Quantitative inference of dynamic regulatory pathways via microarray data
Directory of Open Access Journals (Sweden)
Chen Bor-Sen
2005-03-01
Full Text Available Abstract Background The cellular signaling pathway (network is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach. Results In this study, a second-order dynamic model is developed to describe the regulatory mechanism of a target gene from the upstream causality point of view. From the expression profile and dynamic model of a target gene, we can estimate its upstream regulatory function. According to this upstream regulatory function, we would deduce the upstream regulatory genes with their regulatory abilities and activation delays, and then link up a regulatory pathway. Iteratively, these regulatory genes are considered as target genes to trace back their upstream regulatory genes. Then we could construct the regulatory pathway (or network to the genome wide. In short, we can infer the genetic regulatory pathways from gene-expression profiles quantitatively, which can confirm some doubted paths or seek some unknown paths in a regulatory pathway (network. Finally, the proposed approach is validated by randomly reshuffling the time order of microarray data. Conclusion We focus our algorithm on the inference of regulatory abilities of the identified causal genes, and how much delay before they regulate the downstream genes. With this information, a regulatory pathway would be built up using microarray data. In the present study, two signaling pathways, i.e. circadian regulatory
Michel, L; Reygagne, P; Benech, P; Jean-Louis, F; Scalvino, S; Ly Ka So, S; Hamidou, Z; Bianovici, S; Pouch, J; Ducos, B; Bonnet, M; Bensussan, A; Patatian, A; Lati, E; Wdzieczak-Bakala, J; Choulot, J-C; Loing, E; Hocquaux, M
2017-11-01
Male androgenetic alopecia (AGA) is the most common form of hair loss in men. It is characterized by a distinct pattern of progressive hair loss starting from the frontal area and the vertex of the scalp. Although several genetic risk loci have been identified, relevant genes for AGA remain to be defined. To identify biomarkers associated with AGA. Molecular biomarkers associated with premature AGA were identified through gene expression analysis using cDNA generated from scalp vertex biopsies of hairless or bald men with premature AGA, and healthy volunteers. This monocentric study reveals that genes encoding mast cell granule enzymes, inflammatory mediators and immunoglobulin-associated immune mediators were significantly overexpressed in AGA. In contrast, underexpressed genes appear to be associated with the Wnt/β-catenin and bone morphogenic protein/transforming growth factor-β signalling pathways. Although involvement of these pathways in hair follicle regeneration is well described, functional interpretation of the transcriptomic data highlights different events that account for their inhibition. In particular, one of these events depends on the dysregulated expression of proopiomelanocortin, as confirmed by polymerase chain reaction and immunohistochemistry. In addition, lower expression of CYP27B1 in patients with AGA supports the notion that changes in vitamin D metabolism contributes to hair loss. This study provides compelling evidence for distinct molecular events contributing to alopecia that may pave the way for new therapeutic approaches. © 2017 British Association of Dermatologists.
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...... (MCMC) techniques. Due to space limitations the focus is on spatial point processes....
Inferring the functional effect of gene expression changes in signaling pathways
Sebastián-León, Patricia; Carbonell, José; Salavert, Francisco; Sanchez, Rubén; Medina, Ignacio; Dopazo, Joaquín
2013-01-01
Signaling pathways constitute a valuable source of information that allows interpreting the way in which alterations in gene activities affect to particular cell functionalities. There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case–control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at: http://pathiways.babelomics.org/. PMID:23748960
DEFF Research Database (Denmark)
Møller, Jesper
(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the ......(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.......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...
Maeda, Hiroshi; Shasany, Ajit K; Schnepp, Jennifer; Orlova, Irina; Taguchi, Goro; Cooper, Bruce R; Rhodes, David; Pichersky, Eran; Dudareva, Natalia
2010-03-01
l-Phe, a protein building block and precursor of numerous phenolic compounds, is synthesized from prephenate via an arogenate and/or phenylpyruvate route in which arogenate dehydratase (ADT) or prephenate dehydratase, respectively, plays a key role. Here, we used Petunia hybrida flowers, which are rich in Phe-derived volatiles, to determine the biosynthetic routes involved in Phe formation in planta. Of the three identified petunia ADTs, expression of ADT1 was the highest in petunia petals and positively correlated with endogenous Phe levels throughout flower development. ADT1 showed strict substrate specificity toward arogenate, although with the lowest catalytic efficiency among the three ADTs. ADT1 suppression via RNA interference in petunia petals significantly reduced ADT activity, levels of Phe, and downstream phenylpropanoid/benzenoid volatiles. Unexpectedly, arogenate levels were unaltered, while shikimate and Trp levels were decreased in transgenic petals. Stable isotope labeling experiments showed that ADT1 suppression led to downregulation of carbon flux toward shikimic acid. However, an exogenous supply of shikimate bypassed this negative regulation and resulted in elevated arogenate accumulation. Feeding with shikimate also led to prephenate and phenylpyruvate accumulation and a partial recovery of the reduced Phe level in transgenic petals, suggesting that the phenylpyruvate route can also operate in planta. These results provide genetic evidence that Phe is synthesized predominantly via arogenate in petunia petals and uncover a novel posttranscriptional regulation of the shikimate pathway.
Optimal structural inference of signaling pathways from unordered and overlapping gene sets.
Acharya, Lipi R; Judeh, Thair; Wang, Guangdi; Zhu, Dongxiao
2012-02-15
A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures. We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and
Inference of miRNA targets using evolutionary conservation and pathway analysis
Directory of Open Access Journals (Sweden)
van Nimwegen Erik
2007-03-01
Full Text Available Abstract Background MicroRNAs have emerged as important regulatory genes in a variety of cellular processes and, in recent years, hundreds of such genes have been discovered in animals. In contrast, functional annotations are available only for a very small fraction of these miRNAs, and even in these cases only partially. Results We developed a general Bayesian method for the inference of miRNA target sites, in which, for each miRNA, we explicitly model the evolution of orthologous target sites in a set of related species. Using this method we predict target sites for all known miRNAs in flies, worms, fish, and mammals. By comparing our predictions in fly with a reference set of experimentally tested miRNA-mRNA interactions we show that our general method performs at least as well as the most accurate methods available to date, including ones specifically tailored for target prediction in fly. An important novel feature of our model is that it explicitly infers the phylogenetic distribution of functional target sites, independently for each miRNA. This allows us to infer species-specific and clade-specific miRNA targeting. We also show that, in long human 3' UTRs, miRNA target sites occur preferentially near the start and near the end of the 3' UTR. To characterize miRNA function beyond the predicted lists of targets we further present a method to infer significant associations between the sets of targets predicted for individual miRNAs and specific biochemical pathways, in particular those of the KEGG pathway database. We show that this approach retrieves several known functional miRNA-mRNA associations, and predicts novel functions for known miRNAs in cell growth and in development. Conclusion We have presented a Bayesian target prediction algorithm without any tunable parameters, that can be applied to sequences from any clade of species. The algorithm automatically infers the phylogenetic distribution of functional sites for each miRNA, and
Large scale statistical inference of signaling pathways from RNAi and microarray data
Directory of Open Access Journals (Sweden)
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.
2018-01-01
Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181
Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways
Directory of Open Access Journals (Sweden)
Sébastien De Landtsheer
2018-05-01
Full Text Available Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.
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.
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
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. © 2016 AlphaMed Press.
Lin, Jo-Fu Lotus; Silva-Pereyra, Juan; Chou, Chih-Che; Lin, Fa-Hsuan
2018-04-11
Variability in neuronal response latency has been typically considered caused by random noise. Previous studies of single cells and large neuronal populations have shown that the temporal variability tends to increase along the visual pathway. Inspired by these previous studies, we hypothesized that functional areas at later stages in the visual pathway of face processing would have larger variability in the response latency. To test this hypothesis, we used magnetoencephalographic data collected when subjects were presented with images of human faces. Faces are known to elicit a sequence of activity from the primary visual cortex to the fusiform gyrus. Our results revealed that the fusiform gyrus showed larger variability in the response latency compared to the calcarine fissure. Dynamic and spectral analyses of the latency variability indicated that the response latency in the fusiform gyrus was more variable than in the calcarine fissure between 70 ms and 200 ms after the stimulus onset and between 4 Hz and 40 Hz, respectively. The sequential processing of face information from the calcarine sulcus to the fusiform sulcus was more reliably detected based on sizes of the response variability than instants of the maximal response peaks. With two areas in the ventral visual pathway, we show that the variability in response latency across brain areas can be used to infer the sequence of cortical activity.
Directory of Open Access Journals (Sweden)
Christopher Y Park
2010-11-01
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/.
Directory of Open Access Journals (Sweden)
Huan Wang
2014-05-01
Full Text Available Background: In advanced atherosclerosis, chronic endoplasmic reticulum (ER stress induces foam cells apoptosis and generates inflammatory reactions. Methods: THP-1 macrophage-derived foam cells (FC were incubated with 1 mM 5-aminolevulinic acid (ALA. After ALA mediated sonodynamic therapy (ALA-SDT, apoptosis of FC was assayed by Annexin V-PI staining. Intracellular reactive oxygen species (ROS and mitochondrial membrane potential were detected by staining with CellROX® Green Reagent and jc-1. Pretreatment of FC with N-acetylcysteine (NAC, Z-VAD-FMK or 4-phenylbutyrate (4-PBA, mitochondria apoptotic pathway associated proteins and C/EBP-homologous (CHOP expressions were assayed by wertern blotting. Results: Burst of apoptosis of FC was observed at 5-hour after ALA-SDT with 6-hour incubation of ALA and 0.4 W/cm2 ultrasound. After ALA-SDT, intracellular ROS level increased and mitochondrial membrane potential collapsed. Translocations of cytochrome c from mitochondria into cytosol and Bax from cytosol into mitochondria, cleaved caspase 9, cleaved caspase 3, upregulation of CHOP, as well as downregulation of Bcl-2 after ALA-SDT were detected, which could be suppressed by NAC. Activation of mitochondria-caspase pathway could not be inhibited by 4-PBA. Cleaved caspase 9 and caspase 3 as well as apoptosis induced by ALA-SDT could be inhibited by Z-VAD-FMK. Conclusion: The mitochondria-caspase pathway is predominant in the apoptosis of FC induced by ALA-SDT though ER stress participates in.
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
International Nuclear Information System (INIS)
Noda, Taichi; Takahashi, Akihisa; Kondo, Natsuko; Mori, Eiichiro; Okamoto, Noritomo; Nakagawa, Yosuke; Ohnishi, Ken; Zdzienicka, Malgorzata Z.; Thompson, Larry H.; Helleday, Thomas; Asada, Hideo
2011-01-01
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 -/- , FANCC -/- , FANCA -/- C -/- , FANCD2 -/- 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 γH2AX-staining assay. Although the sensitivity of FANCA -/- , FANCC -/- and FANCA -/- C -/- cells to formaldehyde was comparable to that of proficient cells, FANCD1mt, FANCGmt and FANCD2 -/- 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, γH2AX 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: → We examined to clarify the repair pathways of formaldehyde-induced DNA damage. Formaldehyde induces DNA double strand breaks (DSBs). → DSBs are repaired through the Fanconi anemia (FA) repair pathway. → This pathway is independent of the FA nuclear core complex. → We also found that homologous recombination repair was induced by formaldehyde.
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.
Hi-Jack: a novel computational framework for pathway-based inference of host–pathogen interactions
Kleftogiannis, Dimitrios A.; Wong, Limsoon; Archer, John A.C.; Kalnis, Panos
2015-01-01
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
Caticha, Ariel
2011-03-01
In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.
Kroese, A.H.; van der Meulen, E.A.; Poortema, Klaas; Schaafsma, W.
1995-01-01
The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is
Caticha, Ariel
2010-01-01
In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEn...
Aggelopoulos, Nikolaos C
2015-08-01
Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. Copyright © 2015 Elsevier Ltd. All rights reserved.
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
Marine plastic pollution in waters around Australia: characteristics, concentrations, and pathways
Reisser, Julia Wiener; Shaw, Jeremy; Wilcox, Chris; Hardesty, Britta Denise; Proietti, Maíra Carneiro; 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 lengt...
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
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.
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....
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.
Bayesian statistical inference
Directory of Open Access Journals (Sweden)
Bruno De Finetti
2017-04-01
Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.
Geometric statistical inference
International Nuclear Information System (INIS)
Periwal, Vipul
1999-01-01
A reparametrization-covariant formulation of the inverse problem of probability is explicitly solved for finite sample sizes. The inferred distribution is explicitly continuous for finite sample size. A geometric solution of the statistical inference problem in higher dimensions is outlined
Bailer-Jones, Coryn A. L.
2017-04-01
Preface; 1. Probability basics; 2. Estimation and uncertainty; 3. Statistical models and inference; 4. Linear models, least squares, and maximum likelihood; 5. Parameter estimation: single parameter; 6. Parameter estimation: multiple parameters; 7. Approximating distributions; 8. Monte Carlo methods for inference; 9. Parameter estimation: Markov chain Monte Carlo; 10. Frequentist hypothesis testing; 11. Model comparison; 12. Dealing with more complicated problems; References; Index.
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
Logical inference and evaluation
International Nuclear Information System (INIS)
Perey, F.G.
1981-01-01
Most methodologies of evaluation currently used are based upon the theory of statistical inference. It is generally perceived that this theory is not capable of dealing satisfactorily with what are called systematic errors. Theories of logical inference should be capable of treating all of the information available, including that not involving frequency data. A theory of logical inference is presented as an extension of deductive logic via the concept of plausibility and the application of group theory. Some conclusions, based upon the application of this theory to evaluation of data, are also given
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.
On quantum statistical inference
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, developments in the theory of quantum measurements have
2018-02-15
expressed a variety of inference techniques on discrete and continuous distributions: exact inference, importance sampling, Metropolis-Hastings (MH...without redoing any math or rewriting any code. And although our main goal is composable reuse, our performance is also good because we can use...control paths. • The Hakaru language can express mixtures of discrete and continuous distributions, but the current disintegration transformation
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.
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.
Type Inference with Inequalities
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff
1991-01-01
of (monotonic) inequalities on the types of variables and expressions. A general result about systems of inequalities over semilattices yields a solvable form. We distinguish between deciding typability (the existence of solutions) and type inference (the computation of a minimal solution). In our case, both......Type inference can be phrased as constraint-solving over types. We consider an implicitly typed language equipped with recursive types, multiple inheritance, 1st order parametric polymorphism, and assignments. Type correctness is expressed as satisfiability of a possibly infinite collection...
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…
Hybrid Optical Inference Machines
1991-09-27
with labels. Now, events. a set of facts cal be generated in the dyadic form "u, R 1,2" Eichmann and Caulfield (19] consider the same type of and can...these enceding-schemes. These architectures are-based pri- 19. G. Eichmann and H. J. Caulfield, "Optical Learning (Inference)marily on optical inner
Inference rule and problem solving
Energy Technology Data Exchange (ETDEWEB)
Goto, S
1982-04-01
Intelligent information processing signifies an opportunity of having man's intellectual activity executed on the computer, in which inference, in place of ordinary calculation, is used as the basic operational mechanism for such an information processing. Many inference rules are derived from syllogisms in formal logic. The problem of programming this inference function is referred to as a problem solving. Although logically inference and problem-solving are in close relation, the calculation ability of current computers is on a low level for inferring. For clarifying the relation between inference and computers, nonmonotonic logic has been considered. The paper deals with the above topics. 16 references.
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.
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......, the complexity has been dramatically improved, from exponential time to low polynomial time. The implementation uses the techniques of incremental graph construction and constraint template instantiation to avoid representing intermediate results, doing superfluous work, and recomputing type information....... 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...
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.
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.
Active inference and learning.
Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni
2016-09-01
This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Learning Convex Inference of Marginals
Domke, Justin
2012-01-01
Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process or the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main ...
Probabilistic inductive inference: a survey
Ambainis, Andris
2001-01-01
Inductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since this simple paradigm has produced the most interesting (and most complex) results.
Multimodel inference and adaptive management
Rehme, S.E.; Powell, L.A.; Allen, Craig 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.
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
Emotional inferences by pragmatics
Iza-Miqueleiz, Mauricio
2017-01-01
It has for long been taken for granted that, along the course of reading a text, world knowledge is often required in order to establish coherent links between sentences (McKoon & Ratcliff 1992, Iza & Ezquerro 2000). The content grasped from a text turns out to be strongly dependent upon the reader’s additional knowledge that allows a coherent interpretation of the text as a whole. The world knowledge directing the inference may be of distinctive nature. Gygax et al. (2007) showed that m...
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
2017-04-20
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.
Feature Inference Learning and Eyetracking
Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.
2009-01-01
Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…
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...
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.
Social Inference Through Technology
Oulasvirta, Antti
Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.
Reinforcement and inference in cross-situational word learning.
Tilles, Paulo F C; Fontanari, José F
2013-01-01
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
Older women play predominant role in building social ties and ...
International Development Research Centre (IDRC) Digital Library (Canada)
2016-08-03
Aug 3, 2016 ... Older women play predominant role in building social ties and preventing ... brief demonstrates their contribution to building social cohesion and driving ... From learning to policy-oriented research: Lessons from South Africa's ...
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.
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
Statistical inference for stochastic processes
National Research Council Canada - National Science Library
Basawa, Ishwar V; Prakasa Rao, B. L. S
1980-01-01
The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects...
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.
Impact of noise on molecular network inference.
Directory of Open Access Journals (Sweden)
Radhakrishnan Nagarajan
Full Text Available Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a common-effect, (b three-chain, and (c coherent type-I feed-forward loop (FFL are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS and Incremental Association Markov Blanket (IAMB that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the
Optimal inference with suboptimal models: Addiction and active Bayesian inference
Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl
2015-01-01
When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321
Interactive Instruction in Bayesian Inference
DEFF Research Database (Denmark)
Khan, Azam; Breslav, Simon; Hornbæk, Kasper
2018-01-01
An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....
On Maximum Entropy and Inference
Directory of Open Access Journals (Sweden)
Luigi Gresele
2017-11-01
Full Text Available Maximum entropy is a powerful concept that entails a sharp separation between relevant and irrelevant variables. It is typically invoked in inference, once an assumption is made on what the relevant variables are, in order to estimate a model from data, that affords predictions on all other (dependent variables. Conversely, maximum entropy can be invoked to retrieve the relevant variables (sufficient statistics directly from the data, once a model is identified by Bayesian model selection. We explore this approach in the case of spin models with interactions of arbitrary order, and we discuss how relevant interactions can be inferred. In this perspective, the dimensionality of the inference problem is not set by the number of parameters in the model, but by the frequency distribution of the data. We illustrate the method showing its ability to recover the correct model in a few prototype cases and discuss its application on a real dataset.
Predominant Role of Cytosolic Phospholipase A2α in Dioxin-induced Neonatal Hydronephrosis in Mice
Yoshioka, Wataru; Kawaguchi, Tatsuya; Fujisawa, Nozomi; Aida-Yasuoka, Keiko; Shimizu, Takao; Matsumura, Fumio; Tohyama, Chiharu
2014-01-01
Hydronephrosis is a common disease characterized by dilation of the renal pelvis and calices, resulting in loss of kidney function in the most severe cases. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) induces nonobstructive hydronephrosis in mouse neonates through upregulation of prostaglandin E2 (PGE2) synthesis pathway consisting of cyclooxygenase-2 (COX-2) and microsomal prostaglandin E synthase-1 (mPGES-1) by a yet unknown mechanism. We here studied possible involvement of cytosolic phospholipase A2α (cPLA2α) in this mechanism. To this end, we used a cPLA2α-null mouse model and found that cPLA2α has a significant role in the upregulation of the PGE2 synthesis pathway through a noncanonical pathway of aryl hydrocarbon receptor. This study is the first to demonstrate the predominant role of cPLA2α in hydronephrosis. Elucidation of the pathway leading to the onset of hydronephrosis using the TCDD-exposed mouse model will deepen our understanding of the molecular basis of nonobstructive hydronephrosis in humans. PMID:24509627
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.
Problem solving and inference mechanisms
Energy Technology Data Exchange (ETDEWEB)
Furukawa, K; Nakajima, R; Yonezawa, A; Goto, S; Aoyama, A
1982-01-01
The heart of the fifth generation computer will be powerful mechanisms for problem solving and inference. A deduction-oriented language is to be designed, which will form the core of the whole computing system. The language is based on predicate logic with the extended features of structuring facilities, meta structures and relational data base interfaces. Parallel computation mechanisms and specialized hardware architectures are being investigated to make possible efficient realization of the language features. The project includes research into an intelligent programming system, a knowledge representation language and system, and a meta inference system to be built on the core. 30 references.
Communication Apprehension among Black Students on Predominantly White Campuses.
Byrd, Marquita L.; Sims, Anntarie L.
1987-01-01
A study of 114 Black undergraduates in two predominantly White midwestern universities demonstrates that communication apprehension (CA) among Blacks appears to be an audience-based phenomenon. Black females scored lower than Black males on the Personal Report of Communication Apprehension-24 (PRCA-24). The higher the CA score, the higher the…
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...
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....
Mixed normal inference on multicointegration
Boswijk, H.P.
2009-01-01
Asymptotic likelihood analysis of cointegration in I(2) models, see Johansen (1997, 2006), Boswijk (2000) and Paruolo (2000), has shown that inference on most parameters is mixed normal, implying hypothesis test statistics with an asymptotic 2 null distribution. The asymptotic distribution of the
Statistical inference and Aristotle's Rhetoric.
Macdonald, Ranald R
2004-11-01
Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical.
Lee, Elizabeth C.; Kelly, Michael R.; Ochocki, Brad M.; Akinwumi, Segun M.; Hamre, Karen E. S.; Tien, Joseph H.; Eisenberg, Marisa C.
2016-01-01
Mathematical models of cholera and waterborne disease vary widely in their structures, in terms of transmission pathways, loss of immunity, and other features. These differences may yield different predictions and parameter estimates from the same data. Given the increasing use of models to inform public health decision-making, it is important to assess distinguishability (whether models can be distinguished based on fit to data) and inference robustness (whether model inferences are robust t...
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 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...
Statistical inference an integrated approach
Migon, Helio S; Louzada, Francisco
2014-01-01
Introduction Information The concept of probability Assessing subjective probabilities An example Linear algebra and probability Notation Outline of the bookElements of Inference Common statistical modelsLikelihood-based functions Bayes theorem Exchangeability Sufficiency and exponential family Parameter elimination Prior Distribution Entirely subjective specification Specification through functional forms Conjugacy with the exponential family Non-informative priors Hierarchical priors Estimation Introduction to decision theoryBayesian point estimation Classical point estimation Empirical Bayes estimation Comparison of estimators Interval estimation Estimation in the Normal model Approximating Methods The general problem of inference Optimization techniquesAsymptotic theory Other analytical approximations Numerical integration methods Simulation methods Hypothesis Testing Introduction Classical hypothesis testingBayesian hypothesis testing Hypothesis testing and confidence intervalsAsymptotic tests Prediction...
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.
System Support for Forensic Inference
Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan
Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.
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.
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.
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...
Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis.
Meng, Yu-Xiu; Liu, Quan-Hong; Chen, Deng-Hong; Meng, Ying
2017-06-01
Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RPpathways in neonatal sepsis. By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RPpathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis. Copyright © 2017
Statistical inference a short course
Panik, Michael J
2012-01-01
A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causal
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...
Nonparametric predictive inference in reliability
International Nuclear Information System (INIS)
Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.
2002-01-01
We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere
Variational inference & deep learning : A new synthesis
Kingma, D.P.
2017-01-01
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.
Variational inference & deep learning: A new synthesis
Kingma, D.P.
2017-01-01
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.
Continuous Integrated Invariant Inference, Phase I
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...
Variations on Bayesian Prediction and Inference
2016-05-09
inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle
Adaptive Inference on General Graphical Models
Acar, Umut A.; Ihler, Alexander T.; Mettu, Ramgopal; Sumer, Ozgur
2012-01-01
Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional ...
Chan, Monica; Lye, David; Win, Mar Kyaw; Chow, Angela; Barkham, Tim
2014-09-01
To describe the clinical features, treatments, outcomes, and subtype prevalence of cryptococcosis in Singapore. All patients with laboratory confirmed cryptococcal infections admitted from 1999 to 2007 to a teaching hospital in Singapore were reviewed retrospectively. Identification and molecular types of Cryptococcus neoformans variants and Cryptococcus gattii were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Serotypes were inferred with a multiplex PCR method. Of 62 patients with cryptococcosis, C. neoformans var. grubii was the predominant subtype (in 95%), affecting mainly immunocompromised hosts (91%) with HIV infection (80%). Patients with HIV were younger (median age 36.5 vs. 49.5 years, p=0.006) and less likely to present with an altered mental status (14% vs. 50%, p=0.013). In contrast, delayed treatment (median 7 days vs. 2 days, p=0.03), pulmonary involvement (58% vs. 14%, p=0.03), and initial treatment with fluconazole (25% vs. 2%, p=0.02) were more common in HIV-negative patients. C. gattii was uncommon, affecting only three patients, all of whom were immunocompetent and had disseminated disease with pulmonary and neurological involvement. All C. gattii were RFLP type VG II, serotype B and all C. neoformans var. grubii were RFLP type VN I, serotype A, except for one that was RFLP type VN II. C. neoformans var. grubii, subtype VN I, was the predominant subtype in Singapore, infecting younger, mainly immunocompromised hosts with HIV. C. gattii was uncommon, causing pulmonary manifestations in older, immunocompetent patients and were RFLP type VG II. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.
Targeted therapies for diarrhea-predominant irritable bowel syndrome
Directory of Open Access Journals (Sweden)
Olden KW
2012-05-01
Full Text Available Kevin W OldenDepartment of Medicine, St Joseph's Hospital and Medical Center, Phoenix, AZ, USAAbstract: Irritable bowel syndrome (IBS causes gastrointestinal symptoms such as abdominal pain, bloating, and bowel pattern abnormalities, which compromise patients' daily functioning. Common therapies address one or two IBS symptoms, while others offer wider symptom control, presumably by targeting pathophysiologic mechanisms of IBS. The aim of this targeted literature review was to capture clinical trial reports of agents receiving the highest recommendation (Grade 1 for treatment of IBS from the 2009 American College of Gastroenterology IBS Task Force, with an emphasis on diarrhea-predominant IBS. Literature searches in PubMed captured articles detailing randomized placebo-controlled trials in IBS/diarrhea-predominant IBS for agents receiving Grade I (strong 2009 American College of Gastroenterology IBS Task Force recommendations: tricyclic antidepressants, nonabsorbable antibiotics, and the 5-HT3 receptor antagonist alosetron. Studies specific for constipation-predominant IBS were excluded. Tricyclic antidepressants appear to improve global IBS symptoms but have variable effects on abdominal pain and uncertain tolerability; effects on stool consistency, frequency, and urgency were not adequately assessed. Nonabsorbable antibiotics show positive effects on global symptoms, abdominal pain, bloating, and stool consistency but may be most efficacious in patients with altered intestinal microbiota. Alosetron improves global symptoms and abdominal pain and normalizes bowel irregularities, including stool frequency, consistency, and fecal urgency. Both the nonabsorbable antibiotic rifaximin and the 5-HT3 receptor antagonist alosetron improve quality of life. Targeted therapies provide more complete relief of IBS symptoms than conventional agents. Familiarization with the quantity and quality of evidence of effectiveness can facilitate more individualized
Targeted therapies for diarrhea-predominant irritable bowel syndrome
Olden, Kevin W
2012-01-01
Irritable bowel syndrome (IBS) causes gastrointestinal symptoms such as abdominal pain, bloating, and bowel pattern abnormalities, which compromise patients’ daily functioning. Common therapies address one or two IBS symptoms, while others offer wider symptom control, presumably by targeting pathophysiologic mechanisms of IBS. The aim of this targeted literature review was to capture clinical trial reports of agents receiving the highest recommendation (Grade 1) for treatment of IBS from the 2009 American College of Gastroenterology IBS Task Force, with an emphasis on diarrhea-predominant IBS. Literature searches in PubMed captured articles detailing randomized placebo-controlled trials in IBS/diarrhea-predominant IBS for agents receiving Grade I (strong) 2009 American College of Gastroenterology IBS Task Force recommendations: tricyclic antidepressants, nonabsorbable antibiotics, and the 5-HT3 receptor antagonist alosetron. Studies specific for constipation-predominant IBS were excluded. Tricyclic antidepressants appear to improve global IBS symptoms but have variable effects on abdominal pain and uncertain tolerability; effects on stool consistency, frequency, and urgency were not adequately assessed. Nonabsorbable antibiotics show positive effects on global symptoms, abdominal pain, bloating, and stool consistency but may be most efficacious in patients with altered intestinal microbiota. Alosetron improves global symptoms and abdominal pain and normalizes bowel irregularities, including stool frequency, consistency, and fecal urgency. Both the nonabsorbable antibiotic rifaximin and the 5-HT3 receptor antagonist alosetron improve quality of life. Targeted therapies provide more complete relief of IBS symptoms than conventional agents. Familiarization with the quantity and quality of evidence of effectiveness can facilitate more individualized treatment plans for patients with this heterogeneous disorder. PMID:22754282
Lymphocyte-predominant Hodgkin disease: a comprehensive overview.
Bose, Sumit; Ganesan, Chitra; Pant, Manish; Lai, Catherine; Tabbara, Imad A
2013-02-01
Lymphocyte-predominant Hodgkin disease is a rare form of Hodgkin lymphoma that is recognized as a separate histopathological entity. This disease tends to have multiple relapses, but has an overall good prognosis. Owing to its rarity, and the prolonged time period between recurrence and transformation events, there is no consensus regarding optimal management. However, the National Comprehensive Cancer Network guidelines indicate that for early stages, appropriate treatment is radiotherapy. Several management options have been reported including observation, radiation, chemotherapy, combined chemoradiotherapy, and anti-CD20 antibody therapy. Salvage therapy remains effective in inducing prolonged remission in patients with relapsed/refractory disease.
DEFF Research Database (Denmark)
Cox, Thomas R; Erler, Janine Terra
2014-01-01
Pathologic organ fibrosis is a condition that can affect all major tissues and is typically ascribed to the excessive accumulation of extracellular matrix components, predominantly collagens. It typically leads to compromise of organ function and subsequent organ failure, and it is estimated...
A Network Inference Workflow Applied to Virulence-Related Processes in Salmonella typhimurium
Energy Technology Data Exchange (ETDEWEB)
Taylor, Ronald C.; Singhal, Mudita; Weller, Jennifer B.; Khoshnevis, Saeed; Shi, Liang; McDermott, Jason E.
2009-04-20
Inference of the structure of mRNA transcriptional regulatory networks, protein regulatory or interaction networks, and protein activation/inactivation-based signal transduction networks are critical tasks in systems biology. In this article we discuss a workflow for the reconstruction of parts of the transcriptional regulatory network of the pathogenic bacterium Salmonella typhimurium based on the information contained in sets of microarray gene expression data now available for that organism, and describe our results obtained by following this workflow. The primary tool is one of the network inference algorithms deployed in the Software Environment for BIological Network Inference (SEBINI). Specifically, we selected the algorithm called Context Likelihood of Relatedness (CLR), which uses the mutual information contained in the gene expression data to infer regulatory connections. The associated analysis pipeline automatically stores the inferred edges from the CLR runs within SEBINI and, upon request, transfers the inferred edges into either Cytoscape or the plug-in Collective Analysis of Biological of Biological Interaction Networks (CABIN) tool for further post-analysis of the inferred regulatory edges. The following article presents the outcome of this workflow, as well as the protocols followed for microarray data collection, data cleansing, and network inference. Our analysis revealed several interesting interactions, functional groups, metabolic pathways, and regulons in S. typhimurium.
Sweller, Naomi; Hayes, Brett K
2010-08-01
Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.
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.
Inferring Molecular Processes Heterogeneity from Transcriptional Data.
Gogolewski, Krzysztof; Wronowska, Weronika; Lech, Agnieszka; Lesyng, Bogdan; Gambin, Anna
2017-01-01
RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs.
Generative inference for cultural evolution.
Kandler, Anne; Powell, Adam
2018-04-05
One of the major challenges in cultural evolution is to understand why and how various forms of social learning are used in human populations, both now and in the past. To date, much of the theoretical work on social learning has been done in isolation of data, and consequently many insights focus on revealing the learning processes or the distributions of cultural variants that are expected to have evolved in human populations. In population genetics, recent methodological advances have allowed a greater understanding of the explicit demographic and/or selection mechanisms that underlie observed allele frequency distributions across the globe, and their change through time. In particular, generative frameworks-often using coalescent-based simulation coupled with approximate Bayesian computation (ABC)-have provided robust inferences on the human past, with no reliance on a priori assumptions of equilibrium. Here, we demonstrate the applicability and utility of generative inference approaches to the field of cultural evolution. The framework advocated here uses observed population-level frequency data directly to establish the likely presence or absence of particular hypothesized learning strategies. In this context, we discuss the problem of equifinality and argue that, in the light of sparse cultural data and the multiplicity of possible social learning processes, the exclusion of those processes inconsistent with the observed data might be the most instructive outcome. Finally, we summarize the findings of generative inference approaches applied to a number of case studies.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'. © 2018 The Author(s).
sick: The Spectroscopic Inference Crank
Casey, Andrew R.
2016-03-01
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
Inferring network structure from cascades
Ghonge, Sushrut; Vural, Dervis Can
2017-07-01
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 offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our 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 several different cascade models.
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
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...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
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...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Inference in hybrid Bayesian networks
International Nuclear Information System (INIS)
Langseth, Helge; Nielsen, Thomas D.; Rumi, Rafael; Salmeron, Antonio
2009-01-01
Since the 1980s, Bayesian networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability techniques (like fault trees and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.
SICK: THE SPECTROSCOPIC INFERENCE CRANK
International Nuclear Information System (INIS)
Casey, Andrew R.
2016-01-01
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
Elder, C.; Xu, X.; Walker, J. C.; Walter Anthony, K. M.; Pohlman, J.; Arp, C. D.; Townsend-Small, A.; Hinkel, K. M.; Czimczik, C. I.
2017-12-01
Lakes in Arctic and Boreal regions are hotspots for atmospheric exchange of the greenhouse gases CO2 and CH4. Thermokarst lakes are a subset of these Northern lakes that may further accelerate climate warming by mobilizing ancient permafrost C (> 11,500 years old) that has been disconnected from the active C cycle for millennia. Northern lakes are thus potentially powerful agents of the permafrost C-climate feedback. While they are critical for projecting the magnitude and timing these feedbacks from the rapidly warming circumpolar region, we lack datasets capturing the diversity of northern lakes, especially regarding their CH4contributions to whole-lake C emissions and their ability to access and mobilize ancient C. We measured the radiocarbon (14C) ages of CH4 and CO2 emitted from 60 understudied lakes and ponds in Arctic and Boreal Alaska during winter and summer to estimate the ages of the C sources yielding these gases. Integrated mean ages for whole-lake emissions were inferred from the 14C-age of dissolved gases sampled beneath seasonal ice. Additionally, we measured concentrations and 14C values of gases emitted by ebullition and diffusion in summer to apportion C emission pathways. Using a multi-sourced mass balance approach, we found that whole-lake CH4 and CO2 emissions were predominantly sourced from relatively young C in most lakes. In Arctic lakes, CH4 originated from 850 14C-year old C on average, whereas dissolved CO2 was sourced from 400 14C-year old C, and represented 99% of total dissolved C flux. Although ancient C had a minimal influence (11% of total emissions), we discovered that lakes in finer-textured aeolian deposits (Yedoma) emitted twice as much ancient C as lakes in sandy regions. In Boreal, yedoma-type lakes, CH4 and CO2 were fueled by significantly older sources, and mass balance results estimated CH4-ebullition to comprise 50-60% of whole-lake CH4 emissions. The mean 14C-age of Boreal emissions was 6,000 14C-years for CH4-C, and 2
Prevalence of the Ancient Wood-Ljungdahl Pathway in a Subseafloor Olivine Community
Smith, A. R.; Mueller, R.; Fisk, M. R.; Mason, O. U.; Popa, R.; Kieft, B.; Colwell, F. S.
2018-05-01
The ancient Wood-Ljungdahl pathway used for biosynthesis and energy generation was found to be the predominant metabolic pathway in a microbial community from olivine grains incubated in the Juan de Fuca subseafloor aquifer.
Acute Infantile Encephalopathy Predominantly Affecting The Frontal Lobes (AIEF).
Raha, Sarbani; Udani, Vrajesh
2012-12-01
Acute Infantile Encephalopathy Predominantly Affecting the Frontal Lobes (AIEF) is a relatively recent described entity. This article includes case reports of two patients who had bifrontal involvement during acute febrile encephalopathy. Case 1 describes a 1-y-old boy who presented with hyperpyrexia and dialeptic seizures. Imaging revealed significant bilateral frontal lobe involvement while serology proved presence of Influenza B infection. Over a period of one wk, he recovered with significant cognitive decline and perseveratory behavior. Another 6-y-old boy presented with language and behavioral problems suggestive of frontal dysfunction after recovering from prolonged impairment of consciousness following a convulsive status epilepticus. Bilateral superior frontal lesions with gyral swelling was evident on neuroimaging. These cases are among the very few cases of AIEF described in recent literature and the article also reviews this unique subtype of acute encephalopathy.
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.
Nonalbumin proteinuria predominates in biopsy-proven tenofovir nephrotoxicity.
Sise, Meghan E; Hirsch, Jamie S; Canetta, Pietro A; Herlitz, Leal; Mohan, Sumit
2015-05-15
Tenofovir disoproxil fumarate (TDF) nephrotoxicity is characterized by proximal renal tubular injury and dysmorphic mitochondria resulting in proteinuria, orthoglycemic glycosuria, and other markers of proximal tubular dysfunction. The objective of this study was to determine the pattern of proteinuria in patients with biopsy-proven TDF nephrotoxicity. Retrospective chart review. Patients with biopsy-proven TDF nephrotoxicity were identified and their medical charts and biopsy reports were reviewed. Comparison was made with HIV-infected patients not on TDF who underwent kidney biopsy. We identified 43 biopsy-proven cases of TDF nephrotoxicity; mean age 54.7 ± 0.4 years, 53% men, 42% whites. Thirty-seven cases reported proteinuria by dipstick of which only 60% had at least 2+ proteinuria. Twenty-seven patients had urine protein quantified by either 24-h collection or spot urine protein-to-creatinine ratio; median proteinuria was 1742 mg/day [interquartile range (IQR) 1200-2000 mg] and 1667 mg/g creatinine (IQR 851-1967 mg/g), respectively. Ten patients had concurrent urinary albumin measured, with a median 236 mg/g creatinine (IQR 137-343 mg/g). The mean urine albumin-to-urine protein ratio (uAPR) was 0.17 (IQR 0.14-0.19), confirming that TDF nephrotoxicity is primarily associated with nonalbumin proteinuria. Control cases had a uAPR of 0.65 (IQR 0.55-0.79) P < 0.001. Histopathology showed the predominance of proximal tubular injury with characteristic mitochondrial abnormalities. In the largest published cohort of patients with biopsy-proven TDF nephrotoxicity, we show that low uAPR is a reliable feature of this disease. Because of the predominance of nonalbumin proteinuria, dipstick urinalysis may be unreliable in TDF nephrotoxicity.
Subjective randomness as statistical inference.
Griffiths, Thomas L; Daniels, Dylan; Austerweil, Joseph L; Tenenbaum, Joshua B
2018-06-01
Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences - which have been the focus of much of the previous work on subjective randomness - but also to binary matrices and spatial clustering. Copyright © 2018 Elsevier Inc. All rights reserved.
Han, Junwei; Li, Chunquan; Yang, Haixiu; Xu, Yanjun; Zhang, Chunlong; Ma, Jiquan; Shi, Xinrui; Liu, Wei; Shang, Desi; Yao, Qianlan; Zhang, Yunpeng; Su, Fei; Feng, Li; Li, Xia
2015-01-01
Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools (http://bioinfo.hrbmu.edu.cn/PAGI). PMID:25551156
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...
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.
Type inference for correspondence types
DEFF Research Database (Denmark)
Hüttel, Hans; Gordon, Andy; Hansen, Rene Rydhof
2009-01-01
We present a correspondence type/effect system for authenticity in a π-calculus with polarized channels, dependent pair types and effect terms and show how one may, given a process P and an a priori type environment E, generate constraints that are formulae in the Alternating Least Fixed......-Point (ALFP) logic. We then show how a reasonable model of the generated constraints yields a type/effect assignment such that P becomes well-typed with respect to E if and only if this is possible. The formulae generated satisfy a finite model property; a system of constraints is satisfiable if and only...... if it has a finite model. As a consequence, we obtain the result that type/effect inference in our system is polynomial-time decidable....
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.
Identification of the predominant volatile compounds produced by Aspergillus flavus.
Kamiński, E; Libbey, L M; Stawicki, S; Wasowicz, E
1972-11-01
A culture of Aspergillus flavus grown on moistened wheat meal was homogenized with a blendor, and the resulting slurry was vacuum-distilled at 5 mm of Hg and 35 C. The aqueous distillate was collected in traps cooled to -10 to -80 C. The culture volatiles were extracted from the distillate with CH(2)Cl(2), and, after removal of the bulk of the solvent, the concentrated volatiles were examined by packed-column gas chromatography. Nineteen peaks were observed, and coupled gas chromatography-mass spectrometry was employed to identify the larger components. The compounds identified were: 3-methyl-butanol, 3-octanone, 3-octanol, 1-octen-3-ol, 1-octanol, and cis-2-octen-1-ol. The two octenols were the predominant compounds, and sufficient sample was trapped from the gas chromatograph for infrared analyses; this confirmed the mass spectral identifications and permitted the assignment of the cis designation to 2-octen-1-ol. Both oct-1-en-3-ol and cis-2-octen-1-ol are thought to be responsible for the characteristic musty-fungal odor of certain fungi; the latter compound may be a useful chemical index of fungal growth.
Predominantly elastic crack growth under combined creep-fatigue cycling
International Nuclear Information System (INIS)
Lloyd, G.J.
1979-01-01
A rationalization of the various observed effects of combined creep-fatigue cycling upon predominantly elastic fatigue-crack propagation in austenitic steel is presented. Existing and new evidence is used to show two main groups of behaviour: (i) material and cycling conditions which lead to modest increases (6-8 times) in the rate of crack growth are associated with relaxation-induced changes in the material deformation characteristics, and (ii) material and cycling conditions severe enough to generate internal fracture damage lead to significant (up to a factor of 30) increases in crack growth rate when compared with fast-cycling crack propagation rates at the same temperature. A working hypothesis is presented to show that the boundary between the two groups occurs when the scale of the nucleated creep damage is of the same magnitude as the crack tip opening displacement. This leads to the possibility of unstable crack advance. Creep crack growth rates are shown to provide an upper bound to creep-fatigue crack growth rates when crack advance is unstable. If the deformation properties only are affected by the creep-fatigue cycling then creep crack growth rates provide a lower bound. The role of intergranular oxygen corrosion in very low frequency crack growth tests is also briefly discussed. (author)
Inference Attacks and Control on Database Structures
Directory of Open Access Journals (Sweden)
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.
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.
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.
Ureter smooth muscle cell orientation in rat is predominantly longitudinal.
Spronck, Bart; Merken, Jort J; Reesink, Koen D; Kroon, Wilco; Delhaas, Tammo
2014-01-01
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.
Abdominal Pain-predominant Functional Gastrointestinal Disorders in Adolescent Nigerians.
Udoh, Ekong; Devanarayana, Niranga Manjuri; Rajindrajith, Shaman; Meremikwu, Martin; Benninga, Marc Alexander
2016-04-01
To determine the prevalence, pattern, and predisposing factors of abdominal pain-predominant functional gastrointestinal disorders (AP-FGIDs) in adolescent Nigerians. A cross-sectional study was conducted in 2 states in the southern part of Nigeria in June 2014. Adolescents of age 10 to 18 years were recruited from 11 secondary schools using a stratified random sampling technique. A validated self-administered questionnaire on Rome III criteria for diagnosing AP-FGIDs and its determinants were filled by the participants in a classroom setting. A total of 874 participants filled the questionnaire. Of this, 818 (93.4%) filled it properly and were included in the final analysis. The mean age of the participants was 14.6 ± 2.0 years with 409 (50.0%) being boys. AP-FGIDs were present in 81 (9.9%) participants. Forty six (5.6%) of the study participants had irritable bowel syndrome (IBS), 21 (2.6%) functional abdominal pain, 15 (1.8%) abdominal migraine while 3 (0.4%) had functional dyspepsia. The difference in AP-FGIDs between adolescents residing in rural and urban areas was not statistically significant (P = 0.22). Intestinal and extra-intestinal symptoms occurred more frequently in those with AP-FGIDs. Nausea was the only symptom independently associated with AP-FGIDs (p = 0.015). Multiple regression analysis showed no significant association between stressful life events and AP-FGIDs. AP-FGIDs are a significant health problem in Nigerian adolescents. In addition to the intestinal symptoms, most of the affected children and others also had extraintestinal symptoms. None of the stressful life events evaluated was significantly associated with FGIDs.
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.
Assessing roles of vocabulary knowledge predominating in contextual clues
Directory of Open Access Journals (Sweden)
Patcharawadee Promduang
2016-07-01
Full Text Available The purpose of this study is to investigate the relationship between vocabulary knowledge and the use of contextual clues and whether EFL learners who are well-equipped with reading skills are able to comprehend the text despite a low level of vocabulary knowledge. Therefore, the study focused on which vocabulary dimensions help students guess unfamiliar words. The study was carried out at Hatyai University in Thailand. The population of this study consisted of 34 undergraduates who were studying International Business English and had taken a course in reading techniques. The present study was conducted to conceptually validate the roles of breadth and depth of vocabulary knowledge to improve skills by contextual clue. Vocabulary Depth was specially employed to evaluate two dimensions namely Paradigmatic and Syntagmatic. The Schmitt and Clapham Vocabulary Level Test was used to test vocabulary breadth, while the vocabulary depth was implemented by Read’s Vocabulary Depth Test. Reading parts of the TOEFL were adopted for contextual clue items. There were two statistical analysis tools also implemented in this study: paired-sample t-test and bivariate correlation. First, in an attempt to find which vocabulary dimension predominates in guessing word meaning from the text, a paired-sample t-test was utilized to compare the difference of two vocabulary dimensions in reading part: vocabulary depth and contextual clues, and vocabulary breadth and contextual clues. Second, a bivariate correlation was used to find the degree of relationship between vocabulary knowledge and contextual clues. The consequences of this study identified empirical results that 1 there was a positive relationship between contextual clues and vocabulary depth, the reverse is true in vocabulary breadth. Moreover, vocabulary depth is more significantly crucial than breadth to enhance student’s ability to guess words’ meaning from the context.
A study of the inferred interplanetary magnetic field polarity periodicities
International Nuclear Information System (INIS)
Xanthakis, J.; Tritakis, V.P.; Zerefos, Ch.
1981-01-01
A detailed Power Spectrum Analysis applied on the daily polarities of the inferred interplanetary magnetic field, published by Svalgaard, has pointed out that the main periodicity apparent in these data is 27-28 days, which suggests a recurrency of a 2-sector structure. There is also a secondary periodicity of 13-14 days which mainly appears in the yers of the descending branch of the solar cycle and superimposes on the 2-sector structure, transforming it into a 4-sector structure. A strict statistical study of the correlation between the predominant polarity of the interplanetary magnetic field and the heliographic latitude of the Earth, also known as the Rosenberg-Coleman effect, pointed out that perhaps there is a faint correspondence between these two elements, but one cannot speak of a systematic effect. (Auth.)
LAIT: a local ancestry inference toolkit.
Hui, Daniel; Fang, Zhou; Lin, Jerome; Duan, Qing; Li, Yun; Hu, Ming; Chen, Wei
2017-09-06
Inferring local ancestry in individuals of mixed ancestry has many applications, most notably in identifying disease-susceptible loci that vary among different ethnic groups. Many software packages are available for inferring local ancestry in admixed individuals. However, most of these existing software packages require specific formatted input files and generate output files in various types, yielding practical inconvenience. We developed a tool set, Local Ancestry Inference Toolkit (LAIT), which can convert standardized files into software-specific input file formats as well as standardize and summarize inference results for four popular local ancestry inference software: HAPMIX, LAMP, LAMP-LD, and ELAI. We tested LAIT using both simulated and real data sets and demonstrated that LAIT provides convenience to run multiple local ancestry inference software. In addition, we evaluated the performance of local ancestry software among different supported software packages, mainly focusing on inference accuracy and computational resources used. We provided a toolkit to facilitate the use of local ancestry inference software, especially for users with limited bioinformatics background.
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.
Generative Inferences Based on Learned Relations
Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.
2017-01-01
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…
Inference in models with adaptive learning
Chevillon, G.; Massmann, M.; Mavroeidis, S.
2010-01-01
Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be
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
Uncertainty in prediction and in inference
Hilgevoord, J.; Uffink, J.
1991-01-01
The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close re-lationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in
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.
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2000-01-01
New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on
The Impact of Disablers on Predictive Inference
Cummins, Denise Dellarosa
2014-01-01
People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive…
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...
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...
Extended likelihood inference in reliability
International Nuclear Information System (INIS)
Martz, H.F. Jr.; Beckman, R.J.; Waller, R.A.
1978-10-01
Extended likelihood methods of inference are developed in which subjective information in the form of a prior distribution is combined with sampling results by means of an extended likelihood function. The extended likelihood function is standardized for use in obtaining extended likelihood intervals. Extended likelihood intervals are derived for the mean of a normal distribution with known variance, the failure-rate of an exponential distribution, and the parameter of a binomial distribution. Extended second-order likelihood methods are developed and used to solve several prediction problems associated with the exponential and binomial distributions. In particular, such quantities as the next failure-time, the number of failures in a given time period, and the time required to observe a given number of failures are predicted for the exponential model with a gamma prior distribution on the failure-rate. In addition, six types of life testing experiments are considered. For the binomial model with a beta prior distribution on the probability of nonsurvival, methods are obtained for predicting the number of nonsurvivors in a given sample size and for predicting the required sample size for observing a specified number of nonsurvivors. Examples illustrate each of the methods developed. Finally, comparisons are made with Bayesian intervals in those cases where these are known to exist
Reinforcement learning or active inference?
Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J
2009-07-29
This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
Reinforcement learning or active inference?
Directory of Open Access Journals (Sweden)
Karl J Friston
2009-07-01
Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
Active inference and epistemic value.
Friston, Karl; Rigoli, Francesco; Ognibene, Dimitri; Mathys, Christoph; Fitzgerald, Thomas; Pezzulo, Giovanni
2015-01-01
We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.
Ancient Biomolecules and Evolutionary Inference.
Cappellini, Enrico; Prohaska, Ana; Racimo, Fernando; Welker, Frido; Pedersen, Mikkel Winther; Allentoft, Morten E; de Barros Damgaard, Peter; Gutenbrunner, Petra; Dunne, Julie; Hammann, Simon; Roffet-Salque, Mélanie; Ilardo, Melissa; Moreno-Mayar, J Víctor; Wang, Yucheng; Sikora, Martin; Vinner, Lasse; Cox, Jürgen; Evershed, Richard P; Willerslev, Eske
2018-04-25
Over the last decade, studies of ancient biomolecules-particularly ancient DNA, proteins, and lipids-have revolutionized our understanding of evolutionary history. Though initially fraught with many challenges, the field now stands on firm foundations. Researchers now successfully retrieve nucleotide and amino acid sequences, as well as lipid signatures, from progressively older samples, originating from geographic areas and depositional environments that, until recently, were regarded as hostile to long-term preservation of biomolecules. Sampling frequencies and the spatial and temporal scope of studies have also increased markedly, and with them the size and quality of the data sets generated. This progress has been made possible by continuous technical innovations in analytical methods, enhanced criteria for the selection of ancient samples, integrated experimental methods, and advanced computational approaches. Here, we discuss the history and current state of ancient biomolecule research, its applications to evolutionary inference, and future directions for this young and exciting field. Expected final online publication date for the Annual Review of Biochemistry Volume 87 is June 20, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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.
Lok, Benjamin H.; Powell, Simon N.
2012-01-01
The Rad52 protein was largely ignored in humans and other mammals when the mouse knockout revealed a largely “no-effect” phenotype. However, using synthetic lethal approaches to investigate context dependent function, new studies have shown that Rad52 plays a key survival role in cells lacking the function of the BRCA1-BRCA2 pathway of homologous recombination. Biochemical studies also showed significant differences between yeast and human Rad52, in which yeast Rad52 can promote strand invasion of RPA-coated single-stranded DNA in the presence of Rad51, but human Rad52 cannot. This results in the paradox of how is human Rad52 providing Rad51 function: presumably there is something missing in the biochemical assays that exists in-vivo, but the nature of this missing factor is currently unknown. Recent studies have suggested that Rad52 provides back-up Rad51 function for all members of the BRCA1-BRCA2 pathway, suggesting that Rad52 may be a target for therapy in BRCA pathway deficient cancers. Screening for ways to inhibit Rad52 would potentially provide a complementary strategy for targeting BRCA-deficient cancers in addition to PARP inhibitors. PMID:23071261
Glutamatergic model psychoses: prediction error, learning, and inference.
Corlett, Philip R; Honey, Garry D; Krystal, John H; Fletcher, Paul C
2011-01-01
Modulating glutamatergic neurotransmission induces alterations in conscious experience that mimic the symptoms of early psychotic illness. We review studies that use intravenous administration of ketamine, focusing on interindividual variability in the profundity of the ketamine experience. We will consider this individual variability within a hypothetical model of brain and cognitive function centered upon learning and inference. Within this model, the brains, neural systems, and even single neurons specify expectations about their inputs and responding to violations of those expectations with new learning that renders future inputs more predictable. We argue that ketamine temporarily deranges this ability by perturbing both the ways in which prior expectations are specified and the ways in which expectancy violations are signaled. We suggest that the former effect is predominantly mediated by NMDA blockade and the latter by augmented and inappropriate feedforward glutamatergic signaling. We suggest that the observed interindividual variability emerges from individual differences in neural circuits that normally underpin the learning and inference processes described. The exact source for that variability is uncertain, although it is likely to arise not only from genetic variation but also from subjects' previous experiences and prior learning. Furthermore, we argue that chronic, unlike acute, NMDA blockade alters the specification of expectancies more profoundly and permanently. Scrutinizing individual differences in the effects of acute and chronic ketamine administration in the context of the Bayesian brain model may generate new insights about the symptoms of psychosis; their underlying cognitive processes and neurocircuitry.
Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.
Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui
2017-01-01
The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.
A graphical user interface for a method to infer kinetics and network architecture (MIKANA).
Mourão, Márcio A; Srividhya, Jeyaraman; McSharry, Patrick E; Crampin, Edmund J; Schnell, Santiago
2011-01-01
One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).
Psimoulis, Panos A.; Houlié, Nicolas; Behr, Yannik
2018-01-01
Earthquake early warning (EEW) systems' performance is driven by the trade-off between the need for a rapid alert and the accuracy of each solution. A challenge for many EEW systems has been the magnitude saturation for large events (MW > 7) and the resulting underestimation of seismic moment magnitude. In this study, we test the performance of high-rate (1 Hz) GPS, based on seven seismic events, to evaluate whether long-period ground motions can be measured well enough to infer reliably earthquake predominant periods. We show that high-rate GPS data allow the computation of a GPS-based predominant period (τg) to estimate lower bounds for the magnitude of earthquakes and distinguish between large (MW > 7) and great (MW > 8) events and thus extend the capability of EEW systems for larger events. It has also identified the impact of the different values of the smoothing factor α on the τg results and how the sampling rate and the computation process differentiate τg from the commonly used τp.
Increased serum free tryptophan in patients with diarrhea-predominant irritable bowel syndrome.
Christmas, David M; Badawy, Abdulla A-B; Hince, Dana; Davies, Simon J C; Probert, Christopher; Creed, Tom; Smithson, John; Afzal, Muhammad; Nutt, David J; Potokar, John P
2010-10-01
Irregularities of serotonin function in irritable bowel syndrome (IBS) may be due to changes in the metabolism of the serotonin precursor l-tryptophan. Dietary alteration of tryptophan intake may impact upon the mood and bowel symptoms of IBS. We hypothesized that diarrhea-predominant irritable bowel syndrome (d-IBS) patients would exhibit an increase in plasma tryptophan due to alterations in tryptophan metabolism. We also hypothesized that a diet low in tryptophan would reverse this change and reduce symptoms. Thirteen patients with d-IBS had fasting serum free and total tryptophan, large neutral amino acids, and 6 kynurenine metabolites measured before and after 2 weeks of a strict dairy-free diet. Baseline tryptophan parameters were compared with an age- and sex-matched control group. Changes in the specific tryptophan parameters before and after dairy-free diet were correlated with symptoms of IBS and mood. Compared with the control group, d-IBS patients at baseline exhibited significantly higher free serum tryptophan (10.5 ± 4.35 vs 4.75 ± 2.43 μmol/L [means ± standard deviation], P = .006) and significantly lower tryptophan dioxygenase and total tryptophan oxidation as measured by the kynurenine to free tryptophan and total kynurenines to free tryptophan ratios (23.37 ± 10.12 vs 55.33 ± 16.02, P < .001 and 49.34 ± 17.84 vs 258.46 ± 98.67, P < .001, respectively). Dairy-free diet did not modulate metabolites of the kynurenine pathway or symptoms. Tryptophan metabolism along the kynurenine pathway is inhibited in d-IBS, and a dairy-free diet does not alter this. Our findings are consistent with possible enhanced serotonin activity in d-IBS. Copyright © 2010 Elsevier Inc. All rights reserved.
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…
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
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.
Statistical inference an integrated Bayesianlikelihood approach
Aitkin, Murray
2010-01-01
Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre
Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo
2017-09-21
Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously
Yamasaki, Y; Kuwatsuru, R; Tsukiyama, Y; Oki, K; Koyano, K
2016-08-01
We aimed to investigate mastication predominance in healthy dentate individuals and patients with unilateral posterior missing teeth using objective and subjective methods. The sample comprised 50 healthy dentate individuals (healthy dentate group) and 30 patients with unilateral posterior missing teeth (partially edentulous group). Subjects were asked to freely chew three kinds of test foods (peanuts, beef jerky and chewing gum). Electromyographic activity of the bilateral masseter muscles was recorded. The chewing side (right side or left side) was judged by the level of root mean square electromyographic amplitude. Mastication predominance was then objectively assessed using the mastication predominant score and the mastication predominant index. Self-awareness of mastication predominance was evaluated using a modified visual analogue scale. Mastication predominance scores of the healthy dentate and partially edentulous groups for each test food were analysed. There was a significant difference in the distribution of the mastication predominant index between the two groups (P mastication predominant score was weakly correlated with self-awareness of mastication predominance in the healthy dentate group, whereas strong correlation was observed in the partially edentulous group (P mastication predominance and were more aware of mastication predominance than healthy dentate individuals. Our findings suggest that an objective evaluation of mastication predominance is more precise than a subjective method. © 2016 John Wiley & Sons Ltd.
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
Inferring Domain Plans in Question-Answering
National Research Council Canada - National Science Library
Pollack, Martha E
1986-01-01
The importance of plan inference in models of conversation has been widely noted in the computational-linguistics literature, and its incorporation in question-answering systems has enabled a range...
Scalable inference for stochastic block models
Peng, Chengbin; Zhang, Zhihua; Wong, Ka-Chun; Zhang, Xiangliang; Keyes, David E.
2017-01-01
Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of "big data," traditional inference
Efficient algorithms for conditional independence inference
Czech Academy of Sciences Publication Activity Database
Bouckaert, R.; Hemmecke, R.; Lindner, S.; Studený, Milan
2010-01-01
Roč. 11, č. 1 (2010), s. 3453-3479 ISSN 1532-4435 R&D Projects: GA ČR GA201/08/0539; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : conditional independence inference * linear programming approach Subject RIV: BA - General Mathematics Impact factor: 2.949, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-efficient algorithms for conditional independence inference.pdf
On the criticality of inferred models
Mastromatteo, Iacopo; Marsili, Matteo
2011-10-01
Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. 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.
On the criticality of inferred models
International Nuclear Information System (INIS)
Mastromatteo, Iacopo; Marsili, Matteo
2011-01-01
Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. 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
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.
A Bayesian Network Schema for Lessening Database Inference
National Research Council Canada - National Science Library
Chang, LiWu; Moskowitz, Ira S
2001-01-01
.... The authors introduce a formal schema for database inference analysis, based upon a Bayesian network structure, which identifies critical parameters involved in the inference problem and represents...
Directory of Open Access Journals (Sweden)
Junha Shin
Full Text Available Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life-Archaea, Bacteria, and Eukaryota-suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co
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.
A formal model of interpersonal inference
Directory of Open Access Journals (Sweden)
Michael eMoutoussis
2014-03-01
Full Text Available Introduction: We propose that active Bayesian inference – a general framework for decision-making – can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. Results: 1. Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to 'mentalising' in the psychological literature, is based upon the outcomes of interpersonal exchanges. 2. We show how some well-known social-psychological phenomena (e.g. self-serving biases can be explained in terms of active interpersonal inference. 3. Mentalising naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one’s own qualities and preferences. Conclusion: We inaugurate a Bayes optimal framework for modelling intersubject variability in mentalising during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalising is distorted.
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
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.
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.
2016-01-01
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
Inferring Phylogenetic Networks Using PhyloNet.
Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay
2018-07-01
PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.
Transuranic element pathways to man
International Nuclear Information System (INIS)
Bennett, B.G.
1976-01-01
Transfer to man of transuranic element contamination may occur by the inhalation or ingestion pathways. The measurements of globally dispersed fall-out radioactivity have provided pertinent data on the environmental behaviour of plutonium. Additional data may eventually become available for americium. From the measured and inferred concentrations of fall-out plutonium, the inhalation intake has been determined and the ICRP Task Group lung model used to estimate deposition in the lung and transfer to other body organs. The computed body burden reached a maximum of 4pCi in 1964 and is currently about 2.5pCi. A complete diet sampling has been conducted to determine ingestion intake. Plutonium concentration in food ranged from 0.01pCi/kg in shellfish to undetected (less than 0.0003pCi/kg) in milk. Annual intake in total diet is estimated to have been 1.6pCi in 1972. Low uptake by the gastrointestinal tract makes contribution to organ burdens from ingestion negligible. Long-term pathway considerations include plant uptake from the cumulative deposit in soil and resuspension. Downward movement in soil may limit the significance of these long-term pathway components. (author)
Goal inferences about robot behavior : goal inferences and human response behaviors
Broers, H.A.T.; Ham, J.R.C.; Broeders, R.; De Silva, P.; Okada, M.
2014-01-01
This explorative research focused on the goal inferences human observers draw based on a robot's behavior, and the extent to which those inferences predict people's behavior in response to that robot. Results show that different robot behaviors cause different response behavior from people.
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.
ShinyKGode: an Interactive Application for ODE Parameter Inference Using Gradient Matching.
Wandy, Joe; Niu, Mu; Giurghita, Diana; Daly, Rónán; Rogers, Simon; Husmeier, Dirk
2018-02-27
Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching. ShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualised alongside diagnostic plots to assess convergence. The R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode. dirk.husmeier@glasgow.ac.uk. None.
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…
Explanatory Preferences Shape Learning and Inference.
Lombrozo, Tania
2016-10-01
Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference. Copyright © 2016 Elsevier Ltd. All rights reserved.
Fuzzy logic controller using different inference methods
International Nuclear Information System (INIS)
Liu, Z.; De Keyser, R.
1994-01-01
In this paper the design of fuzzy controllers by using different inference methods is introduced. Configuration of the fuzzy controllers includes a general rule-base which is a collection of fuzzy PI or PD rules, the triangular fuzzy data model and a centre of gravity defuzzification algorithm. The generalized modus ponens (GMP) is used with the minimum operator of the triangular norm. Under the sup-min inference rule, six fuzzy implication operators are employed to calculate the fuzzy look-up tables for each rule base. The performance is tested in simulated systems with MATLAB/SIMULINK. Results show the effects of using the fuzzy controllers with different inference methods and applied to different test processes
Uncertainty in prediction and in inference
International Nuclear Information System (INIS)
Hilgevoord, J.; Uffink, J.
1991-01-01
The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close relationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in inference can be obtained by means of the so-called statistical distance between probability distributions. When applied to quantum mechanics, this distance leads to a measure of the distinguishability of quantum states, which essentially is the absolute value of the matrix element between the states. The importance of this result to the quantum mechanical uncertainty principle is noted. The second part of the paper provides a derivation of the statistical distance on the basis of the so-called method of support
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.
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...
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.>.
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...
Sarbottam Piya; Madhav P. Nepal; Achal Neupane; Gary E. Larson; Jack L. Butler
2012-01-01
Herbarium records were studied to infer the introduction history and spread of the exotic Eurasian sickleweed (Falcaria vulgaris Bernh.) in the United States. The spread of the plant was reconstructed using the location of early collections as the possible sites of primary introduction, and the location of subsequent collections as potential pathways along which this...
Improved Inference of Heteroscedastic Fixed Effects Models
Directory of Open Access Journals (Sweden)
Afshan Saeed
2016-12-01
Full Text Available Heteroscedasticity is a stern problem that distorts estimation and testing of panel data model (PDM. Arellano (1987 proposed the White (1980 estimator for PDM with heteroscedastic errors but it provides erroneous inference for the data sets including high leverage points. In this paper, our attempt is to improve heteroscedastic consistent covariance matrix estimator (HCCME for panel dataset with high leverage points. To draw robust inference for the PDM, our focus is to improve kernel bootstrap estimators, proposed by Racine and MacKinnon (2007. The Monte Carlo scheme is used for assertion of the results.
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisova, K.
2010-01-01
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...... process, where the germs are the centres and the marks are the associated radii of the discs. We propose to use a recent parametric class of interacting disc process models, where the minimal sufficient statistic depends on various geometric properties of the random set, and the density is specified......-based maximum likelihood inference and the effect of specifying different reference Poisson models....
IMAGINE: Interstellar MAGnetic field INference Engine
Steininger, Theo
2018-03-01
IMAGINE (Interstellar MAGnetic field INference Engine) performs inference on generic parametric models of the Galaxy. The modular open source framework uses highly optimized tools and technology such as the MultiNest sampler (ascl:1109.006) and the information field theory framework NIFTy (ascl:1302.013) to create an instance of the Milky Way based on a set of parameters for physical observables, using Bayesian statistics to judge the mismatch between measured data and model prediction. The flexibility of the IMAGINE framework allows for simple refitting for newly available data sets and makes state-of-the-art Bayesian methods easily accessible particularly for random components of the Galactic magnetic field.
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...
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....
Perturbation Biology: Inferring Signaling Networks in Cellular Systems
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. PMID:24367245
Directory of Open Access Journals (Sweden)
Elisa M. Uribe Echevarría
2011-08-01
Full Text Available Eosinophil is considered to be a main protagonist in asthma; however, often discordances between clinical manifestations and response to treatment are observed. We aimed to determine the occurrence of neutrophil predominance in asthma and to identify its characteristics on the basis of clinical-functional features, induced sputum cellular pattern and soluble molecules, to guide the appropriated anti-inflammatory therapy. A total of 41 patients were included in randomized groups: 21-40 year-old, with stable mild-to-severe asthma, steroid-naïve and non-smokers. An induced sputum sample was obtained under basal conditions, a second one after treatment with budesonide (400 µg b.i.d. or montelukast (10 mg/d for six weeks, and a final one after a 4-week washout period. By cytospin we evaluated eosinophil (EP or neutrophil predominance (NP, and in supernatant we determined LTE4, and CC16. Peak expiratory flow variability (PEFV was measured. A total of 23/41 patients corresponded to EP and 18/41 patients to NP. The PEFV was higher in EP than in NP. LTE4 was higher with NP than with EP. No difference was found for CC16. Montelukast reduced the predominant cell in both subsets, whereas budesonide only reduced eosinophils in EP. Budesonide and montelukast reduced PEFV in EP but not in NP. Considering the total treated-samples in each subset, CC16 level increased significantly in EP. In conclusion: a NP subset of asthmatic patients was identified. These patients show a lower bronchial lability; the leukotriene pathway is involved which responds to anti-leukotriene treatment. This phenotype shows a poor recovery of CC16 level after treatment.
Tichy, Elisia D; Pillai, Resmi; Deng, Li; Liang, Li; Tischfield, Jay; Schwemberger, Sandy J; Babcock, George F; Stambrook, Peter J
2010-11-01
Embryonic stem (ES) cells give rise to all cell types of an organism. Since mutations at this embryonic stage would affect all cells and be detrimental to the overall health of an organism, robust mechanisms must exist to ensure that genomic integrity is maintained. To test this proposition, we compared the capacity of murine ES cells to repair DNA double-strand breaks with that of differentiated cells. Of the 2 major pathways that repair double-strand breaks, error-prone nonhomologous end joining (NHEJ) predominated in mouse embryonic fibroblasts, whereas the high fidelity homologous recombinational repair (HRR) predominated in ES cells. Microhomology-mediated end joining, an emerging repair pathway, persisted at low levels in all cell types examined. The levels of proteins involved in HRR and microhomology-mediated end joining were highly elevated in ES cells compared with mouse embryonic fibroblasts, whereas those for NHEJ were quite variable, with DNA Ligase IV expression low in ES cells. The half-life of DNA Ligase IV protein was also low in ES cells. Attempts to increase the abundance of DNA Ligase IV protein by overexpression or inhibition of its degradation, and thereby elevate NHEJ in ES cells, were unsuccessful. When ES cells were induced to differentiate, however, the level of DNA Ligase IV protein increased, as did the capacity to repair by NHEJ. The data suggest that preferential use of HRR rather than NHEJ may lend ES cells an additional layer of genomic protection and that the limited levels of DNA Ligase IV may account for the low level of NHEJ activity.
Pathway Distiller - multisource biological pathway consolidation.
Doderer, Mark S; Anguiano, Zachry; Suresh, Uthra; Dashnamoorthy, Ravi; Bishop, Alexander J R; Chen, Yidong
2012-01-01
One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow
Reconstructing biochemical pathways from time course data.
Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago
2007-03-01
Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.
Model averaging, optimal inference and habit formation
Directory of Open Access Journals (Sweden)
Thomas H B FitzGerald
2014-06-01
Full Text Available Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour.
Efficient Bayesian inference for ARFIMA processes
Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.
2015-03-01
Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more flexible Autoregressive Fractional Integrated Moving Average (ARFIMA) model which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LRD, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g. short memory effects) can be integrated over in order to focus on long memory parameters, and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data, with favorable comparison to the standard estimators.
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…
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 ɛ-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 ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-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.
HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR
International Nuclear Information System (INIS)
Schneider, Michael D.; Dawson, William A.; Hogg, David W.; Marshall, Philip J.; Bard, Deborah J.; Meyers, Joshua; Lang, Dustin
2015-01-01
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 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
Interest, Inferences, and Learning from Texts
Clinton, Virginia; van den Broek, Paul
2012-01-01
Topic interest and learning from texts have been found to be positively associated with each other. However, the reason for this positive association is not well understood. The purpose of this study is to examine a cognitive process, inference generation, that could explain the positive association between interest and learning from texts. In…
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...
Inverse Ising inference with correlated samples
International Nuclear Information System (INIS)
Obermayer, Benedikt; Levine, Erel
2014-01-01
Correlations between two variables of a high-dimensional system can be indicative of an underlying interaction, but can also result from indirect effects. Inverse Ising inference is a method to distinguish one from the other. Essentially, the parameters of the least constrained statistical model are learned from the observed correlations such that direct interactions can be separated from indirect correlations. Among many other applications, this approach has been helpful for protein structure prediction, because residues which interact in the 3D structure often show correlated substitutions in a multiple sequence alignment. In this context, samples used for inference are not independent but share an evolutionary history on a phylogenetic tree. Here, we discuss the effects of correlations between samples on global inference. Such correlations could arise due to phylogeny but also via other slow dynamical processes. We present a simple analytical model to address the resulting inference biases, and develop an exact method accounting for background correlations in alignment data by combining phylogenetic modeling with an adaptive cluster expansion algorithm. We find that popular reweighting schemes are only marginally effective at removing phylogenetic bias, suggest a rescaling strategy that yields better results, and provide evidence that our conclusions carry over to the frequently used mean-field approach to the inverse Ising problem. (paper)
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.
Culture and Pragmatic Inference in Interpersonal Communication
African Journals Online (AJOL)
cognitive process, and that the human capacity for inference is crucially important ... been noted that research in interpersonal communication is currently pushing the ... communicative actions, the social-cultural world of everyday life is not only ... personal experiences of the authors', as documented over time and recreated ...
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…
Statistical Inference on the Canadian Middle Class
Directory of Open Access Journals (Sweden)
Russell Davidson
2018-03-01
Full Text Available Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for inference on these shares, by means of deriving expressions for their asymptotic variances of sample estimates, and the covariance of the estimates. Asymptotic inference can be undertaken based on asymptotic normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an illustration, samples of individual earnings drawn from Canadian census data are used to test various hypotheses about the middle-class shares, and confidence intervals for them are computed. It is found that, for the earlier censuses, sample sizes are large enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap fails on account of a strange phenomenon whereby many presumably different incomes in the data are rounded to one and the same value. Another difference between the centuries is the appearance of heavy right-hand tails in the income distributions of both men and women.
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...
Cortical information flow during inferences of agency
Dogge, Myrthel; Hofman, Dennis; Boersma, Maria; Dijkerman, H Chris; Aarts, Henk
2014-01-01
Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome
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…
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.
Colligation, Or the Logical Inference of Interconnection
DEFF Research Database (Denmark)
Falster, Peter
1998-01-01
laws or assumptions. Yet interconnection as an abstract concept seems to be without scientific underpinning in pure 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...
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 motion and location using WLAN RSSI
Kavitha Muthukrishnan, K.; van der Zwaag, B.J.; Havinga, Paul J.M.; Fuller, R.; Koutsoukos, X.
2009-01-01
We present novel algorithms to infer movement by making use of inherent fluctuations in the received signal strengths from existing WLAN infrastructure. We evaluate the performance of the presented algorithms based on classification metrics such as recall and precision using annotated traces
Active inference, sensory attenuation and illusions.
Brown, Harriet; Adams, Rick A; Parees, Isabel; Edwards, Mark; Friston, Karl
2013-11-01
Active inference provides a simple and neurobiologically plausible account of how action and perception are coupled in producing (Bayes) optimal behaviour. This can be seen most easily as minimising prediction error: we can either change our predictions to explain sensory input through perception. Alternatively, we can actively change sensory input to fulfil our predictions. In active inference, this action is mediated by classical reflex arcs that minimise proprioceptive prediction error created by descending proprioceptive predictions. However, this creates a conflict between action and perception; in that, self-generated movements require predictions to override the sensory evidence that one is not actually moving. However, ignoring sensory evidence means that externally generated sensations will not be perceived. Conversely, attending to (proprioceptive and somatosensory) sensations enables the detection of externally generated events but precludes generation of actions. This conflict can be resolved by attenuating the precision of sensory evidence during movement or, equivalently, attending away from the consequences of self-made acts. We propose that this Bayes optimal withdrawal of precise sensory evidence during movement is the cause of psychophysical sensory attenuation. Furthermore, it explains the force-matching illusion and reproduces empirical results almost exactly. Finally, if attenuation is removed, the force-matching illusion disappears and false (delusional) inferences about agency emerge. This is important, given the negative correlation between sensory attenuation and delusional beliefs in normal subjects--and the reduction in the magnitude of the illusion in schizophrenia. Active inference therefore links the neuromodulatory optimisation of precision to sensory attenuation and illusory phenomena during the attribution of agency in normal subjects. It also provides a functional account of deficits in syndromes characterised by false inference
Modeling and inferring cleavage patterns in proliferating epithelia.
Directory of Open Access Journals (Sweden)
Ankit B Patel
2009-06-01
Full Text Available The regulation of cleavage plane orientation is one of the key mechanisms driving epithelial morphogenesis. Still, many aspects of the relationship between local cleavage patterns and tissue-level properties remain poorly understood. Here we develop a topological model that simulates the dynamics of a 2D proliferating epithelium from generation to generation, enabling the exploration of a wide variety of biologically plausible cleavage patterns. We investigate a spectrum of models that incorporate the spatial impact of neighboring cells and the temporal influence of parent cells on the choice of cleavage plane. Our findings show that cleavage patterns generate "signature" equilibrium distributions of polygonal cell shapes. These signatures enable the inference of local cleavage parameters such as neighbor impact, maternal influence, and division symmetry from global observations of the distribution of cell shape. Applying these insights to the proliferating epithelia of five diverse organisms, we find that strong division symmetry and moderate neighbor/maternal influence are required to reproduce the predominance of hexagonal cells and low variability in cell shape seen empirically. Furthermore, we present two distinct cleavage pattern models, one stochastic and one deterministic, that can reproduce the empirical distribution of cell shapes. Although the proliferating epithelia of the five diverse organisms show a highly conserved cell shape distribution, there are multiple plausible cleavage patterns that can generate this distribution, and experimental evidence suggests that indeed plants and fruitflies use distinct division mechanisms.
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
Quantitative trait loci and metabolic pathways
McMullen, M. D.; Byrne, P. F.; Snook, M. E.; Wiseman, B. R.; Lee, E. A.; Widstrom, N. W.; Coe, E. H.
1998-01-01
The interpretation of quantitative trait locus (QTL) studies is limited by the lack of information on metabolic pathways leading to most economic traits. Inferences about the roles of the underlying genes with a pathway or the nature of their interaction with other loci are generally not possible. An exception is resistance to the corn earworm Helicoverpa zea (Boddie) in maize (Zea mays L.) because of maysin, a C-glycosyl flavone synthesized in silks via a branch of the well characterized flavonoid pathway. Our results using flavone synthesis as a model QTL system indicate: (i) the importance of regulatory loci as QTLs, (ii) the importance of interconnecting biochemical pathways on product levels, (iii) evidence for “channeling” of intermediates, allowing independent synthesis of related compounds, (iv) the utility of QTL analysis in clarifying the role of specific genes in a biochemical pathway, and (v) identification of a previously unknown locus on chromosome 9S affecting flavone level. A greater understanding of the genetic basis of maysin synthesis and associated corn earworm resistance should lead to improved breeding strategies. More broadly, the insights gained in relating a defined genetic and biochemical pathway affecting a quantitative trait should enhance interpretation of the biological basis of variation for other quantitative traits. PMID:9482823
A comparative study of covariance selection models for the inference of gene regulatory networks.
Stifanelli, Patrizia F; Creanza, Teresa M; Anglani, Roberto; Liuzzi, Vania C; Mukherjee, Sayan; Schena, Francesco P; Ancona, Nicola
2013-10-01
The inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology. In this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) 'ℓ(2C)' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that ℓ(2C) outperformed the other two methods having the most predictive partial correlation estimates and the highest values of sensitivity to infer conditional dependencies between genes even when a few number of observations was available. The application of this method for inferring gene networks of the isoprenoid biosynthesis pathways in Arabidopsis thaliana allowed to enlighten a negative partial correlation coefficient between the two hubs in the two isoprenoid pathways and, more importantly, provided an evidence of cross-talk between genes in the plastidial and the cytosolic pathways. When applied to gene expression data relative to a signature of HRAS oncogene in human cell cultures, the method revealed 9 genes (p-value<0.0005) directly interacting with HRAS, sharing the same Ras-responsive binding site for the transcription factor RREB1. This result suggests that the transcriptional activation of these genes is mediated by a common transcription factor downstream of Ras signaling. Software implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip. Copyright © 2013 The Authors. Published by
Lee, Sun Young; Song, Kwang Hoon; Koo, Imhoi; Lee, Kee-Ho; Suh, Kyung-Suk; Kim, Bu-Yeo
2012-06-01
Molecular signatures causing hepatocellular carcinoma (HCC) from chronic infection of hepatitis B virus (HBV) or hepatitis C virus (HCV) are not clearly known. Using microarray datasets composed of HCV-positive HCC or HBV-positive HCC, pathways that could discriminate tumor tissue from adjacent non-tumor liver tissue were selected by implementing nearest shrunken centroid algorithm. Cancer-related signaling pathways and lipid metabolism-related pathways were predominantly enriched in HCV-positive HCC, whereas functionally diverse pathways including immune-related pathways, cell cycle pathways, and RNA metabolism pathways were mainly enriched in HBV-positive HCC. In addition to differentially involved pathways, signaling pathways such as TGF-β, MAPK, and p53 pathways were commonly significant in both HCCs, suggesting the presence of common hepatocarcinogenesis process. The pathway clustering also verified segregation of pathways into the functional subgroups in both HCCs. This study indicates the functional distinction and similarity on the pathways implicated in the development of HCV- and/or HBV-positive HCC. Copyright © 2012 Elsevier Inc. All rights reserved.
Krishnan, Neeraja M; Seligmann, Hervé; Stewart, Caro-Beth; De Koning, A P Jason; Pollock, David D
2004-10-01
Reconstruction of ancestral DNA and amino acid sequences is an important means of inferring information about past evolutionary events. Such reconstructions suggest changes in molecular function and evolutionary processes over the course of evolution and are used to infer adaptation and convergence. Maximum likelihood (ML) is generally thought to provide relatively accurate reconstructed sequences compared to parsimony, but both methods lead to the inference of multiple directional changes in nucleotide frequencies in primate mitochondrial DNA (mtDNA). To better understand this surprising result, as well as to better understand how parsimony and ML differ, we constructed a series of computationally simple "conditional pathway" methods that differed in the number of substitutions allowed per site along each branch, and we also evaluated the entire Bayesian posterior frequency distribution of reconstructed ancestral states. We analyzed primate mitochondrial cytochrome b (Cyt-b) and cytochrome oxidase subunit I (COI) genes and found that ML reconstructs ancestral frequencies that are often more different from tip sequences than are parsimony reconstructions. In contrast, frequency reconstructions based on the posterior ensemble more closely resemble extant nucleotide frequencies. Simulations indicate that these differences in ancestral sequence inference are probably due to deterministic bias caused by high uncertainty in the optimization-based ancestral reconstruction methods (parsimony, ML, Bayesian maximum a posteriori). In contrast, ancestral nucleotide frequencies based on an average of the Bayesian set of credible ancestral sequences are much less biased. The methods involving simpler conditional pathway calculations have slightly reduced likelihood values compared to full likelihood calculations, but they can provide fairly unbiased nucleotide reconstructions and may be useful in more complex phylogenetic analyses than considered here due to their speed and
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisová, Katarina
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...... 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...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....
An Intuitive Dashboard for Bayesian Network Inference
International Nuclear Information System (INIS)
Reddy, Vikas; Farr, Anna Charisse; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K D V
2014-01-01
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++
The NIFTY way of Bayesian signal inference
International Nuclear Information System (INIS)
Selig, Marco
2014-01-01
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 3 PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy
The NIFTy way of Bayesian signal inference
Selig, Marco
2014-12-01
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 D3PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.
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.
Dopamine, reward learning, and active inference
Directory of Open Access Journals (Sweden)
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.
Dopamine, reward learning, and active inference.
FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl
2015-01-01
Temporal difference learning models propose phasic dopamine signaling 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 behavior. 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.
Inferring genetic interactions from comparative fitness data.
Crona, Kristina; Gavryushkin, Alex; Greene, Devin; Beerenwinkel, Niko
2017-12-20
Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax , the fungus Aspergillus niger , and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.
An emergent approach to analogical inference
Thibodeau, Paul H.; Flusberg, Stephen J.; Glick, Jeremy J.; Sternberg, Daniel A.
2013-03-01
In recent years, a growing number of researchers have proposed that analogy is a core component of human cognition. According to the dominant theoretical viewpoint, analogical reasoning requires a specific suite of cognitive machinery, including explicitly coded symbolic representations and a mapping or binding mechanism that operates over these representations. Here we offer an alternative approach: we find that analogical inference can emerge naturally and spontaneously from a relatively simple, error-driven learning mechanism without the need to posit any additional analogy-specific machinery. The results also parallel findings from the developmental literature on analogy, demonstrating a shift from an initial reliance on surface feature similarity to the use of relational similarity later in training. Variants of the model allow us to consider and rule out alternative accounts of its performance. We conclude by discussing how these findings can potentially refine our understanding of the processes that are required to perform analogical inference.
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. Copyright © 2013 Elsevier Ltd. All rights reserved.
Statistical inference from imperfect photon detection
International Nuclear Information System (INIS)
Audenaert, Koenraad M R; Scheel, Stefan
2009-01-01
We consider the statistical properties of photon detection with imperfect detectors that exhibit dark counts and less than unit efficiency, in the context of tomographic reconstruction. In this context, the detectors are used to implement certain positive operator-valued measures (POVMs) that would allow us to reconstruct the quantum state or quantum process under consideration. Here we look at the intermediate step of inferring outcome probabilities from measured outcome frequencies, and show how this inference can be performed in a statistically sound way in the presence of detector imperfections. Merging outcome probabilities for different sets of POVMs into a consistent quantum state picture has been treated elsewhere (Audenaert and Scheel 2009 New J. Phys. 11 023028). Single-photon pulsed measurements as well as continuous wave measurements are covered.
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++.
Working with sample data exploration and inference
Chaffe-Stengel, Priscilla
2014-01-01
Managers and analysts routinely collect and examine key performance measures to better understand their operations and make good decisions. Being able to render the complexity of operations data into a coherent account of significant events requires an understanding of how to work well with raw data and to make appropriate inferences. Although some statistical techniques for analyzing data and making inferences are sophisticated and require specialized expertise, there are methods that are understandable and applicable by anyone with basic algebra skills and the support of a spreadsheet package. By applying these fundamental methods themselves rather than turning over both the data and the responsibility for analysis and interpretation to an expert, managers will develop a richer understanding and potentially gain better control over their environment. This text is intended to describe these fundamental statistical techniques to managers, data analysts, and students. Statistical analysis of sample data is enh...
Parametric inference for biological sequence analysis.
Pachter, Lior; Sturmfels, Bernd
2004-11-16
One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.
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.
Directory of Open Access Journals (Sweden)
Daniel Lobo
2015-06-01
Full Text Available 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
Inferring Genetic Ancestry: Opportunities, Challenges, and Implications
Royal, Charmaine D.; Novembre, John; Fullerton, Stephanie M.; Goldstein, David B.; Long, Jeffrey C.; Bamshad, Michael J.; Clark, Andrew G.
2010-01-01
Increasing public interest in direct-to-consumer (DTC) genetic ancestry testing has been accompanied by growing concern about issues ranging from the personal and societal implications of the testing to the scientific validity of ancestry inference. The very concept of “ancestry” is subject to misunderstanding in both the general and scientific communities. What do we mean by ancestry? How exactly is ancestry measured? How far back can such ancestry be defined and by which genetic tools? How ...
Spatial Inference Based on Geometric Proportional Analogies
Mullally, Emma-Claire; O'Donoghue, Diarmuid P.
2006-01-01
We describe an instance-based reasoning solution to a variety of spatial reasoning problems. The solution centers on identifying an isomorphic mapping between labelled graphs that represent some problem data and a known solution instance. We describe a number of spatial reasoning problems that are solved by generating non-deductive inferences, integrating topology with area (and other) features. We report the accuracy of our algorithm on different categories of spatial reasoning tasks from th...
Inferring ontology graph structures using OWL reasoning
Rodriguez-Garcia, Miguel Angel
2018-01-05
Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies\\' semantic content remains a challenge.We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies\\' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph .Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.
Role of Speaker Cues in Attention Inference
Jin Joo Lee; Cynthia Breazeal; David DeSteno
2017-01-01
Current state-of-the-art approaches to emotion recognition primarily focus on modeling the nonverbal expressions of the sole individual without reference to contextual elements such as the co-presence of the partner. In this paper, we demonstrate that the accurate inference of listeners’ social-emotional state of attention depends on accounting for the nonverbal behaviors of their storytelling partner, namely their speaker cues. To gain a deeper understanding of the role of speaker cues in at...
Inferring ontology graph structures using OWL reasoning.
Rodríguez-García, Miguel Ángel; Hoehndorf, Robert
2018-01-05
Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.
Constrained bayesian inference of project performance models
Sunmola, Funlade
2013-01-01
Project performance models play an important role in the management of project success. When used for monitoring projects, they can offer predictive ability such as indications of possible delivery problems. Approaches for monitoring project performance relies on available project information including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in ...
Using metacognitive cues to infer others' thinking
André Mata; Tiago Almeida
2014-01-01
Three studies tested whether people use cues about the way other people think---for example, whether others respond fast vs. slow---to infer what responses other people might give to reasoning problems. People who solve reasoning problems using deliberative thinking have better insight than intuitive problem-solvers into the responses that other people might give to the same problems. Presumably because deliberative responders think of intuitive responses before they think 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.
Directory of Open Access Journals (Sweden)
Monica Chan
2014-09-01
Conclusion: C. neoformans var. grubii, subtype VN I, was the predominant subtype in Singapore, infecting younger, mainly immunocompromised hosts with HIV. C. gattii was uncommon, causing pulmonary manifestations in older, immunocompetent patients and were RFLP type VG II.
Bootstrap inference when using multiple imputation.
Schomaker, Michael; Heumann, Christian
2018-04-16
Many modern estimators require bootstrapping to calculate confidence intervals because either no analytic standard error is available or the distribution of the parameter of interest is nonsymmetric. It remains however unclear how to obtain valid bootstrap inference when dealing with multiple imputation to address missing data. We present 4 methods that are intuitively appealing, easy to implement, and combine bootstrap estimation with multiple imputation. We show that 3 of the 4 approaches yield valid inference, but that the performance of the methods varies with respect to the number of imputed data sets and the extent of missingness. Simulation studies reveal the behavior of our approaches in finite samples. A topical analysis from HIV treatment research, which determines the optimal timing of antiretroviral treatment initiation in young children, demonstrates the practical implications of the 4 methods in a sophisticated and realistic setting. This analysis suffers from missing data and uses the g-formula for inference, a method for which no standard errors are available. Copyright © 2018 John Wiley & Sons, Ltd.
Inferring epidemic network topology from surveillance data.
Directory of Open Access Journals (Sweden)
Xiang Wan
Full Text Available 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.
Role of Speaker Cues in Attention Inference
Directory of Open Access Journals (Sweden)
Jin Joo Lee
2017-10-01
Full Text Available Current state-of-the-art approaches to emotion recognition primarily focus on modeling the nonverbal expressions of the sole individual without reference to contextual elements such as the co-presence of the partner. In this paper, we demonstrate that the accurate inference of listeners’ social-emotional state of attention depends on accounting for the nonverbal behaviors of their storytelling partner, namely their speaker cues. To gain a deeper understanding of the role of speaker cues in attention inference, we conduct investigations into real-world interactions of children (5–6 years old storytelling with their peers. Through in-depth analysis of human–human interaction data, we first identify nonverbal speaker cues (i.e., backchannel-inviting cues and listener responses (i.e., backchannel feedback. We then demonstrate how speaker cues can modify the interpretation of attention-related backchannels as well as serve as a means to regulate the responsiveness of listeners. We discuss the design implications of our findings toward our primary goal of developing attention recognition models for storytelling robots, and we argue that social robots can proactively use speaker cues to form more accurate inferences about the attentive state of their human partners.
Cortical information flow during inferences of agency
Directory of Open Access Journals (Sweden)
Myrthel eDogge
2014-08-01
Full Text Available Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome are independent. Participants completed a computerized task in which they pressed a button followed by one of two color words (red or blue and rated their experienced agency over producing the color. Before executing the action, a matching or mismatching color word was pre-activated by explicitly instructing participants to produce the color (goal condition or by briefly presenting the color word (prime condition. In both conditions, experienced agency was higher in matching versus mismatching trials. Furthermore, increased electroencephalography (EEG-based connectivity strength was observed between parietal and frontal nodes and within the (prefrontal cortex when color-outcomes matched with goals and participants reported high agency. This pattern of increased connectivity was not identified in trials where outcomes were pre-activated through primes. These results suggest that different connections are involved in the experience and in the loss of agency, as well as in inferences of agency resulting from different types of pre-activation. Moreover, the findings provide novel support for the involvement of a fronto-parietal network in agency inferences.
Phylogenetic Inference of HIV Transmission Clusters
Directory of Open Access Journals (Sweden)
Vlad Novitsky
2017-10-01
Full Text Available Better understanding the structure and dynamics of HIV transmission networks is essential for designing the most efficient interventions to prevent new HIV transmissions, and ultimately for gaining control of the HIV epidemic. The inference of phylogenetic relationships and the interpretation of results rely on the definition of the HIV transmission cluster. The definition of the HIV cluster is complex and dependent on multiple factors, including the design of sampling, accuracy of sequencing, precision of sequence alignment, evolutionary models, the phylogenetic method of inference, and specified thresholds for cluster support. While the majority of studies focus on clusters, non-clustered cases could also be highly informative. A new dimension in the analysis of the global and local HIV epidemics is the concept of phylogenetically distinct HIV sub-epidemics. The identification of active HIV sub-epidemics reveals spreading viral lineages and may help in the design of targeted interventions.HIVclustering can also be affected by sampling density. Obtaining a proper sampling density may increase statistical power and reduce sampling bias, so sampling density should be taken into account in study design and in interpretation of phylogenetic results. Finally, recent advances in long-range genotyping may enable more accurate inference of HIV transmission networks. If performed in real time, it could both inform public-health strategies and be clinically relevant (e.g., drug-resistance testing.
Causal inference of asynchronous audiovisual speech
Directory of Open Access Journals (Sweden)
John F Magnotti
2013-11-01
Full Text Available During speech perception, humans integrate auditory information from the voice with visual information from the face. This multisensory integration increases perceptual precision, but only if the two cues come from the same talker; this requirement has been largely ignored by current models of speech perception. We describe a generative model of multisensory speech perception that includes this critical step of determining the likelihood that the voice and face information have a common cause. A key feature of the model is that it is based on a principled analysis of how an observer should solve this causal inference problem using the asynchrony between two cues and the reliability of the cues. This allows the model to make predictions abut the behavior of subjects performing a synchrony judgment task, predictive power that does not exist in other approaches, such as post hoc fitting of Gaussian curves to behavioral data. We tested the model predictions against the performance of 37 subjects performing a synchrony judgment task viewing audiovisual speech under a variety of manipulations, including varying asynchronies, intelligibility, and visual cue reliability. The causal inference model outperformed the Gaussian model across two experiments, providing a better fit to the behavioral data with fewer parameters. Because the causal inference model is derived from a principled understanding of the task, model parameters are directly interpretable in terms of stimulus and subject properties.
Functional neuroanatomy of intuitive physical inference.
Fischer, Jason; Mikhael, John G; Tenenbaum, Joshua B; Kanwisher, Nancy
2016-08-23
To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.
Elements of Causal Inference: Foundations and Learning Algorithms
DEFF Research Database (Denmark)
Peters, Jonas Martin; Janzing, Dominik; Schölkopf, Bernhard
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning......A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning...
Integrating distributed Bayesian inference and reinforcement learning for sensor management
Grappiolo, C.; Whiteson, S.; Pavlin, G.; Bakker, B.
2009-01-01
This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically
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.
Rossi, Adriana Suzart Ungaretti; de Moura, Luciana Monteiro; de Mello, Claudia Berlim; de Souza, Altay Alves Lino; Muszkat, Mauro; Bueno, Orlando Francisco Amodeo
2015-01-01
Attention-deficit/hyperactivity disorder (ADHD) is a widely studied neurodevelopmental disorder. It is a highly heterogeneous condition, encompassing different types of expression. The predominantly inattentive type is the most prevalent and the most stable over the lifetime, yet it is the least-studied presentation. To increase understanding of its cognitive profile, 29 children with Attention-deficit/hyperactivity disorder of predominantly inattentive type (ADHD-I) and 29 matched controls, ...
Open questions in the management of nodular lymphocyte predominant hodgkin lymphoma.
Tyran, Marguerite; Gonzague, Laurence; Bouabdallah, Reda; Resbeut, Michel
2014-01-01
Localized Nodular Lymphocyte Predominant Hodgkin Lymphoma is a rare disease with an overall good prognosis but frequent late relapses. Due to it's rarity there is no standard therapeutic approach and pathological diagnosis may be hard. In this paper we discuss the technical aspects of the radiation therapy and histological issues. The new fields reductions proposed for classical Hodgkin lymphoma cannot be applied to early stages Nodular Lymphocyte Predominant Hodgkin lymphomas which are usually treated with radiation therapy without systemic chemotherapy.
Abdominal Pain-Predominant Functional Gastrointestinal Disorders in Jordanian School Children.
Altamimi, Eyad M; Al-Safadi, Mohammad H
2014-12-01
Recurrent abdominal pain (RAP) is a common complaint in children. Significant portion of them are of functional origin. This study aimed to assess the prevalence of abdominal pain-predominant functional gastrointestinal disorder (FGID) and its types in Jordanian school children. This is a school-based survey at south Jordan. Information using the self-reporting form of the Questionnaire on Pediatric Gastrointestinal Symptoms-Rome III Version (QPGS-RIII) - the official Arabic translation - was collected. Classes from academic years (grades) 6 - 8 were selected. SPSS Statistical Package Version 17 (IBM, Armonk, NY, USA) was used. Categorical data were analyzed using Fisher's exact test, and continuous data were analyzed using t -test. P abdominal pain-predominant FGID. Seventy-nine (68%) of them were females. Forty-seven (10.6%) had irritable bowel syndrome (IBS). Thirty-six (8%), 17 (3.8%), 11 (2.4%) and five (1.1%) had abdominal migraine, functional abdominal pain, functional abdominal pain syndrome and functional dyspepsia, respectively. Abdominal pain-predominant FGID has become a major health issue in Jordanian children. One of four children between the ages of 11 and 15 years exhibits at least one abdominal pain-predominant FGID. The most common form of abdominal pain-predominant FGID in our children was IBS. Females are affected more often than males. Intestinal and extra-intestinal symptoms are seen regularly with abdominal pain-predominant FGIDs.
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
Bootstrapping phylogenies inferred from rearrangement data.
Lin, Yu; Rajan, Vaibhav; Moret, Bernard Me
2012-08-29
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. 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. Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its support values follow a similar scale and its receiver
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...
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…
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…
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. (c) 2012 APA, all rights reserved.
Hofmann, B
2008-06-01
Are there similarities between scientific and moral inference? This is the key question in this article. It takes as its point of departure an instance of one person's story in the media changing both Norwegian public opinion and a brand-new Norwegian law prohibiting the use of saviour siblings. The case appears to falsify existing norms and to establish new ones. The analysis of this case reveals similarities in the modes of inference in science and morals, inasmuch as (a) a single case functions as a counter-example to an existing rule; (b) there is a common presupposition of stability, similarity and order, which makes it possible to reason from a few cases to a general rule; and (c) this makes it possible to hold things together and retain order. In science, these modes of inference are referred to as falsification, induction and consistency. In morals, they have a variety of other names. Hence, even without abandoning the fact-value divide, there appear to be similarities between inference in science and inference in morals, which may encourage communication across the boundaries between "the two cultures" and which are relevant to medical humanities.
An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.
Liang, Ying; Liao, Bo; Zhu, Wen
2017-01-01
Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.
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.
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.
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 Hardware
Directory of Open Access Journals (Sweden)
Chetan Singh Thakur
2016-03-01
Full Text Available 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.
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.
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
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.
Survival pathways under stress
Indian Academy of Sciences (India)
First page Back Continue Last page Graphics. Survival pathways under stress. Bacteria survive by changing gene expression. pattern. Three important pathways will be discussed: Stringent response. Quorum sensing. Proteins performing function to control oxidative damage.
Pillow, Bradford H; Pearson, Raeanne M; Hecht, Mary; Bremer, Amanda
2010-01-01
Children and adults rated their own certainty following inductive inferences, deductive inferences, and guesses. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Deductions were rated as more certain than strong inductions beginning in Grade 3, and fourth-grade children and adults differentiated strong inductions, weak inductions, and informed guesses from pure guesses. By Grade 3, participants also gave different types of explanations for their deductions and inductions. These results are discussed in relation to children's concepts of cognitive processes, logical reasoning, and epistemological development.
Robust Inference with Multi-way Clustering
A. Colin Cameron; Jonah B. Gelbach; Douglas L. Miller; Doug Miller
2009-01-01
In this paper we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our...
Approximate Inference and Deep Generative Models
CERN. Geneva
2018-01-01
Advances in deep generative models are at the forefront of deep learning research because of the promise they offer for allowing data-efficient learning, and for model-based reinforcement learning. In this talk I'll review a few standard methods for approximate inference and introduce modern approximations which allow for efficient large-scale training of a wide variety of generative models. Finally, I'll demonstrate several important application of these models to density estimation, missing data imputation, data compression and planning.
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....
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...
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.
Dynamic Behavior of Spicules Inferred from Perpendicular Velocity Components
Energy Technology Data Exchange (ETDEWEB)
Sharma, Rahul; Verth, Gary; Erdélyi, Robertus [Solar Physics and Space Plasma Research Centre, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH (United Kingdom)
2017-05-10
Understanding the dynamic behavior of spicules, e.g., in terms of magnetohydrodynamic (MHD) wave mode(s), is key to unveiling their role in energy and mass transfer from the photosphere to corona. The transverse, torsional, and field-aligned motions of spicules have previously been observed in imaging spectroscopy and analyzed separately for embedded wave-mode identification. Similarities in the Doppler signatures of spicular structures for both kink and torsional Alfvén wave modes have led to the misinterpretation of the dominant wave mode in these structures and is a subject of debate. Here, we aim to combine line- of-sight (LOS) and plane-of-sky (POS) velocity components using the high spatial/temporal resolution H α imaging-spectroscopy data from the CRisp Imaging SpectroPolarimeter based at the Swedish Solar Telescope to achieve better insight into the underlying nature of these motions as a whole. The resultant three-dimensional velocity vectors and the other derived quantities (e.g., magnetic pressure perturbations) are used to identify the MHD wave mode(s) responsible for the observed spicule motion. We find a number of independent examples where the bulk transverse motion of the spicule is dominant either in the POS or along the LOS. It is shown that the counterstreaming action of the displaced external plasma due to spicular bulk transverse motion has a similar Doppler profile to that of the m = 0 torsional Alfvén wave when this motion is predominantly perpendicular to the LOS. Furthermore, the inferred magnetic pressure perturbations support the kink wave interpretation of observed spicular bulk transverse motion rather than any purely incompressible MHD wave mode, e.g., the m = 0 torsional Alfvén wave.
Inferring patterns of folktale diffusion using genomic data
Bortolini, Eugenio; Pagani, Luca; Sarno, Stefania; Boattini, Alessio; Sazzini, Marco; da Silva, Sara Graça; Martini, Gessica; Metspalu, Mait; Pettener, Davide; Luiselli, Donata; Tehrani, Jamshid J.
2017-01-01
Observable patterns of cultural variation are consistently intertwined with demic movements, cultural diffusion, and adaptation to different ecological contexts [Cavalli-Sforza and Feldman (1981) Cultural Transmission and Evolution: A Quantitative Approach; Boyd and Richerson (1985) Culture and the Evolutionary Process]. The quantitative study of gene–culture coevolution has focused in particular on the mechanisms responsible for change in frequency and attributes of cultural traits, the spread of cultural information through demic and cultural diffusion, and detecting relationships between genetic and cultural lineages. Here, we make use of worldwide whole-genome sequences [Pagani et al. (2016) Nature 538:238–242] to assess the impact of processes involving population movement and replacement on cultural diversity, focusing on the variability observed in folktale traditions (n = 596) [Uther (2004) The Types of International Folktales: A Classification and Bibliography. Based on the System of Antti Aarne and Stith Thompson] in Eurasia. We find that a model of cultural diffusion predicted by isolation-by-distance alone is not sufficient to explain the observed patterns, especially at small spatial scales (up to ∼4,000 km). We also provide an empirical approach to infer presence and impact of ethnolinguistic barriers preventing the unbiased transmission of both genetic and cultural information. After correcting for the effect of ethnolinguistic boundaries, we show that, of the alternative models that we propose, the one entailing cultural diffusion biased by linguistic differences is the most plausible. Additionally, we identify 15 tales that are more likely to be predominantly transmitted through population movement and replacement and locate putative focal areas for a set of tales that are spread worldwide. PMID:28784786
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.
A case study in pathway knowledgebase verification.
Racunas, Stephen A; Shah, Nigam H; Fedoroff, Nina V
2006-04-08
Biological databases and pathway knowledge-bases 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 knowledge-base as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference. We design a series of logical tests to detect potential problems we might encounter using a particular knowledge-base, 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. 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 knowledge-base to support hypothesis evaluation tools. The methodology we use is general and is in no way restricted to the specific knowledge-base 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.
Huggett, Daniel James
2017-01-01
The National Aeronautics and Space Administration (NASA) provides a formal training program for prospective employees titled, Pathways Intern Employment. The Pathways program targets graduate and undergraduate students who strive to become an active contributor to NASA's goal of space exploration. The report herein provides an account of Daniel Huggett's Pathways experience for the Spring and Summer 2017 semesters.
Neurophysiology and itch pathways.
Schmelz, Martin
2015-01-01
As we all can easily differentiate the sensations of itch and pain, the most straightforward neurophysiologic concept would consist of two specific pathways that independently encode itch and pain. Indeed, a neuronal pathway for histamine-induced itch in the peripheral and central nervous system has been described in animals and humans, and recently several non-histaminergic pathways for itch have been discovered in rodents that support a dichotomous concept differentiated into a pain and an itch pathway, with both pathways being composed of different "flavors." Numerous markers and mediators have been found that are linked to itch processing pathways. Thus, the delineation of neuronal pathways for itch from pain pathways seemingly proves that all sensory aspects of itch are based on an itch-specific neuronal pathway. However, such a concept is incomplete as itch can also be induced by the activation of the pain pathway in particular when the stimulus is applied in a highly localized spatial pattern. These opposite views reflect the old dispute between specificity and pattern theories of itch. Rather than only being of theoretic interest, this conceptual problem has key implication for the strategy to treat chronic itch as key therapeutic targets would be either itch-specific pathways or unspecific nociceptive pathways.
Long-Range Correlation in alpha-Wave Predominant EEG in Human
Sharif, Asif; Chyan Lin, Der; Kwan, Hon; Borette, D. S.
2004-03-01
The background noise in the alpha-predominant EEG taken from eyes-open and eyes-closed neurophysiological states is studied. Scale-free characteristic is found in both cases using the wavelet approach developed by Simonsen and Nes [1]. The numerical results further show the scaling exponent during eyes-closed is consistently lower than eyes-open. We conjecture the origin of this difference is related to the temporal reconfiguration of the neural network in the brain. To further investigate the scaling structure of the EEG background noise, we extended the second order statistics to higher order moments using the EEG increment process. We found that the background fluctuation in the alpha-predominant EEG is predominantly monofractal. Preliminary results are given to support this finding and its implication in brain functioning is discussed. [1] A.H. Simonsen and O.M. Nes, Physical Review E, 58, 2779¡V2748 (1998).
de Rugy, Aymar; Riek, Stephan; Carson, Richard G
2006-01-01
The authors tested for predominant patterns of coordination in the combination of rhythmic flexion-extension (FE) and supination- (SP) at the elbow-joint complex. Participants (N=10) spontaneously established in-phase (supination synchronized with flexion) and antiphase (pronation synchronized with flexion) patterns. In addition, the authors used a motorized robot arm to generate involuntary SP movements with different phase relations with respect to voluntary FE. The involuntarily induced in-phase pattern was accentuated and was more consistent than other patterns. The result provides evidence that the predominance of the in-phase pattern originates in the influence of neuromuscular-skeletal constraints rather than in a preference dictated by perceptual-cognitive factors implicated in voluntary control. Neuromuscular-skeletal constraints involved in the predominance of the in-phase and the antiphase patterns are discussed.
Enhanced thyroid iodine metabolism in patients with triiodothyronine-predominant Graves' disease
International Nuclear Information System (INIS)
Takamatsu, J.; Hosoya, T.; Naito, N.
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
Quantum Enhanced Inference in Markov Logic Networks.
Wittek, Peter; Gogolin, Christian
2017-04-19
Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.
Inferring network topology from complex dynamics
International Nuclear Information System (INIS)
Shandilya, Srinivas Gorur; Timme, Marc
2011-01-01
Inferring the network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method for inferring the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is hardly restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology from observing a time series of state variables only. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstructing both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of external noise that distorts the original dynamics substantially. The method provides a conceptually new step towards reconstructing a variety of real-world networks, including gene and protein interaction networks and neuronal circuits.
Inferring climate sensitivity from volcanic events
Energy Technology Data Exchange (ETDEWEB)
Boer, G.J. [Environment Canada, University of Victoria, Canadian Centre for Climate Modelling and Analysis, Victoria, BC (Canada); Stowasser, M.; Hamilton, K. [University of Hawaii, International Pacific Research Centre, Honolulu, HI (United States)
2007-04-15
The possibility of estimating the equilibrium climate sensitivity of the earth-system from observations following explosive volcanic eruptions is assessed in the context of a perfect model study. Two modern climate models (the CCCma CGCM3 and the NCAR CCSM2) with different equilibrium climate sensitivities are employed in the investigation. The models are perturbed with the same transient volcano-like forcing and the responses analysed to infer climate sensitivities. For volcano-like forcing the global mean surface temperature responses of the two models are very similar, despite their differing equilibrium climate sensitivities, indicating that climate sensitivity cannot be inferred from the temperature record alone even if the forcing is known. Equilibrium climate sensitivities can be reasonably determined only if both the forcing and the change in heat storage in the system are known very accurately. The geographic patterns of clear-sky atmosphere/surface and cloud feedbacks are similar for both the transient volcano-like and near-equilibrium constant forcing simulations showing that, to a considerable extent, the same feedback processes are invoked, and determine the climate sensitivity, in both cases. (orig.)
Facility Activity Inference Using Radiation Networks
Energy Technology Data Exchange (ETDEWEB)
Rao, Nageswara S. [ORNL; Ramirez Aviles, Camila A. [ORNL
2017-11-01
We consider the problem of inferring the operational status of a reactor facility using measurements from a radiation sensor network deployed around the facility’s ventilation off-gas stack. The intensity of stack emissions decays with distance, and the sensor counts or measurements are inherently random with parameters determined by the intensity at the sensor’s location. We utilize the measurements to estimate the intensity at the stack, and use it in a one-sided Sequential Probability Ratio Test (SPRT) to infer on/off status of the reactor. We demonstrate the superior performance of this method over conventional majority fusers and individual sensors using (i) test measurements from a network of 21 NaI detectors, and (ii) effluence measurements collected at the stack of a reactor facility. We also analytically establish the superior detection performance of the network over individual sensors with fixed and adaptive thresholds by utilizing the Poisson distribution of the counts. We quantify the performance improvements of the network detection over individual sensors using the packing number of the intensity space.
Models for inference in dynamic metacommunity systems
Dorazio, Robert M.; Kery, Marc; Royle, J. Andrew; Plattner, Matthias
2010-01-01
A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species- and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.
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. Published 2013. This article is a US Government work and is in the public domain in the USA.
Inferring relevance in a changing world
Directory of Open Access Journals (Sweden)
Robert C Wilson
2012-01-01
Full Text Available Reinforcement learning models of human and animal learning usually concentrate on how we learn the relationship between different stimuli or actions and rewards. However, in real world situations stimuli are ill-defined. On the one hand, our immediate environment is extremely multi-dimensional. On the other hand, in every decision-making scenario only a few aspects of the environment are relevant for obtaining reward, while most are irrelevant. Thus a key question is how do we learn these relevant dimensions, that is, how do we learn what to learn about? We investigated this process of representation learning experimentally, using a task in which one stimulus dimension was relevant for determining reward at each point in time. As in real life situations, in our task the relevant dimension can change without warning, adding ever-present uncertainty engendered by a constantly changing environment. We show that human performance on this task is better described by a suboptimal strategy based on selective attention and serial hypothesis testing rather than a normative strategy based on probabilistic inference. From this, we conjecture that the problem of inferring relevance in general scenarios is too computationally demanding for the brain to solve optimally. As a result the brain utilizes approximations, employing these even in simplified scenarios in which optimal representation learning is tractable, such as the one in our experiment.
Automated adaptive inference of phenomenological dynamical models
Daniels, Bryan
Understanding the dynamics of biochemical systems can seem impossibly complicated at the microscopic level: detailed properties of every molecular species, including those that have not yet been discovered, could be important for producing macroscopic behavior. The profusion of data in this area has raised the hope that microscopic dynamics might be recovered in an automated search over possible models, yet the combinatorial growth of this space has limited these techniques to systems that contain only a few interacting species. We take a different approach inspired by coarse-grained, phenomenological models in physics. Akin to a Taylor series producing Hooke's Law, forgoing microscopic accuracy allows us to constrain the search over dynamical models to a single dimension. This makes it feasible to infer dynamics with very limited data, including cases in which important dynamical variables are unobserved. We name our method Sir Isaac after its ability to infer the dynamical structure of the law of gravitation given simulated planetary motion data. Applying the method to output from a microscopically complicated but macroscopically simple biological signaling model, it is able to adapt the level of detail to the amount of available data. Finally, using nematode behavioral time series data, the method discovers an effective switch between behavioral attractors after the application of a painful stimulus.
Graphical models for inferring single molecule dynamics
Directory of Open Access Journals (Sweden)
Gonzalez Ruben L
2010-10-01
Full Text Available Abstract Background The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM. The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM with Gaussian observables. A detailed description of smFRET is provided as well. Results The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME, and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML optimized by the expectation maximization (EM algorithm, the most important being a natural form of model selection and a well-posed (non-divergent optimization problem. Conclusions The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.
Quantum Enhanced Inference in Markov Logic Networks
Wittek, Peter; Gogolin, Christian
2017-04-01
Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.
Causal Inference in the Perception of Verticality.
de Winkel, Ksander N; Katliar, Mikhail; Diers, Daniel; Bülthoff, Heinrich H
2018-04-03
The perceptual upright is thought to be constructed by the central nervous system (CNS) as a vector sum; by combining estimates on the upright provided by the visual system and the body's inertial sensors with prior knowledge that upright is usually above the head. Recent findings furthermore show that the weighting of the respective sensory signals is proportional to their reliability, consistent with a Bayesian interpretation of a vector sum (Forced Fusion, FF). However, violations of FF have also been reported, suggesting that the CNS may rely on a single sensory system (Cue Capture, CC), or choose to process sensory signals based on inferred signal causality (Causal Inference, CI). We developed a novel alternative-reality system to manipulate visual and physical tilt independently. We tasked participants (n = 36) to indicate the perceived upright for various (in-)congruent combinations of visual-inertial stimuli, and compared models based on their agreement with the data. The results favor the CI model over FF, although this effect became unambiguous only for large discrepancies (±60°). We conclude that the notion of a vector sum does not provide a comprehensive explanation of the perception of the upright, and that CI offers a better alternative.
Predominant CD4 T-lymphocyte tropism of human herpesvirus 6-related virus.
Takahashi, K; Sonoda, S; Higashi, K; Kondo, T; Takahashi, H; Takahashi, M; Yamanishi, K
1989-01-01
Human herpesvirus 6 (HHV-6)-related virus was isolated from CD4+ CD8- and CD3+ CD4+ mature T lymphocytes but could not be isolated from CD4- CD8+, CD4- CD8-, and CD3- T cells in the peripheral blood of exanthem subitum patients. HHV-6-related virus predominantly infected CD4+ CD8+, CD4+ CD8-, and CD3+ CD4+ cells with mature phenotypes and rarely infected CD4- CD8+ cells from cord blood mononuclear cells, which suggested predominant CD4 mature T-lymphocyte tropism of HHV-6-related virus.
Haber, Noah; Smith, Emily R; Moscoe, Ellen; Andrews, Kathryn; Audy, Robin; Bell, Winnie; Brennan, Alana T; Breskin, Alexander; Kane, Jeremy C; Karra, Mahesh; McClure, Elizabeth S; Suarez, Elizabeth A
2018-01-01
The pathway from evidence generation to consumption contains many steps which can lead to overstatement or misinformation. The proliferation of internet-based health news may encourage selection of media and academic research articles that overstate strength of causal inference. We investigated the state of causal inference in health research as it appears at the end of the pathway, at the point of social media consumption. We screened the NewsWhip Insights database for the most shared media articles on Facebook and Twitter reporting about peer-reviewed academic studies associating an exposure with a health outcome in 2015, extracting the 50 most-shared academic articles and media articles covering them. We designed and utilized a review tool to systematically assess and summarize studies' strength of causal inference, including generalizability, potential confounders, and methods used. These were then compared with the strength of causal language used to describe results in both academic and media articles. Two randomly assigned independent reviewers and one arbitrating reviewer from a pool of 21 reviewers assessed each article. We accepted the most shared 64 media articles pertaining to 50 academic articles for review, representing 68% of Facebook and 45% of Twitter shares in 2015. Thirty-four percent of academic studies and 48% of media articles used language that reviewers considered too strong for their strength of causal inference. Seventy percent of academic studies were considered low or very low strength of inference, with only 6% considered high or very high strength of causal inference. The most severe issues with academic studies' causal inference were reported to be omitted confounding variables and generalizability. Fifty-eight percent of media articles were found to have inaccurately reported the question, results, intervention, or population of the academic study. We find a large disparity between the strength of language as presented to the
Constraint Satisfaction Inference : Non-probabilistic Global Inference for Sequence Labelling
Canisius, S.V.M.; van den Bosch, A.; Daelemans, W.; Basili, R.; Moschitti, A.
2006-01-01
We present a new method for performing sequence labelling based on the idea of using a machine-learning classifier to generate several possible output sequences, and then applying an inference procedure to select the best sequence among those. Most sequence labelling methods following a similar
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...
Flores, Judith; Garcia, Silvia
2009-01-01
Judith Flores and Silvia Garcia (University of Utah) draw from the work of their mentor, Rina Benmayor and "Telling to live: Latina feminist testimonios" to establish an organization for Latinas who are staff, faculty, students, alumni, and community members at a predominantly White institution (PWI). Critical race feminism (CRF),…
Stanley, Christine A.
2006-01-01
This article, based on a larger, autoethnographic qualitative research project, focuses on the first-hand experiences of 27 faculty of color teaching in predominantly White colleges and universities. The 27 faculty represented a variety of institutions, disciplines, academic titles, and ranks. They identified themselves as African American,…
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…
Social Functioning in Predominantly Inattentive and Combined Subtypes of Children with ADHD
Solanto, Mary V.; Pope-Boyd, Sabrina A.; Tryon, Warren W.; Stepak, Brenda
2009-01-01
Objective: The objective of this study was to compare the social functioning of children with the Combined (CB) and Predominantly Inattentive (PI) subtypes of Attention Deficit/Hyperactivity Disorder (ADHD), controlling for comorbidity and medication-status, which may have confounded the results of previous research. Method: Parents and teachers…
Thompson, Loren Wright
2017-01-01
The purpose of this study was to examine of stereotype vulnerability, sense of belonging and campus climate for African American college students at a Predominately White Institution (PWI) in the Southeast. This research used a sociocultural model to explore African American student perceptions at a PWI in the southeast of the United States. This…
Miller, Hannah; Robinson, Michelle; Valentine, Jessa Lewis; Fish, Rachel
2016-01-01
Strong parent-teacher relationships are critical to students' academic success. Mismatches in parents' and teachers' perceptions of each other may negatively affect children's outcomes. Using survey data collected from parents and teachers in 52 low-income, predominantly Latino schools, we explore subgroup variation in parents' and teachers'…
Stebleton, Michael J.; Aleixo, Marina B.
2016-01-01
A growing number of college-age Blacks in the United States are Black African immigrants. Using a constructivist grounded theory approach, the researchers interviewed 12 undergraduate Black African immigrant college students attending a predominately White institution (PWI) about their experiences and perceptions of belonging. Findings suggest…
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…
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. Copyright © 2016 Elsevier Ltd. All rights reserved.
Pre- and perinatal risk factors for pyloric stenosis and their influence on the male predominance
DEFF Research Database (Denmark)
Krogh, Camilla; Gørtz, Sanne; Wohlfahrt, Jan
2012-01-01
whether these factors modified the male predominance. Information on pre- and perinatal factors and pyloric stenosis was obtained from national registers. Poisson regression models were used to estimate rate ratios. Among 1,925,313 children, 3,174 had surgery for pyloric stenosis. The authors found...
Being White in Black Spaces: Teaching and Learning at a Predominately Black Institution
Harrington, Timothy E.; Thomas, Michael
2018-01-01
This paper serves as a beginning conversation of how two White males perspectives' were shaped and how those perspectives evolved while attending and teaching at a Predominately Black Institution (PBI). Their initial understandings of Whiteness are introduced. This is an ethnographic study that utilized personal narratives from a college professor…
A Predominately Female Accounting Profession: Lessons from the Past and Other Professions
Whitten, Donna
2016-01-01
Currently, the accounting profession is in the process of transitioning from a male dominated profession to a predominantly female one. Other professions that have undergone this switch experienced declines in the status of the profession and the salaries. So, although women have not yet gained equal access to all levels of the accounting…
Ross, Henry H.; Edwards, Willie J.
2016-01-01
A Delphi method was used with a panel of 24 African American faculty employed at 43 predominantly white doctoral extensive universities to arrive at a group consensus on a list of concerns that African American faculty in general experienced or held. Using the Delphi method a panel of African American faculty initially worked from a list of eight…
Breast milk and energy intake in exclusively, predominantly, and partially breast-fed infants
Haisma, H; Coward, WA; Albernaz, E; Visser, GH; Wells, JCK; Wright, A; Victoria, CG; Victora, C.G.
2003-01-01
Objective: To investigate the extent to which breast milk is replaced by intake of other liquids or foods, and to estimate energy intake of infants defined as exclusively (EBF), predominantly (PBF) and partially breast-fed (PartBF). Design: Cross-sectional. Setting: Community-based study in urban
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…
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…
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
DEFF Research Database (Denmark)
Rügge, Kirsten; Hofstetter, Thomas B.; Haderlein, Stefan B.
1998-01-01
The biogeochemical processes controlling the reductive transformation of contaminants in an anaerobic aquifer were inferred from the relative reactivity patterns of redox-sensitive probe compounds. The fate of five nitroaromatic compounds (NACs) was monitored under different redox conditions in a...... results suggest that Fe(ll) associated with ferric iron minerals is a highly reactive reductant in anaerobic aquifers, which may also determine the fate of other classes of reducible contaminants such as halogenated solvents, azo compounds, sulfoxides, chromate, or arsenate....
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
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
Ngo, Chinh C.; Massa, Helen M.; Thornton, Ruth B.; Cripps, Allan W.
2016-01-01
Background 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. Methods 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. Results 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. Conclusions 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
Active Inference, homeostatic regulation and adaptive behavioural control.
Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl
2015-11-01
We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
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...
Bayesian inference and updating of reliability data
International Nuclear Information System (INIS)
Sabri, Z.A.; Cullingford, M.C.; David, H.T.; Husseiny, A.A.
1980-01-01
A Bayes methodology for inference of reliability values using available but scarce current data is discussed. The method can be used to update failure rates as more information becomes available from field experience, assuming that the performance of a given component (or system) exhibits a nonhomogeneous Poisson process. Bayes' theorem is used to summarize the historical evidence and current component data in the form of a posterior distribution suitable for prediction and for smoothing or interpolation. An example is given. It may be appropriate to apply the methodology developed here to human error data, in which case the exponential model might be used to describe the learning behavior of the operator or maintenance crew personnel
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.
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. Keywords: Oncology, Cancer research, Genetics, Computational biology
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.
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.
The PANTHER database of protein families, subfamilies, functions and pathways
Mi, Huaiyu; Lazareva-Ulitsky, Betty; Loo, Rozina; Kejariwal, Anish; Vandergriff, Jody; Rabkin, Steven; Guo, Nan; Muruganujan, Anushya; Doremieux, Olivier; Campbell, Michael J.; Kitano, Hiroaki; Thomas, Paul D.
2004-01-01
PANTHER is a large collection of protein families that have been subdivided into functionally related subfamilies, using human expertise. These subfamilies model the divergence of specific functions within protein families, allowing more accurate association with function (ontology terms and pathways), as well as inference of amino acids important for functional specificity. Hidden Markov models (HMMs) are built for each family and subfamily for classifying additional protein sequences. The l...
Genes encoding enzymes of the lignin biosynthesis pathway in Eucalyptus
Directory of Open Access Journals (Sweden)
Ricardo Harakava
2005-01-01
Full Text Available Eucalyptus ESTs libraries were screened for genes involved in lignin biosynthesis. This search was performed under the perspective of recent revisions on the monolignols biosynthetic pathway. Eucalyptus orthologues of all genes of the phenylpropanoid pathway leading to lignin biosynthesis reported in other plant species were identified. A library made with mRNAs extracted from wood was enriched for genes involved in lignin biosynthesis and allowed to infer the isoforms of each gene family that play a major role in wood lignin formation. Analysis of the wood library suggests that, besides the enzymes of the phenylpropanoids pathway, chitinases, laccases, and dirigent proteins are also important for lignification. Colocalization of several enzymes on the endoplasmic reticulum membrane, as predicted by amino acid sequence analysis, supports the existence of metabolic channeling in the phenylpropanoid pathway. This study establishes a framework for future investigations on gene expression level, protein expression and enzymatic assays, sequence polymorphisms, and genetic engineering.
Gene expression inference with deep learning.
Chen, Yifei; Li, Yi; Narayan, Rajiv; Subramanian, Aravind; Xie, Xiaohui
2016-06-15
Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. D-GEX is available at https://github.com/uci-cbcl/D-GEX CONTACT: xhx@ics.uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Systematic parameter inference in stochastic mesoscopic modeling
Energy Technology Data Exchange (ETDEWEB)
Lei, Huan; Yang, Xiu [Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Li, Zhen [Division of Applied Mathematics, Brown University, Providence, RI 02912 (United States); Karniadakis, George Em, E-mail: george_karniadakis@brown.edu [Division of Applied Mathematics, Brown University, Providence, RI 02912 (United States)
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.
State-Space Inference and Learning with Gaussian Processes
Turner, R; Deisenroth, MP; Rasmussen, CE
2010-01-01
18.10.13 KB. Ok to add author version to spiral, authors hold copyright. State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. C...
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
Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds
Directory of Open Access Journals (Sweden)
Neelam Redekar
2017-11-01
Full Text Available A dominant loss of function mutation in myo-inositol phosphate synthase (MIPS gene and recessive loss of function mutations in two multidrug resistant protein type-ABC transporter genes not only reduce the seed phytic acid levels in soybean, but also affect the pathways associated with seed development, ultimately resulting in low emergence. To understand the regulatory mechanisms and identify key genes that intervene in the seed development process in low phytic acid crops, we performed computational inference of gene regulatory networks in low and normal phytic acid soybeans using a time course transcriptomic data and multiple network inference algorithms. We identified a set of putative candidate transcription factors and their regulatory interactions with genes that have functions in myo-inositol biosynthesis, auxin-ABA signaling, and seed dormancy. We evaluated the performance of our unsupervised network inference method by comparing the predicted regulatory network with published regulatory interactions in Arabidopsis. Some contrasting regulatory interactions were observed in low phytic acid mutants compared to non-mutant lines. These findings provide important hypotheses on expression regulation of myo-inositol metabolism and phytohormone signaling in developing low phytic acid soybeans. The computational pipeline used for unsupervised network learning in this study is provided as open source software and is freely available at https://lilabatvt.github.io/LPANetwork/.
MoCha: Molecular Characterization of Unknown Pathways.
Lobo, Daniel; Hammelman, Jennifer; Levin, Michael
2016-04-01
Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.
Enhanced thyroid iodine metabolism in patients with triiodothyronine-predominant Graves' disease
Energy Technology Data Exchange (ETDEWEB)
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.
Serang, Oliver
2014-01-01
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 and the space to where 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. PMID:24626234
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.
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.
Hereditary motor and sensory neuropathy with proximal predominance (HMSN-P).
Campellone, Joseph V
2013-06-01
Hereditary motor and sensory neuropathy with proximal predominance (HMSN-P) is a rare disorder inherited in an autosomal dominant fashion. Patients present with slowly progressive proximal-predominant weakness, painful muscle cramps, fasciculations, large-fiber sensory loss, and areflexia. Electrodiagnostic (EDX) studies typically reveal abnormalities consistent with a sensorimotor neuronopathy. A patient with HMSN-P underwent EDX studies, revealing ongoing and chronic neurogenic denervation, motor unit instability, and neuromyotonic discharges, further defining the spectrum of EDX findings in HMSN-P. The clinical, pathological, and genetic features are also reviewed. The appearance of HMSN-P in the United States and elsewhere calls for clinicians in nonendemic regions to be familiar with this rare disorder, which has typically been geographically confined.
Health and Well-being of Women Migrating from Predominantly Muslim Countries to the United States.
Kamimura, Akiko; Pye, Mu; Sin, Kai; Nourian, Maziar M; Assasnik, Nushean; Stoddard, Mary; Frost, Caren J
2018-01-01
The purpose of this study was to examine the health and well-being of women migrating from predominantly Muslim countries to the U.S. Women from predominantly Muslim countries completed a paper survey on the following topics from June to December in 2016 (N=102): depression; physical functioning; self-reported general health; experiences with health care; and demographic characteristics. There were several women's health-related issues: low rates for mammography and Pap smear screening, and preference for female physicians and/or physicians from the same culture. Only one-third of the participants had received a physical exam in the past year, and having done so was related to higher levels of depression and worse physical functioning. The participants who were not in a refugee camp reported higher levels of depression than those who were.
International Nuclear Information System (INIS)
Haas, Rick L.M.; Girinsky, Theo; Aleman, Berthe; Henry-Amar, Michel; Boer, Jan-Paul de; Jong, Daphne de
2009-01-01
Purpose: Nodular lymphocyte predominance Hodgkin's lymphoma is a very rare disease, characterized by an indolent clinical course, with sometimes very late relapses occurring in a minority of all patients. Considerable discussion is ongoing on the treatment of primary and relapsed disease. Patients and Methods: A group of 9 patients were irradiated to a dose of 4 Gy on involved areas only. Results: After a median follow-up of 37 months (range, 6-66), the overall response rate was 89%. Six patients had complete remission (67%), two had partial remission (22%), and one had stable disease (11%). Of 8 patients, 5 developed local relapse 9-57 months after radiotherapy. No toxicity was noted. Conclusion: In nodular lymphocyte predominance Hodgkin's lymphoma, low-dose radiotherapy provided excellent response rates and lasting remissions without significant toxicity.
The Lived Experience of Black Nurse Faculty in Predominantly White Schools of Nursing.
Whitfield-Harris, Lisa; Lockhart, Joan Such; Zoucha, Richard; Alexander, Rumay
2017-03-01
This study explored the experiences of Black nurse faculty employed in predominantly White schools of nursing. High attrition rates of this group were noted in previous literature. Understanding their experiences is important to increase nurse diversity. Hermeneutic phenomenology was used to explore the experiences of 15 Black nurse faculty using interviews. Four themes were extracted as the following: cultural norms of the workplace, coping with improper assets, life as a "Lone Ranger," and surviving the workplace environment. The study provided insight to understand the meaning that Black faculty members give to their experiences working in predominantly White schools of nursing. Findings exemplify the need to improve culturally competent work environments and mentoring programs. Results suggest that better communication and proper respect from students, colleagues, and administrators are necessary. The limited research on this topic illustrates that Black nurse faculty remain under investigated; research is necessary to determine effective change strategies.
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.
Abdominal Pain-Predominant Functional Gastrointestinal Disorders in Jordanian School Children
Altamimi, Eyad M.; Al-Safadi, Mohammad H.
2014-01-01
Background Recurrent abdominal pain (RAP) is a common complaint in children. Significant portion of them are of functional origin. This study aimed to assess the prevalence of abdominal pain-predominant functional gastrointestinal disorder (FGID) and its types in Jordanian school children. Methods This is a school-based survey at south Jordan. Information using the self-reporting form of the Questionnaire on Pediatric Gastrointestinal Symptoms-Rome III Version (QPGS-RIII) - the official Arabi...
Pulmonary microRNA profiling: implications in upper lobe predominant lung disease
Armstrong, David A.; Nymon, Amanda B.; Ringelberg, Carol S.; Lesseur, Corina; Hazlett, Haley F.; Howard, Louisa; Marsit, Carmen J.; Ashare, Alix
2017-01-01
Background Numerous pulmonary diseases manifest with upper lobe predominance including cystic fibrosis, smoking-related chronic obstructive pulmonary disease, and tuberculosis. Zonal hypoxia, characteristic of these pulmonary maladies, and oxygen stress in general is known to exert profound effects on various important aspects of cell biology. Lung macrophages are major participants in the pulmonary innate immune response and regional differences in macrophage responsiveness to hypoxia may co...
Predominantly Electronic or Personal Service Delivery? A Case in the Wealth Management Context
Sunikka, Anne
2009-01-01
Financial services have been a recurrent subject of a multichannel inquiry but investigation into the wealth management area is scarce. This paper intends to fill the gap and presents the results of a questionnaire directed at customers of a financial conglomerate. The objective of this research is to examine which variables influence consumers’ channel preferences in the wealth management context,and to find out possible differences between the customers who prefer predominantly electronic s...
Nonrapid Eye Movement-Predominant Obstructive Sleep Apnea: Detection and Mechanism.
Yamauchi, Motoo; Fujita, Yukio; Kumamoto, Makiko; Yoshikawa, Masanori; Ohnishi, Yoshinobu; Nakano, Hiroshi; Strohl, Kingman P; Kimura, Hiroshi
2015-09-15
Obstructive sleep apnea (OSA) can be severe and present in higher numbers during rapid eye movement (REM) than nonrapid eye movement (NREM) sleep; however, OSA occurs in NREM sleep and can be predominant. In general, ventilation decreases an average 10% to 15% during transition from wakefulness to sleep, and there is variability in just how much ventilation decreases. As dynamic changes in ventilation contribute to irregular breathing and breathing during NREM sleep is mainly under chemical control, our hypothesis is that patients with a more pronounced reduction in ventilation during the transition from wakefulness to NREM sleep will have NREM- predominant rather than REM-predominant OSA. A retrospective analysis of 451 consecutive patients (apnea-hypopnea index [AHI] > 5) undergoing diagnostic polysomnography was performed, and breath-to-breath analysis of the respiratory cycle duration, tidal volume, and estimated minute ventilation before and after sleep onset were examined. Values were calculated using respiratory inductance plethysmography. The correlation between the percent change in estimated minute ventilation during wake-sleep transitions and the percentage of apnea-hypopneas in NREM sleep (%AHI in NREM; defined as (AHI-NREM) / [(AHI-NREM) + (AHI-REM)] × 100) was the primary outcome. The decrease in estimated minute ventilation during wake-sleep transitions was 15.0 ± 16.6% (mean ± standard deviation), due to a decrease in relative tidal volume. This decrease in estimated minute ventilation was significantly correlated with %AHI in NREM (r = -0.222, p sleep contributes to the NREM predominant OSA phenotype via induced ventilatory instability. © 2015 American Academy of Sleep Medicine.
Correlated cone noise decreases rod signal contributions to the post-receptoral pathways.
Hathibelagal, Amithavikram R; Feigl, Beatrix; Zele, Andrew J
2018-04-01
This study investigated how invisible extrinsic temporal white noise that correlates with the activity of one of the three [magnocellular (MC), parvocellular (PC), or koniocellular (KC)] post-receptoral pathways alters mesopic rod signaling. A four-primary photostimulator provided independent control of the rod and three cone photoreceptor excitations. The rod contributions to the three post-receptoral pathways were estimated by perceptually matching a 20% contrast rod pulse by independently varying the LMS (MC pathway), +L-M (PC pathway), and S-cone (KC pathway) excitations. We show that extrinsic cone noise caused a predominant decrease in the overall magnitude and ratio of the rod contributions to each pathway. Thus, the relative cone activity in the post-receptoral pathways determines the relative mesopic rod inputs to each pathway.
Morcillo, N; Zumarraga, M; Imperiale, B; Di Giulio, B; Chirico, C; Kuriger, A; Alito, A; Kremer, K; Cataldi, A
2007-01-01
In 2003, the incidence of tuberculosis in Argentina showed an increase compared to 2002. The severe national crisis at the end of the 90s has probably strongly contributed to this situation. The goal of this work was to estimate the extent of the spread of the most predominant Mycobacterium tuberculosis strains and to assess the spread of predominant M. tuberculosis clusters as determined by spoligotyping and IS6110 RFLP. The study involved 590 pulmonary, smear-positive TB cases receiving medical attention at health centers and hospitals in Northern Buenos Aires (NBA) suburbs, from October 2001 to December 2002. From a total of 208 clinical isolates belonging to 6 major clusters, 63 (30.2%) isolates had identical spoligotyping and IS6110 RFLP pattern. Only 22.2% were shown to have epidemiological connections with another member of their respective cluster. In these major clusters, 30.2% of the 208 TB cases studied by both molecular techniques and contact tracing could be convincingly attributable to a recently acquired infection. This knowledge may be useful to assess the clonal distribution of predominant M. tuberculosis clusters in Argentina, which may make an impact on TB control strategies.
Is microscopic colitis a missed diagnosis in diarrhea-predominant Irritable Bowel Syndrome?
Directory of Open Access Journals (Sweden)
Hamid Tavakoli
2008-08-01
Full Text Available
Feng, Xiaomin; Dong, Honghong; Yang, Pan; Yang, Ruijuan; Lu, Jun; Lv, Jie; Sheng, Jun
2016-08-01
The fermentation process of Yunnan arabica coffee is a typical wet fermentation. Its excellent quality is closely related to microbes in the process of fermentation. The purpose of this study was to isolate and identify the microorganisms in the wet method of coffee processing in Yunnan Province, China. Microbial community structure and dominant bacterial species were evaluated by traditional cultivated separation method and PCR-DGGE technology, and were further analyzed in combination with the changes of organic acid content, activity of pectinase, and physical parameters (pH and temperature). A large number of microorganisms which can produce pectinase were found. Among them, Enterobacter cowanii, Pantoea agglomerans, Enterobacteriaceae bacterium, and Rahnella aquatilis were the predominant gram-negative bacteria, Bacillus cereus was the predominant gram-positive bacterium, Pichia kluyveri, Hanseniaspora uvarum, and Pichia fermentans were the predominant yeasts, and all those are pectinase-producing microorganisms. As for the contents of organic acids, oxalic was the highest, followed by acetic and lactic acids. Butyrate and propionate, which were unfavorable during the fermentation period, were barely discovered.
Paré, Pierre; Math, Joanna Lee M; Hawes, Ian A
2010-01-01
OBJECTIVE: To determine whether strategies to counsel and empower patients with heartburn-predominant dyspepsia could improve health-related quality of life. METHODS: Using a cluster randomized, parallel group, multicentre design, nine centres were assigned to provide either basic or comprehensive counselling to patients (age range 18 to 50 years) presenting with heartburn-predominant upper gastrointestinal symptoms, who would be considered for drug therapy without further investigation. Patients were treated for four weeks with esomeprazole 40 mg once daily, followed by six months of treatment that was at the physician’s discretion. The primary end point was the baseline change in Quality of Life in Reflux and Dyspepsia (QOLRAD) questionnaire score. RESULTS: A total of 135 patients from nine centres were included in the intention-to-treat analysis. There was a statistically significant baseline improvement in all domains of the QOLRAD questionnaire in both study arms at four and seven months (Pheartburn-predominant uninvestigated dyspepsia. Further investigation is needed to confirm the potential benefits of providing patients with comprehensive counselling regarding disease management. PMID:20352148
Baldwin, Barbara, Ed.
1995-01-01
Articles in this theme issue are based on presentations at the Pathways from Poverty Workshop held in Albuquerque, New Mexico, on May 18-25, 1995. The event aimed to foster development of a network to address rural poverty issues in the Western Rural Development Center (WRDC) region. Articles report on outcomes from the Pathways from Poverty…
Making Inferences in Adulthood: Falling Leaves Mean It's Fall.
Zandi, Taher; Gregory, Monica E.
1988-01-01
Assessed age differences in making inferences from prose. Older adults correctly answered mean of 10 questions related to implicit information and 8 related to explicit information. Young adults answered mean of 7 implicit and 12 explicit information questions. In spite of poorer recall of factual details, older subjects made inferences to greater…
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…
Causal Effect Inference with Deep Latent-Variable Models
Louizos, C; Shalit, U.; Mooij, J.; Sontag, D.; Zemel, R.; Welling, M.
2017-01-01
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
Fuzzy inference engine has found successful applications in a wide variety of fields, such as automatic control, data classification, decision analysis, expert engines, time series prediction, robotics, pattern recognition, etc. This paper presents a comparative analysis of three fuzzy inference engines, max-product, max-min ...
General Purpose Probabilistic Programming Platform with Effective Stochastic Inference
2018-04-01
REFERENCES 74 LIST OF ACRONYMS 80 ii List of Figures Figure 1. The problem of inferring curves from data while simultaneously choosing the...bottom path) as the inverse problem to computer graphics (top path). ........ 18 Figure 18. An illustration of generative probabilistic graphics for 3D...Building these systems involves simultaneously developing mathematical models, inference algorithms and optimized software implementations. Small changes
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
Fuzzy Inference engine is an important part of reasoning systems capable of extracting correct conclusions from ... is known as the inference, or rule definition portion, of fuzzy .... minimal set of decision rules based on input- ... The study uses Mamdani FIS model and. Sugeno FIS ... control of induction motor drive. [18] study.
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…
Causal inference in survival analysis using pseudo-observations
DEFF Research Database (Denmark)
Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T
2017-01-01
Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs ...
Crystallization Pathways in Biomineralization
Weiner, Steve; Addadi, Lia
2011-08-01
A crystallization pathway describes the movement of ions from their source to the final product. Cells are intimately involved in biological crystallization pathways. In many pathways the cells utilize a unique strategy: They temporarily concentrate ions in intracellular membrane-bound vesicles in the form of a highly disordered solid phase. This phase is then transported to the final mineralization site, where it is destabilized and crystallizes. We present four case studies, each of which demonstrates specific aspects of biological crystallization pathways: seawater uptake by foraminifera, calcite spicule formation by sea urchin larvae, goethite formation in the teeth of limpets, and guanine crystal formation in fish skin and spider cuticles. Three representative crystallization pathways are described, and aspects of the different stages of crystallization are discussed. An in-depth understanding of these complex processes can lead to new ideas for synthetic crystallization processes of interest to materials science.
Bayesian inference of radiation belt loss timescales.
Camporeale, E.; Chandorkar, M.
2017-12-01
Electron fluxes in the Earth's radiation belts are routinely studied using the classical quasi-linear radial diffusion model. Although this simplified linear equation has proven to be an indispensable tool in understanding the dynamics of the radiation belt, it requires specification of quantities such as the diffusion coefficient and electron loss timescales that are never directly measured. Researchers have so far assumed a-priori parameterisations for radiation belt quantities and derived the best fit using satellite data. The state of the art in this domain lacks a coherent formulation of this problem in a probabilistic framework. We present some recent progress that we have made in performing Bayesian inference of radial diffusion parameters. We achieve this by making extensive use of the theory connecting Gaussian Processes and linear partial differential equations, and performing Markov Chain Monte Carlo sampling of radial diffusion parameters. These results are important for understanding the role and the propagation of uncertainties in radiation belt simulations and, eventually, for providing a probabilistic forecast of energetic electron fluxes in a Space Weather context.
Scalable inference for stochastic block models
Peng, Chengbin
2017-12-08
Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of "big data," traditional inference algorithms for such a model are increasingly limited due to their high time complexity and poor scalability. In this paper, we propose a multi-stage maximum likelihood approach to recover the latent parameters of the stochastic block model, in time linear with respect to the number of edges. We also propose a parallel algorithm based on message passing. Our algorithm can overlap communication and computation, providing speedup without compromising accuracy as the number of processors grows. For example, to process a real-world graph with about 1.3 million nodes and 10 million edges, our algorithm requires about 6 seconds on 64 cores of a contemporary commodity Linux cluster. Experiments demonstrate that the algorithm can produce high quality results on both benchmark and real-world graphs. An example of finding more meaningful communities is illustrated consequently in comparison with a popular modularity maximization algorithm.
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
Heuristics as Bayesian inference under extreme priors.
Parpart, Paula; Jones, Matt; Love, Bradley C
2018-05-01
Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Aesthetic quality inference for online fashion shopping
Chen, Ming; Allebach, Jan
2014-03-01
On-line fashion communities in which participants post photos of personal fashion items for viewing and possible purchase by others are becoming increasingly popular. Generally, these photos are taken by individuals who have no training in photography with low-cost mobile phone cameras. It is desired that photos of the products have high aesthetic quality to improve the users' online shopping experience. In this work, we design features for aesthetic quality inference in the context of online fashion shopping. Psychophysical experiments are conducted to construct a database of the photos' aesthetic evaluation, specifically for photos from an online fashion shopping website. We then extract both generic low-level features and high-level image attributes to represent the aesthetic quality. Using a support vector machine framework, we train a predictor of the aesthetic quality rating based on the feature vector. Experimental results validate the efficacy of our approach. Metadata such as the product type are also used to further improve the result.
Information-Theoretic Inference of Common Ancestors
Directory of Open Access Journals (Sweden)
Bastian Steudel
2015-04-01
Full Text Available A directed acyclic graph (DAG partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of conditional independence relations. We are interested in properties of this class that can be derived from observations of a subsystem only. To this end, we prove an information-theoretic inequality that allows for the inference of common ancestors of observed parts in any DAG representing some unknown larger system. More explicitly, we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information. Within the causal interpretation of DAGs, our result can be seen as a quantitative extension of Reichenbach’s principle of common cause to more than two variables. Our conclusions are valid also for non-probabilistic observations, such as binary strings, since we state the proof for an axiomatized notion of “mutual information” that includes the stochastic as well as the algorithmic version.
Probabilistic learning and inference in schizophrenia.
Averbeck, Bruno B; Evans, Simon; Chouhan, Viraj; Bristow, Eleanor; Shergill, Sukhwinder S
2011-04-01
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 behavior 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 behavior, 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 a 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. Published by Elsevier B.V.
Active Inference and Learning in the Cerebellum.
Friston, Karl; Herreros, Ivan
2016-09-01
This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.
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.; 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 = 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.
BAYESIAN INFERENCE OF CMB GRAVITATIONAL LENSING
Energy Technology Data Exchange (ETDEWEB)
Anderes, Ethan [Department of Statistics, University of California, Davis, CA 95616 (United States); Wandelt, Benjamin D.; Lavaux, Guilhem [Sorbonne Universités, UPMC Univ Paris 06 and CNRS, UMR7095, Institut d’Astrophysique de Paris, F-75014, Paris (France)
2015-08-01
The Planck satellite, along with several ground-based telescopes, has mapped the cosmic microwave background (CMB) at sufficient resolution and signal-to-noise so as to allow a detection of the subtle distortions due to the gravitational influence of the intervening matter distribution. A natural modeling approach is to write a Bayesian hierarchical model for the lensed CMB in terms of the unlensed CMB and the lensing potential. So far there has been no feasible algorithm for inferring the posterior distribution of the lensing potential from the lensed CMB map. We propose a solution that allows efficient Markov Chain Monte Carlo sampling from the joint posterior of the lensing potential and the unlensed CMB map using the Hamiltonian Monte Carlo technique. The main conceptual step in the solution is a re-parameterization of CMB lensing in terms of the lensed CMB and the “inverse lensing” potential. We demonstrate a fast implementation on simulated data, including noise and a sky cut, that uses a further acceleration based on a very mild approximation of the inverse lensing potential. We find that the resulting Markov Chain has short correlation lengths and excellent convergence properties, making it promising for applications to high-resolution CMB data sets in the future.
Virtual reality and consciousness inference in dreaming.
Hobson, J Allan; Hong, Charles C-H; Friston, Karl J
2014-01-01
This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that - through experience-dependent plasticity - becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep - and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain's generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis - evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research.
Inferring human mobility using communication patterns
Palchykov, Vasyl; Mitrović, Marija; Jo, Hang-Hyun; Saramäki, Jari; Pan, Raj Kumar
2014-08-01
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 alone. 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 data is required, the model helps to avoid potential privacy problems.
Inference-based procedural modeling of solids
Biggers, Keith
2011-11-01
As virtual environments become larger and more complex, there is an increasing need for more automated construction algorithms to support the development process. We present an approach for modeling solids by combining prior examples with a simple sketch. Our algorithm uses an inference-based approach to incrementally fit patches together in a consistent fashion to define the boundary of an object. This algorithm samples and extracts surface patches from input models, and develops a Petri net structure that describes the relationship between patches along an imposed parameterization. Then, given a new parameterized line or curve, we use the Petri net to logically fit patches together in a manner consistent with the input model. This allows us to easily construct objects of varying sizes and configurations using arbitrary articulation, repetition, and interchanging of parts. The result of our process is a solid model representation of the constructed object that can be integrated into a simulation-based environment. © 2011 Elsevier Ltd. All rights reserved.
Multiple sequence alignment accuracy and phylogenetic inference.
Ogden, T Heath; Rosenberg, Michael S
2006-04-01
Phylogenies are often thought to be more dependent upon the specifics of the sequence alignment rather than on the method of reconstruction. Simulation of sequences containing insertion and deletion events was performed in order to determine the role that alignment accuracy plays during phylogenetic inference. Data sets were simulated for pectinate, balanced, and random tree shapes under different conditions (ultrametric equal branch length, ultrametric random branch length, nonultrametric random branch length). Comparisons between hypothesized alignments and true alignments enabled determination of two measures of alignment accuracy, that of the total data set and that of individual branches. In general, our results indicate that as alignment error increases, topological accuracy decreases. This trend was much more pronounced for data sets derived from more pectinate topologies. In contrast, for balanced, ultrametric, equal branch length tree shapes, alignment inaccuracy had little average effect on tree reconstruction. These conclusions are based on average trends of many analyses under different conditions, and any one specific analysis, independent of the alignment accuracy, may recover very accurate or inaccurate topologies. Maximum likelihood and Bayesian, in general, outperformed neighbor joining and maximum parsimony in terms of tree reconstruction accuracy. Results also indicated that as the length of the branch and of the neighboring branches increase, alignment accuracy decreases, and the length of the neighboring branches is the major factor in topological accuracy. Thus, multiple-sequence alignment can be an important factor in downstream effects on topological reconstruction.
Phylogenetic inference with weighted codon evolutionary distances.
Criscuolo, Alexis; Michel, Christian J
2009-04-01
We develop a new approach to estimate a matrix of pairwise evolutionary distances from a codon-based alignment based on a codon evolutionary model. The method first computes a standard distance matrix for each of the three codon positions. Then these three distance matrices are weighted according to an estimate of the global evolutionary rate of each codon position and averaged into a unique distance matrix. Using a large set of both real and simulated codon-based alignments of nucleotide sequences, we show that this approach leads to distance matrices that have a significantly better treelikeness compared to those obtained by standard nucleotide evolutionary distances. We also propose an alternative weighting to eliminate the part of the noise often associated with some codon positions, particularly the third position, which is known to induce a fast evolutionary rate. Simulation results show that fast distance-based tree reconstruction algorithms on distance matrices based on this codon position weighting can lead to phylogenetic trees that are at least as accurate as, if not better, than those inferred by maximum likelihood. Finally, a well-known multigene dataset composed of eight yeast species and 106 codon-based alignments is reanalyzed and shows that our codon evolutionary distances allow building a phylogenetic tree which is similar to those obtained by non-distance-based methods (e.g., maximum parsimony and maximum likelihood) and also significantly improved compared to standard nucleotide evolutionary distance estimates.
Primate diversification inferred from phylogenies and fossils.
Herrera, James P
2017-12-01
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 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. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.
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.
Contingency inferences driven by base rates: Valid by sampling
Directory of Open Access Journals (Sweden)
Florian Kutzner
2011-04-01
Full Text Available Fiedler et al. (2009, reviewed evidence for the utilization of a contingency inference strategy termed pseudocontingencies (PCs. In PCs, the more frequent levels (and, by implication, the less frequent levels are assumed to be associated. PCs have been obtained using a wide range of task settings and dependent measures. Yet, the readiness with which decision makers rely on PCs is poorly understood. A computer simulation explored two potential sources of subjective validity of PCs. First, PCs are shown to perform above chance level when the task is to infer the sign of moderate to strong population contingencies from a sample of observations. Second, contingency inferences based on PCs and inferences based on cell frequencies are shown to partially agree across samples. Intriguingly, this criterion and convergent validity are by-products of random sampling error, highlighting the inductive nature of contingency inferences.
Quantum-Like Representation of Non-Bayesian Inference
Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.
2013-01-01
This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.
Statistical causal inferences and their applications in public health research
Wu, Pan; Chen, Ding-Geng
2016-01-01
This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
Human Inferences about Sequences: A Minimal Transition Probability Model.
Directory of Open Access Journals (Sweden)
Florent Meyniel
2016-12-01
Full Text Available The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.
Bayesian methods for hackers probabilistic programming and Bayesian inference
Davidson-Pilon, Cameron
2016-01-01
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...
Zhang, Yan; Liu, Dianming; Wang, Lihong; Wang, Shuyuan; Yu, Xuexin; Dai, Enyu; Liu, Xinyi; Luo, Shanshun; Jiang, Wei
2015-12-01
Coronary artery disease (CAD) is the most common type of heart disease. However, the molecular mechanisms of CAD remain elusive. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, inferring risk regulatory pathways is an important step toward elucidating the mechanisms underlying CAD. With advances in high-throughput data, we developed an integrated systems approach to identify CAD risk regulatory pathways and key regulators. Firstly, a CAD-related core subnetwork was identified from a curated transcription factor (TF) and microRNA (miRNA) regulatory network based on a random walk algorithm. Secondly, candidate risk regulatory pathways were extracted from the subnetwork by applying a breadth-first search (BFS) algorithm. Then, risk regulatory pathways were prioritized based on multiple CAD-associated data sources. Finally, we also proposed a new measure to prioritize upstream regulators. We inferred that phosphatase and tensin homolog (PTEN) may be a key regulator in the dysregulation of risk regulatory pathways. This study takes a closer step than the identification of disease subnetworks or modules. From the risk regulatory pathways, we could understand the flow of regulatory information in the initiation and progression of the disease. Our approach helps to uncover its potential etiology. We developed an integrated systems approach to identify risk regulatory pathways. We proposed a new measure to prioritize the key regulators in CAD. PTEN may be a key regulator in dysregulation of the risk regulatory pathways.
Silvestro, Daniele; Zizka, Alexander; Bacon, Christine D; Cascales-Miñana, Borja; Salamin, Nicolas; Antonelli, Alexandre
2016-04-05
Methods in historical biogeography have revolutionized our ability to infer the evolution of ancestral geographical ranges from phylogenies of extant taxa, the rates of dispersals, and biotic connectivity among areas. However, extant taxa are likely to provide limited and potentially biased information about past biogeographic processes, due to extinction, asymmetrical dispersals and variable connectivity among areas. Fossil data hold considerable information about past distribution of lineages, but suffer from largely incomplete sampling. Here we present a new dispersal-extinction-sampling (DES) model, which estimates biogeographic parameters using fossil occurrences instead of phylogenetic trees. The model estimates dispersal and extinction rates while explicitly accounting for the incompleteness of the fossil record. Rates can vary between areas and through time, thus providing the opportunity to assess complex scenarios of biogeographic evolution. We implement the DES model in a Bayesian framework and demonstrate through simulations that it can accurately infer all the relevant parameters. We demonstrate the use of our model by analysing the Cenozoic fossil record of land plants and inferring dispersal and extinction rates across Eurasia and North America. Our results show that biogeographic range evolution is not a time-homogeneous process, as assumed in most phylogenetic analyses, but varies through time and between areas. In our empirical assessment, this is shown by the striking predominance of plant dispersals from Eurasia into North America during the Eocene climatic cooling, followed by a shift in the opposite direction, and finally, a balance in biotic interchange since the middle Miocene. We conclude by discussing the potential of fossil-based analyses to test biogeographic hypotheses and improve phylogenetic methods in historical biogeography. © 2016 The Author(s).
Wu, Bill X; Li, Anqi; Lei, Liming; Kaneko, Satoshi; Wallace, Caroline; Li, Xue; Li, Zihai
2017-11-03
Glycoprotein A repetitions predominant (GARP) (encoded by the Lrrc32 gene) plays important roles in cell-surface docking and activation of TGFβ. However, GARP's role in organ development in mammalian systems is unclear. To determine the function of GARP in vivo , we generated a GARP KO mouse model. Unexpectedly, the GARP KO mice died within 24 h after birth and exhibited defective palatogenesis without apparent abnormalities in other major organs. Furthermore, we observed decreased apoptosis and SMAD2 phosphorylation in the medial edge epithelial cells of the palatal shelf of GARP KO embryos at embryonic day 14.5 (E14.5), indicating a defect in the TGFβ signaling pathway in the GARP-null developing palates. Of note, the failure to develop the secondary palate and concurrent reduction of SMAD phosphorylation without other defects in GARP KO mice phenocopied TGFβ3 KO mice, although GARP has not been suggested previously to interact with TGFβ3. We found that GARP and TGFβ3 co-localize in medial edge epithelial cells at E14.5. In vitro studies confirmed that GARP and TGFβ3 directly interact and that GARP is indispensable for the surface expression of membrane-associated latent TGFβ3. Our findings indicate that GARP is essential for normal morphogenesis of the palate and demonstrate that GARP plays a crucial role in regulating TGFβ3 signaling during embryogenesis. In conclusion, we have uncovered a novel function of GARP in positively regulating TGFβ3 activation and function. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.
International Nuclear Information System (INIS)
Gronow, J.R.
1986-01-01
This study looked at diffusive migration through three types of deformation; the projectile pathways, hydraulic fractures of the sediments and faults, and was divided into three experimental areas: autoradiography, the determination of diffusion coefficients and electron microscopy of model projectile pathways in clay. For the autoradiography, unstressed samples were exposed to two separate isotopes, Pm-147 (a possible model for Am behaviour) and the poorly sorbed iodide-125. The results indicated that there was no enhanced migration through deformed kaolin samples nor through fractured Great Meteor East (GME) sediment, although some was evident through the projectile pathways in GME and possibly through the GME sheared samples. The scanning electron microscopy of projectile pathways in clay showed that emplacement of a projectile appeared to have no effect on the orientation of particles at distances greater than two projectile radii from the centre of a projectile pathway. It showed that the particles were not simply aligned with the direction of motion of the projectile but that, the closer to the surface of a particular pathway, the closer the particles lay to their original orientation. This finding was of interest from two points of view: i) the ease of migration of a pollutant along the pathway, and ii) possible mechanisms of hole closure. It was concluded that, provided that there is no advective migration, the transport of radionuclides through sediments containing these defects would not be significantly more rapid than in undeformed sediments. (author)
Inferring climate variability from skewed proxy records
Emile-Geay, J.; Tingley, M.
2013-12-01
Many paleoclimate analyses assume a linear relationship between the proxy and the target climate variable, and that both the climate quantity and the errors follow normal distributions. An ever-increasing number of proxy records, however, are better modeled using distributions that are heavy-tailed, skewed, or otherwise non-normal, on account of the proxies reflecting non-normally distributed climate variables, or having non-linear relationships with a normally distributed climate variable. The analysis of such proxies requires a different set of tools, and this work serves as a cautionary tale on the danger of making conclusions about the underlying climate from applications of classic statistical procedures to heavily skewed proxy records. Inspired by runoff proxies, we consider an idealized proxy characterized by a nonlinear, thresholded relationship with climate, and describe three approaches to using such a record to infer past climate: (i) applying standard methods commonly used in the paleoclimate literature, without considering the non-linearities inherent to the proxy record; (ii) applying a power transform prior to using these standard methods; (iii) constructing a Bayesian model to invert the mechanistic relationship between the climate and the proxy. We find that neglecting the skewness in the proxy leads to erroneous conclusions and often exaggerates changes in climate variability between different time intervals. In contrast, an explicit treatment of the skewness, using either power transforms or a Bayesian inversion of the mechanistic model for the proxy, yields significantly better estimates of past climate variations. We apply these insights in two paleoclimate settings: (1) a classical sedimentary record from Laguna Pallcacocha, Ecuador (Moy et al., 2002). Our results agree with the qualitative aspects of previous analyses of this record, but quantitative departures are evident and hold implications for how such records are interpreted, and
Vertically Integrated Seismological Analysis II : Inference
Arora, N. S.; Russell, S.; Sudderth, E.
2009-12-01
Methods for automatically associating detected waveform features with hypothesized seismic events, and localizing those events, are a critical component of efforts to verify the Comprehensive Test Ban Treaty (CTBT). As outlined in our companion abstract, we have developed a hierarchical model which views detection, association, and localization as an integrated probabilistic inference problem. In this abstract, we provide more details on the Markov chain Monte Carlo (MCMC) methods used to solve this inference task. MCMC generates samples from a posterior distribution π(x) over possible worlds x by defining a Markov chain whose states are the worlds x, and whose stationary distribution is π(x). In the Metropolis-Hastings (M-H) method, transitions in the Markov chain are constructed in two steps. First, given the current state x, a candidate next state x‧ is generated from a proposal distribution q(x‧ | x), which may be (more or less) arbitrary. Second, the transition to x‧ is not automatic, but occurs with an acceptance probability—α(x‧ | x) = min(1, π(x‧)q(x | x‧)/π(x)q(x‧ | x)). The seismic event model outlined in our companion abstract is quite similar to those used in multitarget tracking, for which MCMC has proved very effective. In this model, each world x is defined by a collection of events, a list of properties characterizing those events (times, locations, magnitudes, and types), and the association of each event to a set of observed detections. The target distribution π(x) = P(x | y), the posterior distribution over worlds x given the observed waveform data y at all stations. Proposal distributions then implement several types of moves between worlds. For example, birth moves create new events; death moves delete existing events; split moves partition the detections for an event into two new events; merge moves combine event pairs; swap moves modify the properties and assocations for pairs of events. Importantly, the rules for
DMPD: Regulatory pathways in inflammation. [Dynamic Macrophage Pathway CSML Database
Lifescience Database Archive (English)
Full Text Available 17967718 Regulatory pathways in inflammation. Mantovani A, Garlanda C, Locati M, Ro....html) (.csml) Show Regulatory pathways in inflammation. PubmedID 17967718 Title Regulatory pathways in infl
Network inference via adaptive optimal design
Directory of Open Access Journals (Sweden)
Stigter Johannes D
2012-09-01
Full Text Available Abstract Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.
On the Hardness of Topology Inference
Acharya, H. B.; Gouda, M. G.
Many systems require information about the topology of networks on the Internet, for purposes like management, efficiency, testing of new protocols and so on. However, ISPs usually do not share the actual topology maps with outsiders; thus, in order to obtain the topology of a network on the Internet, a system must reconstruct it from publicly observable data. The standard method employs traceroute to obtain paths between nodes; next, a topology is generated such that the observed paths occur in the graph. However, traceroute has the problem that some routers refuse to reveal their addresses, and appear as anonymous nodes in traces. Previous research on the problem of topology inference with anonymous nodes has demonstrated that it is at best NP-complete. In this paper, we improve upon this result. In our previous research, we showed that in the special case where nodes may be anonymous in some traces but not in all traces (so all node identifiers are known), there exist trace sets that are generable from multiple topologies. This paper extends our theory of network tracing to the general case (with strictly anonymous nodes), and shows that the problem of computing the network that generated a trace set, given the trace set, has no general solution. The weak version of the problem, which allows an algorithm to output a "small" set of networks- any one of which is the correct one- is also not solvable. Any algorithm guaranteed to output the correct topology outputs at least an exponential number of networks. Our results are surprisingly robust: they hold even when the network is known to have exactly two anonymous nodes, and every node as well as every edge in the network is guaranteed to occur in some trace. On the basis of this result, we suggest that exact reconstruction of network topology requires more powerful tools than traceroute.
Statistical Inference for Data Adaptive Target Parameters.
Hubbard, Alan E; Kherad-Pajouh, Sara; van der Laan, Mark J
2016-05-01
Consider one observes n i.i.d. copies of a random variable with a probability distribution that is known to be an element of a particular statistical model. In order to define our statistical target we partition the sample in V equal size sub-samples, and use this partitioning to define V splits in an estimation sample (one of the V subsamples) and corresponding complementary parameter-generating sample. For each of the V parameter-generating samples, we apply an algorithm that maps the sample to a statistical target parameter. We define our sample-split data adaptive statistical target parameter as the average of these V-sample specific target parameters. We present an estimator (and corresponding central limit theorem) of this type of data adaptive target parameter. This general methodology for generating data adaptive target parameters is demonstrated with a number of practical examples that highlight new opportunities for statistical learning from data. This new framework provides a rigorous statistical methodology for both exploratory and confirmatory analysis within the same data. Given that more research is becoming "data-driven", the theory developed within this paper provides a new impetus for a greater involvement of statistical inference into problems that are being increasingly addressed by clever, yet ad hoc pattern finding methods. To suggest such potential, and to verify the predictions of the theory, extensive simulation studies, along with a data analysis based on adaptively determined intervention rules are shown and give insight into how to structure such an approach. The results show that the data adaptive target parameter approach provides a general framework and resulting methodology for data-driven science.
High cancer-related mortality in an urban, predominantly African-American, HIV-infected population.
Riedel, David J; Mwangi, Evelyn Ivy W; Fantry, Lori E; Alexander, Carla; Hossain, Mian B; Pauza, C David; Redfield, Robert R; Gilliam, Bruce L
2013-04-24
To determine mortality associated with a new cancer diagnosis in an urban, predominantly African-American, HIV-infected population. Retrospective cohort study. All HIV-infected patients diagnosed with cancer between 1 January 2000 and 30 June 2010 were reviewed. Mortality was examined using Kaplan-Meier estimates and Cox proportional hazards models. There were 470 cases of cancer among 447 patients. Patients were predominantly African-American (85%) and male (79%). Non-AIDS-defining cancers (NADCs, 69%) were more common than AIDS-defining cancers (ADCs, 31%). Cumulative cancer incidence increased significantly over the study period. The majority (55.9%) was taking antiretroviral therapy (ART) at cancer diagnosis or started afterward (26.9%); 17.2% never received ART. Stage 3 or 4 cancer was diagnosed in 67%. There were 226 deaths during 1096 person years of follow-up, yielding an overall mortality rate of 206 per 1000 person years. The cumulative mortality rate at 30 days, 1 year, and 2 years was 6.5, 32.2, and 41.4%, respectively. Mortality was similar between patients on ART whether they started before or after the cancer diagnosis but was higher in patients who never received ART. In patients with a known cause of death, 68% were related to progression of the underlying cancer. In a large cohort of urban, predominantly African-American patients with HIV and cancer, many patients presented with late-stage cancer. There was substantial 30-day and 2-year mortality, although ART had a significant mortality benefit. Deaths were most often caused by progression of cancer and not from another HIV-related or AIDS-related event.
Cortical restricted diffusion as the predominant MRI finding in sporadic Creutzfeldt-Jakob disease
Energy Technology Data Exchange (ETDEWEB)
Talbott, Sabrina D.; Sattenberg, Ronald J.; Heidenreich, Jens O. (Dept. of Radiology, Univ. of Louisville, Louisville (United States)), e-mail: sdtalb02@gwise.louisville.edu; Plato, Brian M (Dept. of Neurology, Univ. of Louisville, Louisville (United States)); Parker, John (Dept. of Pathology and Laboratory Medicine, Univ. of Louisville, Louisville (United States))
2011-04-15
Creutzfeldt-Jakob disease is a rare and fatal neurodegenerative disorder with MR findings predominantly limited to the grey matter of the cortex and the basal ganglia. Sporadic Creutzfeldt-Jakob disease can produce a spectrum of MR imaging findings of the brain, most notably on DWI and FLAIR sequences. Involvement of the basal ganglia and neocortex is the most common finding, but isolated involvement of the cortex can also be seen. We describe the clinical history and MRI findings of three patients with sporadic Creutzfeldt-Jakob disease confirmed by brain biopsy or autopsy and review the literature of imaging manifestations of this disease
Fault current limiter-predominantly resistive behavior of a BSCCO shielded-core reactor
International Nuclear Information System (INIS)
Ennis, M. G.; Tobin, T. J.; Cha, Y. S.; Hull, J. R.
2000-01-01
Tests were conducted to determine the electrical and magnetic characteristics of a superconductor shielded core reactor (SSCR). The results show that a closed-core SSCR is predominantly a resistive device and an open-core SSCR is a hybrid resistive/inductive device. The open-core SSCR appears to dissipate less than the closed-core SSCR. However, the impedance of the open-core SSCR is less than that of the closed-core SSCR. Magnetic and thermal diffusion are believed to be the mechanism that facilitates the penetration of the superconductor tube under fault conditions
Cortical restricted diffusion as the predominant MRI finding in sporadic Creutzfeldt-Jakob disease
International Nuclear Information System (INIS)
Talbott, Sabrina D.; Sattenberg, Ronald J.; Heidenreich, Jens O.; Plato, Brian M; Parker, John
2011-01-01
Creutzfeldt-Jakob disease is a rare and fatal neurodegenerative disorder with MR findings predominantly limited to the grey matter of the cortex and the basal ganglia. Sporadic Creutzfeldt-Jakob disease can produce a spectrum of MR imaging findings of the brain, most notably on DWI and FLAIR sequences. Involvement of the basal ganglia and neocortex is the most common finding, but isolated involvement of the cortex can also be seen. We describe the clinical history and MRI findings of three patients with sporadic Creutzfeldt-Jakob disease confirmed by brain biopsy or autopsy and review the literature of imaging manifestations of this disease
Ensemble stacking mitigates biases in inference of synaptic connectivity.
Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N
2018-01-01
A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.
Causal inference in biology networks with integrated belief propagation.
Chang, Rui; Karr, Jonathan R; Schadt, Eric E
2015-01-01
Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.
kpath: integration of metabolic pathway linked data.
Navas-Delgado, Ismael; García-Godoy, María Jesús; López-Camacho, Esteban; Rybinski, Maciej; Reyes-Palomares, Armando; Medina, Miguel Ángel; Aldana-Montes, José F
2015-01-01
In the last few years, the Life Sciences domain has experienced a rapid growth in the amount of available biological databases. The heterogeneity of these databases makes data integration a challenging issue. Some integration challenges are locating resources, relationships, data formats, synonyms or ambiguity. The Linked Data approach partially solves the heterogeneity problems by introducing a uniform data representation model. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. This article introduces kpath, a database that integrates information related to metabolic pathways. kpath also provides a navigational interface that enables not only the browsing, but also the deep use of the integrated data to build metabolic networks based on existing disperse knowledge. This user interface has been used to showcase relationships that can be inferred from the information available in several public databases. © The Author(s) 2015. Published by Oxford University Press.
Antonijevic, Sasa; Bodenhausen, Geoffrey
2006-06-01
A set of graphical conventions called quadrupolar transfer pathways is proposed to describe a wide range of experiments designed for the study of quadrupolar nuclei with spin quantum numbers I = 1, 3/2, 2, 5/2, etc. These pathways, which inter alea allow one to appreciate the distinction between quadrupolar and Zeeman echoes, represent a generalization of the well-known coherence transfer pathways. Quadrupolar transfer pathways not merely distinguish coherences with different orders -2 I ⩽ p ⩽ +2 I, but allow one to follow the fate of coherences associated with single transitions that have the same coherence orderp=mIr-mIs but can be distinguished by a satellite orderq=(mIr)2-(mIs)2.
Department of Veterans Affairs — Pathways is a SOAP/REST web service interface accessed via HTTPS that provides administrative data (Appointments, Exam Requests and Exams information) from VistA in...
Yu, Jia; Virshup, David M.
2014-01-01
In the three decades since the discovery of the Wnt1 proto-oncogene in virus-induced mouse mammary tumours, our understanding of the signalling pathways that are regulated by the Wnt proteins has progressively expanded. Wnts are involved in an complex signalling network that governs multiple biological processes and cross-talk with multiple additional signalling cascades, including the Notch, FGF (fibroblast growth factor), SHH (Sonic hedgehog), EGF (epidermal growth factor) and Hippo pathways. The Wnt signalling pathway also illustrates the link between abnormal regulation of the developmental processes and disease manifestation. Here we provide an overview of Wnt-regulated signalling cascades and highlight recent advances. We focus on new findings regarding the dedicated Wnt production and secretion pathway with potential therapeutic targets that might be beneficial for patients with Wnt-related diseases. PMID:25208913
Lee, Elizabeth C; Kelly, Michael R; Ochocki, Brad M; Akinwumi, Segun M; Hamre, Karen E S; Tien, Joseph H; Eisenberg, Marisa C
2017-05-07
Mathematical models of cholera and waterborne disease vary widely in their structures, in terms of transmission pathways, loss of immunity, and a range of other features. These differences can affect model dynamics, with different models potentially yielding different predictions and parameter estimates from the same data. Given the increasing use of mathematical models to inform public health decision-making, it is important to assess model distinguishability (whether models can be distinguished based on fit to data) and inference robustness (whether inferences from the model are robust to realistic variations in model structure). In this paper, we examined the effects of uncertainty in model structure in the context of epidemic cholera, testing a range of models with differences in transmission and loss of immunity structure, based on known features of cholera epidemiology. We fit these models to simulated epidemic and long-term data, as well as data from the 2006 Angola epidemic. We evaluated model distinguishability based on fit to data, and whether the parameter values, model behavior, and forecasting ability can accurately be inferred from incidence data. In general, all models were able to successfully fit to all data sets, both real and simulated, regardless of whether the model generating the simulated data matched the fitted model. However, in the long-term data, the best model fits were achieved when the loss of immunity structures matched those of the model that simulated the data. Two parameters, one representing person-to-person transmission and the other representing the reporting rate, were accurately estimated across all models, while the remaining parameters showed broad variation across the different models and data sets. The basic reproduction number (R 0 ) was often poorly estimated even using the correct model, due to practical unidentifiability issues in the waterborne transmission pathway which were consistent across all models. Forecasting
Iwashita, Hayato; Tsukiyama, Yoshihiro; Kori, Hidehiro; Kuwatsuru, Rika; Yamasaki, Yo; Koyano, Kiyoshi
2014-10-01
Missing posterior teeth can decrease masticatory function and cause horizontal mastication deviation, i.e., mastication predominance. Mastication predominance may lead to abnormal tooth attrition and temporomandibular disorders. This study evaluated masticatory performance and mastication predominance in patients with missing posterior teeth to investigate effects of missing posterior teeth on masticatory performance and mastication predominance. Thirty volunteers with normal dentition (control group), 30 patients with unilateral missing posterior teeth (unilateral group), and 23 patients with bilateral missing posterior teeth (bilateral group) participated. Gummy jellies were used to evaluate participants' masticatory performance, and electromyography was used to assess the degree of mastication predominance. Chewing gums, gummy jellies, and peanuts were used as foods of various hardnesses for evaluating mastication predominance. Compared with the control group, masticatory performance did not differ in the unilateral group but was significantly decreased in the bilateral group. With chewing gum and gummy jellies, the degree of mastication predominance was significantly increased in both unilateral and bilateral groups than the control group. With peanuts, the degree of mastication predominance was significantly increased in the unilateral group than the control group. Although masticatory performance was not decreased in the unilateral group, the degree of mastication predominance was increased. Decreased masticatory performance was observed in the bilateral group, and for foods with normal hardness and soft foods, the degree of mastication predominance was increased. These results suggested that mastication predominance should be considered in the recovery of masticatory performance in patients with missing posterior teeth. Copyright © 2014 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.
Tarlowski, Andrzej
2018-01-01
There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.
Directory of Open Access Journals (Sweden)
Andrzej Tarlowski
2018-04-01
Full Text Available There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.
Bayesian inference of substrate properties from film behavior
International Nuclear Information System (INIS)
Aggarwal, R; Demkowicz, M J; Marzouk, Y M
2015-01-01
We demonstrate that by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred with quantified uncertainty using Bayesian methods. We carry out this demonstration on an illustrative film/substrate model where the substrate is a Gaussian random field and the film is a two-component mixture that obeys the Cahn–Hilliard equation. We construct a stochastic reduced order model to describe the film/substrate interaction and use it to infer substrate properties from film behavior. This quantitative inference strategy may be adapted to other film/substrate systems. (paper)
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation
DEFF Research Database (Denmark)
Brouwer, Thomas; Frellsen, Jes; Liò, Pietro
2017-01-01
In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri......-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real...
Working memory supports inference learning just like classification learning.
Craig, Stewart; Lewandowsky, Stephan
2013-08-01
Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.
Surrogate based approaches to parameter inference in ocean models
Knio, Omar
2016-01-06
This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.
Ensemble stacking mitigates biases in inference of synaptic connectivity
Directory of Open Access Journals (Sweden)
Brendan Chambers
2018-03-01
Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures
Brain Imaging, Forward Inference, and Theories of Reasoning
Heit, Evan
2015-01-01
This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities. PMID:25620926
Fast and scalable inference of multi-sample cancer lineages.
Popic, Victoria
2015-05-06
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .
Fast and scalable inference of multi-sample cancer lineages.
Popic, Victoria; Salari, Raheleh; Hajirasouliha, Iman; Kashef-Haghighi, Dorna; West, Robert B; Batzoglou, Serafim
2015-01-01
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .
International Conference on Trends and Perspectives in Linear Statistical Inference
Rosen, Dietrich
2018-01-01
This volume features selected contributions on a variety of topics related to linear statistical inference. The peer-reviewed papers from the International Conference on Trends and Perspectives in Linear Statistical Inference (LinStat 2016) held in Istanbul, Turkey, 22-25 August 2016, cover topics in both theoretical and applied statistics, such as linear models, high-dimensional statistics, computational statistics, the design of experiments, and multivariate analysis. The book is intended for statisticians, Ph.D. students, and professionals who are interested in statistical inference. .
Brain imaging, forward inference, and theories of reasoning.
Heit, Evan
2014-01-01
This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities.
Surrogate based approaches to parameter inference in ocean models
Knio, Omar
2016-01-01
This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.
Phylogenetic origin and diversification of RNAi pathway genes in insects
DEFF Research Database (Denmark)
Dowling, Daniel; Pauli, Thomas; Donath, Alexander
2016-01-01
RNAinterference (RNAi) refers tothe set ofmolecular processes foundin eukaryotic organisms in which smallRNAmolecules mediate the silencing or down-regulation of target genes. In insects, RNAi serves a number of functions, including regulation of endogenous genes, anti-viral defense, and defense...... against transposable elements. Despite being well studied in model organisms, such as Drosophila, the distribution of core RNAi pathway genes and their evolution in insects is not well understood. Here we present the most comprehensive overview of the distribution and diversity of core RNAi pathway genes...... across 100 insect species, encompassing all currently recognized insect orders. We inferred the phylogenetic origin of insect-specific RNAi pathway genes and also identified several hitherto unrecorded gene expansions using whole-body transcriptome data from the international 1KITE (1000 Insect...
Blue Rubber Bleb Nevus Syndrome Showing Vascular Skin Lesions Predominantly on the Face
Directory of Open Access Journals (Sweden)
Ayumi Korekawa
2015-07-01
Full Text Available An 81-year-old Japanese man presented with dark blue papules and nodules on his face. There were multiple soft papules and nodules, dark blue in color, compressive, and ranging in size from 2 to 10 mm. A few similar lesions were seen on the patient's right dorsal second toe and right buccal mucosa. There were no skin lesions on his trunk and upper limbs. The patient's past history did not include gastrointestinal bleeding or anemia. Histopathological examination showed dilated vascular spaces lined by the normal epithelium extending beneath the dermis and into the subcutaneous fat. Endoscopy of the gastrointestinal tract to check for colon involvement was not performed. X-ray images of the limbs revealed no abnormalities in the bones or joints. Laboratory investigations did not show anemia. Although we failed to confirm a diagnosis by endoscopy, the skin lesions, histopathological findings, lack of abnormal X-ray findings, and the presence of oral lesions as a part of gastrointestinal tract guided the diagnosis of blue rubber bleb nevus syndrome (BRBNS. Skin lesions of BRBNS occur predominantly on the trunk and upper limbs. However, the present case showed multiple skin lesions predominantly on the face. Therefore, it is important for clinicians to know about a possible atypical distribution of skin lesions in BRBNS.
Acculturation and Intention to Breastfeed among a Population of Predominantly Puerto Rican Women.
Barcelona de Mendoza, Veronica; Harville, Emily; Theall, Katherine; Buekens, Pierre; Chasan-Taber, Lisa
2016-03-01
Latinas have high overall breastfeeding initiation rates, yet Puerto Ricans have among the lowest exclusive breastfeeding rates. This study sought to determine if acculturation was associated with intent to breastfeed in a predominantly Puerto Rican population. A cohort of Latina women were enrolled in Proyecto Buena Salud, and provided information on infant feeding intent (n = 1,323). Acculturation was assessed via the Psychological Acculturation Scale (PAS), language preference, and generation in the United States. Increasing acculturation as measured by English language preference (aOR 0.61 [95% CI 0.42-0.88]) and second or third generation in the United States (aOR 0.70 [95% CI 0.52-0.95)] was inversely associated with odds of intending to exclusively breastfeed. Similarly, women with higher levels of acculturation as measured by the PAS (aOR 0.67 [95% CI 0.45-0.99]), English language preference (aOR 0.48 [95% CI 0.33-0.70]) and second or third generation in the United States (aOR 0.42 [95% CI 0.31-0.58]) were less likely to report intent to combination feed as compared with women with lower acculturation. Acculturation was inversely associated with intent to exclusively breastfeed and intent to combination feed in this predominantly Puerto Rican sample. © 2015 Wiley Periodicals, Inc.
Eluxadoline in the treatment of diarrhea-predominant irritable bowel syndrome: The SEPD perspective
Directory of Open Access Journals (Sweden)
Isabel Vera
Full Text Available Functional gut disorders, including diarrhea-predominant irritable bowel syndrome, are highly prevalent conditions worldwide that significantly impact health economy and patient quality of life, yet lacking fully satisfactory therapeutic options. These circumstances fostered research on various molecules with more specific therapeutic targets, including opioid receptors. Eluxadoline (Allergan's Vibercy® in the USA, Truberzi® in Europe is a locally-acting mixed µ- and κ-opioid receptor agonist, and δ-opioid receptor antagonist, that was licensed in 2015 by the Food and Drug Administration (FDA and in 2016 by the European Medicines Agency (EMA for use in diarrhea-predominant irritable bowel syndrome. Eluxadoline provides, with advantage over the current standard of care, control of both stool consistency and abdominal pain, good tolerability in most cases, and improved quality of life, hence it deserves consideration when approaching a patient with this disorder. As with any recently approved therapy, adequate pharmacovigilance is to be expected, as well as studies to inform on different scenarios such as on-demand therapy, loss of response assessment, use as rescue therapy for other molecules, and cost-effectiveness, to further characterize and more accurately position eluxadoline within the therapeutic spectrum.
Filteau, Marie; Lagacé, Luc; Lapointe, Gisèle; Roy, Denis
2012-03-01
Maple sap processing and microbial contamination are significant aspects that affect maple syrup quality. In this study, two sample sets from 2005 and 2008 were used to assess the maple syrup quality variation and its relationship to microbial populations, with respect to processing, production site and harvesting period. The abundance of maple sap predominant bacteria (Pseudomonas fluorescens group and two subgroups, Rahnella spp., Janthinobacterium spp., Leuconostoc mesenteroides) and yeast (Mrakia spp., Mrakiella spp.,Guehomyces pullulans) was assessed by quantitative PCR. Maple syrup properties were analyzed by physicochemical and sensorial methods. Results indicate that P. fluorescens, Mrakia spp., Mrakiella spp. G. pullulans and Rahnella spp. are stable contaminants of maple sap, as they were found for every production site throughout the flow period. Multiple factor analysis reports a link between the relative abundance of P. fluorescens group and Mrakia spp. in maple sap with maple and vanilla odor as well as flavor of maple syrup. This evidence supports the contribution of these microorganisms or a consortium of predominant microbial contaminants to the characteristic properties of maple syrup. Copyright © 2011 Elsevier B.V. All rights reserved.
Dolash, Karry; He, Meizi; Yin, Zenong; Sosa, Erica T
2015-04-01
Park features' association with physical activity among predominantly Hispanic communities is not extensively researched. The purpose of this study was to assess factors associated with park use and physical activity among park users in predominantly Hispanic neighborhoods. Data were collected across 6 parks and included park environmental assessments to evaluate park features, physical activity observations to estimate physical activity energy expenditure as kcal/kg/ minute per person, and park user interviews to assess motivators for park use. Quantitative data analysis included independent t tests and ANOVA. Thematic analysis of park user interviews was conducted collectively and by parks. Parks that were renovated had higher physical activity energy expenditure scores (mean = .086 ± .027) than nonrenovated parks (mean = .077 ± .028; t = -3.804; P motivation to be physically active, using the play spaces in the park, parks as the main place for physical activity, and social support for using parks. Renovations to park amenities, such as increasing basketball courts and trail availability, could potentially increase physical activity among low-socioeconomic-status populations.
Lhomme, Emilie; Orain, Servane; Courcoux, Philippe; Onno, Bernard; Dousset, Xavier
2015-11-20
Fourteen bakeries located in different regions of France were selected. These bakers use natural sourdough and organic ingredients. Consequently, different organic sourdoughs used for the manufacture of French bread were studied by the enumeration of lactic acid bacteria (LAB) and 16S rRNA sequencing of the isolates. In addition, after DNA extraction the bacterial diversity was assessed by pyrosequencing of the 16S rDNA V1-V3 region. Although LAB counts showed significant variations (7.6-9.5log10CFU/g) depending on the sourdough studied, their identification through a polyphasic approach revealed a large predominance of Lactobacillus sanfranciscensis in all samples. In ten sourdoughs, both culture and independent methods identified L. sanfranciscensis as the dominant LAB species identified. In the remaining sourdoughs, culture methods identified 30-80% of the LAB as L. sanfranciscensis whereas more than 95% of the reads obtained by pyrosequencing belonged to L. sanfranciscensis. Other sub-dominant species, such as Lactobacillus curvatus, Lactobacillus hammesii, Lactobacillus paralimentarius, Lactobacillus plantarum, Lactobacillus pentosus, and Lactobacillus sakei, were also identified. Quantification of L. sanfranciscensis by real-time PCR confirmed the predominance of this species ranging from 8.24 to 10.38log10CFU/g. Regarding the acidification characteristics, sourdough and related bread physico-chemical characteristics varied, questioning the involvement of sub-dominant species or L. sanfranciscensis intra-species diversity and/or the role of the baker's practices. Copyright © 2015 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Lee Hoyong
2006-12-01
Full Text Available Abstract Background Maturation of spermatozoa, including development of motility and the ability to fertilize the oocyte, occurs during transit through the microenvironment of the epididymis. Comprehensive understanding of sperm maturation requires identification and characterization of unique genes expressed in the epididymis. Results We systematically identified 32 novel genes with epididymis-specific or -predominant expression in the mouse epididymis UniGene library, containing 1505 gene-oriented transcript clusters, by in silico and in vitro analyses. The Northern blot analysis revealed various characteristics of the genes at the transcript level, such as expression level, size and the presence of isoform. We found that expression of the half of the genes is regulated by androgens. Further expression analyses demonstrated that the novel genes are region-specific and developmentally regulated. Computational analysis showed that 15 of the genes lack human orthologues, suggesting their implication in male reproduction unique to the mouse. A number of the novel genes are putative epididymal protease inhibitors or β-defensins. We also found that six of the genes have secretory activity, indicating that they may interact with sperm and have functional roles in sperm maturation. Conclusion We identified and characterized 32 novel epididymis-specific or -predominant genes by an integrative approach. Our study is unique in the aspect of systematic identification of novel epididymal genes and should be a firm basis for future investigation into molecular mechanisms underlying sperm maturation in the epididymis.
Mineral Depositions of Calcifying Skin Disorders are Predominantly Composed of Carbonate Apatite
Directory of Open Access Journals (Sweden)
Michael Franzen
2017-08-01
Full Text Available Subcutaneous calcifications can lead to complications, including pain, inflammation, ulceration and immobilization. Studies on the pathophysiology of mineral compositions and effective treatment modalities are limited. We therefore studied 14 patients with subcutaneous calcifications. Mineral material was collected and analysed by Fourier transform infrared spectrometry. Blood analyses were run to evaluate systemic alterations of mineral metabolism. Carbonate apatite (CAP was found to be the single constituent in the majority of patients (n = 9, 64.3%, 3 cases (21.4% had a composition of CAP and calcium oxalate dihydrate and one case had a combination of CAP and magnesium ammonium phosphate, whereas CAP was the major component in all 4 cases. Only one case showed predominantly calcium oxalate. Thus, CAP was found to be the only or predominant component in most cases of subcutaneous calcifications. Chemical analyses of the mineral compositions may aid in the development of new treatment regimes to improve the solubility of mineral components and to decrease extraosseous calcifications.
New challenges for text mining: mapping between text and manually curated pathways
Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi
2008-01-01
Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550
Models and Inference for Multivariate Spatial Extremes
Vettori, Sabrina
2017-12-07
The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical
Indirect Inference for Stochastic Differential Equations Based on Moment Expansions
Ballesio, Marco; Tempone, Raul; Vilanova, Pedro
2016-01-01
We provide an indirect inference method to estimate the parameters of timehomogeneous scalar diffusion and jump diffusion processes. We obtain a system of ODEs that approximate the time evolution of the first two moments of the process
ESPRIT: Exercise Sensing and Pose Recovery Inference Tool, Phase I
National Aeronautics and Space Administration — We propose to develop ESPRIT: an Exercise Sensing and Pose Recovery Inference Tool, in support of NASA's effort in developing crew exercise technologies for...
Automated Flight Safety Inference Engine (AFSIE) System, Phase I
National Aeronautics and Space Administration — We propose to develop an innovative Autonomous Flight Safety Inference Engine (AFSIE) system to autonomously and reliably terminate the flight of an errant launch...
Classification versus inference learning contrasted with real-world categories.
Jones, Erin L; Ross, Brian H
2011-07-01
Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.
Efficient Exact Inference With Loss Augmented Objective in Structured Learning.
Bauer, Alexander; Nakajima, Shinichi; Muller, Klaus-Robert
2016-08-19
Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms--the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference algorithm for maximizing nondecomposable objectives due to special type of a high-order potential having a decomposable internal structure. As an important application, our method covers the loss augmented inference, which enables the slack and margin scaling formulations of structural SVM with a variety of dissimilarity measures, e.g., Hamming loss, precision and recall, Fβ-loss, intersection over union, and many other functions that can be efficiently computed from the contingency table. We demonstrate the advantages of our approach in natural language parsing and sequence segmentation applications.
BagReg: Protein inference through machine learning.
Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou
2015-08-01
Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.
The Human Cochlear Mechanical Nonlinearity Inferred via Psychometric Functions
Directory of Open Access Journals (Sweden)
Nizami Lance
2013-12-01
Extension of the model of Schairer and colleagues results in credible cochlear nonlinearities in man, suggesting that forward-masking provides a non-invasive way to infer the human mechanical cochlear nonlinearity.
A general Bayes weibull inference model for accelerated life testing
International Nuclear Information System (INIS)
Dorp, J. Rene van; Mazzuchi, Thomas A.
2005-01-01
This article presents the development of a general Bayes inference model for accelerated life testing. The failure times at a constant stress level are assumed to belong to a Weibull distribution, but the specification of strict adherence to a parametric time-transformation function is not required. Rather, prior information is used to indirectly define a multivariate prior distribution for the scale parameters at the various stress levels and the common shape parameter. Using the approach, Bayes point estimates as well as probability statements for use-stress (and accelerated) life parameters may be inferred from a host of testing scenarios. The inference procedure accommodates both the interval data sampling strategy and type I censored sampling strategy for the collection of ALT test data. The inference procedure uses the well-known MCMC (Markov Chain Monte Carlo) methods to derive posterior approximations. The approach is illustrated with an example
Inference method using bayesian network for diagnosis of pulmonary nodules
International Nuclear Information System (INIS)
Kawagishi, Masami; Iizuka, Yoshio; Yamamoto, Hiroyuki; Yakami, Masahiro; Kubo, Takeshi; Fujimoto, Koji; Togashi, Kaori
2010-01-01
This report describes the improvements of a naive Bayes model that infers the diagnosis of pulmonary nodules in chest CT images based on the findings obtained when a radiologist interprets the CT images. We have previously introduced an inference model using a naive Bayes classifier and have reported its clinical value based on evaluation using clinical data. In the present report, we introduce the following improvements to the original inference model: the selection of findings based on correlations and the generation of a model using only these findings, and the introduction of classifiers that integrate several simple classifiers each of which is specialized for specific diagnosis. These improvements were found to increase the inference accuracy by 10.4% (p<.01) as compared to the original model in 100 cases (222 nodules) based on leave-one-out evaluation. (author)
Bayesian inference of chemical kinetic models from proposed reactions
Galagali, Nikhil; Marzouk, Youssef M.
2015-01-01
© 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
Inference of beliefs and emotions in patients with Alzheimer's disease.
Zaitchik, Deborah; Koff, Elissa; Brownell, Hiram; Winner, Ellen; Albert, Marilyn
2006-01-01
The present study compared 20 patients with mild to moderate Alzheimer's disease with 20 older controls (ages 69-94 years) on their ability to make inferences about emotions and beliefs in others. Six tasks tested their ability to make 1st-order and 2nd-order inferences as well as to offer explanations and moral evaluations of human action by appeal to emotions and beliefs. Results showed that the ability to infer emotions and beliefs in 1st-order tasks remains largely intact in patients with mild to moderate Alzheimer's. Patients were able to use mental states in the prediction, explanation, and moral evaluation of behavior. Impairment on 2nd-order tasks involving inference of mental states was equivalent to impairment on control tasks, suggesting that patients' difficulty is secondary to their cognitive impairments. ((c) 2006 APA, all rights reserved).
Efficient design and inference in distributed Bayesian networks: an overview
de Oude, P.; Groen, F.C.A.; Pavlin, G.; Bezhanishvili, N.; Löbner, S.; Schwabe, K.; Spada, L.
2011-01-01
This paper discusses an approach to distributed Bayesian modeling and inference, which is relevant for an important class of contemporary real world situation assessment applications. By explicitly considering the locality of causal relations, the presented approach (i) supports coherent distributed
SDG multiple fault diagnosis by real-time inverse inference
International Nuclear Information System (INIS)
Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng
2005-01-01
In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency
SDG multiple fault diagnosis by real-time inverse inference
Energy Technology Data Exchange (ETDEWEB)
Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng
2005-02-01
In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency.
Bayesian Information Criterion as an Alternative way of Statistical Inference
Directory of Open Access Journals (Sweden)
Nadejda Yu. Gubanova
2012-05-01
Full Text Available The article treats Bayesian information criterion as an alternative to traditional methods of statistical inference, based on NHST. The comparison of ANOVA and BIC results for psychological experiment is discussed.
Predominant typologies of psychopathology in the United States: a latent class analysis.
El-Gabalawy, Renée; Tsai, Jack; Harpaz-Rotem, Ilan; Hoff, Rani; Sareen, Jitender; Pietrzak, Robert H
2013-11-01
Latent class analysis (LCA) offers a parsimonious way of classifying common typologies of psychiatric comorbidity. We used LCA to identify the nature and correlates of predominant typologies of Axis I and II disorders in a large and comprehensive population-based sample of U.S. adults. We analyzed data from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (2004-2005; n = 34,653), a population-based sample of U.S. adults. We derived latent classes based on all assessed Axis I and II disorders and examined the relationship between the identified Axis I classes and lifetime psychiatric disorders and suicide attempts, and physical and mental health-related quality of life. A four-class solution was optimal in characterizing predominant typologies of both Axis I and II disorders. For Axis I disorders, these included low psychopathology (n = 28,935, 84.0%), internalizing (n = 3693, 9.9%), externalizing (n = 1426, 4.5%), and high psychopathology (n = 599, 1.6%) classes. For Axis II disorders, these included no/low personality disorders (n = 31,265, 90.9%), obsessive/paranoid (n = 1635, 4.6%), borderline/dysregulated (n = 1319, 3.4%), and highly comorbid (n = 434, 1.1%) classes. Compared to the low psychopathology class, all other Axis I classes had significantly increased odds of mental disorders, elevated Axis II classes, suicide attempts and poorer quality of life, with the high psychopathology class having the overall highest rates of these correlates, with the exception of substance use disorders. Compared to the low psychopathology class, the internalizing and externalizing classes had increased rates of mood and anxiety disorders, and substance use disorders, respectively. Axis I and II psychopathology among U.S. adults may be best represented by four predominant typologies. Characterizing co-occurring patterns of psychopathology using person-based typologies represents a higher-order classification system that may be useful in clinical
Statistical inferences for bearings life using sudden death test
Directory of Open Access Journals (Sweden)
Morariu Cristin-Olimpiu
2017-01-01
Full Text Available In this paper we propose a calculus method for reliability indicators estimation and a complete statistical inferences for three parameters Weibull distribution of bearings life. Using experimental values regarding the durability of bearings tested on stands by the sudden death tests involves a series of particularities of the estimation using maximum likelihood method and statistical inference accomplishment. The paper detailing these features and also provides an example calculation.
Inference in {open_quotes}poor{close_quotes} languages
Energy Technology Data Exchange (ETDEWEB)
Petrov, S. [Oak Ridge National Lab., TN (United States)
1996-12-31
Languages with a solvable implication problem but without complete and consistent systems of inference rules ({open_quote}poor{close_quote} languages) are considered. The problem of existence of a finite, complete, and consistent inference rule system for a {open_quotes}poor{close_quotes} language is stated independently of the language or the rule syntax. Several properties of the problem are proved. An application of the results to the language of join dependencies is given.
Inference of a Nonlinear Stochastic Model of the Cardiorespiratory Interaction
Smelyanskiy, V. N.; Luchinsky, D. G.; Stefanovska, A.; McClintock, P. V.
2005-03-01
We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.
Towards Bayesian Inference of the Fast-Ion Distribution Function
DEFF Research Database (Denmark)
Stagner, L.; Heidbrink, W.W.; Salewski, Mirko
2012-01-01
sensitivity of the measurements are incorporated into Bayesian likelihood probabilities, while prior probabilities enforce physical constraints. As an initial step, this poster uses Bayesian statistics to infer the DIII-D electron density profile from multiple diagnostic measurements. Likelihood functions....... However, when theory and experiment disagree (for one or more diagnostics), it is unclear how to proceed. Bayesian statistics provides a framework to infer the DF, quantify errors, and reconcile discrepant diagnostic measurements. Diagnostic errors and ``weight functions" that describe the phase space...
Completion is an Instance of Abstract Canonical System Inference
Burel , Guillaume; Kirchner , Claude
2006-01-01
http://www.springerlink.com/content/u222753gl333221p/; Abstract canonical systems and inference (ACSI) were introduced to formalize the intuitive notions of good proof and good inference appearing typically in first-order logic or in Knuth-Bendix like completion procedures. Since this abstract framework is intended to be generic, it is of fundamental interest to show its adequacy to represent the main systems of interest. This has been done for ground completion (where all equational axioms a...
Geostatistical inference using crosshole ground-penetrating radar
DEFF Research Database (Denmark)
Looms, Majken C; Hansen, Thomas Mejer; Cordua, Knud Skou
2010-01-01
of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance...... reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisturecontent measurements, obtained gravimetrically from samples collected at the field site....
Fused Regression for Multi-source Gene Regulatory Network Inference.
Directory of Open Access Journals (Sweden)
Kari Y Lam
2016-12-01
Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.
Bayesian Inference and Online Learning in Poisson Neuronal Networks.
Huang, Yanping; Rao, Rajesh P N
2016-08-01
Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.
Automatic physical inference with information maximizing neural networks
Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.
2018-04-01
Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.
Eluxadoline in the treatment of diarrhea-predominant irritable bowel syndrome
Directory of Open Access Journals (Sweden)
Özdener AE
2017-09-01
Full Text Available Ayşe Elif Özdener, Anastasia Rivkin School of Pharmacy and Health Sciences, Fairleigh Dickinson University, Florham Park, NJ, USA Abstract: Eluxadoline is a novel drug approved for the management of diarrhea predominant irritable bowel syndrome (IBS-D. It has unique pharmacology and works on three different opioid receptors. Several Phase II and III clinical trials have demonstrated eluxadoline’s efficacy in reducing symptoms related to IBS-D. Clinical trial results and postmarketing reports show a risk of pancreatitis in patients without a gallbladder or those abusing alcohol. This review article will include information on clinical trial results related to IBS-D management as well as eluxadoline’s limitations. Keywords: IBS-D, eluxadoline, diarrhea, gastrointestinal, Viberzi
Observable lepton number violation with predominantly Dirac nature of active neutrinos
Energy Technology Data Exchange (ETDEWEB)
Borah, Debasish [Department of Physics, Indian Institute of Technology Guwahati,Assam-781039 (India); Dasgupta, Arnab [Institute of Physics, HBNI,Sachivalaya Marg, Bhubaneshwar-751005 (India)
2017-01-17
We study a specific version of SU(2){sub R}×SU(2){sub L}×U(1){sub B−L} models extended by discrete symmetries where the new physics sector responsible for tiny neutrino masses at leading order remains decoupled from the new physics sector that can give rise to observable signatures of lepton number violation such as neutrinoless double beta decay. More specifically, the dominant contribution to light neutrino masses comes from a one-loop Dirac mass. At higher loop level, a tiny Majorana mass also appears which remains suppressed by many order of magnitudes in comparison to the Dirac mass. Such a model where the active neutrinos are predominantly of Dirac type, also predicts observable charged lepton flavour violation like μ→3e,μ→eγ and multi-component dark matter.
Santos, Richard; Huerta, Gabriel; Karki, Menuka; Cantarero, Andrea
This study analyzes the social determinants associated with the overweight or obesity prevalence of 85 elementary schools during the 2010-11 academic year in a predominantly Hispanic school district. A binomial logistic regression is used to analyze the aggregate overweight or obesity rate of a school by the percent of Hispanic students in each school, selected school and neighborhood characteristics, and its geographical location. The proportion of Hispanic enrollment more readily explains a school's aggregate overweight or obesity rate than social determinants or spatial location. Number of fast food establishments and the academic ranking of a school appear to slightly impact the aggregate prevalence rate. Spatial location of school is not a significant factor, controlling for other determinants. An elementary school's overall overweight or obesity rate provides a valuable health indicator to study the social determinants of obesity among Hispanics and other students within a local neighborhood. Copyright © 2017 Elsevier Inc. All rights reserved.
Upper motor neuron predominant degeneration with frontal and temporal lobe atrophy.
Konagaya, M; Sakai, M; Matsuoka, Y; Konagaya, Y; Hashizume, Y
1998-11-01
The autopsy findings of a 78-year-old man mimicking primary lateral sclerosis (PLS) are reported. He showed slowly progressive spasticity, pseudobulbar palsy and character change, and died 32 months after the onset of symptoms. Autopsy revealed severe atrophy of the frontal and temporal lobes, remarkable neuronal loss and gliosis in the precentral gyrus, left temporal lobe pole and amygdala, mild degeneration of the Ammon's horn, degeneration of the corticospinal tract, and very mild involvement of the lower motor neurons. The anterior horn cells only occasionally demonstrated Bunina body by cystatin-C staining, and skein-like inclusions by ubiquitin staining. This is a peculiar case with concomitant involvement in the motor cortex and temporal lobe in motor neuron disease predominantly affecting the upper motor neuron.
An outcome of nuclear safety research in JAERI. Predominance of research
International Nuclear Information System (INIS)
Yanagisawa, Kazuaki; Kawashima, Kei; Ito, Keishiro; Katsuki, Chisato
2010-02-01
Bibliometric study by means of research papers revealed the followings; (1) Nuclear Safety Research (NSR) performed in Japan is the 2nd highest in the world followed by USA. The share of JAERI for safety paper publication is about 25% in Japan (2) During past 25 years, JAERI is predominant at 39 safety fields out of 97, that is, 40% to the total. This is the fact revealed from comparison of published number of research papers with those of other organizations. (3) JAERI is recently changing its stress point from reactor-oriented accidents to the down stream of nuclear fuel cycling. There existed impact of TMI-2 accident on NSR-JAERI, especially in the field of thermal hydraulics, LOCA, severe accident and risk analysis. (author)
Bose-Einstein atoms in atomic traps with predominantly attractive two-body interactions
International Nuclear Information System (INIS)
Hussein, M.S.; Vorov, O.K.
2002-01-01
Using the Perron-Frobenius theorem, we prove that the results by Wilkin, Gunn, and Smith [Phys. Rev. Lett. 80, 2265 (1998)] for the ground states at angular momentum L of N harmonically trapped Bose atoms, interacting via weak attractive δ 2 (r) forces, are valid for a broad class of predominantly attractive interactions V(r), not necessarily attractive for any r. This class is described by sufficient conditions on the two-body matrix elements of the potential V(r). It includes, in particular, the Gaussian attraction of arbitrary radius, -1/r-Coulomb and log(r)-Coulomb forces, as well as all the short-range interactions satisfying inequality ∫d 2 r-vectorV(r)<0. In the precollapse regime, the angular momentum L is concentrated in the collective 'center-of-mass' mode, and there is no condensation at high L
Hawkes, Eliza A; Wotherspoon, Andrew; Cunningham, David
2012-03-01
Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare disease constituting only 3-8% of all Hodgkin lymphoma. It has a distinct histological and clinical presentation as well as significantly different natural history compared to the classical form of Hodgkin lymphoma. Presenting most often as early-stage disease, NLPHL is characterized by frequent relapses, but paradoxically an overall good prognosis. The approach to management should therefore reflect this pattern and focus on attaining prolonged remissions, with long-term follow-up paramount. Due to the rarity of the disease, few prospective data exist. Options for treatment include radiotherapy, chemotherapy or combined chemotherapy plus radiotherapy and targeted anti-CD20 antibody therapy, as well as observation in selected patients.
Hassouneh, Dena
2006-07-01
Despite the significant effects of systems of oppression on health, nursing education tends not to include anti-racist pedagogy in its curricula, preferring instead to focus more narrowly on culture. This narrow focus allows nurses to depoliticize discussions of race and other social differences, largely ignoring the influence that systems of oppression, imperialism, and historical trauma have had on health in marginalized populations. In contrast, anti-racist pedagogy educates students in ways that make racialized power relations explicit, deconstruct the social construction of race, and analyze interlocking systems of oppression that serve to marginalize and exclude some groups while privileging others. This article describes anti-racist pedagogy from the perspective of a faculty member of color, drawing on personal experience and a review of the anti-racist pedagogical literature. Specifically, this article highlights some of the personal and professional challenges faced by faculty of color when engaged in anti-racist pedagogy in predominantly white schools of nursing.
Probabilistic pathway construction.
Yousofshahi, Mona; Lee, Kyongbum; Hassoun, Soha
2011-07-01
Expression of novel synthesis pathways in host organisms amenable to genetic manipulations has emerged as an attractive metabolic engineering strategy to overproduce natural products, biofuels, biopolymers and other commercially useful metabolites. We present a pathway construction algorithm for identifying viable synthesis pathways compatible with balanced cell growth. Rather than exhaustive exploration, we investigate probabilistic selection of reactions to construct the pathways. Three different selection schemes are investigated for the selection of reactions: high metabolite connectivity, low connectivity and uniformly random. For all case studies, which involved a diverse set of target metabolites, the uniformly random selection scheme resulted in the highest average maximum yield. When compared to an exhaustive search enumerating all possible reaction routes, our probabilistic algorithm returned nearly identical distributions of yields, while requiring far less computing time (minutes vs. years). The pathways identified by our algorithm have previously been confirmed in the literature as viable, high-yield synthesis routes. Prospectively, our algorithm could facilitate the design of novel, non-native synthesis routes by efficiently exploring the diversity of biochemical transformations in nature. Copyright © 2011 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Matsen Frederick A
2012-05-01
Full Text Available Abstract Background Although taxonomy is often used informally to evaluate the results of phylogenetic inference and the root of phylogenetic trees, algorithmic methods to do so are lacking. Results In this paper we formalize these procedures and develop algorithms to solve the relevant problems. In particular, we introduce a new algorithm that solves a "subcoloring" problem to express the difference between a taxonomy and a phylogeny at a given rank. This algorithm improves upon the current best algorithm in terms of asymptotic complexity for the parameter regime of interest; we also describe a branch-and-bound algorithm that saves orders of magnitude in computation on real data sets. We also develop a formalism and an algorithm for rooting phylogenetic trees according to a taxonomy. Conclusions The algorithms in this paper, and the associated freely-available software, will help biologists better use and understand taxonomically labeled phylogenetic trees.
Acculturation and Adverse Birth Outcomes in a Predominantly Puerto Rican Population.
Barcelona de Mendoza, Veronica; Harville, Emily; Theall, Katherine; Buekens, Pierre; Chasan-Taber, Lisa
2016-06-01
Introduction Latinas in the United States on average have poorer birth outcomes than Whites, yet considerable heterogeneity exists within Latinas. Puerto Ricans have some of the highest rates of adverse outcomes and are understudied. The goal of this study was to determine if acculturation was associated with adverse birth outcomes in a predominantly Puerto Rican population. Methods We conducted a secondary analysis of Proyecto Buena Salud, a prospective cohort study conducted from 2006 to 2011. A convenience sample of pregnant Latina women were recruited from a tertiary care hospital in Massachusetts. Acculturation was measured in early pregnancy; directly via the Psychological Acculturation Scale, and via proxies of language preference and generation in the United States. Birth outcomes (gestational age and birthweight) were abstracted from medical records (n = 1362). Results After adjustment, psychological acculturation, language preference, and generation was not associated with odds of preterm birth. However, every unit increase in psychological acculturation score was associated with an increase in gestational age of 0.22 weeks (SE = 0.1, p = 0.04) among all births. Women who preferred to speak Spanish (β = -0.39, SE = 0.2, p = 0.02) and who were first generation in the US (β = -0.33, SE = 0.1, p = 0.02) had significantly lower gestational ages than women who preferred English or who were later generation, respectively. Similarly, women who were first generation had babies who weighed 76.11 g less (SE = 35.2, p = 0.03) than women who were later generation. Discussion We observed a small, but statistically significant adverse impact of low acculturation on gestational age and birthweight in this predominantly Puerto Rican population.
Ice-associated norovirus outbreak predominantly caused by GII.17 in Taiwan, 2015.
Cheng, Hao-Yuan; Hung, Min-Nan; Chen, Wan-Chin; Lo, Yi-Chun; Su, Ying-Shih; Wei, Hsin-Yi; Chen, Meng-Yu; Tuan, Yen-Chang; Lin, Hui-Chen; Lin, Hsu-Yang; Liu, Tsung-Yen; Wang, Yu-Ying; Wu, Fang-Tzy
2017-11-07
On 5 March 2015, Taiwan Centers for Disease Control was notified of more than 200 students with gastroenteritis at a senior high school during excursion to Kenting. We conducted an outbreak investigation to identify the causative agent and possible vehicle of the pathogen. We conducted a retrospective cohort study by using a structured questionnaire to interview all students for consumed food items during their stay at the resort. Students were defined as a gastroenteritis case while having vomiting or diarrhea after the breakfast on 4 March. We inspected the environment to identify possible contamination route. We collected stool or vomitus samples from ill students, food handlers and environmental specimens for bacterial culture for common enteropathogens, reverse transcription polymerase chain reaction (RT-PCR) for norovirus and enzyme-linked immunosorbent assay (ELISA) for rotavirus. Norovirus PCR-positive products were then sequenced and genotyped. Of 267 students enrolled, 144 (54%) met our case definition. Regression analysis revealed elevated risk associated with iced tea, which was made from tea powder mixed with hot water and self-made ice (risk ratio 1.54, 95% confidence interval 1.22-1.98). Ice used for beverages, water before and after water filter of the ice machine and 16 stool and vomitus samples from ill students were tested positive for norovirus; Multiple genotypes were identified including GI.2, GI.4 and GII.17. GII.17 was the predominant genotype and phylogenetic analyses showed that noroviruses identified in ice, water and human samples were clustered into the same genotypes. Environmental investigation revealed the ice was made by inadequate-filtered and un-boiled water. We identified the ice made by norovirus-contaminated un-boiled water caused the outbreak and the predominant genotype was GII.17. Adequately filtered or boiled water should be strongly recommended for making ice to avoid possible contamination.
Directory of Open Access Journals (Sweden)
Adriana Suzart Ungaretti Rossi
2015-09-01
Full Text Available Attention-deficit/hyperactivity disorder (ADHD is a widely studied neurodevelopmental disorder. It is a highly heterogeneous condition, encompassing different types of expression. The predominantly inattentive type is the most prevalent and the most stable over the lifetime, yet it is the least-studied presentation. To increase understanding of its cognitive profile, 29 children with Attention-deficit/hyperactivity disorder of predominantly inattentive type (ADHD-I and 29 matched controls, aged 7 to 15 years, had their attentional abilities assessed through the Conners’ Continuous Performance Test. Diffusion tensor imaging data were collected for all of the participants using a 3.0 Tesla MRI system. Fractional anisotropy values were obtained for 20 fibre tracts, and brain-behaviour correlations were calculated for 42 of the children. The ADHD-I children differed significantly from the typically developing children with respect to attentional measures, such as the ability to maintain response-time consistency throughout the task (Hit RT SE and Variability, vigilance (Hit RT ISI and Hit RT ISI SE, processing speed (Hit RT, selective attention (Omissions, sustained attention (Hit RT Block Change, error profile (Response Style and inhibitory control (Perseverations. Evidence of significant differences between the ADHD-I and the typically developing participants was not found with respect to the mean FA values in the fibre tracts analysed. Moderate and strong correlations between performance on the attention indicators and the tract-average fractional anisotropy values were found for the ADHD-I group. Our results contribute to a better characterization of the attentional profile of ADHD-I individuals and suggest that in children and adolescents with ADHD-I, attentional performance is mainly associated with the white-matter structure of the long associative fibres that connect anterior-posterior brain areas.
Rossi, Adriana Suzart Ungaretti; de Moura, Luciana Monteiro; de Mello, Claudia Berlim; de Souza, Altay Alves Lino; Muszkat, Mauro; Bueno, Orlando Francisco Amodeo
2015-01-01
Attention-deficit/hyperactivity disorder (ADHD) is a widely studied neurodevelopmental disorder. It is a highly heterogeneous condition, encompassing different types of expression. The predominantly inattentive type is the most prevalent and the most stable over the lifetime, yet it is the least-studied presentation. To increase understanding of its cognitive profile, 29 children with attention-deficit/hyperactivity disorder of predominantly inattentive type (ADHD-I) and 29 matched controls, aged 7-15 years, had their attentional abilities assessed through the Conners' continuous performance test. Diffusion tensor imaging data were collected for all of the participants using a 3.0-T MRI system. Fractional anisotropy (FA) values were obtained for 20 fiber tracts, and brain-behavior correlations were calculated for 42 of the children. The ADHD-I children differed significantly from the typically developing (TD) children with respect to attentional measures, such as the ability to maintain response-time consistency throughout the task (Hit RT SE and Variability), vigilance (Hit RT ISI and Hit RT ISI SE), processing speed (Hit RT), selective attention (Omissions), sustained attention (Hit RT Block Change), error profile (Response Style), and inhibitory control (Perseverations). Evidence of significant differences between the ADHD-I and the TD participants was not found with respect to the mean FA values in the fiber tracts analyzed. Moderate and strong correlations between performance on the attention indicators and the tract-average FA values were found for the ADHD-I group. Our results contribute to a better characterization of the attentional profile of ADHD-I individuals and suggest that in children and adolescents with ADHD-I, attentional performance is mainly associated with the white matter structure of the long associative fibers that connect anterior-posterior brain areas.
Bashir, Gulnaz; Wani, Tehmeena; Sharma, Pragya; Katoch, V M; Lone, Rubina; Shah, Azra; Katoch, Kiran; Kakru, D K; Chauhan, Devendra Singh
2017-10-01
As there are no data available regarding the strains of Mycobacterium tuberculosis circulating in Kashmir Valley, India, the current study aimed at describing the genetic diversity of M. tuberculosis strains in this region, by spoligotyping and 12-locus-based MIRU-VNTR typing (Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat). Sputa from 207 smear positive cases with newly diagnosed pulmonary tuberculosis were subjected to culture for M. tuberculosis. Eighty-five isolates confirmed as M. tuberculosis were subjected to drug susceptibility testing and molecular typing by spoligotyping and MIRU-VNTRs. Drug susceptibility results of 72 isolates revealed 76.3% as fully sensitive while 5.5% as multidrug resistant (MDR). Spoligotyping of 85 isolates detected 42 spoligotypes with 50 isolates (58.8%) clustered into seven spoligotypes. SIT26/CAS1_Del was the major spoligotype (23, 27%) followed by SIT127/H4 (12, 14.1%); CAS lineage (37.6%) was predominant, followed by Haarlem (25.8%) and ill-defined T clade (23.5%). MIRU-VNTR analysis displayed 82 MIRU patterns from 85 strains, including 3 small clusters and 79 unique. MIRU 26 was found to be the most discriminatory locus. Kashmir Valley has CAS as the predominant lineage of M. tuberculosis similar to the rest of the Indian sub-continent, while it is peculiar in having Euro American lineages such as Haarlem and ill-defined T clade. Copyright © 2017 Tuberculosis Association of India. Published by Elsevier B.V. All rights reserved.
Predominance of Blastocystis sp. Infection among School Children in Peninsular Malaysia.
Nithyamathi, Kalimuthu; Chandramathi, Samudi; Kumar, Suresh
2016-01-01
One of the largest cross-sectional study in recent years was carried out to investigate the prevalence of intestinal parasitic infections among urban and rural school children from five states namely Selangor, Perak, Pahang, Kedah and Johor in Peninsula Malaysia. This information would be vital for school authorities to influence strategies for providing better health especially in terms of reducing intestinal parasitism. A total of 3776 stool cups was distributed to 26 schools throughout the country. 1760 (46.61%) responded. The overall prevalence of intestinal parasitic infection in both rural and urban areas was 13.3%, with Blastocystis sp (10.6%) being the most predominant, followed by Trichuris trichiura (3.4%), Ascaris lumbricoides (1.5%) and hook worm infection (0.9%). Only rural school children had helminthic infection. In general Perak had the highest infection (37.2%, total, n = 317), followed by Selangor (10.4%, total, n = 729), Pahang (8.6%, total, n = 221), Kedah (6.2%, total, n = 195) and Johor (3.4%, total, n = 298). School children from rural schools had higher infection (13.7%, total, n = 922) than urban school children (7.2%, total, n = 838). Subtype (ST) 3 (54.3%) is the most predominant ST with persons infected with only ST1 and ST3 showing symptoms. Blastocystis sp infection significantly associated with low household income, low parent's education and presence of symptoms (p<0.05). It is critical that we institute deworming and treatment to eradicate the parasite especially in rural school children.
Taylor, Sean W; Laughlin, Ruple S; Kumar, Neeraj; Goodman, Brent; Klein, Christopher J; Dyck, Peter J; Dyck, P James B
2017-10-01
Myelopathy is considered the most common neurological complication of copper deficiency. Concurrent peripheral neuropathy has been recognised in association with copper deficiency but has not been well characterised. To characterise the clinical, physiological and pathological features of copper-deficient peripheral neuropathy. Patients with simultaneous copper deficiency (peripheral neuropathy seen at the Mayo Clinic from 1985 to 2005 were identified. 34 patients were identified (median age 55 years, range 36-78) including 24 women and 10 men. Myelopathy was found in 21 patients. Median serum copper level was 0.11 μg/mL (range 0-0.58). The most frequent clinical and electrophysiological pattern of neuropathy was a sensory predominant length-dependent peripheral neuropathy (71%). Somatosensory evoked potentials demonstrated central slowing supporting myelopathy (96%). Quantitative sensory testing demonstrated both small and large fibre involvement (100%). Autonomic reflex screens (77%) and thermoregulatory sweat test (67%) confirmed sudomotor dysfunction. 14 cutaneous nerve biopsies revealed loss of myelinated nerve fibres (86%), increased regenerative clusters (50%), increased rates of axonal degeneration (91%) and increased numbers of empty nerve strands (73%). 71% of biopsies demonstrated epineurial perivascular inflammation. An axonal, length-dependent sensory predominant peripheral neuropathy causing sensory ataxia is characteristic of copper deficiency usually co-occurring with myelopathy. Neurophysiological testing confirms involvement of large, greater than small fibres. The pathological findings suggest axonal degeneration and repair. Inflammatory infiltrates are common but are small and of doubtful pathological significance. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Levine, Stephen Z; Leucht, Stefan
2013-04-01
The treatment and measurement of negative symptoms are currently at issue in schizophrenia, but the clinical meaning of symptom severity and change is unclear. To offer a clinically meaningful interpretation of severity and change scores on the Scale for the Assessment of Negative Symptoms (SANS). Patients were intention-to-treat participants (n=383) in two double-blind randomized placebo-controlled clinical trials that compared amisulpride with placebo for the treatment of predominant negative symptoms. Equipercentile linking was used to examine extrapolation from (a) CGI-S to SANS severity ratings, and (b) CGI-I to SANS percentage change (n=383). Linking was conducted at baseline, 8-14 days, 28-30 days, and 56-60 days of the trials. Across visits, CGI-S ratings of 'not ill' linked to SANS scores of 0-13, and ranged to 'extreme' ratings that linked to SANS scores of 102-105. The relationship between the CGI-S and the SANS severity scores assumed a linear trend (1=0-13, 2=15-56, 3=37-61, 4=49-66, 5=63-75, 6=79-89, 7=102-105). Similarly the relationship between CGI-I ratings and SANS percentage change followed a linear trend. For instance, CGI-I ratings of 'very much improved' were linked to SANS percent changes of -90 to -67, 'much improved' to -50 to -42, and 'minimally improved' to -21 to -13. The current results uniquely contribute to the debate surrounding negative symptoms by providing clinical meaning to SANS severity and change scores and so offer direction regarding clinically meaningful response cut-off scores to guide treatment targets of predominant negative symptoms. Copyright © 2013 Elsevier B.V. All rights reserved.
Nodular lymphocyte-predominant Hodgkin lymphoma: a unique disease deserving unique management.
Eichenauer, Dennis A; Engert, Andreas
2017-12-08
Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare lymphoma entity with an incidence of 0.1 to 0.2/100 000/y. Compared with the more common subtypes of classical Hodgkin lymphoma, NLPHL is characterized by distinct pathological and clinical features. Histologically, the disease-defining lymphocyte predominant cells consistently express CD20 but lack CD30. Clinically, NLPHL mostly has a rather indolent course, and patients usually are diagnosed in early stages. The prognosis of early-stage NLPHL is excellent, with progression-free survival and overall survival rates exceeding 90% after involved-field radiotherapy (IF-RT) alone (stage IA) or combined modality treatment consisting of a brief chemotherapy with 2 cycles of ABVD (doxorubicin, bleomycin, vinblastine, dacarbazine) chemotherapy followed by IF-RT (early stages other than stage IA). In contrast, patients with advanced disease at diagnosis tend to relapse either with NLPHL histology or with histological transformation into aggressive B-cell non-Hodgkin lymphoma despite more aggressive first-line treatment with 6 to 8 cycles of multiagent chemotherapy. However, even NLPHL patients with multiple relapses successfully respond to salvage therapy in many cases. Salvage therapies range from single-agent anti-CD20 antibody treatment to high-dose chemotherapy followed by autologous stem cell transplantation. Treatment at disease recurrence should be chosen on the basis of various factors, including histology at relapse, time to relapse, extent of disease at relapse, and prior treatment. Because death among NLPHL patients is more often caused by therapy-related late effects than lymphoma-related complications, optimizing the risk-benefit ratio of treatment by decreasing toxicity whenever possible is the major goal of clinical research in this disease. © 2016 by The American Society of Hematology. All rights reserved.
Inferences of the deep solar meridional flow
Böning, Vincent G. A.
2017-10-01
Understanding the solar meridional flow is important for uncovering the origin of the solar activity cycle. Yet, recent helioseismic estimates of this flow have come to conflicting conclusions in deeper layers of the solar interior, i.e., at depths below about 0.9 solar radii. The aim of this thesis is to contribute to a better understanding of the deep solar meridional flow. Time-distance helioseismology is the major method for investigating this flow. In this method, travel times of waves propagating between pairs of locations on the solar surface are measured. Until now, the travel-time measurements have been modeled using the ray approximation, which assumes that waves travel along infinitely thin ray paths between these locations. In contrast, the scattering of the full wave field in the solar interior due to the flow is modeled in first order by the Born approximation. It is in general a more accurate model of the physics in the solar interior. In a first step, an existing model for calculating the sensitivity of travel-time measurements to solar interior flows using the Born approximation is extended from Cartesian to spherical geometry. The results are succesfully compared to the Cartesian ones and are tested for self-consistency. In a second step, the newly developed model is validated using an existing numerical simulation of linear wave propagation in the Sun. An inversion of artificial travel times for meridional flow shows excellent agreement for noiseless data and reproduces many features in the input flow profile in the case of noisy data. Finally, the new method is used to infer the deep meridional flow. I used Global Oscillation Network Group (GONG) data that were earlier analyzed using the ray approximation and I employed the same Substractive Optimized Local Averaging (SOLA) inversion technique as in the earlier study. Using an existing formula for the covariance of travel-time measurements, it is shown that the assumption of uncorrelated errors
Wells, Julie; Rivera, Miguel N; Kim, Woo Jae; Starbuck, Kristen; Haber, Daniel A
2010-07-01
WT1 encodes a tumor suppressor first identified by its inactivation in Wilms' Tumor. Although one WT1 splicing variant encodes a well-characterized zinc finger transcription factor, little is known about the function of the most prevalent WT1 isoform, whose DNA binding domain is disrupted by a three-amino acid (KTS) insertion. Using cells that conditionally express WT1(+KTS), we undertook a genome-wide chromatin immunoprecipitation and cloning analysis to identify candidate WT1(+KTS)-regulated promoters. We identified the planar cell polarity gene Scribble (SCRB) as the first WT1(+KTS) target gene in podocytes of the kidney. WT1 and SCRB expression patterns overlap precisely in developing renal glomeruli of mice, and WT1(+KTS) binds to a 33-nucleotide region within the Scribble promoter in mouse and human cell lines and kidneys. Together, our results support a role for the predominant WT1(+KTS) isoform in transcriptional regulation and suggest a link between the WT1-dependent tumor suppressor pathway and a key component of the planar cell polarity pathway.