approach to foundations of probability theory and statistical inference, based on relative entropy and category theory, and aimed to bypass the mathematical and conceptual problems of existing foundational approaches.
Type Inference for Guarded Recursive Data Types
Stuckey, Peter J.; Sulzmann, Martin
2005-01-01
We consider type inference for guarded recursive data types (GRDTs) -- a recent generalization of algebraic data types. We reduce type inference for GRDTs to unification under a mixed prefix. Thus, we obtain efficient type inference. Inference is incomplete because the set of type constraints allowed to appear in the type system is only a subset of those type constraints generated by type inference. Hence, inference only succeeds if the program is sufficiently type annotated. We present refin...
Statistical Inference in Graphical Models
2008-06-17
Probabilistic Network Library ( PNL ). While not fully mature, PNL does provide the most commonly-used algorithms for inference and learning with the efficiency...of C++, and also offers interfaces for calling the library from MATLAB and R 1361. Notably, both BNT and PNL provide learning and inference algorithms...mature and has been used for research purposes for several years, it is written in MATLAB and thus is not suitable to be used in real-time settings. PNL
Implementing Deep Inference in Tom
Kahramanogullari, Ozan; Moreau, Pierre-Etienne; Reilles, Antoine
2005-01-01
ISSN 1430-211X; The calculus of structures is a proof theoretical formalism which generalizes sequent calculus with the feature of deep inference: in contrast to sequent calculus, the calculus of structures does not rely on the notion of main connective and, like in term rewriting, it permits the application of the inference rules at any depth inside a formula. Tom is a pattern matching processor that integrates term rewriting facilities into imperative languages. In this paper, relying on th...
An Inference Language for Imaging
DEFF Research Database (Denmark)
Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen
2014-01-01
We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framewor......-accelerated primitives specializes iLang to the spatial data-structures that arise in imaging applications. We illustrate the framework through a challenging application: spatio-temporal tomographic reconstruction with compressive sensing....
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.
Statistical Inference: The Big Picture.
Kass, Robert E
2011-02-01
Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labelled here statistical pragmatism, serves as a foundation for inference. Statistical pragmatism is inclusive and emphasizes the assumptions that connect statistical models with observed data. I argue that introductory courses often mis-characterize the process of statistical inference and I propose an alternative "big picture" depiction.
Abductive inference and delusional belief.
Coltheart, Max; Menzies, Peter; Sutton, John
2010-01-01
Delusional beliefs have sometimes been considered as rational inferences from abnormal experiences. We explore this idea in more detail, making the following points. First, the abnormalities of cognition that initially prompt the entertaining of a delusional belief are not always conscious and since we prefer to restrict the term "experience" to consciousness we refer to "abnormal data" rather than "abnormal experience". Second, we argue that in relation to many delusions (we consider seven) one can clearly identify what the abnormal cognitive data are which prompted the delusion and what the neuropsychological impairment is which is responsible for the occurrence of these data; but one can equally clearly point to cases where this impairment is present but delusion is not. So the impairment is not sufficient for delusion to occur: a second cognitive impairment, one that affects the ability to evaluate beliefs, must also be present. Third (and this is the main thrust of our paper), we consider in detail what the nature of the inference is that leads from the abnormal data to the belief. This is not deductive inference and it is not inference by enumerative induction; it is abductive inference. We offer a Bayesian account of abductive inference and apply it to the explanation of delusional belief.
Active inference, communication and hermeneutics.
Friston, Karl J; Frith, Christopher D
2015-07-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Inferring tumor progression from genomic heterogeneity.
Navin, Nicholas; Krasnitz, Alexander; Rodgers, Linda; Cook, Kerry; Meth, Jennifer; Kendall, Jude; Riggs, Michael; Eberling, Yvonne; Troge, Jennifer; Grubor, Vladimir; Levy, Dan; Lundin, Pär; Månér, Susanne; Zetterberg, Anders; Hicks, James; Wigler, Michael
2010-01-01
Cancer progression in humans is difficult to infer because we do not routinely sample patients at multiple stages of their disease. However, heterogeneous breast tumors provide a unique opportunity to study human tumor progression because they still contain evidence of early and intermediate subpopulations in the form of the phylogenetic relationships. We have developed a method we call Sector-Ploidy-Profiling (SPP) to study the clonal composition of breast tumors. SPP involves macro-dissecting tumors, flow-sorting genomic subpopulations by DNA content, and profiling genomes using comparative genomic hybridization (CGH). Breast carcinomas display two classes of genomic structural variation: (1) monogenomic and (2) polygenomic. Monogenomic tumors appear to contain a single major clonal subpopulation with a highly stable chromosome structure. Polygenomic tumors contain multiple clonal tumor subpopulations, which may occupy the same sectors, or separate anatomic locations. In polygenomic tumors, we show that heterogeneity can be ascribed to a few clonal subpopulations, rather than a series of gradual intermediates. By comparing multiple subpopulations from different anatomic locations, we have inferred pathways of cancer progression and the organization of tumor growth.
Inferring interaction partners from protein sequences
Bitbol, Anne-Florence; Colwell, Lucy J; Wingreen, Ned S
2016-01-01
Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners. Hence, the sequences of interacting partners are correlated. Here we exploit these correlations to accurately identify which proteins are specific interaction partners from sequence data alone. Our general approach, which employs a pairwise maximum entropy model to infer direct couplings between residues, has been successfully used to predict the three-dimensional structures of proteins from sequences. Building on this approach, we introduce an iterative algorithm to predict specific interaction partners from among the members of two protein families. We assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. The algorithm proves successful without any a pri...
An Inference Language for Imaging
DEFF Research Database (Denmark)
Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen
2014-01-01
We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framework...... is composed of a set of language primitives and of an inference engine based on a message-passing system that integrates cutting-edge computational tools, including proximal algorithms and high performance Hamiltonian Markov Chain Monte Carlo techniques. A set of domain-specific highly optimized GPU......-accelerated primitives specializes iLang to the spatial data-structures that arise in imaging applications. We illustrate the framework through a challenging application: spatio-temporal tomographic reconstruction with compressive sensing....
Locative inferences in medical texts.
Mayer, P S; Bailey, G H; Mayer, R J; Hillis, A; Dvoracek, J E
1987-06-01
Medical research relies on epidemiological studies conducted on a large set of clinical records that have been collected from physicians recording individual patient observations. These clinical records are recorded for the purpose of individual care of the patient with little consideration for their use by a biostatistician interested in studying a disease over a large population. Natural language processing of clinical records for epidemiological studies must deal with temporal, locative, and conceptual issues. This makes text understanding and data extraction of clinical records an excellent area for applied research. While much has been done in making temporal or conceptual inferences in medical texts, parallel work in locative inferences has not been done. This paper examines the locative inferences as well as the integration of temporal, locative, and conceptual issues in the clinical record understanding domain by presenting an application that utilizes two key concepts in its parsing strategy--a knowledge-based parsing strategy and a minimal lexicon.
Sick, the spectroscopic inference crank
Casey, Andrew R
2016-01-01
There exists an inordinate amount of spectral data in both public and private astronomical archives which remain severely under-utilised. The lack of reliable open-source tools for analysing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this Article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick can be used to provide a nearest-neighbour estimate of model parameters, a numerically optimised point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalise on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-di...
T-cell-predominant lymphoid hyperplasia in a tattoo*
Souza, Erica Sales; Rocha, Bruno de Oliveira; Batista, Everton da Silva; de Oliveira, Rodrigo Ferreira; Farre, Lourdes; Bittencourt, Achilea Lisboa
2014-01-01
Cutaneous lymphoid hyperplasia (CLH) can be idiopathic or secondary to external stimuli, and is considered rare in tattoos. The infiltrate can be predominantly of B or T-cells, the latter being seldom reported in tattoos. We present a case of a predominantly T CLH, secondary to the black pigment of tattooing in a 35-year-old patient, with a dense infiltrate of small, medium and scarce large T-cells. Analysis of the rearrangement of T-cells receptor revealed a polyclonal proliferation. Since the infiltrate of CLH can simulate a T lymphoma, it is important to show that lesions from tattoos can have a predominance of T-cells. PMID:25387518
Eight challenges in phylodynamic inference
Directory of Open Access Journals (Sweden)
Simon D.W. Frost
2015-03-01
Full Text Available The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.
Automatic Inference of DATR Theories
Barg, P
1996-01-01
This paper presents an approach for the automatic acquisition of linguistic knowledge from unstructured data. The acquired knowledge is represented in the lexical knowledge representation language DATR. A set of transformation rules that establish inheritance relationships and a default-inference algorithm make up the basis components of the system. Since the overall approach is not restricted to a special domain, the heuristic inference strategy uses criteria to evaluate the quality of a DATR theory, where different domains may require different criteria. The system is applied to the linguistic learning task of German noun inflection.
Perception, illusions and Bayesian inference.
Nour, Matthew M; Nour, Joseph M
2015-01-01
Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms.
Object-Oriented Type Inference
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Palsberg, Jens
1991-01-01
We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op-timizing......We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op...
Career Advice: Finding a Job at a Predominantly Undergraduate Institution.
Ramirez, Julio J
2016-01-01
Seeking a teaching job at a predominantly undergraduate college or university can be a daunting proposition. Although reports from the Bureau of Labor Statistics suggest that the job market for teaching positions at postsecondary institutions will be healthy over the coming decade, competition for these positions will likely be intense. This essay explores the profiles of predominantly undergraduate institutions (PUIs), the nature of faculty positions at PUIs, the elements that make for a competitive job applicant, and strategies to consider during negotiations. Seeking a position at a PUI may be arduous at times, but the rewards reaped from a successful search for a PUI position are well worth the investment.
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....
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
On principles of inductive inference
Kostecki, Ryszard Paweł
2011-01-01
We discuss the mathematical and conceptual problems of main approaches to foundations of probability theory and statistical inference and propose new foundational approach, aimed to improve the mathematical structure of the theory and to bypass the old conceptual problems. In particular, we introduce the intersubjective interpretation of probability, which is designed to deal with the troubles of `subjective' and `objective' bayesian interpretations.
Regular inference as vertex coloring
Costa Florêncio, C.; Verwer, S.
2012-01-01
This paper is concerned with the problem of supervised learning of deterministic finite state automata, in the technical sense of identification in the limit from complete data, by finding a minimal DFA consistent with the data (regular inference). We solve this problem by translating it in its enti
Type inference for COBOL systems
Deursen, A. van; Moonen, L.M.F.
1998-01-01
Types are a good starting point for various software reengineering tasks. Unfortunately, programs requiring reengineering most desperately are written in languages without an adequate type system (such as COBOL). To solve this problem, we propose a method of automated type inference for these lang
Regular inference as vertex coloring
Costa Florêncio, C.; Verwer, S.
2012-01-01
This paper is concerned with the problem of supervised learning of deterministic finite state automata, in the technical sense of identification in the limit from complete data, by finding a minimal DFA consistent with the data (regular inference). We solve this problem by translating it in its
Statistical inference on variance components
Verdooren, L.R.
1988-01-01
In several sciences but especially in animal and plant breeding, the general mixed model with fixed and random effects plays a great role. Statistical inference on variance components means tests of hypotheses about variance components, constructing confidence intervals for them, estimating them,
Covering, Packing and Logical Inference
1993-10-01
of Operations Research 43 (1993). [34] *Hooker, J. N., Generalized resolution for 0-1 linear inequalities, Annals of Mathematics and A 16 271-286. [35...Hooker, J. N. and C. Fedjki, Branch-and-cut solution of inference prob- lems in propositional logic, Annals of Mathematics and AI 1 (1990) 123-140. [40
Mathematical Programming and Logical Inference
1990-12-01
solution of inference problems in propositional logic, to appear in Annals of Mathematics and Al. (271 Howard, R. A., and J. E. Matheson, Influence...1981). (281 Jeroslow, R., and J. Wang, Solving propositional satisfiability problems, to appear in Annals of Mathematics and Al. [29] Nilsson, N. J
An Introduction to Causal Inference
2009-11-02
legitimize causal inference, has removed causation from its natural habitat, and distorted its face beyond recognition. This exclusivist attitude is...In contrast, when the mediation problem is approached from an exclusivist potential-outcome viewpoint, void of the structural guidance of Eq. (28
CLEARANCE OF INDOMETHACIN OCCURS PREDOMINANTLY BY RENAL GLUCURONIDATION
MOOLENAAR, F; CRANCRINUS, S; VISSER, J; DEZEEUW, D; MEIJER, DKF
1992-01-01
In this report we describe the conditions of collection, storage and handling of urine samples, collected after oral dosing with indometacin in man, in order to maintain the integrity of the labile glucuronide formed. We found that the body clearance occurs predominantly by renal metabolism, due to
Spontaneous evaluative inferences and their relationship to spontaneous trait inferences.
Schneid, Erica D; Carlston, Donal E; Skowronski, John J
2015-05-01
Three experiments are reported that explore affectively based spontaneous evaluative impressions (SEIs) of stimulus persons. Experiments 1 and 2 used modified versions of the savings in relearning paradigm (Carlston & Skowronski, 1994) to confirm the occurrence of SEIs, indicating that they are equivalent whether participants are instructed to form trait impressions, evaluative impressions, or neither. These experiments also show that SEIs occur independently of explicit recall for the trait implications of the stimuli. Experiment 3 provides a single dissociation test to distinguish SEIs from spontaneous trait inferences (STIs), showing that disrupting cognitive processing interferes with a trait-based prediction task that presumably reflects STIs, but not with an affectively based social approach task that presumably reflects SEIs. Implications of these findings for the potential independence of spontaneous trait and evaluative inferences, as well as limitations and important steps for future study are discussed. (c) 2015 APA, all rights reserved).
Transcriptional networks inferred from molecular signatures of breast cancer.
Tongbai, Ron; Idelman, Gila; Nordgard, Silje H; Cui, Wenwu; Jacobs, Jonathan L; Haggerty, Cynthia M; Chanock, Stephen J; Børresen-Dale, Anne-Lise; Livingston, Gary; Shaunessy, Patrick; Chiang, Chih-Hung; Kristensen, Vessela N; Bilke, Sven; Gardner, Kevin
2008-02-01
Global genomic approaches in cancer research have provided new and innovative strategies for the identification of signatures that differentiate various types of human cancers. Computational analysis of the promoter composition of the genes within these signatures may provide a powerful method for deducing the regulatory transcriptional networks that mediate their collective function. In this study we have systematically analyzed the promoter composition of gene classes derived from previously established genetic signatures that recently have been shown to reliably and reproducibly distinguish five molecular subtypes of breast cancer associated with distinct clinical outcomes. Inferences made from the trends of transcription factor binding site enrichment in the promoters of these gene groups led to the identification of regulatory pathways that implicate discrete transcriptional networks associated with specific molecular subtypes of breast cancer. One of these inferred pathways predicted a role for nuclear factor-kappaB in a novel feed-forward, self-amplifying, autoregulatory module regulated by the ERBB family of growth factor receptors. The existence of this pathway was verified in vivo by chromatin immunoprecipitation and shown to be deregulated in breast cancer cells overexpressing ERBB2. This analysis indicates that approaches of this type can provide unique insights into the differential regulatory molecular programs associated with breast cancer and will aid in identifying specific transcriptional networks and pathways as potential targets for tumor subtype-specific therapeutic intervention.
Chronic recurrent multifocal osteomyelitis exhibiting predominance of periosteal reaction.
Queiroz, Rodolfo Mendes; Rocha, Pedro Henrique Pereira; Lauar, Lara Zupelli; Costa, Mauro José Brandão da; Laguna, Claudio Benedini; Oliveira, Rafael Gouvêa Gomes de
2017-04-01
Chronic recurrent multifocal osteomyelitis is an idiopathic nonpyogenic autoinflammatory bone disorder involving multiple sites, with clinical progression persisting for more than 6 months and which may have episodes of remission and exacerbation in the long term. It represents up to 2-5% of the cases of osteomyelitis, with an approximate incidence of up to 4/1,000,000 individuals, and average age of disease onset estimated between 8-11 years, predominantly in females. The legs are the most affected, with a predilection for metaphyseal regions along the growth plate. We describe the case of a female patient, aged 2 years and 5 months, with involvement of the left ulna, right jaw and left tibia, showing a predominance of periosteal reaction as main finding.
Predominant cartilaginous hamartoma: an unusual variant of chondromatous hamartoma.
Seda, Gilbert; Amundson, Dennis; Lin, Mercury Y
2010-02-01
Chondromatous hamartomas are the most common benign lung tumors and the third most common pulmonary nodule. Histologically, they are characteristically composed of hyaline cartilage mixed with fibromyxoid stroma and adipose tissue surrounded by epithelial cells. We report the case of a healthy, 60-year-old woman with an incidentally discovered chondromatous hamartoma that was thorascopically excised. Her pulmonary hamartoma was predominantly cartilaginous, which only occurs in 1% of hamartomas.
Predominant cultivable microflora of human dental fissure plaque.
Theilade, E; Fejerskov, O; Karring, T; Theilade, J
1982-01-01
Plaque developed in 10 occlusal fissures from unerupted third molars during implantation for 200 to 270 days in lower molars of dental students was studied. To characterize the predominant cultivable flora, 592 isolates (51 to 67 from each fissure) were subcultured from anaerobic roll tubes. Twenty-eight of the isolates were lost. Streptococci constituted 8 to 86% (median, 45%) of the isolates, Streptococcus mutans constituted 0 to 86% (median, 25%) and S. sanguis constituted 0 to 15% (median...
Statistical inference on residual life
Jeong, Jong-Hyeon
2014-01-01
This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.
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.
Bayesian Inference for Radio Observations
Lochner, Michelle; Zwart, Jonathan T L; Smirnov, Oleg; Bassett, Bruce A; Oozeer, Nadeem; Kunz, Martin
2015-01-01
(Abridged) New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inaccurate uncertainty estimates and biased results because such methods ignore any correlations between parameters. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realisation of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. Thi...
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Network Inference from Grouped Data
Zhao, Yunpeng
2016-01-01
In medical research, economics, and the social sciences data frequently appear as subsets of a set of objects. Over the past century a number of descriptive statistics have been developed to construct network structure from such data. However, these measures lack a generating mechanism that links the inferred network structure to the observed groups. To address this issue, we propose a model-based approach called the Hub Model which assumes that every observed group has a leader and that the leader has brought together the other members of the group. The performance of Hub Models is demonstrated by simulation studies. We apply this model to infer the relationships among Senators serving in the 110th United States Congress, the characters in a famous 18th century Chinese novel, and the distribution of flora in North America.
Bayesian inference with ecological applications
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...
Inferring Centrality from Network Snapshots
Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng
2017-01-01
The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data. PMID:28098166
Statistical learning and selective inference.
Taylor, Jonathan; Tibshirani, Robert J
2015-06-23
We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.
Bayesian inference on proportional elections.
Directory of Open Access Journals (Sweden)
Gabriel Hideki Vatanabe Brunello
Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
Causal inference based on counterfactuals
Directory of Open Access Journals (Sweden)
Höfler M
2005-09-01
Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
Minimising Unnecessary Mastectomies in a Predominantly Chinese Community
Directory of Open Access Journals (Sweden)
Mona P. Tan
2015-01-01
Full Text Available Background. Recent data shows that the use of breast conservation treatment (BCT for breast cancer may result in superior outcomes when compared with mastectomy. However, reported rates of BCT in predominantly Chinese populations are significantly lower than those reported in Western countries. Low BCT rates may now be a concern as they may translate into suboptimal outcomes. A study was undertaken to evaluate BCT rates in a cohort of predominantly Chinese women. Methods. All patients who underwent surgery on the breast at the authors’ healthcare facility between October 2008 and December 2011 were included in the study and outcomes of treatment were evaluated. Results. A total of 171 patients were analysed. Two-thirds of the patients were of Chinese ethnicity. One hundred and fifty-six (85.9% underwent BCT. Ninety-eight of 114 Chinese women (86% underwent BCT. There was no difference in the proportion of women undergoing BCT based on ethnicity. After a median of 49 months of follow-up, three patients (1.8% had local recurrence and 5 patients (2.9% suffered distant metastasis. Four patients (2.3% have died from their disease. Conclusion. BCT rates exceeding 80% in a predominantly Chinese population are possible with acceptable local and distant control rates, thereby minimising unnecessary mastectomies.
Applied statistical inference with MINITAB
Lesik, Sally
2009-01-01
Through clear, step-by-step mathematical calculations, Applied Statistical Inference with MINITAB enables students to gain a solid understanding of how to apply statistical techniques using a statistical software program. It focuses on the concepts of confidence intervals, hypothesis testing, validating model assumptions, and power analysis.Illustrates the techniques and methods using MINITABAfter introducing some common terminology, the author explains how to create simple graphs using MINITAB and how to calculate descriptive statistics using both traditional hand computations and MINITAB. Sh
Security Inference from Noisy Data
2008-04-08
Junk Mail Samples (JMS)” later) is collected from Hotmail using a different method. JMS is collected from email in inboxes that is reported as spam (or...The data consist of side channel traces from attackers: spam email messages received by Hotmail, one of the largest Web mail services. The basic...similar content and determining the senders of these email messages, one can infer the composition of the botnet. This approach can analyze botnets re
Optimal Inference in Cointegrated Systems
1988-01-01
This paper studies the properties of maximum likelihood estimates of co-integrated systems. Alternative formulations of such models are considered including a new triangular system error correction mechanism. It is shown that full system maximum likelihood brings the problem of inference within the family that is covered by the locally asymptotically mixed normal asymptotic theory provided that all unit roots in the system have been eliminated by specification and data transformation. This re...
Inferring Centrality from Network Snapshots
Haibin Shao; Mehran Mesbahi; Dewei Li; Yugeng Xi
2017-01-01
The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagati...
On Quantum Statistical Inference, II
Barndorff-Nielsen, O. E.; Gill, R. D.; Jupp, P.E.
2003-01-01
Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, theoretical developments in the theory of quantum measurements have brought the basic mathematical framework for the probability calculations much closer to that of classical probability theory. The present paper reviews this field and proposes and inte...
An introduction to causal inference.
Pearl, Judea
2010-02-26
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.
BowTieBuilder: modeling signal transduction pathways
Directory of Open Access Journals (Sweden)
Schröder Adrian
2009-06-01
Full Text Available Abstract Background Sensory proteins react to changing environmental conditions by transducing signals into the cell. These signals are integrated into core proteins that activate downstream target proteins such as transcription factors (TFs. This structure is referred to as a bow tie, and allows cells to respond appropriately to complex environmental conditions. Understanding this cellular processing of information, from sensory proteins (e.g., cell-surface proteins to target proteins (e.g., TFs is important, yet for many processes the signaling pathways remain unknown. Results Here, we present BowTieBuilder for inferring signal transduction pathways from multiple source and target proteins. Given protein-protein interaction (PPI data signaling pathways are assembled without knowledge of the intermediate signaling proteins while maximizing the overall probability of the pathway. To assess the inference quality, BowTieBuilder and three alternative heuristics are applied to several pathways, and the resulting pathways are compared to reference pathways taken from KEGG. In addition, BowTieBuilder is used to infer a signaling pathway of the innate immune response in humans and a signaling pathway that potentially regulates an underlying gene regulatory network. Conclusion We show that BowTieBuilder, given multiple source and/or target proteins, infers pathways with satisfactory recall and precision rates and detects the core proteins of each pathway.
El: A Program for Ecological Inference
King, Gary
2004-01-01
The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997). Ecological inference, as traditionally defined, is the process of using aggregate (i.e., “ecological”) data to infer discrete individual-level relationships of interest when individual- level data are not avai...
EI: A Program for Ecological Inference
Gary King
2004-01-01
The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997). Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological") data to infer discrete individual-level relationships of interest when individual-level data are not ava...
Heinz, Tanja; Alvarez-Iglesias, Vanesa; Pardo-Seco, Jacobo; Taboada-Echalar, Patricia; Gómez-Carballa, Alberto; Torres-Balanza, Antonio; Rocabado, Omar; Carracedo, Angel; Vullo, Carlos; Salas, Antonio
2013-09-01
We have genotyped 46 Ancestry Informative Markers (AIMs) in two of the most populated areas in Bolivia, namely, La Paz (Andean region; n=105), and Chuquisaca (Sub-Andean region; n=73). Using different analytical tools, we inferred admixture proportions of these two American communities by comparing the genetic profiles with those publicly available from the CEPH (Centre d'Etude du Polymorphisme Humain) panel representing three main continental groups (Africa, Europe, and America). By way of simulations, we first evaluated the minimum sample size needed in order to obtain accurate estimates of ancestry proportions. The results indicated that sample sizes above 30 individuals could be large enough to estimate main continental ancestry proportions using the 46 AIMs panel. With the exception of a few individuals, the results also indicated that Bolivians showed a predominantly Native American ancestry with variable levels of European admixture. The proportions of ancestry were statistically different in La Paz and Chuquisaca: the Native American component was 86% and 77% (Mann-Whitney U-test: un-adjusted P-value=2.1×10(-5)), while the European ancestry was 13% and 21% (Mann-Whitney U-test: un-adjusted P-value=3.6×10(-5)), respectively. The African ancestry in Bolivians captured by the AIMs analyzed in the present study was below 2%. The inferred ancestry of Bolivians fits well with previous studies undertaken on haplotype data, indicating a major proportion of Native American lineages. The genetic differences observed in these two groups suggest that forensic genetic analysis should be better performed based on local databases built in the main Bolivian areas.
Evidence and Inference in Educational Assessment.
1995-02-01
Educational assessment concerns inference about students’ knowledge, skills, and accomplishments. Because data are never so comprehensive and...techniques can be viewed as applications of more general principles for inference in the presence of uncertainty. Issues of evidence and inference in educational assessment are discussed from this perspective. (AN)
Hierarchical animal movement models for population-level inference
Hooten, Mevin B.; Buderman, Frances E.; Brost, Brian M.; Hanks, Ephraim M.; Ivans, Jacob S.
2016-01-01
New methods for modeling animal movement based on telemetry data are developed regularly. With advances in telemetry capabilities, animal movement models are becoming increasingly sophisticated. Despite a need for population-level inference, animal movement models are still predominantly developed for individual-level inference. Most efforts to upscale the inference to the population level are either post hoc or complicated enough that only the developer can implement the model. Hierarchical Bayesian models provide an ideal platform for the development of population-level animal movement models but can be challenging to fit due to computational limitations or extensive tuning required. We propose a two-stage procedure for fitting hierarchical animal movement models to telemetry data. The two-stage approach is statistically rigorous and allows one to fit individual-level movement models separately, then resample them using a secondary MCMC algorithm. The primary advantages of the two-stage approach are that the first stage is easily parallelizable and the second stage is completely unsupervised, allowing for an automated fitting procedure in many cases. We demonstrate the two-stage procedure with two applications of animal movement models. The first application involves a spatial point process approach to modeling telemetry data, and the second involves a more complicated continuous-time discrete-space animal movement model. We fit these models to simulated data and real telemetry data arising from a population of monitored Canada lynx in Colorado, USA.
Perceptual inference and autistic traits
DEFF Research Database (Denmark)
Skewes, Joshua; Jegindø, Else-Marie Elmholdt; Gebauer, Line
2015-01-01
Autistic people are better at perceiving details. Major theories explain this in terms of bottom-up sensory mechanisms, or in terms of top-down cognitive biases. Recently, it has become possible to link these theories within a common framework. This framework assumes that perception is implicit...... neural inference, combining sensory evidence with prior perceptual knowledge. Within this framework, perceptual differences may occur because of enhanced precision in how sensory evidence is represented, or because sensory evidence is weighted much higher than prior perceptual knowledge...
Logical inferences in discourse analysis
Institute of Scientific and Technical Information of China (English)
刘峰廷
2014-01-01
Cohesion and coherence are two important characteristics of discourses. Halliday and Hasan have pointed out that cohesion is the basis of coherence and coherence is the premise of forming discourse. The commonly used cohesive devices are: preference, ellipsis, substitution, etc. Discourse coherence is mainly manifested in sentences and paragraphs. However, in real discourse analysis environment, traditional methods on cohesion and coherence are not enough. This article talks about the conception of discourse analysis at the beginning. Then, we list some of the traditional cohesive devices and its uses. Following that, we make corpus analysis. Finally, we explore and find a new device in textual analysis:discourse logical inferences.
SICK: THE SPECTROSCOPIC INFERENCE CRANK
Energy Technology Data Exchange (ETDEWEB)
Casey, Andrew R., E-mail: arc@ast.cam.ac.uk [Institute of Astronomy, University of Cambridge, Madingley Road, Cambdridge, CB3 0HA (United Kingdom)
2016-03-15
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal
Universum Inference and Corpus Homogeneity
Vogel, Carl; Lynch, Gerard; Janssen, Jerom
Universum Inference is re-interpreted for assessment of corpus homogeneity in computational stylometry. Recent stylometric research quantifies strength of characterization within dramatic works by assessing the homogeneity of corpora associated with dramatic personas. A methodological advance is suggested to mitigate the potential for the assessment of homogeneity to be achieved by chance. Baseline comparison analysis is constructed for contributions to debates by nonfictional participants: the corpus analyzed consists of transcripts of US Presidential and Vice-Presidential debates from the 2000 election cycle. The corpus is also analyzed in translation to Italian, Spanish and Portuguese. Adding randomized categories makes assessments of homogeneity more conservative.
Inferring Network Structure from Cascades
Ghonge, Sushrut
2016-01-01
Many physical, biological and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we solve the dynamics of general cascade processes. We then offer three topological inversion methods to infer the structure of any directed network given a set of cascade arrival times. Our forward and inverse formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for 5 different cascade models.
Inferring interaction partners from protein sequences
Bitbol, Anne-Florence; Dwyer, Robert S.; Colwell, Lucy J.; Wingreen, Ned S.
2016-01-01
Specific protein−protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm’s performance on histidine kinases and response regulators from bacterial two-component signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data. PMID:27663738
Inferring gene networks from discrete expression data
Zhang, L.
2013-07-18
The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.
Predominant enterobacteria on modified-atmosphere packaged meat and poultry.
Säde, Elina; Murros, Anna; Björkroth, Johanna
2013-06-01
Enterobacteria on modified-atmosphere (MA) packaged meat (n = 54) and poultry (n = 32) products were enumerated, and 899 isolates were picked and ribotyped. For identification, 16S rRNA genes of representative strains were sequenced and analyzed. Altogether 54 (60%) of the samples contained enterobacteria >10(4) CFU/g. In 34% of the poultry samples, enterobacteria counts were >10(6) CFU/g suggesting that enterobacteria may contribute to spoilage of MA packaged poultry. The enterobacteria identified were predominantly Hafnia spp. (40%) and Serratia spp. (42%) with Hafnia alvei, Hafnia paralvei, Serratia fonticola, Serratia grimesii, Serratia liquefaciens, Serratia proteamaculans, and Serratia quinivorans being the species identified. In addition, 6% of the isolates were identified as Rahnella spp., 3% as Yersinia spp., and 1% as Buttiauxella spp. Percentage distributions of the predominant genera in different products showed that 89% of the Serratia spp. were from products packaged under a high-O2 MA containing CO2 (25-35%), whereas most (76%) isolates of Hafnia originated from anaerobically packaged red meat and poultry. These findings suggest that the gas mixture used for MA packaging influence the selection of enterobacteria growing on meat and poultry. Copyright © 2012 Elsevier Ltd. All rights reserved.
Bayesian inference for OPC modeling
Burbine, Andrew; Sturtevant, John; Fryer, David; Smith, Bruce W.
2016-03-01
The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.
Dopamine, Affordance and Active Inference
Friston, Karl J.; Shiner, Tamara; FitzGerald, Thomas; Galea, Joseph M.; Adams, Rick; Brown, Harriet; Dolan, Raymond J.; Moran, Rosalyn; Stephan, Klaas Enno; Bestmann, Sven
2012-01-01
The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level. PMID:22241972
Dopamine, affordance and active inference.
Directory of Open Access Journals (Sweden)
Karl J Friston
2012-01-01
Full Text Available The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order in which cues are presented. These simulations provide a (Bayes-optimal model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.
ERP time course and brain areas of spontaneous and intentional goal inferences.
Van der Cruyssen, Laurens; Van Duynslaeger, Marijke; Cortoos, Aisha; Van Overwalle, Frank
2009-01-01
This study measured event-related potentials during spontaneous and intentional goal inferences. Participants read sentences describing the behavior of a target person from which a strong goal or intention could be inferred. The last word of each sentence determined the consistency with the goal induced during preceding sentences. In comparison with behaviors that were consistent with the implied goal, a stronger P200 waveform was obtained when the behaviors were irrelevant with that goal or did not contain goal-directed behavior at all, and this P200 showed considerable parallels between spontaneous and intentional inferences. This indicates that goals were inferred rapidly and automatically while reading the behaviors, irrespective of the participants' spontaneous or intentional instructions. In line with this, source localization (LORETA) of the event-related potentials shows predominantly activation in the temporoparietal junction (TPJ) during and immediately after goal detection (225-300 ms). Before and after this time interval, however, activation is stronger at the TPJ during spontaneous processing, and stronger at the medial prefrontal cortex (mPFC) during intentional processing. Memory measures taken after the presentation of the stimulus materials support the occurrence of goal inferences and show significant correlations with the neural components, indicating that these components are valid neural indices of spontaneous and intentional goal inferences. The results are highly similar to previous ERP research on trait inferences that revealed a similar division of brain activation for spontaneous (TPJ) and intentional (mPFC) processes, but appearing later at about 600 ms, pointing to similar brain areas recruited for social inferences, but at different timings for different inference types.
Nephrotic presentation in hydatid cyst disease with predominant tubulointerstital disease
Directory of Open Access Journals (Sweden)
Feroz Aziz
2009-06-01
Full Text Available Feroz Aziz1, Tanmay Pandya1, Himanshu V Patel1, Paladugu Ramakrishna1, Kamal R Goplani1, Manoj Gumber1, Aruna V Vanikar2, Kamal Kanodia2, Pankaj R Shah1, Hargovind L Trivedi11Department of Nephrology and Transplantation Medicine; 2Department of Pathology, Lab Medicine, Transfusion Services and Immunohematology, G.R. Doshi and K.M. Mehta Institute of Kidney Diseases and Research Centre (IKDRC, Ahmedabad, Gujarat, IndiaAbstract: Renal involvement, which can rarely occur in echinococcosis, more commonly manifests as hydatid cyst of the kidney. Scattered case reports of nephrotic syndrome secondary to hydatid cyst in the liver or lung have been reported for over two decades. The glomerular picture varied from minimal change lesion to mesangiocapillary glomerulonephritis. We report a case of predominantly tubulointerstitial nephritis with mesangioproliferative glomerulonephritis in a patient with hepatic hydatid cyst which responded to cyst resection alone. Keywords: echinococcosis, hydatid cyst, kidney, nephrotic syndrome, tubulointerstitial nephritis
Enterobacter cloacae: A predominant pathogen in neonatal septicaemia
Directory of Open Access Journals (Sweden)
Mahapatra A
2002-01-01
Full Text Available A total of 120 blood samples from neonates presenting with clinical signs of septicaemia were subjected for culture using brain heart infusion agar biphasic medium (BHI BPM and glucose broth. Bacterial agents were isolated from 48 numbers (40% of cultures. Gram-negative bacilli were isolated in maximum percentage (88.45% of cases whereas gram-positive bacteria (coagulase negative staphylococci and group B streptococci in 11.6% of cultures. E.cloacae (39.5% was maximally isolated among the pathogenic bacteria followed by K.pneumoniae (23.2%, E.coli (11.6% and others like Acinetobacter spp. (6.9%, Citrobacter freundi (4.6% and P.mirabillis (2.3%. All the gram-negative bacilli isolates showed 100% susceptibility to amikacin, whereas 85% of E.cloacae isolates were sensitive to the same. Thus E.cloacae was found to be a predominant moderately sensitive pathogen in neonatal septicemia.
Nodular lymphocyte predominant hodgkin lymphoma: biology, diagnosis and treatment.
Goel, Anupama; Fan, Wen; Patel, Amit A; Devabhaktuni, Madhuri; Grossbard, Michael L
2014-08-01
Nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) is an uncommon variant of classical Hodgkin lymphoma. It is characterized histologically by presence of lymphohistiocytic cells which have B-cell phenotype, are positive for CD19, CD20, CD45, CD79a, BOB.1, Oct.2, and negative for CD15 and CD30. Patients often present with early stage of disease and do not have classical B symptoms. The clinical behavior appears to mimic that of an indolent non-Hodgkin lymphoma more than that of classical Hodgkin disease. The purpose of the present report is to define the biology of NLPHL, review its clinical presentation, and summarize the available clinical data regarding treatment.
HCV prevalence and predominant genotype in IV drug users
Directory of Open Access Journals (Sweden)
Asad Andalibalshohada
2014-07-01
Full Text Available Hepatitis C virus (HCV causes 308000 deaths due to liver cancer and 758000 deaths due to cirrhosis every year. Almost 170 million people have HCV infection around the world. Information regarding this virus helps us to determine the prevalence of other hepatitis C genotypes in population, especially in intravenous drug users. It is assumed that some genotypes are more common in certain areas or groups of people. A recent study strongly confirms the central role of injecting network traits, not only as a transmission factor but also as a predictor of HCV genotype and phylogenetic determination in different communities. Hepatitis C genotypes and subtypes have different prevalence considering the country. Risk factors such as transfusion, hemodialysis, root of acquisition and etc, are detected in intravenous drug users. Several conducted studies have investigated the prevalence, risk factors, and predominance of HCV genotypes infection in different parts of Iran.
Nodular Lymphocyte Predominant Hodgkin Lymphoma of the Thyroid
Cassis, João; Simões, Helder; Sequeira Duarte, João
2016-01-01
Thyroid lymphomas are rare clinical entities that may result from either the primary intrathyroid de novo or secondary thyroid gland involvement of a lymphoma. Among these, the Hodgkin's subtype is quite uncommon, accounting for 0.6–5% of all thyroid malignancies. The authors report on a 76-year-old female presenting with a thyroid nodule that, upon surgical excision, was found to be a nodular lymphocyte predominant Hodgkin lymphoma of the thyroid. So far, thyroid involvement by this variant has never been reported. Upon reporting on this clinical case, the authors emphasize the difficulties usually found in establishing the diagnosis and in defining the best management strategy. A thorough review of the available literature is done. PMID:28044111
Plasma-cell-predominant B-cell pseudolymphoma.
Nervi, Stephen J; Schwartz, R A
2008-10-15
A 46-year-old woman with no history of foreign travel presented to the New Jersey Medical School Dermatology Clinic in July, 2007, with pruritic ulcerating facial masses that had been present since October, 2006. Clinical and histopathologic findings were most consistent with a diagnosis of cutaneous plasma cell predominant B cell pseudolymphoma. An extensive search using special stains for an etiologic organism was negative. The term cutaneous pseudolymphoma has been coined to describe the accumulation of either T or B cell lymphocytes in the skin that is caused by a nonmalignant stimulus and encompasses several different terms depending on etiology. In cases of cutaneous pseudolymphoma where a cause is identified, treatment entails removing the underlying causative agent. Idiopathic cases tend to be recalcitrant to treatment.
Predominant Nearshore Sediment Dispersal Patterns in Manila Bay
Directory of Open Access Journals (Sweden)
Fernando Siringan
1997-12-01
Full Text Available Net nearshore sediment drift patterns in Manila Bay were determined by combining the coastal geomorphology depicted in 1 : 50,000scale topographic maps and Synthetic Aperture Radar (SAR images, with changes in shoreline position and predominant longshore current directions derived from the interaction of locally generated waves and bay morphology.Manila Bay is fringed by a variety of coastal subenvironments that reflect changing balances of fluvial, wave, and tidal processes. Along the northern coast, a broad tidal-river delta plain stretching from Bataan to Bulacan indicates the importance of tides, where the lateral extent of tidal influences is amplified by the very gentle coastal gradients. In contrast, along the Cavite coast sandy strandplains, spits, and wave-dominated deltas attest to the geomorphic importance of waves that enter the bay from the South China Sea.The estimates of net sediment drift derived from geomorphological, shoreline-change, and meteorological information are generally in good agreement. Sediment drift directions are predominantly to the northeast along Cavite, to the northwest along Manila and Bulacan, and to the north along Bataan. Wave refraction and eddy formation at the tip of the Cavite Spit cause southwestward sediment drift along the coast from Zapote to Kawit. Geomorphology indicates that onshore-offshore sediment transport is probably more important than alongshore transport along the coast fronting the tidal delta plain of northern Manila Bay. Disagreements between the geomorphic-derived and predicted net sediment drift directions may be due to interactions of wave-generated longshore currents with wind- and tide-generated currents.
Reverse Engineering Adverse Outcome Pathways
Energy Technology Data Exchange (ETDEWEB)
Perkins, Edward; Chipman, J.K.; Edwards, Stephen; Habib, Tanwir; Falciani, Francesco; Taylor, Ronald C.; Van Aggelen, Graham; Vulpe, Chris; Antczak, Philipp; Loguinov, Alexandre
2011-01-30
The toxicological effects of many stressors are mediated through unknown, or poorly characterized, mechanisms of action. We describe the application of reverse engineering complex interaction networks from high dimensional omics data (gene, protein, metabolic, signaling) to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis in fathead minnows. Gene expression changes in fathead minnow ovaries in response to 7 different chemicals, over different times, doses, and in vivo versus in vitro conditions were captured in a large data set of 868 arrays. We examined potential AOPs of the antiandrogen flutamide using two mutual information theory methods, ARACNE and CLR to infer gene regulatory networks and potential adverse outcome pathways. Representative networks from these studies were used to predict a network path from stressor to adverse outcome as a candidate AOP. The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment leading to the nodes associated with the adverse outcome. Identification of candidate pathways allows for formation of testable hypotheses about key biologic processes, biomarkers or alternative endpoints, which could be used to monitor an adverse outcome pathway. Finally, we identify the unique challenges facing the application of this approach in ecotoxicology, and attempt to provide a road map for the utilization of these tools. Key Words: mechanism of action, toxicology, microarray, network inference
Lower complexity bounds for lifted inference
DEFF Research Database (Denmark)
Jaeger, Manfred
2015-01-01
instances of the model. Numerous approaches for such “lifted inference” techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show...... the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about...... that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing...
Spontaneous Trait Inferences on Social Media
Utz, Sonja
2016-01-01
The present research investigates whether spontaneous trait inferences occur under conditions characteristic of social media and networking sites: nonextreme, ostensibly self-generated content, simultaneous presentation of multiple cues, and self-paced browsing. We used an established measure of trait inferences (false recognition paradigm) and a direct assessment of impressions. Without being asked to do so, participants spontaneously formed impressions of people whose status updates they saw. Our results suggest that trait inferences occurred from nonextreme self-generated content, which is commonly found in social media updates (Experiment 1) and when nine status updates from different people were presented in parallel (Experiment 2). Although inferences did occur during free browsing, the results suggest that participants did not necessarily associate the traits with the corresponding status update authors (Experiment 3). Overall, the findings suggest that spontaneous trait inferences occur on social media. We discuss implications for online communication and research on spontaneous trait inferences. PMID:28123646
Causal inference in public health.
Glass, Thomas A; Goodman, Steven N; Hernán, Miguel A; Samet, Jonathan M
2013-01-01
Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action's consequences rather than the less precise notion of a risk factor's causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world.
Statistical inference for financial engineering
Taniguchi, Masanobu; Ogata, Hiroaki; Taniai, Hiroyuki
2014-01-01
This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e.g., discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc., which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.
Polynomial Regressions and Nonsense Inference
Directory of Open Access Journals (Sweden)
Daniel Ventosa-Santaulària
2013-11-01
Full Text Available Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340. by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
Nor, Igor; Charlat, Sylvain; Engelstadter, Jan; Reuter, Max; Duron, Olivier; Sagot, Marie-France
2010-01-01
We address in this paper a new computational biology problem that aims at understanding a mechanism that could potentially be used to genetically manipulate natural insect populations infected by inherited, intra-cellular parasitic bacteria. In this problem, that we denote by \\textsc{Mod/Resc Parsimony Inference}, we are given a boolean matrix and the goal is to find two other boolean matrices with a minimum number of columns such that an appropriately defined operation on these matrices gives back the input. We show that this is formally equivalent to the \\textsc{Bipartite Biclique Edge Cover} problem and derive some complexity results for our problem using this equivalence. We provide a new, fixed-parameter tractability approach for solving both that slightly improves upon a previously published algorithm for the \\textsc{Bipartite Biclique Edge Cover}. Finally, we present experimental results where we applied some of our techniques to a real-life data set.
Bayesian Inference with Optimal Maps
Moselhy, Tarek A El
2011-01-01
We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure. Existence and uniqueness of a suitable measure-preserving map is established by formulating the problem in the context of optimal transport theory. We discuss various means of explicitly parameterizing the map and computing it efficiently through solution of an optimization problem, exploiting gradient information from the forward model when possible. The resulting algorithm overcomes many of the computational bottlenecks associated with Markov chain Monte Carlo. Advantages of a map-based representation of the posterior include analytical expressions for posterior moments and the ability to generate arbitrary numbers of independent posterior samples without additional likelihood evaluations or forward solves. The optimization approach also provides clear convergence criteria for posterior approximation and facilitates model selectio...
Relevance-driven Pragmatic Inferences
Institute of Scientific and Technical Information of China (English)
王瑞彪
2013-01-01
Relevance theory, an inferential approach to pragmatics, claims that the hearer is expected to pick out the input of op-timal relevance from a mass of alternative inputs produced by the speaker in order to interpret the speaker ’s intentions. The de-gree of the relevance of an input can be assessed in terms of cognitive effects and the processing effort. The input of optimal rele-vance is the one yielding the greatest positive cognitive effect and requiring the least processing effort. This paper attempts to as-sess the degrees of the relevance of a mass of alternative inputs produced by an imaginary speaker from the perspective of her cor-responding hearer in terms of cognitive effects and the processing effort with a view to justifying the feasibility of the principle of relevance in pragmatic inferences.
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...... and differentiating these circuits in time linear in their size. We report on experimental results showing the successful compilation, and efficient inference, on relational Bayesian networks whose {\\primula}--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Definitive Consensus for Distributed Data Inference
2011-01-01
Inference from data is of key importance in many applications of informatics. The current trend in performing such a task of inference from data is to utilise machine learning algorithms. Moreover, in many applications that it is either required or is preferable to infer from the data in a distributed manner. Many practical difficulties arise from the fact that in many distributed applications we avert from transferring data or parts of it due to cost...
Constraint Processing in Lifted Probabilistic Inference
Kisynski, Jacek
2012-01-01
First-order probabilistic models combine representational power of first-order logic with graphical models. There is an ongoing effort to design lifted inference algorithms for first-order probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through this viewpoint, we analyze and compare existing approaches and expose their advantages and limitations. Our theoretical results show that the wrong choice of constraint processing method can lead to exponential increase in computational complexity. Our empirical tests confirm the importance of constraint processing in lifted inference. This is the first theoretical and empirical study of constraint processing in lifted inference.
Inference Attacks and Control on Database Structures
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Muhamed Turkanovic
2015-02-01
Full Text Available Today’s databases store information with sensitivity levels that range from public to highly sensitive, hence ensuring confidentiality can be highly important, but also requires costly control. This paper focuses on the inference problem on different database structures. It presents possible treats on privacy with relation to the inference, and control methods for mitigating these treats. The paper shows that using only access control, without any inference control is inadequate, since these models are unable to protect against indirect data access. Furthermore, it covers new inference problems which rise from the dimensions of new technologies like XML, semantics, etc.
State Sampling Dependence of Hopfield Network Inference
Institute of Scientific and Technical Information of China (English)
黄海平
2012-01-01
The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations. We present the system in the glassy phase with low temperature and high memory load. We find that the inference error is very sensitive to the form of state sampling. When a single state is sampled to compute magnetizations and correlations, the inference error is almost indistinguishable irrespective of the sampled state. However, the error can be greatly reduced if the data is collected with state transitions. Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.
Bayesian inference of chemical kinetic models from proposed reactions
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.
Modeling cancer progression via pathway dependencies.
Directory of Open Access Journals (Sweden)
Elena J Edelman
2008-02-01
Full Text Available Cancer is a heterogeneous disease often requiring a complexity of alterations to drive a normal cell to a malignancy and ultimately to a metastatic state. Certain genetic perturbations have been implicated for initiation and progression. However, to a great extent, underlying mechanisms often remain elusive. These genetic perturbations are most likely reflected by the altered expression of sets of genes or pathways, rather than individual genes, thus creating a need for models of deregulation of pathways to help provide an understanding of the mechanisms of tumorigenesis. We introduce an integrative hierarchical analysis of tumor progression that discovers which a priori defined pathways are relevant either throughout or in particular steps of progression. Pathway interaction networks are inferred for these relevant pathways over the steps in progression. This is followed by the refinement of the relevant pathways to those genes most differentially expressed in particular disease stages. The final analysis infers a gene interaction network for these refined pathways. We apply this approach to model progression in prostate cancer and melanoma, resulting in a deeper understanding of the mechanisms of tumorigenesis. Our analysis supports previous findings for the deregulation of several pathways involved in cell cycle control and proliferation in both cancer types. A novel finding of our analysis is a connection between ErbB4 and primary prostate cancer.
NeuroData; Mishchenko, Y.; AM, Packer; TA, Machado; Yuste, R.; Paninski, L
2015-01-01
Vogelstein JT, Mishchenko Y, Packer AM, Machado TA, Yuste R, Paninski L. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. NIPS Workshop on Connectivity Inference in Neuroimaging, 2009
Lubiprostone: in constipation-predominant irritable bowel syndrome.
Carter, Natalie J; Scott, Lesley J
2009-06-18
Lubiprostone is an oral bicyclic fatty acid that selectively activates type 2 chloride channels in the apical membrane of human gastrointestinal epithelial cells, thereby increasing chloride-rich fluid secretion. Although the mechanism is unclear, this may then decrease intestinal transit time, allowing the passage of stool and alleviating symptoms of constipation. Oral lubiprostone was effective in the treatment of patients with constipation-predominant irritable bowel syndrome (IBS-C) in large (n = 193-583) phase II (dose-finding) and phase III randomized, double-blind, placebo-controlled, multicentre trials. The number of patients with IBS-C demonstrating an overall response to treatment (primary endpoint) in the two phase III trials was significantly greater in patients receiving lubiprostone 8 microg twice daily for 3 months than in those receiving placebo. In addition, a randomized, 4-week withdrawal period at the end of one of the phase III trials demonstrated that discontinuation of lubiprostone was not associated with rebound of IBS symptoms. Lubiprostone was generally well tolerated in clinical trials, with the majority of adverse events being of mild to moderate severity. In patients with IBS-C who received lubiprostone 8 microg twice daily, nausea was the most frequently occurring adverse event that was considered possibly or probably treatment related. No serious treatment-related adverse events were reported in a 36-week open-label extension to the phase III trials.
Predominance of single bacterial cells in composting bioaerosols
Galès, Amandine; Bru-Adan, Valérie; Godon, Jean-Jacques; Delabre, Karine; Catala, Philippe; Ponthieux, Arnaud; Chevallier, Michel; Birot, Emmanuel; Steyer, Jean-Philippe; Wéry, Nathalie
2015-04-01
Bioaerosols emitted from composting plants have become an issue because of their potential harmful impact on public or workers' health. Accurate knowledge of the particle-size distribution in bioaerosols emitted from open-air composting facilities during operational activity is a requirement for improved modeling of air dispersal. In order to investigate the aerodynamic diameter of bacteria in composting bioaerosols this study used an Electrical Low Pressure Impactor for sampling and quantitative real-time PCR for quantification. Quantitative PCR results show that the size of bacteria peaked between 0.95 μm and 2.4 μm and that the geometric mean diameter of the bacteria was 1.3 μm. In addition, total microbial cells were counted by flow cytometry and revealed that these qPCR results corresponded to single whole bacteria. Finally, the enumeration of cultivable thermophilic microorganisms allowed us to set the upper size limit for fragments at an aerodynamic diameter of ∼0.3 μm. Particle-size distributions of microbial groups previously used to monitor composting bioaerosols were also investigated. In collected the bioaerosols, the aerodynamic diameter of the actinomycetes Saccharopolyspora rectivirgula-and-relatives and also of the fungus Aspergillus fumigatus, appeared to be consistent with a majority of individual cells. Together, this study provides the first culture-independent data on particle-size distribution of composting bioaerosols and reveals that airborne single bacteria were emitted predominantly from open-air composting facilities.
Proteobacteria become predominant during regrowth after water disinfection.
Becerra-Castro, Cristina; Macedo, Gonçalo; Silva, Adrian M T; Manaia, Célia M; Nunes, Olga C
2016-12-15
Disinfection processes aim at reducing the number of viable cells through the generation of damages in different cellular structures and molecules. Since disinfection involves unspecific mechanisms, some microbial populations may be selected due to resilience to treatment and/or to high post-treatment fitness. In this study, the bacterial community composition of secondarily treated urban wastewater and of surface water collected in the intake area of a drinking water treatment plant was compared before and 3-days after disinfection with ultraviolet radiation, ozonation or photocatalytic ozonation. The aim was to assess the dynamics of the bacterial communities during regrowth after disinfection. In all the freshly collected samples, Proteobacteria and Bacteroidetes were the predominant phyla (40-50% and 20-30% of the reads, respectively). Surface water differed from wastewater mainly in the relative abundance of Actinobacteria (17% and disinfected samples presented a shift of Gammaproteobacteria (from 8 to 10% to 33-65% of the reads) and Betaproteobacteria (from 14 to 20% to 31-37% of the reads), irrespective of the type of water and disinfection process used. Genera such as Pseudomonas, Acinetobacter or Rheinheimera presented a selective advantage after water disinfection. These variations were not observed in the non-disinfected controls. Given the ubiquity and genome plasticity of these bacteria, the results obtained suggest that disinfection processes may have implications on the microbiological quality of the disinfected water. Copyright Â© 2016 Elsevier B.V. All rights reserved.
Protein inference: A protein quantification perspective.
He, Zengyou; Huang, Ting; Liu, Xiaoqing; Zhu, Peijun; Teng, Ben; Deng, Shengchun
2016-08-01
In mass spectrometry-based shotgun proteomics, protein quantification and protein identification are two major computational problems. To quantify the protein abundance, a list of proteins must be firstly inferred from the raw data. Then the relative or absolute protein abundance is estimated with quantification methods, such as spectral counting. Until now, most researchers have been dealing with these two processes separately. In fact, the protein inference problem can be regarded as a special protein quantification problem in the sense that truly present proteins are those proteins whose abundance values are not zero. Some recent published papers have conceptually discussed this possibility. However, there is still a lack of rigorous experimental studies to test this hypothesis. In this paper, we investigate the feasibility of using protein quantification methods to solve the protein inference problem. Protein inference methods aim to determine whether each candidate protein is present in the sample or not. Protein quantification methods estimate the abundance value of each inferred protein. Naturally, the abundance value of an absent protein should be zero. Thus, we argue that the protein inference problem can be viewed as a special protein quantification problem in which one protein is considered to be present if its abundance is not zero. Based on this idea, our paper tries to use three simple protein quantification methods to solve the protein inference problem effectively. The experimental results on six data sets show that these three methods are competitive with previous protein inference algorithms. This demonstrates that it is plausible to model the protein inference problem as a special protein quantification task, which opens the door of devising more effective protein inference algorithms from a quantification perspective. The source codes of our methods are available at: http://code.google.com/p/protein-inference/.
Inferring modules from human protein interactome classes
Directory of Open Access Journals (Sweden)
Chaurasia Gautam
2010-07-01
Full Text Available Abstract Background The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features. Results We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules. Conclusions Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.
Ham, J.R.C.; Vonk, R.
2003-01-01
Social perceivers have been shown to draw spontaneous trait inferences (STI's) about the behavior of an actor as well as spontaneous situational inferences (SSI's) about the situation the actor is in. In two studies, we examined inferences about behaviors that allow for both an STI and an SSI. In
Validating Inductive Hypotheses by Mode Inference
Institute of Scientific and Technical Information of China (English)
王志坚
1993-01-01
Sme criteria based on mode inference for validating inductive hypotheses are presented in this paper.Mode inference is caried out mechanically,thus such kind of validation can result in low overhead in consistency check and high efficiency in performance.
Causal inference in economics and marketing.
Varian, Hal R
2016-07-05
This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.
Local and Global Thinking in Statistical Inference
Pratt, Dave; Johnston-Wilder, Peter; Ainley, Janet; Mason, John
2008-01-01
In this reflective paper, we explore students' local and global thinking about informal statistical inference through our observations of 10- to 11-year-olds, challenged to infer the unknown configuration of a virtual die, but able to use the die to generate as much data as they felt necessary. We report how they tended to focus on local changes…
The Reasoning behind Informal Statistical Inference
Makar, Katie; Bakker, Arthur; Ben-Zvi, Dani
2011-01-01
Informal statistical inference (ISI) has been a frequent focus of recent research in statistics education. Considering the role that context plays in developing ISI calls into question the need to be more explicit about the reasoning that underpins ISI. This paper uses educational literature on informal statistical inference and philosophical…
Forward and backward inference in spatial cognition.
Directory of Open Access Journals (Sweden)
Will D Penny
Full Text Available This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.
Fiducial inference - A Neyman-Pearson interpretation
Salome, D; VonderLinden, W; Dose,; Fischer, R; Preuss, R
1999-01-01
Fisher's fiducial argument is a tool for deriving inferences in the form of a probability distribution on the parameter space, not based on Bayes's Theorem. Lindley established that in exceptional situations fiducial inferences coincide with posterior distributions; in the other situations fiducial
Active Inference: A Process Theory.
Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; Pezzulo, Giovanni
2017-01-01
This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.
Redshift data and statistical inference
Newman, William I.; Haynes, Martha P.; Terzian, Yervant
1994-01-01
Frequency histograms and the 'power spectrum analysis' (PSA) method, the latter developed by Yu & Peebles (1969), have been widely employed as techniques for establishing the existence of periodicities. We provide a formal analysis of these two classes of methods, including controlled numerical experiments, to better understand their proper use and application. In particular, we note that typical published applications of frequency histograms commonly employ far greater numbers of class intervals or bins than is advisable by statistical theory sometimes giving rise to the appearance of spurious patterns. The PSA method generates a sequence of random numbers from observational data which, it is claimed, is exponentially distributed with unit mean and variance, essentially independent of the distribution of the original data. We show that the derived random processes is nonstationary and produces a small but systematic bias in the usual estimate of the mean and variance. Although the derived variable may be reasonably described by an exponential distribution, the tail of the distribution is far removed from that of an exponential, thereby rendering statistical inference and confidence testing based on the tail of the distribution completely unreliable. Finally, we examine a number of astronomical examples wherein these methods have been used giving rise to widespread acceptance of statistically unconfirmed conclusions.
Ureter smooth muscle cell orientation in rat is predominantly longitudinal.
Directory of Open Access Journals (Sweden)
Bart Spronck
Full Text Available In ureter peristalsis, the orientation of the contracting smooth muscle cells is essential, yet current descriptions of orientation and composition of the smooth muscle layer in human as well as in rat ureter are inconsistent. The present study aims to improve quantification of smooth muscle orientation in rat ureters as a basis for mechanistic understanding of peristalsis. A crucial step in our approach is to use two-photon laser scanning microscopy and image analysis providing objective, quantitative data on smooth muscle cell orientation in intact ureters, avoiding the usual sectioning artifacts. In 36 rat ureter segments, originating from a proximal, middle or distal site and from a left or right ureter, we found close to the adventitia a well-defined longitudinal smooth muscle orientation. Towards the lamina propria, the orientation gradually became slightly more disperse, yet the main orientation remained longitudinal. We conclude that smooth muscle cell orientation in rat ureter is predominantly longitudinal, though the orientation gradually becomes more disperse towards the proprial side. These findings do not support identification of separate layers. The observed longitudinal orientation suggests that smooth muscle contraction would rather cause local shortening of the ureter, than cause luminal constriction. However, the net-like connective tissue of the ureter wall may translate local longitudinal shortening into co-local luminal constriction, facilitating peristalsis. Our quantitative, minimally invasive approach is a crucial step towards more mechanistic insight into ureter peristalsis, and may also be used to study smooth muscle cell orientation in other tube-like structures like gut and blood vessels.
Predominant Campylobacter jejuni sequence types persist in Finnish chicken production.
Directory of Open Access Journals (Sweden)
Ann-Katrin Llarena
Full Text Available Consumption and handling of chicken meat are well-known risk factors for acquiring campylobacteriosis. This study aimed to describe the Campylobacter jejuni population in Finnish chickens and to investigate the distribution of C. jejuni genotypes on Finnish chicken farms over a period of several years. We included 89.8% of the total C. jejuni population recovered in Finnish poultry during 2004, 2006, 2007, 2008, and 2012 and used multilocus sequence typing (MLST and pulsed-field gel electrophoresis (PFGE to characterize the 380 isolates. The typing data was combined with isolate information on collection-time and farm of origin. The C. jejuni prevalence in chicken slaughter batches was low (mean 3.0%, CI95% [1.8%, 4.2%], and approximately a quarter of Finnish chicken farms delivered at least one positive chicken batch yearly. In general, the C. jejuni population was diverse as represented by a total of 63 sequence types (ST, but certain predominant MLST lineages were identified. ST-45 clonal complex (CC accounted for 53% of the isolates while ST-21 CC and ST-677 CC covered 11% and 9% of the isolates, respectively. Less than half of the Campylobacter positive farms (40.3% delivered C. jejuni-contaminated batches in multiple years, but the genotypes (ST and PFGE types generally varied from year to year. Therefore, no evidence for a persistent C. jejuni source for the colonization of Finnish chickens emerged. Finnish chicken farms are infrequently contaminated with C. jejuni compared to other European Union (EU countries, making Finland a valuable model for further epidemiological studies of the C. jejuni in poultry flocks.
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...
EI: A Program for Ecological Inference
Directory of Open Access Journals (Sweden)
Gary King
2004-09-01
Full Text Available The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997. Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological" data to infer discrete individual-level relationships of interest when individual-level data are not available. Ecological inferences are required in political science research when individual-level surveys are unavailable (e.g., local or comparative electoral politics, unreliable (racial politics, insufficient (political geography, or infeasible (political history. They are also required in numerous areas of ma jor significance in public policy (e.g., for applying the Voting Rights Act and other academic disciplines ranging from epidemiology and marketing to sociology and quantitative history.
On the criticality of inferred models
Mastromatteo, Iacopo
2011-01-01
Advanced inference techniques allow one to reconstruct the pattern of interaction from high dimensional data sets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to a phase transition. On one side, we show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher Information) is directly related to the model's susceptibility. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. On the other, this region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time-scales naturally yield models which are close to criticality.
Trace elements induce predominance among methanogenic activity in anaerobic digestion
Directory of Open Access Journals (Sweden)
Babett Wintsche
2016-12-01
Full Text Available Trace elements play an essential role in all organisms due to their functions in enzyme complexes. In anaerobic digesters, control and supplementation of trace elements lead to stable and more efficient methane production processes while trace element deficits cause process imbalances. However, the underlying metabolic mechanisms and the adaptation of the affected microbial communities to such deficits are not yet fully understood. Here, we investigated the microbial community dynamics and resulting process changes induced by trace element deprivation. Two identical lab-scale continuous stirred tank reactors fed with distiller’s grains and supplemented with trace elements (cobalt, molybdenum, nickel, tungsten and a commercial iron additive were operated in parallel. After 72 weeks of identical operation, the feeding regime of one reactor was changed by omitting trace element supplements and reducing the amount of iron additive. Both reactors were operated for further 21 weeks. Various process parameters (biogas production and composition, total solids and volatile solids, trace element concentration, volatile fatty acids, total ammonium nitrogen, total organic acids/alkalinity ratio, and pH and the composition and activity of the microbial communities were monitored over the total experimental time. While the methane yield remained stable, the concentrations of hydrogen sulfide, total ammonia nitrogen, and acetate increased in the trace element-depleted reactor compared to the well-supplied control reactor. Methanosarcina and Methanoculleus dominated the methanogenic communities in both reactors. However, the activity ratio of these two genera was shown to depend on trace element supplementation explainable by different trace element requirements of their energy conservation systems. Methanosarcina dominated the well-supplied anaerobic digester, pointing to acetoclastic methanogenesis as the dominant methanogenic pathway. Under trace element
Causal inference in obesity research.
Franks, P W; Atabaki-Pasdar, N
2017-03-01
Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.
DEFF Research Database (Denmark)
Cox, Thomas R; Erler, Janine Terra
2014-01-01
that 45% of deaths in the developed world are linked to fibrotic disease. Fibrosis and cancer are known to be inextricably linked; however, we are only just beginning to understand the common and overlapping molecular pathways between the two. Here, we discuss what is known about the intersection...... of fibrosis and cancer, with a focus on cancer metastasis, and highlight some of the exciting new potential clinical targets that are emerging from analysis of the molecular pathways associated with these two devastating diseases. Clin Cancer Res; 20(14); 3637-43. ©2014 AACR....
Linguistic Markers of Inference Generation While Reading.
Clinton, Virginia; Carlson, Sarah E; Seipel, Ben
2016-06-01
Words can be informative linguistic markers of psychological constructs. The purpose of this study is to examine associations between word use and the process of making meaningful connections to a text while reading (i.e., inference generation). To achieve this purpose, think-aloud data from third-fifth grade students ([Formula: see text]) reading narrative texts were hand-coded for inferences. These data were also processed with a computer text analysis tool, Linguistic Inquiry and Word Count, for percentages of word use in the following categories: cognitive mechanism words, nonfluencies, and nine types of function words. Findings indicate that cognitive mechanisms were an independent, positive predictor of connections to background knowledge (i.e., elaborative inference generation) and nonfluencies were an independent, negative predictor of connections within the text (i.e., bridging inference generation). Function words did not provide unique variance towards predicting inference generation. These findings are discussed in the context of a cognitive reflection model and the differences between bridging and elaborative inference generation. In addition, potential practical implications for intelligent tutoring systems and computer-based methods of inference identification are presented.
Using biologically interrelated experiments to identify pathway genes in Arabidopsis
Kim, Kyungpil; Jiang, Keni; Teng, Siew Leng; Feldman, Lewis J.; Huang, Haiyan
2012-01-01
Motivation: Pathway genes are considered as a group of genes that work cooperatively in the same pathway constituting a fundamental functional grouping in a biological process. Identifying pathway genes has been one of the major tasks in understanding biological processes. However, due to the difficulty in characterizing/inferring different types of biological gene relationships, as well as several computational issues arising from dealing with high-dimensional biological data, deducing ge...
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...... by evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose PRIMULA--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Inference and the introductory statistics course
Pfannkuch, Maxine; Regan, Matt; Wild, Chris; Budgett, Stephanie; Forbes, Sharleen; Harraway, John; Parsonage, Ross
2011-10-01
This article sets out some of the rationale and arguments for making major changes to the teaching and learning of statistical inference in introductory courses at our universities by changing from a norm-based, mathematical approach to more conceptually accessible computer-based approaches. The core problem of the inferential argument with its hypothetical probabilistic reasoning process is examined in some depth. We argue that the revolution in the teaching of inference must begin. We also discuss some perplexing issues, problematic areas and some new insights into language conundrums associated with introducing the logic of inference through randomization methods.
Hotchkins, Bryan K.
2013-01-01
This study addresses African American students' leadership experiences at predominantly White institutions. Findings indicated participants utilized servant leadership in historically Black organizations and transformational leadership in predominantly White organizations. The differences displayed showed that participants' leadership perceptions…
Are Evaluations Inferred Directly From Overt Actions?
Brown, Donald; And Others
1975-01-01
The operation of a covert information processing mechanism was investigated in two experiments of the self-persuasion phenomena; i. e., making an inference about a stimulus on the basis of one's past behavior. (Editor)
Autonomous forward inference via DNA computing
Institute of Scientific and Technical Information of China (English)
Fu Yan; Li Gen; Li Yin; Meng Dazhi
2007-01-01
Recent studies direct the researchers into building DNA computing machines with intelligence, which is measured by three main points: autonomous, programmable and able to learn and adapt. Logical inference plays an important role in programmable information processing or computing. Here we present a new method to perform autonomous molecular forward inference for expert system.A novel repetitive recognition site (RRS) technique is invented to design rule-molecules in knowledge base. The inference engine runs autonomously by digesting the rule-molecule, using a Class ⅡB restriction enzyme PpiⅠ. Concentration model has been built to show the feasibility of the inference process under ideal chemical reaction conditions. Moreover, we extend to implement a triggering communication between molecular automata, as a further application of the RRS technique in our model.
Inferring AS Relationships from BGP Attributes
Giotsas, Vasileios
2011-01-01
Business relationships between autonomous systems (AS) are crucial for Internet routing. Existing algorithms used heuristics to infer AS relationships from AS topology data. In this paper we propose a different approach to infer AS relationships from more informative data sources, namely the BGP Community and Local Preference attributes. These data contain rich information on AS routing policies and therefore closely reflect AS relationships. We accumulate the BGP data from RouteViews, RIPE RIS and route servers in August 2010 and February 2011. We infer the AS relationships for 39% of links that are visible in our BGP data. They cover the majority of links among the Tier-1 and Tier-2 ASes. The BGP data also allow us to discover special relationship types, namely hybrid relationship, partial-transit relationship, indirect peering relationship and backup links. Finally we evaluate and analyse the problems of the existing inference algorithms.
Bayesian Cosmological inference beyond statistical isotropy
Souradeep, Tarun; Das, Santanu; Wandelt, Benjamin
2016-10-01
With advent of rich data sets, computationally challenge of inference in cosmology has relied on stochastic sampling method. First, I review the widely used MCMC approach used to infer cosmological parameters and present a adaptive improved implementation SCoPE developed by our group. Next, I present a general method for Bayesian inference of the underlying covariance structure of random fields on a sphere. We employ the Bipolar Spherical Harmonic (BipoSH) representation of general covariance structure on the sphere. We illustrate the efficacy of the method with a principled approach to assess violation of statistical isotropy (SI) in the sky maps of Cosmic Microwave Background (CMB) fluctuations. The general, principled, approach to a Bayesian inference of the covariance structure in a random field on a sphere presented here has huge potential for application to other many aspects of cosmology and astronomy, as well as, more distant areas of research like geosciences and climate modelling.
Metacognitive inferences from other people's memory performance.
Smith, Robert W; Schwarz, Norbert
2016-09-01
Three studies show that people draw metacognitive inferences about events from how well others remember the event. Given that memory fades over time, detailed accounts of distant events suggest that the event must have been particularly memorable, for example, because it was extreme. Accordingly, participants inferred that a physical assault (Study 1) or a poor restaurant experience (Studies 2-3) were more extreme when they were well remembered one year rather than one week later. These inferences influence behavioral intentions. For example, participants recommended a more severe punishment for a well-remembered distant rather than recent assault (Study 1). These metacognitive inferences are eliminated when people attribute the reporter's good memory to an irrelevant cause (e.g., photographic memory), thus undermining the informational value of memory performance (Study 3). These studies illuminate how people use lay theories of memory to learn from others' memory performance about characteristics of the world. (PsycINFO Database Record
Artificial Hydrocarbon Networks Fuzzy Inference System
Directory of Open Access Journals (Sweden)
Hiram Ponce
2013-01-01
Full Text Available This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications.
Experimental evidence for circular inference in schizophrenia
Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S.; Denève, Sophie
2017-01-01
Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to `see what we expect' (through descending loops), to `expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ.
An inference engine for embedded diagnostic systems
Fox, Barry R.; Brewster, Larry T.
1987-01-01
The implementation of an inference engine for embedded diagnostic systems is described. The system consists of two distinct parts. The first is an off-line compiler which accepts a propositional logical statement of the relationship between facts and conclusions and produces data structures required by the on-line inference engine. The second part consists of the inference engine and interface routines which accept assertions of fact and return the conclusions which necessarily follow. Given a set of assertions, it will generate exactly the conclusions which logically follow. At the same time, it will detect any inconsistencies which may propagate from an inconsistent set of assertions or a poorly formulated set of rules. The memory requirements are fixed and the worst case execution times are bounded at compile time. The data structures and inference algorithms are very simple and well understood. The data structures and algorithms are described in detail. The system has been implemented on Lisp, Pascal, and Modula-2.
Composite likelihood method for inferring local pedigrees
Nielsen, Rasmus
2017-01-01
Pedigrees contain information about the genealogical relationships among individuals and are of fundamental importance in many areas of genetic studies. However, pedigrees are often unknown and must be inferred from genetic data. Despite the importance of pedigree inference, existing methods are limited to inferring only close relationships or analyzing a small number of individuals or loci. We present a simulated annealing method for estimating pedigrees in large samples of otherwise seemingly unrelated individuals using genome-wide SNP data. The method supports complex pedigree structures such as polygamous families, multi-generational families, and pedigrees in which many of the member individuals are missing. Computational speed is greatly enhanced by the use of a composite likelihood function which approximates the full likelihood. We validate our method on simulated data and show that it can infer distant relatives more accurately than existing methods. Furthermore, we illustrate the utility of the method on a sample of Greenlandic Inuit. PMID:28827797
Operation of the Bayes Inference Engine
Energy Technology Data Exchange (ETDEWEB)
Hanson, K.M.; Cunningham, G.S.
1998-07-27
The authors have developed a computer application, called the Bayes Inference Engine, to enable one to make inferences about models of a physical object from radiographs taken of it. In the BIE calculational models are represented by a data-flow diagram that can be manipulated by the analyst in a graphical-programming environment. The authors demonstrate the operation of the BIE in terms of examples of two-dimensional tomographic reconstruction including uncertainty estimation.
Causal inference in economics and marketing
Varian, Hal R.
2016-01-01
This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144
Role of Utility and Inference in the Evolution of Functional Information
Sharov, Alexei A.
2009-01-01
Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and the function (phenotype) and determines the shape of the fitness landscape. The utility factor operates at multiple time scales: short-term selection drives evolution towards higher survival and reproduction rate within a given fitness landscape, and long-term selection favors those fitness landscapes that support adaptability and lead to evolutionary expansion of certain lineages. Inference rules make short-term selection possible by shaping the fitness landscape and defining possible directions of evolution, but they are under control of the long-term selection of lineages. Communication normally occurs within a set of agents with compatible interpretation systems, which I call communication system. Functional information cannot be directly transferred between communication systems with incompatible inference rules. Each biological species is a genetic communication system that carries unique functional information together with inference rules that determine evolutionary directions and constraints. This view of the relation between utility and inference can resolve the conflict between realism/positivism and pragmatism. Realism overemphasizes the role of inference in evolution of human knowledge because it assumes that logic is embedded in reality. Pragmatism substitutes usefulness for truth and therefore ignores the advantage of inference. The proposed concept of evolutionary pragmatism rejects the idea that logic is embedded in reality; instead, inference rules are
Polynomial Chaos Surrogates for Bayesian Inference
Le Maitre, Olivier
2016-01-06
The Bayesian inference is a popular probabilistic method to solve inverse problems, such as the identification of field parameter in a PDE model. The inference rely on the Bayes rule to update the prior density of the sought field, from observations, and derive its posterior distribution. In most cases the posterior distribution has no explicit form and has to be sampled, for instance using a Markov-Chain Monte Carlo method. In practice the prior field parameter is decomposed and truncated (e.g. by means of Karhunen- Lo´eve decomposition) to recast the inference problem into the inference of a finite number of coordinates. Although proved effective in many situations, the Bayesian inference as sketched above faces several difficulties requiring improvements. First, sampling the posterior can be a extremely costly task as it requires multiple resolutions of the PDE model for different values of the field parameter. Second, when the observations are not very much informative, the inferred parameter field can highly depends on its prior which can be somehow arbitrary. These issues have motivated the introduction of reduced modeling or surrogates for the (approximate) determination of the parametrized PDE solution and hyperparameters in the description of the prior field. Our contribution focuses on recent developments in these two directions: the acceleration of the posterior sampling by means of Polynomial Chaos expansions and the efficient treatment of parametrized covariance functions for the prior field. We also discuss the possibility of making such approach adaptive to further improve its efficiency.
Marine plastic pollution in waters around Australia: characteristics, concentrations, and pathways.
Reisser, Julia; Shaw, Jeremy; Wilcox, Chris; Hardesty, Britta Denise; Proietti, Maira; Thums, Michele; Pattiaratchi, Charitha
2013-01-01
Plastics represent the vast majority of human-made debris present in the oceans. However, their characteristics, accumulation zones, and transport pathways remain poorly assessed. We characterised and estimated the concentration of marine plastics in waters around Australia using surface net tows, and inferred their potential pathways using particle-tracking models and real drifter trajectories. The 839 marine plastics recorded were predominantly small fragments ("microplastics", median length = 2.8 mm, mean length = 4.9 mm) resulting from the breakdown of larger objects made of polyethylene and polypropylene (e.g. packaging and fishing items). Mean sea surface plastic concentration was 4256.4 pieces km(-2), and after incorporating the effect of vertical wind mixing, this value increased to 8966.3 pieces km(-2). These plastics appear to be associated with a wide range of ocean currents that connect the sampled sites to their international and domestic sources, including populated areas of Australia's east coast. This study shows that plastic contamination levels in surface waters of Australia are similar to those in the Caribbean Sea and Gulf of Maine, but considerably lower than those found in the subtropical gyres and Mediterranean Sea. Microplastics such as the ones described here have the potential to affect organisms ranging from megafauna to small fish and zooplankton.
Marine plastic pollution in waters around Australia: characteristics, concentrations, and pathways.
Directory of Open Access Journals (Sweden)
Julia Reisser
Full Text Available Plastics represent the vast majority of human-made debris present in the oceans. However, their characteristics, accumulation zones, and transport pathways remain poorly assessed. We characterised and estimated the concentration of marine plastics in waters around Australia using surface net tows, and inferred their potential pathways using particle-tracking models and real drifter trajectories. The 839 marine plastics recorded were predominantly small fragments ("microplastics", median length = 2.8 mm, mean length = 4.9 mm resulting from the breakdown of larger objects made of polyethylene and polypropylene (e.g. packaging and fishing items. Mean sea surface plastic concentration was 4256.4 pieces km(-2, and after incorporating the effect of vertical wind mixing, this value increased to 8966.3 pieces km(-2. These plastics appear to be associated with a wide range of ocean currents that connect the sampled sites to their international and domestic sources, including populated areas of Australia's east coast. This study shows that plastic contamination levels in surface waters of Australia are similar to those in the Caribbean Sea and Gulf of Maine, but considerably lower than those found in the subtropical gyres and Mediterranean Sea. Microplastics such as the ones described here have the potential to affect organisms ranging from megafauna to small fish and zooplankton.
Marine Plastic Pollution in Waters around Australia: Characteristics, Concentrations, and Pathways
Reisser, Julia; Shaw, Jeremy; Wilcox, Chris; Hardesty, Britta Denise; Proietti, Maira; Thums, Michele; Pattiaratchi, Charitha
2013-01-01
Plastics represent the vast majority of human-made debris present in the oceans. However, their characteristics, accumulation zones, and transport pathways remain poorly assessed. We characterised and estimated the concentration of marine plastics in waters around Australia using surface net tows, and inferred their potential pathways using particle-tracking models and real drifter trajectories. The 839 marine plastics recorded were predominantly small fragments (“microplastics”, median length = 2.8 mm, mean length = 4.9 mm) resulting from the breakdown of larger objects made of polyethylene and polypropylene (e.g. packaging and fishing items). Mean sea surface plastic concentration was 4256.4 pieces km−2, and after incorporating the effect of vertical wind mixing, this value increased to 8966.3 pieces km−2. These plastics appear to be associated with a wide range of ocean currents that connect the sampled sites to their international and domestic sources, including populated areas of Australia's east coast. This study shows that plastic contamination levels in surface waters of Australia are similar to those in the Caribbean Sea and Gulf of Maine, but considerably lower than those found in the subtropical gyres and Mediterranean Sea. Microplastics such as the ones described here have the potential to affect organisms ranging from megafauna to small fish and zooplankton. PMID:24312224
Mood Inference Machine: Framework to Infer Affective Phenomena in ROODA Virtual Learning Environment
Directory of Open Access Journals (Sweden)
Magalí Teresinha Longhi
2012-02-01
Full Text Available This article presents a mechanism to infer mood states, aiming to provide virtual learning environments (VLEs with a tool able to recognize the student’s motivation. The inference model has as its parameters personality traits, motivational factors obtained through behavioral standards and the affective subjectivity identified in texts made available in the communication functionalities of the VLE. In the inference machine, such variables are treated under probability reasoning, more precisely by Bayesian networks.
Multisensory oddity detection as bayesian inference.
Directory of Open Access Journals (Sweden)
Timothy Hospedales
Full Text Available A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm--that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments--involving cues across and within modalities--for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new 'oddity detection' paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain.
Multisensory oddity detection as bayesian inference.
Hospedales, Timothy; Vijayakumar, Sethu
2009-01-01
A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm--that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments--involving cues across and within modalities--for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new 'oddity detection' paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain.
Inference of Isoforms from Short Sequence Reads
Feng, Jianxing; Li, Wei; Jiang, Tao
Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms (or splicing variants) is a difficult problem. Traditional experimental methods for this purpose are time consuming and cost ineffective. The emerging RNA-Seq technology provides a possible effective method to address this problem. Although the advantages of RNA-Seq over traditional methods in transcriptome analysis have been confirmed by many studies, the inference of isoforms from millions of short sequence reads (e.g., Illumina/Solexa reads) has remained computationally challenging. In this work, we propose a method to calculate the expression levels of isoforms and infer isoforms from short RNA-Seq reads using exon-intron boundary, transcription start site (TSS) and poly-A site (PAS) information. We first formulate the relationship among exons, isoforms, and single-end reads as a convex quadratic program, and then use an efficient algorithm (called IsoInfer) to search for isoforms. IsoInfer can calculate the expression levels of isoforms accurately if all the isoforms are known and infer novel isoforms from scratch. Our experimental tests on known mouse isoforms with both simulated expression levels and reads demonstrate that IsoInfer is able to calculate the expression levels of isoforms with an accuracy comparable to the state-of-the-art statistical method and a 60 times faster speed. Moreover, our tests on both simulated and real reads show that it achieves a good precision and sensitivity in inferring isoforms when given accurate exon-intron boundary, TSS and PAS information, especially for isoforms whose expression levels are significantly high.
DEFF Research Database (Denmark)
Scheuer, John Damm
2010-01-01
The theoretical background in this chapter is organizational studies and especially theories about design and design processes in organizations. The concept of design is defined as a particular kind of work aimed at making arrangements in order to change existing situations into desired ones....... The illustrative case example is the introduction of clinical pathways in a psychiatric department. The contribution to a general core of design research is the development of the concept of design work and a critical discussion of the role of technological rules in design work....
DEFF Research Database (Denmark)
2010-01-01
The theoretical background in this chapter is organizational studies and especially theories about design and design processes in organizations. The concept of design is defined as a particular kind of work aimed at making arrangements in order to change existing situations into desired ones....... The illustrative case example is the introduction of clinical pathways in a psychiatric department. The contribution to a general core of design research is the development of the concept of design work and a critical discussion of the role of technological rules in design work....
Improved functional overview of protein complexes using inferred epistatic relationships
LENUS (Irish Health Repository)
Ryan, Colm
2011-05-23
Abstract Background Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization. Results We introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus. Conclusion Overall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.
Estimating uncertainty of inference for validation
Energy Technology Data Exchange (ETDEWEB)
Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM
2010-09-30
We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the
Marine plastic pollution in waters around Australia: characteristics, concentrations, and pathways
National Research Council Canada - National Science Library
Reisser, Julia; Shaw, Jeremy; Wilcox, Chris; Hardesty, Britta Denise; Proietti, Maira; Thums, Michele; Pattiaratchi, Charitha
2013-01-01
.... We characterised and estimated the concentration of marine plastics in waters around Australia using surface net tows, and inferred their potential pathways using particle-tracking models and real drifter trajectories...
National Research Council Canada - National Science Library
Julia Reisser; Jeremy Shaw; Chris Wilcox; Britta Denise Hardesty; Maira Proietti; Michele Thums; Charitha Pattiaratchi
2013-01-01
.... We characterised and estimated the concentration of marine plastics in waters around Australia using surface net tows, and inferred their potential pathways using particle-tracking models and real drifter trajectories...
Deep Learning for Population Genetic Inference.
Directory of Open Access Journals (Sweden)
Sara Sheehan
2016-03-01
Full Text Available Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data to the output (e.g., population genetic parameters of interest. We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history. Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.
Deep Learning for Population Genetic Inference.
Sheehan, Sara; Song, Yun S
2016-03-01
Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.
Deep Learning for Population Genetic Inference
Sheehan, Sara; Song, Yun S.
2016-01-01
Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908
Generative Inferences Based on Learned Relations.
Chen, Dawn; Lu, Hongjing; Holyoak, Keith J
2016-11-17
A key property of relational representations is their generativity: From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from non-relational inputs. In the present paper, we show that a bottom-up model of relation learning, initially developed to discriminate between positive and negative examples of comparative relations (e.g., deciding whether a sheep is larger than a rabbit), can be extended to make generative inferences. The model is able to make quasi-deductive transitive inferences (e.g., "If A is larger than B and B is larger than C, then A is larger than C") and to qualitatively account for human responses to generative questions such as "What is an animal that is smaller than a dog?" These results provide evidence that relational models based on bottom-up learning mechanisms are capable of supporting generative inferences.
Computationally efficient Bayesian inference for inverse problems.
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef M.; Najm, Habib N.; Rahn, Larry A.
2007-10-01
Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced.
Pathway collages: personalized multi-pathway diagrams.
Paley, Suzanne; O'Maille, Paul E; Weaver, Daniel; Karp, Peter D
2016-12-13
Metabolic pathway diagrams are a classical way of visualizing a linked cascade of biochemical reactions. However, to understand some biochemical situations, viewing a single pathway is insufficient, whereas viewing the entire metabolic network results in information overload. How do we enable scientists to rapidly construct personalized multi-pathway diagrams that depict a desired collection of interacting pathways that emphasize particular pathway interactions? We define software for constructing personalized multi-pathway diagrams called pathway-collages using a combination of manual and automatic layouts. The user specifies a set of pathways of interest for the collage from a Pathway/Genome Database. Layouts for the individual pathways are generated by the Pathway Tools software, and are sent to a Javascript Pathway Collage application implemented using Cytoscape.js. That application allows the user to re-position pathways; define connections between pathways; change visual style parameters; and paint metabolomics, gene expression, and reaction flux data onto the collage to obtain a desired multi-pathway diagram. We demonstrate the use of pathway collages in two application areas: a metabolomics study of pathogen drug response, and an Escherichia coli metabolic model. Pathway collages enable facile construction of personalized multi-pathway diagrams.
Using Alien Coins to Test Whether Simple Inference Is Bayesian
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…
Identification of the Entner-Doudoroff pathway in an antibiotic-producing actinomycete species
DEFF Research Database (Denmark)
Gunnarsson, Nina; Mortensen, Uffe Hasbro; Sosio, M.
2004-01-01
the primary metabolic pathways of the poorly characterized antibiotic-producing actinomycete Nonomuraea sp. ATCC 39727. Surprisingly, it was found that Nonomuraea sp. ATCC 39272 predominantly metabolizes glucose via the Entner-Doudoroff (ED) pathway. This represents the first time that the ED pathway has been...... recognized as the main catabolic pathway in an actinomycete. The Nonomuraea genes encoding the key enzymes of the ED pathway were subsequently identified, sequenced and functionally described....
Statistical inference based on divergence measures
Pardo, Leandro
2005-01-01
The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, prese...
Hierarchical probabilistic inference of cosmic shear
Schneider, Michael D; Marshall, Philip J; Dawson, William A; Meyers, Joshua; Bard, Deborah J; Lang, Dustin
2014-01-01
Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the glo...
Lifted Inference for Relational Continuous Models
Choi, Jaesik; Hill, David J
2012-01-01
Relational Continuous Models (RCMs) represent joint probability densities over attributes of objects, when the attributes have continuous domains. With relational representations, they can model joint probability distributions over large numbers of variables compactly in a natural way. This paper presents a new exact lifted inference algorithm for RCMs, thus it scales up to large models of real world applications. The algorithm applies to Relational Pairwise Models which are (relational) products of potentials of arity 2. Our algorithm is unique in two ways. First, it substantially improves the efficiency of lifted inference with variables of continuous domains. When a relational model has Gaussian potentials, it takes only linear-time compared to cubic time of previous methods. Second, it is the first exact inference algorithm which handles RCMs in a lifted way. The algorithm is illustrated over an example from econometrics. Experimental results show that our algorithm outperforms both a groundlevel inferenc...
On Tidal Inference in the Diurnal Band
Ray, R. D.
2017-01-01
Standard methods of tidal inference should be revised to account for a known resonance that occurs mostly within the K(sub 1) tidal group in the diurnal band. The resonance arises from a free rotational mode of Earth caused by the fluid core. In a set of 110 bottom-pressure tide stations, the amplitude of the P(sub 1) tidal constituent is shown to be suppressed relative to K(sub 1), which is in good agreement with the resonance theory. Standard formulas for the K(sub 1) nodal modulation remain essentially unaffected. Two examples are given of applications of the refined inference methodology: one with monthly tide gauge data and one with satellite altimetry. For some altimeter-constrained tide models, an inferred P(sub 1) constituent is found to be more accurate than a directly determined one.
Grammatical inference algorithms, routines and applications
Wieczorek, Wojciech
2017-01-01
This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>.
Parameter inference with estimated covariance matrices
Sellentin, Elena
2015-01-01
When inferring parameters from a Gaussian-distributed data set by computing a likelihood, a covariance matrix is needed that describes the data errors and their correlations. If the covariance matrix is not known a priori, it may be estimated and thereby becomes a random object with some intrinsic uncertainty itself. We show how to infer parameters in the presence of such an estimated covariance matrix, by marginalising over the true covariance matrix, conditioned on its estimated value. This leads to a likelihood function that is no longer Gaussian, but rather an adapted version of a multivariate $t$-distribution, which has the same numerical complexity as the multivariate Gaussian. As expected, marginalisation over the true covariance matrix improves inference when compared with Hartlap et al.'s method, which uses an unbiased estimate of the inverse covariance matrix but still assumes that the likelihood is Gaussian.
Inferring epidemic network topology from surveillance data.
Wan, Xiang; Liu, Jiming; Cheung, William K; Tong, Tiejun
2014-01-01
The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases.
Examples in parametric inference with R
Dixit, Ulhas Jayram
2016-01-01
This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory cou...
Picturing classical and quantum Bayesian inference
Coecke, Bob
2011-01-01
We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers density operators rather than probability distributions as representative of degrees of belief. The diagrammatic framework is stated in the graphical language of symmetric monoidal categories and of compact structures and Frobenius structures therein, in which Bayesian inversion boils down to transposition with respect to an appropriate compact structure. We characterize classical Bayesian inference in terms of a graphical property and demonstrate that our approach eliminates some purely conventional elements that appear in common representations thereof, such as whether degrees of belief are represented by probabilities or entropic quantities. We also introduce a quantum-like calculus wherein the Frobenius structure is noncommutative and show that it can accommodate Leifer's calculus of `cond...
Inferring causality from noisy time series data
DEFF Research Database (Denmark)
Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian;
2016-01-01
Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...... injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Given the inherent noisy nature of real-world systems, our findings enable a more accurate evaluation of CCM applicability and advance suggestions on how to overcome its weaknesses....
A Learning Algorithm for Multimodal Grammar Inference.
D'Ulizia, A; Ferri, F; Grifoni, P
2011-12-01
The high costs of development and maintenance of multimodal grammars in integrating and understanding input in multimodal interfaces lead to the investigation of novel algorithmic solutions in automating grammar generation and in updating processes. Many algorithms for context-free grammar inference have been developed in the natural language processing literature. An extension of these algorithms toward the inference of multimodal grammars is necessary for multimodal input processing. In this paper, we propose a novel grammar inference mechanism that allows us to learn a multimodal grammar from its positive samples of multimodal sentences. The algorithm first generates the multimodal grammar that is able to parse the positive samples of sentences and, afterward, makes use of two learning operators and the minimum description length metrics in improving the grammar description and in avoiding the over-generalization problem. The experimental results highlight the acceptable performances of the algorithm proposed in this paper since it has a very high probability of parsing valid sentences.
Generalized Collective Inference with Symmetric Clique Potentials
Gupta, Rahul; Dewan, Ajit A
2009-01-01
Collective graphical models exploit inter-instance associative dependence to output more accurate labelings. However existing models support very limited kind of associativity which restricts accuracy gains. This paper makes two major contributions. First, we propose a general collective inference framework that biases data instances to agree on a set of {\\em properties} of their labelings. Agreement is encouraged through symmetric clique potentials. We show that rich properties leads to bigger gains, and present a systematic inference procedure for a large class of such properties. The procedure performs message passing on the cluster graph, where property-aware messages are computed with cluster specific algorithms. This provides an inference-only solution for domain adaptation. Our experiments on bibliographic information extraction illustrate significant test error reduction over unseen domains. Our second major contribution consists of algorithms for computing outgoing messages from clique clusters with ...
Inference of gene-phenotype associations via protein-protein interaction and orthology.
Directory of Open Access Journals (Sweden)
Panwen Wang
Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.
Chow, Ho Ming; Kaup, Barbara; Raabe, Markus; Greenlee, Mark W
2008-04-01
We investigated how readers strategically infer context-appropriate information on the basis of the presented text and their world knowledge during passage reading. In the main experimental condition, participants were instructed to read short passages and to predict the development of the situation described in each passage during reading. To accomplish this task, we assumed that participants need to draw strategic inferences relevant to the contexts. Comparing this condition with a passage-reading condition without prediction, we found out that the left anterior prefrontal cortex (aPFC) in Brodmann area 9/10 and the left anterior ventral inferior frontal gyrus (vIFG) in Brodmann area 47 elicited increased hemodynamic responses. These two regions are probably critical in coherence evaluation and in drawing strategic inferences. Additionally, we used dynamic causal modelling (DCM) to investigate the fronto-temporal interactions induced by the experimental conditions. Ten models with different plausible ways to modulate the connections between frontal and temporal regions were compared. The DCM results showed a consistent conclusion: The connectivity between the left posterior superior temporal sulcus (pSTS) and the left dorsal lateral inferior frontal gyrus (dIFG) were enhanced when participants made inferential predictions during reading. The results support the role of top-down influences mediated by the neural pathways between dIFG and pSTS in retrieving strategic inferences. With these findings we discuss functional roles of aPFC, vIFG and dIFG-pSTS connections in drawing strategic inferences.
Role of Utility and Inference in the Evolution of Functional Information
Sharov, Alexei A
2009-01-01
Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and th...
Evaluation of probabilistic and logical inference for a SNP annotation system.
Shen, Terry H; Tarczy-Hornoch, Peter; Detwiler, Landon T; Cadag, Eithon; Carlson, Christopher S
2010-06-01
Genome wide association studies (GWAS) are an important approach to understanding the genetic mechanisms behind human diseases. Single nucleotide polymorphisms (SNPs) are the predominant markers used in genome wide association studies, and the ability to predict which SNPs are likely to be functional is important for both a priori and a posteriori analyses of GWA studies. This article describes the design, implementation and evaluation of a family of systems for the purpose of identifying SNPs that may cause a change in phenotypic outcomes. The methods described in this article characterize the feasibility of combinations of logical and probabilistic inference with federated data integration for both point and regional SNP annotation and analysis. Evaluations of the methods demonstrate the overall strong predictive value of logical, and logical with probabilistic, inference applied to the domain of SNP annotation.
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisova, K.
2010-01-01
with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analysing Peter Diggle's heather data set, where we discuss the results of simulation......This is probably the first paper which discusses likelihood inference for a random set using a germ-grain model, where the individual grains are unobservable, edge effects occur and other complications appear. We consider the case where the grains form a disc process modelled by a marked point......-based maximum likelihood inference and the effect of specifying different reference Poisson models....
Variational Bayesian Inference of Line Spectra
DEFF Research Database (Denmark)
Badiu, Mihai Alin; Hansen, Thomas Lundgaard; Fleury, Bernard Henri
2017-01-01
In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coeffici......In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid...
Statistical Inference for Partially Observed Diffusion Processes
DEFF Research Database (Denmark)
Jensen, Anders Christian
-dimensional Ornstein-Uhlenbeck where one coordinate is completely unobserved. This model does not have the Markov property and it makes parameter inference more complicated. Next we take a Bayesian approach and introduce some basic Markov chain Monte Carlo methods. In chapter ve and six we describe an Bayesian method...... to perform parameter inference in multivariate diffusion models that may be only partially observed. The methodology is applied to the stochastic FitzHugh-Nagumo model and the two-dimensional Ornstein-Uhlenbeck process. Chapter seven focus on parameter identifiability in the aprtially observed Ornstein...
National Research Council Canada - National Science Library
Heber Silva-Díaz; Olinda Bustamante-Canelo; Franklin-Rómulo Aguilar-Gamboa; Katya Mera-Villasis; Jhonatan Ipanaque-Chozo; Eberth Seclen-Bernabe; Martha Vergara-Espinoza
2017-01-01
Objective: To determine the type and frequency of predominant enteropathogens in acute diarrhea and their associated characteristics in children treated at Hospital Regional Lambayeque (HRL) - Peru...
Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data
Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie
2016-01-01
Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993
Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data
Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie
2016-10-01
Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine.
Inference making ability and the function of inferences in reading comprehension
Directory of Open Access Journals (Sweden)
Salih Özenici
2011-05-01
Full Text Available The aim of this study is to explain the relation between reading comprehension and inference. The main target of reading process is to create a coherent mental representation of the text, therefore it is necessary to recognize relations between different parts of the texts and to relate them to one another. During reading process, to complete the missing information in the text or to add new information is necessary. All these processes require inference making ability beyond the information in the text. When the readers use such active reading strategies as monitoring the comprehension, prediction, inferring and background knowledge, they learn a lot more from the text and understand it better. In reading comprehension, making inference is a constructive thinking process, because it is a cognitive process in order to form the meaning. When reading comprehension models are considered, it can be easily seen that linguistics elements cannot explain these processes by themselves, therefore the ability of thinking and inference making is needed. During reading process, general world knowledge is necessary to form coherent relations between sentences. Information which comes from context of the text will not be adequate to understand the text. In order to overcome this deficiency and to integrate the meanings from different sentences witch each other, it is necessary to make inference. Readers make inference in order to completely understand what the writer means, to interpret the sentences and also to form the combinations and relations between them.
Inference making ability and the function of inferences in reading comprehension
Directory of Open Access Journals (Sweden)
Salih Özenici
2011-05-01
Full Text Available The aim of this study is to explain the relation of reading comprehension and inference. The main target of reading process is to create a coherent mental representation of the text, therefore it is necessary to recognize relations between different parts of the texts and to relate them to one another. During reading process, to complete the missing information in the text or to add new information is necessary. All these processes require inference making ability beyond the information in the text. When the readers use such active reading strategies as monitoring the comprehension, prediction, inferring and background knowledge, they learn a lot more from the text and understand it better. In reading comprehension, making inference is a constructive thinking process, because it is a cognitive process in order to form the meaning. When reading comprehension models are considered, it can be easily seen that linguistics elements cannot explain these processes by themselves, therefore the ability of thinking and inference making is needed. During reading process, general world knowledge is necessary to form coherent relations between sentences. Information which comes from context of the text will not be adequate to understand the text. In order to overcome this deficiency and to integrate the meanings from different sentences witch each other, it is necessary to make inference. Readers make inference in order to completely understand what the writer means, to interpret the sentences and also to form the combinations and relations between them.
Perturbation biology: inferring signaling networks in cellular systems.
Molinelli, Evan J; Korkut, Anil; Wang, Weiqing; Miller, Martin L; Gauthier, Nicholas P; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B; Pratilas, Christine A; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris
2013-01-01
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.
Understanding COBOL systems using inferred types
A. van Deursen (Arie); L.M.F. Moonen (Leon)
1999-01-01
textabstractIn a typical COBOL program, the data division consists of 50 of the lines of code. Automatic type inference can help to understand the large collections of variable declarations contained therein, showing how variables are related based on their actual usage. The most problematic aspect
John Updike and Norman Mailer: Sport Inferences.
Upshaw, Kathryn Jane
The phenomenon of writer use of sport inferences in the literary genre of the novel is examined in the works of Updike and Mailer. Novels of both authors were reviewed in order to study the pattern of usage in each novel. From these patterns, concepts which illustrated the sport philosophies of each author were used for general comparisons of the…
HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR
Energy Technology Data Exchange (ETDEWEB)
Schneider, Michael D.; Dawson, William A. [Lawrence Livermore National Laboratory, Livermore, CA 94551 (United States); Hogg, David W. [Center for Cosmology and Particle Physics, New York University, New York, NY 10003 (United States); Marshall, Philip J.; Bard, Deborah J. [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Meyers, Joshua [Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, 452 Lomita Mall, Stanford, CA 94035 (United States); Lang, Dustin, E-mail: schneider42@llnl.gov [Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213 (United States)
2015-07-01
Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics.
Inferring Internet Denial-of-Service Activity
2007-11-02
Inferring Internet Denial-of-Service Activity David Moore CAIDA San Diego Supercomputer Center University of California, San Diego dmoore@caida.org...the local network topology. kc claffy and Colleen Shannon at CAIDA provided support and valuable feed- back throughout the project. David Wetherall
GAMBIT: Global And Modular BSM Inference Tool
GAMBIT Collaboration; Athron, Peter; Balazs, Csaba; Bringmann, Torsten; Buckley, Andy; Chrzä Szcz, Marcin; Conrad, Jan; Cornell, Jonathan M.; Dal, Lars A.; Dickinson, Hugh; Edsjö, Joakim; Farmer, Ben; Jackson, Paul; Krislock, Abram; Kvellestad, Anders; Lundberg, Johan; McKay, James; Mahmoudi, Farvah; Martinez, Gregory D.; Putze, Antje Raklev, Are; Ripken, Joachim; Rogan, Christopher; Saavedra, Aldo; Savage, Christopher; Scott, Pat; Seo, Seon-Hee; Serra, Nicola; Weniger, Christoph; White, Martin; Wild, Sebastian
2017-08-01
GAMBIT (Global And Modular BSM Inference Tool) performs statistical global fits of generic physics models using a wide range of particle physics and astrophysics data. Modules provide native simulations of collider and astrophysics experiments, a flexible system for interfacing external codes (the backend system), a fully featured statistical and parameter scanning framework, and additional tools for implementing and using hierarchical models.
Linguistic Markers of Inference Generation While Reading
Clinton, Virginia; Carlson, Sarah E.; Seipel, Ben
2016-01-01
Words can be informative linguistic markers of psychological constructs. The purpose of this study is to examine associations between word use and the process of making meaningful connections to a text while reading (i.e., inference generation). To achieve this purpose, think-aloud data from third-fifth grade students (N = 218) reading narrative…
New Inference Rules for Max-SAT
Li, C M; Planes, J; 10.1613/jair.2215
2011-01-01
Exact Max-SAT solvers, compared with SAT solvers, apply little inference at each node of the proof tree. Commonly used SAT inference rules like unit propagation produce a simplified formula that preserves satisfiability but, unfortunately, solving the Max-SAT problem for the simplified formula is not equivalent to solving it for the original formula. In this paper, we define a number of original inference rules that, besides being applied efficiently, transform Max-SAT instances into equivalent Max-SAT instances which are easier to solve. The soundness of the rules, that can be seen as refinements of unit resolution adapted to Max-SAT, are proved in a novel and simple way via an integer programming transformation. With the aim of finding out how powerful the inference rules are in practice, we have developed a new Max-SAT solver, called MaxSatz, which incorporates those rules, and performed an experimental investigation. The results provide empirical evidence that MaxSatz is very competitive, at least, on ran...
Ignorability in Statistical and Probabilistic Inference
DEFF Research Database (Denmark)
Jaeger, Manfred
2005-01-01
When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed...
Nonparametric Bayes inference for concave distribution functions
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted; Lauritzen, Steffen Lilholt
2002-01-01
Bayesian inference for concave distribution functions is investigated. This is made by transforming a mixture of Dirichlet processes on the space of distribution functions to the space of concave distribution functions. We give a method for sampling from the posterior distribution using a Pólya urn...
Campbell's and Rubin's Perspectives on Causal Inference
West, Stephen G.; Thoemmes, Felix
2010-01-01
Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…
Decision generation tools and Bayesian inference
Jannson, Tomasz; Wang, Wenjian; Forrester, Thomas; Kostrzewski, Andrew; Veeris, Christian; Nielsen, Thomas
2014-05-01
Digital Decision Generation (DDG) tools are important software sub-systems of Command and Control (C2) systems and technologies. In this paper, we present a special type of DDGs based on Bayesian Inference, related to adverse (hostile) networks, including such important applications as terrorism-related networks and organized crime ones.
Campbell's and Rubin's Perspectives on Causal Inference
West, Stephen G.; Thoemmes, Felix
2010-01-01
Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…
"Comments on Slavin": Synthesizing Causal Inferences
Briggs, Derek C.
2008-01-01
When causal inferences are to be synthesized across multiple studies, efforts to establish the magnitude of a causal effect should be balanced by an effort to evaluate the generalizability of the effect. The evaluation of generalizability depends on two factors that are given little attention in current syntheses: construct validity and external…
On Measurement Bias in Causal Inference
Pearl, Judea
2012-01-01
This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias-free effect estimates in such models.
Evolutionary inference via the Poisson Indel Process.
Bouchard-Côté, Alexandre; Jordan, Michael I
2013-01-22
We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.
Making statistical inferences about software reliability
Miller, Douglas R.
1988-01-01
Failure times of software undergoing random debugging can be modelled as order statistics of independent but nonidentically distributed exponential random variables. Using this model inferences can be made about current reliability and, if debugging continues, future reliability. This model also shows the difficulty inherent in statistical verification of very highly reliable software such as that used by digital avionics in commercial aircraft.
Spurious correlations and inference in landscape genetics
Samuel A. Cushman; Erin L. Landguth
2010-01-01
Reliable interpretation of landscape genetic analyses depends on statistical methods that have high power to identify the correct process driving gene flow while rejecting incorrect alternative hypotheses. Little is known about statistical power and inference in individual-based landscape genetics. Our objective was to evaluate the power of causalmodelling with partial...
Understanding COBOL systems using inferred types
Deursen, A. van; Moonen, L.M.F.
1999-01-01
In a typical COBOL program, the data division consists of 50 of the lines of code. Automatic type inference can help to understand the large collections of variable declarations contained therein, showing how variables are related based on their actual usage. The most problematic aspect of type infe
Double jeopardy in inferring cognitive processes.
Fific, Mario
2014-01-01
Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2 (n) . In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs.
Tactile length contraction as Bayesian inference.
Tong, Jonathan; Ngo, Vy; Goldreich, Daniel
2016-08-01
To perceive, the brain must interpret stimulus-evoked neural activity. This is challenging: The stochastic nature of the neural response renders its interpretation inherently uncertain. Perception would be optimized if the brain used Bayesian inference to interpret inputs in light of expectations derived from experience. Bayesian inference would improve perception on average but cause illusions when stimuli violate expectation. Intriguingly, tactile, auditory, and visual perception are all prone to length contraction illusions, characterized by the dramatic underestimation of the distance between punctate stimuli delivered in rapid succession; the origin of these illusions has been mysterious. We previously proposed that length contraction illusions occur because the brain interprets punctate stimulus sequences using Bayesian inference with a low-velocity expectation. A novel prediction of our Bayesian observer model is that length contraction should intensify if stimuli are made more difficult to localize. Here we report a tactile psychophysical study that tested this prediction. Twenty humans compared two distances on the forearm: a fixed reference distance defined by two taps with 1-s temporal separation and an adjustable comparison distance defined by two taps with temporal separation t ≤ 1 s. We observed significant length contraction: As t was decreased, participants perceived the two distances as equal only when the comparison distance was made progressively greater than the reference distance. Furthermore, the use of weaker taps significantly enhanced participants' length contraction. These findings confirm the model's predictions, supporting the view that the spatiotemporal percept is a best estimate resulting from a Bayesian inference process.
Colligation or, The Logical Inference of Interconnection
DEFF Research Database (Denmark)
Franksen, Ole Immanuel; Falster, Peter
2000-01-01
laws or assumptions. Yet interconnection as an abstract concept seems to be without scientific underpinning in oure logic. Adopting a historical viewpoint, our aim is to show that the reasoning of interconnection may be identified with a neglected kind of logical inference, called "colligation...
Inferring comprehensible business/ICT alignment rules
Cumps, B.; Martens, D.; De Backer, M.; Haesen, R.; Viaene, S.; Dedene, G.; Baesens, B.; Snoeck, M.
2009-01-01
We inferred business rules for business/ICT alignment by applying a novel rule induction algorithm on a data set containing rich alignment information polled from 641 organisations in 7 European countries. The alignment rule set was created using AntMiner+, a rule induction technique with a reputati
Quasi-Experimental Designs for Causal Inference
Kim, Yongnam; Steiner, Peter
2016-01-01
When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…
How to Make Inference in Reading
Institute of Scientific and Technical Information of China (English)
何少芳
2013-01-01
Students often have difficulties in reading comprehension because of too many new and unfamiliar words, too little background knowledge and different patterns of thinking among different countries. In this thesis, I recommend applying context clues, synonyms or antonyms, examples, definitions or explanations, cause/effect clues and background clues to make inference when we read texts.
Investigating Mathematics Teachers' Thoughts of Statistical Inference
Yang, Kai-Lin
2012-01-01
Research on statistical cognition and application suggests that statistical inference concepts are commonly misunderstood by students and even misinterpreted by researchers. Although some research has been done on students' misunderstanding or misconceptions of confidence intervals (CIs), few studies explore either students' or mathematics…
Non-Parametric Inference in Astrophysics
Wasserman, L H; Nichol, R C; Genovese, C; Jang, W; Connolly, A J; Moore, A W; Schneider, J; Wasserman, Larry; Miller, Christopher J.; Nichol, Robert C.; Genovese, Chris; Jang, Woncheol; Connolly, Andrew J.; Moore, Andrew W.; Schneider, Jeff; group, the PICA
2001-01-01
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
The importance of learning when making inferences
Directory of Open Access Journals (Sweden)
Jorg Rieskamp
2008-03-01
Full Text Available The assumption that people possess a repertoire of strategies to solve the inference problems they face has been made repeatedly. The experimental findings of two previous studies on strategy selection are reexamined from a learning perspective, which argues that people learn to select strategies for making probabilistic inferences. This learning process is modeled with the strategy selection learning (SSL theory, which assumes that people develop subjective expectancies for the strategies they have. They select strategies proportional to their expectancies, which are updated on the basis of experience. For the study by Newell, Weston, and Shanks (2003 it can be shown that people did not anticipate the success of a strategy from the beginning of the experiment. Instead, the behavior observed at the end of the experiment was the result of a learning process that can be described by the SSL theory. For the second study, by Br"oder and Schiffer (2006, the SSL theory is able to provide an explanation for why participants only slowly adapted to new environments in a dynamic inference situation. The reanalysis of the previous studies illustrates the importance of learning for probabilistic inferences.
Active interoceptive inference and the emotional brain
Friston, Karl J.
2016-01-01
We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’. PMID:28080966
Bayesian structural inference for hidden processes.
Strelioff, Christopher C; Crutchfield, James P
2014-04-01
We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.
Inference and the Introductory Statistics Course
Pfannkuch, Maxine; Regan, Matt; Wild, Chris; Budgett, Stephanie; Forbes, Sharleen; Harraway, John; Parsonage, Ross
2011-01-01
This article sets out some of the rationale and arguments for making major changes to the teaching and learning of statistical inference in introductory courses at our universities by changing from a norm-based, mathematical approach to more conceptually accessible computer-based approaches. The core problem of the inferential argument with its…
DEFF Research Database (Denmark)
Brighton, Cheryl A.; Rievaj, Juraj; Kuhre, Rune E.;
2015-01-01
Bile acids are well-recognized stimuli of glucagon-like peptide-1 (GLP-1) secretion. This action has been attributed to activation of the G protein-coupled bile acid receptor GPBAR1 (TGR5), although other potential bile acid sensors include the nuclear farnesoid receptor and the apical sodium......-coupled bile acid transporter ASBT. The aim of this study was to identify pathways important for GLP-1 release and to determine whether bile acids target their receptors on GLP-1-secreting L-cells from the apical or basolateral compartment. Using transgenic mice expressing fluorescent sensors specifically in L...... to either TLCA or TDCA. We conclude that the action of bile acids on GLP-1 secretion is predominantly mediated by GPBAR1 located on the basolateral L-cell membrane, suggesting that stimulation of gut hormone secretion may include postabsorptive mechanisms....
Ruan, Jianqing; Liao, Cangsong; Ye, Yang; Lin, Ge
2014-01-21
Pyrrolizidine alkaloid (PA) poisoning is well-known because of the intake of PA-containing plant-derived natural products and PA-contaminated foodstuffs. Based on different structures of the necine bases, PAs are classified into three types: retronecine, otonecine, and platynecine type. The former two type PAs possessing an unsaturated necine base with a 1,2-double bond are hepatotoxic due to the P450-mediated metabolic activation to generate reactive pyrrolic ester, which interacts with cellular macromolecules leading to toxicity. With a saturated necine base, platynecine-type PAs are reported to be nontoxic and their nontoxicity was hypothesized to be due to the absence of metabolic activation; however, the metabolic pathway responsible for their nontoxic nature is largely unknown. In the present study, to prove the absence of metabolic activation in nontoxic platynecine-type PAs, hepatic metabolism of platyphylline (PLA), a representative platynecine-type PA, was investigated and directly compared with the representatives of two toxic types of PAs in parallel. By determining the pyrrolic ester-derived glutathione conjugate, our results confirmed that the major metabolic pathway of PLA did not lead to formation of the reactive pyrrolic ester. More interestingly, having a metabolic rate similar to that of toxic PAs, PLA also underwent oxidative metabolisms mediated by P450s, especially P450 3A4, the same enzyme that catalyzes metabolic activation of two toxic types of PAs. However, the predominant oxidative dehydrogenation pathway of PLA formed a novel metabolite, dehydroplatyphylline carboxylic acid, which was water-soluble, readily excreted, and could not interact with cellular macromolecules. In conclusion, our study confirmed that the saturated necine bases determine the absence of metabolic activation and thus govern the metabolic pathway responsible for the nontoxic nature of platynecine-type PAs.
Segers, Frank J J; Meijer, Martin; Houbraken, Jos; Samson, Robert A; Wösten, Han A B; Dijksterhuis, Jan
2015-01-01
Indoor fungi are a major cause of cosmetic and structural damage of buildings worldwide and prolonged exposure of these fungi poses a health risk. Aspergillus, Penicillium and Cladosporium species are the most predominant fungi in indoor environments. Cladosporium species predominate under ambient c
Ishimoto, Haruka; Oshima, Tadayuki; Sei, Hiroo; Yamasaki, Takahisa; Kondo, Takashi; Tozawa, Katsuyuki; Tomita, Toshihiko; Ohda, Yoshio; Fukui, Hirokazu; Watari, Jiro; Miwa, Hiroto
2017-01-01
Intestinal epithelial barrier function is impaired in irritable bowel syndrome patients. Claudins are highly expressed in cells with tight junctions and are involved in the intestinal epithelial barrier function. The expression pattern of tight junction proteins in diarrhea-predominant irritable bowel syndrome have not been fully elucidated. We therefore recruited 17 diarrhea-predominant irritable bowel syndrome patients and 20 healthy controls. The expression of the tight junction-related proteins was examined in the ileal, cecal, and rectal mucosa of diarrhea-predominant irritable bowel syndrome patients using real-time PCR and immunofluorescence. Claudin-2 expression was high in the ileum of diarrhea-predominant irritable bowel syndrome patients. Claudin-2 expression was the same in cecum and rectal mucosa of control and diarrhea-predominant irritable bowel syndrome patients. Similarly, the expression of clauidn-1, claudin-7, JAM-A, occludin, and ZO-1 in the ileal, cecal, and rectal mucosa did not change between control and diarrhea-predominant irritable bowel syndrome samples. Infiltration of eosinophil and mast cells in the mucosa of ileum, cecum and rectum was evaluated using immunohistochemical staining and was not affected by diarrhea-predominant irritable bowel syndrome. Claudin-2 was expressed on the apical side of villi and crypts of ileal mucosal epithelial cells. Clauidn-2 expression is upregulated in diarrhea-predominant irritable bowel syndrome patients and may contribute to the pathogenesis of this condition. PMID:28366996
Inference and Assumption in Historical Seismology
Musson, R. M. W.
The principal aim in studies of historical earthquakes is usually to be able to derive parameters for past earthquakes from macroseismic or other data and thus extend back in time parametric earthquake catalogues, often with improved seismic hazard studies as the ultimate goal. In cases of relatively recent historical earthquakes, for example, those of the 18th and 19th centuries, it is often the case that there is such an abundance of available macroseismic data that estimating earthquake parameters is relatively straightforward. For earlier historical periods, especially medieval and earlier, and also for areas where settlement or documentation are sparse, the situation is much harder. The seismologist often finds that he has only a few data points (or even one) for an earthquake that nevertheless appears to be regionally significant.In such cases, it is natural that the investigator will attempt to make the most of the available data, expanding it by making working assumptions, and from these deriving conclusions by inference (i.e. the process of proceeding logically from some premise). This can be seen in a number of existing studies; in some cases extremely slight data are so magnified by the use of inference that one must regard the results as tentative in the extreme. Two main types of inference can be distinguished. The first type is inference from documentation. This is where assumptions are made such as: the absence of a report of the earthquake from this monastic chronicle indicates that at this locality the earthquake was not felt. The second type is inference from seismicity. Here one deals with arguments such as all recent earthquakes felt at town X are events occurring in seismic zone Y, therefore this ancient earthquake which is only reported at town X probably also occurred in this zone.
Directory of Open Access Journals (Sweden)
Wang Woei-Fuh
2008-03-01
Full Text Available Abstract Background With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality. Results Motivated by inferring transcriptional compensation (TC interactions in yeast, a stepwise structural equation modeling algorithm (SSEM is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC SSEM works best. SSEM with Bayesian information criterion (BIC results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1 small groups of genes that are synthetic sick or lethal (SSL to SGS1, and (2 a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1, SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2 are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted. Conclusion SSEM is a useful tool for inferring genetic networks, and the
Genetic dissection of cardiac growth control pathways
MacLellan, W. R.; Schneider, M. D.
2000-01-01
Cardiac muscle cells exhibit two related but distinct modes of growth that are highly regulated during development and disease. Cardiac myocytes rapidly proliferate during fetal life but exit the cell cycle irreversibly soon after birth, following which the predominant form of growth shifts from hyperplastic to hypertrophic. Much research has focused on identifying the candidate mitogens, hypertrophic agonists, and signaling pathways that mediate these processes in isolated cells. What drives the proliferative growth of embryonic myocardium in vivo and the mechanisms by which adult cardiac myocytes hypertrophy in vivo are less clear. Efforts to answer these questions have benefited from rapid progress made in techniques to manipulate the murine genome. Complementary technologies for gain- and loss-of-function now permit a mutational analysis of these growth control pathways in vivo in the intact heart. These studies have confirmed the importance of suspected pathways, have implicated unexpected pathways as well, and have led to new paradigms for the control of cardiac growth.
Treatment Implications of Predominant Polarity and the Polarity Index: A Comprehensive Review
Quevedo, João; McIntyre, Roger S.; Soeiro-de-Souza, Márcio G.; Fountoulakis, Konstantinos N.; Berk, Michael; Hyphantis, Thomas N.; Vieta, Eduard
2015-01-01
Background: Bipolar disorder (BD) is a serious and recurring condition that affects approximately 2.4% of the global population. About half of BD sufferers have an illness course characterized by either a manic or a depressive predominance. This predominant polarity in BD may be differentially associated with several clinical correlates. The concept of a polarity index (PI) has been recently proposed as an index of the antimanic versus antidepressive efficacy of various maintenance treatments for BD. Notwithstanding its potential clinical utility, predominant polarity was not included in the DSM-5 as a BD course specifier. Methods: Here we searched computerized databases for original clinical studies on the role of predominant polarity for selection of and response to pharmacological treatments for BD. Furthermore, we systematically searched the Pubmed database for maintenance randomized controlled trials (RCTs) for BD to determine the PI of the various pharmacological agents for BD. Results: We found support from naturalistic studies that bipolar patients with a predominantly depressive polarity are more likely to be treated with an antidepressive stabilization package, while BD patients with a manic-predominant polarity are more frequently treated with an antimanic stabilization package. Furthermore, predominantly manic BD patients received therapeutic regimens with a higher mean PI. The calculated PI varied from 0.4 (for lamotrigine) to 12.1 (for aripiprazole). Conclusions: This review supports the clinical relevance of predominant polarity as a course specifier for BD. Future studies should investigate the role of baseline, predominant polarity as an outcome predictor of BD maintenance RCTs. PMID:25522415
Terrorism Event Classification Using Fuzzy Inference Systems
Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat
2010-01-01
Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluat...
Inferring Planetary Obliquity Using Rotational & Orbital Photometry
Schwartz, Joel C; Haggard, Hal M; Pallé, Eric; Cowan, Nicolas B
2015-01-01
The obliquity of a terrestrial planet is an important clue about its formation and critical to its climate. Previous studies using simulated photometry of Earth show that continuous observations over most of a planet's orbit can be inverted to infer obliquity. We extend this approach to single-epoch observations for planets with arbitrary albedo maps. For diffuse reflection, the flux seen by a distant observer is the product of the planet's albedo map, the host star's illumination, and the observer's visibility of different planet regions. It is useful to treat the product of illumination and visibility as the kernel of a convolution; this kernel is unimodal and symmetric. For planets with unknown obliquity, the kernel is not known a priori, but could be inferred by fitting a rotational light curve. We analyze this kernel under different viewing geometries, finding it well described by its longitudinal width and latitudinal position. We use Monte Carlo simulation to estimate uncertainties on these kernel char...
Human collective intelligence as distributed Bayesian inference
Krafft, Peter M; Pan, Wei; Della Penna, Nicolás; Altshuler, Yaniv; Shmueli, Erez; Tenenbaum, Joshua B; Pentland, Alex
2016-01-01
Collective intelligence is believed to underly the remarkable success of human society. The formation of accurate shared beliefs is one of the key components of human collective intelligence. How are accurate shared beliefs formed in groups of fallible individuals? Answering this question requires a multiscale analysis. We must understand both the individual decision mechanisms people use, and the properties and dynamics of those mechanisms in the aggregate. As of yet, mathematical tools for such an approach have been lacking. To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference. Distributed inference occurs through information processing at the individual level, and yields rational belief formation at the group level. We instantiate this framework in a new model of human social decision-making, which we validate using a dataset we collected of over 50,000 users of an online social trading platform where inves...
Bayesianism and inference to the best explanation
Directory of Open Access Journals (Sweden)
Valeriano IRANZO
2008-01-01
Full Text Available Bayesianism and Inference to the best explanation (IBE are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” (IBE-Bay and “frequentist-Bayesianism” (Freq-Bay. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: (i endorses a role for explanatory value in the assessment of scientific hypotheses; (ii avoids a purely subjectivist reading of prior probabilities; and (iii fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned.
Inference on power law spatial trends
Robinson, Peter M
2012-01-01
Power law or generalized polynomial regressions with unknown real-valued exponents and coefficients, and weakly dependent errors, are considered for observations over time, space or space--time. Consistency and asymptotic normality of nonlinear least-squares estimates of the parameters are established. The joint limit distribution is singular, but can be used as a basis for inference on either exponents or coefficients. We discuss issues of implementation, efficiency, potential for improved estimation and possibilities of extension to more general or alternative trending models to allow for irregularly spaced data or heteroscedastic errors; though it focusses on a particular model to fix ideas, the paper can be viewed as offering machinery useful in developing inference for a variety of models in which power law trends are a component. Indeed, the paper also makes a contribution that is potentially relevant to many other statistical models: Our problem is one of many in which consistency of a vector of parame...
The NIFTY way of Bayesian signal inference
Energy Technology Data Exchange (ETDEWEB)
Selig, Marco, E-mail: mselig@mpa-Garching.mpg.de [Max Planck Institut für Astrophysik, Karl-Schwarzschild-Straße 1, D-85748 Garching, Germany, and Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, D-80539 München (Germany)
2014-12-05
We introduce NIFTY, 'Numerical Information Field Theory', a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTY as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D{sup 3}PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.
Inferences on Children’s Reading Groups
Directory of Open Access Journals (Sweden)
Javier González García
2009-05-01
Full Text Available This article focuses on the non-literal information of a text, which can be inferred from key elements or clues offered by the text itself. This kind of text is called implicit text or inference, due to the thinking process that it stimulates. The explicit resources that lead to information retrieval are related to others of implicit information, which have increased their relevance. In this study, during two courses, how two teachers interpret three stories and how they establish a debate dividing the class into three student groups, was analyzed. The sample was formed by two classes of two urban public schools of Burgos capital (Spain, and two of public schools of Tampico (Mexico. This allowed us to observe an increasing percentage value of the group focused in text comprehension, and a lesser percentage of the group perceiving comprehension as a secondary objective.
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisová, Katarina
is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analyzing Peter Diggle's heather dataset, where we discuss the results......To the best of our knowledge, this is the first paper which discusses likelihood inference or a random set using a germ-grain model, where the individual grains are unobservable edge effects occur, and other complications appear. We consider the case where the grains form a disc process modelled...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....
Introductory statistical inference with the likelihood function
Rohde, Charles A
2014-01-01
This textbook covers the fundamentals of statistical inference and statistical theory including Bayesian and frequentist approaches and methodology possible without excessive emphasis on the underlying mathematics. This book is about some of the basic principles of statistics that are necessary to understand and evaluate methods for analyzing complex data sets. The likelihood function is used for pure likelihood inference throughout the book. There is also coverage of severity and finite population sampling. The material was developed from an introductory statistical theory course taught by the author at the Johns Hopkins University’s Department of Biostatistics. Students and instructors in public health programs will benefit from the likelihood modeling approach that is used throughout the text. This will also appeal to epidemiologists and psychometricians. After a brief introduction, there are chapters on estimation, hypothesis testing, and maximum likelihood modeling. The book concludes with secti...
Dopamine, reward learning, and active inference
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Thomas eFitzgerald
2015-11-01
Full Text Available Temporal difference learning models propose phasic dopamine signalling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behaviour. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.
Variational Bayesian Inference of Line Spectra
DEFF Research Database (Denmark)
Badiu, Mihai Alin; Hansen, Thomas Lundgaard; Fleury, Bernard Henri
2016-01-01
In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coeffici......In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid......; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection. Unlike earlier works which retain only point estimates of the frequencies, we undertake a more complete Bayesian treatment by estimating the posterior probability density functions (pdfs...
Improved testing inference in mixed linear models
Melo, Tatiane F N; Cribari-Neto, Francisco; 10.1016/j.csda.2008.12.007
2011-01-01
Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Oftentimes, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test and also to a test obtained from a modified profile likelihood function. Our results generalize those in Zucker et al. (Journal of the Royal Statistical Society B, 2000, 62, 827-838) by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report numerical evidence which shows that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presente...
Towards Stratification Learning through Homology Inference
Bendich, Paul; Wang, Bei
2010-01-01
A topological approach to stratification learning is developed for point cloud data drawn from a stratified space. Given such data, our objective is to infer which points belong to the same strata. First we define a multi-scale notion of a stratified space, giving a stratification for each radius level. We then use methods derived from kernel and cokernel persistent homology to cluster the data points into different strata, and we prove a result which guarantees the correctness of our clustering, given certain topological conditions; some geometric intuition for these topological conditions is also provided. Our correctness result is then given a probabilistic flavor: we give bounds on the minimum number of sample points required to infer, with probability, which points belong to the same strata. Finally, we give an explicit algorithm for the clustering, prove its correctness, and apply it to some simulated data.
Bayesian inference of structural brain networks.
Hinne, Max; Heskes, Tom; Beckmann, Christian F; van Gerven, Marcel A J
2013-02-01
Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring brain networks from the streamline distributions which tractography produces. State-of-the-art methods rely on an arbitrary threshold or, alternatively, yield weighted results that are difficult to interpret. In this paper, we provide a generative model that explicitly describes how structural brain networks lead to observed streamline distributions. This allows us to draw principled conclusions about brain networks, which we validate using simultaneously acquired resting-state functional MRI data. Inference may be further informed by means of a prior which combines connectivity estimates from multiple subjects. Based on this prior, we obtain networks that significantly improve on the conventional approach.
Statistical Methods in Phylogenetic and Evolutionary Inferences
Directory of Open Access Journals (Sweden)
Luigi Bertolotti
2013-05-01
Full Text Available Molecular instruments are the most accurate methods in organisms’identification and characterization. Biologists are often involved in studies where the main goal is to identify relationships among individuals. In this framework, it is very important to know and apply the most robust approaches to infer correctly these relationships, allowing the right conclusions about phylogeny. In this review, we will introduce the reader to the most used statistical methods in phylogenetic analyses, the Maximum Likelihood and the Bayesian approaches, considering for simplicity only analyses regardingDNA sequences. Several studieswill be showed as examples in order to demonstrate how the correct phylogenetic inference can lead the scientists to highlight very peculiar features in pathogens biology and evolution.
Inferring network topology via the propagation process
Zeng, An
2013-01-01
Inferring the network topology from the dynamics is a fundamental problem with wide applications in geology, biology and even counter-terrorism. Based on the propagation process, we present a simple method to uncover the network topology. The numerical simulation on artificial networks shows that our method enjoys a high accuracy in inferring the network topology. We find the infection rate in the propagation process significantly influences the accuracy, and each network is corresponding to an optimal infection rate. Moreover, the method generally works better in large networks. These finding are confirmed in both real social and nonsocial networks. Finally, the method is extended to directed networks and a similarity measure specific for directed networks is designed.
Inference for ordered parameters in multinomial distributions
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
This paper discusses inference for ordered parameters of multinomial distributions. We first show that the asymptotic distributions of their maximum likelihood estimators (MLEs) are not always normal and the bootstrap distribution estimators of the MLEs can be inconsistent. Then a class of weighted sum estimators (WSEs) of the ordered parameters is proposed. Properties of the WSEs are studied, including their asymptotic normality. Based on those results, large sample inferences for smooth functions of the ordered parameters can be made. Especially, the confidence intervals of the maximum cell probabilities are constructed. Simulation results indicate that this interval estimation performs much better than the bootstrap approaches in the literature. Finally, the above results for ordered parameters of multinomial distributions are extended to more general distribution models.
An Intuitive Dashboard for Bayesian Network Inference
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++.
Pointwise probability reinforcements for robust statistical inference.
Frénay, Benoît; Verleysen, Michel
2014-02-01
Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation.
Kobayashi, Zen; Watanabe, Mayumi; Karibe, Yuri; Nakazawa, Chika; Numasawa, Yoshiyuki; Tomimitsu, Hiroyuki; Shintani, Shuzo
2014-01-01
A 74-year-old right-handed woman without cognitive impairment suddenly developed nonfluent aphasia. Brain MRI showed acute infarction in the right frontal lobe and insula without involvement of the corpus callosum. A neurological examination demonstrated not only transcortical motor aphasia, but also ideomotor apraxia and right hand predominant constructional apraxia (CA). To date, right hand predominant CA has only been reported in patients with corpus callosum lesions. The right hand predominant CA observed in our patient may be associated with the failure to transfer information on the spatial structure from the right hemisphere to the motor cortex of the left hemisphere.
Data analysis recipes: Probability calculus for inference
Hogg, David W.
2012-01-01
In this pedagogical text aimed at those wanting to start thinking about or brush up on probabilistic inference, I review the rules by which probability distribution functions can (and cannot) be combined. I connect these rules to the operations performed in probabilistic data analysis. Dimensional analysis is emphasized as a valuable tool for helping to construct non-wrong probabilistic statements. The applications of probability calculus in constructing likelihoods, marginalized likelihoods,...
Data analysis recipes: Probability calculus for inference
Hogg, David W
2012-01-01
In this pedagogical text aimed at those wanting to start thinking about or brush up on probabilistic inference, I review the rules by which probability distribution functions can (and cannot) be combined. I connect these rules to the operations performed in probabilistic data analysis. Dimensional analysis is emphasized as a valuable tool for helping to construct non-wrong probabilistic statements. The applications of probability calculus in constructing likelihoods, marginalized likelihoods, posterior probabilities, and posterior predictions are all discussed.
Analysis of KATRIN data using Bayesian inference
DEFF Research Database (Denmark)
Riis, Anna Sejersen; Hannestad, Steen; Weinheimer, Christian
2011-01-01
The KATRIN (KArlsruhe TRItium Neutrino) experiment will be analyzing the tritium beta-spectrum to determine the mass of the neutrino with a sensitivity of 0.2 eV (90% C.L.). This approach to a measurement of the absolute value of the neutrino mass relies only on the principle of energy conservati...... the KATRIN chi squared function in the COSMOMC package - an MCMC code using Bayesian parameter inference - solved the task at hand very nicely....
Inferring Trust Based on Similarity with TILLIT
Tavakolifard, Mozhgan; Herrmann, Peter; Knapskog, Svein J.
A network of people having established trust relations and a model for propagation of related trust scores are fundamental building blocks in many of today’s most successful e-commerce and recommendation systems. However, the web of trust is often too sparse to predict trust values between non-familiar people with high accuracy. Trust inferences are transitive associations among users in the context of an underlying social network and may provide additional information to alleviate the consequences of the sparsity and possible cold-start problems. Such approaches are helpful, provided that a complete trust path exists between the two users. An alternative approach to the problem is advocated in this paper. Based on collaborative filtering one can exploit the like-mindedness resp. similarity of individuals to infer trust to yet unknown parties which increases the trust relations in the web. For instance, if one knows that with respect to a specific property, two parties are trusted alike by a large number of different trusters, one can assume that they are similar. Thus, if one has a certain degree of trust to the one party, one can safely assume a very similar trustworthiness of the other one. In an attempt to provide high quality recommendations and proper initial trust values even when no complete trust propagation path or user profile exists, we propose TILLIT — a model based on combination of trust inferences and user similarity. The similarity is derived from the structure of the trust graph and users’ trust behavior as opposed to other collaborative-filtering based approaches which use ratings of items or user’s profile. We describe an algorithm realizing the approach based on a combination of trust inferences and user similarity, and validate the algorithm using a real large-scale data-set.
Towards a Faster Randomized Parcellation Based Inference
Hoyos-Idrobo, Andrés; Varoquaux, Gaël; Thirion, Bertrand
2017-01-01
International audience; In neuroimaging, multi-subject statistical analysis is an essential step, as it makes it possible to draw conclusions for the population under study. However, the lack of power in neuroimaging studies combined with the lack of stability and sensitivity of voxel-based methods may lead to non-reproducible results. A method designed to tackle this problem is Randomized Parcellation-Based Inference (RPBI), which has shown good empirical performance. Nevertheless, the use o...
Thermodynamics of statistical inference by cells.
Lang, Alex H; Fisher, Charles K; Mora, Thierry; Mehta, Pankaj
2014-10-03
The deep connection between thermodynamics, computation, and information is now well established both theoretically and experimentally. Here, we extend these ideas to show that thermodynamics also places fundamental constraints on statistical estimation and learning. To do so, we investigate the constraints placed by (nonequilibrium) thermodynamics on the ability of biochemical signaling networks to estimate the concentration of an external signal. We show that accuracy is limited by energy consumption, suggesting that there are fundamental thermodynamic constraints on statistical inference.
Unified Theory of Inference for Text Understanding
1986-11-25
reataurant script is recognized, script application would lead to inferences such as identifying the waiter as ’ ’the waiter who is employed by the...relations between the objects. Objects have names as a convenience for the system modeler, but the names are not used for purposes other than...intent is that we can consider talking to be a frame with a talker slot which must be filled by a person. This is just a convenient notation; the
Inferring sparse networks for noisy transient processes
Tran, Hoang M.; Bukkapatnam, Satish T. S.
2016-02-01
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the -min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of -min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues.
Intuitive Mechanics: Inferences of Vertical Projectile Motion
Directory of Open Access Journals (Sweden)
Milana Damjenić
2016-07-01
Full Text Available Our intuitive knowledge of physics mechanics, i.e. knowledge defined through personal experience about velocity, acceleration, motion causes, etc., is often wrong. This research examined whether similar misconceptions occur systematically in the case of vertical projectiles launched upwards. The first experiment examined inferences of velocity and acceleration of the ball moving vertically upwards, while the second experiment examined whether the mass of the thrown ball and force of the throw have an impact on the inference. The results showed that more than three quarters of the participants wrongly assumed that maximum velocity and peak acceleration did not occur at the initial launch of the projectile. There was no effect of object mass or effect of the force of the throw on the inference relating to the velocity and acceleration of the ball. The results exceed the explanatory reach of the impetus theory, most commonly used to explain the naive understanding of the mechanics of object motion. This research supports that the actions on objects approach and the property transmission heuristics may more aptly explain the dissidence between perceived and actual implications in projectile motion.
Combinatorics of distance-based tree inference.
Pardi, Fabio; Gascuel, Olivier
2012-10-01
Several popular methods for phylogenetic inference (or hierarchical clustering) are based on a matrix of pairwise distances between taxa (or any kind of objects): The objective is to construct a tree with branch lengths so that the distances between the leaves in that tree are as close as possible to the input distances. If we hold the structure (topology) of the tree fixed, in some relevant cases (e.g., ordinary least squares) the optimal values for the branch lengths can be expressed using simple combinatorial formulae. Here we define a general form for these formulae and show that they all have two desirable properties: First, the common tree reconstruction approaches (least squares, minimum evolution), when used in combination with these formulae, are guaranteed to infer the correct tree when given enough data (consistency); second, the branch lengths of all the simple (nearest neighbor interchange) rearrangements of a tree can be calculated, optimally, in quadratic time in the size of the tree, thus allowing the efficient application of hill climbing heuristics. The study presented here is a continuation of that by Mihaescu and Pachter on branch length estimation [Mihaescu R, Pachter L (2008) Proc Natl Acad Sci USA 105:13206-13211]. The focus here is on the inference of the tree itself and on providing a basis for novel algorithms to reconstruct trees from distances.
Inference of magnetic fields in inhomogeneous prominences
Milić, I.; Faurobert, M.; Atanacković, O.
2017-01-01
Context. Most of the quantitative information about the magnetic field vector in solar prominences comes from the analysis of the Hanle effect acting on lines formed by scattering. As these lines can be of non-negligible optical thickness, it is of interest to study the line formation process further. Aims: We investigate the multidimensional effects on the interpretation of spectropolarimetric observations, particularly on the inference of the magnetic field vector. We do this by analyzing the differences between multidimensional models, which involve fully self-consistent radiative transfer computations in the presence of spatial inhomogeneities and velocity fields, and those which rely on simple one-dimensional geometry. Methods: We study the formation of a prototype line in ad hoc inhomogeneous, isothermal 2D prominence models. We solve the NLTE polarized line formation problem in the presence of a large-scale oriented magnetic field. The resulting polarized line profiles are then interpreted (i.e. inverted) assuming a simple 1D slab model. Results: We find that differences between input and the inferred magnetic field vector are non-negligible. Namely, we almost universally find that the inferred field is weaker and more horizontal than the input field. Conclusions: Spatial inhomogeneities and radiative transfer have a strong effect on scattering line polarization in the optically thick lines. In real-life situations, ignoring these effects could lead to a serious misinterpretation of spectropolarimetric observations of chromospheric objects such as prominences.
Inferring Pedigree Graphs from Genetic Distances
Tamura, Takeyuki; Ito, Hiro
In this paper, we study a problem of inferring blood relationships which satisfy a given matrix of genetic distances between all pairs of n nodes. Blood relationships are represented by our proposed graph class, which is called a pedigree graph. A pedigree graph is a directed acyclic graph in which the maximum indegree is at most two. We show that the number of pedigree graphs which satisfy the condition of given genetic distances may be exponential, but they can be represented by one directed acyclic graph with n nodes. Moreover, an O(n3) time algorithm which solves the problem is also given. Although phylogenetic trees and phylogenetic networks are similar data structures to pedigree graphs, it seems that inferring methods for phylogenetic trees and networks cannot be applied to infer pedigree graphs since nodes of phylogenetic trees and networks represent species whereas nodes of pedigree graphs represent individuals. We also show an O(n2) time algorithm which detects a contradiction between a given pedigreee graph and distance matrix of genetic distances.
National Research Council Canada - National Science Library
Kathryn Hughes Barry; Stella Koutros; Sonja I. Berndt; Gabriella Andreotti; Jane A. Hoppin; Dale P. Sandler; Laurie A. Burdette; Meredith Yeager; Laura E. Beane Freeman; Jay H. Lubin; Xiaomei Ma; Tongzhang Zheng; Michael C. R. Alavanja
2011-01-01
.... OBJECTIVES: Because base excision repair (BER) is the predominant pathway involved in repairing oxidative damage, we evaluated interactions between 39 pesticides and 394 tag single-nucleotide polymorphisms (SNPs...
An empirical Bayesian approach for model-based inference of cellular signaling networks
Directory of Open Access Journals (Sweden)
Klinke David J
2009-11-01
Full Text Available Abstract Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.
Bootstrapping phylogenies inferred from rearrangement data
Directory of Open Access Journals (Sweden)
Lin Yu
2012-08-01
Full Text Available Abstract Background Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. Results We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Conclusions Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its
Lobo, Daniel; Levin, Michael
2015-06-01
Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated
2011-01-28
... Copyright Office 37 CFR Part 202 Deposit Requirements for Registration of Automated Databases That... databases that consist predominantly of photographs and group registration of published photographs (the ``Interim Regulations''), governing the deposit requirements for applications for automated databases that...
Mediational Inferences in the Process of Counselor Judgment.
Haase, Richard F.; And Others
1983-01-01
Replicates research on the process of moving from observations to clinical judgments. Counselors (N=20) made status inferences, attributional inferences, and diagnostic classification of clients based on case folders. Results suggest the clinical judgment process was stagewise mediated, and attributional inferences had little direct impact on…
Type Inference for Session Types in the Pi-Calculus
DEFF Research Database (Denmark)
Graversen, Eva Fajstrup; Harbo, Jacob Buchreitz; Huttel, Hans
2014-01-01
In this paper we present a direct algorithm for session type inference for the π-calculus. Type inference for session types has previously been achieved by either imposing limitations and restriction on the π-calculus, or by reducing the type inference problem to that for linear types. Our approach...
Type Inference for Session Types in the Pi-Calculus
DEFF Research Database (Denmark)
Huttel, Hans; Graversen, Eva Fajstrup; Wahl, Sebastian
2014-01-01
In this paper we present a direct algorithm for session type inference for the π-calculus. Type inference for session types has previously been achieved by either imposing limitations and restriction on the π-calculus, or by reducing the type inference problem to that for linear types. Our approa...
Classical and Bayesian aspects of robust unit root inference
H. Hoek (Henk); H.K. van Dijk (Herman)
1995-01-01
textabstractThis paper has two themes. First, we classify some effects which outliers in the data have on unit root inference. We show that, both in a classical and a Bayesian framework, the presence of additive outliers moves ‘standard’ inference towards stationarity. Second, we base inference on a
Statistical Inference at Work: Statistical Process Control as an Example
Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia
2008-01-01
To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…
Reasoning about Informal Statistical Inference: One Statistician's View
Rossman, Allan J.
2008-01-01
This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical…
IgM MGUS anti-MAG neuropathy with predominant muscle weakness and extensive muscle atrophy.
Kawagashira, Yuichi; Kondo, Naohide; Atsuta, Naoki; Iijima, Masahiro; Koike, Haruki; Katsuno, Masahisa; Tanaka, Fumiaki; Kusunoki, Susumu; Sobue, Gen
2010-09-01
We report a patient with anti-myelin-associated glycoprotein (MAG) neuropathy, predominantly exhibiting severe motor symptoms, accompanied by extensive muscle atrophy mimicking Charcot-Marie-Tooth disease. Nerve conduction studies revealed mild retardation of motor conduction velocities and significant prolongation of distal latency. Sural nerve biopsy revealed widely spaced myelin and positive staining of myelinated fibers with an IgM antibody. Predominant motor symptoms with muscle atrophy can be one of the clinical manifestations of anti-MAG neuropathy.
Malle, Bertram F; Holbrook, Jess
2012-04-01
People interpret behavior by making inferences about agents' intentionality, mind, and personality. Past research studied such inferences 1 at a time; in real life, people make these inferences simultaneously. The present studies therefore examined whether 4 major inferences (intentionality, desire, belief, and personality), elicited simultaneously in response to an observed behavior, might be ordered in a hierarchy of likelihood and speed. To achieve generalizability, the studies included a wide range of stimulus behaviors, presented them verbally and as dynamic videos, and assessed inferences both in a retrieval paradigm (measuring the likelihood and speed of accessing inferences immediately after they were made) and in an online processing paradigm (measuring the speed of forming inferences during behavior observation). Five studies provide evidence for a hierarchy of social inferences-from intentionality and desire to belief to personality-that is stable across verbal and visual presentations and that parallels the order found in developmental and primate research.
Inferring Acceptance and Rejection in Dialogue by Default Rules of Inference
Walker, M A
1996-01-01
This paper discusses the processes by which conversants in a dialogue can infer whether their assertions and proposals have been accepted or rejected by their conversational partners. It expands on previous work by showing that logical consistency is a necessary indicator of acceptance, but that it is not sufficient, and that logical inconsistency is sufficient as an indicator of rejection, but it is not necessary. I show how conversants can use information structure and prosody as well as logical reasoning in distinguishing between acceptances and logically consistent rejections, and relate this work to previous work on implicature and default reasoning by introducing three new classes of rejection: {\\sc implicature rejections}, {\\sc epistemic rejections} and {\\sc deliberation rejections}. I show how these rejections are inferred as a result of default inferences, which, by other analyses, would have been blocked by the context. In order to account for these facts, I propose a model of the common ground that...
Nonparametric inference of network structure and dynamics
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Bayesian Estimation and Inference Using Stochastic Electronics.
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.
Inference with Linear Equality and Inequality Constraints Using R: The Package ic.infer
Directory of Open Access Journals (Sweden)
Ulrike Grömping
2010-02-01
Full Text Available In linear models and multivariate normal situations, prior information in linear inequality form may be encountered, or linear inequality hypotheses may be subjected to statistical tests. R package ic.infer has been developed to support inequality-constrained estimation and testing for such situations. This article gives an overview of the principles underlying inequality-constrained inference that are far less well-known than methods for unconstrained or equality-constrained models, and describes their implementation in the package.
Inferring Boolean network states from partial information
2013-01-01
Networks of molecular interactions regulate key processes in living cells. Therefore, understanding their functionality is a high priority in advancing biological knowledge. Boolean networks are often used to describe cellular networks mathematically and are fitted to experimental datasets. The fitting often results in ambiguities since the interpretation of the measurements is not straightforward and since the data contain noise. In order to facilitate a more reliable mapping between datasets and Boolean networks, we develop an algorithm that infers network trajectories from a dataset distorted by noise. We analyze our algorithm theoretically and demonstrate its accuracy using simulation and microarray expression data. PMID:24006954
Inferring Evolutionary Scenarios for Protein Domain Compositions
Wiedenhoeft, John; Krause, Roland; Eulenstein, Oliver
Essential cellular processes are controlled by functional interactions of protein domains, which can be inferred from their evolutionary histories. Methods to reconstruct these histories are challenged by the complexity of reconstructing macroevolutionary events. In this work we model these events using a novel network-like structure that represents the evolution of domain combinations, called plexus. We describe an algorithm to find a plexus that represents the evolution of a given collection of domain histories as phylogenetic trees with the minimum number of macroevolutionary events, and demonstrate its effectiveness in practice.
Defeasible modes of inference: A preferential perspective
CSIR Research Space (South Africa)
Britz, K
2012-06-01
Full Text Available of normality from the antecedent of an infer- ence to the effect of an action, and, importantly, use it in the scope of other logical constructors. The importance of defeasibility in specific modes of rea- soning is also illustrated by the following example..., then there are no accessi- ble worlds at all (and vice versa). j= p pi? $ 2i? (2) From (2) and contraposition we conclude j= 3i> $ p p i>. The following two equivalences are also worthy of men- tion (their proofs are straightforward): j= p pi> $ > and j= p p i...
Abductive Inference using Array-Based Logic
DEFF Research Database (Denmark)
Frisvad, Jeppe Revall; Falster, Peter; Møller, Gert L.;
The notion of abduction has found its usage within a wide variety of AI fields. Computing abductive solutions has, however, shown to be highly intractable in logic programming. To avoid this intractability we present a new approach to logicbased abduction; through the geometrical view of data...... employed in array-based logic we embrace abduction in a simple structural operation. We argue that a theory of abduction on this form allows for an implementation which, at runtime, can perform abductive inference quite efficiently on arbitrary rules of logic representing knowledge of finite domains....
Inferring cultural models from corpus data
DEFF Research Database (Denmark)
Jensen, Kim Ebensgaard
2015-01-01
developed methods of inferring cultural models from observed behavior – in particular observed verbal behavior (including both spoken and written language). While there are plenty of studies of the reflection of cultural models in artificially generated verbal behavior, not much research has been made...... of constructional discursive behavior, the present paper offers a covarying collexeme analysis of the [too ADJ to V]-construction in the Corpus of Contemporary American English. The purpose is to discover the extent to which its force-dynamic constructional semantics interacts with cultural models. We focus...
Conditional statistical inference with multistage testing designs.
Zwitser, Robert J; Maris, Gunter
2015-03-01
In this paper it is demonstrated how statistical inference from multistage test designs can be made based on the conditional likelihood. Special attention is given to parameter estimation, as well as the evaluation of model fit. Two reasons are provided why the fit of simple measurement models is expected to be better in adaptive designs, compared to linear designs: more parameters are available for the same number of observations; and undesirable response behavior, like slipping and guessing, might be avoided owing to a better match between item difficulty and examinee proficiency. The results are illustrated with simulated data, as well as with real data.
Inverse Ising Inference Using All the Data
Aurell, Erik; Ekeberg, Magnus
2012-03-01
We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of l1 regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.
DEFF Research Database (Denmark)
Andersen, Jesper; Lawall, Julia Laetitia
2008-01-01
A key issue in maintaining Linux device drivers is the need to update drivers in response to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spfind, that identifies common changes made...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...
Inferences on the common coefficient of variation.
Tian, Lili
2005-07-30
The coefficient of variation is often used as a measure of precision and reproducibility of data in medical and biological science. This paper considers the problem of making inference about the common population coefficient of variation when it is a priori suspected that several independent samples are from populations with a common coefficient of variation. The procedures for confidence interval estimation and hypothesis testing are developed based on the concepts of generalized variables. The coverage properties of the proposed confidence intervals and type-I errors of the proposed tests are evaluated by simulation. The proposed methods are illustrated by a real life example.
Mathematical inference and control of molecular networks from perturbation experiments
Mohammed-Rasheed, Mohammed
One of the main challenges facing biologists and mathematicians in the post genomic era is to understand the behavior of molecular networks and harness this understanding into an educated intervention of the cell. The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks (GRNs) or molecular networks. GRNs can be viewed as dynamical systems with inherent properties and mechanisms, such as steady-state equilibriums and stability, that determine the behavior of the cell. The biological relevance of the mathematical concepts are important as they may predict the differentiation of a stem cell, the maintenance of a normal cell, the development of cancer and its aberrant behavior, and the design of drugs and response to therapy. Uncovering the underlying GRN structure from gene/protein expression data, e.g., microarrays or perturbation experiments, is called inference or reverse engineering of the molecular network. Because of the high cost and time consuming nature of biological experiments, the number of available measurements or experiments is very small compared to the number of molecules (genes, RNA and proteins). In addition, the observations are noisy, where the noise is due to the measurements imperfections as well as the inherent stochasticity of genetic expression levels. Intra-cellular activities and extra-cellular environmental attributes are also another source of variability. Thus, the inference of GRNs is, in general, an under-determined problem with a highly noisy set of observations. The ultimate goal of GRN inference and analysis is to be able to intervene within the network, in order to force it away from undesirable cellular states and into desirable ones. However, it remains a major challenge to design optimal intervention strategies
Occurrence and evolutionary inferences about Kranz anatomy in Cyperaceae (Poales
Directory of Open Access Journals (Sweden)
SHIRLEY MARTINS
2015-12-01
Full Text Available ABSTRACT Cyperaceae is an angiosperm family with the greatest diversity of species with Kranz anatomy. Four different types of Kranz anatomy (chlorocyperoid, eleocharoid, fimbristyloid and rhynchosporoid have been described for this angiosperm family, and the occurrence and structural characteristics of these types are important to trace evolutionary hypotheses. The purpose of this study was to examine the available data on Cyperaceae Kranz anatomy, emphasizing taxonomy, geographic distribution, habitat and anatomy, to infer the potential origin of the Kranz anatomy in this family. The results showed that the four types of Kranz anatomy (associated with C4 photosynthesis in Cyperaceae emerged numerous times in unrelated phylogenetic groups. However, the convergence of these anatomical types, except rhynchosporoid, was observed in certain groups. Thus, the diverse origin of these species might result from different environmental pressures that promote photorespiration. Greater variation in occurrence of Kranz anatomy and anatomical types was observed inEleocharis, whose emergence of the C4 pathway was recent compared with other genera in the family, and the species of this genus are located in aquatic environments.
Chia, Kenny; Lau, Tze Liang
2017-07-01
Despite categorized as low seismicity group, until being affected by distant earthquake ground motion from Sumatra and the recent 2015 Sabah Earthquake, Malaysia has come to realize that seismic hazard in the country is real and has the potential to threaten the public safety and welfare. The major concern in this paper is to study the effect of local site condition, where it could amplify the magnitude of ground vibration at sites. The aim for this study is to correlate the thickness of soft stratum with the predominant frequency of soil. Single point microtremor measurements were carried out at 24 selected points where the site investigation reports are available. Predominant period and frequency at each site are determined by Nakamura's method. The predominant period varies from 0.22 s to 0.98 s. Generally, the predominant period increases when getting closer to the shoreline which has thicker sediments. As far as the thickness of the soft stratum could influence the amplification of seismic wave, the advancement of micotremor observation to predict the thickness of soft stratum (h) from predominant frequency (fr) is of the concern. Thus an empirical relationship h =54.917 fr-1.314 is developed based on the microtremor observation data. The empirical relationship will be benefited in the prediction of thickness of soft stratum based on microtremor observation for seismic design with minimal cost compared to conventional boring method.
Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization.
Yang, Qiang; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Deng, Jeremiah D; Li, Yun; Zhang, Jun
2016-10-24
Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.
Segers, Frank J J; Meijer, Martin; Houbraken, Jos; Samson, Robert A; Wösten, Han A B; Dijksterhuis, Jan
2015-01-01
Indoor fungi are a major cause of cosmetic and structural damage of buildings worldwide and prolonged exposure of these fungi poses a health risk. Aspergillus, Penicillium and Cladosporium species are the most predominant fungi in indoor environments. Cladosporium species predominate under ambient conditions. A total of 123 Cladosporium isolates originating from indoor air and indoor surfaces of archives, industrial factories, laboratories, and other buildings from four continents were identified by sequencing the internal transcribed spacer (ITS), and a part of the translation elongation factor 1α gene (TEF) and actin gene (ACT). Species from the Cladosporium sphaerospermum species complex were most predominant representing 44.7% of all isolates, while the Cladosporium cladosporioides and Cladosporium herbarum species complexes represented 33.3% and 22.0%, respectively. The contribution of the C. sphaerospermum species complex was 23.1% and 58.2% in the indoor air and isolates from indoor surfaces, respectively. Isolates from this species complex showed growth at lower water activity (≥ 0.82) when compared to species from the C. cladosporioides and C. herbarum species complexes (≥ 0.85). Together, these data indicate that xerotolerance provide the C. sphaerospermum species complex advantage in colonizing indoor surfaces. As a consequence, C. sphaerospermum are proposed to be the most predominant fungus at these locations under ambient conditions. Findings are discussed in relation to the specificity of allergy test, as the current species of Cladosporium used to develop these tests are not the predominant indoor species.
Inferring Taxi Status Using GPS Trajectories
Zhu, Yin; Zhang, Liuhang; Santani, Darshan; Xie, Xing; Yang, Qiang
2012-01-01
In this paper, we infer the statuses of a taxi, consisting of occupied, non-occupied and parked, in terms of its GPS trajectory. The status information can enable urban computing for improving a city's transportation systems and land use planning. In our solution, we first identify and extract a set of effective features incorporating the knowledge of a single trajectory, historical trajectories and geographic data like road network. Second, a parking status detection algorithm is devised to find parking places (from a given trajectory), dividing a trajectory into segments (i.e., sub-trajectories). Third, we propose a two-phase inference model to learn the status (occupied or non-occupied) of each point from a taxi segment. This model first uses the identified features to train a local probabilistic classifier and then carries out a Hidden Semi-Markov Model (HSMM) for globally considering long term travel patterns. We evaluated our method with a large-scale real-world trajectory dataset generated by 600 taxis...
Inference with the Median of a Prior
Directory of Open Access Journals (Sweden)
Ali Mohammad-Djafari
2006-06-01
Full Text Available We consider the problem of inference on one of the two parameters of a probability distribution when we have some prior information on a nuisance parameter. When a prior probability distribution on this nuisance parameter is given, the marginal distribution is the classical tool to account for it. If the prior distribution is not given, but we have partial knowledge such as a fixed number of moments, we can use the maximum entropy principle to assign a prior law and thus go back to the previous case. In this work, we consider the case where we only know the median of the prior and propose a new tool for this case. This new inference tool looks like a marginal distribution. It is obtained by first remarking that the marginal distribution can be considered as the mean value of the original distribution with respect to the prior probability law of the nuisance parameter, and then, by using the median in place of the mean.
Causal inference, probability theory, and graphical insights.
Baker, Stuart G
2013-11-10
Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design.
Natural frequencies facilitate diagnostic inferences of managers.
Hoffrage, Ulrich; Hafenbrädl, Sebastian; Bouquet, Cyril
2015-01-01
In Bayesian inference tasks, information about base rates as well as hit rate and false-alarm rate needs to be integrated according to Bayes' rule after the result of a diagnostic test became known. Numerous studies have found that presenting information in a Bayesian inference task in terms of natural frequencies leads to better performance compared to variants with information presented in terms of probabilities or percentages. Natural frequencies are the tallies in a natural sample in which hit rate and false-alarm rate are not normalized with respect to base rates. The present research replicates the beneficial effect of natural frequencies with four tasks from the domain of management, and with management students as well as experienced executives as participants. The percentage of Bayesian responses was almost twice as high when information was presented in natural frequencies compared to a presentation in terms of percentages. In contrast to most tasks previously studied, the majority of numerical responses were lower than the Bayesian solutions. Having heard of Bayes' rule prior to the study did not affect Bayesian performance. An implication of our work is that textbooks explaining Bayes' rule should teach how to represent information in terms of natural frequencies instead of how to plug probabilities or percentages into a formula.
Hierarchical Bayesian inference in the visual cortex
Lee, Tai Sing; Mumford, David
2003-07-01
Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas. 2003 Optical Society of America
Rahmati, Vahid; Kirmse, Knut; Marković, Dimitrije; Holthoff, Knut; Kiebel, Stefan J
2016-02-01
Calcium imaging has been used as a promising technique to monitor the dynamic activity of neuronal populations. However, the calcium trace is temporally smeared which restricts the extraction of quantities of interest such as spike trains of individual neurons. To address this issue, spike reconstruction algorithms have been introduced. One limitation of such reconstructions is that the underlying models are not informed about the biophysics of spike and burst generations. Such existing prior knowledge might be useful for constraining the possible solutions of spikes. Here we describe, in a novel Bayesian approach, how principled knowledge about neuronal dynamics can be employed to infer biophysical variables and parameters from fluorescence traces. By using both synthetic and in vitro recorded fluorescence traces, we demonstrate that the new approach is able to reconstruct different repetitive spiking and/or bursting patterns with accurate single spike resolution. Furthermore, we show that the high inference precision of the new approach is preserved even if the fluorescence trace is rather noisy or if the fluorescence transients show slow rise kinetics lasting several hundred milliseconds, and inhomogeneous rise and decay times. In addition, we discuss the use of the new approach for inferring parameter changes, e.g. due to a pharmacological intervention, as well as for inferring complex characteristics of immature neuronal circuits.
Radiolabeling as a tool to study uptake pathways in plants
Energy Technology Data Exchange (ETDEWEB)
Schymura, Stefan; Hildebrand, Heike; Franke, Karsten [Helmholtz-Zentrum Dresden-Rossendorf e.V., Dresden (Germany). Reactive Transport; Fricke, T. [Vita34 BioPlanta, Leipzig (Germany)
2017-06-01
The identification of major uptake pathways in plants is an important factor when evaluation the fate of manufactured nanoparticles in the environment and the associated risks. Using different radiolabeling techniques we were able to show a predominantly particulate uptake for CeO{sub 2} nanoparticles (NPs) in contrast to a possible uptake in the form of ionic cerium.
Does index tumor predominant location influence prognostic factors in radical prostatectomies?
Billis, Athanase; Freitas, Leandro L. L.; Costa, Larissa B. E.; de Angelis, Camila M.; Carvalho, Kelson R.; Magna, Luis A.; Ferreira, Ubirajara
2017-01-01
ABSTRACT Purpose To find any influence on prognostic factors of index tumor according to predominant location. Materials and Methods Prostate surgical specimens from 499 patients submitted to radical retropubic prostatectomy were step-sectioned. Each transverse section was subdivided into 2 anterolateral and 2 posterolateral quadrants. Tumor extent was evaluated by a semi-quantitative point-count method. The index tumor (dominant nodule) was recorded as the maximal number of positive points of the most extensive tumor area from the quadrants and the predominant location was considered anterior (anterolateral quadrants), posterior (posterolateral quadrants), basal (quadrants in upper half of the prostate), apical (quadrants in lower half of the prostate), left (left quadrants) or right (right quadrants). Time to biochemical recurrence was analyzed by Kaplan-Meier product-limit analysis and prediction of shorter time to biochemical recurrence using univariate and multivariate Cox proportional hazards model. Results Index tumors with predominant posterior location were significantly associated with higher total tumor extent, needle and radical prostatectomy Gleason score, positive lymph nodes and preoperative prostate-specific antigen. Index tumors with predominant basal location were significantly associated with higher preoperative prostate-specific antigen, pathological stage higher than pT2, extra-prostatic extension, and seminal vesicle invasion. Index tumors with predominant basal location were significantly associated with time to biochemical recurrence in Kaplan-Meier estimates and significantly predicted shorter time to biochemical recurrence on univariate analysis but not on multivariate analysis. Conclusions The study suggests that index tumor predominant location is associated with prognosis in radical prostatectomies, however, in multivariate analysis do not offer advantage over other well-established prognostic factors. PMID:28379672
Human brain lesion-deficit inference remapped.
Mah, Yee-Haur; Husain, Masud; Rees, Geraint; Nachev, Parashkev
2014-09-01
Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant
Species tree inference by minimizing deep coalescences.
Directory of Open Access Journals (Sweden)
Cuong Than
2009-09-01
Full Text Available In a 1997 seminal paper, W. Maddison proposed minimizing deep coalescences, or MDC, as an optimization criterion for inferring the species tree from a set of incongruent gene trees, assuming the incongruence is exclusively due to lineage sorting. In a subsequent paper, Maddison and Knowles provided and implemented a search heuristic for optimizing the MDC criterion, given a set of gene trees. However, the heuristic is not guaranteed to compute optimal solutions, and its hill-climbing search makes it slow in practice. In this paper, we provide two exact solutions to the problem of inferring the species tree from a set of gene trees under the MDC criterion. In other words, our solutions are guaranteed to find the tree that minimizes the total number of deep coalescences from a set of gene trees. One solution is based on a novel integer linear programming (ILP formulation, and another is based on a simple dynamic programming (DP approach. Powerful ILP solvers, such as CPLEX, make the first solution appealing, particularly for very large-scale instances of the problem, whereas the DP-based solution eliminates dependence on proprietary tools, and its simplicity makes it easy to integrate with other genomic events that may cause gene tree incongruence. Using the exact solutions, we analyze a data set of 106 loci from eight yeast species, a data set of 268 loci from eight Apicomplexan species, and several simulated data sets. We show that the MDC criterion provides very accurate estimates of the species tree topologies, and that our solutions are very fast, thus allowing for the accurate analysis of genome-scale data sets. Further, the efficiency of the solutions allow for quick exploration of sub-optimal solutions, which is important for a parsimony-based criterion such as MDC, as we show. We show that searching for the species tree in the compatibility graph of the clusters induced by the gene trees may be sufficient in practice, a finding that helps
Logical inference techniques for loop parallelization
DEFF Research Database (Denmark)
Oancea, Cosmin Eugen; Rauchwerger, Lawrence
2012-01-01
This paper presents a fully automatic approach to loop parallelization that integrates the use of static and run-time analysis and thus overcomes many known difficulties such as nonlinear and indirect array indexing and complex control flow. Our hybrid analysis framework validates...... the parallelization transformation by verifying the independence of the loop's memory references. To this end it represents array references using the USR (uniform set representation) language and expresses the independence condition as an equation, S={}, where S is a set expression representing array indexes. Using...... ( F(S) => S = {} ). Loop parallelization is then validated using a novel logic inference algorithm that factorizes the obtained complex predicates F(S) into a sequence of sufficient-independence conditions that are evaluated first statically and, when needed, dynamically, in increasing order...
Inferences from Genomic Models in Stratified Populations
DEFF Research Database (Denmark)
Janss, Luc; de los Campos, Gustavo; Sheehan, Nuala
2012-01-01
Unaccounted population stratification can lead to spurious associations in genome-wide association studies (GWAS) and in this context several methods have been proposed to deal with this problem. An alternative line of research uses whole-genome random regression (WGRR) models that fit all markers...... are unsatisfactory. Here we address this problem and describe a reparameterization of a WGRR model, based on an eigenvalue decomposition, for simultaneous inference of parameters and unobserved population structure. This allows estimation of genomic parameters with and without inclusion of marker......-derived eigenvectors that account for stratification. The method is illustrated with grain yield in wheat typed for 1279 genetic markers, and with height, HDL cholesterol and systolic blood pressure from the British 1958 cohort study typed for 1 million SNP genotypes. Both sets of data show signs of population...
Inferences about infants' visual brain mechanisms.
Atkinson, Janette; Braddick, Oliver
2013-11-01
We discuss hypotheses that link the measurements we can make with infants to inferences about their developing neural mechanisms. First, we examine evidence from the sensitivity to visual stimulus properties seen in infants' responses, using both electrophysiological measures (transient and steady-state recordings of visual evoked potentials/visual event-related potentials) and behavioral measures and compare this with the sensitivity of brain processes, known from data on mammalian neurophysiology and human neuroimaging. The evidence for multiple behavioral systems with different patterns of visual sensitivity is discussed. Second, we consider the analogies which can be made between infants' behavior and that of adults with identified brain damage, and extend these links to hypothesize about the brain basis of visual deficits in infants and children with developmental disorders. Last, we consider how these lines of data might allow us to form "inverse linking hypotheses" about infants' visual experience.
Bayesian inference data evaluation and decisions
Harney, Hanns Ludwig
2016-01-01
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. New sections feature factorizing parameters, commuting parameters, observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge. Requiring no knowledge of quantum mechanics, the book is written on introductory level, with man...
Robust Spectroscopic Inference with Imperfect Models
Czekala, Ian; Mandel, Kaisey S; Hogg, David W; Green, Gregory M
2014-01-01
We present a modular, extensible framework for the spectroscopic inference of physical parameters based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. In the limit of high signal-to-noise data with large spectral range that is common for stellar parameter estimation, that covariant structure can bias the parameter determinations. We have designed a likelihood function formalism to account for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. We specifically address the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic or molecular data, or radiative transfer treatment) by developing a novel local covariance kernel framework that identifies and self-consistently downweights pathological spectral line "outliers." By fitting multiple spec...
Inferring human intentions from the brain data
DEFF Research Database (Denmark)
Stanek, Konrad
The human brain is a massively complex organ composed of approximately a hundred billion densely interconnected, interacting neural cells. The neurons are not wired randomly - instead, they are organized in local functional assemblies. It is believed that the complex patterns of dynamic electric...... discharges across the neural tissue are responsible for emergence of high cognitive function, conscious perception and voluntary action. The brain’s capacity to exercise free will, or internally generated free choice, has long been investigated by philosophers, psychologists and neuroscientists. Rather than...... assuming a causal power of conscious will, the neuroscience of volition is based on the premise that "mental states rest on brain processes”, and hence by measuring spatial and temporal correlates of volition in carefully controlled experiments we can infer about their underlying mind processes, including...
Inferring human mobility using communication patterns
Palchykov, Vasyl; Jo, Hang-Hyun; Saramäki, Jari; Pan, Raj Kumar
2014-01-01
Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g., tracking mobile phone locations, or by estimations from models. Typically, such models build on accurate knowledge of the population size at each location. However, when this information is not readily available, their applicability is rather limited. As mobile phones are ubiquitous, our aim is to investigate if mobility patterns can be inferred from aggregated mobile phone call data. Using data released by Orange for Ivory Coast, we show that human mobility is well predicted by a simple model based on the frequency of mobile phone calls between two locations and their geographical distance. We argue that the strength of the model comes from directly incorporating the social dimension of mobility. Furthermore, as only aggregated call da...
Ignorability in Statistical and Probabilistic Inference
Jaeger, M
2011-01-01
When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed by maintaining this proper distinction are often prohibitive, one asks for conditions under which it can be safely ignored. Such conditions are given by the missing at random (mar) and coarsened at random (car) assumptions. In this paper we provide an in-depth analysis of several questions relating to mar/car assumptions. Main purpose of our study is to provide criteria by which one may evaluate whether a car assumption is reasonable for a particular data collecting or observational process. This question is complicated by the fact that several distinct versions of mar/car assumptions exist. We therefore first provide an overview over these different versions, in which we highlight the distinction between distributional an...
Seasonal constraints on inferred planetary heat content
McKinnon, Karen A.; Huybers, Peter
2016-10-01
Planetary heating can be quantified using top of the atmosphere energy fluxes or through monitoring the heat content of the Earth system. It has been difficult, however, to compare the two methods with each other because of biases in satellite measurements and incomplete spatial coverage of ocean observations. Here we focus on the the seasonal cycle whose amplitude is large relative to satellite biases and observational errors. The seasonal budget can be closed through inferring contributions from high-latitude oceans and marginal seas using the covariance structure of National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM1). In contrast, if these regions are approximated as the average across well-observed regions, the amplitude of the seasonal cycle is overestimated relative to satellite constraints. Analysis of the same CESM1 simulation indicates that complete measurement of the upper ocean would increase the magnitude and precision of interannual trend estimates in ocean heating more than fully measuring the deep ocean.
The renormalisation group via statistical inference
Bény, Cédric
2014-01-01
In physics one attempts to infer the rules governing a system given only the results of imperfect measurements. Hence, microscopic theories may be effectively indistinguishable experimentally. We develop an operationally motivated procedure to identify the corresponding equivalence classes of theories. Here it is argued that the renormalisation group arises from the inherent ambiguities in constructing the classes: one encounters flow parameters as, e.g., a regulator, a scale, or a measure of precision, which specify representatives of the equivalence classes. This provides a unifying framework and identifies the role played by information in renormalisation. We validate this idea by showing that it justifies the use of low-momenta n-point functions as relevant observables around a gaussian hypothesis. Our methods also provide a way to extend renormalisation techniques to effective models which are not based on the usual quantum-field formalism, and elucidates the distinctions between various type of RG.
Automatic inference of indexing rules for MEDLINE
Directory of Open Access Journals (Sweden)
Shooshan Sonya E
2008-11-01
Full Text Available Abstract Background: Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. Methods: In this paper, we describe the use and the customization of Inductive Logic Programming (ILP to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Results: Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI, a system producing automatic indexing recommendations for MEDLINE. Conclusion: We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.
Supplier Selection Using Fuzzy Inference System
Directory of Open Access Journals (Sweden)
hamidreza kadhodazadeh
2014-01-01
Full Text Available Suppliers are one of the most vital parts of supply chain whose operation has significant indirect effect on customer satisfaction. Since customer's expectations from organization are different, organizations should consider different standards, respectively. There are many researches in this field using different standards and methods in recent years. The purpose of this study is to propose an approach for choosing a supplier in a food manufacturing company considering cost, quality, service, type of relationship and structure standards of the supplier organization. To evaluate supplier according to the above standards, the fuzzy inference system has been used. Input data of this system includes supplier's score in any standard that is achieved by AHP approach and the output is final score of each supplier. Finally, a supplier has been selected that although is not the best in price and quality, has achieved good score in all of the standards.
Network Topology Inference from Spectral Templates
Segarra, Santiago; Mateos, Gonzalo; Ribeiro, Alejandro
2016-01-01
We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh look advocated here permeates benefits from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors. These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end we develop efficient inference algorithms stemming from provably-tight convex relaxations of natural nonconvex criteria, particularizing the results for two shifts: the...
Bayesian inference for pulsar timing models
Vigeland, Sarah J
2013-01-01
The extremely regular, periodic radio emission from millisecond pulsars make them useful tools for studying neutron star astrophysics, general relativity, and low-frequency gravitational waves. These studies require that the observed pulse time of arrivals are fit to complicated timing models that describe numerous effects such as the astrometry of the source, the evolution of the pulsar's spin, the presence of a binary companion, and the propagation of the pulses through the interstellar medium. In this paper, we discuss the benefits of using Bayesian inference to obtain these timing solutions. These include the validation of linearized least-squares model fits when they are correct, and the proper characterization of parameter uncertainties when they are not; the incorporation of prior parameter information and of models of correlated noise; and the Bayesian comparison of alternative timing models. We describe our computational setup, which combines the timing models of tempo2 with the nested-sampling integ...
Inferring Networks of Diffusion and Influence
Gomez-Rodriguez, Manuel; Krause, Andreas
2010-01-01
Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a...
Inference of magnetic fields in inhomogeneous prominences
Milic, Ivan; Atanackovic, Olga
2016-01-01
Most of the quantitative information about the magnetic field vector in solar prominences comes from the analysis of the Hanle effect acting on lines formed by scattering. As these lines can be of non-negligible optical thickness, it is of interest to study the line formation process further. We investigate the multidimensional effects on the interpretation of spectropolarimetric observations, particularly on the inference of the magnetic field vector. We do this by analyzing the differences between multidimensional models, which involve fully self-consistent radiative transfer computations in the presence of spatial inhomogeneities and velocity fields, and those which rely on simple one-dimensional geometry. We study the formation of a prototype line in ad hoc inhomogeneous, isothermal 2D prominence models. We solve the NLTE polarized line formation problem in the presence of a large-scale oriented magnetic field. The resulting polarized line profiles are then interpreted (i.e. inverted) assuming a simple 1D...
Inferring Phylogenetic Networks from Gene Order Data
Directory of Open Access Journals (Sweden)
Alexey Anatolievich Morozov
2013-01-01
Full Text Available Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary, sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm. Binary encoding can also be useful, but only when the methods mentioned above cannot be used.
MISTIC: mutual information server to infer coevolution
DEFF Research Database (Denmark)
Simonetti, Franco L.; Teppa, Elin; Chernomoretz, Ariel
2013-01-01
MISTIC (mutual information server to infer coevolution) is a web server for graphical representation of the information contained within a MSA (multiple sequence alignment) and a complete analysis tool for Mutual Information networks in protein families. The server outputs a graphical visualization...... of several information-related quantities using a circos representation. This provides an integrated view of the MSA in terms of (i) the mutual information (MI) between residue pairs, (ii) sequence conservation and (iii) the residue cumulative and proximity MI scores. Further, an interactive interface...... containing all results can be downloaded. The server is available at http://mistic.leloir.org.ar. In summary, MISTIC allows for a comprehensive, compact, visually rich view of the information contained within an MSA in a manner unique to any other publicly available web server. In particular, the use...
Progression inference for somatic mutations in cancer
Directory of Open Access Journals (Sweden)
Leif E. Peterson
2017-04-01
Full Text Available Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer.
Chemical agnostic hazard prediction: Statistical inference of toxicity pathways - data for Figure 2
U.S. Environmental Protection Agency — This dataset comprises one SigmaPlot 13 file containing measured survival data and survival data predicted from the model coefficients selected by the LASSO...
Air-ice carbon pathways inferred from a sea ice tank experiment
Marie Kotovitch; Sébastien Moreau; Jiayun Zhou; Martin Vancoppenolle; Dieckmann, Gerhard S.; Karl-Ulrich Evers; Fanny Van der Linden; Thomas, David N.; Jean-Louis Tison; Bruno Delille
2016-01-01
Abstract Given rapid sea ice changes in the Arctic Ocean in the context of climate warming, better constraints on the role of sea ice in CO2 cycling are needed to assess the capacity of polar oceans to buffer the rise of atmospheric CO2 concentration. Air-ice CO2 fluxes were measured continuously using automated chambers from the initial freezing of a sea ice cover until its decay during the INTERICE V experiment at the Hamburg Ship Model Basin. Cooling seawater prior to sea ice formation act...
Sympatry inference and network analysis in biogeography.
Dos Santos, Daniel A; Fernández, Hugo R; Cuezzo, María Gabriela; Domínguez, Eduardo
2008-06-01
A new approach for biogeography to find patterns of sympatry, based on network analysis, is proposed. Biogeographic analysis focuses basically on sympatry patterns of species. Sympatry is a network (= relational) datum, but it has never been analyzed before using relational tools such as Network Analysis. Our approach to biogeographic analysis consists of two parts: first the sympatry inference and second the network analysis method (NAM). The sympatry inference method was designed to propose sympatry hypothesis, constructing a basal sympatry network based on punctual data, independent of a priori distributional area determination. In this way, two or more species are considered sympatric when there is interpenetration and relative proximity among their records of occurrence. In nature, groups of species presenting within-group sympatry and between-group allopatry constitute natural units (units of co-occurrence). These allopatric units are usually connected by intermediary species. The network analysis method (NAM) that we propose here is based on the identification and removal of intermediary species to segregate units of co-occurrence, using the betweenness measure and the clustering coefficient. The species ranges of the units of co-occurrence obtained are transferred to a map, being considered as candidates to areas of endemism. The new approach was implemented on three different real complex data sets (one of them a classic example previously used in biogeography) resulting in (1) independence of predefined spatial units; (2) definition of co-occurrence patterns from the sympatry network structure, not from species range similarities; (3) higher stability in results despite scale changes; (4) identification of candidates to areas of endemism supported by strictly endemic species; (5) identification of intermediary species with particular biological attributes.
Robust inference for group sequential trials.
Ganju, Jitendra; Lin, Yunzhi; Zhou, Kefei
2017-03-01
For ethical reasons, group sequential trials were introduced to allow trials to stop early in the event of extreme results. Endpoints in such trials are usually mortality or irreversible morbidity. For a given endpoint, the norm is to use a single test statistic and to use that same statistic for each analysis. This approach is risky because the test statistic has to be specified before the study is unblinded, and there is loss in power if the assumptions that ensure optimality for each analysis are not met. To minimize the risk of moderate to substantial loss in power due to a suboptimal choice of a statistic, a robust method was developed for nonsequential trials. The concept is analogous to diversification of financial investments to minimize risk. The method is based on combining P values from multiple test statistics for formal inference while controlling the type I error rate at its designated value.This article evaluates the performance of 2 P value combining methods for group sequential trials. The emphasis is on time to event trials although results from less complex trials are also included. The gain or loss in power with the combination method relative to a single statistic is asymmetric in its favor. Depending on the power of each individual test, the combination method can give more power than any single test or give power that is closer to the test with the most power. The versatility of the method is that it can combine P values from different test statistics for analysis at different times. The robustness of results suggests that inference from group sequential trials can be strengthened with the use of combined tests. Copyright © 2017 John Wiley & Sons, Ltd.
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Yang, Xiu; Li, Zhen; Karniadakis, George Em
2017-02-01
We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are "sparse". The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.
Inferring Protein Associations Using Protein Pulldown Assays
Energy Technology Data Exchange (ETDEWEB)
Sharp, Julia L.; Anderson, Kevin K.; Daly, Don S.; Auberry, Deanna L.; Borkowski, John J.; Cannon, William R.
2007-02-01
Background: One method to infer protein-protein associations is through a “bait-prey pulldown” assay using a protein affinity agent and an LC-MS (liquid chromatography-mass spectrometry)-based protein identification method. False positive and negative protein identifications are not uncommon, however, leading to incorrect inferences. Methods: A pulldown experiment generates a protein association matrix wherein each column represents a sample from one bait protein, each row represents one prey protein and each cell contains a presence/absence association indicator. Our method evaluates the presence/absence pattern across a prey protein (row) with a Likelihood Ratio Test (LRT), computing its p-value with simulated LRT test statistic distributions after a check with simulated binomial random variates disqualified the large sample 2 test. A pulldown experiment often involves hundreds of tests so we apply the false discovery rate method to control the false positive rate. Based on the p-value, each prey protein is assigned a category (specific association, non-specific association, or not associated) and appraised with respect to the pulldown experiment’s goal and design. The method is illustrated using a pulldown experiment investigating the protein complexes of Shewanella oneidensis MR-1. Results: The Monte Carlo simulated LRT p-values objectively reveal specific and ubiquitous prey, as well as potential systematic errors. The example analysis shows the results to be biologically sensible and more realistic than the ad hoc screening methods previously utilized. Conclusions: The method presented appears to be informative for screening for protein-protein associations.
Energy Technology Data Exchange (ETDEWEB)
Stekhoven, Daniel J. [Univ. of Zurich (Switzerland); Omasits, Ulrich [Univ. of Zurich (Switzerland); ETH Zurich (Switzerland); Quebatte, Maxime [Univ. of Basel (Switzerland); Dehio, Christoph [Univ. of Basel (Switzerland); Ahrens, Christian H. [Univ. of Zurich (Switzerland)
2014-03-01
Proteomics data provide unique insights into biological systems, including the predominant subcellular localization (SCL) of proteins, which can reveal important clues about their functions. Here we analyzed data of a complete prokaryotic proteome expressed under two conditions mimicking interaction of the emerging pathogen Bartonella henselae with its mammalian host. Normalized spectral count data from cytoplasmic, total membrane, inner and outer membrane fractions allowed us to identify the predominant SCL for 82% of the identified proteins. The spectral count proportion of total membrane versus cytoplasmic fractions indicated the propensity of cytoplasmic proteins to co-fractionate with the inner membrane, and enabled us to distinguish cytoplasmic, peripheral innermembrane and bona fide inner membrane proteins. Principal component analysis and k-nearest neighbor classification training on selected marker proteins or predominantly localized proteins, allowed us to determine an extensive catalog of at least 74 expressed outer membrane proteins, and to extend the SCL assignment to 94% of the identified proteins, including 18% where in silico methods gave no prediction. Suitable experimental proteomics data combined with straightforward computational approaches can thus identify the predominant SCL on a proteome-wide scale. Finally, we present a conceptual approach to identify proteins potentially changing their SCL in a condition-dependent fashion.
Shavers, Marjorie C.; Moore, James L., III
2014-01-01
Drawing on a larger study, this qualitative investigation uses Black feminist thought as the interpretive lens to investigate perceptions and experiences of African American female doctoral students at predominately White institutions (PWIs). Semistructured interviews were used to gain an understanding of their experiences and how these…
Harper, Shaun R.
2015-01-01
In this article, Shaun R. Harper investigates how Black undergraduate men respond to and resist the internalization of racist stereotypes at predominantly White colleges and universities. Prior studies consistently show that racial stereotypes are commonplace on many campuses, that their effects are usually psychologically and academically…
Harper, Shaun R.
2015-01-01
In this article, Shaun R. Harper investigates how Black undergraduate men respond to and resist the internalization of racist stereotypes at predominantly White colleges and universities. Prior studies consistently show that racial stereotypes are commonplace on many campuses, that their effects are usually psychologically and academically…
Black Undergraduate Women and Their Sense of Belonging in STEM at Predominantly White Institutions
Dortch, Deniece; Patel, Chirag
2017-01-01
Because little work exists on the sense of belonging focusing on just Black undergraduate women in science, technology, engineering, and math (STEM), especially at highly selective predominantly white institutions (PWIs), this study takes a phenomenological approach to understand the lived experiences of Black undergraduate women in STEM by…
Achuonye, Keziah Akuoma
2015-01-01
This descriptive survey is hinged on predominant teaching strategies in schools, implications for curriculum implementation in Mathematics, Science and Technology. Target population consisted of teachers in primary, secondary and tertiary schools. However, purposive sample of 900 respondents was drawn from the six BRACED states namely Bayelsa,…
Public Platitudes and Hidden Tensions: Racial Climates at Predominantly White Liberal Arts Colleges.
McClelland, Katherine E.
1990-01-01
Theories of intergroup attitudes suggest that the period of relative calm on college campuses was only superficial. Theories are supported by a study of a "quiet" predominantly White liberal arts college. Findings indicate significant differences between Blacks and Whites on a variety of measures of interracial attitudes and interaction patterns.…
Origin of Predominance of Cementite among Iron Carbides in Steel at Elevated Temperature
Fang, C.M.; Sluiter, M.H.F.; Van Huis, M.A.; Ande, C.K.; Zandbergen, H.W.
2010-01-01
A long-standing challenge in physics is to understand why cementite is the predominant carbide in steel. Here we show that the prevalent formation of cementite can be explained only by considering its stability at elevated temperature. A systematic highly accurate quantum mechanical study was conduc
Identification of predominant aroma components of raw, dry roasted and oil roasted almonds.
Erten, Edibe S; Cadwallader, Keith R
2017-02-15
Volatile components of raw, dry roasted and oil roasted almonds were isolated by solvent extraction/solvent-assisted flavor evaporation and predominant aroma compounds identified by gas chromatography-olfactometry (GCO) and aroma extract dilutions analysis (AEDA). Selected odorants were quantitated by GC-mass spectrometry and odor-activity values (OAVs) determined. Results of AEDA indicated that 1-octen-3-one and acetic acid were important aroma compounds in raw almonds. Those predominant in dry roasted almonds were methional, 2- and 3-methylbutanal, 2-acetyl-1-pyrroline and 2,3-pentanedione; whereas, in oil roasted almonds 4-hydroxy-2,5-dimethyl-3(2H)-furanone, 2,3-pentanedione, methional and 2-acetyl-1-pyrroline were the predominant aroma compounds. Overall, oil roasted almonds contained a greater number and higher abundance of aroma compounds than either raw or dry roasted almonds. The results of this study demonstrate the importance of lipid-derived volatile compounds in raw almond aroma. Meanwhile, in dry and oil roasted almonds, the predominant aroma compounds were derived via the Maillard reaction, lipid degradation/oxidation and sugar degradation.
Jackson, Alicia D.
2013-01-01
African American women represented a growing proportion within the field of education in attaining leadership roles as school principals. As the numbers continued to rise slowly, African American women principals found themselves leading in diverse or even predominately White school settings. Leading in such settings encouraged African American…
H. Wang (Hongling); G. Zhou (Guoying); L. Luo (Linjie); J.B.A. Crusius; A. Yuan (Anlong); J. Kou (Jiguang); G. Yang (Guifang); M. Wang (Min); J. Wu (Jing); B.M.E. von Blomberg (Mary); S.A. Morré (Servaas); A. Salvador Pena; B. Xia (Bing)
2015-01-01
textabstractCeliac disease (CD) is common in Caucasians, but thought to be rare in Asians. Our aim was to determine the prevalence of CD in Chinese patients with chronic diarrhea predominant irritable bowel syndrome (IBS-D). From July 2010 to August 2012, 395 adult patients with IBS-D and 363 age
The Construction of Black High-Achiever Identities in a Predominantly White High School
Andrews, Dorinda J. Carter
2009-01-01
In this article, I examine how black students construct their racial and achievement self-concepts in a predominantly white high school to enact a black achiever identity. By listening to these students talk about the importance of race and achievement to their lives, I came to understand how racialized the task of achieving was for them even…
Schatorjé, E J H; Gathmann, B; van Hout, R W N M; de Vries, E; Schölvinck, Elisabeth H.
2014-01-01
Hypogammaglobulinaemias are the most common primary immunodeficiency diseases. This group of diseases is very heterogeneous, and little is known about these diseases in children. In the Pediatric Predominantly Antibody Deficiencies (PedPAD) study, we analysed data from the European Society for Immun
Perceptions of Financial Aid: Black Students at a Predominantly White Institution
Tichavakunda, Antar A.
2017-01-01
This study provides qualitative context for statistics concerning Black college students and financial aid. Using the financial nexus model as a framework, this research draws upon interviews with 29 Black juniors and seniors at a selective, -private, and predominantly White university. The data suggest that students -generally exhibited high…
Predominance of N-acetyl transferase 2 slow acetylator alleles in ...
African Journals Online (AJOL)
Student
acetylator phenotype were the most predominant NAT2 allelic type and individuals with the phenotype were more likely to ... influence individual variation in cancer susceptibility, responses to ... development of bladder (Hein, 2002) and colon cancers ... temperature ≥ 37.5°C) or having a history of fever in the preceding.
Harwood, Stacy A.; Huntt, Margaret Browne; Mendenhall, Ruby; Lewis, Jioni A.
2012-01-01
Students of color often perceive the campus climate more negatively than do their White counterparts. Our study begins to uncover what students of color experience in residence halls. Using focus group data from a larger study exploring racial microaggressions at a predominantly White institution (PWI), we identified over 70 racial…
Public Platitudes and Hidden Tensions: Racial Climates at Predominantly White Liberal Arts Colleges.
McClelland, Katherine E.
1990-01-01
Theories of intergroup attitudes suggest that the period of relative calm on college campuses was only superficial. Theories are supported by a study of a "quiet" predominantly White liberal arts college. Findings indicate significant differences between Blacks and Whites on a variety of measures of interracial attitudes and interaction patterns.…
The peptidoglycan of Mycobacterium abscessus is predominantly cross-linked by L,D-transpeptidases.
Lavollay, Marie; Fourgeaud, Martine; Herrmann, Jean-Louis; Dubost, Lionel; Marie, Arul; Gutmann, Laurent; Arthur, Michel; Mainardi, Jean-Luc
2011-02-01
Few therapeutic alternatives remain for the treatment of infections due to multiresistant Mycobacterium abscessus. Here we show that the peptidoglycans of the "rough" and "smooth" morphotypes contain predominantly 3→3 cross-links generated by l,d-transpeptidases, indicating that these enzymes are attractive targets for the development of efficient drugs.
Achuonye, Keziah Akuoma
2015-01-01
This descriptive survey is hinged on predominant teaching strategies in schools, implications for curriculum implementation in Mathematics, Science and Technology. Target population consisted of teachers in primary, secondary and tertiary schools. However, purposive sample of 900 respondents was drawn from the six BRACED states namely Bayelsa,…
Lewis, Amanda E.; Chesler, Mark; Forman, Tyrone A.
2000-01-01
Investigated the experiences of minority students with their white peers on predominantly white campuses. Focus groups revealed how white students' purported color-blindness regarding race often blinded them to their own color conscious behavior and the subsequent stereotyping effects. Participants' discussions examined stereotyping, assimilation,…
Reduced sense of agency in chronic schizophrenia with predominant negative symptoms.
Maeda, Takaki; Takahata, Keisuke; Muramatsu, Taro; Okimura, Tsukasa; Koreki, Akihiro; Iwashita, Satoru; Mimura, Masaru; Kato, Motoichiro
2013-10-30
Self-disturbances in schizophrenia have been regarded as a fundamental vulnerability marker for this disease, and have begun to be studied from the standpoint of an abnormal "sense of agency (SoA)" in cognitive neuroscience. To clarify the nature of aberrant SoA in schizophrenia, it needs to be investigated in various clinical subtypes and stages. The residual type of chronic schizophrenia with predominant negative symptoms (NS) has never been investigated for SoA. Accordingly, we investigated SoA by an original agency attribution task in NS-predominant schizophrenia, and evaluated the dynamic interplay between the predictive and postdictive components of SoA in the optimal cue integration framework. We studied 20 patients with NS-predominant schizophrenia, and compared with 30 patients with paranoid-type schizophrenia and 35 normal volunteers. NS-predominant schizophrenia showed markedly diminished SoA compared to normal controls and paranoid-type schizophrenia, indicating a completely opposite direction in agency attribution compared with excessive SoA demonstrated in paranoid-type schizophrenia. Reduced SoA was detected in experimental studies of schizophrenia for the first time. According to the optimal cue integration framework, these results indicate that there was no increase in compensatory contributions of the postdictive processes despite the existence of inadequate predictions, contrary to the exaggerated postdictive component in paranoid-type schizophrenia.
Perceptions of Financial Aid: Black Students at a Predominantly White Institution
Tichavakunda, Antar A.
2017-01-01
This study provides qualitative context for statistics concerning Black college students and financial aid. Using the financial nexus model as a framework, this research draws upon interviews with 29 Black juniors and seniors at a selective, -private, and predominantly White university. The data suggest that students -generally exhibited high…
Diemer, Matthew A.
2007-01-01
Negotiating 2 worlds, a predominantly White opportunity structure and one's community of origin, often produces distress among persons of color. In this qualitative study, the author examines the perspectives and competencies of African American men who negotiate 2 worlds and suggests that bicultural competence may facilitate participation in the…
Experiences of Black Women Who Persist to Graduation at Predominantly White Schools of Nursing
Thomas, Francine Simms
2009-01-01
This study was designed to explore the experiences of Black women who attended predominantly White nursing schools. A phenomenological design was used to investigate eight nurses who persisted through to graduation from their nursing programs in the 21st century. The study examined persistence through the lens of academic involvement, alienation,…
Johnson, Lakitta
2013-01-01
Since the passage of the Civil Rights Act of 1964, the retention of African American students at predominately White colleges and universities continues to be problematic. Although many of these institutions have implemented retention programs for African American students, few have incorporated a comprehensive program that utilizes multi-program…
Dynamical Logic Driven by Classified Inferences Including Abduction
Sawa, Koji; Gunji, Yukio-Pegio
2010-11-01
We propose a dynamical model of formal logic which realizes a representation of logical inferences, deduction and induction. In addition, it also represents abduction which is classified by Peirce as the third inference following deduction and induction. The three types of inference are represented as transformations of a directed graph. The state of a relation between objects of the model fluctuates between the collective and the distinctive. In addition, the location of the relation in the sequence of the relation influences its state.
Conditional likelihood inference in generalized linear mixed models.
Sartori, Nicola; Severini , T.A
2002-01-01
Consider a generalized linear model with a canonical link function, containing both fixed and random effects. In this paper, we consider inference about the fixed effects based on a conditional likelihood function. It is shown that this conditional likelihood function is valid for any distribution of the random effects and, hence, the resulting inferences about the fixed effects are insensitive to misspecification of the random effects distribution. Inferences based on the conditional likelih...
A Reasoning System using Inductive Inference of Analogical Union
Miyahara, Tetsuhiro
1988-01-01
Analogical reasoning derives a new fact based on the analogous facts previously known. Inductive inference is a process of gaining a general rule from examples. We propose a new reasoning system using inductive inference and analogical reasoning. which is applicable to intellectual information processing and we characterize its power. Given an enumeration of paired examples. this system inductively infers a program representing the paring and constructs an analogical union. It reasons by anal...
Inferring angiosperm phylogeny from EST data with widespread gene duplication
Sanderson, Michael J.; McMahon, Michelle M.
2007-01-01
Background Most studies inferring species phylogenies use sequences from single copy genes or sets of orthologs culled from gene families. For taxa such as plants, with very high levels of gene duplication in their nuclear genomes, this has limited the exploitation of nuclear sequences for phylogenetic studies, such as those available in large EST libraries. One rarely used method of inference, gene tree parsimony, can infer species trees from gene families undergoing duplication and loss, bu...
Directory of Open Access Journals (Sweden)
Camiciottoli G
2016-09-01
Full Text Available Gianna Camiciottoli,1,2 Francesca Bigazzi,1 Chiara Magni,1 Viola Bonti,1 Stefano Diciotti,3 Maurizio Bartolucci,4 Mario Mascalchi,5 Massimo Pistolesi1 1Section of Respiratory Medicine, Department of Clinical and Experimental Medicine, 2Department of Clinical and Experimental Biomedical Sciences, University of Florence, Florence, 3Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi,” University of Bologna, Cesena, 4Department of Diagnostic Imaging, Careggi University Hospital, 5Radiodiagnostic Section, Department of Clinical and Experimental Biomedical Sciences, University of Florence, Florence, Italy Background: In addition to lung involvement, several other diseases and syndromes coexist in patients with chronic obstructive pulmonary disease (COPD. Our purpose was to investigate the prevalence of idiopathic arterial hypertension (IAH, ischemic heart disease, heart failure, peripheral vascular disease (PVD, diabetes, osteoporosis, and anxious depressive syndrome in a clinical setting of COPD outpatients whose phenotypes (predominant airway disease and predominant emphysema and severity (mild and severe diseases were determined by clinical and functional parameters. Methods: A total of 412 outpatients with COPD were assigned either a predominant airway disease or a predominant emphysema phenotype of mild or severe degree according to predictive models based on pulmonary functions (forced expiratory volume in 1 second/vital capacity; total lung capacity %; functional residual capacity %; and diffusing capacity of lung for carbon monoxide % and sputum characteristics. Comorbidities were assessed by objective medical records. Results: Eighty-four percent of patients suffered from at least one comorbidity and 75% from at least one cardiovascular comorbidity, with IAH and PVD being the most prevalent ones (62% and 28%, respectively. IAH prevailed significantly in predominant airway disease, osteoporosis prevailed
Probabilistic logic networks a comprehensive framework for uncertain inference
Goertzel, Ben; Goertzel, Izabela Freire; Heljakka, Ari
2008-01-01
This comprehensive book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. A broad scope of reasoning types are considered.
Parametric statistical inference basic theory and modern approaches
Zacks, Shelemyahu; Tsokos, C P
1981-01-01
Parametric Statistical Inference: Basic Theory and Modern Approaches presents the developments and modern trends in statistical inference to students who do not have advanced mathematical and statistical preparation. The topics discussed in the book are basic and common to many fields of statistical inference and thus serve as a jumping board for in-depth study. The book is organized into eight chapters. Chapter 1 provides an overview of how the theory of statistical inference is presented in subsequent chapters. Chapter 2 briefly discusses statistical distributions and their properties. Chapt
Spike Inference from Calcium Imaging using Sequential Monte Carlo Methods
NeuroData; Paninski, L
2015-01-01
Vogelstein JT, Paninski L. Spike Inference from Calcium Imaging using Sequential Monte Carlo Methods. Statistical and Applied Mathematical Sciences Institute (SAMSI) Program on Sequential Monte Carlo Methods, 2008
Inferring word meanings by assuming that speakers are informative.
Frank, Michael C; Goodman, Noah D
2014-12-01
Language comprehension is more than a process of decoding the literal meaning of a speaker's utterance. Instead, by making the assumption that speakers choose their words to be informative in context, listeners routinely make pragmatic inferences that go beyond the linguistic data. If language learners make these same assumptions, they should be able to infer word meanings in otherwise ambiguous situations. We use probabilistic tools to formalize these kinds of informativeness inferences-extending a model of pragmatic language comprehension to the acquisition setting-and present four experiments whose data suggest that preschool children can use informativeness to infer word meanings and that adult judgments track quantitatively with informativeness.
Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology.
Karp, Peter D; Paley, Suzanne M; Krummenacker, Markus; Latendresse, Mario; Dale, Joseph M; Lee, Thomas J; Kaipa, Pallavi; Gilham, Fred; Spaulding, Aaron; Popescu, Liviu; Altman, Tomer; Paulsen, Ian; Keseler, Ingrid M; Caspi, Ron
2010-01-01
Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry.
Nodular lymphocyte predominant Hodgkin lymphoma: a Lymphoma Study Association retrospective study
Lazarovici, Julien; Dartigues, Peggy; Brice, Pauline; Obéric, Lucie; Gaillard, Isabelle; Hunault-Berger, Mathilde; Broussais-Guillaumot, Florence; Gyan, Emmanuel; Bologna, Serge; Nicolas-Virelizier, Emmanuelle; Touati, Mohamed; Casasnovas, Olivier; Delarue, Richard; Orsini-Piocelle, Frédérique; Stamatoullas, Aspasia; Gabarre, Jean; Fornecker, Luc-Matthieu; Gastinne, Thomas; Peyrade, Fréderic; Roland, Virginie; Bachy, Emmanuel; André, Marc; Mounier, Nicolas; Fermé, Christophe
2015-01-01
Nodular lymphocyte predominant Hodgkin lymphoma represents a distinct entity from classical Hodgkin lymphoma. We conducted a retrospective study to investigate the management of patients with nodular lymphocyte predominant Hodgkin lymphoma. Clinical characteristics, treatment and outcome of adult patients with nodular lymphocyte predominant Hodgkin lymphoma were collected in Lymphoma Study Association centers. Progression-free survival (PFS) and overall survival (OS) were analyzed, and the competing risks formulation of a Cox regression model was used to control the effect of risk factors on relapse or death as competing events. Among 314 evaluable patients, 82.5% had early stage nodular lymphocyte predominant Hodgkin lymphoma. Initial management consisted in watchful waiting (36.3%), radiotherapy (20.1%), rituximab (8.9%), chemotherapy or immuno-chemotherapy (21.7%), combined modality treatment (12.7%), or radiotherapy plus rituximab (0.3%). With a median follow-up of 55.8 months, the 10-year PFS and OS estimates were 44.2% and 94.9%, respectively. The 4-year PFS estimates were 79.6% after radiotherapy, 77.0% after rituximab alone, 78.8% after chemotherapy or immuno-chemotherapy, and 93.9% after combined modality treatment. For the whole population, early treatment with chemotherapy or radiotherapy, but not rituximab alone (Hazard ratio 0.695 [0.320–1.512], P=0.3593) significantly reduced the risk of progression compared to watchful waiting (HR 0.388 [0.234–0.643], P=0.0002). Early treatment appears more beneficial compared to watchful waiting in terms of progression-free survival, but has no impact on overall survival. Radiotherapy in selected early stage nodular lymphocyte predominant Hodgkin lymphoma, and combined modality treatment, chemotherapy or immuno-chemotherapy for other patients, are the main options to treat adult patients with a curative intent. PMID:26430172
Ngo, Chinh C; Massa, Helen M; Thornton, Ruth B; Cripps, Allan W
2016-01-01
Otitis media (OM) is amongst the most common childhood diseases and is associated with multiple microbial pathogens within the middle ear. Global and temporal monitoring of predominant bacterial pathogens is important to inform new treatment strategies, vaccine development and to monitor the impact of vaccine implementation to improve progress toward global OM prevention. A systematic review of published reports of microbiology of acute otitis media (AOM) and otitis media with effusion (OME) from January, 1970 to August 2014, was performed using PubMed databases. This review confirmed that Streptococcus pneumoniae and Haemophilus influenzae, remain the predominant bacterial pathogens, with S. pneumoniae the predominant bacterium in the majority reports from AOM patients. In contrast, H. influenzae was the predominant bacterium for patients experiencing chronic OME, recurrent AOM and AOM with treatment failure. This result was consistent, even where improved detection sensitivity from the use of polymerase chain reaction (PCR) rather than bacterial culture was conducted. On average, PCR analyses increased the frequency of detection of S. pneumoniae and H. influenzae 3.2 fold compared to culture, whilst Moraxella catarrhalis was 4.5 times more frequently identified by PCR. Molecular methods can also improve monitoring of regional changes in the serotypes and identification frequency of S. pneumoniae and H. influenzae over time or after vaccine implementation, such as after introduction of the 7-valent pneumococcal conjugate vaccine. Globally, S. pneumoniae and H. influenzae remain the predominant otopathogens associated with OM as identified through bacterial culture; however, molecular methods continue to improve the frequency and accuracy of detection of individual serotypes. Ongoing monitoring with appropriate detection methods for OM pathogens can support development of improved vaccines to provide protection from the complex combination of otopathogens within
Directory of Open Access Journals (Sweden)
Chinh C Ngo
Full Text Available Otitis media (OM is amongst the most common childhood diseases and is associated with multiple microbial pathogens within the middle ear. Global and temporal monitoring of predominant bacterial pathogens is important to inform new treatment strategies, vaccine development and to monitor the impact of vaccine implementation to improve progress toward global OM prevention.A systematic review of published reports of microbiology of acute otitis media (AOM and otitis media with effusion (OME from January, 1970 to August 2014, was performed using PubMed databases.This review confirmed that Streptococcus pneumoniae and Haemophilus influenzae, remain the predominant bacterial pathogens, with S. pneumoniae the predominant bacterium in the majority reports from AOM patients. In contrast, H. influenzae was the predominant bacterium for patients experiencing chronic OME, recurrent AOM and AOM with treatment failure. This result was consistent, even where improved detection sensitivity from the use of polymerase chain reaction (PCR rather than bacterial culture was conducted. On average, PCR analyses increased the frequency of detection of S. pneumoniae and H. influenzae 3.2 fold compared to culture, whilst Moraxella catarrhalis was 4.5 times more frequently identified by PCR. Molecular methods can also improve monitoring of regional changes in the serotypes and identification frequency of S. pneumoniae and H. influenzae over time or after vaccine implementation, such as after introduction of the 7-valent pneumococcal conjugate vaccine.Globally, S. pneumoniae and H. influenzae remain the predominant otopathogens associated with OM as identified through bacterial culture; however, molecular methods continue to improve the frequency and accuracy of detection of individual serotypes. Ongoing monitoring with appropriate detection methods for OM pathogens can support development of improved vaccines to provide protection from the complex combination of
A case study in pathway knowledgebase verification
Directory of Open Access Journals (Sweden)
Shah Nigam H
2006-04-01
Full Text Available Abstract Background Biological databases and pathway knowledgebases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledgebase as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference. Results We design a series of logical tests to detect potential problems we might encounter using a particular knowledgebase, the Reactome database, with a particular computer-aided hypothesis evaluation tool, HyBrow. We develop an explicit formal language from the language implicit in the Reactome data format and specify a logic to evaluate models expressed using this language. We use the formalism of finite model theory in this work. We then use this logic to formulate tests for desirable properties (such as completeness, consistency, and well-formedness for pathways stored in Reactome. We apply these tests to the publicly available Reactome releases (releases 10 through 14 and compare the results, which highlight Reactome's steady improvement in terms of decreasing inconsistencies. We also investigate and discuss Reactome's potential for supporting computer-aided inference tools. Conclusion The case study described in this work demonstrates that it is possible to use our model theory based approach to identify problems one might encounter using a knowledgebase to support hypothesis evaluation tools. The methodology we use is general and is in no way restricted to the specific knowledgebase employed in this case study. Future application of this methodology will enable us to compare pathway resources with respect to the generic properties such resources will need to possess if they are to support automated reasoning.
Making inference from wildlife collision data: inferring predator absence from prey strikes
Hosack, Geoffrey R.; Barry, Simon C.
2017-01-01
Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application. PMID:28243534
Making inference from wildlife collision data: inferring predator absence from prey strikes
Directory of Open Access Journals (Sweden)
Peter Caley
2017-02-01
Full Text Available Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.
Directory of Open Access Journals (Sweden)
Oliver Serang
Full Text Available Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called "causal independence". For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to O(k log(k2 and the space to O(k log(k where k is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions.
Making inference from wildlife collision data: inferring predator absence from prey strikes.
Caley, Peter; Hosack, Geoffrey R; Barry, Simon C
2017-01-01
Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.
Efficient Probabilistic Inference with Partial Ranking Queries
Huang, Jonathan; Guestrin, Carlos E
2012-01-01
Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distributions. We approach the modeling problem from the computational principle that one should make structural assumptions which allow for efficient calculation of typical probabilistic queries. For ranking models, "typical" queries predominantly take the form of partial ranking queries (e.g., given a user's top-k favorite movies, what are his preferences over remaining movies?). In this paper, we argue that riffled independence factorizations proposed in recent literature [7, 8] are a natural structural assumption for ranking distributions, allowing for particularly efficient processing of partial ranking queries.
DEFF Research Database (Denmark)
Pedersen, Jørn Bjarke Torp; Kaufmann, Laura Hauch; Kroon, Aart
2010-01-01
’, and examines its spatial and temporal dynamics by analysis of recent satellite imagery, modelled meteorological data and historical data covering the last decade. The dominating mechanisms to form and sustain the polynya are inferred and the persistence and inter-annual variability of the phenomenon...... are estimated. The polynya formation is predominantly governed by mechanical forcing caused by northerly gales, and it is classified as a wind-driven shelf water polynya. A marked seasonal difference in the surface wind field, together with the obvious seasonal cycle in insolation, creates distinct winter...... and summer regimes in the seasonal evolution of the polynya. During the winter regime, both the size of and the ice cover within the polynya varies significantly on a temporal and spatial scale. Intermittent wind-driven openings of the polynya alternate with periods of increasing ice cover. Some of the most...
Campbell, Kieran R.
2016-01-01
Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ‘pseudotime’ where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference. PMID:27870852
Ahl, Richard E; Keil, Frank C
2016-09-26
Four studies explored the abilities of 80 adults and 180 children (4-9 years), from predominantly middle-class families in the Northeastern United States, to use information about machines' observable functional capacities to infer their internal, "hidden" mechanistic complexity. Children as young as 4 and 5 years old used machines' numbers of functions as indications of complexity and matched machines performing more functions with more complex "insides" (Study 1). However, only older children (6 and older) and adults used machines' functional diversity alone as an indication of complexity (Studies 2-4). The ability to use functional diversity as a complexity cue therefore emerges during the early school years, well before the use of diversity in most categorical induction tasks.
Enhanced thyroid iodine metabolism in patients with triiodothyronine-predominant Graves' disease
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Takamatsu, J.; Hosoya, T.; Naito, N.; Yoshimura, H.; Kohno, Y.; Tarutani, O.; Kuma, K.; Sakane, S.; Takeda, K.; Mozai, T.
1988-01-01
Some patients with hyperthyroid Graves' disease have increased serum T3 and normal or even low serum T4 levels during treatment with antithyroid drugs. These patients with elevated serum T3 to T4 ratios rarely have a remission of their hyperthyroidism. The aim of this study was to investigate thyroid iodine metabolism in such patients, whom we termed T3-predominant Graves' disease. Mean thyroid radioactive iodine uptake was 51.0 +/- 18.1% ( +/- SD) at 3 h, and it decreased to 38.9 +/- 20.1% at 24 h in 31 patients with T3-predominant Graves' disease during treatment. It was 20.0 +/- 11.4% at 3 h and increased to 31.9 +/- 16.0% at 24 h in 17 other patients with hyperthyroid Graves' disease who had normal serum T3 and T4 levels and a normal serum T3 to T4 ratio during treatment (control Graves' disease). The activity of serum TSH receptor antibodies was significantly higher in the patients with T3-predominant Graves' disease than in control Graves' disease patients. From in vitro studies of thyroid tissue obtained at surgery, both thyroglobulin content and iodine content in thyroglobulin were significantly lower in patients with T3-predominant Graves' disease than in the control Graves' disease patients. Thyroid peroxidase (TPO) activity determined by a guaiacol assay was 0.411 +/- 0.212 g.u./mg protein in the T3-predominant Graves' disease patients, significantly higher than that in the control Graves' disease patients. Serum TPO autoantibody levels determined by immunoprecipitation also were greater in T3-predominant Graves' disease patients than in control Graves' disease patients. Binding of this antibody to TPO slightly inhibited the enzyme activity of TPO, but this effect of the antibody was similar in the two groups of patients.
Inferring gene regression networks with model trees
Directory of Open Access Journals (Sweden)
Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Nuclear Forensic Inferences Using Iterative Multidimensional Statistics
Energy Technology Data Exchange (ETDEWEB)
Robel, M; Kristo, M J; Heller, M A
2009-06-09
Nuclear forensics involves the analysis of interdicted nuclear material for specific material characteristics (referred to as 'signatures') that imply specific geographical locations, production processes, culprit intentions, etc. Predictive signatures rely on expert knowledge of physics, chemistry, and engineering to develop inferences from these material characteristics. Comparative signatures, on the other hand, rely on comparison of the material characteristics of the interdicted sample (the 'questioned sample' in FBI parlance) with those of a set of known samples. In the ideal case, the set of known samples would be a comprehensive nuclear forensics database, a database which does not currently exist. In fact, our ability to analyze interdicted samples and produce an extensive list of precise materials characteristics far exceeds our ability to interpret the results. Therefore, as we seek to develop the extensive databases necessary for nuclear forensics, we must also develop the methods necessary to produce the necessary inferences from comparison of our analytical results with these large, multidimensional sets of data. In the work reported here, we used a large, multidimensional dataset of results from quality control analyses of uranium ore concentrate (UOC, sometimes called 'yellowcake'). We have found that traditional multidimensional techniques, such as principal components analysis (PCA), are especially useful for understanding such datasets and drawing relevant conclusions. In particular, we have developed an iterative partial least squares-discriminant analysis (PLS-DA) procedure that has proven especially adept at identifying the production location of unknown UOC samples. By removing classes which fell far outside the initial decision boundary, and then rebuilding the PLS-DA model, we have consistently produced better and more definitive attributions than with a single pass classification approach. Performance of the
Signal transduction pathway profiling of individual tumor samples
Directory of Open Access Journals (Sweden)
Peterson Carsten
2005-06-01
Full Text Available Abstract Background Signal transduction pathways convey information from the outside of the cell to transcription factors, which in turn regulate gene expression. Our objective is to analyze tumor gene expression data from microarrays in the context of such pathways. Results We use pathways compiled from the TRANSPATH/TRANSFAC databases and the literature, and three publicly available cancer microarray data sets. Variation in pathway activity, across the samples, is gauged by the degree of correlation between downstream targets of a pathway. Two correlation scores are applied; one considers all pairs of downstream targets, and the other considers only pairs without common transcription factors. Several pathways are found to be differentially active in the data sets using these scores. Moreover, we devise a score for pathway activity in individual samples, based on the average expression value of the downstream targets. Statistical significance is assigned to the scores using permutation of genes as null model. Hence, for individual samples, the status of a pathway is given as a sign, + or -, and a p-value. This approach defines a projection of high-dimensional gene expression data onto low-dimensional pathway activity scores. For each dataset and many pathways we find a much larger number of significant samples than expected by chance. Finally, we find that several sample-wise pathway activities are significantly associated with clinical classifications of the samples. Conclusion This study shows that it is feasible to infer signal transduction pathway activity, in individual samples, from gene expression data. Furthermore, these pathway activities are biologically relevant in the three cancer data sets.
Parsons, Matthew J; Long, David T; Giesy, John P; Kannan, Kurunthachalam
2014-09-20
Sediment chronologies from inland lakes suggest the influence of local to sub-regional scale sources for mercury (Hg). However, apportionment of sources for Hg using sediment chronologies is difficult due to the mixing of sources and pathways. Mercury and polycyclic aromatic hydrocarbons (PAH) often share common sources and pathways into the environment. Thus, chronologies of PAHs in dated cores of sediments might be a useful tool to infer sources of Hg. Sediment cores from seven inland lakes of Michigan were collected for measurement of PAHs and Hg and dated by use of (210)Pb. PAH concentrations and ratios of kinetic and thermodynamic PAH compounds were used to infer sources of Hg. Ratios indicate the existence of modern combustion sources to each lake and historic combustion sources to lakes near cement kilns and an iron foundry. Coal combustion sources were identified for two lakes near urban centers. Whereas a petroleum combustion source was identified for a lake that has a coal fired power plant along its shoreline. These results have implications for the cycling of Hg on local to regional scales.
iPoint: an integer programming based algorithm for inferring protein subnetworks.
Atias, Nir; Sharan, Roded
2013-07-01
Large scale screening experiments have become the workhorse of molecular biology, producing data at an ever increasing scale. The interpretation of such data, particularly in the context of a protein interaction network, has the potential to shed light on the molecular pathways underlying the phenotype or the process in question. A host of approaches have been developed in recent years to tackle this reconstruction challenge. These approaches aim to infer a compact subnetwork that connects the genes revealed by the screen while optimizing local (individual path lengths) or global (likelihood) aspects of the subnetwork. Yosef et al. [Mol. Syst. Biol., 2009, 5, 248] were the first to provide a joint optimization of both criteria, albeit approximate in nature. Here we devise an integer linear programming formulation for the joint optimization problem, allowing us to solve it to optimality in minutes on current networks. We apply our algorithm, iPoint, to various data sets in yeast and human and evaluate its performance against state-of-the-art algorithms. We show that iPoint attains very compact and accurate solutions that outperform previous network inference algorithms with respect to their local and global attributes, their consistency across multiple experiments targeting the same pathway, and their agreement with current biological knowledge.
The Workplace Environment for African-American Faculty Employed in Predominately White Institutions.
Whitfield-Harris, Lisa; Lockhart, Joan Such
2016-01-01
Diversity in academia requires attention, especially with the expected increase in minority populations in the United States (American Association of Colleges of Nursing, (AACN) 2014). Despite theoretical papers that suggest that several challenges are encountered by minority faculty employed in predominately White institutions, a dearth of research on this topic has been published. The purpose of this literature review was to analyze the published research that addressed the workplace environment of African-American faculty employed in predominately White institutions. In utilizing the keywords in various combinations, 236 articles were retrieved through multiple databases. After applying inclusion and exclusion criteria, 15 studies were reviewed with only three related to nursing. Two themes were extracted from the review: 1) the cultural climate of the workplace environment and, 2) underrepresentation of African-American faculty. It is apparent from this review that additional research is needed to understand the experiences of this group of faculty to target effective recruitment and retention strategies.
Dornblaser, Mark M.; Striegl, Robert G.
2015-01-01
Hydrologic exports of dissolved inorganic and organic carbon (DIC, DOC) reflect permafrost conditions in arctic and subarctic river basins. DIC yields in particular, increase with decreased permafrost extent. We investigated the influence of permafrost extent on DIC and DOC yield in a tributary of the Yukon River, where the upper watershed has continuous permafrost and the lower watershed has discontinuous permafrost. Our results indicate that DIC versus DOC predominance switches with interannual changes in water availability and flow routing in intermediate-size watersheds having mixed permafrost coverage. Large water yield and small concentrations from mountainous headwaters and small water yield and high concentrations from lowlands produced similar upstream and downstream carbon yields. However, DOC export exceeded DIC export during high-flow 2011 while DIC predominated during low-flow 2010. The majority of exported carbon derived from near-surface organic sources when landscapes were wet or frozen and from mineralized subsurface sources when infiltration increased.
Deontic Introduction: A Theory of Inference from Is to Ought
Elqayam, Shira; Thompson, Valerie A.; Wilkinson, Meredith R.; Evans, Jonathan St. B. T.; Over, David E.
2015-01-01
Humans have a unique ability to generate novel norms. Faced with the knowledge that there are hungry children in Somalia, we easily and naturally infer that we ought to donate to famine relief charities. Although a contentious and lively issue in metaethics, such inference from "is" to "ought" has not been systematically…
The Role of Causal Models in Analogical Inference
Lee, Hee Seung; Holyoak, Keith J.
2008-01-01
Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3…
Strategic Processing and Predictive Inference Generation in L2 Reading
Nahatame, Shingo
2014-01-01
Predictive inference is the anticipation of the likely consequences of events described in a text. This study investigated predictive inference generation during second language (L2) reading, with a focus on the effects of strategy instructions. In this experiment, Japanese university students read several short narrative passages designed to…
Statistical Inference and Patterns of Inequality in the Global North
Moran, Timothy Patrick
2006-01-01
Cross-national inequality trends have historically been a crucial field of inquiry across the social sciences, and new methodological techniques of statistical inference have recently improved the ability to analyze these trends over time. This paper applies Monte Carlo, bootstrap inference methods to the income surveys of the Luxembourg Income…
The Importance of Statistical Modeling in Data Analysis and Inference
Rollins, Derrick, Sr.
2017-01-01
Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound…
Inferencing Processes after Right Hemisphere Brain Damage: Maintenance of Inferences
Blake, Margaret Lehman
2009-01-01
Purpose: This study was designed to replicate and extend a previous study of inferencing in which some adults with right hemisphere damage (RHD) generated but did not maintain predictive inferences over time (M. Lehman-Blake & C. Tompkins, 2001). Two hypotheses were tested: (a) inferences were deactivated, and (b) selection of previously generated…
A Probability Index of the Robustness of a Causal Inference
Pan, Wei; Frank, Kenneth A.
2003-01-01
Causal inference is an important, controversial topic in the social sciences, where it is difficult to conduct experiments or measure and control for all confounding variables. To address this concern, the present study presents a probability index to assess the robustness of a causal inference to the impact of a confounding variable. The…
2006-01-01
Bariatric surgery is frequently indicated in the treatment of morbid obesity. Previously unreported complications have been associated to this surgery; among them, neurological complications have gained attention. We report the case of a 25-year-old man submitted to gastric surgery for treatment of morbid obesity who developed, two months after surgery, acute proximal weakness in lower limbs. The electroneuromyography revealed axonal peripheral polyneuropathy with predominant proximal involve...
Predominance of CRF06_cpx and Transmitted HIV Resistance in Algeria: Update 2013-2014.
Abdellaziz, Akila; Papuchon, Jennifer; Khaled, Safia; Ouerdane, Dalila; Fleury, Hervé; Recordon-Pinson, Patricia
2016-04-01
Since 2008, no data on HIV diversity or the transmission rate of HIV resistance mutations in naive patients have been presented for Algeria, a country of MENA region. Between 2013 and 2014, we studied 152 samples including 89 naive patients. The current study describes the change in HIV diversity in Algeria with the predominance of CRF06_cpx and the huge increase of transmitted HIV resistance, which now reaches 15%.
Murugesan, Latha; Kucerova, Zuzana; Knabel, Stephen J; LaBorde, Luke F
2015-11-01
A longitudinal study was conducted to determine the prevalence of Listeria spp. in a commercial fresh mushroom slicing and packaging environment. Samples were collected at three different sampling periods within a 13-month time interval. Of the 255 environmental samples collected, 18.8% tested positive for L. monocytogenes, 4.3% for L. innocua, and 2.0% for L. grayi. L. monocytogenes was most often found on wet floors within the washing and slicing and packaging areas. Each of the 171 L. monocytogenes isolates found in the environment could be placed into one of three different serotypes; 1/2c was predominant (93.6%), followed by 1/2b (3.5%) and 1/2a (2.9%). Of 58 isolates subtyped using multi-virulence-locus sequence typing, all 1/2c isolates were identified as virulence type (VT) 11 (VT11), all 1/2b isolates were VT105, and 1/2a isolates were either VT107 or VT56. VT11 was designated as the predominant and persistent clone in the environment because it was isolated repeatedly at numerous locations throughout the study. The overall predominance and persistence of VT11 indicates that it likely colonized the mushroom processing environment. Areas adjacent to the trench drain in the washing and slicing area and a floor crack in the packaging area may represent primary harborage sites (reservoirs) for VT11. Improvements made to sanitation procedures by company management after period 2 coincided with a significant (P ≤ 0.001) reduction in the prevalence of L. monocytogenes from 17.8% in period 1 and 30.7% in period 2 to 8.5% in period 3. This suggests that targeted cleaning and sanitizing procedures can be effective in minimizing the occurrence of L. monocytogenes contamination in processing facilities. Additional research is needed to understand why VT11 was predominant and persistent in the mushroom processing environment.
The predominant leadership style in a nurse group which frequent a post-graduation courses
Brandão Chaves, Enaura Helena; S. Souto De Moura, Gisela
2008-01-01
This study identify the leadership style is adopt for nurses which frequent Post-Graduation Courses offer by Schools of Nursing of Metropolitan region of Porto Alegre, Brazil. The data collection used an instrument proposed by David R. Frew was used in a sample of 184 nurses. The instrument classify the leadership in five styles: very autocratic, autocratic moderate mixed, democratic moderate and very democratic. The results shows the predominant utilization of the mixed style (83,15%) follow...
Why cholesterol should be found predominantly in the cytoplasmic leaf of the plasma membrane
Giang, Ha
2014-01-01
In the mammalian plasma membrane, cholesterol can translocate rapidly between the exoplasmic and cytoplasmic leaves, and is found predominantly in the latter. We hypothesize that it is drawn to the inner leaf to reduce the bending free energy of the membrane caused by the presence there of phosphatidylethanolamine. Incorporating this mechanism into a model free energy for the bilayer, we calculate that approximately two thirds of the total cholesterol should be in the inner leaf.
Glickstein, Lisa; Moore, Brian; Bledsoe, Tara; Damle, Nitin; Sikand, Vijay; Steere, Allen C
2003-10-01
In a study of cytokine production ex vivo by Borrelia burgdorferi-stimulated peripheral blood mononuclear cells from 27 patients with culture-positive erythema migrans, production of inflammatory cytokines predominated, particularly gamma interferon and, to a lesser degree, tumor necrosis factor alpha. In contrast, with the exception of interleukin-13, anti-inflammatory cytokine production was negligible. Thus, B. burgdorferi antigens in early Lyme disease often induce a strong inflammatory response.
Energy Technology Data Exchange (ETDEWEB)
Kim, Ja Young; Kim, Ah Hyun; Moon, Hee Jung; Kim, Eun Kyung; Kwak, Jin Young [Yonsei University College of Medicine, Seoul (Korea, Republic of); Choi, Jun Jeong [Wonju College of Medicine, Wonju (Korea, Republic of); Kim, Myung Hyun [Gangnam MizMedi Hospital, Seoul (Korea, Republic of)
2012-03-15
Most medullary thyroid carcinomas show suspicious malignant features such as hypoechogenicity, a spiculated margin and/or intranodular calcifications, which are well known features of papillary carcinoma. We report here on a case of medullary carcinoma that was seen as a predominantly cystic thyroid mass on ultrasonography. This type of case is not common in the literature and we discuss the way to diagnose a medullary thyroid carcinoma
Institute of Scientific and Technical Information of China (English)
Anna; Mrzljak; Slavko; Gasparov; Ika; Kardum-Skelin; Vesna; Colic-Cvrlje; Slobodanka; Ostojic; Kolonic
2010-01-01
Febrile cholestatic liver disease is an extremely unusual presentation of Hodgkin lymphoma(HL).The liver biopsy of a 40-year-old man with febrile episodes and cholestatic laboratory pattern disclosed an uncommon subtype of HL,a nodular lymphocyte-predominant HL(NLPHL).Liver involvement in the early stage of the usually indolent NLPHL's clinical course suggests an aggressiveness and unfavorable outcome.Emphasizing a liver biopsy early in the diagnostic algorithm enables accurate diagnosis and appropriate tre...
Predominant pathogen competition and core microbiota divergence in chronic airway infection.
Rogers, Geraint B; van der Gast, Christopher J; Serisier, David J
2015-01-01
Chronic bacterial lung infections associated with non-cystic fibrosis bronchiectasis represent a substantial and growing health-care burden. Where Pseudomonas aeruginosa is the numerically dominant species within these infections, prognosis is significantly worse. However, in many individuals, Haemophilus influenzae predominates, a scenario associated with less severe disease. The mechanisms that determine which pathogen is most abundant are not known. We hypothesised that the distribution of H. influenzae and P. aeruginosa would be consistent with strong interspecific competition effects. Further, we hypothesised that where P. aeruginosa is predominant, it is associated with a distinct 'accessory microbiota' that reflects a significant interaction between this pathogen and the wider bacterial community. To test these hypotheses, we analysed 16S rRNA gene pyrosequencing data generated previously from 60 adult bronchiectasis patients, whose airway microbiota was dominated by either P. aeruginosa or H. influenzae. The relative abundances of the two dominant species in their respective groups were not significantly different, and when present in the opposite pathogen group the two species were found to be in very low abundance, if at all. These findings are consistent with strong competition effects, moving towards competitive exclusion. Ordination analysis indicated that the distribution of the core microbiota associated with each pathogen, readjusted after removal of the dominant species, was significantly divergent (analysis of similarity (ANOSIM), R=0.07, P=0.019). Taken together, these findings suggest that both interspecific competition and also direct and/or indirect interactions between the predominant species and the wider bacterial community may contribute to the predominance of P. aeruginosa in a subset of bronchiectasis lung infections.
Refilming with depth-inferred videos.
Zhang, Guofeng; Dong, Zilong; Jia, Jiaya; Wan, Liang; Wong, Tien-Tsin; Bao, Hujun
2009-01-01
Compared to still image editing, content-based video editing faces the additional challenges of maintaining the spatiotemporal consistency with respect to geometry. This brings up difficulties of seamlessly modifying video content, for instance, inserting or removing an object. In this paper, we present a new video editing system for creating spatiotemporally consistent and visually appealing refilming effects. Unlike the typical filming practice, our system requires no labor-intensive construction of 3D models/surfaces mimicking the real scene. Instead, it is based on an unsupervised inference of view-dependent depth maps for all video frames. We provide interactive tools requiring only a small amount of user input to perform elementary video content editing, such as separating video layers, completing background scene, and extracting moving objects. These tools can be utilized to produce a variety of visual effects in our system, including but not limited to video composition, "predator" effect, bullet-time, depth-of-field, and fog synthesis. Some of the effects can be achieved in real time.
Functional network inference of the suprachiasmatic nucleus.
Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel; St John, Peter C; Wang, Thomas J; Bales, Benjamin B; Doyle, Francis J; Herzog, Erik D; Petzold, Linda R
2016-04-19
In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure.
Primate diversification inferred from phylogenies and fossils.
Herrera, James P
2017-09-14
Biodiversity arises from the balance between speciation and extinction. Fossils record the origins and disappearance of organisms, and the branching patterns of molecular phylogenies allow estimation of speciation and extinction rates, but the patterns of diversification are frequently incongruent between these two data sources. I tested two hypotheses about the diversification of primates based on ∼600 fossil species and 90% complete phylogenies of living species: 1) diversification rates increased through time; 2) a significant extinction event occurred in the Oligocene. Consistent with the first hypothesis, analyses of phylogenies consistently supported increasing speciation rates and negligible extinction rates. In contrast, fossils showed that while speciation rates increased, speciation and extinction rates tended to be nearly equal, resulting in zero net diversification. Partially supporting the second hypothesis, the fossil data recorded a clear pattern of diversity decline in the Oligocene, although diversification rates were near zero. The phylogeny supported increased extinction ∼34 Ma, but also elevated extinction ∼10 Ma, coinciding with diversity declines in some fossil clades. The results demonstrated that estimates of speciation and extinction ignoring fossils are insufficient to infer diversification and information on extinct lineages should be incorporated into phylogenetic analyses. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Probabilistic learning and inference in schizophrenia
Averbeck, Bruno B.; Evans, Simon; Chouhan, Viraj; Bristow, Eleanor; Shergill, Sukhwinder S.
2010-01-01
Patients with schizophrenia make decisions on the basis of less evidence when required to collect information to make an inference, a behavior often called jumping to conclusions. The underlying basis for this behaviour remains controversial. We examined the cognitive processes underpinning this finding by testing subjects on the beads task, which has been used previously to elicit jumping to conclusions behaviour, and a stochastic sequence learning task, with a similar decision theoretic structure. During the sequence learning task, subjects had to learn a sequence of button presses, while receiving noisy feedback on their choices. We fit a Bayesian decision making model to the sequence task and compared model parameters to the choice behavior in the beads task in both patients and healthy subjects. We found that patients did show a jumping to conclusions style; and those who picked early in the beads task tended to learn less from positive feedback in the sequence task. This favours the likelihood of patients selecting early because they have a low threshold for making decisions, and that they make choices on the basis of relatively little evidence. PMID:20810252
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Logical inference techniques for loop parallelization
Oancea, Cosmin E.
2012-01-01
This paper presents a fully automatic approach to loop parallelization that integrates the use of static and run-time analysis and thus overcomes many known difficulties such as nonlinear and indirect array indexing and complex control flow. Our hybrid analysis framework validates the parallelization transformation by verifying the independence of the loop\\'s memory references. To this end it represents array references using the USR (uniform set representation) language and expresses the independence condition as an equation, S = Ø, where S is a set expression representing array indexes. Using a language instead of an array-abstraction representation for S results in a smaller number of conservative approximations but exhibits a potentially-high runtime cost. To alleviate this cost we introduce a language translation F from the USR set-expression language to an equally rich language of predicates (F(S) ⇒ S = Ø). Loop parallelization is then validated using a novel logic inference algorithm that factorizes the obtained complex predicates (F(S)) into a sequence of sufficient-independence conditions that are evaluated first statically and, when needed, dynamically, in increasing order of their estimated complexities. We evaluate our automated solution on 26 benchmarks from PERFECTCLUB and SPEC suites and show that our approach is effective in parallelizing large, complex loops and obtains much better full program speedups than the Intel and IBM Fortran compilers. Copyright © 2012 ACM.