Causal pathways linking Farm to School to childhood obesity prevention.

PubMed

Joshi, Anupama; Ratcliffe, Michelle M

2012-08-01

Farm to School programs are rapidly gaining attention as a potential strategy for preventing childhood obesity; however, the causal linkages between Farm to School activities and health outcomes are not well documented. To capitalize on the increased interest in and momentum for Farm to School, researchers and practitioners need to move from developing and implementing evidence informed programs and policies to ones that are evidence-based. The purpose of this article is to outline a framework for facilitating an evidence base for Farm to School programs and policies through a systematic and coordinated approach. Employing the concepts of causal pathways, the authors introduce a proposed framework for organizing and systematically testing out multiple hypotheses (or potential causal links) for how, why, and under what conditions Farm to School Inputs and Activities may result in what Outputs, Effects, and Impacts. Using the causal pathways framework may help develop and test competing hypotheses, identify multicausality, strength, and interactions of causes, and discern the difference between catalysts and causes. In this article, we introduce causal pathways, present menus of potential independent and dependent variables from which to create and test causal pathways linking Farm to School interventions and their role in preventing childhood obesity, discuss their applicability to Farm to School research and practice, and outline proposed next steps for developing a coordinated research framework for Farm to School programs.

Mechanisms and mediation in survival analysis: towards an integrated analytical framework.

PubMed

Pratschke, Jonathan; Haase, Trutz; Comber, Harry; Sharp, Linda; de Camargo Cancela, Marianna; Johnson, Howard

2016-02-29

A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall). In addition to the substantial direct effect of this variable (-0.60), its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12), on the one hand, and hospital caseload, on the other, (-0.10) are of similar size. The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of Structural Equation Modelling. The authors demonstrate the efficacy of this approach in identifying complex causal pathways that mediate the effects of a socio-economic baseline covariate on the hazard of death from colon cancer. The results show that this approach has the potential to shed light on a class of research questions which is of particular relevance in health research.

Examination of tetrahydrobiopterin pathway genes in autism.

PubMed

Schnetz-Boutaud, N C; Anderson, B M; Brown, K D; Wright, H H; Abramson, R K; Cuccaro, M L; Gilbert, J R; Pericak-Vance, M A; Haines, J L

2009-11-01

Autism is a complex disorder with a high degree of heritability and significant phenotypic and genotypic heterogeneity. Although candidate gene studies and genome-wide screens have failed to identify major causal loci associated with autism, numerous studies have proposed association with several variations in genes in the dopaminergic and serotonergic pathways. Because tetrahydrobiopterin (BH4) is the essential cofactor in the synthesis of these two neurotransmitters, we genotyped 25 SNPs in nine genes of the BH4 pathway in a total of 403 families. Significant nominal association was detected in the gene for 6-pyruvoyl-tetrahydropterin synthase, PTS (chromosome 11), with P = 0.009; this result was not restricted to an affected male-only subset. Multilocus interaction was detected in the BH4 pathway alone, but not across the serotonin, dopamine and BH4 pathways.

Causal mechanisms of soil organic matter decomposition: Deconstructing salinity and flooding impacts in coastal wetlands

USGS Publications Warehouse

Stagg, Camille L.; Schoolmaster, Donald; Krauss, Ken W.; Cormier, Nicole; Conner, William H.

2017-01-01

Coastal wetlands significantly contribute to global carbon storage potential. Sea-level rise and other climate change-induced disturbances threaten coastal wetland sustainability and carbon storage capacity. It is critical that we understand the mechanisms controlling wetland carbon loss so that we can predict and manage these resources in anticipation of climate change. However, our current understanding of the mechanisms that control soil organic matter decomposition, in particular the impacts of elevated salinity, are limited, and literature reports are contradictory. In an attempt to improve our understanding of these complex processes, we measured root and rhizome decomposition and developed a causal model to identify and quantify the mechanisms that influence soil organic matter decomposition in coastal wetlands that are impacted by sea-level rise. We identified three causal pathways: 1) a direct pathway representing the effects of flooding on soil moisture, 2) a direct pathway representing the effects of salinity on decomposer microbial communities and soil biogeochemistry, and 3) an indirect pathway representing the effects of salinity on litter quality through changes in plant community composition over time. We used this model to test the effects of alternate scenarios on the response of tidal freshwater forested wetlands and oligohaline marshes to short- and long-term climate-induced disturbances of flooding and salinity. In tidal freshwater forested wetlands, the model predicted less decomposition in response to drought, hurricane salinity pulsing, and long-term sea-level rise. In contrast, in the oligohaline marsh, the model predicted no change in response to sea-level rise, and increased decomposition following a drought or a hurricane salinity pulse. Our results show that it is critical to consider the temporal scale of disturbance and the magnitude of exposure when assessing the effects of salinity intrusion on carbon mineralization in coastal wetlands. Here we identify three causal mechanisms that can reconcile disparities between long-term and short-term salinity impacts on organic matter decomposition.

Causal mechanisms of soil organic matter decomposition: deconstructing salinity and flooding impacts in coastal wetlands.

PubMed

Stagg, Camille L; Schoolmaster, Donald R; Krauss, Ken W; Cormier, Nicole; Conner, William H

2017-08-01

Coastal wetlands significantly contribute to global carbon storage potential. Sea-level rise and other climate-change-induced disturbances threaten coastal wetland sustainability and carbon storage capacity. It is critical that we understand the mechanisms controlling wetland carbon loss so that we can predict and manage these resources in anticipation of climate change. However, our current understanding of the mechanisms that control soil organic matter decomposition, in particular the impacts of elevated salinity, are limited, and literature reports are contradictory. In an attempt to improve our understanding of these complex processes, we measured root and rhizome decomposition and developed a causal model to identify and quantify the mechanisms that influence soil organic matter decomposition in coastal wetlands that are impacted by sea-level rise. We identified three causal pathways: (1) a direct pathway representing the effects of flooding on soil moisture, (2) a direct pathway representing the effects of salinity on decomposer microbial communities and soil biogeochemistry, and (3) an indirect pathway representing the effects of salinity on litter quality through changes in plant community composition over time. We used this model to test the effects of alternate scenarios on the response of tidal freshwater forested wetlands and oligohaline marshes to short- and long-term climate-induced disturbances of flooding and salinity. In tidal freshwater forested wetlands, the model predicted less decomposition in response to drought, hurricane salinity pulsing, and long-term sea-level rise. In contrast, in the oligohaline marsh, the model predicted no change in response to drought and sea-level rise, and increased decomposition following a hurricane salinity pulse. Our results show that it is critical to consider the temporal scale of disturbance and the magnitude of exposure when assessing the effects of salinity intrusion on carbon mineralization in coastal wetlands. Here, we identify three causal mechanisms that can reconcile disparities between long-term and short-term salinity impacts on organic matter decomposition. © 2017 by the Ecological Society of America.

What Can Causal Networks Tell Us about Metabolic Pathways?

PubMed Central

Blair, Rachael Hageman; Kliebenstein, Daniel J.; Churchill, Gary A.

2012-01-01

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies. PMID:22496633

Processing of positive-causal and negative-causal coherence relations in primary school children and adults: a test of the cumulative cognitive complexity approach in German.

PubMed

Knoepke, Julia; Richter, Tobias; Isberner, Maj-Britt; Naumann, Johannes; Neeb, Yvonne; Weinert, Sabine

2017-03-01

Establishing local coherence relations is central to text comprehension. Positive-causal coherence relations link a cause and its consequence, whereas negative-causal coherence relations add a contrastive meaning (negation) to the causal link. According to the cumulative cognitive complexity approach, negative-causal coherence relations are cognitively more complex than positive-causal ones. Therefore, they require greater cognitive effort during text comprehension and are acquired later in language development. The present cross-sectional study tested these predictions for German primary school children from Grades 1 to 4 and adults in reading and listening comprehension. Accuracy data in a semantic verification task support the predictions of the cumulative cognitive complexity approach. Negative-causal coherence relations are cognitively more demanding than positive-causal ones. Moreover, our findings indicate that children's comprehension of negative-causal coherence relations continues to develop throughout the course of primary school. Findings are discussed with respect to the generalizability of the cumulative cognitive complexity approach to German.

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity.

PubMed

Kogelman, Lisette J A; Zhernakova, Daria V; Westra, Harm-Jan; Cirera, Susanna; Fredholm, Merete; Franke, Lude; Kadarmideen, Haja N

2015-10-20

Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data. Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B. To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

Temporal Expression Profiling Identifies Pathways Mediating Effect of Causal Variant on Phenotype

PubMed Central

Gupta, Saumya; Radhakrishnan, Aparna; Raharja-Liu, Pandu; Lin, Gen; Steinmetz, Lars M.; Gagneur, Julien; Sinha, Himanshu

2015-01-01

Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants’ effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is primarily used to get insights into the biological bases of diseases, it makes differentiating the causative from the correlative effects difficult, as the dynamics of the underlying biological processes are not monitored. Using yeast as a model, we studied genome-wide gene expression dynamics in the presence of a causal variant as the sole genetic determinant, and performed allele-specific functional validation to delineate the causal effects of the genetic variant on the phenotype. Here, we characterized the precise genetic effects of a functional MKT1 allelic variant in sporulation efficiency variation. A mathematical model describing meiotic landmark events and conditional activation of MKT1 expression during sporulation specified an early meiotic role of this variant. By analyzing the early meiotic genome-wide transcriptional response, we demonstrate an MKT1-dependent role of novel modulators, namely, RTG1/3, regulators of mitochondrial retrograde signaling, and DAL82, regulator of nitrogen starvation, in additively effecting sporulation efficiency. In the presence of functional MKT1 allele, better respiration during early sporulation was observed, which was dependent on the mitochondrial retrograde regulator, RTG3. Furthermore, our approach showed that MKT1 contributes to sporulation independent of Puf3, an RNA-binding protein that steady-state transcription profiling studies have suggested to mediate MKT1-pleiotropic effects during mitotic growth. These results uncover interesting regulatory links between meiosis and mitochondrial retrograde signaling. In this study, we highlight the advantage of analyzing allele-specific transcriptional dynamics of mediating genes. Applications in higher eukaryotes can be valuable for inferring causal molecular pathways underlying complex dynamic processes, such as development, physiology and disease progression. PMID:26039065

AOP: An R Package For Sufficient Causal Analysis in Pathway ...

EPA Pesticide Factsheets

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharma-cology, toxicology and risk assessment. By identifying these sufficient causal key events, we have fewer events to monitor for a pathway, thereby decreasing assay costs and time, while maximizing the value of the information. I have developed the “aop” package which uses backdoor analysis of causal net-works to identify these minimal sets of key events that are suf-ficient for making causal predictions. Availability and Implementation: The source and binary are available online through the Bioconductor project (http://www.bioconductor.org/) as an R package titled “aop”. The R/Bioconductor package runs within the R statistical envi-ronment. The package has functions that can take pathways (as directed graphs) formatted as a Cytoscape JSON file as input, or pathways can be represented as directed graphs us-ing the R/Bioconductor “graph” package. The “aop” package has functions that can perform backdoor analysis to identify the minimal set of key events for making causal predictions.Contact: burgoon.lyle@epa.gov This paper describes an R/Bioconductor package that was developed to facilitate the identification of key events within an AOP that are the minimal set of sufficient key events that need to be tested/monit

The Neural Circuits that Generate Tics in Gilles de la Tourette Syndrome

PubMed Central

Wang, Zhishun; Maia, Tiago V.; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S.

2014-01-01

Objective To study neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette syndrome. Method We acquired fMRI data from 13 participants with Tourette syndrome and 21 controls during spontaneous or simulated tics. We used independent component analysis with hierarchical partner matching to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. We used Granger causality to investigate causal interactions among these regions. Results We found that the Tourette group exhibited stronger neural activity and interregional causality than controls throughout all portions of the motor pathway including sensorimotor cortex, putamen, pallidum, and substania nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette group was stronger during spontaneous tics than during voluntary tics in somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette group than in controls within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may fail to control tic behaviors or the premonitory urges that generate them. Conclusions Our findings taken together suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits. PMID:21955933

Genetic architecture for human aggression: A study of gene-phenotype relationship in OMIM.

PubMed

Zhang-James, Yanli; Faraone, Stephen V

2016-07-01

Genetic studies of human aggression have mainly focused on known candidate genes and pathways regulating serotonin and dopamine signaling and hormonal functions. These studies have taught us much about the genetics of human aggression, but no genetic locus has yet achieved genome-significance. We here present a review based on a paradoxical hypothesis that studies of rare, functional genetic variations can lead to a better understanding of the molecular mechanisms underlying complex multifactorial disorders such as aggression. We examined all aggression phenotypes catalogued in Online Mendelian Inheritance in Man (OMIM), an Online Catalog of Human Genes and Genetic Disorders. We identified 95 human disorders that have documented aggressive symptoms in at least one individual with a well-defined genetic variant. Altogether, we retrieved 86 causal genes. Although most of these genes had not been implicated in human aggression by previous studies, the most significantly enriched canonical pathways had been previously implicated in aggression (e.g., serotonin and dopamine signaling). Our findings provide strong evidence to support the causal role of these pathways in the pathogenesis of aggression. In addition, the novel genes and pathways we identified suggest additional mechanisms underlying the origins of human aggression. Genome-wide association studies with very large samples will be needed to determine if common variants in these genes are risk factors for aggression. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Autoimmune chronic spontaneous urticaria: What we know and what we do not know.

PubMed

Kolkhir, Pavel; Church, Martin K; Weller, Karsten; Metz, Martin; Schmetzer, Oliver; Maurer, Marcus

2017-06-01

Chronic spontaneous urticaria (CSU) is a mast cell-driven skin disease characterized by the recurrence of transient wheals, angioedema, or both for more than 6 weeks. Autoimmunity is thought to be one of the most frequent causes of CSU. Type I and II autoimmunity (ie, IgE to autoallergens and IgG autoantibodies to IgE or its receptor, respectively) have been implicated in the etiology and pathogenesis of CSU. We analyzed the relevant literature and assessed the existing evidence in support of a role for type I and II autoimmunity in CSU with the help of Hill's criteria of causality. For each of these criteria (ie, strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, and analogy), we categorized the strength of evidence as "insufficient," "low," "moderate," or "high" and then assigned levels of causality for type I and II autoimmunity in patients with CSU from level 1 (causal relationship) to level 5 (causality not likely). Based on the evidence in support of Hill's criteria, type I autoimmunity in patients with CSU has level 3 causality (causal relationship suggested), and type II autoimmunity has level 2 causality (causal relationship likely). There are still many aspects of the pathologic mechanisms of CSU that need to be resolved, but it is becoming clear that there are at least 2 distinct pathways, type I and type II autoimmunity, that contribute to the pathogenesis of this complex disease. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Using Qualitative Comparative Analysis (QCA) of Key Informant Interviews in Health Services Research: Enhancing a Study of Adjuvant Therapy Use in Breast Cancer Care

PubMed Central

McAlearney, Ann Scheck; Walker, Daniel; Moss, Alexandra DeNardis; Bickell, Nina A.

2015-01-01

Background Qualitative Comparative Analysis (QCA) is a methodology created to address causal complexity in social sciences research by preserving the objectivity of quantitative data analysis without losing detail inherent in qualitative research. However, its use in health services research (HSR) is limited, and questions remain about its application in this context. Objective To explore the strengths and weaknesses of using QCA for HSR. Research Design Using data from semi-structured interviews conducted as part of a multiple case study about adjuvant treatment underuse among underserved breast cancer patients, findings were compared using qualitative approaches with and without QCA to identify strengths, challenges, and opportunities presented by QCA. Subjects Ninety administrative and clinical key informants interviewed across ten NYC area safety net hospitals. Measures Transcribed interviews were coded by three investigators using an iterative and interactive approach. Codes were calibrated for QCA, as well as examined using qualitative analysis without QCA. Results Relative to traditional qualitative analysis, QCA strengths include: (1) addressing causal complexity, (2) results presentation as pathways as opposed to a list, (3) identification of necessary conditions, (4) the option of fuzzy-set calibrations, and (5) QCA-specific parameters of fit that allow researchers to compare outcome pathways. Weaknesses include: (1) few guidelines and examples exist for calibrating interview data, (2) not designed to create predictive models, and (3) unidirectionality. Conclusions Through its presentation of results as pathways, QCA can highlight factors most important for production of an outcome. This strength can yield unique benefits for HSR not available through other methods. PMID:26908085

Using Qualitative Comparative Analysis of Key Informant Interviews in Health Services Research: Enhancing a Study of Adjuvant Therapy Use in Breast Cancer Care.

PubMed

McAlearney, Ann Scheck; Walker, Daniel; Moss, Alexandra D; Bickell, Nina A

2016-04-01

Qualitative comparative analysis (QCA) is a methodology created to address causal complexity in social sciences research by preserving the objectivity of quantitative data analysis without losing detail inherent in qualitative research. However, its use in health services research (HSR) is limited, and questions remain about its application in this context. To explore the strengths and weaknesses of using QCA for HSR. Using data from semistructured interviews conducted as part of a multiple case study about adjuvant treatment underuse among underserved breast cancer patients, findings were compared using qualitative approaches with and without QCA to identify strengths, challenges, and opportunities presented by QCA. Ninety administrative and clinical key informants interviewed across 10 NYC area safety net hospitals. Transcribed interviews were coded by 3 investigators using an iterative and interactive approach. Codes were calibrated for QCA, as well as examined using qualitative analysis without QCA. Relative to traditional qualitative analysis, QCA strengths include: (1) addressing causal complexity, (2) results presentation as pathways as opposed to a list, (3) identification of necessary conditions, (4) the option of fuzzy-set calibrations, and (5) QCA-specific parameters of fit that allow researchers to compare outcome pathways. Weaknesses include: (1) few guidelines and examples exist for calibrating interview data, (2) not designed to create predictive models, and (3) unidirectionality. Through its presentation of results as pathways, QCA can highlight factors most important for production of an outcome. This strength can yield unique benefits for HSR not available through other methods.

Assessing natural direct and indirect effects through multiple pathways.

PubMed

Lange, Theis; Rasmussen, Mette; Thygesen, Lau Caspar

2014-02-15

Within the fields of epidemiology, interventions research and social sciences researchers are often faced with the challenge of decomposing the effect of an exposure into different causal pathways working through defined mediator variables. The goal of such analyses is often to understand the mechanisms of the system or to suggest possible interventions. The case of a single mediator, thus implying only 2 causal pathways (direct and indirect) from exposure to outcome, has been extensively studied. By using the framework of counterfactual variables, researchers have established theoretical properties and developed powerful tools. However, in practical problems, it is not uncommon to have several distinct causal pathways from exposure to outcome operating through different mediators. In this article, we suggest a widely applicable approach to quantifying and ranking different causal pathways. The approach is an extension of the natural effect models proposed by Lange et al. (Am J Epidemiol. 2012;176(3):190-195). By allowing the analysis of distinct multiple pathways, the suggested approach adds to the capabilities of modern mediation techniques. Furthermore, the approach can be implemented using standard software, and we have included with this article implementation examples using R (R Foundation for Statistical Computing, Vienna, Austria) and Stata software (StataCorp LP, College Station, Texas).

A soft pillow for hard times? Economic insecurity, food intake and body weight in Russia.

PubMed

Staudigel, Matthias

2016-12-01

This study investigates causal effects of economic insecurity on subjective anxiety, food intake, and weight outcomes. A review of psychological and nutrition studies highlights the complexity of processes at work on each stage of this causal chain. Econometric analyses trace the effects along the hypothesized pathway using detailed household panel data from the Russia Longitudinal Monitoring Survey from 1994 to 2005. Economic insecurity measures serve as key explanatory variables in regressions and are instrumented by exogenous regional indicators. Results support a causal chain from economic insecurity to weight outcomes for some population subgroups. In contrast to the leading hypothesis that economic insecurity increases body weight, I find strong evidence of a decreasing effect among women. Results suggest further that consumption of foods rich in sugar responds strongly to higher levels of economic insecurity. Heterogeneous impacts of economic insecurity on body weight call for individual-level interventions rather than large-scale action. Copyright © 2016 Elsevier B.V. All rights reserved.

Processing of Positive-Causal and Negative-Causal Coherence Relations in Primary School Children and Adults: A Test of the Cumulative Cognitive Complexity Approach in German

ERIC Educational Resources Information Center

Knoepke, Julia; Richter, Tobias; Isberner, Maj-Britt; Naumann, Johannes; Neeb, Yvonne; Weinert, Sabine

2017-01-01

Establishing local coherence relations is central to text comprehension. Positive-causal coherence relations link a cause and its consequence, whereas negative-causal coherence relations add a contrastive meaning (negation) to the causal link. According to the cumulative cognitive complexity approach, negative-causal coherence relations are…

Pathway Analysis and the Search for Causal Mechanisms

ERIC Educational Resources Information Center

Weller, Nicholas; Barnes, Jeb

2016-01-01

The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting…

Pathways from Cannabis to Psychosis: A Review of the Evidence

PubMed Central

Burns, Jonathan K.

2013-01-01

The nature of the relationship between cannabis use (CU) and psychosis is complex and remains unclear. Researchers and clinicians remain divided regarding key issues such as whether or not cannabis is an independent cause of psychosis and schizophrenia. This paper reviews the field in detail, examining questions of causality, the neurobiological basis for such causality and for differential inter-individual risk, the clinical and cognitive features of psychosis in cannabis users, and patterns of course and outcome of psychosis in the context of CU. The author proposes two major pathways from cannabis to psychosis based on a differentiation between early-initiated lifelong CU and a scenario where vulnerable individuals without a lifelong pattern of use consume cannabis over a relatively brief period of time just prior to psychosis onset. Additional key factors determining the clinical and neurobiological manifestation of psychosis as well as course and outcome in cannabis users include: underlying genetic and developmental vulnerability to schizophrenia-spectrum disorders; and whether or not CU ceases or continues after the onset of psychosis. Finally, methodological guidelines are presented for future research aimed at both elucidating the pathways that lead from cannabis to psychosis and clarifying the long-term outcome of the disorder in those who have a history of using cannabis. PMID:24133460

Systems Genetics Analysis of GWAS reveals Novel Associations between Key Biological Processes and Coronary Artery Disease

PubMed Central

Ghosh, Sujoy; Vivar, Juan; Nelson, Christopher P; Willenborg, Christina; Segrè, Ayellet V; Mäkinen, Ville-Petteri; Nikpay, Majid; Erdmann, Jeannette; Blankenberg, Stefan; O'Donnell, Christopher; März, Winfried; Laaksonen, Reijo; Stewart, Alexandre FR; Epstein, Stephen E; Shah, Svati H; Granger, Christopher B; Hazen, Stanley L; Kathiresan, Sekar; Reilly, Muredach P; Yang, Xia; Quertermous, Thomas; Samani, Nilesh J; Schunkert, Heribert; Assimes, Themistocles L; McPherson, Ruth

2016-01-01

Objective Genome-wide association (GWA) studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, individually these explain only a small fraction of the heritability of CAD and for most, the causal biological mechanisms remain unclear. We sought to obtain further insights into potential causal processes of CAD by integrating large-scale GWA data with expertly curated databases of core human pathways and functional networks. Approaches and Results Employing pathways (gene sets) from Reactome, we carried out a two-stage gene set enrichment analysis strategy. From a meta-analyzed discovery cohort of 7 CADGWAS data sets (9,889 cases/11,089 controls), nominally significant gene-sets were tested for replication in a meta-analysis of 9 additional studies (15,502 cases/55,730 controls) from the CARDIoGRAM Consortium. A total of 32 of 639 Reactome pathways tested showed convincing association with CAD (replication p<0.05). These pathways resided in 9 of 21 core biological processes represented in Reactome, and included pathways relevant to extracellular matrix integrity, innate immunity, axon guidance, and signaling by PDRF, NOTCH, and the TGF-β/SMAD receptor complex. Many of these pathways had strengths of association comparable to those observed in lipid transport pathways. Network analysis of unique genes within the replicated pathways further revealed several interconnected functional and topologically interacting modules representing novel associations (e.g. semaphorin regulated axonal guidance pathway) besides confirming known processes (lipid metabolism). The connectivity in the observed networks was statistically significant compared to random networks (p<0.001). Network centrality analysis (‘degree’ and ‘betweenness’) further identified genes (e.g. NCAM1, FYN, FURIN etc.) likely to play critical roles in the maintenance and functioning of several of the replicated pathways. Conclusions These findings provide novel insights into how genetic variation, interpreted in the context of biological processes and functional interactions among genes, may help define the genetic architecture of CAD. PMID:25977570

HIA, the next step: Defining models and roles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Putters, Kim

If HIA is to be an effective instrument for optimising health interests in the policy making process it has to recognise the different contests in which policy is made and the relevance of both technical rationality and political rationality. Policy making may adopt a rational perspective in which there is a systematic and orderly progression from problem formulation to solution or a network perspective in which there are multiple interdependencies, extensive negotiation and compromise, and the steps from problem to formulation are not followed sequentially or in any particular order. Policy problems may be simple with clear causal pathways andmore » responsibilities or complex with unclear causal pathways and disputed responsibilities. Network analysis is required to show which stakeholders are involved, their support for health issues and the degree of consensus. From this analysis three models of HIA emerge. The first is the phases model which is fitted to simple problems and a rational perspective of policymaking. This model involves following structured steps. The second model is the rounds (Echternach) model that is fitted to complex problems and a network perspective of policymaking. This model is dynamic and concentrates on network solutions taking these steps in no particular order. The final model is the 'garbage can' model fitted to contexts which combine simple and complex problems. In this model HIA functions as a problem solver and signpost keeping all possible solutions and stakeholders in play and allowing solutions to emerge over time. HIA models should be the beginning rather than the conclusion of discussion the worlds of HIA and policymaking.« less

Simplifying Causal Complexity: How Interactions between Modes of Causal Induction and Information Availability Lead to Heuristic-Driven Reasoning

ERIC Educational Resources Information Center

Grotzer, Tina A.; Tutwiler, M. Shane

2014-01-01

This article considers a set of well-researched default assumptions that people make in reasoning about complex causality and argues that, in part, they result from the forms of causal induction that we engage in and the type of information available in complex environments. It considers how information often falls outside our attentional frame…

The neural circuits that generate tics in Tourette's syndrome.

PubMed

Wang, Zhishun; Maia, Tiago V; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S

2011-12-01

The purpose of this study was to examine neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette's syndrome. Functional magnetic resonance imaging data were acquired from 13 individuals with Tourette's syndrome and 21 healthy comparison subjects during spontaneous or simulated tics. Independent component analysis with hierarchical partner matching was used to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. Granger causality was used to investigate causal interactions among these regions. The Tourette's syndrome group exhibited stronger neural activity and interregional causality than healthy comparison subjects throughout all portions of the motor pathway, including the sensorimotor cortex, putamen, pallidum, and substantia nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette's syndrome group was stronger during spontaneous tics than during voluntary tics in the somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette's syndrome group than in the healthy comparison group within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (the caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may result in their failure to control tic behaviors or the premonitory urges that generate them. Our findings, taken together, suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits.

Brain Networks Shaping Religious Belief

PubMed Central

Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P.; Grafman, Jordan Henry

2014-01-01

Abstract We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing. PMID:24279687

Brain networks shaping religious belief.

PubMed

Kapogiannis, Dimitrios; Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P; Grafman, Jordan Henry

2014-02-01

We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing.

Optimal causal inference: estimating stored information and approximating causal architecture.

PubMed

Still, Susanne; Crutchfield, James P; Ellison, Christopher J

2010-09-01

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.

Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways

PubMed Central

Burgess, Stephen; Daniel, Rhian M; Butterworth, Adam S; Thompson, Simon G

2015-01-01

Background: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. Methods: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. Results: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. Conclusions: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes. PMID:25150977

Assessing Understanding of Complex Causal Networks Using an Interactive Game

ERIC Educational Resources Information Center

Ross, Joel

2013-01-01

Assessing people's understanding of the causal relationships found in large-scale complex systems may be necessary for addressing many critical social concerns, such as environmental sustainability. Existing methods for assessing systems thinking and causal understanding frequently use the technique of cognitive causal mapping. However, the…

The direct, not V1-mediated, functional influence between the thalamus and middle temporal complex in the human brain is modulated by the speed of visual motion.

PubMed

Gaglianese, A; Costagli, M; Ueno, K; Ricciardi, E; Bernardi, G; Pietrini, P; Cheng, K

2015-01-22

The main visual pathway that conveys motion information to the middle temporal complex (hMT+) originates from the primary visual cortex (V1), which, in turn, receives spatial and temporal features of the perceived stimuli from the lateral geniculate nucleus (LGN). In addition, visual motion information reaches hMT+ directly from the thalamus, bypassing the V1, through a direct pathway. We aimed at elucidating whether this direct route between LGN and hMT+ represents a 'fast lane' reserved to high-speed motion, as proposed previously, or it is merely involved in processing motion information irrespective of speeds. We evaluated functional magnetic resonance imaging (fMRI) responses elicited by moving visual stimuli and applied connectivity analyses to investigate the effect of motion speed on the causal influence between LGN and hMT+, independent of V1, using the Conditional Granger Causality (CGC) in the presence of slow and fast visual stimuli. Our results showed that at least part of the visual motion information from LGN reaches hMT+, bypassing V1, in response to both slow and fast motion speeds of the perceived stimuli. We also investigated whether motion speeds have different effects on the connections between LGN and functional subdivisions within hMT+: direct connections between LGN and MT-proper carry mainly slow motion information, while connections between LGN and MST carry mainly fast motion information. The existence of a parallel pathway that connects the LGN directly to hMT+ in response to both slow and fast speeds may explain why MT and MST can still respond in the presence of V1 lesions. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Inflammatory pathways in cervical cancer - the UCT contribution.

PubMed

Sales, Kurt Jason; Katz, Arieh Anthony

2012-03-23

Cervical cancer is the leading gynaecological malignancy in Southern Africa. The main causal factor for development of the disease is infection of the cervix with human papillomavirus. It is a multi-step disease with several contributing co-factors including multiple sexual partners, a compromised immune system and cervical inflammation caused by infections with Chlamydia trachomatis or Neisseria gonorrhoeae. Inflammation involves extensive tissue remodelling events which are orchestrated by complex networks of cytokines, chemokines and bio-active lipids working across multiple cellular compartments to maintain tissue homeostasis. Many pathological disorders or diseases, including cervical cancer, are characterised by the exacerbated activation and maintenance of inflammatory pathways. In this review we highlight our findings pertaining to activation of inflammatory pathways in cervical cancers, addressing their potential role in pathological changes of the cervix and the significance of these findings for intervention strategies.

Clinical Applications of Molecular Genetic Discoveries

PubMed Central

Marian, A.J.

2015-01-01

Genome-wide association studies (GWAS) of complex traits have mapped more than 15,000 common single nucleotide variants (SNVs). Likewise, applications of massively parallel nucleic acid sequencing technologies often referred to as Next Generation Sequencing, to molecular genetic studies of complex traits have catalogued a large number of rare variants (population frequency of <0.01) in cases with complex traits. Moreover, high throughput nucleic acid sequencing, variant burden analysis, and linkage studies are illuminating the presence of large number of SNVs in cases and families with single gene disorders. The plethora of the genetic variants has exposed the formidable challenge of identifying the causal and pathogenic variants from the enormous number of innocuous common and rare variants that exist in the population as well as in an individual genome. The arduous task of identifying the causal and pathogenic variants is further compounded by the pleiotropic effects of the variants, complexity of cis and trans interactions in the genome, variability in phenotypic expression of the disease, as well as phenotypic plasticity, and the multifarious determinants of the phenotype. Population genetic studies offer the initial roadmaps and have the potential to elucidate novel pathways involved in the pathogenesis of the disease. However, the genome of an individual is unique, rendering unambiguous identification of the causal or pathogenic variant in a single individual exceedingly challenging. Yet, the focus of the practice of medicine is on the individual, as Sir William Osler elegantly expressed in his insightful quotation: “The good physician treats the disease; the great physician treats the patient who has the disease.” The daunting task facing physicians, patients, and researchers alike is to apply the modern genetic discoveries to care of the individual with or at risk of the disease. PMID:26548329

Causal Link between the Cortico-Rubral Pathway and Functional Recovery through Forced Impaired Limb Use in Rats with Stroke

PubMed Central

Ishida, Akimasa; Isa, Kaoru; Umeda, Tatsuya; Kobayashi, Kazuto; Kobayashi, Kenta; Hida, Hideki

2016-01-01

Intensive rehabilitation is believed to induce use-dependent plasticity in the injured nervous system; however, its causal relationship to functional recovery is unclear. Here, we performed systematic analysis of the effects of forced use of an impaired forelimb on the recovery of rats after lesioning the internal capsule with intracerebral hemorrhage (ICH). Forced limb use (FLU) group rats exhibited better recovery of skilled forelimb functions and their cortical motor area with forelimb representation was restored and enlarged on the ipsilesional side. In addition, abundant axonal sprouting from the reemerged forelimb area was found in the ipsilateral red nucleus after FLU. To test the causal relationship between the plasticity in the cortico-rubral pathway and recovery, loss-of-function experiments were conducted using a double-viral vector technique, which induces selective blockade of the target pathway. Blockade of the cortico-rubral tract resulted in deficits of the recovered forelimb function in FLU group rats. These findings suggest that the cortico-rubral pathway is a substrate for recovery induced by intensive rehabilitation after ICH. SIGNIFICANCE STATEMENT The research aimed at determining the causal linkage between reorganization of the motor pathway induced by intensive rehabilitative training and recovery after stroke. We clarified the expansion of the forelimb representation area of the ipsilesional motor cortex by forced impaired forelimb use (FLU) after lesioning the internal capsule with intracerebral hemorrhaging (ICH) in rats. Anterograde tracing showed robust axonal sprouting from the forelimb area to the red nucleus in response to FLU. Selective blockade of the cortico-rubral pathway by the novel double-viral vector technique clearly revealed that the increased cortico-rubral axonal projections had causal linkage to the recovery of reaching movements induced by FLU. Our data demonstrate that the cortico-rubral pathway is responsible for the effect of intensive limb use. PMID:26758837

Causality, apparent ``superluminality,'' and reshaping in barrier penetration

NASA Astrophysics Data System (ADS)

Sokolovski, D.

2010-04-01

We consider tunneling of a nonrelativistic particle across a potential barrier. It is shown that the barrier acts as an effective beam splitter which builds up the transmitted pulse from the copies of the initial envelope shifted in the coordinate space backward relative to the free propagation. Although along each pathway causality is explicitly obeyed, in special cases reshaping can result an overall reduction of the initial envelope, accompanied by an arbitrary coordinate shift. In the case of a high barrier the delay amplitude distribution (DAD) mimics a Dirac δ function, the transmission amplitude is superoscillatory for finite momenta and tunneling leads to an accurate advancement of the (reduced) initial envelope by the barrier width. In the case of a wide barrier, initial envelope is accurately translated into the complex coordinate plane. The complex shift, given by the first moment of the DAD, accounts for both the displacement of the maximum of the transmitted probability density and the increase in its velocity. It is argued that analyzing apparent “superluminality” in terms of spacial displacements helps avoid contradiction associated with time parameters such as the phase time.

Trust and health: testing the reverse causality hypothesis

PubMed Central

Giordano, Giuseppe Nicola; Lindström, Martin

2016-01-01

Background Social capital research has consistently shown positive associations between generalised trust and health outcomes over 2 decades. Longitudinal studies attempting to test causal relationships further support the theory that trust is an independent predictor of health. However, as the reverse causality hypothesis has yet to be empirically tested, a knowledge gap remains. The aim of this study, therefore, was to investigate if health status predicts trust. Methods Data employed in this study came from 4 waves of the British Household Panel Survey between years 2000 and 2007 (N=8114). The sample was stratified by baseline trust to investigate temporal relationships between prior self-rated health (SRH) and changes in trust. We used logistic regression models with random effects, as trust was expected to be more similar within the same individuals over time. Results From the ‘Can trust at baseline’ cohort, poor SRH at time (t−1) predicted low trust at time (t) (OR=1.38). Likewise, good health predicted high trust within the ‘Cannot’ trust cohort (OR=1.30). These patterns of positive association remained after robustness checks, which adjusted for misclassification of outcome (trust) status and the existence of other temporal pathways. Conclusions This study offers empirical evidence to support the circular nature of trust/health relationship. The stability of association between prior health status and changes in trust over time differed between cohorts, hinting at the existence of complex pathways rather than a simple positive feedback loop. PMID:26546287

Ends, Principles, and Causal Explanation in Educational Justice

ERIC Educational Resources Information Center

Dum, Jenn

2017-01-01

Many principles characterize educational justice in terms of the relationship between educational inputs, outputs and distributive standards. Such principles depend upon the "causal pathway view" of education. It is implicit in this view that the causally effective aspects of education can be understood as separate from the normative…

Finding the Cause: Verbal Framing Helps Children Extract Causal Evidence Embedded in a Complex Scene

ERIC Educational Resources Information Center

Butler, Lucas P.; Markman, Ellen M.

2012-01-01

In making causal inferences, children must both identify a causal problem and selectively attend to meaningful evidence. Four experiments demonstrate that verbally framing an event ("Which animals make Lion laugh?") helps 4-year-olds extract evidence from a complex scene to make accurate causal inferences. Whereas framing was unnecessary when…

Detecting causal drivers and empirical prediction of the Indian Summer Monsoon

NASA Astrophysics Data System (ADS)

Di Capua, G.; Vellore, R.; Raghavan, K.; Coumou, D.

2017-12-01

The Indian summer monsoon (ISM) is crucial for the economy, society and natural ecosystems on the Indian peninsula. Predict the total seasonal rainfall at several months lead time would help to plan effective water management strategies, improve flood or drought protection programs and prevent humanitarian crisis. However, the complexity and strong internal variability of the ISM circulation system make skillful seasonal forecasting challenging. Moreover, to adequately identify the low-frequency, and far-away processes which influence ISM behavior novel tools are needed. We applied a Response-Guided Causal Precursor Detection (RGCPD) scheme, which is a novel empirical prediction method which unites a response-guided community detection scheme with a causal discovery algorithm (CEN). These tool allow us to assess causal pathways between different components of the ISM circulation system and with far-away regions in the tropics, mid-latitudes or Arctic. The scheme has successfully been used to identify causal precursors of the Stratospheric polar vortex enabling skillful predictions at (sub) seasonal timescales (Kretschmer et al. 2016, J.Clim., Kretschmer et al. 2017, GRL). We analyze observed ISM monthly rainfall over the monsoon trough region. Applying causal discovery techniques, we identify several causal precursor communities in the fields of 2m-temperature, sea level pressure and snow depth over Eurasia. Specifically, our results suggest that surface temperature conditions in both tropical and Arctic regions contribute to ISM variability. A linear regression prediction model based on the identified set of communities has good hindcasting skills with 4-5 months lead times. Further we separate El Nino, La Nina and ENSO-neutral years from each other and find that the causal precursors are different dependent on ENSO state. The ENSO-state dependent causal precursors give even higher skill, especially for La Nina years when the ISM is relatively strong. These findings are promising results that might ultimately contribute to both improved understanding of the ISM circulation system and help improving seasonal ISM forecasts.

CADDIS Volume 4. Data Analysis: Getting Started

EPA Pesticide Factsheets

Assembling data for an ecological causal analysis, matching biological and environmental samples in time and space, organizing data along conceptual causal pathways, data quality and quantity requirements, Data Analysis references.

Thalamocortical and corticothalamic pathways differentially contribute to goal-directed behaviors in the rat

PubMed Central

Alcaraz, Fabien; Fresno, Virginie; Marchand, Alain R; Kremer, Eric J; Coutureau, Etienne

2018-01-01

Highly distributed neural circuits are thought to support adaptive decision-making in volatile and complex environments. Notably, the functional interactions between prefrontal and reciprocally connected thalamic nuclei areas may be important when choices are guided by current goal value or action-outcome contingency. We examined the functional involvement of selected thalamocortical and corticothalamic pathways connecting the dorsomedial prefrontal cortex (dmPFC) and the mediodorsal thalamus (MD) in the behaving rat. Using a chemogenetic approach to inhibit projection-defined dmPFC and MD neurons during an instrumental learning task, we show that thalamocortical and corticothalamic pathways differentially support goal attributes. Both pathways participate in adaptation to the current goal value, but only thalamocortical neurons are required to integrate current causal relationships. These data indicate that antiparallel flow of information within thalamocortical circuits may convey qualitatively distinct aspects of adaptive decision-making and highlight the importance of the direction of information flow within neural circuits. PMID:29405119

Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology

PubMed Central

Marshall, Brandon D. L.; Galea, Sandro

2015-01-01

Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry. PMID:25480821

Rethinking the relationship between socio-economic status and health: Making the case for sociological theory in health inequality research.

PubMed

Øversveen, Emil; Rydland, Håvard T; Bambra, Clare; Eikemo, Terje A

2017-03-01

The aim of this study is to analyse previous explanations of social inequality in health and argue for a closer integration of sociological theory into future empirical research. We examine cultural-behavioural, materialist, psychosocial and life-course approaches, in addition to fundamental cause theory. Giddens' structuration theory and a neo-materialist approach, inspired by Bruno Latour, Gilles Deleuze and Felix Guattari, are proposed as ways of rethinking the causal relationship between socio-economic status and health. Much of the empirical research on health inequalities has tended to rely on explanations with a static and unidirectional view of the association between socio-economic status and health, assuming a unidirectional causal relationship between largely static categories. We argue for the use of sociological theory to develop more dynamic models that enhance the understanding of the complex pathways and mechanisms linking social structures to health.

Systems Genetics Analysis of Genome-Wide Association Study Reveals Novel Associations Between Key Biological Processes and Coronary Artery Disease.

PubMed

Ghosh, Sujoy; Vivar, Juan; Nelson, Christopher P; Willenborg, Christina; Segrè, Ayellet V; Mäkinen, Ville-Petteri; Nikpay, Majid; Erdmann, Jeannette; Blankenberg, Stefan; O'Donnell, Christopher; März, Winfried; Laaksonen, Reijo; Stewart, Alexandre F R; Epstein, Stephen E; Shah, Svati H; Granger, Christopher B; Hazen, Stanley L; Kathiresan, Sekar; Reilly, Muredach P; Yang, Xia; Quertermous, Thomas; Samani, Nilesh J; Schunkert, Heribert; Assimes, Themistocles L; McPherson, Ruth

2015-07-01

Genome-wide association studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, individually these explain only a small fraction of the heritability of CAD and for most, the causal biological mechanisms remain unclear. We sought to obtain further insights into potential causal processes of CAD by integrating large-scale GWA data with expertly curated databases of core human pathways and functional networks. Using pathways (gene sets) from Reactome, we carried out a 2-stage gene set enrichment analysis strategy. From a meta-analyzed discovery cohort of 7 CAD genome-wide association study data sets (9889 cases/11 089 controls), nominally significant gene sets were tested for replication in a meta-analysis of 9 additional studies (15 502 cases/55 730 controls) from the Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) Consortium. A total of 32 of 639 Reactome pathways tested showed convincing association with CAD (replication P<0.05). These pathways resided in 9 of 21 core biological processes represented in Reactome, and included pathways relevant to extracellular matrix (ECM) integrity, innate immunity, axon guidance, and signaling by PDRF (platelet-derived growth factor), NOTCH, and the transforming growth factor-β/SMAD receptor complex. Many of these pathways had strengths of association comparable to those observed in lipid transport pathways. Network analysis of unique genes within the replicated pathways further revealed several interconnected functional and topologically interacting modules representing novel associations (eg, semaphoring-regulated axonal guidance pathway) besides confirming known processes (lipid metabolism). The connectivity in the observed networks was statistically significant compared with random networks (P<0.001). Network centrality analysis (degree and betweenness) further identified genes (eg, NCAM1, FYN, FURIN, etc) likely to play critical roles in the maintenance and functioning of several of the replicated pathways. These findings provide novel insights into how genetic variation, interpreted in the context of biological processes and functional interactions among genes, may help define the genetic architecture of CAD. © 2015 American Heart Association, Inc.

Causality and complexity: the myth of objectivity in science.

PubMed

Mikulecky, Donald C

2007-10-01

Two distinctly different worldviews dominate today's thinking in science and in the world of ideas outside of science. Using the approach advocated by Robert M. Hutchins, it is possible to see a pattern of interaction between ideas in science and in other spheres such as philosophy, religion, and politics. Instead of compartmentalizing these intellectual activities, it is worthwhile to look for common threads of mutual influence. Robert Rosen has created an approach to scientific epistemology that might seem radical to some. However, it has characteristics that resemble ideas in other fields, in particular in the writings of George Lakoff, Leo Strauss, and George Soros. Historically, the atmosphere at the University of Chicago during Hutchins' presidency gave rise to Rashevsky's relational biology, which Rosen carried forward. Strauss was writing his political philosophy there at the same time. One idea is paramount in all this, and it is Lakoff who gives us the most insight into how the worldviews differ using this idea. The central difference has to do with causality, the fundamental concept that we use to build a worldview. Causal entailment has two distinct forms in Lakoff 's analysis: direct causality and complex causality. Rosen's writings on complexity create a picture of complex causality that is extremely useful in its detail, grounding in the ideas of Aristotle. Strauss asks for a return to the ancients to put philosophy back on track. Lakoff sees the weaknesses in Western philosophy in a similar way, and Rosen provides tools for dealing with the problem. This introduction to the relationships between the thinking of these authors is meant to stimulate further discourse on the role of complex causal entailment in all areas of thought, and how it brings them together in a holistic worldview. The worldview built on complex causality is clearly distinct from that built around simple, direct causality. One important difference is that the impoverished causal entailment that accompanies the machine metaphor in science is unable to give us a clear way to distinguish living organisms from machines. Complex causality finds a dichotomy between organisms, which are closed to efficient cause, and machines, which require entailment from outside. An argument can be made that confusing living organisms with machines, as is done in the worldview using direct cause, makes religion a necessity to supply the missing causal entailment.

A Complex Systems Approach to Causal Discovery in Psychiatry.

PubMed

Saxe, Glenn N; Statnikov, Alexander; Fenyo, David; Ren, Jiwen; Li, Zhiguo; Prasad, Meera; Wall, Dennis; Bergman, Nora; Briggs, Ernestine C; Aliferis, Constantin

2016-01-01

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

Prenatal nutrition, epigenetics and schizophrenia risk: can we test causal effects?

PubMed

Kirkbride, James B; Susser, Ezra; Kundakovic, Marija; Kresovich, Jacob K; Davey Smith, George; Relton, Caroline L

2012-06-01

We posit that maternal prenatal nutrition can influence offspring schizophrenia risk via epigenetic effects. In this article, we consider evidence that prenatal nutrition is linked to epigenetic outcomes in offspring and schizophrenia in offspring, and that schizophrenia is associated with epigenetic changes. We focus upon one-carbon metabolism as a mediator of the pathway between perturbed prenatal nutrition and the subsequent risk of schizophrenia. Although post-mortem human studies demonstrate DNA methylation changes in brains of people with schizophrenia, such studies cannot establish causality. We suggest a testable hypothesis that utilizes a novel two-step Mendelian randomization approach, to test the component parts of the proposed causal pathway leading from prenatal nutritional exposure to schizophrenia. Applied here to a specific example, such an approach is applicable for wider use to strengthen causal inference of the mediating role of epigenetic factors linking exposures to health outcomes in population-based studies.

A causal analysis framework for land-use change and the potential role of bioenergy policy

DOE PAGES

Efroymson, Rebecca A.; Kline, Keith L.; Angelsen, Arild; ...

2016-10-05

Here we propose a causal analysis framework to increase the reliability of land-use change (LUC) models and the accuracy of net greenhouse gas (GHG) emissions calculations for biofuels. The health-sciences-inspired framework is used here to determine probable causes of LUC, with an emphasis on bioenergy and deforestation. Calculations of net GHG emissions for LUC are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under national (U.S., U.K.), state (California), and European Union regulations. Biofuel policymakers and scientists continue to discuss whether presumed indirect land-use change (ILUC) estimates, which often involve deforestation, should be includedmore » in GHG accounting for biofuel pathways. Current estimates of ILUC for bioenergy rely largely on economic simulation models that focus on causal pathways involving global commodity trade and use coarse land cover data with simple land classification systems. ILUC estimates are highly uncertain, partly because changes are not clearly defined and key causal links are not sufficiently included in the models. The proposed causal analysis framework begins with a definition of the change that has occurred and proceeds to a strength-of-evidence approach based on types of epidemiological evidence including plausibility of the relationship, completeness of the causal pathway, spatial co-occurrence, time order, analogous agents, simulation model results, and quantitative agent response relationships.Lastly, we discuss how LUC may be allocated among probable causes for policy purposes and how the application of the framework has the potential to increase the validity of LUC models and resolve ILUC and biofuel controversies.« less

A causal analysis framework for land-use change and the potential role of bioenergy policy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Efroymson, Rebecca A.; Kline, Keith L.; Angelsen, Arild

Here we propose a causal analysis framework to increase the reliability of land-use change (LUC) models and the accuracy of net greenhouse gas (GHG) emissions calculations for biofuels. The health-sciences-inspired framework is used here to determine probable causes of LUC, with an emphasis on bioenergy and deforestation. Calculations of net GHG emissions for LUC are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under national (U.S., U.K.), state (California), and European Union regulations. Biofuel policymakers and scientists continue to discuss whether presumed indirect land-use change (ILUC) estimates, which often involve deforestation, should be includedmore » in GHG accounting for biofuel pathways. Current estimates of ILUC for bioenergy rely largely on economic simulation models that focus on causal pathways involving global commodity trade and use coarse land cover data with simple land classification systems. ILUC estimates are highly uncertain, partly because changes are not clearly defined and key causal links are not sufficiently included in the models. The proposed causal analysis framework begins with a definition of the change that has occurred and proceeds to a strength-of-evidence approach based on types of epidemiological evidence including plausibility of the relationship, completeness of the causal pathway, spatial co-occurrence, time order, analogous agents, simulation model results, and quantitative agent response relationships.Lastly, we discuss how LUC may be allocated among probable causes for policy purposes and how the application of the framework has the potential to increase the validity of LUC models and resolve ILUC and biofuel controversies.« less

PPARα siRNA–Treated Expression Profiles Uncover the Causal Sufficiency Network for Compound-Induced Liver Hypertrophy

PubMed Central

Dai, Xudong; Souza, Angus T. De; Dai, Hongyue; Lewis, David L; Lee, Chang-kyu; Spencer, Andy G; Herweijer, Hans; Hagstrom, Jim E; Linsley, Peter S; Bassett, Douglas E; Ulrich, Roger G; He, Yudong D

2007-01-01

Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor α (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARα-induced liver hypertrophy is supported by their ability to predict non-PPARα–induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events. PMID:17335344

Effective connectivity of ascending and descending frontal-thalamic pathways during sustained attention: Complex brain network interactions in adolescence

PubMed Central

Jagtap, Pranav; Diwadkar, Vaibhav A.

2016-01-01

Frontal-thalamic interactions are crucial for bottom-up gating and top-down control, yet have not been well studied from brain network perspectives. We applied network modeling of fMRI signals (Dynamic Causal Modeling; DCM) to investigate frontal-thalamic interactions during an attention task with parametrically varying levels of demand. fMRI was collected while subjects participated in a sustained continuous performance task with low and high attention demands. 162 competing model architectures were employed in DCM to evaluate hypotheses on bilateral frontal-thalamic connections and their modulation by attention demand, selected at a second level using Bayesian Model Selection. The model architecture evinced significant contextual modulation by attention of ascending (thalamus → dPFC) and descending (dPFC → thalamus) pathways. However, modulation of these pathways was asymmetric: While positive modulation of the ascending pathway was comparable across attention demand, modulation of the descending pathway was significantly greater when attention demands were increased. Increased modulation of the (dPFC → thalamus) pathway in response to increased attention demand constitutes novel evidence of attention-related gain in the connectivity of the descending attention pathway. By comparison demand-independent modulation of the ascending (thalamus → dPFC) pathway suggests unbiased thalamic inputs to the cortex in the context of the paradigm. PMID:27145923

Formalizing the role of agent-based modeling in causal inference and epidemiology.

PubMed

Marshall, Brandon D L; Galea, Sandro

2015-01-15

Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Social and ecological synergy: local rulemaking, forest livelihoods, and biodiversity conservation.

PubMed

Persha, Lauren; Agrawal, Arun; Chhatre, Ashwini

2011-03-25

Causal pathways to achieve social and ecological benefits from forests are unclear, because there are few systematic multicountry empirical analyses that identify important factors and their complex relationships with social and ecological outcomes. This study examines biodiversity conservation and forest-based livelihood outcomes using a data set on 84 sites from six countries in East Africa and South Asia. We find both positive and negative relationships, leading to joint wins, losses, and trade-offs depending on specific contextual factors; participation in forest governance institutions by local forest users is strongly associated with jointly positive outcomes for forests in our study.

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations

PubMed Central

Swings, Toon; Weytjens, Bram; Schalck, Thomas; Bonte, Camille; Verstraeten, Natalie; Michiels, Jan

2017-01-01

Abstract Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well. PMID:28961727

An assessment of molecular pathways of obesity susceptible to nutrient, toxicant and genetically induced epigenetic perturbation

PubMed Central

Xue, Jing; Ideraabdullah, Folami Y.

2015-01-01

In recent years, the etiology of human disease has greatly improved with the inclusion of epigenetic mechanisms, in particular as a common link between environment and disease. However, for most diseases we lack a detailed interpretation of the epigenetic regulatory pathways perturbed by environment and causal mechanisms. Here, we focus on recent findings elucidating nutrient-related epigenetic changes linked to obesity. We highlight studies demonstrating that obesity is a complex disease linked to disruption of epigenetically regulated metabolic pathways in the brain, adipose tissue and liver. These pathways regulate (1) homeostatic and hedonic eating behaviors (2) adipocyte differentiation and fat accumulation, and (3) energy expenditure. By compiling these data we illustrate that obesity-related phenotypes are repeatedly linked to disruption of critical epigenetic mechanisms that regulate of key metabolic genes. These data are supported by genetic mutation of key epigenetic regulators and many of the diet induced epigenetic mechanisms of obesity are also perturbed by exposure to environmental toxicants. Identifying similarly perturbed epigenetic mechanisms in multiple experimental models of obesity strengthens the translational applications of these findings. We also discuss many of the ongoing challenges to understanding the role of environmentally-induced epigenetic pathways in obesity and suggest future studies to elucidate these roles. This assessment illustrates our current understanding of molecular pathways of obesity that are susceptible to environmental perturbation via epigenetic mechanisms. Thus, it lays the groundwork for dissecting the complex interactions between diet, genes, and toxicants that contribute to obesity and obesity-related phenotypes. PMID:27012616

Genetic Correction of SOD1 Mutant iPSCs Reveals ERK and JNK Activated AP1 as a Driver of Neurodegeneration in Amyotrophic Lateral Sclerosis.

PubMed

Bhinge, Akshay; Namboori, Seema C; Zhang, Xiaoyu; VanDongen, Antonius M J; Stanton, Lawrence W

2017-04-11

Although mutations in several genes with diverse functions have been known to cause amyotrophic lateral sclerosis (ALS), it is unknown to what extent causal mutations impinge on common pathways that drive motor neuron (MN)-specific neurodegeneration. In this study, we combined induced pluripotent stem cells-based disease modeling with genome engineering and deep RNA sequencing to identify pathways dysregulated by mutant SOD1 in human MNs. Gene expression profiling and pathway analysis followed by pharmacological screening identified activated ERK and JNK signaling as key drivers of neurodegeneration in mutant SOD1 MNs. The AP1 complex member JUN, an ERK/JNK downstream target, was observed to be highly expressed in MNs compared with non-MNs, providing a mechanistic insight into the specific degeneration of MNs. Importantly, investigations of mutant FUS MNs identified activated p38 and ERK, indicating that network perturbations induced by ALS-causing mutations converge partly on a few specific pathways that are drug responsive and provide immense therapeutic potential. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps

PubMed Central

Silver, Matt; Montana, Giovanni

2012-01-01

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways. We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our “pathways group lasso with adaptive weights” (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets. In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small. PMID:22499682

Experimental Approaches to Systematic Discovery and Development of Reproductive Adverse Outcome Pathways in Fish

EPA Science Inventory

Adverse outcome pathways (AOPs) are conceptual frameworks that portray causal and predictive linkages between key events at multiple scales of biological organization that connect molecular initiating events and early cellular perturbations (e.g., initiation of toxicity pathways)...

The Nature of Nurture: Using a Virtual-Parent Design to Test Parenting Effects on Children's Educational Attainment in Genotyped Families.

PubMed

Bates, Timothy C; Maher, Brion S; Medland, Sarah E; McAloney, Kerrie; Wright, Margaret J; Hansell, Narelle K; Kendler, Kenneth S; Martin, Nicholas G; Gillespie, Nathan A

2018-04-01

Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on alleles not transmitted to the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children's own EA PRS significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.

A Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD).

PubMed

Sokolova, Elena; Oerlemans, Anoek M; Rommelse, Nanda N; Groot, Perry; Hartman, Catharina A; Glennon, Jeffrey C; Claassen, Tom; Heskes, Tom; Buitelaar, Jan K

2017-06-01

Autism spectrum disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) are often comorbid. The purpose of this study is to explore the relationships between ASD and ADHD symptoms by applying causal modeling. We used a large phenotypic data set of 417 children with ASD and/or ADHD, 562 affected and unaffected siblings, and 414 controls, to infer a structural equation model using a causal discovery algorithm. Three distinct pathways between ASD and ADHD were identified: (1) from impulsivity to difficulties with understanding social information, (2) from hyperactivity to stereotypic, repetitive behavior, (3) a pairwise pathway between inattention, difficulties with understanding social information, and verbal IQ. These findings may inform future studies on understanding the pathophysiological mechanisms behind the overlap between ASD and ADHD.

Metabolomics and Diabetes: Analytical and Computational Approaches

PubMed Central

Sas, Kelli M.; Karnovsky, Alla; Michailidis, George

2015-01-01

Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field. PMID:25713200

Pathways to Aggression in Urban Elementary School Youth

ERIC Educational Resources Information Center

Ozkol, Hivren; Zucker, Marla; Spinazzola, Joseph

2011-01-01

This study examined the pathways from violence exposure to aggressive behaviors in urban, elementary school youth. We utilized structural equation modeling to examine putative causal pathways between children's exposure to violence, development of posttraumatic stress symptoms, permissive attitudes towards violence, and engagement in aggressive…

Complex Causal Process Diagrams for Analyzing the Health Impacts of Policy Interventions

PubMed Central

Joffe, Michael; Mindell, Jennifer

2006-01-01

Causal diagrams are rigorous tools for controlling confounding. They also can be used to describe complex causal systems, which is done routinely in communicable disease epidemiology. The use of change diagrams has advantages over static diagrams, because change diagrams are more tractable, relate better to interventions, and have clearer interpretations. Causal diagrams are a useful basis for modeling. They make assumptions explicit, provide a framework for analysis, generate testable predictions, explore the effects of interventions, and identify data gaps. Causal diagrams can be used to integrate different types of information and to facilitate communication both among public health experts and between public health experts and experts in other fields. Causal diagrams allow the use of instrumental variables, which can help control confounding and reverse causation. PMID:16449586

Causal inference in public health.

PubMed

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.

Unveiling causal activity of complex networks

NASA Astrophysics Data System (ADS)

Williams-García, Rashid V.; Beggs, John M.; Ortiz, Gerardo

2017-07-01

We introduce a novel tool for analyzing complex network dynamics, allowing for cascades of causally-related events, which we call causal webs (c-webs), to be separated from other non-causally-related events. This tool shows that traditionally-conceived avalanches may contain mixtures of spatially-distinct but temporally-overlapping cascades of events, and dynamical disorder or noise. In contrast, c-webs separate these components, unveiling previously hidden features of the network and dynamics. We apply our method to mouse cortical data with resulting statistics which demonstrate for the first time that neuronal avalanches are not merely composed of causally-related events. The original version of this article was uploaded to the arXiv on March 17th, 2016 [1].

Intimate Relationships and Depression: Is There a Causal Connection?

ERIC Educational Resources Information Center

Burns, David D.; And Others

1994-01-01

Estimated causal pathways that link depression and dissatisfaction in intimate relationships in 115 depressed patients during first 12 weeks of treatment. Depression severity, as measured by Beck Depression Inventory, was negatively correlated with relationship satisfaction at intake and at 12 weeks. Structural equation modeling was not consistent…

Understanding causal pathways within health systems policy evaluation through mediation analysis: an application to payment for performance (P4P) in Tanzania.

PubMed

Anselmi, Laura; Binyaruka, Peter; Borghi, Josephine

2017-02-02

The evaluation of payment for performance (P4P) programmes has focused mainly on understanding contributions to health service coverage, without unpacking causal mechanisms. The overall aim of the paper is to test the causal pathways through which P4P schemes may (or may not) influence maternal care outcomes. We used data from an evaluation of a P4P programme in Tanzania. Data were collected from a sample of 3000 women who delivered in the 12 months prior to interview and 200 health workers at 150 health facilities from seven intervention and four comparison districts in Tanzania in January 2012 and in February 2013. We applied causal mediation analysis using a linear structural equation model to identify direct and indirect effects of P4P on institutional delivery rates and on the uptake of two doses of an antimalarial drug during pregnancy. We first ran a series of linear difference-in-difference regression models to test the effect of P4P on potential mediators, which we then included in a linear difference-in-difference model evaluating the impact of P4P on the outcome. We tested the robustness of our results to unmeasured confounding using semi-parametric methods. P4P reduced the probability of women paying for delivery care (-4.5 percentage points) which mediates the total effect of P4P on institutional deliveries (by 48%) and on deliveries in a public health facility (by 78%). P4P reduced the stock-out rate for some essential drugs, specifically oxytocin (-36 percentage points), which mediated the total effect of P4P on institutional deliveries (by 22%) and deliveries in a public health facility (by 30%). P4P increased kindness at delivery (5 percentage points), which mediated the effect of P4P on institutional deliveries (by 48%) and on deliveries in a public health facility (by 49%). P4P increased the likelihood of supervision visits taking place within the last 90 days (18 percentage points), which mediated 15% of the total P4P effect on the uptake of two antimalarial doses during antenatal care (IPT2). Kindness during deliveries and the probability of paying out of pocket for delivery care were the mediators most robust to unmeasured confounding. The effect of P4P on institutional deliveries is mediated by financing and human resources factors, while uptake of antimalarials in pregnancy is mediated by governance factors. Further research is required to explore additional and more complex causal pathways.

A Study of Students' Reasoning about Probabilistic Causality: Implications for Understanding Complex Systems and for Instructional Design

ERIC Educational Resources Information Center

Grotzer, Tina A.; Solis, S. Lynneth; Tutwiler, M. Shane; Cuzzolino, Megan Powell

2017-01-01

Understanding complex systems requires reasoning about causal relationships that behave or appear to behave probabilistically. Features such as distributed agency, large spatial scales, and time delays obscure co-variation relationships and complex interactions can result in non-deterministic relationships between causes and effects that are best…

Regression and Data Mining Methods for Analyses of Multiple Rare Variants in the Genetic Analysis Workshop 17 Mini-Exome Data

PubMed Central

Bailey-Wilson, Joan E.; Brennan, Jennifer S.; Bull, Shelley B; Culverhouse, Robert; Kim, Yoonhee; Jiang, Yuan; Jung, Jeesun; Li, Qing; Lamina, Claudia; Liu, Ying; Mägi, Reedik; Niu, Yue S.; Simpson, Claire L.; Wang, Libo; Yilmaz, Yildiz E.; Zhang, Heping; Zhang, Zhaogong

2012-01-01

Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors. PMID:22128066

Food for Thought ... Mechanistic Validation

PubMed Central

Hartung, Thomas; Hoffmann, Sebastian; Stephens, Martin

2013-01-01

Summary Validation of new approaches in regulatory toxicology is commonly defined as the independent assessment of the reproducibility and relevance (the scientific basis and predictive capacity) of a test for a particular purpose. In large ring trials, the emphasis to date has been mainly on reproducibility and predictive capacity (comparison to the traditional test) with less attention given to the scientific or mechanistic basis. Assessing predictive capacity is difficult for novel approaches (which are based on mechanism), such as pathways of toxicity or the complex networks within the organism (systems toxicology). This is highly relevant for implementing Toxicology for the 21st Century, either by high-throughput testing in the ToxCast/ Tox21 project or omics-based testing in the Human Toxome Project. This article explores the mostly neglected assessment of a test's scientific basis, which moves mechanism and causality to the foreground when validating/qualifying tests. Such mechanistic validation faces the problem of establishing causality in complex systems. However, pragmatic adaptations of the Bradford Hill criteria, as well as bioinformatic tools, are emerging. As critical infrastructures of the organism are perturbed by a toxic mechanism we argue that by focusing on the target of toxicity and its vulnerability, in addition to the way it is perturbed, we can anchor the identification of the mechanism and its verification. PMID:23665802

Developing Causal Understanding with Causal Maps: The Impact of Total Links, Temporal Flow, and Lateral Position of Outcome Nodes

ERIC Educational Resources Information Center

Jeong, Allan; Lee, Woon Jee

2012-01-01

This study examined some of the methodological approaches used by students to construct causal maps in order to determine which approaches help students understand the underlying causes and causal mechanisms in a complex system. This study tested the relationship between causal understanding (ratio of root causes correctly/incorrectly identified,…

Causal pathways linking environmental change with health behaviour change: Natural experimental study of new transport infrastructure and cycling to work.

PubMed

Prins, R G; Panter, J; Heinen, E; Griffin, S J; Ogilvie, D B

2016-06-01

Mechanisms linking changes to the environment with changes in physical activity are poorly understood. Insights into mechanisms of interventions can help strengthen causal attribution and improve understanding of divergent response patterns. We examined the causal pathways linking exposure to new transport infrastructure with changes in cycling to work. We used baseline (2009) and follow-up (2012) data (N=469) from the Commuting and Health in Cambridge natural experimental study (Cambridge, UK). Exposure to new infrastructure in the form of the Cambridgeshire Guided Busway was defined using residential proximity. Mediators studied were changes in perceptions of the route to work, theory of planned behaviour constructs and self-reported use of the new infrastructure. Outcomes were modelled as an increase, decrease or no change in weekly cycle commuting time. We used regression analyses to identify combinations of mediators forming potential pathways between exposure and outcome. We then tested these pathways in a path model and stratified analyses by baseline level of active commuting. We identified changes in perceptions of the route to work, and use of the cycle path, as potential mediators. Of these potential mediators, only use of the path significantly explained (85%) the effect of the infrastructure in increasing cycling. Path use also explained a decrease in cycling among more active commuters. The findings strengthen the causal argument that changing the environment led to changes in health-related behaviour via use of the new infrastructure, but also show how some commuters may have spent less time cycling as a result. Copyright © 2016. Published by Elsevier Inc.

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations.

PubMed

Swings, Toon; Weytjens, Bram; Schalck, Thomas; Bonte, Camille; Verstraeten, Natalie; Michiels, Jan; Marchal, Kathleen

2017-11-01

Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Reducing Oral Health Disparities: A Focus on Social and Cultural Determinants

PubMed Central

Patrick, Donald L; Lee, Rosanna Shuk Yin; Nucci, Michele; Grembowski, David; Jolles, Carol Zane; Milgrom, Peter

2006-01-01

Oral health is essential to the general health and well-being of individuals and the population. Yet significant oral health disparities persist in the U.S. population because of a web of influences that include complex cultural and social processes that affect both oral health and access to effective dental health care. This paper introduces an organizing framework for addressing oral health disparities. We present and discuss how the multiple influences on oral health and oral health disparities operate using this framework. Interventions targeted at different causal pathways bring new directions and implications for research and policy in reducing oral health disparities. PMID:16934121

Complexity VIII. Ontology of closure in complex systems: The C* hypothesis and the O° notation

NASA Astrophysics Data System (ADS)

Chandler, Jerry LR

1999-03-01

Closure is a common characteristic of mathematical, natural and socio-cultural systems. Whether one is describing a graph, a molecule, a cell, a human, or a nation state, closure is implicitly understood. An objective of this paper is to continue a construction of a systematic framework for closure which is sufficient for future quantitative transdisciplinary investigations. A further objective is to extend the Birkhoff-von Neumann criterion for quantum systems to complex natural objects. The C* hypothesis is being constructed to be consistent with algebraic category theory (Ehresmann and Vanbremeersch, 1987, 1997, Chandler, 1990, 1991, Chandler, Ehresmann and Vanbremeersch, 1996). Five aspects of closure will be used to construct a framework for categories of complex systems: 1. Truth functions in mathematics and the natural sciences 2. Systematic descriptions in the mks and O° notations 3. Organizational structures in hierarchical scientific languages 4. Transitive organizational pathways in the causal structures of complex behaviors 5. Composing additive, multiplicative and exponential operations in complex systems Truth functions can be formal or objective or subjective, depending on the complexity of the system and on our capability to represent the fine structure of the system symbolically, observationally or descriptively. "Complete" material representations of the fine structure of a system may allow truth functions to be created over sets of one to one correspondences. Less complete descriptions can support less stringent truth functions based on coherence or subjective judgments. The role of human values in creating and perpetuating truth functions can be placed in context of the degree of fine structure in the system's description. The organization of complex systems are hypothesized to be categorizable into degrees relative to one another, thereby creating an ordering relationship. This ordering relationship is denoted by the symbols: O°1, O°2,O°3... For example, for material systems, an ordering relation such as particles, atoms, molecules, cells, tissues, organs, individuals and social groups might be assigned to classify observations for medical purposes. The C* hypothesis asserts that any complex system can be described in terms of four enumerable concepts: closure, conformation, concatenation and cyclicity. Mappings between objects are constructed within a notation for organization. Causality is organized within C* as pathways of relationships in time. The notation of organizational degrees is used to distinguish a directionality for causality: 1. bottom-up (energy flows) 2. top-down (control processes or dominating variables), 3. outside — inward (ecoment on organism) and 4. inside — outward (organism on ecoment). Closures are asserted to emerge from evolutionary cooperation. It is asserted that truth functions emerged from the necessity of an organism to identify ecoments where life can prosper. For example, basic truth functions of mathematics (operations of addition, multiplication and exponentiation) are made operationally consistent within the biochemical operations of sustaining exponential cellular growth. These fundamental mathematical functions can provide a logical basis (in conjunction with conservation rules) for a construction of complex material categories at higher degrees of organization. It is remarked that these simple functions suggests a biochemical origin for the intuitionistic philosophy of mathematics. The emergence and success of mathematics is conjectured to result from the need to acquire a consistent basis for communication among individuals seeking to cooperate socially. This suggests a cultural closure over a collection of individual closures.

Entropy Transfer between Residue Pairs and Allostery in Proteins: Quantifying Allosteric Communication in Ubiquitin.

PubMed

Hacisuleyman, Aysima; Erman, Burak

2017-01-01

It has recently been proposed by Gunasakaran et al. that allostery may be an intrinsic property of all proteins. Here, we develop a computational method that can determine and quantify allosteric activity in any given protein. Based on Schreiber's transfer entropy formulation, our approach leads to an information transfer landscape for the protein that shows the presence of entropy sinks and sources and explains how pairs of residues communicate with each other using entropy transfer. The model can identify the residues that drive the fluctuations of others. We apply the model to Ubiquitin, whose allosteric activity has not been emphasized until recently, and show that there are indeed systematic pathways of entropy and information transfer between residues that correlate well with the activities of the protein. We use 600 nanosecond molecular dynamics trajectories for Ubiquitin and its complex with human polymerase iota and evaluate entropy transfer between all pairs of residues of Ubiquitin and quantify the binding susceptibility changes upon complex formation. We explain the complex formation propensities of Ubiquitin in terms of entropy transfer. Important residues taking part in allosteric communication in Ubiquitin predicted by our approach are in agreement with results of NMR relaxation dispersion experiments. Finally, we show that time delayed correlation of fluctuations of two interacting residues possesses an intrinsic causality that tells which residue controls the interaction and which one is controlled. Our work shows that time delayed correlations, entropy transfer and causality are the required new concepts for explaining allosteric communication in proteins.

Inferring causal molecular networks: empirical assessment through a community-based effort.

PubMed

Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-04-01

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Binocular depth processing in the ventral visual pathway

PubMed Central

Vogels, Rufin

2016-01-01

One of the most powerful forms of depth perception capitalizes on the small relative displacements, or binocular disparities, in the images projected onto each eye. The brain employs these disparities to facilitate various computations, including sensori-motor transformations (reaching, grasping), scene segmentation and object recognition. In accordance with these different functions, disparity activates a large number of regions in the brain of both humans and monkeys. Here, we review how disparity processing evolves along different regions of the ventral visual pathway of macaques, emphasizing research based on both correlational and causal techniques. We will discuss the progression in the ventral pathway from a basic absolute disparity representation to a more complex three-dimensional shape code. We will show that, in the course of this evolution, the underlying neuronal activity becomes progressively more bound to the global perceptual experience. We argue that these observations most probably extend beyond disparity processing per se, and pertain to object processing in the ventral pathway in general. We conclude by posing some important unresolved questions whose answers may significantly advance the field, and broaden its scope. This article is part of the themed issue ‘Vision in our three-dimensional world’. PMID:27269602

An integrated analysis of genes and functional pathways for aggression in human and rodent models.

PubMed

Zhang-James, Yanli; Fernàndez-Castillo, Noèlia; Hess, Jonathan L; Malki, Karim; Glatt, Stephen J; Cormand, Bru; Faraone, Stephen V

2018-06-01

Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression.

Binocular depth processing in the ventral visual pathway.

PubMed

Verhoef, Bram-Ernst; Vogels, Rufin; Janssen, Peter

2016-06-19

One of the most powerful forms of depth perception capitalizes on the small relative displacements, or binocular disparities, in the images projected onto each eye. The brain employs these disparities to facilitate various computations, including sensori-motor transformations (reaching, grasping), scene segmentation and object recognition. In accordance with these different functions, disparity activates a large number of regions in the brain of both humans and monkeys. Here, we review how disparity processing evolves along different regions of the ventral visual pathway of macaques, emphasizing research based on both correlational and causal techniques. We will discuss the progression in the ventral pathway from a basic absolute disparity representation to a more complex three-dimensional shape code. We will show that, in the course of this evolution, the underlying neuronal activity becomes progressively more bound to the global perceptual experience. We argue that these observations most probably extend beyond disparity processing per se, and pertain to object processing in the ventral pathway in general. We conclude by posing some important unresolved questions whose answers may significantly advance the field, and broaden its scope.This article is part of the themed issue 'Vision in our three-dimensional world'. © 2016 The Author(s).

Life Strain, Social Control, Social Learning, and Delinquency: The Effects of Gender, Age, and Family SES Among Chinese Adolescents.

PubMed

Bao, Wan-Ning; Haas, Ain; Xie, Yunping

2016-09-01

Very few studies have examined the pathways to delinquency and causal factors for demographic subgroups of adolescents in a different culture. This article explores the effects of gender, age, and family socioeconomic status (SES) in an integrated model of strain, social control, social learning, and delinquency among a sample of Chinese adolescents. ANOVA is used to check for significant differences between categories of demographic groups on the variables in the integrated model, and the differential effects of causal factors in the theoretical path models are examined. Further tests of interaction effects are conducted to compare path coefficients between "high-risk" youths (i.e., male, mid-teen, and low family SES adolescents) and other subgroups. The findings identified similar pathways to delinquency across subgroups and clarified the salience of causal factors for male, mid-teen, and low SES adolescents in a different cultural context. © The Author(s) 2015.

Diabetic Neuropathy: Mechanisms to Management

PubMed Central

Edwards, James L.; Vincent, Andrea; Cheng, Thomas; Feldman, Eva L.

2014-01-01

Neuropathy is the most common and debilitating complication of diabetes and results in pain, decreased motility, and amputation. Diabetic neuropathy encompasses a variety of forms whose impact ranges from discomfort to death. Hyperglycemia induces oxidative stress in diabetic neurons and results in activation of multiple biochemical pathways. These activated pathways are a major source of damage and are potential therapeutic targets in diabetic neuropathy. Though therapies are available to alleviate the symptoms of diabetic neuropathy, few options are available to eliminate the root causes. The immense physical, psychological, and economic cost of diabetic neuropathy underscores the need for causally targeted therapies. This review covers the pathology, epidemiology, biochemical pathways, and prevention of diabetic neuropathy, as well as discusses current symptomatic and causal therapies and novel approaches to identify therapeutic targets. PMID:18616962

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases.

PubMed

Zhang, Qin; Yao, Quanying

2018-05-01

The dynamic uncertain causality graph (DUCG) is a newly presented framework for uncertain causality representation and probabilistic reasoning. It has been successfully applied to online fault diagnoses of large, complex industrial systems, and decease diagnoses. This paper extends the DUCG to model more complex cases than what could be previously modeled, e.g., the case in which statistical data are in different groups with or without overlap, and some domain knowledge and actions (new variables with uncertain causalities) are introduced. In other words, this paper proposes to use -mode, -mode, and -mode of the DUCG to model such complex cases and then transform them into either the standard -mode or the standard -mode. In the former situation, if no directed cyclic graph is involved, the transformed result is simply a Bayesian network (BN), and existing inference methods for BNs can be applied. In the latter situation, an inference method based on the DUCG is proposed. Examples are provided to illustrate the methodology.

Rigor, vigor, and the study of health disparities

PubMed Central

Adler, Nancy; Bush, Nicole R.; Pantell, Matthew S.

2012-01-01

Health disparities research spans multiple fields and methods and documents strong links between social disadvantage and poor health. Associations between socioeconomic status (SES) and health are often taken as evidence for the causal impact of SES on health, but alternative explanations, including the impact of health on SES, are plausible. Studies showing the influence of parents’ SES on their children’s health provide evidence for a causal pathway from SES to health, but have limitations. Health disparities researchers face tradeoffs between “rigor” and “vigor” in designing studies that demonstrate how social disadvantage becomes biologically embedded and results in poorer health. Rigorous designs aim to maximize precision in the measurement of SES and health outcomes through methods that provide the greatest control over temporal ordering and causal direction. To achieve precision, many studies use a single SES predictor and single disease. However, doing so oversimplifies the multifaceted, entwined nature of social disadvantage and may overestimate the impact of that one variable and underestimate the true impact of social disadvantage on health. In addition, SES effects on overall health and functioning are likely to be greater than effects on any one disease. Vigorous designs aim to capture this complexity and maximize ecological validity through more complete assessment of social disadvantage and health status, but may provide less-compelling evidence of causality. Newer approaches to both measurement and analysis may enable enhanced vigor as well as rigor. Incorporating both rigor and vigor into studies will provide a fuller understanding of the causes of health disparities. PMID:23045672

Setting the stage to advance the adverse outcome pathway (AOP) framework through horizon scanning

EPA Science Inventory

Recognizing the international interest surrounding the adverse outcome pathway framework, which captures existing information describing causal linkages between a molecular initiating event through levels of biological organization to an adverse outcome of regulatory significance...

Adverse Outcome Pathways and Extrapolation Tools to Advance the Three Rs in Ecotoxicology

EPA Science Inventory

Adverse outcome pathways (AOPs) are conceptual frameworks for identifying and organizing predictive and causal linkages between cellular-level responses and endpoints conventionally considered in ecological risk assessment (e.g., effects on survival, growth/development, and repro...

.Network analytics for adverse outcome pathways

EPA Science Inventory

Adverse Outcome Pathways (AOPs) organize toxicological knowledge from the molecular level up to the population level, providing evidence-based causal linkages at each step. The AOPWiki serves as a repository of AOPs. With the international adoption of the AOP framework, the AOPw...

University Students' Causal Conceptions about Social Mobility: Diverging Pathways for Believers in Personal Merit and Luck

ERIC Educational Resources Information Center

Shane, Jacob; Heckhausen, Jutta

2013-01-01

Many college students hold ambitious goals for upward social mobility via post-college careers. However, in the current economic recession such optimistic expectations are not a given. The present study examines how college students' current social status and beliefs in causal factors for socioeconomic status (SES) attainment lead to diverging…

Causality: Physics and Philosophy

ERIC Educational Resources Information Center

Chatterjee, Atanu

2013-01-01

Nature is a complex causal network exhibiting diverse forms and species. These forms or rather systems are physically open, structurally complex and naturally adaptive. They interact with the surrounding media by operating a positive-feedback loop through which, they adapt, organize and self-organize themselves in response to the ever-changing…

Evolution of Religious Beliefs

ScienceCinema

None

2018-05-11

Humans may be distinguished from all other animals in having beliefs about the causal interaction of physical objects. Causal beliefs are a developmental primitive in human children; animals, by contrast, have very few causal beliefs. The origin of human causal beliefs comes from the evolutionary advantage it gave in relation to complex tool making and use. Causal beliefs gave rise religion and mystical thinking as our ancestors wanted to know the causes of events that affected their lives.

Evolution of Religious Beliefs

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

Humans may be distinguished from all other animals in having beliefs about the causal interaction of physical objects. Causal beliefs are a developmental primitive in human children; animals, by contrast, have very few causal beliefs. The origin of human causal beliefs comes from the evolutionary advantage it gave in relation to complex tool making and use. Causal beliefs gave rise religion and mystical thinking as our ancestors wanted to know the causes of events that affected their lives.

Effective connectivity of ascending and descending frontalthalamic pathways during sustained attention: Complex brain network interactions in adolescence.

PubMed

Jagtap, Pranav; Diwadkar, Vaibhav A

2016-07-01

Frontal-thalamic interactions are crucial for bottom-up gating and top-down control, yet have not been well studied from brain network perspectives. We applied network modeling of fMRI signals [dynamic causal modeling (DCM)] to investigate frontal-thalamic interactions during an attention task with parametrically varying levels of demand. fMRI was collected while subjects participated in a sustained continuous performance task with low and high attention demands. 162 competing model architectures were employed in DCM to evaluate hypotheses on bilateral frontal-thalamic connections and their modulation by attention demand, selected at a second level using Bayesian model selection. The model architecture evinced significant contextual modulation by attention of ascending (thalamus → dPFC) and descending (dPFC → thalamus) pathways. However, modulation of these pathways was asymmetric: while positive modulation of the ascending pathway was comparable across attention demand, modulation of the descending pathway was significantly greater when attention demands were increased. Increased modulation of the (dPFC → thalamus) pathway in response to increased attention demand constitutes novel evidence of attention-related gain in the connectivity of the descending attention pathway. By comparison demand-independent modulation of the ascending (thalamus → dPFC) pathway suggests unbiased thalamic inputs to the cortex in the context of the paradigm. Hum Brain Mapp 37:2557-2570, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

AOP: An R Package For Sufficient Causal Analysis in Pathway-based Screening of Drugs and Chemicals for Adversity

EPA Science Inventory

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharma-cology, toxicology and risk assessment. ...

Causal Learning with Local Computations

ERIC Educational Resources Information Center

Fernbach, Philip M.; Sloman, Steven A.

2009-01-01

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require…

Fragile X syndrome neurobiology translates into rational therapy.

PubMed

Braat, Sien; Kooy, R Frank

2014-04-01

Causal genetic defects have been identified for various neurodevelopmental disorders. A key example in this respect is fragile X syndrome, one of the most frequent genetic causes of intellectual disability and autism. Since the discovery of the causal gene, insights into the underlying pathophysiological mechanisms have increased exponentially. Over the past years, defects were discovered in pathways that are potentially amendable by pharmacological treatment. These findings have inspired the initiation of clinical trials in patients. The targeted pathways converge in part with those of related neurodevelopmental disorders raising hopes that the treatments developed for this specific disorder might be more broadly applicable. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluations of Structural Interventions for HIV Prevention: A Review of Approaches and Methods.

PubMed

Iskarpatyoti, Brittany S; Lebov, Jill; Hart, Lauren; Thomas, Jim; Mandal, Mahua

2018-04-01

Structural interventions alter the social, economic, legal, political, and built environments that underlie processes affecting population health. We conducted a systematic review of evaluations of structural interventions for HIV prevention in low- and middle-income countries (LMICs) to better understand methodological and other challenges and identify effective evaluation strategies. We included 27 peer-reviewed articles on interventions related to economic empowerment, education, and substance abuse in LMICs. Twenty-one evaluations included clearly articulated theories of change (TOCs); 14 of these assessed the TOC by measuring intermediary variables in the causal pathway between the intervention and HIV outcomes. Although structural interventions address complex interactions, no evaluation included methods designed to evaluate complex systems. To strengthen evaluations of structural interventions, we recommend clearly articulating a TOC and measuring intermediate variables between the predictor and outcome. We additionally recommend adapting study designs and analytic methods outside traditional epidemiology to better capture complex results, influences external to the intervention, and unintended consequences.

Inferring causal molecular networks: empirical assessment through a community-based effort

PubMed Central

Hill, Steven M.; Heiser, Laura M.; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; Mills, Gordon B.; Gray, Joe W.; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul T.; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-01-01

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks. PMID:26901648

Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.

PubMed

Wilson, Paul; Larminie, Christopher; Smith, Rona

2016-01-01

To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation. Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms. This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation. Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

Applying causal mediation analysis to personality disorder research.

PubMed

Walters, Glenn D

2018-01-01

This article is designed to address fundamental issues in the application of causal mediation analysis to research on personality disorders. Causal mediation analysis is used to identify mechanisms of effect by testing variables as putative links between the independent and dependent variables. As such, it would appear to have relevance to personality disorder research. It is argued that proper implementation of causal mediation analysis requires that investigators take several factors into account. These factors are discussed under 5 headings: variable selection, model specification, significance evaluation, effect size estimation, and sensitivity testing. First, care must be taken when selecting the independent, dependent, mediator, and control variables for a mediation analysis. Some variables make better mediators than others and all variables should be based on reasonably reliable indicators. Second, the mediation model needs to be properly specified. This requires that the data for the analysis be prospectively or historically ordered and possess proper causal direction. Third, it is imperative that the significance of the identified pathways be established, preferably with a nonparametric bootstrap resampling approach. Fourth, effect size estimates should be computed or competing pathways compared. Finally, investigators employing the mediation method are advised to perform a sensitivity analysis. Additional topics covered in this article include parallel and serial multiple mediation designs, moderation, and the relationship between mediation and moderation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A System Dynamics Model for Planning Cardiovascular Disease Interventions

PubMed Central

Homer, Jack; Evans, Elizabeth; Zielinski, Ann

2010-01-01

Planning programs for the prevention and treatment of cardiovascular disease (CVD) is a challenge to every community that wants to make the best use of its limited resources. Selecting programs that provide the greatest impact is difficult because of the complex set of causal pathways and delays that link risk factors to CVD. We describe a system dynamics simulation model developed for a county health department that incorporates and tracks the effects of those risk factors over time on both first-time and recurrent events. We also describe how the model was used to evaluate the potential impacts of various intervention strategies for reducing the county's CVD burden and present the results of those policy tests. PMID:20167899

Causal reasoning with forces

PubMed Central

Wolff, Phillip; Barbey, Aron K.

2015-01-01

Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed. PMID:25653611

Indole-3-acetic acid biosynthesis in Fusarium delphinoides strain GPK, a causal agent of Wilt in Chickpea.

PubMed

Kulkarni, Guruprasad B; Sanjeevkumar, S; Kirankumar, B; Santoshkumar, M; Karegoudar, T B

2013-02-01

Fusarium delphinoides (Ascomycota; Nectriaceae) is an indole-3-acetic acid (IAA) producing plant pathogen and a causal agent of wilt in chickpea. The IAA biosynthetic pathway in F. delphinoides strain GPK (FDG) was examined by analyzing metabolic intermediates and by feeding experiments. Gas chromatograph (GC) analysis of FDG culture filtrates showed the presence of metabolic intermediates of indole-3-pyruvic acid (IPyA), indole-3-acetamide (IAM), and tryptamine (TRA) pathways. The different IAA biosynthetic pathways were further confirmed by identifying the presence of different enzymes of these pathways. Substrate specificity study of aromatic amino acid aminotransferase revealed that the enzyme is highly specific for tryptophan (Trp) and α-ketoglutarate (α-kg) as amino group donor and acceptor, respectively. Furthermore, the concentration-dependent effect of exogenous IAA on fungal growth was established. Low concentration of exogenous IAA increases the fungal growth and at high concentration it decreases the growth of FDG.

Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease

PubMed Central

Relton, Caroline L; Davey Smith, George

2012-01-01

The burgeoning interest in the field of epigenetics has precipitated the need to develop approaches to strengthen causal inference when considering the role of epigenetic mediators of environmental exposures on disease risk. Epigenetic markers, like any other molecular biomarker, are vulnerable to confounding and reverse causation. Here, we present a strategy, based on the well-established framework of Mendelian randomization, to interrogate the causal relationships between exposure, DNA methylation and outcome. The two-step approach first uses a genetic proxy for the exposure of interest to assess the causal relationship between exposure and methylation. A second step then utilizes a genetic proxy for DNA methylation to interrogate the causal relationship between DNA methylation and outcome. The rationale, origins, methodology, advantages and limitations of this novel strategy are presented. PMID:22422451

Annual research review: Rare genotypes and childhood psychopathology--uncovering diverse developmental mechanisms of ADHD risk.

PubMed

Scerif, Gaia; Baker, Kate

2015-03-01

Through the increased availability and sophistication of genetic testing, it is now possible to identify causal diagnoses in a growing proportion of children with neurodevelopmental disorders. In addition to developmental delay and intellectual disability, many genetic disorders are associated with high risks of psychopathology, which curtail the wellbeing of affected individuals and their families. Beyond the identification of significant clinical needs, understanding the diverse pathways from rare genetic mutations to cognitive dysfunction and emotional-behavioural disturbance has theoretical and practical utility. We overview (based on a strategic search of the literature) the state-of-the-art on causal mechanisms leading to one of the most common childhood behavioural diagnoses - attention deficit hyperactivity disorder (ADHD) - in the context of specific genetic disorders. We focus on new insights emerging from the mapping of causal pathways from identified genetic differences to neuronal biology, brain abnormalities, cognitive processing differences and ultimately behavioural symptoms of ADHD. First, ADHD research in the context of rare genotypes highlights the complexity of multilevel mechanisms contributing to psychopathology risk. Second, comparisons between genetic disorders associated with similar psychopathology risks can elucidate convergent or distinct mechanisms at each level of analysis, which may inform therapeutic interventions and prognosis. Third, genetic disorders provide an unparalleled opportunity to observe dynamic developmental interactions between neurocognitive risk and behavioural symptoms. Fourth, variation in expression of psychopathology risk within each genetic disorder points to putative moderating and protective factors within the genome and the environment. A common imperative emerging within psychopathology research is the need to investigate mechanistically how developmental trajectories converge or diverge between and within genotype-defined groups. Crucially, as genetic predispositions modify interaction dynamics from the outset, longitudinal research is required to understand the multi-level developmental processes that mediate symptom evolution. © 2014 Association for Child and Adolescent Mental Health.

Entropy Transfer between Residue Pairs and Allostery in Proteins: Quantifying Allosteric Communication in Ubiquitin

PubMed Central

2017-01-01

It has recently been proposed by Gunasakaran et al. that allostery may be an intrinsic property of all proteins. Here, we develop a computational method that can determine and quantify allosteric activity in any given protein. Based on Schreiber's transfer entropy formulation, our approach leads to an information transfer landscape for the protein that shows the presence of entropy sinks and sources and explains how pairs of residues communicate with each other using entropy transfer. The model can identify the residues that drive the fluctuations of others. We apply the model to Ubiquitin, whose allosteric activity has not been emphasized until recently, and show that there are indeed systematic pathways of entropy and information transfer between residues that correlate well with the activities of the protein. We use 600 nanosecond molecular dynamics trajectories for Ubiquitin and its complex with human polymerase iota and evaluate entropy transfer between all pairs of residues of Ubiquitin and quantify the binding susceptibility changes upon complex formation. We explain the complex formation propensities of Ubiquitin in terms of entropy transfer. Important residues taking part in allosteric communication in Ubiquitin predicted by our approach are in agreement with results of NMR relaxation dispersion experiments. Finally, we show that time delayed correlation of fluctuations of two interacting residues possesses an intrinsic causality that tells which residue controls the interaction and which one is controlled. Our work shows that time delayed correlations, entropy transfer and causality are the required new concepts for explaining allosteric communication in proteins. PMID:28095404

Proposal for an integrated evaluation model for the study of whole systems health care in cancer.

PubMed

Jonas, Wayne B; Beckner, William; Coulter, Ian

2006-12-01

For more than 200 years, biomedicine has approached the treatment of disease by studying disease processes (patho-genesis), inferring causal connections and developing specific approaches for therapeutically interfering with those processes. This pathogenic approach has been highly successful in acute and traumatic disease but less successful in chronic disease, primarily because of the complex, multi-factorial nature of most chronic disease, which does not allow for simple causal inference or for simple therapeutic interventions. This article suggests that chronic disease is best approached by enhancing healing processes (salutogenesis) as a whole system. Because of the nature of complex systems in chronic disease, an evaluation model based on integrative medicine is felt to be more appropriate than a disease model. The authors propose and describe an integrated model for the evaluation of healing (IMEH) that collects multilevel "thick case" observational data in assessing complex practices for chronic disease. If successful, this approach could become a blueprint for studying healing capacity in whole medical systems, including complementary medicine, traditional medicine, and conventional primary care. In addition, streamlining data collection and applying rapid informatics management might allow for such data to be used in guiding clinical practice. The IMEH involves collection, integration, and potentially feedback of relevant variables in the following areas: (1) sociocultural, (2) psychological and behavioral, (3) clinical (diagnosis based), and (4) biological. Evaluation and integration of these components would involve specialized research teams that feed their data into a single data management and information analysis center. These data can then be subjected to descriptive and pathway analysis providing "bench and bedside" information.

A Hypothesis for Using Pathway Genetic Load Analysis for Understanding Complex Outcomes in Bilirubin Encephalopathy

PubMed Central

Riordan, Sean M.; Bittel, Douglas C.; Le Pichon, Jean-Baptiste; Gazzin, Silvia; Tiribelli, Claudio; Watchko, Jon F.; Wennberg, Richard P.; Shapiro, Steven M.

2016-01-01

Genetic-based susceptibility to bilirubin neurotoxicity and chronic bilirubin encephalopathy (kernicterus) is still poorly understood. Neonatal jaundice affects 60–80% of newborns, and considerable effort goes into preventing this relatively benign condition from escalating into the development of kernicterus making the incidence of this potentially devastating condition very rare in more developed countries. The current understanding of the genetic background of kernicterus is largely comprised of mutations related to alterations of bilirubin production, elimination, or both. Less is known about mutations that may predispose or protect against CNS bilirubin neurotoxicity. The lack of a monogenetic source for this risk of bilirubin neurotoxicity suggests that disease progression is dependent upon an overall decrease in the functionality of one or more essential genetically controlled metabolic pathways. In other words, a “load” is placed on key pathways in the form of multiple genetic variants that combine to create a vulnerable phenotype. The idea of epistatic interactions creating a pathway genetic load (PGL) that affects the response to a specific insult has been previously reported as a PGL score. We hypothesize that the PGL score can be used to investigate whether increased susceptibility to bilirubin-induced CNS damage in neonates is due to a mutational load being placed on key genetic pathways important to the central nervous system's response to bilirubin neurotoxicity. We propose a modification of the PGL score method that replaces the use of a canonical pathway with custom gene lists organized into three tiers with descending levels of evidence combined with the utilization of single nucleotide polymorphism (SNP) causality prediction methods. The PGL score has the potential to explain the genetic background of complex bilirubin induced neurological disorders (BIND) such as kernicterus and could be the key to understanding ranges of outcome severity in complex diseases. We anticipate that this method could be useful for improving the care of jaundiced newborns through its use as an at-risk screen. Importantly, this method would also be useful in uncovering basic knowledge about this and other polygenetic diseases whose genetic source is difficult to discern through traditional means such as a genome-wide association study. PMID:27587993

Forces and Motion: How Young Children Understand Causal Events

ERIC Educational Resources Information Center

Goksun, Tilbe; George, Nathan R.; Hirsh-Pasek, Kathy; Golinkoff, Roberta M.

2013-01-01

How do children evaluate complex causal events? This study investigates preschoolers' representation of "force dynamics" in causal scenes, asking whether (a) children understand how single and dual forces impact an object's movement and (b) this understanding varies across cause types (Cause, Enable, Prevent). Three-and-a half- to…

The Model Analyst’s Toolkit: Scientific Model Development, Analysis, and Validation

DTIC Science & Technology

2013-11-20

Granger causality F-test validation 3.1.2. Dynamic time warping for uneven temporal relationships Many causal relationships are imperfectly...mapping for dynamic feedback models Granger causality and DTW can identify causal relationships and consider complex temporal factors. However, many ...variant of the tf-idf algorithm (Manning, Raghavan, Schutze et al., 2008), typically used in search engines, to “score” features. The (-log tf) in

Combining Propensity Score Methods and Complex Survey Data to Estimate Population Treatment Effects

ERIC Educational Resources Information Center

Stuart, Elizabeth A.; Dong, Nianbo; Lenis, David

2016-01-01

Complex surveys are often used to estimate causal effects regarding the effects of interventions or exposures of interest. Propensity scores (Rosenbaum & Rubin, 1983) have emerged as one popular and effective tool for causal inference in non-experimental studies, as they can help ensure that groups being compared are similar with respect to a…

Domain-specific perceptual causality in children depends on the spatio-temporal configuration, not motion onset

PubMed Central

Schlottmann, Anne; Cole, Katy; Watts, Rhianna; White, Marina

2013-01-01

Humans, even babies, perceive causality when one shape moves briefly and linearly after another. Motion timing is crucial in this and causal impressions disappear with short delays between motions. However, the role of temporal information is more complex: it is both a cue to causality and a factor that constrains processing. It affects ability to distinguish causality from non-causality, and social from mechanical causality. Here we study both issues with 3- to 7-year-olds and adults who saw two computer-animated squares and chose if a picture of mechanical, social or non-causality fit each event best. Prior work fit with the standard view that early in development, the distinction between the social and physical domains depends mainly on whether or not the agents make contact, and that this reflects concern with domain-specific motion onset, in particular, whether the motion is self-initiated or not. The present experiments challenge both parts of this position. In Experiments 1 and 2, we showed that not just spatial, but also animacy and temporal information affect how children distinguish between physical and social causality. In Experiments 3 and 4 we showed that children do not seem to use spatio-temporal information in perceptual causality to make inferences about self- or other-initiated motion onset. Overall, spatial contact may be developmentally primary in domain-specific perceptual causality in that it is processed easily and is dominant over competing cues, but it is not the only cue used early on and it is not used to infer motion onset. Instead, domain-specific causal impressions may be automatic reactions to specific perceptual configurations, with a complex role for temporal information. PMID:23874308

Practical approaches to adverse outcome pathway development and weight‐of‐evidence evaluation as illustrated by ecotoxicological case studies

EPA Science Inventory

Adverse Outcome Pathways (AOPs) describe toxicant effects as a sequential chain of causally linked events beginning with a molecular perturbation and culminating in an adverse outcome at an individual or population level. Strategies for developing AOPs are still evolving and dep...

A genetically informative developmental study of the relationship between conduct disorder and peer deviance in males

PubMed Central

Kendler, K. S.; Jacobson, K.; Myers, J. M.; Eaves, L. J.

2014-01-01

Background Conduct disorder (CD) and peer deviance (PD) both powerfully predict future externalizing behaviors. Although levels of CD and PD are strongly correlated, the causal relationship between them has remained controversial and has not been examined by a genetically informative study. Method Levels of CD and PD were assessed in 746 adult male–male twin pairs at personal interview for ages 8–11, 12–14 and 15–17 years using a life history calendar. Model fitting was performed using the Mx program. Results The best-fit model indicated an active developmental relationship between CD and PD including forward transmission of both traits over time and strong causal relationships between CD and PD within time periods. The best-fit model indicated that the causal relationship for genetic risk factors was from CD to PD and was constant over time. For common environmental factors, the causal pathways ran from PD to CD and were stronger in earlier than later age periods. Conclusions A genetically informative model revealed causal pathways difficult to elucidate by other methods. Genes influence risk for CD, which, through social selection, impacts on the deviance of peers. Shared environment, through family and community processes, encourages or discourages adolescent deviant behavior, which, via social influence, alters risk for CD. Social influence is more important than social selection in childhood, but by late adolescence social selection becomes predominant. These findings have implications for prevention efforts for CD and associated externalizing disorders. PMID:17935643

Causality networks from multivariate time series and application to epilepsy.

PubMed

Siggiridou, Elsa; Koutlis, Christos; Tsimpiris, Alkiviadis; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

2015-08-01

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.

Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review.

PubMed

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 research consumer and the underlying strength of causal inference among the studies most widely shared on social media. However, because this sample was designed to be representative of the articles selected and shared on social media, it is unlikely to be representative of all academic and media work. More research is needed to determine how academic institutions, media organizations, and social network sharing patterns impact causal inference and language as received by the research consumer.

Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review

PubMed Central

Smith, Emily R.; Moscoe, Ellen; Audy, Robin; Bell, Winnie; Brennan, Alana T.; Breskin, Alexander; Kane, Jeremy C.; Suarez, Elizabeth A.

2018-01-01

Background 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. Methods 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. Results 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. Conclusions We find a large disparity between the strength of language as presented to the research consumer and the underlying strength of causal inference among the studies most widely shared on social media. However, because this sample was designed to be representative of the articles selected and shared on social media, it is unlikely to be representative of all academic and media work. More research is needed to determine how academic institutions, media organizations, and social network sharing patterns impact causal inference and language as received by the research consumer. PMID:29847549

Genetic aspects of autism spectrum disorders: insights from animal models

PubMed Central

Banerjee, Swati; Riordan, Maeveen; Bhat, Manzoor A.

2014-01-01

Autism spectrum disorders (ASDs) are a complex neurodevelopmental disorder that display a triad of core behavioral deficits including restricted interests, often accompanied by repetitive behavior, deficits in language and communication, and an inability to engage in reciprocal social interactions. ASD is among the most heritable disorders but is not a simple disorder with a singular pathology and has a rather complex etiology. It is interesting to note that perturbations in synaptic growth, development, and stability underlie a variety of neuropsychiatric disorders, including ASD, schizophrenia, epilepsy, and intellectual disability. Biological characterization of an increasing repertoire of synaptic mutants in various model organisms indicates synaptic dysfunction as causal in the pathophysiology of ASD. Our understanding of the genes and genetic pathways that contribute toward the formation, stabilization, and maintenance of functional synapses coupled with an in-depth phenotypic analysis of the cellular and behavioral characteristics is therefore essential to unraveling the pathogenesis of these disorders. In this review, we discuss the genetic aspects of ASD emphasizing on the well conserved set of genes and genetic pathways implicated in this disorder, many of which contribute to synapse assembly and maintenance across species. We also review how fundamental research using animal models is providing key insights into the various facets of human ASD. PMID:24605088

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

PubMed

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

Assessing Command and Control System Vulnerabilities in Underdeveloped, Degraded and Denied Operational Environments

DTIC Science & Technology

2013-06-01

simulation of complex systems (Sterman 2000, Meadows 2008): a) Causal Loop Diagrams. A Causal Loop Diagram ( CLD ) is used to represent the feedback...structure of the dynamic system. CLDs consist of variables in the system being connected by arrows to show their causal influences and relationships. It is...distribution of orders will be included in the model. 6.4.2 Causal Loop Diagrams The CLD , as seen in Figure 5, is derived from the WDA constructs for the

Causal Loop Analysis of coastal geomorphological systems

NASA Astrophysics Data System (ADS)

Payo, Andres; Hall, Jim W.; French, Jon; Sutherland, James; van Maanen, Barend; Nicholls, Robert J.; Reeve, Dominic E.

2016-03-01

As geomorphologists embrace ever more sophisticated theoretical frameworks that shift from simple notions of evolution towards single steady equilibria to recognise the possibility of multiple response pathways and outcomes, morphodynamic modellers are facing the problem of how to keep track of an ever-greater number of system feedbacks. Within coastal geomorphology, capturing these feedbacks is critically important, especially as the focus of activity shifts from reductionist models founded on sediment transport fundamentals to more synthesist ones intended to resolve emergent behaviours at decadal to centennial scales. This paper addresses the challenge of mapping the feedback structure of processes controlling geomorphic system behaviour with reference to illustrative applications of Causal Loop Analysis at two study cases: (1) the erosion-accretion behaviour of graded (mixed) sediment beds, and (2) the local alongshore sediment fluxes of sand-rich shorelines. These case study examples are chosen on account of their central role in the quantitative modelling of geomorphological futures and as they illustrate different types of causation. Causal loop diagrams, a form of directed graph, are used to distil the feedback structure to reveal, in advance of more quantitative modelling, multi-response pathways and multiple outcomes. In the case of graded sediment bed, up to three different outcomes (no response, and two disequilibrium states) can be derived from a simple qualitative stability analysis. For the sand-rich local shoreline behaviour case, two fundamentally different responses of the shoreline (diffusive and anti-diffusive), triggered by small changes of the shoreline cross-shore position, can be inferred purely through analysis of the causal pathways. Explicit depiction of feedback-structure diagrams is beneficial when developing numerical models to explore coastal morphological futures. By explicitly mapping the feedbacks included and neglected within a model, the modeller can readily assess if critical feedback loops are included.

Reflecting on explanatory ability: A mechanism for detecting gaps in causal knowledge.

PubMed

Johnson, Dan R; Murphy, Meredith P; Messer, Riley M

2016-05-01

People frequently overestimate their understanding-with a particularly large blind-spot for gaps in their causal knowledge. We introduce a metacognitive approach to reducing overestimation, termed reflecting on explanatory ability (REA), which is briefly thinking about how well one could explain something in a mechanistic, step-by-step, causally connected manner. Nine experiments demonstrated that engaging in REA just before estimating one's understanding substantially reduced overestimation. Moreover, REA reduced overestimation with nearly the same potency as generating full explanations, but did so 20 times faster (although only for high complexity objects). REA substantially reduced overestimation by inducing participants to quickly evaluate an object's inherent causal complexity (Experiments 4-7). REA reduced overestimation by also fostering step-by-step, causally connected processing (Experiments 2 and 3). Alternative explanations for REA's effects were ruled out including a general conservatism account (Experiments 4 and 5) and a covert explanation account (Experiment 8). REA's overestimation-reduction effect generalized beyond objects (Experiments 1-8) to sociopolitical policies (Experiment 9). REA efficiently detects gaps in our causal knowledge with implications for improving self-directed learning, enhancing self-insight into vocational and academic abilities, and even reducing extremist attitudes. (c) 2016 APA, all rights reserved).

The impact of human development on individual health: a causal mediation analysis examining pathways through education and body mass index.

PubMed

Wang, Aolin; Arah, Onyebuchi A

2017-01-01

The macro environment we live in projects what we can achieve and how we behave, and in turn, shapes our health in complex ways. Policymaking will benefit from insights into the mechanisms underlying how national socioeconomic context affects health. This study examined the impact of human development on individual health and the possible mediating roles of education and body mass index (BMI). We analyzed World Health Survey data on 109,448 participants aged 25 or older from 42 low- and middle-income countries with augmented human development index (HDI) in 1990. We used principal components method to create a health score based on measures from eight health state domains, used years of schooling as education indicator and calculated BMI from self-reported height and weight. We used causal mediation analysis technique with random intercepts to account for the multilevel structure. Below a reference HDI level of 0.48, HDI was negatively associated with good health (total effect at HDI of 0.23: b  =  - 3.44, 95% CI [-6.39--0.49] for males and b  =  - 5.16, 95% CI [-9.24,--1.08] for females) but was positively associated with good health above this reference level (total effect at HDI of 0.75: b  = 4.16, 95% CI [-0.33-8.66] for males and b  = 6.62, 95% CI [0.85-12.38] for females). We found a small positive effect of HDI on health via education across reference HDI levels ( b ranging from 0.24 to 0.29 for males and 0.40 to 0.49 for females) but not via pathways involving BMI only. Human development has a non-linear effect on individual health, but the impact appears to be mainly through pathways other than education and BMI.

Transcriptomic analysis of genetically defined autism candidate genes reveals common mechanisms of action

PubMed Central

2013-01-01

Background Austism spectrum disorder (ASD) is a heterogeneous behavioral disorder or condition characterized by severe impairment of social engagement and the presence of repetitive activities. The molecular etiology of ASD is still largely unknown despite a strong genetic component. Part of the difficulty in turning genetics into disease mechanisms and potentially new therapeutics is the sheer number and diversity of the genes that have been associated with ASD and ASD symptoms. The goal of this work is to use shRNA-generated models of genetic defects proposed as causative for ASD to identify the common pathways that might explain how they produce a core clinical disability. Methods Transcript levels of Mecp2, Mef2a, Mef2d, Fmr1, Nlgn1, Nlgn3, Pten, and Shank3 were knocked-down in mouse primary neuron cultures using shRNA constructs. Whole genome expression analysis was conducted for each of the knockdown cultures as well as a mock-transduced culture and a culture exposed to a lentivirus expressing an anti-luciferase shRNA. Gene set enrichment and a causal reasoning engine was employed to identify pathway level perturbations generated by the transcript knockdown. Results Quantification of the shRNA targets confirmed the successful knockdown at the transcript and protein levels of at least 75% for each of the genes. After subtracting out potential artifacts caused by viral infection, gene set enrichment and causal reasoning engine analysis showed that a significant number of gene expression changes mapped to pathways associated with neurogenesis, long-term potentiation, and synaptic activity. Conclusions This work demonstrates that despite the complex genetic nature of ASD, there are common molecular mechanisms that connect many of the best established autism candidate genes. By identifying the key regulatory checkpoints in the interlinking transcriptional networks underlying autism, we are better able to discover the ideal points of intervention that provide the broadest efficacy across the diverse population of autism patients. PMID:24238429

Transcriptomic analysis of genetically defined autism candidate genes reveals common mechanisms of action.

PubMed

Lanz, Thomas A; Guilmette, Edward; Gosink, Mark M; Fischer, James E; Fitzgerald, Lawrence W; Stephenson, Diane T; Pletcher, Mathew T

2013-11-15

Austism spectrum disorder (ASD) is a heterogeneous behavioral disorder or condition characterized by severe impairment of social engagement and the presence of repetitive activities. The molecular etiology of ASD is still largely unknown despite a strong genetic component. Part of the difficulty in turning genetics into disease mechanisms and potentially new therapeutics is the sheer number and diversity of the genes that have been associated with ASD and ASD symptoms. The goal of this work is to use shRNA-generated models of genetic defects proposed as causative for ASD to identify the common pathways that might explain how they produce a core clinical disability. Transcript levels of Mecp2, Mef2a, Mef2d, Fmr1, Nlgn1, Nlgn3, Pten, and Shank3 were knocked-down in mouse primary neuron cultures using shRNA constructs. Whole genome expression analysis was conducted for each of the knockdown cultures as well as a mock-transduced culture and a culture exposed to a lentivirus expressing an anti-luciferase shRNA. Gene set enrichment and a causal reasoning engine was employed to identify pathway level perturbations generated by the transcript knockdown. Quantification of the shRNA targets confirmed the successful knockdown at the transcript and protein levels of at least 75% for each of the genes. After subtracting out potential artifacts caused by viral infection, gene set enrichment and causal reasoning engine analysis showed that a significant number of gene expression changes mapped to pathways associated with neurogenesis, long-term potentiation, and synaptic activity. This work demonstrates that despite the complex genetic nature of ASD, there are common molecular mechanisms that connect many of the best established autism candidate genes. By identifying the key regulatory checkpoints in the interlinking transcriptional networks underlying autism, we are better able to discover the ideal points of intervention that provide the broadest efficacy across the diverse population of autism patients.

Situation models and memory: the effects of temporal and causal information on recall sequence.

PubMed

Brownstein, Aaron L; Read, Stephen J

2007-10-01

Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.

Recognising discourse causality triggers in the biomedical domain.

PubMed

Mihăilă, Claudiu; Ananiadou, Sophia

2013-12-01

Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types.

Using complex networks to characterize international business cycles.

PubMed

Caraiani, Petre

2013-01-01

There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles. We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries' GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples. The use of complex networks is valuable for understanding the business cycle comovements at an international level.

The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke.

PubMed

Jobe, Thomas H.; Helgason, Cathy M.

1998-04-01

Twentieth century medical science has embraced nineteenth century Boolean probability theory based upon two-valued Aristotelian logic. With the later addition of bit-based, von Neumann structured computational architectures, an epistemology based on randomness has led to a bivalent epidemiological methodology that dominates medical decision making. In contrast, fuzzy logic, based on twentieth century multi-valued logic, and computational structures that are content addressed and adaptively modified, has advanced a new scientific paradigm for the twenty-first century. Diseases such as stroke involve multiple concomitant causal factors that are difficult to represent using conventional statistical methods. We tested which paradigm best represented this complex multi-causal clinical phenomenon-stroke. We show that the fuzzy logic paradigm better represented clinical complexity in cerebrovascular disease than current probability theory based methodology. We believe this finding is generalizable to all of clinical science since multiple concomitant causal factors are involved in nearly all known pathological processes.

Tools for Detecting Causality in Space Systems

NASA Astrophysics Data System (ADS)

Johnson, J.; Wing, S.

2017-12-01

Complex systems such as the solar and magnetospheric envivonment often exhibit patterns of behavior that suggest underlying organizing principles. Causality is a key organizing principle that is particularly difficult to establish in strongly coupled nonlinear systems, but essential for understanding and modeling the behavior of systems. While traditional methods of time-series analysis can identify linear correlations, they do not adequately quantify the distinction between causal and coincidental dependence. We discuss tools for detecting causality including: granger causality, transfer entropy, conditional redundancy, and convergent cross maps. The tools are illustrated by applications to magnetospheric and solar physics including radiation belt, Dst (a magnetospheric state variable), substorm, and solar cycle dynamics.

Causal networks clarify productivity-richness interrelations, bivariate plots do not

USGS Publications Warehouse

Grace, James B.; Adler, Peter B.; Harpole, W. Stanley; Borer, Elizabeth T.; Seabloom, Eric W.

2014-01-01

We urge ecologists to consider productivity–richness relationships through the lens of causal networks to advance our understanding beyond bivariate analysis. Further, we emphasize that models based on a causal network conceptualization can also provide more meaningful guidance for conservation management than can a bivariate perspective. Measuring only two variables does not permit the evaluation of complex ideas nor resolve debates about underlying mechanisms.

Assessing Causality in a Complex Security Environment

DTIC Science & Technology

2015-01-01

social sciences that could genuinely benefit those students. Causality is one of these critical issues. Causality has many definitions, but we might...protests (Ivan Bandura ) Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated...relatively simple theory of what leads to a stable deter- rent relationship between two states. Mearsheimer argued that when State A fields a

Foundational perspectives on causality in large-scale brain networks

NASA Astrophysics Data System (ADS)

Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

Foundational perspectives on causality in large-scale brain networks.

PubMed

Mannino, Michael; Bressler, Steven L

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain. Copyright © 2015 Elsevier B.V. All rights reserved.

Students' reasons for preferring teleological explanations

NASA Astrophysics Data System (ADS)

Trommler, Friederike; Gresch, Helge; Hammann, Marcus

2018-01-01

The teleological bias, a major learning obstacle, involves explaining biological phenomena in terms of purposes and goals. To probe the teleological bias, researchers have used acceptance judgement tasks and preference judgement tasks. In the present study, such tasks were used with German high school students (N = 353) for 10 phenomena from human biology, that were explained both teleologically and causally. A sub-sample (n = 26) was interviewed about the reasons for their preferences. The results showed that the students favoured teleological explanations over causal explanations. Although the students explained their preference judgements etiologically (i.e. teleologically and causally), they also referred to a wide range of non-etiological criteria (i.e. familiarity, complexity, relevance and five more criteria). When elaborating on their preference for causal explanations, the students often focused not on the causality of the phenomenon, but on mechanisms whose complexity they found attractive. When explaining their preference for teleological explanations, they often focused not teleologically on purposes and goals, but rather on functions, which they found familiar and relevant. Generally, students' preference judgements rarely allowed for making inferences about causal reasoning and teleological reasoning, an issue that is controversial in the literature. Given that students were largely unaware of causality and teleology, their attention must be directed towards distinguishing between etiological and non-etiological reasoning. Implications for educational practice as well as for future research are discussed.

Gang membership and substance use: guilt as a gendered causal pathway

PubMed Central

Coffman, Donna L.; Melde, Chris; Esbensen, Finn-Aage

2014-01-01

Objectives We examine whether anticipated guilt for substance use is a gendered mechanism underlying the noted enhancement effect of gang membership on illegal drug use. We also demonstrate a method for making stronger causal inferences when assessing mediation in the presence of moderation and time-varying confounding. Methods We estimate a series of inverse propensity weighted models to obtain unbiased estimates of mediation in the presence of confounding of the exposure (i.e., gang membership) and mediator (i.e., anticipated guilt) using three waves of data from a multi-site panel study of a law-related education program for youth (N=1,113). Results The onset of gang membership significantly decreased anticipated substance use guilt among both male and female respondents. This reduction was significantly associated with increased frequency of substance use only for female respondents, however, suggesting that gender moderates the mechanism through which gang membership influences substance use. Conclusions Criminologists are often concerned with identifying causal pathways for antisocial and/or delinquent behavior, but confounders of the exposure, mediator, and outcome often interfere with efforts to assess mediation. Many new approaches have been proposed for strengthening causal inference for mediation effects. After controlling for confounding using inverse propensity weighting, our results suggest that interventions aimed at reducing substance use by current and former female gang members should focus on the normative aspects of these behaviors. PMID:26190954

Gang membership and substance use: guilt as a gendered causal pathway.

PubMed

Coffman, Donna L; Melde, Chris; Esbensen, Finn-Aage

2015-03-01

We examine whether anticipated guilt for substance use is a gendered mechanism underlying the noted enhancement effect of gang membership on illegal drug use. We also demonstrate a method for making stronger causal inferences when assessing mediation in the presence of moderation and time-varying confounding. We estimate a series of inverse propensity weighted models to obtain unbiased estimates of mediation in the presence of confounding of the exposure (i.e., gang membership) and mediator (i.e., anticipated guilt) using three waves of data from a multi-site panel study of a law-related education program for youth ( N =1,113). The onset of gang membership significantly decreased anticipated substance use guilt among both male and female respondents. This reduction was significantly associated with increased frequency of substance use only for female respondents, however, suggesting that gender moderates the mechanism through which gang membership influences substance use. Criminologists are often concerned with identifying causal pathways for antisocial and/or delinquent behavior, but confounders of the exposure, mediator, and outcome often interfere with efforts to assess mediation. Many new approaches have been proposed for strengthening causal inference for mediation effects. After controlling for confounding using inverse propensity weighting, our results suggest that interventions aimed at reducing substance use by current and former female gang members should focus on the normative aspects of these behaviors.

Causal discovery in the geosciences-Using synthetic data to learn how to interpret results

NASA Astrophysics Data System (ADS)

Ebert-Uphoff, Imme; Deng, Yi

2017-02-01

Causal discovery algorithms based on probabilistic graphical models have recently emerged in geoscience applications for the identification and visualization of dynamical processes. The key idea is to learn the structure of a graphical model from observed spatio-temporal data, thus finding pathways of interactions in the observed physical system. Studying those pathways allows geoscientists to learn subtle details about the underlying dynamical mechanisms governing our planet. Initial studies using this approach on real-world atmospheric data have shown great potential for scientific discovery. However, in these initial studies no ground truth was available, so that the resulting graphs have been evaluated only by whether a domain expert thinks they seemed physically plausible. The lack of ground truth is a typical problem when using causal discovery in the geosciences. Furthermore, while most of the connections found by this method match domain knowledge, we encountered one type of connection for which no explanation was found. To address both of these issues we developed a simulation framework that generates synthetic data of typical atmospheric processes (advection and diffusion). Applying the causal discovery algorithm to the synthetic data allowed us (1) to develop a better understanding of how these physical processes appear in the resulting connectivity graphs, and thus how to better interpret such connectivity graphs when obtained from real-world data; (2) to solve the mystery of the previously unexplained connections.

DNA methylation as a potential mediator of environmental risks in the development of childhood acute lymphoblastic leukemia.

PubMed

Timms, Jessica A; Relton, Caroline L; Rankin, Judith; Strathdee, Gordon; McKay, Jill A

2016-04-01

5-year survival rate for childhood acute lymphoblastic leukemia (ALL) has risen to approximately 90%, yet the causal disease pathway is still poorly understood. Evidence suggests multiple 'hits' are required for disease progression; an initial genetic abnormality followed by additional secondary 'hits'. It is plausible that environmental influences may trigger these secondary hits, and with the peak incidence of diagnosis between 2 and 5 years of age, early life exposures are likely to be key. DNA methylation can be modified by many environmental exposures and is dramatically altered in cancers, including childhood ALL. Here we explore the potential that DNA methylation may be involved in the causal pathway toward disease by acting as a mediator between established environmental factors and childhood ALL development.

Applied mediation analyses: a review and tutorial.

PubMed

Lange, Theis; Hansen, Kim Wadt; Sørensen, Rikke; Galatius, Søren

2017-01-01

In recent years, mediation analysis has emerged as a powerful tool to disentangle causal pathways from an exposure/treatment to clinically relevant outcomes. Mediation analysis has been applied in scientific fields as diverse as labour market relations and randomized clinical trials of heart disease treatments. In parallel to these applications, the underlying mathematical theory and computer tools have been refined. This combined review and tutorial will introduce the reader to modern mediation analysis including: the mathematical framework; required assumptions; and software implementation in the R package medflex. All results are illustrated using a recent study on the causal pathways stemming from the early invasive treatment of acute coronary syndrome, for which the rich Danish population registers allow us to follow patients' medication use and more after being discharged from hospital.

Inductive reasoning about causally transmitted properties.

PubMed

Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D; Tenenbaum, Joshua B

2008-11-01

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates' context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.

Depression as a systemic syndrome: mapping the feedback loops of major depressive disorder.

PubMed

Wittenborn, A K; Rahmandad, H; Rick, J; Hosseinichimeh, N

2016-02-01

Depression is a complex public health problem with considerable variation in treatment response. The systemic complexity of depression, or the feedback processes among diverse drivers of the disorder, contribute to the persistence of depression. This paper extends prior attempts to understand the complex causal feedback mechanisms that underlie depression by presenting the first broad boundary causal loop diagram of depression dynamics. We applied qualitative system dynamics methods to map the broad feedback mechanisms of depression. We used a structured approach to identify candidate causal mechanisms of depression in the literature. We assessed the strength of empirical support for each mechanism and prioritized those with support from validation studies. Through an iterative process, we synthesized the empirical literature and created a conceptual model of major depressive disorder. The literature review and synthesis resulted in the development of the first causal loop diagram of reinforcing feedback processes of depression. It proposes candidate drivers of illness, or inertial factors, and their temporal functioning, as well as the interactions among drivers of depression. The final causal loop diagram defines 13 key reinforcing feedback loops that involve nine candidate drivers of depression. Future research is needed to expand upon this initial model of depression dynamics. Quantitative extensions may result in a better understanding of the systemic syndrome of depression and contribute to personalized methods of evaluation, prevention and intervention.

Depression as a systemic syndrome: mapping the feedback loops of major depressive disorder

PubMed Central

Wittenborn, A. K.; Rahmandad, H.; Rick, J.; Hosseinichimeh, N.

2016-01-01

Background Depression is a complex public health problem with considerable variation in treatment response. The systemic complexity of depression, or the feedback processes among diverse drivers of the disorder, contribute to the persistence of depression. This paper extends prior attempts to understand the complex causal feedback mechanisms that underlie depression by presenting the first broad boundary causal loop diagram of depression dynamics. Method We applied qualitative system dynamics methods to map the broad feedback mechanisms of depression. We used a structured approach to identify candidate causal mechanisms of depression in the literature. We assessed the strength of empirical support for each mechanism and prioritized those with support from validation studies. Through an iterative process, we synthesized the empirical literature and created a conceptual model of major depressive disorder. Results The literature review and synthesis resulted in the development of the first causal loop diagram of reinforcing feedback processes of depression. It proposes candidate drivers of illness, or inertial factors, and their temporal functioning, as well as the interactions among drivers of depression. The final causal loop diagram defines 13 key reinforcing feedback loops that involve nine candidate drivers of depression. Conclusions Future research is needed to expand upon this initial model of depression dynamics. Quantitative extensions may result in a better understanding of the systemic syndrome of depression and contribute to personalized methods of evaluation, prevention and intervention. PMID:26621339

Encoding dependence in Bayesian causal networks

USDA-ARS?s Scientific Manuscript database

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...

A Tox21 Approach to Altered Epigenetic Landscapes: Assessing Epigenetic Toxicity Pathways Leading to Altered Gene Expression and Oncogenic Transformation In Vitro

PubMed Central

Parfett, Craig L.; Desaulniers, Daniel

2017-01-01

An emerging vision for toxicity testing in the 21st century foresees in vitro assays assuming the leading role in testing for chemical hazards, including testing for carcinogenicity. Toxicity will be determined by monitoring key steps in functionally validated molecular pathways, using tests designed to reveal chemically-induced perturbations that lead to adverse phenotypic endpoints in cultured human cells. Risk assessments would subsequently be derived from the causal in vitro endpoints and concentration vs. effect data extrapolated to human in vivo concentrations. Much direct experimental evidence now shows that disruption of epigenetic processes by chemicals is a carcinogenic mode of action that leads to altered gene functions playing causal roles in cancer initiation and progression. In assessing chemical safety, it would therefore be advantageous to consider an emerging class of carcinogens, the epigenotoxicants, with the ability to change chromatin and/or DNA marks by direct or indirect effects on the activities of enzymes (writers, erasers/editors, remodelers and readers) that convey the epigenetic information. Evidence is reviewed supporting a strategy for in vitro hazard identification of carcinogens that induce toxicity through disturbance of functional epigenetic pathways in human somatic cells, leading to inactivated tumour suppressor genes and carcinogenesis. In the context of human cell transformation models, these in vitro pathway measurements ensure high biological relevance to the apical endpoint of cancer. Four causal mechanisms participating in pathways to persistent epigenetic gene silencing were considered: covalent histone modification, nucleosome remodeling, non-coding RNA interaction and DNA methylation. Within these four interacting mechanisms, 25 epigenetic toxicity pathway components (SET1, MLL1, KDM5, G9A, SUV39H1, SETDB1, EZH2, JMJD3, CBX7, CBX8, BMI, SUZ12, HP1, MPP8, DNMT1, DNMT3A, DNMT3B, TET1, MeCP2, SETDB2, BAZ2A, UHRF1, CTCF, HOTAIR and ANRIL) were found to have experimental evidence showing that functional perturbations played “driver” roles in human cellular transformation. Measurement of epigenotoxicants presents challenges for short-term carcinogenicity testing, especially in the high-throughput modes emphasized in the Tox21 chemicals testing approach. There is need to develop and validate in vitro tests to detect both, locus-specific, and genome-wide, epigenetic alterations with causal links to oncogenic cellular phenotypes. Some recent examples of cell-based high throughput chemical screening assays are presented that have been applied or have shown potential for application to epigenetic endpoints. PMID:28587163

Drug- and Herb-Induced Liver Injury in Clinical and Translational Hepatology: Causality Assessment Methods, Quo Vadis?

PubMed Central

Eickhoff, Axel; Schulze, Johannes

2013-01-01

Drug-induced liver injury (DILI) and herb-induced liver injury (HILI) are typical diseases of clinical and translational hepatology. Their diagnosis is complex and requires an experienced clinician to translate basic science into clinical judgment and identify a valid causality algorithm. To prospectively assess causality starting on the day DILI or HILI is suspected, the best approach for physicians is to use the Council for International Organizations of Medical Sciences (CIOMS) scale in its original or preferably its updated version. The CIOMS scale is validated, liver-specific, structured, and quantitative, providing final causality grades based on scores of specific items for individual patients. These items include latency period, decline in liver values after treatment cessation, risk factors, co-medication, alternative diagnoses, hepatotoxicity track record of the suspected product, and unintentional re-exposure. Provided causality is established as probable or highly probable, data of the CIOMS scale with all individual items, a short clinical report, and complete raw data should be transmitted to the regulatory agencies, manufacturers, expert panels, and possibly to the scientific community for further refinement of the causality evaluation in a setting of retrospective expert opinion. Good-quality case data combined with thorough CIOMS-based assessment as a standardized approach should avert subsequent necessity for other complex causality assessment methods that may have inter-rater problems because of poor-quality data. In the future, the CIOMS scale will continue to be the preferred tool to assess causality of DILI and HILI cases and should be used consistently, both prospectively by physicians, and retrospectively for subsequent expert opinion if needed. For comparability and international harmonization, all parties assessing causality in DILI and HILI cases should attempt this standardized approach using the updated CIOMS scale. PMID:26357608

Controversies about a common etiology for eating and mood disorders

PubMed Central

Rossetti, Clara; Halfon, Olivier; Boutrel, Benjamin

2014-01-01

Obesity and depression represent a growing health concern worldwide. For many years, basic science and medicine have considered obesity as a metabolic illness, while depression was classified a psychiatric disorder. Despite accumulating evidence suggesting that obesity and depression may share commonalities, the causal link between eating and mood disorders remains to be fully understood. This etiology is highly complex, consisting of multiple environmental and genetic risk factors that interact with each other. In this review, we sought to summarize the preclinical and clinical evidence supporting a common etiology for eating and mood disorders, with a particular emphasis on signaling pathways involved in the maintenance of energy balance and mood stability, among which orexigenic and anorexigenic neuropeptides, metabolic factors, stress responsive hormones, cytokines, and neurotrophic factors. PMID:25386150

Exploring Students' Mapping Behaviors and Interactive Discourses in a Case Diagnosis Problem: Sequential Analysis of Collaborative Causal Map Drawing Processes

ERIC Educational Resources Information Center

Lee, Woon Jee

2012-01-01

The purpose of this study was to explore the nature of students' mapping and discourse behaviors while constructing causal maps to articulate their understanding of a complex, ill-structured problem. In this study, six graduate-level students were assigned to one of three pair groups, and each pair used the causal mapping software program,…

Right external globus pallidus changes are associated with altered causal awareness in youth with depression

PubMed Central

Griffiths, K R; Lagopoulos, J; Hermens, D F; Hickie, I B; Balleine, B W

2015-01-01

Cognitive impairment is a functionally disabling feature of depression contributing to maladaptive decision-making, a loss of behavioral control and an increased disease burden. The ability to calculate the causal efficacy of ones actions in achieving specific goals is critical to normal decision-making and, in this study, we combined voxel-based morphometry (VBM), shape analysis and diffusion tensor tractography to investigate the relationship between cortical–basal ganglia structural integrity and such causal awareness in 43 young subjects with depression and 21 demographically similar healthy controls. Volumetric analysis determined a relationship between right pallidal size and sensitivity to the causal status of specific actions. More specifically, shape analysis identified dorsolateral surface vertices where an inward location was correlated with reduced levels of causal awareness. Probabilistic tractography revealed that affected parts of the pallidum were primarily connected with the striatum, dorsal thalamus and hippocampus. VBM did not reveal any whole-brain gray matter regions that correlated with causal awareness. We conclude that volumetric reduction within the indirect pathway involving the right dorsolateral pallidum is associated with reduced awareness of the causal efficacy of goal-directed actions in young depressed individuals. This causal awareness task allows for the identification of a functionally and biologically relevant subgroup to which more targeted cognitive interventions could be applied, potentially enhancing the long-term outcomes for these individuals. PMID:26440541

A Method for Gene-Based Pathway Analysis Using Genomewide Association Study Summary Statistics Reveals Nine New Type 1 Diabetes Associations

PubMed Central

Evangelou, Marina; Smyth, Deborah J; Fortune, Mary D; Burren, Oliver S; Walker, Neil M; Guo, Hui; Onengut-Gumuscu, Suna; Chen, Wei-Min; Concannon, Patrick; Rich, Stephen S; Todd, John A; Wallace, Chris

2014-01-01

Pathway analysis can complement point-wise single nucleotide polymorphism (SNP) analysis in exploring genomewide association study (GWAS) data to identify specific disease-associated genes that can be candidate causal genes. We propose a straightforward methodology that can be used for conducting a gene-based pathway analysis using summary GWAS statistics in combination with widely available reference genotype data. We used this method to perform a gene-based pathway analysis of a type 1 diabetes (T1D) meta-analysis GWAS (of 7,514 cases and 9,045 controls). An important feature of the conducted analysis is the removal of the major histocompatibility complex gene region, the major genetic risk factor for T1D. Thirty-one of the 1,583 (2%) tested pathways were identified to be enriched for association with T1D at a 5% false discovery rate. We analyzed these 31 pathways and their genes to identify SNPs in or near these pathway genes that showed potentially novel association with T1D and attempted to replicate the association of 22 SNPs in additional samples. Replication P-values were skewed () with 12 of the 22 SNPs showing . Support, including replication evidence, was obtained for nine T1D associated variants in genes ITGB7 (rs11170466, ), NRP1 (rs722988, ), BAD (rs694739, ), CTSB (rs1296023, ), FYN (rs11964650, ), UBE2G1 (rs9906760, ), MAP3K14 (rs17759555, ), ITGB1 (rs1557150, ), and IL7R (rs1445898, ). The proposed methodology can be applied to other GWAS datasets for which only summary level data are available. PMID:25371288

A qualitative study evaluating causality attribution for serious adverse events during early phase oncology clinical trials.

PubMed

Mukherjee, Som D; Coombes, Megan E; Levine, Mitch; Cosby, Jarold; Kowaleski, Brenda; Arnold, Andrew

2011-10-01

In early phase oncology trials, novel targeted therapies are increasingly being tested in combination with traditional agents creating greater potential for enhanced and new toxicities. When a patient experiences a serious adverse event (SAE), investigators must determine whether the event is attributable to the investigational drug or not. This study seeks to understand the clinical reasoning, tools used and challenges faced by the researchers who assign causality to SAE's. Thirty-two semi-structured interviews were conducted with medical oncologists and trial coordinators at six Canadian academic cancer centres. Interviews were recorded and transcribed verbatim. Individual interview content analysis was followed by thematic analysis across the interview set. Our study found that causality assessment tends to be a rather complex process, often without complete clinical and investigational data at hand. Researchers described using a common processing strategy whereby they gather pertinent information, eliminate alternative explanations, and consider whether or not the study drug resulted in the SAE. Many of the interviewed participants voiced concern that causality assessments are often conducted quickly and tend to be highly subjective. Many participants were unable to identify any useful tools to help in assigning causality and welcomed more objectivity in the overall process. Attributing causality to SAE's is a complex process. Clinical trial researchers apply a logical system of reasoning, but feel that the current method of assigning causality could be improved. Based on these findings, future research involving the development of a new causality assessment tool specifically for use in early phase oncology clinical trials may be useful.

The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models

DTIC Science & Technology

2011-10-27

through (25), (26) and (27), rather than going through (23) ( van der Laan and Rubin, 2006). 29 values, though disparities in parameters may not...graphs. Epidemiology 22 378–381. Petersen, M., Sinisi, S. and van der Laan, M. (2006). Estimation of direct causal effects. Epidemiology 17 276–284...and J. Halpern, eds.). College Publications, UK, 415–444. van der Laan, M. J. and Rubin, D. (2006). Targeted maximum likelihood learning. The

Causal learning is collaborative: Examining explanation and exploration in social contexts.

PubMed

Legare, Cristine H; Sobel, David M; Callanan, Maureen

2017-10-01

Causal learning in childhood is a dynamic and collaborative process of explanation and exploration within complex physical and social environments. Understanding how children learn causal knowledge requires examining how they update beliefs about the world given novel information and studying the processes by which children learn in collaboration with caregivers, educators, and peers. The objective of this article is to review evidence for how children learn causal knowledge by explaining and exploring in collaboration with others. We review three examples of causal learning in social contexts, which elucidate how interaction with others influences causal learning. First, we consider children's explanation-seeking behaviors in the form of "why" questions. Second, we examine parents' elaboration of meaning about causal relations. Finally, we consider parents' interactive styles with children during free play, which constrains how children explore. We propose that the best way to understand children's causal learning in social context is to combine results from laboratory and natural interactive informal learning environments.

Investigating the multi-causal and complex nature of the accident causal influence of construction project features.

PubMed

Manu, Patrick A; Ankrah, Nii A; Proverbs, David G; Suresh, Subashini

2012-09-01

Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement. Copyright © 2011 Elsevier Ltd. All rights reserved.

Causal Relation Analysis Tool of the Case Study in the Engineer Ethics Education

NASA Astrophysics Data System (ADS)

Suzuki, Yoshio; Morita, Keisuke; Yasui, Mitsukuni; Tanada, Ichirou; Fujiki, Hiroyuki; Aoyagi, Manabu

In engineering ethics education, the virtual experiencing of dilemmas is essential. Learning through the case study method is a particularly effective means. Many case studies are, however, difficult to deal with because they often include many complex causal relationships and social factors. It would thus be convenient if there were a tool that could analyze the factors of a case example and organize them into a hierarchical structure to get a better understanding of the whole picture. The tool that was developed applies a cause-and-effect matrix and simple graph theory. It analyzes the causal relationship between facts in a hierarchical structure and organizes complex phenomena. The effectiveness of this tool is shown by presenting an actual example.

DNA methylation as a potential mediator of environmental risks in the development of childhood acute lymphoblastic leukemia

PubMed Central

Timms, Jessica A; Relton, Caroline L; Rankin, Judith; Strathdee, Gordon; McKay, Jill A

2016-01-01

5-year survival rate for childhood acute lymphoblastic leukemia (ALL) has risen to approximately 90%, yet the causal disease pathway is still poorly understood. Evidence suggests multiple ‘hits’ are required for disease progression; an initial genetic abnormality followed by additional secondary ‘hits’. It is plausible that environmental influences may trigger these secondary hits, and with the peak incidence of diagnosis between 2 and 5 years of age, early life exposures are likely to be key. DNA methylation can be modified by many environmental exposures and is dramatically altered in cancers, including childhood ALL. Here we explore the potential that DNA methylation may be involved in the causal pathway toward disease by acting as a mediator between established environmental factors and childhood ALL development. PMID:27035209

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

Using Complex Networks to Characterize International Business Cycles

PubMed Central

Caraiani, Petre

2013-01-01

Background There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles. Methodology/Principal Findings We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries’ GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples. Conclusion The use of complex networks is valuable for understanding the business cycle comovements at an international level. PMID:23483979

Causal inference in biology networks with integrated belief propagation.

PubMed

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.

Causal Analysis After Haavelmo

PubMed Central

Heckman, James; Pinto, Rodrigo

2014-01-01

Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123

Proximal, Distal, and the Politics of Causation: What’s Level Got to Do With It?

PubMed Central

Krieger, Nancy

2008-01-01

Causal thinking in public health, and especially in the growing literature on social determinants of health, routinely employs the terminology of proximal (or downstream) and distal (or upstream). I argue that the use of these terms is problematic and adversely affects public health research, practice, and causal accountability. At issue are distortions created by conflating measures of space, time, level, and causal strength. To make this case, I draw on an ecosocial perspective to show how public health got caught in the middle of the problematic proximal–distal divide—surprisingly embraced by both biomedical and social determinist frameworks—and propose replacing the terms proximal and distal with explicit language about levels, pathways, and power. PMID:18172144

Proximal, distal, and the politics of causation: what's level got to do with it?

PubMed

Krieger, Nancy

2008-02-01

Causal thinking in public health, and especially in the growing literature on social determinants of health, routinely employs the terminology of proximal (or downstream) and distal (or upstream). I argue that the use of these terms is problematic and adversely affects public health research, practice, and causal accountability. At issue are distortions created by conflating measures of space, time, level, and causal strength. To make this case, I draw on an ecosocial perspective to show how public health got caught in the middle of the problematic proximal-distal divide--surprisingly embraced by both biomedical and social determinist frameworks--and propose replacing the terms proximal and distal with explicit language about levels, pathways, and power.

Strategic approaches to unraveling genetic causes of cardiovascular diseases

USDA-ARS?s Scientific Manuscript database

DNA sequence variants are major components of the "causal field" for virtually all medical phenotypes, whether single gene familial disorders or complex traits without a clear familial aggregation. The causal variants in single gene disorders are necessary and sufficient to impart large effects. In ...

Designing and Undertaking a Health Economics Study of Digital Health Interventions.

PubMed

McNamee, Paul; Murray, Elizabeth; Kelly, Michael P; Bojke, Laura; Chilcott, Jim; Fischer, Alastair; West, Robert; Yardley, Lucy

2016-11-01

This paper introduces and discusses key issues in the economic evaluation of digital health interventions. The purpose is to stimulate debate so that existing economic techniques may be refined or new methods developed. The paper does not seek to provide definitive guidance on appropriate methods of economic analysis for digital health interventions. This paper describes existing guides and analytic frameworks that have been suggested for the economic evaluation of healthcare interventions. Using selected examples of digital health interventions, it assesses how well existing guides and frameworks align to digital health interventions. It shows that digital health interventions may be best characterized as complex interventions in complex systems. Key features of complexity relate to intervention complexity, outcome complexity, and causal pathway complexity, with much of this driven by iterative intervention development over time and uncertainty regarding likely reach of the interventions among the relevant population. These characteristics imply that more-complex methods of economic evaluation are likely to be better able to capture fully the impact of the intervention on costs and benefits over the appropriate time horizon. This complexity includes wider measurement of costs and benefits, and a modeling framework that is able to capture dynamic interactions among the intervention, the population of interest, and the environment. The authors recommend that future research should develop and apply more-flexible modeling techniques to allow better prediction of the interdependency between interventions and important environmental influences. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Global issues in allergy and immunology: Parasitic infections and allergy.

PubMed

Cruz, Alvaro A; Cooper, Philip J; Figueiredo, Camila A; Alcantara-Neves, Neuza M; Rodrigues, Laura C; Barreto, Mauricio L

2017-11-01

Allergic diseases are on the increase globally in parallel with a decrease in parasitic infection. The inverse association between parasitic infections and allergy at an ecological level suggests a causal association. Studies in human subjects have generated a large knowledge base on the complexity of the interrelationship between parasitic infection and allergy. There is evidence for causal links, but the data from animal models are the most compelling: despite the strong type 2 immune responses they induce, helminth infections can suppress allergy through regulatory pathways. Conversely, many helminths can cause allergic-type inflammation, including symptoms of "classical" allergic disease. From an evolutionary perspective, subjects with an effective immune response against helminths can be more susceptible to allergy. This narrative review aims to inform readers of the most relevant up-to-date evidence on the relationship between parasites and allergy. Experiments in animal models have demonstrated the potential benefits of helminth infection or administration of helminth-derived molecules on chronic inflammatory diseases, but thus far, clinical trials in human subjects have not demonstrated unequivocal clinical benefits. Nevertheless, there is sufficiently strong evidence to support continued investigation of the potential benefits of helminth-derived therapies for the prevention or treatment of allergic and other inflammatory diseases. Copyright © 2017 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Unraveling interrelationships among psychopathology symptoms, cognitive domains and insight dimensions in chronic schizophrenia.

PubMed

Xavier, Rose Mary; Pan, Wei; Dungan, Jennifer R; Keefe, Richard S E; Vorderstrasse, Allison

2018-03-01

Insight in schizophrenia is long known to have a complex relationship with psychopathology symptoms and cognition. However, very few studies have examined models that explain these interrelationships. In a large sample derived from the NIMH Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial (N=1391), we interrogated these interrelationships for potential causal pathways using structural equation modeling. Using the NIMH consensus model, latent variables were constructed for psychopathology symptom dimensions, including positive, negative, disorganized, excited and depressed from the Positive and Negative Syndrome Scale (PANSS) items. Neurocognitive variables were created from five predefined domains of working memory, verbal memory, reasoning, vigilance and processing speed. Illness insight and treatment insight were tested using latent variables constructed from the Illness and Treatment Attitude Questionnaire (ITAQ). Disorganized symptoms had the strongest effect on insight. Illness insight mediated the relationship of positive, depressed, and disorganized symptoms with treatment insight. Neurocognition mediated the relationship between disorganized and treatment insight and depressed symptoms and treatment insight. There was no effect of negative symptoms on either illness insight or treatment insight. Taken together, our results indicate overlapping and unique relational paths for illness and treatment insight dimensions, which could suggest differences in causal mechanisms and potential interventions to improve insight. Copyright © 2017 Elsevier B.V. All rights reserved.

Establishing causal coherence across sentences: an ERP study

PubMed Central

Kuperberg, Gina R.; Paczynski, Martin; Ditman, Tali

2011-01-01

This study examined neural activity associated with establishing causal relationships across sentences during online comprehension. ERPs were measured while participants read and judged the relatedness of three-sentence scenarios in which the final sentence was highly causally related, intermediately related and causally unrelated to its context. Lexico-semantic co-occurrence was matched across the three conditions using a Latent Semantic Analysis. Critical words in causally unrelated scenarios evoked a larger N400 than words in both highly causally related and intermediately related scenarios, regardless of whether they appeared before or at the sentence-final position. At midline sites, the N400 to intermediately related sentence-final words was attenuated to the same degree as to highly causally related words, but otherwise the N400 to intermediately related words fell in between that evoked by highly causally related and intermediately related words. No modulation of the Late Positivity/P600 component was observed across conditions. These results indicate that both simple and complex causal inferences can influence the earliest stages of semantically processing an incoming word. Further, they suggest that causal coherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relationships between individual words are matched. PMID:20175676

Influencing preferences for different types of causal explanation of complex events.

PubMed

Klein, Gary; Rasmussen, Louise; Lin, Mei-Hua; Hoffman, Robert R; Case, Jason

2014-12-01

We examined preferences for different forms of causal explanations for indeterminate situations. Background: Klein and Hoffman distinguished several forms of causal explanations for indeterminate, complex situations: single-cause explanations, lists of causes, and explanations that interrelate several causes. What governs our preferences for single-cause (simple) versus multiple- cause (complex) explanations? In three experiments, we examined the effect of target audience, explanatory context, participant nationality, and explanation type. All participants were college students. Participants were given two scenarios, one regarding the U.S. economic collapse in 2007 to 2008 and the other about the sudden success of the U.S. military in Iraq in 2007. The participants were asked to assess various types of causal explanations for each of the scenarios, with reference to one or more purposes or audience for the explanations. Participants preferred simple explanations for presentation to less sophisticated audiences. Malaysian students of Chinese ethnicity preferred complex explanations more than did American students. The form of presentation made a difference: Participants preferred complex to simple explanations when given a chance to compare the two, but the preference for simple explanations increased when there was no chance for compari- son, and the difference between Americans and Malaysians disappeared. Preferences for explanation forms can vary with the context and with the audience, and they depend on the nature of the alternatives that are provided. Guidance for decision-aiding technology and training systems that provide explanations need to involve consideration of the form and depth of the accounts provided as well as the intended audience.

The "Mevalonate hypothesis": a cholesterol-independent alternative for the etiology of atherosclerosis.

PubMed

Keizer, Hiskias G

2012-11-05

The "cholesterol hypothesis" is the leading theory to explain the cause of atherosclerosis. The "cholesterol hypothesis" assumes that plasma (LDL) cholesterol is an important causal factor for atherosclerosis.However, data of at least seven placebo controlled randomized prospective trials with various cholesterol lowering drugs show that plasma cholesterol lowering does not necessarily lead to protection against cardiovascular disease. Therefore an alternative hypothesis for the etiology of cardiovascular disease is formulated. This alternative hypothesis, the "mevalonate hypothesis", assumes that after stimulation of the mevalonate pathway in endothelial cells by inflammatory factors, these cells start producing cholesterol and free radicals. In this hypothesis, only the latter play a role in the etiology of atherosclerosis by contributing to the formation of oxidized cholesterol which is a widely accepted causal factor for atherosclerosis.Regardless of how the mevalonate pathway is activated (by withdrawal of statin drugs, by inflammatory factors or indirectly by reduced intracellular cholesterol levels) in all these cases free radical production is observed as well as cardiovascular disease. Since in the "mevalonate hypothesis" cholesterol is produced at the same time as the free radicals causing atherosclerosis, this hypothesis provides an explanation for the correlation which exists between cardiovascular disease and plasma cholesterol levels. From an evolutionary perspective, concomitant cholesterol production and free radical production in response to inflammatory factors makes sense if one realizes that both activities potentially protect cells and organisms from infection by gram-negative bacteria.In conclusion, data have been collected which suggest that activation of the mevalonate pathway in endothelial cells is likely to be a causal factor for atherosclerosis. This "mevalonate hypothesis" provides a better explanation for results obtained from recent clinical studies with cholesterol lowering drugs than the "cholesterol hypothesis". Furthermore, this hypothesis explains how cholesterol can be correlated with cardiovascular disease without being a causal factor for it. Finally it provides a logical explanation for the etiology of this disease.

Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits.

PubMed

Feltus, F Alex

2014-06-01

Understanding the control of any trait optimally requires the detection of causal genes, gene interaction, and mechanism of action to discover and model the biochemical pathways underlying the expressed phenotype. Functional genomics techniques, including RNA expression profiling via microarray and high-throughput DNA sequencing, allow for the precise genome localization of biological information. Powerful genetic approaches, including quantitative trait locus (QTL) and genome-wide association study mapping, link phenotype with genome positions, yet genetics is less precise in localizing the relevant mechanistic information encoded in DNA. The coupling of salient functional genomic signals with genetically mapped positions is an appealing approach to discover meaningful gene-phenotype relationships. Techniques used to define this genetic-genomic convergence comprise the field of systems genetics. This short review will address an application of systems genetics where RNA profiles are associated with genetically mapped genome positions of individual genes (eQTL mapping) or as gene sets (co-expression network modules). Both approaches can be applied for knowledge independent selection of candidate genes (and possible control mechanisms) underlying complex traits where multiple, likely unlinked, genomic regions might control specific complex traits. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Probable Cause: A Decision Making Framework.

DTIC Science & Technology

1984-08-01

Thus, a biochemist may see the causal link between smoking and lung cancer as due to chemical effects of tar, nicotine , and the like, on cell structure...long term and complex effects like poverty can have short term and simple causes. Determinants of Gros Strength Causal chains. While the causal...explained is a standing state. For example, what is the cause of " poverty ?" Since the effect to be explained in a standing state of large duration and

Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies.

PubMed

Liu, Shao-Hsien; Ulbricht, Christine M; Chrysanthopoulou, Stavroula A; Lapane, Kate L

2016-07-20

Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown. We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized. Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce. Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.

The transformative potential of an integrative approach to pregnancy.

PubMed

Eidem, Haley R; McGary, Kriston L; Capra, John A; Abbot, Patrick; Rokas, Antonis

2017-09-01

Complex traits typically involve diverse biological pathways and are shaped by numerous genetic and environmental factors. Pregnancy-associated traits and pathologies are further complicated by extensive communication across multiple tissues in two individuals, interactions between two genomes-maternal and fetal-that obscure causal variants and lead to genetic conflict, and rapid evolution of pregnancy-associated traits across mammals and in the human lineage. Given the multi-faceted complexity of human pregnancy, integrative approaches that synthesize diverse data types and analyses harbor tremendous promise to identify the genetic architecture and environmental influences underlying pregnancy-associated traits and pathologies. We review current research that addresses the extreme complexities of traits and pathologies associated with human pregnancy. We find that successful efforts to address the many complexities of pregnancy-associated traits and pathologies often harness the power of many and diverse types of data, including genome-wide association studies, evolutionary analyses, multi-tissue transcriptomic profiles, and environmental conditions. We propose that understanding of pregnancy and its pathologies will be accelerated by computational platforms that provide easy access to integrated data and analyses. By simplifying the integration of diverse data, such platforms will provide a comprehensive synthesis that transcends many of the inherent challenges present in studies of pregnancy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Untangling the Web: The Diverse Functions of the PIWI/piRNA Pathway

PubMed Central

Mani, Sneha Ramesh; Juliano, Celina E.

2014-01-01

SUMMARY Small RNAs impact several cellular processes through gene regulation. Argonaute proteins bind small RNAs to form effector complexes that control transcriptional and post-transcriptional gene expression. PIWI proteins belong to the Argonaute protein family, and bind PIWI-interacting RNAs (piRNAs). They are highly abundant in the germline, but are also expressed in some somatic tissues. The PIWI/piRNA pathway has a role in transposon repression in Drosophila, which occurs both by epigenetic regulation and post-transcriptional degradation of transposon mRNAs. These functions are conserved, but clear differences in the extent and mechanism of transposon repression exist between species. Mutations in piwi genes lead to the upregulation of transposon mRNAs. It is hypothesized that this increased transposon mobilization leads to genomic instability and thus sterility, although no causal link has been established between transposon upregulation and genome instability. An alternative scenario could be that piwi mutations directly affect genomic instability, and thus lead to increased transposon expression. We propose that the PIWI/piRNA pathway controls genome stability in several ways: suppression of transposons, direct regulation of chromatin architecture and regulation of genes that control important biological processes related to genome stability. The PIWI/piRNA pathway also regulates at least some, if not many, protein-coding genes, which further lends support to the idea that piwi genes may have broader functions beyond transposon repression. An intriguing possibility is that the PIWI/piRNA pathway is using transposon sequences to coordinate the expression of large groups of genes to regulate cellular function. PMID:23712694

Mapping rare and common causal alleles for complex human diseases

PubMed Central

Raychaudhuri, Soumya

2011-01-01

Advances in genotyping and sequencing technologies have revolutionized the genetics of complex disease by locating rare and common variants that influence an individual’s risk for diseases, such as diabetes, cancers, and psychiatric disorders. However, to capitalize on this data for prevention and therapies requires the identification of causal alleles and a mechanistic understanding for how these variants contribute to the disease. After discussing the strategies currently used to map variants for complex diseases, this Primer explores how variants may be prioritized for follow-up functional studies and the challenges and approaches for assessing the contributions of rare and common variants to disease phenotypes. PMID:21962507

Characterizing time series: when Granger causality triggers complex networks

NASA Astrophysics Data System (ADS)

Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants

PubMed Central

Zaitlen, Noah A.; Ye, Chun Jimmie; Witte, John S.

2016-01-01

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature. PMID:27197206

DEVELOPMENT PLAN FOR THE CAUSAL ANALYSIS ...

EPA Pesticide Factsheets

The Causal Analysis/Diagnosis Decision Information System (CADDIS) is a web-based system that provides technical support for states, tribes and other users of the Office of Water's Stressor Identification Guidance. The Stressor Identification Guidance provides a rigorous and scientifically defensible method for determining the causes of biological impairments of aquatic ecosystems. It is being used by states as part of the TMDL process and is being applied to other impaired ecosystems such as Superfund sites. However, because of the complexity of causal relationships in ecosystems, and because the guidance includes a strength-of-evidence analysis which uses multiple causal considerations, the process is complex and information intensive. CADDIS helps users deal with that inherent complexity. Increasingly, the regulatory, remedial, and restoration actions taken to manage impaired environments are based on measurement and analysis of the biotic community. When an aquatic assemblage has been identified as impaired, an accurate and defensible assessment of the cause can help ensure that appropriate actions are taken. The U.S. EPA's Stressor Identification Guidance describes a methodology for identifying the most likely causes of observed impairments in aquatic systems. Stressor identification requires extensive knowledge of the mechanisms, symptoms, and stressor-response relationships for various specific stressors as well as the ability to use that knowledge in a

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

NASA Astrophysics Data System (ADS)

Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

2018-04-01

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Mechanisms regulating neuronal excitability and seizure development following mTOR pathway hyperactivation.

PubMed

Lasarge, Candi L; Danzer, Steve C

2014-01-01

The phosphatidylinositol-3-kinase/phosphatase and tensin homolog (PTEN)-mammalian target of rapamycin (mTOR) pathway regulates a variety of neuronal functions, including cell proliferation, survival, growth, and plasticity. Dysregulation of the pathway is implicated in the development of both genetic and acquired epilepsies. Indeed, several causal mutations have been identified in patients with epilepsy, the most prominent of these being mutations in PTEN and tuberous sclerosis complexes 1 and 2 (TSC1, TSC2). These genes act as negative regulators of mTOR signaling, and mutations lead to hyperactivation of the pathway. Animal models deleting PTEN, TSC1, and TSC2 consistently produce epilepsy phenotypes, demonstrating that increased mTOR signaling can provoke neuronal hyperexcitability. Given the broad range of changes induced by altered mTOR signaling, however, the mechanisms underlying seizure development in these animals remain uncertain. In transgenic mice, cell populations with hyperactive mTOR have many structural abnormalities that support recurrent circuit formation, including somatic and dendritic hypertrophy, aberrant basal dendrites, and enlargement of axon tracts. At the functional level, mTOR hyperactivation is commonly, but not always, associated with enhanced synaptic transmission and plasticity. Moreover, these populations of abnormal neurons can affect the larger network, inducing secondary changes that may explain paradoxical findings reported between cell and network functioning in different models or at different developmental time points. Here, we review the animal literature examining the link between mTOR hyperactivation and epileptogenesis, emphasizing the impact of enhanced mTOR signaling on neuronal form and function.

Putative adverse outcome pathways relevant to neurotoxicity

PubMed Central

Bal-Price, Anna; Crofton, Kevin M.; Sachana, Magdalini; Shafer, Timothy J.; Behl, Mamta; Forsby, Anna; Hargreaves, Alan; Landesmann, Brigitte; Lein, Pamela J.; Louisse, Jochem; Monnet-Tschudi, Florianne; Paini, Alicia; Rolaki, Alexandra; Schrattenholz, André; Suñol, Cristina; van Thriel, Christoph; Whelan, Maurice; Fritsche, Ellen

2016-01-01

The Adverse Outcome Pathway (AOP) framework provides a template that facilitates understanding of complex biological systems and the pathways of toxicity that result in adverse outcomes (AOs). The AOP starts with an molecular initiating event (MIE) in which a chemical interacts with a biological target(s), followed by a sequential series of KEs, which are cellular, anatomical, and/or functional changes in biological processes, that ultimately result in an AO manifest in individual organisms and populations. It has been developed as a tool for a knowledge-based safety assessment that relies on understanding mechanisms of toxicity, rather than simply observing its adverse outcome. A large number of cellular and molecular processes are known to be crucial to proper development and function of the central (CNS) and peripheral nervous systems (PNS). However, there are relatively few examples of well-documented pathways that include causally linked MIEs and KEs that result in adverse outcomes in the CNS or PNS. As a first step in applying the AOP framework to adverse health outcomes associated with exposure to exogenous neurotoxic substances, the EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) organized a workshop (March 2013, Ispra, Italy) to identify potential AOPs relevant to neurotoxic and developmental neurotoxic outcomes. Although the AOPs outlined during the workshop are not fully described, they could serve as a basis for further, more detailed AOP development and evaluation that could be useful to support human health risk assessment in a variety of ways. PMID:25605028

Causal influence in neural systems: Reconciling mechanistic-reductionist and statistical perspectives. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino & S.L. Bressler

NASA Astrophysics Data System (ADS)

Griffiths, John D.

2015-12-01

The modern understanding of the brain as a large, complex network of interacting elements is a natural consequence of the Neuron Doctrine [1,2] that has been bolstered in recent years by the tools and concepts of connectomics. In this abstracted, network-centric view, the essence of neural and cognitive function derives from the flows between network elements of activity and information - or, more generally, causal influence. The appropriate characterization of causality in neural systems, therefore, is a question at the very heart of systems neuroscience.

Children's and Adolescents' Thoughts on Pollution: Cognitive Abilities Required to Understand Environmental Systems

ERIC Educational Resources Information Center

Rodríguez, Manuel; Kohen, Raquel; Delval, Juan

2015-01-01

Pollution phenomena are complex systems in which different parts are integrated by means of causal and temporal relationships. To understand pollution, children must develop some cognitive abilities related to system thinking and temporal and causal inferential reasoning. These cognitive abilities constrain and guide how children understand…

CAUSAL ANALYSIS OF BIOLOGICAL IMPAIRMENT IN LONG CREEK, A SANDY-BOTTOMED STREAM IN COASTAL SOUTHERN MAINE (Final Report)

EPA Science Inventory

This assessment presents results from a complex causal assessment of a biologically impaired, urbanized coastal watershed located primarily in South Portland, Maine, USA—the Long Creek watershed. This case study serves as an example implementation of U.S. Environmental Protectio...

Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.

PubMed

Gandal, Michael J; Haney, Jillian R; Parikshak, Neelroop N; Leppa, Virpi; Ramaswami, Gokul; Hartl, Chris; Schork, Andrew J; Appadurai, Vivek; Buil, Alfonso; Werge, Thomas M; Liu, Chunyu; White, Kevin P; Horvath, Steve; Geschwind, Daniel H

2018-02-09

The predisposition to neuropsychiatric disease involves a complex, polygenic, and pleiotropic genetic architecture. However, little is known about how genetic variants impart brain dysfunction or pathology. We used transcriptomic profiling as a quantitative readout of molecular brain-based phenotypes across five major psychiatric disorders-autism, schizophrenia, bipolar disorder, depression, and alcoholism-compared with matched controls. We identified patterns of shared and distinct gene-expression perturbations across these conditions. The degree of sharing of transcriptional dysregulation is related to polygenic (single-nucleotide polymorphism-based) overlap across disorders, suggesting a substantial causal genetic component. This comprehensive systems-level view of the neurobiological architecture of major neuropsychiatric illness demonstrates pathways of molecular convergence and specificity. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Pathways to psychosis in cannabis abuse.

PubMed

Shrivastava, Amresh; Johnston, Megan; Terpstra, Kristen; Bureau, Yves

2015-04-01

Cannabis has been implicated as a risk factor for the development of schizophrenia, but the exact biological mechanisms remain unclear. In this review, we attempt to understand the neurobiological pathways that link cannabis use to schizophrenia. This has been an area of great debate; despite similarities between cannabis users and schizophrenia patients, the evidence is not sufficient to establish cause-and-effect. There have been advances in the understanding of the mechanisms of cannabis dependence as well as the role of the cannabinoid system in the development of psychosis and schizophrenia. The neurobiological mechanisms associated with the development of psychosis and effects from cannabis use may be similar but remain elusive. In order to better understand these associations, this paper will show common neurobiological and neuroanatomical changes as well as common cognitive dysfunction in cannabis users and patients of schizophrenia. We conclude that epidemiologic evidence highlights potential causal links; however, neurobiological evidence for causality remains weak.

Inactivation of the Parietal Reach Region Causes Optic Ataxia, Impairing Reaches but Not Saccades

PubMed Central

Hwang, Eun Jung; Hauschild, Markus; Wilke, Melanie; Andersen, Richard A.

2013-01-01

SUMMARY Lesions in human posterior parietal cortex can cause optic ataxia (OA), in which reaches but not saccades to visual objects are impaired, suggesting separate visuomotor pathways for the two effectors. In monkeys, one potentially crucial area for reach control is the parietal reach region (PRR), in which neurons respond preferentially during reach planning as compared to saccade planning. However, direct causal evidence linking the monkey PRR to the deficits observed in OA is missing. We thus inactivated part of the macaque PRR, in the medial wall of the intraparietal sulcus, and produced the hallmarks of OA, misreaching for peripheral targets but unimpaired saccades. Furthermore, reach errors were larger for the targets preferred by the neural population local to the injection site. These results demonstrate that PRR is causally involved in reach-specific visuomotor pathways, and reach goal disruption in PRR can be a neural basis of OA. PMID:23217749

Regional Heritability Mapping Provides Insights into Dry Matter Content in African White and Yellow Cassava Populations.

PubMed

Okeke, Uche Godfrey; Akdemir, Deniz; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-03-01

The HarvestPlus program for cassava ( Crantz) fortifies cassava with β-carotene by breeding for carotene-rich tubers (yellow cassava). However, a negative correlation between yellowness and dry matter (DM) content has been identified. We investigated the genetic control of DM in white and yellow cassava. We used regional heritability mapping (RHM) to associate DM with genomic segments in both subpopulations. Significant segments were subjected to candidate gene analysis and candidates were validated with prediction accuracies. The RHM procedure was validated via a simulation approach and revealed significant hits for white cassava on chromosomes 1, 4, 5, 10, 17, and 18, whereas hits for the yellow were on chromosome 1. Candidate gene analysis revealed genes in the carbohydrate biosynthesis pathway including plant serine-threonine protein kinases (SnRKs), UDP (uridine diphosphate)-glycosyltransferases, UDP-sugar transporters, invertases, pectinases, and regulons. Validation using 1252 unique identifiers from the SnRK gene family genome-wide recovered 50% of the predictive accuracy of whole-genome single nucleotide polymorphisms for DM, whereas validation using 53 likely genes (extracted from the literature) from significant segments recovered 32%. Genes including an acid invertase, a neutral or alkaline invertase, and a glucose-6-phosphate isomerase were validated on the basis of an a priori list for the cassava starch pathway, and also a fructose-biphosphate aldolase from the Calvin cycle pathway. The power of the RHM procedure was estimated as 47% when the causal quantitative trait loci generated 10% of the phenotypic variance (sample size = 451). Cassava DM genetics are complex and RHM may be useful for complex traits. Copyright © 2018 Crop Science Society of America.

Success in Acquisition: Using Archetypes to Beat the Odds

DTIC Science & Technology

2010-09-01

Burnout and Turnover 49 5.7 Underbidding the Contract 52 5.8 Longer Begets Bigger 55 5.9 Robbing Peter to Pay Paul 58 5.10 “Happy Path” Testing 61...Causal Loop Diagram of “PMO vs. Contractor Hostility” 47 Figure 17: Causal Loop Diagram of “Staff Burnout and Turnover” 50 Figure 18: Causal Loop...backs to avoid Sudden Infant Death Syndrome (SIDS), and so on. People want to believe that there are straightforward solutions to complex and

A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data

PubMed Central

Zhang, Wanhong; Zhou, Tong

2015-01-01

Motivation Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations y = Φx. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity. Results In this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered. PMID:26207991

Smoking-related microRNAs and mRNAs in human peripheral blood mononuclear cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Su, Ming-Wei

Teenager smoking is of great importance in public health. Functional roles of microRNAs have been documented in smoke-induced gene expression changes, but comprehensive mechanisms of microRNA-mRNA regulation and benefits remained poorly understood. We conducted the Teenager Smoking Reduction Trial (TSRT) to investigate the causal association between active smoking reduction and whole-genome microRNA and mRNA expression changes in human peripheral blood mononuclear cells (PBMC). A total of 12 teenagers with a substantial reduction in smoke quantity and a decrease in urine cotinine/creatinine ratio were enrolled in genomic analyses. In Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA), differentially expressedmore » genes altered by smoke reduction were mainly associated with glucocorticoid receptor signaling pathway. The integrative analysis of microRNA and mRNA found eleven differentially expressed microRNAs negatively correlated with predicted target genes. CD83 molecule regulated by miR-4498 in human PBMC, was critical for the canonical pathway of communication between innate and adaptive immune cells. Our data demonstrated that microRNAs could regulate immune responses in human PBMC after habitual smokers quit smoking and support the potential translational value of microRNAs in regulating disease-relevant gene expression caused by tobacco smoke. - Highlights: • We conducted a smoke reduction trial program and investigated the causal relationship between smoke and gene regulation. • MicroRNA and mRNA expression changes were examined in human PBMC. • MicroRNAs are important in regulating disease-causal genes after tobacco smoke reduction.« less

Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality.

PubMed

Yang, Guanxue; Wang, Lin; Wang, Xiaofan

2017-06-07

Reconstruction of networks underlying complex systems is one of the most crucial problems in many areas of engineering and science. In this paper, rather than identifying parameters of complex systems governed by pre-defined models or taking some polynomial and rational functions as a prior information for subsequent model selection, we put forward a general framework for nonlinear causal network reconstruction from time-series with limited observations. With obtaining multi-source datasets based on the data-fusion strategy, we propose a novel method to handle nonlinearity and directionality of complex networked systems, namely group lasso nonlinear conditional granger causality. Specially, our method can exploit different sets of radial basis functions to approximate the nonlinear interactions between each pair of nodes and integrate sparsity into grouped variables selection. The performance characteristic of our approach is firstly assessed with two types of simulated datasets from nonlinear vector autoregressive model and nonlinear dynamic models, and then verified based on the benchmark datasets from DREAM3 Challenge4. Effects of data size and noise intensity are also discussed. All of the results demonstrate that the proposed method performs better in terms of higher area under precision-recall curve.

Dimensionality and R4P: A Health Equity Framework for Research Planning and Evaluation in African American Populations.

PubMed

Hogan, Vijaya; Rowley, Diane L; White, Stephanie Baker; Faustin, Yanica

2018-02-01

Introduction Existing health disparities frameworks do not adequately incorporate unique interacting contributing factors leading to health inequities among African Americans, resulting in public health stakeholders' inability to translate these frameworks into practice. Methods We developed dimensionality and R4P to integrate multiple theoretical perspectives into a framework of action to eliminate health inequities experienced by African Americans. Results The dimensional framework incorporates Critical Race Theory and intersectionality, and includes dimensions of time-past, present and future. Dimensionality captures the complex linear and non-linear array of influences that cause health inequities, but these pathways do not lend themselves to approaches to developing empirically derived programs, policies and interventions to promote health equity. R4P provides a framework for addressing the scope of actions needed. The five components of R4P are (1) Remove, (2) Repair, (3) Remediate, (4) Restructure and (5) Provide. Conclusion R4P is designed to translate complex causality into a public health equity planning, assessment, evaluation and research tool.

Neural Connectivity in Epilepsy as Measured by Granger Causality.

PubMed

Coben, Robert; Mohammad-Rezazadeh, Iman

2015-01-01

Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in nearby regions and pathways. There is also early evidence to suggest that these patterns change during ictal events and that these changes may even by related to the occurrence or triggering of seizure events. We present data showing how Granger causality can be used with EEG data to measure connectivity across brain regions involved in ictal events and their resolution. We have provided two case examples as a demonstration of how to obtain and interpret such data. EEG data of ictal events are processed, converted to independent components and their dipole localizations, and these are used to measure causality and connectivity between these locations. Both examples have shown hypercoupling near the seizure foci and low causality across nearby and associated neuronal pathways. This technique also allows us to track how these measures change over time and during the ictal and post-ictal periods. Areas for further research into this technique, its application to epilepsy, and the formation of more effective therapeutic interventions are recommended.

Investigating Causality Between Interacting Brain Areas with Multivariate Autoregressive Models of MEG Sensor Data

PubMed Central

Michalareas, George; Schoffelen, Jan-Mathijs; Paterson, Gavin; Gross, Joachim

2013-01-01

Abstract In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. PMID:22328419

A Causal Modelling Approach to the Development of Theory-Based Behaviour Change Programmes for Trial Evaluation

ERIC Educational Resources Information Center

Hardeman, Wendy; Sutton, Stephen; Griffin, Simon; Johnston, Marie; White, Anthony; Wareham, Nicholas J.; Kinmonth, Ann Louise

2005-01-01

Theory-based intervention programmes to support health-related behaviour change aim to increase health impact and improve understanding of mechanisms of behaviour change. However, the science of intervention development remains at an early stage. We present a causal modelling approach to developing complex interventions for evaluation in…

The relationship of family characteristics and bipolar disorder using causal-pie models.

PubMed

Chen, Y-C; Kao, C-F; Lu, M-K; Yang, Y-K; Liao, S-C; Jang, F-L; Chen, W J; Lu, R-B; Kuo, P-H

2014-01-01

Many family characteristics were reported to increase the risk of bipolar disorder (BPD). The development of BPD may be mediated through different pathways, involving diverse risk factor profiles. We evaluated the associations of family characteristics to build influential causal-pie models to estimate their contributions on the risk of developing BPD at the population level. We recruited 329 clinically diagnosed BPD patients and 202 healthy controls to collect information in parental psychopathology, parent-child relationship, and conflict within family. Other than logistic regression models, we applied causal-pie models to identify pathways involved with different family factors for BPD. The risk of BPD was significantly increased with parental depression, neurosis, anxiety, paternal substance use problems, and poor relationship with parents. Having a depressed mother further predicted early onset of BPD. Additionally, a greater risk for BPD was observed with higher numbers of paternal/maternal psychopathologies. Three significant risk profiles were identified for BPD, including paternal substance use problems (73.0%), maternal depression (17.6%), and through poor relationship with parents and conflict within the family (6.3%). Our findings demonstrate that different aspects of family characteristics elicit negative impacts on bipolar illness, which can be utilized to target specific factors to design and employ efficient intervention programs. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Network cosmology.

PubMed

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

Network Cosmology

PubMed Central

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

Identifying Causal Variants at Loci with Multiple Signals of Association

PubMed Central

Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar

2014-01-01

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20–50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/. PMID:25104515

Identifying causal variants at loci with multiple signals of association.

PubMed

Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar

2014-10-01

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/. Copyright © 2014 by the Genetics Society of America.

Looking forward in geriatric anxiety and depression: implications of basic science for the future.

PubMed

Gershenfeld, Howard K; Philibert, Robert A; Boehm, Gary W

2005-12-01

Major depression and anxiety are common psychiatric illnesses whose etiology remains incompletely understood. This review highlights progress in understanding the etiology of these illnesses through genetic strategies and looks forward to their impact on geriatric psychiatry. We briefly address three broad domains of progress, namely 1) genetic approaches to etiology, including linkage and association studies, pharmacogenetics ("personalized medicine"), and gene x environment interactions; 2) mechanisms of thyroid and testosterone action via nuclear receptors, given these hormones' status as possible augmenters of antidepressants; and 3) the role of the neuroimmune system as a contributor to the stress response. Genetic strategies offer one path for converting correlational findings into causal pathways while complementing studies of a gene's function at the molecular, cellular, network, and whole-organismal levels. Neuroendocrine supplementation (thyroid and testosterone) has a long history and tradition. A molecular understanding of nuclear receptor pathways and their coactivators, the mediator complex proteins, provides a rationale for improved targeting of hormonal action in a tissue-selective manner, yielding drugs with improved safety and efficacy. Neural-immune interactions in psychiatric illness remain tantalizing topics. Research suggests that cytokine pathways may contribute to the maintenance or susceptibility to stress, anxiety, and depressive disorders. The reciprocal and recursive interactions among basic science, drug discovery, and clinical science will continue to provide hopeful themes for improving the lives of patients with treatment-refractive psychiatric illness.

Protecting Urban Health and Safety: Balancing Care and Harm in the Era of Mass Incarceration.

PubMed

Gaber, Nadia; Wright, Anthony

2016-04-01

This paper explores theoretical, spatial, and mediatized pathways through which policing poses harms to the health of marginalized communities in the urban USA, including analysis of two recent and widely publicized incidents of officer-involved killings in Ferguson, Missouri and Staten Island, New York. We examine the influence of the "broken windows" model in both policing and public health, revealing alternate institutional strategies for responding to urban disorder in the interests of the health and safety of the city. Drawing on ecosocial theory and medical anthropology, we consider the roles of the segregated built environment and historical experience in the embodiment of structural vulnerability with respect to police violence. We examine the recent shootings of Eric Garner and Michael Brown as the most visible, most circulated symbols of this complex and contradictory terrain, focusing on the pathways through which theories of causality authorize violent and/or caring intervention by the state. We show how police killings reveal an underlying and racialized association between disorder and deviance that becomes institutionalized and embodied through spatial and symbolic pathways. If public health workers and advocates are to play a role in responding to the call of the Black Lives Matter movement, it is important to understand the interpretations and translations of urban social life that circulate on the streets, in the media, in public policy, and in institutional practice.

Can Joined-Up Data Lead to Joined-Up Thinking? The Western Australian Developmental Pathways Project

PubMed Central

Stanley, Fiona; Glauert, Rebecca; McKenzie, Anne; O'Donnell, Melissa

2011-01-01

Modern societies are challenged by “wicked problems” – by definition, those that are difficult to define, multi-causal and hard to treat. Problems such as low birth weight, obesity, mental ill health, teenage pregnancy, educational difficulties and juvenile crime fit this category. Given the complex nature of these problems, they require the best data in order to measure them, guide policy frameworks and evaluate whether the steps taken to address them are actually making a difference. What such problems really require are joined-up approaches to enable effective solutions. In this paper, we describe a unique initiative to encourage a more preventive, whole-of-government approach to these problems – the Developmental Pathways Project, which has enabled the linkage of a large number of de-identified administrative databases in order to explore the pathways into and out of the negative outcomes affecting our children and youth. This project has not only enabled the linkage of agency data, but also of agency personnel, in order to improve and promote cross-agency research, policy and preventive solutions. Through the use of these linkages we are attempting to shift the paradigm to encourage agencies to appreciate that these “wicked problems” demand a preventive approach, as well as the provision of effective services for those already affected. PMID:24933374

Identification of causal genes for complex traits

PubMed Central

Hormozdiari, Farhad; Kichaev, Gleb; Yang, Wen-Yun; Pasaniuc, Bogdan; Eskin, Eleazar

2015-01-01

Motivation: Although genome-wide association studies (GWAS) have identified thousands of variants associated with common diseases and complex traits, only a handful of these variants are validated to be causal. We consider ‘causal variants’ as variants which are responsible for the association signal at a locus. As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD. This is particularly important for model organisms such as inbred mice, where LD extends much further than in human populations, resulting in large stretches of the genome with significantly associated variants. Furthermore, these model organisms are highly structured and require correction for population structure to remove potential spurious associations. Results: In this work, we propose CAVIAR-Gene (CAusal Variants Identification in Associated Regions), a novel method that is able to operate across large LD regions of the genome while also correcting for population structure. A key feature of our approach is that it provides as output a minimally sized set of genes that captures the genes which harbor causal variants with probability ρ. Through extensive simulations, we demonstrate that our method not only speeds up computation, but also have an average of 10% higher recall rate compared with the existing approaches. We validate our method using a real mouse high-density lipoprotein data (HDL) and show that CAVIAR-Gene is able to identify Apoa2 (a gene known to harbor causal variants for HDL), while reducing the number of genes that need to be tested for functionality by a factor of 2. Availability and implementation: Software is freely available for download at genetics.cs.ucla.edu/caviar. Contact: eeskin@cs.ucla.edu PMID:26072484

Identification of causal genes for complex traits.

PubMed

Hormozdiari, Farhad; Kichaev, Gleb; Yang, Wen-Yun; Pasaniuc, Bogdan; Eskin, Eleazar

2015-06-15

Although genome-wide association studies (GWAS) have identified thousands of variants associated with common diseases and complex traits, only a handful of these variants are validated to be causal. We consider 'causal variants' as variants which are responsible for the association signal at a locus. As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD. This is particularly important for model organisms such as inbred mice, where LD extends much further than in human populations, resulting in large stretches of the genome with significantly associated variants. Furthermore, these model organisms are highly structured and require correction for population structure to remove potential spurious associations. In this work, we propose CAVIAR-Gene (CAusal Variants Identification in Associated Regions), a novel method that is able to operate across large LD regions of the genome while also correcting for population structure. A key feature of our approach is that it provides as output a minimally sized set of genes that captures the genes which harbor causal variants with probability ρ. Through extensive simulations, we demonstrate that our method not only speeds up computation, but also have an average of 10% higher recall rate compared with the existing approaches. We validate our method using a real mouse high-density lipoprotein data (HDL) and show that CAVIAR-Gene is able to identify Apoa2 (a gene known to harbor causal variants for HDL), while reducing the number of genes that need to be tested for functionality by a factor of 2. Software is freely available for download at genetics.cs.ucla.edu/caviar. © The Author 2015. Published by Oxford University Press.

Causal learning with local computations.

PubMed

Fernbach, Philip M; Sloman, Steven A

2009-05-01

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure. Copyright 2009 APA, all rights reserved.

Contrasting cue-density effects in causal and prediction judgments.

PubMed

Vadillo, Miguel A; Musca, Serban C; Blanco, Fernando; Matute, Helena

2011-02-01

Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.

Causal Mediation Analysis for the Cox Proportional Hazards Model with a Smooth Baseline Hazard Estimator.

PubMed

Wang, Wei; Albert, Jeffrey M

2017-08-01

An important problem within the social, behavioral, and health sciences is how to partition an exposure effect (e.g. treatment or risk factor) among specific pathway effects and to quantify the importance of each pathway. Mediation analysis based on the potential outcomes framework is an important tool to address this problem and we consider the estimation of mediation effects for the proportional hazards model in this paper. We give precise definitions of the total effect, natural indirect effect, and natural direct effect in terms of the survival probability, hazard function, and restricted mean survival time within the standard two-stage mediation framework. To estimate the mediation effects on different scales, we propose a mediation formula approach in which simple parametric models (fractional polynomials or restricted cubic splines) are utilized to approximate the baseline log cumulative hazard function. Simulation study results demonstrate low bias of the mediation effect estimators and close-to-nominal coverage probability of the confidence intervals for a wide range of complex hazard shapes. We apply this method to the Jackson Heart Study data and conduct sensitivity analysis to assess the impact on the mediation effects inference when the no unmeasured mediator-outcome confounding assumption is violated.

Discovering causal signaling pathways through gene-expression patterns

PubMed Central

Parikh, Jignesh R.; Klinger, Bertram; Xia, Yu; Marto, Jarrod A.; Blüthgen, Nils

2010-01-01

High-throughput gene-expression studies result in lists of differentially expressed genes. Most current meta-analyses of these gene lists include searching for significant membership of the translated proteins in various signaling pathways. However, such membership enrichment algorithms do not provide insight into which pathways caused the genes to be differentially expressed in the first place. Here, we present an intuitive approach for discovering upstream signaling pathways responsible for regulating these differentially expressed genes. We identify consistently regulated signature genes specific for signal transduction pathways from a panel of single-pathway perturbation experiments. An algorithm that detects overrepresentation of these signature genes in a gene group of interest is used to infer the signaling pathway responsible for regulation. We expose our novel resource and algorithm through a web server called SPEED: Signaling Pathway Enrichment using Experimental Data sets. SPEED can be freely accessed at http://speed.sys-bio.net/. PMID:20494976

Physical Injury and Somatic Complaints: The Mediating Role of Posttraumatic Stress Symptoms in Young Survivors of a Terror Attack.

PubMed

Bugge, Ingrid; Dyb, Grete; Stensland, Synne Øien; Ekeberg, Øivind; Wentzel-Larsen, Tore; Diseth, Trond H

2017-06-01

Physically injured trauma survivors have particularly high risk for later somatic complaints and posttraumatic stress symptoms (PTSS). However, the potential mediating role of PTSS linking injury to later somatic complaints has been poorly investigated. In this study, survivors (N = 255) were interviewed longitudinally at 2 timepoints after the terror attack on Utøya Island, Norway, in 2011. Assessments included injury sustained during the attack, PTSS (after 4-5 months), somatic complaints (after 14-15 months), and background factors. Causal mediation analysis was conducted to evaluate the potential mediating role of PTSS in linking injury to somatic complaints comparing 2 groups of injured survivors with noninjured survivors. For the nonhospitalized injured versus the noninjured survivors, the mediated pathway was significant (average causal mediation effect; ACME = 0.09, p = .028, proportion = 55.8%). For the hospitalized versus the noninjured survivors, the mediated pathway was not significant (ACME = 0.04, p = .453, proportion = 11.6%). PTSS may play a significant mediating role in the development of somatic complaints among nonhospitalized injured trauma survivors. Intervening health professionals should be aware of this possible pathway to somatic complaints. Copyright © 2017 International Society for Traumatic Stress Studies.

Using multiple cause-of-death data to investigate associations and causality between conditions listed on the death certificate.

PubMed

Redelings, Matthew D; Wise, Matthew; Sorvillo, Frank

2007-07-01

Death rarely results from only one cause, and it can be caused by a variety of factors. Multiple cause-of-death data files can list as many as 20 contributing causes of death in addition to the reported underlying cause of death. Analysis of multiple cause-of-death data can provide information on associations between causes of death, revealing common combinations of events or conditions which lead to death. Additionally, physicians report the causal train of events through which they believe that different conditions or events may have led to each other and ultimately caused death. In this paper, the authors discuss methods used in studying associations between reported causes of death and in investigating commonly reported causal pathways between events or conditions listed on the death certificate.

Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity

PubMed Central

Albantakis, Larissa; Hintze, Arend; Koch, Christof; Adami, Christoph; Tononi, Giulio

2014-01-01

Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment's causal structure. We investigated the evolution of small, adaptive logic-gate networks (“animats”) in task environments where falling blocks of different sizes have to be caught or avoided in a ‘Tetris-like’ game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks (“brains”) with many concepts, leading to an increase in their internal complexity. PMID:25521484

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants.

PubMed

Uricchio, Lawrence H; Zaitlen, Noah A; Ye, Chun Jimmie; Witte, John S; Hernandez, Ryan D

2016-07-01

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature. © 2016 Uricchio et al.; Published by Cold Spring Harbor Laboratory Press.

Could Plasmodium vivax malaria trigger malnutrition? Revisiting the Bradford Hill criteria to assess a causal relationship between two neglected problems.

PubMed

Monteiro, Wuelton Marcelo; Alexandre, Márcia Araújo; Siqueira, André; Melo, Gisely; Romero, Gustavo Adolfo Sierra; d'Ávila, Efrem; Benzecry, Silvana Gomes; Leite, Heitor Pons; Lacerda, Marcus Vinícius Guimarães

2016-01-01

The benign characteristics formerly attributed to Plasmodium vivax infections have recently changed owing to the increasing number of reports of severe vivax malaria resulting in a broad spectrum of clinical complications, probably including undernutrition. Causal inference is a complex process, and arriving at a tentative inference of the causal or non-causal nature of an association is a subjective process limited by the existing evidence. Applying classical epidemiology principles, such as the Bradford Hill criteria, may help foster an understanding of causality and lead to appropriate interventions being proposed that may improve quality of life and decrease morbidity in neglected populations. Here, we examined these criteria in the context of the available data suggesting that vivax malaria may substantially contribute to childhood malnutrition. We found the data supported a role for P. vivax in the etiology of undernutrition in endemic areas. Thus, the application of modern causal inference tools, in future studies, may be useful in determining causation.

Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

PubMed

Barnett, Lionel; Seth, Anil K

2017-01-01

Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process. While it is well known that subsampling can lead to imputation of spurious causal connections where none exist, less is known about the effects of subsampling on the ability to reliably detect causal connections which do exist. We present a theoretical analysis of the effects of subsampling on Granger-causal inference. Neurophysiological processes typically feature signal propagation delays on multiple time scales; accordingly, we base our analysis on a distributed-lag, continuous-time stochastic model, and consider Granger causality in continuous time at finite prediction horizons. Via exact analytical solutions, we identify relationships among sampling frequency, underlying causal time scales and detectability of causalities. We reveal complex interactions between the time scale(s) of neural signal propagation and sampling frequency. We demonstrate that detectability decays exponentially as the sample time interval increases beyond causal delay times, identify detectability "black spots" and "sweet spots", and show that downsampling may potentially improve detectability. We also demonstrate that the invariance of Granger causality under causal, invertible filtering fails at finite prediction horizons, with particular implications for inference of Granger causality from fMRI data. Our analysis emphasises that sampling rates for causal analysis of neurophysiological time series should be informed by domain-specific time scales, and that state-space modelling should be preferred to purely autoregressive modelling. On the basis of a very general model that captures the structure of neurophysiological processes, we are able to help identify confounds, and offer practical insights, for successful detection of causal connectivity from neurophysiological recordings. Copyright © 2016 Elsevier B.V. All rights reserved.

A Theory of Diagnostic Inference. II. Judging Causality.

DTIC Science & Technology

1982-09-01

spent on social programs in the 160s and 󈨊s could have had little or no effect or that long term and complex effects like poverty can have short term...biochemist may see the causal link between smoking and lung cancer as due to chemical effects of tar, nicotine , and the like, on cell structure, while an

In defence of story-telling.

PubMed

Currie, Adrian; Sterelny, Kim

2017-04-01

We argue that narratives are central to the success of historical reconstruction. Narrative explanation involves tracing causal trajectories across time. The construction of narrative, then, often involves postulating relatively speculative causal connections between comparatively well-established events. But speculation is not always idle or harmful: it also aids in overcoming local underdetermination by forming scaffolds from which new evidence becomes relevant. Moreover, as our understanding of the past's causal milieus become richer, the constraints on narrative plausibility become increasingly strict: a narrative's admissibility does not turn on mere logical consistency with background data. Finally, narrative explanation and explanation generated by simple, formal models complement one another. Where models often achieve isolation and precision at the cost of simplification and abstraction, narratives can track complex changes in a trajectory over time at the cost of simplicity and precision. In combination both allow us to understand and explain highly complex historical sequences. Copyright © 2017 Elsevier Ltd. All rights reserved.

Biological interpretation of genome-wide association studies using predicted gene functions.

PubMed

Pers, Tune H; Karjalainen, Juha M; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R; Yang, Jian; Lui, Julian C; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S N; Hirschhorn, Joel N; Franke, Lude

2015-01-19

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

INTERNATIONAL CHILDHOOD CANCER COHORT CONSORTIUM (Journal Article)

EPA Science Inventory

Childhood cancers are rare conditions whose etiology is poorly understood. There is evidence that for some, the causal pathway may commence in utero or during peri-conception. One traditional epidemiologic approach to the study of rare diseases is the use of a retrospective cas...

Conceptualizing and Treating Social Anxiety in Autism Spectrum Disorder: A Focus Group Study with Multidisciplinary Professionals.

PubMed

Spain, Debbie; Rumball, Freya; O'Neill, Lucy; Sin, Jacqueline; Prunty, Jonathan; Happé, Francesca

2017-12-01

Individuals who have autism spectrum disorders (ASD) commonly experience social anxiety (SA). Disentangling SA symptoms from core ASD characteristics is complex, partly due to diagnostic overshadowing and co-occurring alexithymia. Causal and maintaining mechanisms for SA in ASD are underexplored, but it is feasible that there is an ASD specificity to the clinical presentation, with implications for the development of targeted treatments. Five focus groups were conducted with multidisciplinary professionals to investigate their perspectives about, and approaches to, working with individuals with ASD and SA. Data were analysed thematically. Data analysis revealed two overarching themes: conceptualizing SA in ASD and service provision. Our results suggest that adaptations to service provision are pertinent, so as to accommodate inherent impairments that can mediate assessment and intervention. Future studies should establish how aspects of the care pathway can be improved for individuals with ASD and SA. © 2016 John Wiley & Sons Ltd.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

2018-06-21

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression.

PubMed

Fairfax, Benjamin P; Humburg, Peter; Makino, Seiko; Naranbhai, Vivek; Wong, Daniel; Lau, Evelyn; Jostins, Luke; Plant, Katharine; Andrews, Robert; McGee, Chris; Knight, Julian C

2014-03-07

To systematically investigate the impact of immune stimulation upon regulatory variant activity, we exposed primary monocytes from 432 healthy Europeans to interferon-γ (IFN-γ) or differing durations of lipopolysaccharide and mapped expression quantitative trait loci (eQTLs). More than half of cis-eQTLs identified, involving hundreds of genes and associated pathways, are detected specifically in stimulated monocytes. Induced innate immune activity reveals multiple master regulatory trans-eQTLs including the major histocompatibility complex (MHC), coding variants altering enzyme and receptor function, an IFN-β cytokine network showing temporal specificity, and an interferon regulatory factor 2 (IRF2) transcription factor-modulated network. Induced eQTL are significantly enriched for genome-wide association study loci, identifying context-specific associations to putative causal genes including CARD9, ATM, and IRF8. Thus, applying pathophysiologically relevant immune stimuli assists resolution of functional genetic variants.

Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli

PubMed Central

Gat-Viks, Irit; Chevrier, Nicolas; Wilentzik, Roni; Eisenhaure, Thomas; Raychowdhury, Raktima; Steuerman, Yael; Shalek, Alex; Hacohen, Nir; Amit, Ido; Regev, Aviv

2013-01-01

Individual genetic variation affects gene expression in response to stimuli, often by influencing complex molecular circuits. Here we combine genomic and intermediate-scale transcriptional profiling with computational methods to identify variants that affect the responsiveness of genes to stimuli (responsiveness QTLs; reQTLs) and to position these variants in molecular circuit diagrams. We apply this approach to study variation in transcriptional responsiveness to pathogen components in dendritic cells from recombinant inbred mouse strains. We identify reQTLs that correlate with particular stimuli and position them in known pathways. For example, in response to a virus-like stimulus, a trans-acting variant acts as an activator of the antiviral response; using RNAi, we identify Rgs16 as the likely causal gene. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in circuits that control responses to stimuli. PMID:23503680

Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli.

PubMed

Gat-Viks, Irit; Chevrier, Nicolas; Wilentzik, Roni; Eisenhaure, Thomas; Raychowdhury, Raktima; Steuerman, Yael; Shalek, Alex K; Hacohen, Nir; Amit, Ido; Regev, Aviv

2013-04-01

Individual genetic variation affects gene responsiveness to stimuli, often by influencing complex molecular circuits. Here we combine genomic and intermediate-scale transcriptional profiling with computational methods to identify variants that affect the responsiveness of genes to stimuli (responsiveness quantitative trait loci or reQTLs) and to position these variants in molecular circuit diagrams. We apply this approach to study variation in transcriptional responsiveness to pathogen components in dendritic cells from recombinant inbred mouse strains. We identify reQTLs that correlate with particular stimuli and position them in known pathways. For example, in response to a virus-like stimulus, a trans-acting variant responds as an activator of the antiviral response; using RNA interference, we identify Rgs16 as the likely causal gene. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in circuits that control responses to stimuli.

Complexity-entropy causality plane: A useful approach for distinguishing songs

NASA Astrophysics Data System (ADS)

Ribeiro, Haroldo V.; Zunino, Luciano; Mendes, Renio S.; Lenzi, Ervin K.

2012-04-01

Nowadays we are often faced with huge databases resulting from the rapid growth of data storage technologies. This is particularly true when dealing with music databases. In this context, it is essential to have techniques and tools able to discriminate properties from these massive sets. In this work, we report on a statistical analysis of more than ten thousand songs aiming to obtain a complexity hierarchy. Our approach is based on the estimation of the permutation entropy combined with an intensive complexity measure, building up the complexity-entropy causality plane. The results obtained indicate that this representation space is very promising to discriminate songs as well as to allow a relative quantitative comparison among songs. Additionally, we believe that the here-reported method may be applied in practical situations since it is simple, robust and has a fast numerical implementation.

Detangling complex relationships in forensic data: principles and use of causal networks and their application to clinical forensic science.

PubMed

Lefèvre, Thomas; Lepresle, Aude; Chariot, Patrick

2015-09-01

The search for complex, nonlinear relationships and causality in data is hindered by the availability of techniques in many domains, including forensic science. Linear multivariable techniques are useful but present some shortcomings. In the past decade, Bayesian approaches have been introduced in forensic science. To date, authors have mainly focused on providing an alternative to classical techniques for quantifying effects and dealing with uncertainty. Causal networks, including Bayesian networks, can help detangle complex relationships in data. A Bayesian network estimates the joint probability distribution of data and graphically displays dependencies between variables and the circulation of information between these variables. In this study, we illustrate the interest in utilizing Bayesian networks for dealing with complex data through an application in clinical forensic science. Evaluating the functional impairment of assault survivors is a complex task for which few determinants are known. As routinely estimated in France, the duration of this impairment can be quantified by days of 'Total Incapacity to Work' ('Incapacité totale de travail,' ITT). In this study, we used a Bayesian network approach to identify the injury type, victim category and time to evaluation as the main determinants of the 'Total Incapacity to Work' (TIW). We computed the conditional probabilities associated with the TIW node and its parents. We compared this approach with a multivariable analysis, and the results of both techniques were converging. Thus, Bayesian networks should be considered a reliable means to detangle complex relationships in data.

Causality in Psychiatry: A Hybrid Symptom Network Construct Model

PubMed Central

Young, Gerald

2015-01-01

Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639

The center for causal discovery of biomedical knowledge from big data

PubMed Central

Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-01-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. PMID:26138794

Epigenetic restriction of Hippo signaling by MORC2 underlies stemness of hepatocellular carcinoma cells.

PubMed

Wang, Tao; Qin, Zhong-Yi; Wen, Liang-Zhi; Guo, Yan; Liu, Qin; Lei, Zeng-Jie; Pan, Wei; Liu, Kai-Jun; Wang, Xing-Wei; Lai, Shu-Jie; Sun, Wen-Jing; Wei, Yan-Ling; Liu, Lei; Guo, Ling; Chen, Yu-Qin; Wang, Jun; Xiao, Hua-Liang; Bian, Xiu-Wu; Chen, Dong-Feng; Wang, Bin

2018-03-19

The evolutionarily conserved Hippo signaling pathway is a key regulator of stem cell self-renewal, differentiation, and organ size. While alterations in Hippo signaling are causally linked to uncontrolled cell growth and a broad range of malignancies, genetic mutations in the Hippo pathway are uncommon and it is unclear how the tumor suppressor function of the Hippo pathway is disrupted in human cancers. Here, we report a novel epigenetic mechanism of Hippo inactivation in the context of hepatocellular carcinoma (HCC). We identify a member of the microrchidia (MORC) protein family, MORC2, as an inhibitor of the Hippo pathway by controlling upstream Hippo regulators, neurofibromatosis 2 (NF2) and kidney and brain protein (KIBRA). Mechanistically, MORC2 forms a complex with DNA methyltransferase 3A (DNMT3A) at the promoters of NF2 and KIBRA, leading to their DNA hyper-methylation and transcriptional repression. As a result, NF2 and KIBRA are crucial targets of MORC2 to regulate confluence-induced activation of Hippo signaling and contact inhibition of cell growth under both physiological and pathological conditions. The MORC2-NF2/KIBRA axis is critical for maintaining self-renewal, sorafenib resistance, and oncogenicity of HCC cells in vitro and in nude mice. Furthermore, MORC2 expression is elevated in HCC tissues, associated with stem-like properties of cancer cells, and disease progression in patients. Collectively, MORC2 promotes cancer stemness and tumorigenesis by facilitating DNA methylation-dependent silencing of Hippo signaling and could be a potential molecular target for cancer therapeutics.

An exploratory statistical approach to depression pattern identification

NASA Astrophysics Data System (ADS)

Feng, Qing Yi; Griffiths, Frances; Parsons, Nick; Gunn, Jane

2013-02-01

Depression is a complex phenomenon thought to be due to the interaction of biological, psychological and social factors. Currently depression assessment uses self-reported depressive symptoms but this is limited in the degree to which it can characterise the different expressions of depression emerging from the complex causal pathways that are thought to underlie depression. In this study, we aimed to represent the different patterns of depression with pattern values unique to each individual, where each value combines all the available information about an individual’s depression. We considered the depressed individual as a subsystem of an open complex system, proposed Generalized Information Entropy (GIE) to represent the general characteristics of information entropy of the system, and then implemented Maximum Entropy Estimates to derive equations for depression patterns. We also introduced a numerical simulation method to process the depression related data obtained by the Diamond Cohort Study which has been underway in Australia since 2005 involving 789 people. Unlike traditional assessment, we obtained a unique value for each depressed individual which gives an overall assessment of the depression pattern. Our work provides a novel way to visualise and quantitatively measure the depression pattern of the depressed individual which could be used for pattern categorisation. This may have potential for tailoring health interventions to depressed individuals to maximize health benefit.

Mixed and Complex Mixed Migration during Armed Conflict: Multidimensional Empirical Evidence from Nepal

PubMed Central

Williams, Nathalie E.

2015-01-01

Historically, legal, policy, and academic communities largely ascribed to a dichotomy between forced and voluntary migration, creating a black and white vision that was convenient for legal and policy purposes. More recently, discussions have begun addressing the possibility of mixed migration, acknowledging that there is likely a wide continuum between forced and voluntary, and most migrants likely move with some amount of compulsion and some volition, even during armed conflict. While the mixed migration hypothesis is well-received, empirical evidence is disparate and somewhat blunt at this point. In this article, I contribute a direct theoretical and causal pathway discussion of mixed migration. I also propose the complex mixed migration hypothesis, which argues that not only do non-conflict related factors influence migration during conflict, but they do so differently than during periods of relative peace. I empirically test both hypotheses in the context of the recent armed conflict in Nepal. Using detailed survey data and event history models, results provide strong evidence for both mixed migration and complex mixed migration during conflict hypotheses. These hypotheses and evidence suggest that armed conflict might have substantial impacts on long-term population growth and change, with significant relevance in both academic and policy spheres. PMID:26366007

Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits.

PubMed

Mancuso, Nicholas; Shi, Huwenbo; Goddard, Pagé; Kichaev, Gleb; Gusev, Alexander; Pasaniuc, Bogdan

2017-03-02

Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies.

PubMed

Meng, Xiang-He; Shen, Hui; Chen, Xiang-Ding; Xiao, Hong-Mei; Deng, Hong-Wen

2018-03-01

Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with diverse complex phenotypes and diseases, and provided tremendous opportunities for further analyses using summary association statistics. Recently, Pickrell et al. developed a robust method for causal inference using independent putative causal SNPs. However, this method may fail to infer the causal relationship between two phenotypes when only a limited number of independent putative causal SNPs identified. Here, we extended Pickrell's method to make it more applicable for the general situations. We extended the causal inference method by replacing the putative causal SNPs with the lead SNPs (the set of the most significant SNPs in each independent locus) and tested the performance of our extended method using both simulation and empirical data. Simulations suggested that when the same number of genetic variants is used, our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al. But in practice, our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs. And including more SNPs, on the other hand, would not cause more false positives. By applying our extended method to summary statistics from GWAS for blood metabolites and femoral neck bone mineral density (FN-BMD), we successfully identified ten blood metabolites that may causally influence FN-BMD. We extended a causal inference method for inferring putative causal relationship between two phenotypes using summary statistics from GWAS, and identified a number of potential causal metabolites for FN-BMD, which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.

A pedagogical walkthrough of computational modeling and simulation of Wnt signaling pathway using static causal models in MATLAB.

PubMed

Sinha, Shriprakash

2016-12-01

Simulation study in systems biology involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition, who intend to work on the modeling of the pathway. This paucity might happen due to restrictive business policies which enforce an unwanted embargo on the sharing of important scientific knowledge. A tutorial introduction to computational modeling of Wnt signaling pathway in a human colorectal cancer dataset using static Bayesian network models is provided. The walkthrough might aid biologists/informaticians in understanding the design of computational experiments that is interleaved with exposition of the Matlab code and causal models from Bayesian network toolbox. The manuscript elucidates the coding contents of the advance article by Sinha (Integr. Biol. 6:1034-1048, 2014) and takes the reader in a step-by-step process of how (a) the collection and the transformation of the available biological information from literature is done, (b) the integration of the heterogeneous data and prior biological knowledge in the network is achieved, (c) the simulation study is designed, (d) the hypothesis regarding a biological phenomena is transformed into computational framework, and (e) results and inferences drawn using d -connectivity/separability are reported. The manuscript finally ends with a programming assignment to help the readers get hands-on experience of a perturbation project. Description of Matlab files is made available under GNU GPL v3 license at the Google code project on https://code.google.com/p/static-bn-for-wnt-signaling-pathway and https: //sites.google.com/site/shriprakashsinha/shriprakashsinha/projects/static-bn-for-wnt-signaling-pathway. Latest updates can be found in the latter website.

Elucidating an Adverse Outcome Pathway of Microcephaly for use in Computational Toxicology (SOT Annual Meeting)

EPA Science Inventory

While evidence now supports a causal link between maternal Zika viral infection and microcephaly, genetic errors and chemical stressors may also precipitate this malformation through disruption of neuroprogenitor cell (NPC) proliferation, migration and differentiation in the earl...

Near miss and minor occupational injury: Does it share a common causal pathway with major injury?

PubMed

Alamgir, Hasanat; Yu, Shicheng; Gorman, Erin; Ngan, Karen; Guzman, Jaime

2009-01-01

An essential assumption of injury prevention programs is the common cause hypothesis that the causal pathways of near misses and minor injuries are similar to those of major injuries. The rates of near miss, minor injury and major injury of all reported incidents and musculoskeletal incidents (MSIs) were calculated for three health regions using information from a surveillance database and productive hours from payroll data. The relative distribution of individual causes and activities involved in near miss, minor injury and major injury were then compared. For all reported incidents, there were significant differences in the relative distribution of causes for near miss, minor, and major injury. However, the relative distribution of causes and activities involved in minor and major MSIs were similar. The top causes and activities involved were the same across near miss, minor, and major injury. Finding from this study support the use of near miss and minor injury data as potential outcome measures for injury prevention programs. (c) 2008 Wiley-Liss, Inc.

Nuclear lamina defects cause ATM-dependent NF-κB activation and link accelerated aging to a systemic inflammatory response.

PubMed

Osorio, Fernando G; Bárcena, Clea; Soria-Valles, Clara; Ramsay, Andrew J; de Carlos, Félix; Cobo, Juan; Fueyo, Antonio; Freije, José M P; López-Otín, Carlos

2012-10-15

Alterations in the architecture and dynamics of the nuclear lamina have a causal role in normal and accelerated aging through both cell-autonomous and systemic mechanisms. However, the precise nature of the molecular cues involved in this process remains incompletely defined. Here we report that the accumulation of prelamin A isoforms at the nuclear lamina triggers an ATM- and NEMO-dependent signaling pathway that leads to NF-κB activation and secretion of high levels of proinflammatory cytokines in two different mouse models of accelerated aging (Zmpste24(-/-) and Lmna(G609G/G609G) mice). Causal involvement of NF-κB in accelerated aging was demonstrated by the fact that both genetic and pharmacological inhibition of NF-κB signaling prevents age-associated features in these animal models, significantly extending their longevity. Our findings provide in vivo proof of principle for the feasibility of pharmacological modulation of the NF-κB pathway to slow down the progression of physiological and pathological aging.

A review of covariate selection for non-experimental comparative effectiveness research.

PubMed

Sauer, Brian C; Brookhart, M Alan; Roy, Jason; VanderWeele, Tyler

2013-11-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for a common cause pathway between treatment and outcome can remove confounding, whereas adjustment for other structural types may increase bias. For this reason, variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely known. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher's knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. Copyright © 2013 John Wiley & Sons, Ltd.

A Review of Covariate Selection for Nonexperimental Comparative Effectiveness Research

PubMed Central

Sauer, Brian C.; Brookhart, Alan; Roy, Jason; Vanderweele, Tyler

2014-01-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research (CER), and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for on a common cause pathway between treatment and outcome can remove confounding, while adjustment for other structural types may increase bias. For this reason variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely know. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses the high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher’s knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically-derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. PMID:24006330

Knowing When Help Is Needed: A Developing Sense of Causal Complexity

ERIC Educational Resources Information Center

Kominsky, Jonathan F.; Zamm, Anna P.; Keil, Frank C.

2018-01-01

Research on the division of cognitive labor has found that adults and children as young as age 5 are able to find appropriate experts for different causal systems. However, little work has explored how children and adults decide when to seek out expert knowledge in the first place. We propose that children and adults rely (in part) on…

Waking up to Complexity: Using Christopher Clark's the Sleepwalkers to Challenge Over-Determined Causal Explanations

ERIC Educational Resources Information Center

Holliss, Claire

2014-01-01

Teaching student to construct causal argument is a staple of history teaching and, in this year, questions about the causes of the First World War are particularly pertinent and once again the public eye. Claire Holliss, however, became dissatisfied with existing approaches to teaching students about the causes of the First World War. In…

Taking a systems approach to ecological systems

USGS Publications Warehouse

Grace, James B.

2015-01-01

Increasingly, there is interest in a systems-level understanding of ecological problems, which requires the evaluation of more complex, causal hypotheses. In this issue of the Journal of Vegetation Science, Soliveres et al. use structural equation modeling to test a causal network hypothesis about how tree canopies affect understorey communities. Historical analysis suggests structural equation modeling has been under-utilized in ecology.

Granger causality for state-space models

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Seth, Anil K.

2015-04-01

Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

Unified framework for information integration based on information geometry

PubMed Central

Oizumi, Masafumi; Amari, Shun-ichi

2016-01-01

Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner. PMID:27930289

Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

PubMed

Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés

2017-06-01

Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.

The good, the bad, and the timely: how temporal order and moral judgment influence causal selection

PubMed Central

Reuter, Kevin; Kirfel, Lara; van Riel, Raphael; Barlassina, Luca

2014-01-01

Causal selection is the cognitive process through which one or more elements in a complex causal structure are singled out as actual causes of a certain effect. In this paper, we report on an experiment in which we investigated the role of moral and temporal factors in causal selection. Our results are as follows. First, when presented with a temporal chain in which two human agents perform the same action one after the other, subjects tend to judge the later agent to be the actual cause. Second, the impact of temporal location on causal selection is almost canceled out if the later agent did not violate a norm while the former did. We argue that this is due to the impact that judgments of norm violation have on causal selection—even if the violated norm has nothing to do with the obtaining effect. Third, moral judgments about the effect influence causal selection even in the case in which agents could not have foreseen the effect and did not intend to bring it about. We discuss our findings in connection to recent theories of the role of moral judgment in causal reasoning, on the one hand, and to probabilistic models of temporal location, on the other. PMID:25477851

Biological interpretation of genome-wide association studies using predicted gene functions

PubMed Central

Pers, Tune H.; Karjalainen, Juha M.; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R.; Yang, Jian; Lui, Julian C.; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K.; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S.N.; Hirschhorn, Joel N.; Franke, Lude

2015-01-01

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. PMID:25597830

The perceived causal structures of smoking: Smoker and non-smoker comparisons

PubMed Central

Lydon, David M; Howard, Matthew C; Wilson, Stephen J; Geier, Charles F

2015-01-01

Despite the detrimental impact of smoking on health, its prevalence remains high. Empirical research has provided insight into the many causes and effects of smoking, yet lay perceptions of smoking remain relatively understudied. The current study used a form of network analysis to gain insight into the causal attributions for smoking of both smoking and non-smoking college students. The analyses resulted in highly endorsed, complex network diagrams that conveyed the perceived causal structures of smoking. Differences in smoker and non-smoker networks emerged with smokers attributing less negative consequences to smoking behaviors. Implications for intervention are discussed. PMID:25690755

Causality analysis in business performance measurement system using system dynamics methodology

NASA Astrophysics Data System (ADS)

Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah

2014-07-01

One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.

Cancer risk and the complexity of the interactions between environmental and host factors: HENVINET interactive diagrams as simple tools for exploring and understanding the scientific evidence.

PubMed

Merlo, Domenico F; Filiberti, Rosangela; Kobernus, Michael; Bartonova, Alena; Gamulin, Marija; Ferencic, Zeljko; Dusinska, Maria; Fucic, Aleksandra

2012-06-28

Development of graphical/visual presentations of cancer etiology caused by environmental stressors is a process that requires combining the complex biological interactions between xenobiotics in living and occupational environment with genes (gene-environment interaction) and genomic and non-genomic based disease specific mechanisms in living organisms. Traditionally, presentation of causal relationships includes the statistical association between exposure to one xenobiotic and the disease corrected for the effect of potential confounders. Within the FP6 project HENVINET, we aimed at considering together all known agents and mechanisms involved in development of selected cancer types. Selection of cancer types for causal diagrams was based on the corpus of available data and reported relative risk (RR). In constructing causal diagrams the complexity of the interactions between xenobiotics was considered a priority in the interpretation of cancer risk. Additionally, gene-environment interactions were incorporated such as polymorphisms in genes for repair and for phase I and II enzymes involved in metabolism of xenobiotics and their elimination. Information on possible age or gender susceptibility is also included. Diagrams are user friendly thanks to multistep access to information packages and the possibility of referring to related literature and a glossary of terms. Diagrams cover both chemical and physical agents (ionizing and non-ionizing radiation) and provide basic information on the strength of the association between type of exposure and cancer risk reported by human studies and supported by mechanistic studies. Causal diagrams developed within HENVINET project represent a valuable source of information for professionals working in the field of environmental health and epidemiology, and as educational material for students. Cancer risk results from a complex interaction of environmental exposures with inherited gene polymorphisms, genetic burden collected during development and non genomic capacity of response to environmental insults. In order to adopt effective preventive measures and the associated regulatory actions, a comprehensive investigation of cancer etiology is crucial. Variations and fluctuations of cancer incidence in human populations do not necessarily reflect environmental pollution policies or population distribution of polymorphisms of genes known to be associated with increased cancer risk. Tools which may be used in such a comprehensive research, including molecular biology applied to field studies, require a methodological shift from the reductionism that has been used until recently as a basic axiom in interpretation of data. The complexity of the interactions between cells, genes and the environment, i.e. the resonance of the living matter with the environment, can be synthesized by systems biology. Within the HENVINET project such philosophy was followed in order to develop interactive causal diagrams for the investigation of cancers with possible etiology in environmental exposure. Causal diagrams represent integrated knowledge and seed tool for their future development and development of similar diagrams for other environmentally related diseases such as asthma or sterility. In this paper development and application of causal diagrams for cancer are presented and discussed.

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

2012-01-01

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

PubMed

Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

2009-06-01

In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

An update on the genetic architecture of hyperuricemia and gout.

PubMed

Merriman, Tony R

2015-04-10

Genome-wide association studies that scan the genome for common genetic variants associated with phenotype have greatly advanced medical knowledge. Hyperuricemia is no exception, with 28 loci identified. However, genetic control of pathways determining gout in the presence of hyperuricemia is still poorly understood. Two important pathways determining hyperuricemia have been confirmed (renal and gut excretion of uric acid with glycolysis now firmly implicated). Major urate loci are SLC2A9 and ABCG2. Recent studies show that SLC2A9 is involved in renal and gut excretion of uric acid and is implicated in antioxidant defense. Although etiological variants at SLC2A9 are yet to be identified, it is clear that considerable genetic complexity exists at the SLC2A9 locus, with multiple statistically independent genetic variants and local epistatic interactions. The positions of implicated genetic variants within or near chromatin regions involved in transcriptional control suggest that this mechanism (rather than structural changes in SLC2A9) is important in regulating the activity of SLC2A9. ABCG2 is involved primarily in extra-renal uric acid under-excretion with the etiological variant influencing expression. At the other 26 loci, probable causal genes can be identified at three (PDZK1, SLC22A11, and INHBB) with strong candidates at a further 10 loci. Confirmation of the causal gene will require a combination of re-sequencing, trans-ancestral mapping, and correlation of genetic association data with expression data. As expected, the urate loci associate with gout, although inconsistent effect sizes for gout require investigation. Finally, there has been no genome-wide association study using clinically ascertained cases to investigate the causes of gout in the presence of hyperuricemia. In such a study, use of asymptomatic hyperurcemic controls would be expected to increase the ability to detect genetic associations with gout.

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph

2015-09-01

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension . We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the -faces of the -dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the -faces.

Breakfast and the Achievement Gap among Urban Minority Youth

ERIC Educational Resources Information Center

Basch, Charles E.

2011-01-01

Objectives: To outline the prevalence and disparities of breakfast consumption among school-aged urban minority youth, causal pathways through which skipping breakfast adversely affects academic achievement, and proven or promising approaches for schools to increase breakfast consumption. Methods: Literature review. Results: On any given day a…

Inattention and Hyperactivity and the Achievement Gap among Urban Minority Youth

ERIC Educational Resources Information Center

Basch, Charles E.

2011-01-01

Objectives: To outline the prevalence and disparities of inattention and hyperactivity among school-aged urban minority youth, causal pathways through which inattention and hyperactivity adversely affects academic achievement, and proven or promising approaches for schools to address these problems. Methods: Literature review. Results:…

Social Cognitive Perspective of Gender Disparities in Undergraduate Physics

ERIC Educational Resources Information Center

Kelly, Angela M.

2016-01-01

This article synthesizes sociopsychological theories and empirical research to establish a framework for exploring causal pathways and targeted interventions for the low representation of women in post-secondary physics. The rationale for this article is based upon disproportionate representation among undergraduate physics majors in the United…

Understanding the Etiology of Complex Traits: Symbiotic Relationships between Psychology and Genetics

ERIC Educational Resources Information Center

Grigorenko, Elena L.

2007-01-01

The present article offers comments on the infusion of methodologies, approaches, reasoning strategies, and findings from the fields of genetics and genomics into studies of complex human behaviors (hereafter, complex phenotypes). Specifically, I discuss issues of generality and specificity, causality, and replicability as they pertain to…

SPV: a JavaScript Signaling Pathway Visualizer.

PubMed

Calderone, Alberto; Cesareni, Gianni

2018-03-24

The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free space where, different entities, cellular compartments and interaction types are hardly distinguishable. SPV (Signaling Pathway Visualizer) is a free open source JavaScript library which offers a series of pre-defined elements, compartments and interaction types meant to facilitate the representation of signaling pathways consisting of causal interactions without neglecting simple protein-protein interaction networks. freely available under Apache version 2 license; Source code: https://github.com/Sinnefa/SPV_Signaling_Pathway_Visualizer_v1.0. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js. sinnefa@gmail.com.

Time, frequency, and time-varying Granger-causality measures in neuroscience.

PubMed

Cekic, Sezen; Grandjean, Didier; Renaud, Olivier

2018-05-20

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned. Copyright © 2018 John Wiley & Sons, Ltd.

Loop Analysis of Causal Feedback in Epidemiology: An Illustration Relating To Urban Neighborhoods and Resident Depressive Experiences

PubMed Central

2008-01-01

The causal feedback implied by urban neighborhood conditions that shape human health experiences, that in turn shape neighborhood conditions through a complex causal web, raises a challenge for traditional epidemiological causal analyses. This article introduces the loop analysis method, and builds off of a core loop model linking neighborhood property vacancy rate, resident depressive symptoms, rate of neighborhood death, and rate of neighborhood exit in a feedback network. I justify and apply loop analysis to the specific example of depressive symptoms and abandoned urban residential property to show how inquiries into the behavior of causal systems can answer different kinds of hypotheses, and thereby compliment those of causal modeling using statistical models. Neighborhood physical conditions that are only indirectly influenced by depressive symptoms may nevertheless manifest in the mental health experiences of their residents; conversely, neighborhood physical conditions may be a significant mental health risk for the population of neighborhood residents. I find that participatory greenspace programs are likely to produce adaptive responses in depressive symptoms and different neighborhood conditions, which are different in character to non-participatory greenspace interventions. PMID:17706851

Causality discovery technology

NASA Astrophysics Data System (ADS)

Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.

2012-11-01

Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.

CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS

PubMed Central

Shpitser, Ilya; Tchetgen, Eric Tchetgen

2017-01-01

Identifying causal parameters from observational data is fraught with subtleties due to the issues of selection bias and confounding. In addition, more complex questions of interest, such as effects of treatment on the treated and mediated effects may not always be identified even in data where treatment assignment is known and under investigator control, or may be identified under one causal model but not another. Increasingly complex effects of interest, coupled with a diversity of causal models in use resulted in a fragmented view of identification. This fragmentation makes it unnecessarily difficult to determine if a given parameter is identified (and in what model), and what assumptions must hold for this to be the case. This, in turn, complicates the development of estimation theory and sensitivity analysis procedures. In this paper, we give a unifying view of a large class of causal effects of interest, including novel effects not previously considered, in terms of a hierarchy of interventions, and show that identification theory for this large class reduces to an identification theory of random variables under interventions from this hierarchy. Moreover, we show that one type of intervention in the hierarchy is naturally associated with queries identified under the Finest Fully Randomized Causally Interpretable Structure Tree Graph (FFRCISTG) model of Robins (via the extended g-formula), and another is naturally associated with queries identified under the Non-Parametric Structural Equation Model with Independent Errors (NPSEM-IE) of Pearl, via a more general functional we call the edge g-formula. Our results motivate the study of estimation theory for the edge g-formula, since we show it arises both in mediation analysis, and in settings where treatment assignment has unobserved causes, such as models associated with Pearl’s front-door criterion. PMID:28919652

CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS.

PubMed

Shpitser, Ilya; Tchetgen, Eric Tchetgen

2016-12-01

Identifying causal parameters from observational data is fraught with subtleties due to the issues of selection bias and confounding. In addition, more complex questions of interest, such as effects of treatment on the treated and mediated effects may not always be identified even in data where treatment assignment is known and under investigator control, or may be identified under one causal model but not another. Increasingly complex effects of interest, coupled with a diversity of causal models in use resulted in a fragmented view of identification. This fragmentation makes it unnecessarily difficult to determine if a given parameter is identified (and in what model), and what assumptions must hold for this to be the case. This, in turn, complicates the development of estimation theory and sensitivity analysis procedures. In this paper, we give a unifying view of a large class of causal effects of interest, including novel effects not previously considered, in terms of a hierarchy of interventions, and show that identification theory for this large class reduces to an identification theory of random variables under interventions from this hierarchy. Moreover, we show that one type of intervention in the hierarchy is naturally associated with queries identified under the Finest Fully Randomized Causally Interpretable Structure Tree Graph (FFRCISTG) model of Robins (via the extended g-formula), and another is naturally associated with queries identified under the Non-Parametric Structural Equation Model with Independent Errors (NPSEM-IE) of Pearl, via a more general functional we call the edge g-formula. Our results motivate the study of estimation theory for the edge g-formula, since we show it arises both in mediation analysis, and in settings where treatment assignment has unobserved causes, such as models associated with Pearl's front-door criterion.

Explanatory models concerning the effects of small-area characteristics on individual health.

PubMed

Voigtländer, Sven; Vogt, Verena; Mielck, Andreas; Razum, Oliver

2014-06-01

Material and social living conditions at the small-area level are assumed to have an effect on individual health. We review existing explanatory models concerning the effects of small-area characteristics on health and describe the gaps future research should try to fill. Systematic literature search for, and analysis of, studies that propose an explanatory model of the relationship between small-area characteristics and health. Fourteen studies met our inclusion criteria. Using various theoretical approaches, almost all of the models are based on a three-tier structure linking social inequalities (posited at the macro-level), small-area characteristics (posited at the meso-level) and individual health (micro-level). No study explicitly defines the geographical borders of the small-area context. The health impact of the small-area characteristics is explained by specific pathways involving mediating factors (psychological, behavioural, biological). These pathways tend to be seen as uni-directional; often, causality is implied. They may be modified by individual factors. A number of issues need more attention in research on explanatory models concerning small-area effects on health. Among them are the (geographical) definition of the small-area context; the systematic description of pathways comprising small-area contextual as well as compositional factors; questions of direction of association and causality; and the integration of a time dimension.

Health in global context; beyond the social determinants of health?

PubMed Central

Krumeich, Anja; Meershoek, Agnes

2014-01-01

The rise of the social determinants of health (SDH) discourse on the basis of statistical evidence that correlates ill health to SDH and pictures causal pathways in comprehensive theoretical frameworks led to widespread awareness that health and health disparities are the outcome of complex pathways of interconnecting SDH. In this paper we explore whether and how SDH frameworks can be translated to effectively inform particular national health policies. To this end we identified major challenges for this translation followed by reflections on ways to overcome them. Most important challenges affecting adequate translation of these frameworks into concrete policy and intervention are 1) overcoming the inclination to conceptualize SDH as mere barriers to health behavior to be modified by lifestyle interventions by addressing them as structural factors instead; 2) obtaining sufficient in-depth insight in and evidence for the exact nature of the relationship between SDs and health; 3) to adequately translate the general determinants and pathways into explanations for ill health and limited access to health care in local settings; 4) to develop and implement policies and other interventions that are adjusted to those local circumstances. We conclude that to transform generic SDH models into useful policy tools and to prevent them to transform in SDH themselves, in depth understanding of the unique interplay between local, national and global SDH in a local setting, gathered by ethnographic research, is needed to be able to address structural SD in the local setting and decrease health inequity.

Asthma and the Achievement Gap among Urban Minority Youth

ERIC Educational Resources Information Center

Basch, Charles E.

2011-01-01

Objectives: To outline the prevalence and disparities of asthma among school-aged urban minority youth, causal pathways through which poorly controlled asthma adversely affects academic achievement, and proven or promising approaches for schools to address these problems. Methods: Literature review. Results: Asthma is the most common chronic…

Internalising Symptoms: An Antecedent or Precedent in Adolescent Peer Victimisation

ERIC Educational Resources Information Center

Lester, Leanne; Dooley, Julian; Cross, Donna; Shaw, Therese

2012-01-01

The transition period from primary to secondary school is a critical time in adolescent development. The high prevalence of adolescent mental health problems makes understanding the causal pathways between peer victimisation and internalising symptoms an important priority during this time. This article utilises data collected from self-completion…

Physical Activity and the Achievement Gap among Urban Minority Youth

ERIC Educational Resources Information Center

Basch, Charles E.

2011-01-01

Objectives: To outline the prevalence and disparities of physical activity among school-aged urban minority youth, causal pathways through which low levels of physical activity and fitness adversely affects academic achievement, and proven or promising approaches for schools to increase physical activity and physical fitness among youth. Methods:…

Vision and the Achievement Gap among Urban Minority Youth

ERIC Educational Resources Information Center

Basch, Charles E.

2011-01-01

Objectives: To outline the prevalence and disparities of vision problems among school-aged urban minority youth, causal pathways through which vision problems adversely affects academic achievement, and proven or promising approaches for schools to address these problems. Methods: Literature review. Results: More than 20% of school-aged youth have…

Aggression and Violence and the Achievement Gap among Urban Minority Youth

ERIC Educational Resources Information Center

Basch, Charles E.

2011-01-01

Objectives: To outline the prevalence and disparities of aggression and violence among school-aged urban minority youth, causal pathways through which aggression and violence adversely affects academic achievement, and proven or promising approaches for schools to address these problems. Methods: Literature review. Results: Recent national data…

The Association of Attention Deficit Hyperactivity Disorder with Socioeconomic Disadvantage: Alternative Explanations and Evidence

ERIC Educational Resources Information Center

Russell, Ginny; Ford, Tamsin; Rosenberg, Rachel; Kelly, Susan

2014-01-01

Background: Studies throughout Northern Europe, the United States and Australia have found an association between childhood attention deficit hyperactivity disorder (ADHD) and family socioeconomic disadvantage. We report further evidence for the association and review potential causal pathways that might explain the link. Methods: Secondary…

Ventilatory Patterning in a Mouse Model of Stroke

PubMed Central

Koo, Brian B; Strohl, Kingman P; Gillombardo, Carl B; Jacono, Frank J

2010-01-01

Cheyne-Stokes respiration (CSR) is a breathing pattern characterized by waxing and waning of breath volume and frequency, and is often recognized following stroke, when causal pathways are often obscure. We used an animal model to address the hypothesis that cerebral infarction is a mechanism for producing breathing instability. Fourteen male A/J mice underwent either stroke (n=7) or sham (n=7) procedure. Ventilation was measured using whole body plethysmography. Respiratory rate (RR), tidal volume (VT) and minute ventilation (Ve) mean values and coefficient of variation were computed for ventilation and oscillatory behavior. In addition, the ventilatory data were computationally fit to models to quantify autocorrelation, mutual information, sample entropy and a nonlinear complexity index. At the same time post procedure, stroke when compared to sham animal breathing consisted of a lower RR and autocorrelation, higher coefficient of variation for VT and higher coefficient of variation for Ve. Mutual information and the nonlinear complexity index were higher in breathing following stroke which also demonstrated a waxing/waning pattern. The absence of stroke in the sham animals was verified anatomically. We conclude that ventilatory pattern following cerebral infarction demonstrated increased variability with increased nonlinear patterning and a waxing/waning pattern, consistent with CSR. PMID:20472101

Designing Better Scaffolding in Teaching Complex Systems with Graphical Simulations

NASA Astrophysics Data System (ADS)

Li, Na

Complex systems are an important topic in science education today, but they are usually difficult for secondary-level students to learn. Although graphic simulations have many advantages in teaching complex systems, scaffolding is a critical factor for effective learning. This dissertation study was conducted around two complementary research questions on scaffolding: (1) How can we chunk and sequence learning activities in teaching complex systems? (2) How can we help students make connections among system levels across learning activities (level bridging)? With a sample of 123 seventh-graders, this study employed a 3x2 experimental design that factored sequencing methods (independent variable 1; three levels) with level-bridging scaffolding (independent variable 2; two levels) and compared the effectiveness of each combination. The study measured two dependent variables: (1) knowledge integration (i.e., integrating and connecting content-specific normative concepts and providing coherent scientific explanations); (2) understanding of the deep causal structure (i.e., being able to grasp and transfer the causal knowledge of a complex system). The study used a computer-based simulation environment as the research platform to teach the ideal gas law as a system. The ideal gas law is an emergent chemical system that has three levels: (1) experiential macro level (EM) (e.g., an aerosol can explodes when it is thrown into the fire); (2) abstract macro level (AM) (i.e., the relationships among temperature, pressure and volume); (3) micro level (Mi) (i.e., molecular activity). The sequencing methods of these levels were manipulated by changing the order in which they were delivered with three possibilities: (1) EM-AM-Mi; (2) Mi-AM-EM; (3) AM-Mi-EM. The level-bridging scaffolding variable was manipulated on two aspects: (1) inserting inter-level questions among learning activities; (2) two simulations dynamically linked in the final learning activity. Addressing the first research question, the Experiential macro-Abstract macro-Micro (EM-AM-Mi) sequencing method, following the "concrete to abstract" principle, produced better knowledge integration while the Micro-Abstract macro-Experiential macro (Mi-AM-EM) sequencing method, congruent with the causal direction of the emergent system, produced better understanding of the deep causal structure only when level-bridging scaffolding was provided. The Abstract macro-Micro-Experiential macro (AM-Mi-EM) sequencing method produced worse performance in general, because it did not follow the "concrete to abstract" principle, nor did it align with the causal structure of the emergent system. As to the second research question, the results showed that level-bridging scaffolding was important for both knowledge integration and understanding of the causal structure in learning the ideal gas law system.

Imputation of adverse drug reactions: Causality assessment in hospitals

PubMed Central

Mastroianni, Patricia de Carvalho

2017-01-01

Background & objectives Different algorithms have been developed to standardize the causality assessment of adverse drug reactions (ADR). Although most share common characteristics, the results of the causality assessment are variable depending on the algorithm used. Therefore, using 10 different algorithms, the study aimed to compare inter-rater and multi-rater agreement for ADR causality assessment and identify the most consistent to hospitals. Methods Using ten causality algorithms, four judges independently assessed the first 44 cases of ADRs reported during the first year of implementation of a risk management service in a medium complexity hospital in the state of Sao Paulo (Brazil). Owing to variations in the terminology used for causality, the equivalent imputation terms were grouped into four categories: definite, probable, possible and unlikely. Inter-rater and multi-rater agreement analysis was performed by calculating the Cohen´s and Light´s kappa coefficients, respectively. Results None of the algorithms showed 100% reproducibility in the causal imputation. Fair inter-rater and multi-rater agreement was found. Emanuele (1984) and WHO-UMC (2010) algorithms showed a fair rate of agreement between the judges (k = 0.36). Interpretation & conclusions Although the ADR causality assessment algorithms were poorly reproducible, our data suggest that WHO-UMC algorithm is the most consistent for imputation in hospitals, since it allows evaluating the quality of the report. However, to improve the ability of assessing the causality using algorithms, it is necessary to include criteria for the evaluation of drug-related problems, which may be related to confounding variables that underestimate the causal association. PMID:28166274

Timing and Causality in the Generation of Learned Eyelid Responses

PubMed Central

Sánchez-Campusano, Raudel; Gruart, Agnès; Delgado-García, José M.

2011-01-01

The cerebellum-red nucleus-facial motoneuron (Mn) pathway has been reported as being involved in the proper timing of classically conditioned eyelid responses. This special type of associative learning serves as a model of event timing for studying the role of the cerebellum in dynamic motor control. Here, we have re-analyzed the firing activities of cerebellar posterior interpositus (IP) neurons and orbicularis oculi (OO) Mns in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. The aim was to revisit the hypothesis that the IP neurons (IPns) can be considered a neuronal phase-modulating device supporting OO Mns firing with an emergent timing mechanism and an explicit correlation code during learned eyelid movements. Optimized experimental and computational tools allowed us to determine the different causal relationships (temporal order and correlation code) during and between trials. These intra- and inter-trial timing strategies expanding from sub-second range (millisecond timing) to longer-lasting ranges (interval timing) expanded the functional domain of cerebellar timing beyond motor control. Interestingly, the results supported the above-mentioned hypothesis. The causal inferences were influenced by the precise motor and pre-motor spike timing in the cause-effect interval, and, in addition, the timing of the learned responses depended on cerebellar–Mn network causality. Furthermore, the timing of CRs depended upon the probability of simulated causal conditions in the cause-effect interval and not the mere duration of the inter-stimulus interval. In this work, the close relation between timing and causality was verified. It could thus be concluded that the firing activities of IPns may be related more to the proper performance of ongoing CRs (i.e., the proper timing as a consequence of the pertinent causality) than to their generation and/or initiation. PMID:21941469

[FROM STATISTICAL ASSOCIATIONS TO SCIENTIFIC CAUSALITY].

PubMed

Golan, Daniel; Linn, Shay

2015-06-01

The pathogenesis of most chronic diseases is complex and probably involves the interaction of multiple genetic and environmental risk factors. One way to learn about disease triggers is from statistically significant associations in epidemiological studies. However, associations do not necessarily prove causation. Associations can commonly result from bias, confounding and reverse causation. Several paradigms for causality inference have been developed. Henle-Koch postulates are mainly applied for infectious diseases. Austin Bradford Hill's criteria may serve as a practical tool to weigh the evidence regarding the probability that a single new risk factor for a given disease is indeed causal. These criteria are irrelevant for estimating the causal relationship between exposure to a risk factor and disease whenever biological causality has been previously established. Thus, it is highly probable that past exposure of an individual to definite carcinogens is related to his cancer, even without proving an association between this exposure and cancer in his group. For multifactorial diseases, Rothman's model of interacting sets of component causes can be applied.

Complexity Thinking and Methodology: The Potential of "Complex Case Study" for Educational Research

ERIC Educational Resources Information Center

Hetherington, Lindsay

2013-01-01

Complexity theories have in common perspectives that challenge linear methodologies and views of causality. In educational research, relatively little has been written explicitly exploring their implications for educational research methodology in general and case study in particular. In this paper, I offer a rationale for case study as a research…

Characterization of autoregressive processes using entropic quantifiers

NASA Astrophysics Data System (ADS)

Traversaro, Francisco; Redelico, Francisco O.

2018-01-01

The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.

Traveling Wave Modes of a Plane Layered Anelastic Earth

DTIC Science & Technology

2016-05-20

dependent anelastic moduli. The anelastic moduli must be frequency dependent and satisfy the Kramers- Kronig relations to preserve causality. The...the complex, frequency dependent moduli satisfy the Kramers- Kronig relations. Stable, well-behaved numerical algorithms exist for solving the complex

Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host's metabolism.

PubMed

Zheng, P; Zeng, B; Zhou, C; Liu, M; Fang, Z; Xu, X; Zeng, L; Chen, J; Fan, S; Du, X; Zhang, X; Yang, D; Yang, Y; Meng, H; Li, W; Melgiri, N D; Licinio, J; Wei, H; Xie, P

2016-06-01

Major depressive disorder (MDD) is the result of complex gene-environment interactions. According to the World Health Organization, MDD is the leading cause of disability worldwide, and it is a major contributor to the overall global burden of disease. However, the definitive environmental mechanisms underlying the pathophysiology of MDD remain elusive. The gut microbiome is an increasingly recognized environmental factor that can shape the brain through the microbiota-gut-brain axis. We show here that the absence of gut microbiota in germ-free (GF) mice resulted in decreased immobility time in the forced swimming test relative to conventionally raised healthy control mice. Moreover, from clinical sampling, the gut microbiotic compositions of MDD patients and healthy controls were significantly different with MDD patients characterized by significant changes in the relative abundance of Firmicutes, Actinobacteria and Bacteroidetes. Fecal microbiota transplantation of GF mice with 'depression microbiota' derived from MDD patients resulted in depression-like behaviors compared with colonization with 'healthy microbiota' derived from healthy control individuals. Mice harboring 'depression microbiota' primarily exhibited disturbances of microbial genes and host metabolites involved in carbohydrate and amino acid metabolism. This study demonstrates that dysbiosis of the gut microbiome may have a causal role in the development of depressive-like behaviors, in a pathway that is mediated through the host's metabolism.

The Microbiome and Complement Activation: A Mechanistic Model for Preterm Birth

PubMed Central

Dunn, Alexis B.; Dunlop, Anne L.; Hogue, Carol J.; Miller, Andrew; Corwin, Elizabeth J.

2018-01-01

Preterm Birth (PTB, < 37 completed weeks' gestation) is one of the leading obstetrical problems in the United States affecting approximately 1 of every 9 births. Even more concerning are the persistent racial disparities in PTB with particularly high rates in African Americans. There are several recognized pathophysiologic pathways to PTB, including infection and/or exaggerated systemic or local inflammation. Intrauterine infection is a causal factor linked to PTB, thought to result most commonly from inflammatory processes triggered by microbial invasion of bacteria ascending from the vaginal microbiome. Trials to treat various infections have shown limited efficacy in reducing PTB risk, suggesting that other complex mechanisms, including those associated with inflammation, may be involved in the relationship between microbes, infection, and PTB. A key mediator of the inflammatory response, and recently shown to be associated with PTB, is the complement system, an innate defense mechanism involved in both normal physiologic processes that occur during pregnancy implantation, as well as processes that promote the elimination of pathogenic microbes. The purpose of this paper is to present a mechanistic model of inflammation-associated PTB, which hypothesizes a relationship between the microbiome and dysregulation of the complement system. Exploring the relationships between the microbial environment and complement biomarkers may elucidate a potentially modifiable biological pathway to preterm birth. PMID:28073296

Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis.

PubMed

Rossi, Sergio; Morin, Hubert; Deslauriers, Annie

2012-03-01

Although habitually considered as a whole, xylogenesis is a complex process of division and maturation of a pool of cells where the relationship between the phenological phases generating such a growth pattern remains essentially unknown. This study investigated the causal relationships in cambium phenology of black spruce [Picea mariana (Mill.) BSP] monitored for 8 years on four sites of the boreal forest of Quebec, Canada. The dependency links connecting the timing of xylem cell differentiation and cell production were defined and the resulting causal model was analysed with d-sep tests and generalized mixed models with repeated measurements, and tested with Fisher's C statistics to determine whether and how causality propagates through the measured variables. The higher correlations were observed between the dates of emergence of the first developing cells and between the ending of the differentiation phases, while the number of cells was significantly correlated with all phenological phases. The model with eight dependency links was statistically valid for explaining the causes and correlations between the dynamics of cambium phenology. Causal modelling suggested that the phenological phases involved in xylogenesis are closely interconnected by complex relationships of cause and effect, with the onset of cell differentiation being the main factor directly or indirectly triggering all successive phases of xylem maturation.

Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis

PubMed Central

Rossi, Sergio; Morin, Hubert; Deslauriers, Annie

2012-01-01

Although habitually considered as a whole, xylogenesis is a complex process of division and maturation of a pool of cells where the relationship between the phenological phases generating such a growth pattern remains essentially unknown. This study investigated the causal relationships in cambium phenology of black spruce [Picea mariana (Mill.) BSP] monitored for 8 years on four sites of the boreal forest of Quebec, Canada. The dependency links connecting the timing of xylem cell differentiation and cell production were defined and the resulting causal model was analysed with d-sep tests and generalized mixed models with repeated measurements, and tested with Fisher’s C statistics to determine whether and how causality propagates through the measured variables. The higher correlations were observed between the dates of emergence of the first developing cells and between the ending of the differentiation phases, while the number of cells was significantly correlated with all phenological phases. The model with eight dependency links was statistically valid for explaining the causes and correlations between the dynamics of cambium phenology. Causal modelling suggested that the phenological phases involved in xylogenesis are closely interconnected by complex relationships of cause and effect, with the onset of cell differentiation being the main factor directly or indirectly triggering all successive phases of xylem maturation. PMID:22174441

Phase Transitions in Living Neural Networks

NASA Astrophysics Data System (ADS)

Williams-Garcia, Rashid Vladimir

Our nervous systems are composed of intricate webs of interconnected neurons interacting in complex ways. These complex interactions result in a wide range of collective behaviors with implications for features of brain function, e.g., information processing. Under certain conditions, such interactions can drive neural network dynamics towards critical phase transitions, where power-law scaling is conjectured to allow optimal behavior. Recent experimental evidence is consistent with this idea and it seems plausible that healthy neural networks would tend towards optimality. This hypothesis, however, is based on two problematic assumptions, which I describe and for which I present alternatives in this thesis. First, critical transitions may vanish due to the influence of an environment, e.g., a sensory stimulus, and so living neural networks may be incapable of achieving "critical" optimality. I develop a framework known as quasicriticality, in which a relative optimality can be achieved depending on the strength of the environmental influence. Second, the power-law scaling supporting this hypothesis is based on statistical analysis of cascades of activity known as neuronal avalanches, which conflate causal and non-causal activity, thus confounding important dynamical information. In this thesis, I present a new method to unveil causal links, known as causal webs, between neuronal activations, thus allowing for experimental tests of the quasicriticality hypothesis and other practical applications.

A causal loop analysis of the sustainability of integrated community case management in Rwanda.

PubMed

Sarriot, Eric; Morrow, Melanie; Langston, Anne; Weiss, Jennifer; Landegger, Justine; Tsuma, Laban

2015-04-01

Expansion of community health services in Rwanda has come with the national scale up of integrated Community Case Management (iCCM) of malaria, pneumonia and diarrhea. We used a sustainability assessment framework as part of a large-scale project evaluation to identify factors affecting iCCM sustainability (2011). We then (2012) used causal-loop analysis to identify systems determinants of iCCM sustainability from a national systems perspective. This allows us to develop three high-probability future scenarios putting the achievements of community health at risk, and to recommend mitigating strategies. Our causal loop diagram highlights both balancing and reinforcing loops of cause and effect in the national iCCM system. Financial, political and technical scenarios carry high probability for threatening the sustainability through: (1) reduction in performance-based financing resources, (2) political shocks and erosion of political commitment for community health, and (3) insufficient progress in resolving district health systems--"building blocks"--performance gaps. In a complex health system, the consequences of choices may be delayed and hard to predict precisely. Causal loop analysis and scenario mapping make explicit complex cause-and-effects relationships and high probability risks, which need to be anticipated and mitigated. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Dynamics of safety performance and culture: a group model building approach.

PubMed

Goh, Yang Miang; Love, Peter E D; Stagbouer, Greg; Annesley, Chris

2012-09-01

The management of occupational health and safety (OHS) including safety culture interventions is comprised of complex problems that are often hard to scope and define. Due to the dynamic nature and complexity of OHS management, the concept of system dynamics (SD) is used to analyze accident prevention. In this paper, a system dynamics group model building (GMB) approach is used to create a causal loop diagram of the underlying factors influencing the OHS performance of a major drilling and mining contractor in Australia. While the organization has invested considerable resources into OHS their disabling injury frequency rate (DIFR) has not been decreasing. With this in mind, rich individualistic knowledge about the dynamics influencing the DIFR was acquired from experienced employees with operations, health and safety and training background using a GMB workshop. Findings derived from the workshop were used to develop a series of causal loop diagrams that includes a wide range of dynamics that can assist in better understanding the causal influences OHS performance. The causal loop diagram provides a tool for organizations to hypothesize the dynamics influencing effectiveness of OHS management, particularly the impact on DIFR. In addition the paper demonstrates that the SD GMB approach has significant potential in understanding and improving OHS management. Copyright © 2011 Elsevier Ltd. All rights reserved.

Computer modeling of Epilepsy

PubMed Central

Lytton, William W.

2009-01-01

Preface Epilepsy is a complex set of disorders that can involve many areas of cortex as well as underlying deep brain systems. The myriad manifestations of seizures, as varied as déjà vu and olfactory hallucination, can thereby give researchers insights into regional functions and relations. Epilepsy is also complex genetically and pathophysiologically, involving microscopic (ion channels, synaptic proteins), macroscopic (brain trauma and rewiring) and intermediate changes in a complex interplay of causality. It has long been recognized that computer modeling will be required to disentangle causality, to better understand seizure spread and to understand and eventually predict treatment efficacy. Over the past few years, substantial progress has been made modeling epilepsy at levels ranging from the molecular to the socioeconomic. We review these efforts and connect them to the medical goals of understanding and treating this disorder. PMID:18594562

Analysis of complex neural circuits with nonlinear multidimensional hidden state models

PubMed Central

Friedman, Alexander; Slocum, Joshua F.; Tyulmankov, Danil; Gibb, Leif G.; Altshuler, Alex; Ruangwises, Suthee; Shi, Qinru; Toro Arana, Sebastian E.; Beck, Dirk W.; Sholes, Jacquelyn E. C.; Graybiel, Ann M.

2016-01-01

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain. PMID:27222584

Spatial and temporal relationships of electrocorticographic alpha and gamma activity during auditory processing.

PubMed

Potes, Cristhian; Brunner, Peter; Gunduz, Aysegul; Knight, Robert T; Schalk, Gerwin

2014-08-15

Neuroimaging approaches have implicated multiple brain sites in musical perception, including the posterior part of the superior temporal gyrus and adjacent perisylvian areas. However, the detailed spatial and temporal relationship of neural signals that support auditory processing is largely unknown. In this study, we applied a novel inter-subject analysis approach to electrophysiological signals recorded from the surface of the brain (electrocorticography (ECoG)) in ten human subjects. This approach allowed us to reliably identify those ECoG features that were related to the processing of a complex auditory stimulus (i.e., continuous piece of music) and to investigate their spatial, temporal, and causal relationships. Our results identified stimulus-related modulations in the alpha (8-12 Hz) and high gamma (70-110 Hz) bands at neuroanatomical locations implicated in auditory processing. Specifically, we identified stimulus-related ECoG modulations in the alpha band in areas adjacent to primary auditory cortex, which are known to receive afferent auditory projections from the thalamus (80 of a total of 15,107 tested sites). In contrast, we identified stimulus-related ECoG modulations in the high gamma band not only in areas close to primary auditory cortex but also in other perisylvian areas known to be involved in higher-order auditory processing, and in superior premotor cortex (412/15,107 sites). Across all implicated areas, modulations in the high gamma band preceded those in the alpha band by 280 ms, and activity in the high gamma band causally predicted alpha activity, but not vice versa (Granger causality, p<1e(-8)). Additionally, detailed analyses using Granger causality identified causal relationships of high gamma activity between distinct locations in early auditory pathways within superior temporal gyrus (STG) and posterior STG, between posterior STG and inferior frontal cortex, and between STG and premotor cortex. Evidence suggests that these relationships reflect direct cortico-cortical connections rather than common driving input from subcortical structures such as the thalamus. In summary, our inter-subject analyses defined the spatial and temporal relationships between music-related brain activity in the alpha and high gamma bands. They provide experimental evidence supporting current theories about the putative mechanisms of alpha and gamma activity, i.e., reflections of thalamo-cortical interactions and local cortical neural activity, respectively, and the results are also in agreement with existing functional models of auditory processing. Copyright © 2014 Elsevier Inc. All rights reserved.

Obesity and infection: reciprocal causality.

PubMed

Hainer, V; Zamrazilová, H; Kunešová, M; Bendlová, B; Aldhoon-Hainerová, I

2015-01-01

Associations between different infectious agents and obesity have been reported in humans for over thirty years. In many cases, as in nosocomial infections, this relationship reflects the greater susceptibility of obese individuals to infection due to impaired immunity. In such cases, the infection is not related to obesity as a causal factor but represents a complication of obesity. In contrast, several infections have been suggested as potential causal factors in human obesity. However, evidence of a causal linkage to human obesity has only been provided for adenovirus 36 (Adv36). This virus activates lipogenic and proinflammatory pathways in adipose tissue, improves insulin sensitivity, lipid profile and hepatic steatosis. The E4orf1 gene of Adv36 exerts insulin senzitizing effects, but is devoid of its pro-inflammatory modalities. The development of a vaccine to prevent Adv36-induced obesity or the use of E4orf1 as a ligand for novel antidiabetic drugs could open new horizons in the prophylaxis and treatment of obesity and diabetes. More experimental and clinical studies are needed to elucidate the mutual relations between infection and obesity, identify additional infectious agents causing human obesity, as well as define the conditions that predispose obese individuals to specific infections.

Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.

PubMed

Giambartolomei, Claudia; Vukcevic, Damjan; Schadt, Eric E; Franke, Lude; Hingorani, Aroon D; Wallace, Chris; Plagnol, Vincent

2014-05-01

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.

Epidemiologic evidence for asthma and exposure to air toxics: linkages between occupational, indoor, and community air pollution research.

PubMed Central

Delfino, Ralph J

2002-01-01

Outdoor ambient air pollutant exposures in communities are relevant to the acute exacerbation and possibly the onset of asthma. However, the complexity of pollutant mixtures and etiologic heterogeneity of asthma has made it difficult to identify causal components in those mixtures. Occupational exposures associated with asthma may yield clues to causal components in ambient air pollution because such exposures are often identifiable as single-chemical agents (e.g., metal compounds). However, translating occupational to community exposure-response relationships is limited. Of the air toxics found to cause occupational asthma, only formaldehyde has been frequently investigated in epidemiologic studies of allergic respiratory responses to indoor air, where general consistency can be shown despite lower ambient exposures. The specific volatile organic compounds (VOCs) identified in association with occupational asthma are generally not the same as those in studies showing respiratory effects of VOC mixtures on nonoccupational adult and pediatric asthma. In addition, experimental evidence indicates that airborne polycyclic aromatic hydrocarbon (PAH) exposures linked to diesel exhaust particles (DEPs) have proinflammatory effects on airways, but there is insufficient supporting evidence from the occupational literature of effects of DEPs on asthma or lung function. In contrast, nonoccupational epidemiologic studies have frequently shown associations between allergic responses or asthma with exposures to ambient air pollutant mixtures with PAH components, including black smoke, high home or school traffic density (particularly truck traffic), and environmental tobacco smoke. Other particle-phase and gaseous co-pollutants are likely causal in these associations as well. Epidemiologic research on the relationship of both asthma onset and exacerbation to air pollution is needed to disentangle effects of air toxics from monitored criteria air pollutants such as particle mass. Community studies should focus on air toxics expected to have adverse respiratory effects based on biological mechanisms, particularly irritant and immunological pathways to asthma onset and exacerbation. PMID:12194890

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

NASA Astrophysics Data System (ADS)

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

From Classification to Causality: Advancing Understanding of Mechanisms of Change in Implementation Science.

PubMed

Lewis, Cara C; Klasnja, Predrag; Powell, Byron J; Lyon, Aaron R; Tuzzio, Leah; Jones, Salene; Walsh-Bailey, Callie; Weiner, Bryan

2018-01-01

The science of implementation has offered little toward understanding how different implementation strategies work. To improve outcomes of implementation efforts, the field needs precise, testable theories that describe the causal pathways through which implementation strategies function. In this perspective piece, we describe a four-step approach to developing causal pathway models for implementation strategies. First, it is important to ensure that implementation strategies are appropriately specified. Some strategies in published compilations are well defined but may not be specified in terms of its core component that can have a reliable and measureable impact. Second, linkages between strategies and mechanisms need to be generated. Existing compilations do not offer mechanisms by which strategies act, or the processes or events through which an implementation strategy operates to affect desired implementation outcomes. Third, it is critical to identify proximal and distal outcomes the strategy is theorized to impact, with the former being direct, measurable products of the strategy and the latter being one of eight implementation outcomes (1). Finally, articulating effect modifiers, like preconditions and moderators, allow for an understanding of where, when, and why strategies have an effect on outcomes of interest. We argue for greater precision in use of terms for factors implicated in implementation processes; development of guidelines for selecting research design and study plans that account for practical constructs and allow for the study of mechanisms; psychometrically strong and pragmatic measures of mechanisms; and more robust curation of evidence for knowledge transfer and use.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects.

PubMed

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

New challenges for text mining: mapping between text and manually curated pathways

PubMed Central

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

Causal network analysis of head and neck keloid tissue identifies potential master regulators.

PubMed

Garcia-Rodriguez, Laura; Jones, Lamont; Chen, Kang Mei; Datta, Indrani; Divine, George; Worsham, Maria J

2016-10-01

To generate novel insights and hypotheses in keloid development from potential master regulators. Prospective cohort. Six fresh keloid and six normal skin samples from 12 anonymous donors were used in a prospective cohort study. Genome-wide profiling was done previously on the cohort using the Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA). The 190 statistically significant CpG islands between keloid and normal tissue mapped to 152 genes (P < .05). The top 10 statistically significant genes (VAMP5, ACTR3C, GALNT3, KCNAB2, LRRC61, SCML4, SYNGR1, TNS1, PLEKHG5, PPP1R13-α, false discovery rate <.015) were uploaded into the Ingenuity Pathway Analysis software's Causal Network Analysis (QIAGEN, Redwood City, CA). To reflect expected gene expression direction in the context of methylation changes, the inverse of the methylation ratio from keloid versus normal tissue was used for the analysis. Causal Network Analysis identified disease-specific master regulator molecules based on downstream differentially expressed keloid-specific genes and expected directionality of expression (hypermethylated vs. hypomethylated). Causal Network Analysis software identified four hierarchical networks that included four master regulators (pyroxamide, tributyrin, PRKG2, and PENK) and 19 intermediate regulators. Causal Network Analysis of differentiated methylated gene data of keloid versus normal skin demonstrated four causal networks with four master regulators. These hierarchical networks suggest potential driver roles for their downstream keloid gene targets in the pathogenesis of the keloid phenotype, likely triggered due to perturbation/injury to normal tissue. NA Laryngoscope, 126:E319-E324, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments

PubMed Central

2014-01-01

Background Knockdown or overexpression of genes is widely used to identify genes that play important roles in many aspects of cellular functions and phenotypes. Because next-generation sequencing generates high-throughput data that allow us to detect genes, it is important to identify genes that drive functional and phenotypic changes of cells. However, conventional methods rely heavily on the assumption of normality and they often give incorrect results when the assumption is not true. To relax the Gaussian assumption in causal inference, we introduce the non-paranormal method to test conditional independence in the PC-algorithm. Then, we present the non-paranormal intervention-calculus when the directed acyclic graph (DAG) is absent (NPN-IDA), which incorporates the cumulative nature of effects through a cascaded pathway via causal inference for ranking causal genes against a phenotype with the non-paranormal method for estimating DAGs. Results We demonstrate that causal inference with the non-paranormal method significantly improves the performance in estimating DAGs on synthetic data in comparison with the original PC-algorithm. Moreover, we show that NPN-IDA outperforms the conventional methods in exploring regulators of the flowering time in Arabidopsis thaliana and regulators that control the browning of white adipocytes in mice. Our results show that performance improvement in estimating DAGs contributes to an accurate estimation of causal effects. Conclusions Although the simplest alternative procedure was used, our proposed method enables us to design efficient intervention experiments and can be applied to a wide range of research purposes, including drug discovery, because of its generality. PMID:24980787

Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments.

PubMed

Teramoto, Reiji; Saito, Chiaki; Funahashi, Shin-ichi

2014-06-30

Knockdown or overexpression of genes is widely used to identify genes that play important roles in many aspects of cellular functions and phenotypes. Because next-generation sequencing generates high-throughput data that allow us to detect genes, it is important to identify genes that drive functional and phenotypic changes of cells. However, conventional methods rely heavily on the assumption of normality and they often give incorrect results when the assumption is not true. To relax the Gaussian assumption in causal inference, we introduce the non-paranormal method to test conditional independence in the PC-algorithm. Then, we present the non-paranormal intervention-calculus when the directed acyclic graph (DAG) is absent (NPN-IDA), which incorporates the cumulative nature of effects through a cascaded pathway via causal inference for ranking causal genes against a phenotype with the non-paranormal method for estimating DAGs. We demonstrate that causal inference with the non-paranormal method significantly improves the performance in estimating DAGs on synthetic data in comparison with the original PC-algorithm. Moreover, we show that NPN-IDA outperforms the conventional methods in exploring regulators of the flowering time in Arabidopsis thaliana and regulators that control the browning of white adipocytes in mice. Our results show that performance improvement in estimating DAGs contributes to an accurate estimation of causal effects. Although the simplest alternative procedure was used, our proposed method enables us to design efficient intervention experiments and can be applied to a wide range of research purposes, including drug discovery, because of its generality.

Quantification of causal couplings via dynamical effects: A unifying perspective

NASA Astrophysics Data System (ADS)

Smirnov, Dmitry A.

2014-12-01

Quantitative characterization of causal couplings from time series is crucial in studies of complex systems of different origin. Various statistical tools for that exist and new ones are still being developed with a tendency to creating a single, universal, model-free quantifier of coupling strength. However, a clear and generally applicable way of interpreting such universal characteristics is lacking. This work suggests a general conceptual framework for causal coupling quantification, which is based on state space models and extends the concepts of virtual interventions and dynamical causal effects. Namely, two basic kinds of interventions (state space and parametric) and effects (orbital or transient and stationary or limit) are introduced, giving four families of coupling characteristics. The framework provides a unifying view of apparently different well-established measures and allows us to introduce new characteristics, always with a definite "intervention-effect" interpretation. It is shown that diverse characteristics cannot be reduced to any single coupling strength quantifier and their interpretation is inevitably model based. The proposed set of dynamical causal effect measures quantifies different aspects of "how the coupling manifests itself in the dynamics," reformulating the very question about the "causal coupling strength."

Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc

Collaborating To Enhance Resilience in Rural At-Risk Students.

ERIC Educational Resources Information Center

Finley, Mary K.

This paper links areas of research with implications for community prevention strategies aimed at high-risk children. Collaborative efforts to reduce the number of at-risk children in rural communities can be advanced by merging knowledge from the following areas: (1) the causal pathways leading to substance abuse and identification of risk…

Childhood Adversity, Poly-Substance Use, and Disordered Eating in Adolescent Latinas: Mediated and Indirect Paths in a Community Sample

ERIC Educational Resources Information Center

Hodson, Christopher; Newcomb, Michael D.; Locke, Thomas F.; Goodyear, Rodney K.

2006-01-01

Objective: Although many studies have identified associations between childhood maltreatment and later substance use and disordered eating, few have examined causal or explanatory pathways, and whether victim characteristics predict the development of one versus the other of these outcomes remains uninvestigated. Furthermore, relatively little…

Demographic Effects of Girls' Education in Developing Countries: Proceedings of a Workshop. In Brief

ERIC Educational Resources Information Center

Samari, Goleen

2017-01-01

Educating girls is a universally accepted strategy for improving lives and advancing development. Girls' schooling is associated with many demographic outcomes, including later age at marriage or union formation, lower fertility, and better child health. However, the causal pathways between education and demographic outcomes are not well…

Lactoferrin and lysozyme to reduce environmental enteric dysfunction and stunting in Malawian children: Study protocol for a randomized controlled trial

USDA-ARS?s Scientific Manuscript database

Chronic childhood malnutrition, as manifested by stunted linear growth, remains a persistent barrier to optimal child growth and societal development. Environmental enteric dysfunction (EED) is a significant underlying factor in the causal pathway to stunting, delayed cognitive development, and ulti...

Teen Pregnancy and the Achievement Gap among Urban Minority Youth

ERIC Educational Resources Information Center

Basch, Charles E.

2011-01-01

Objectives: To outline the prevalence and disparities of teen pregnancy among school-aged urban minority youth, causal pathways through which nonmarital teen births adversely affects academic achievement, and proven or promising approaches for schools to address this problem. Methods: Literature review. Results: In 2006, the birth rate among 15-…

Predictors of Victimisation across Direct Bullying, Indirect Bullying and Cyberbullying

ERIC Educational Resources Information Center

Brighi, Antonella; Guarini, Annalisa; Melotti, Giannino; Galli, Silvia; Genta, Maria Luisa

2012-01-01

Cyberbullying may sometimes be an extension of traditional bullying. However, some particular features of cyberbullying suggest that it may have a distinct causal pathway, due to the social context of a virtual environment within which peer social processes occur. Moreover, boys and girls may perceive and respond differentially to their social…

Effects of education on cognition at older ages: evidence from China's Great Famine.

PubMed

Huang, Wei; Zhou, Yi

2013-12-01

This paper explores whether educational attainment has a cognitive reserve capacity in elder life. Using pilot data from the China Health and Retirement Longitudinal Study (CHARLS), we examined the impact of education on cognitive abilities at old ages. OLS results showed that respondents who completed primary school obtained 18.2 percent higher scores on cognitive tests than those who did not. We then constructed an instrumental variable (IV) by leveraging China's Great Famine of 1959-1961 as a natural experiment to estimate the causal effect of education on cognition. Two-stage least squares (2SLS) results provided sound evidence that completing primary school significantly increases cognition scores, especially in episode memory, by almost 20 percent on average. Moreover, Regression Discontinuity (RD) analysis provides further evidence for the causal interpretation, and shows that the effects are different for the different measures of cognition we explored. Our results also show that the Great Famine can result in long-term health consequences through the pathway of losing educational opportunities other than through the pathway of nutrition deprivation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nuclear lamina defects cause ATM-dependent NF-κB activation and link accelerated aging to a systemic inflammatory response

PubMed Central

Osorio, Fernando G.; Bárcena, Clea; Soria-Valles, Clara; Ramsay, Andrew J.; de Carlos, Félix; Cobo, Juan; Fueyo, Antonio; Freije, José M.P.; López-Otín, Carlos

2012-01-01

Alterations in the architecture and dynamics of the nuclear lamina have a causal role in normal and accelerated aging through both cell-autonomous and systemic mechanisms. However, the precise nature of the molecular cues involved in this process remains incompletely defined. Here we report that the accumulation of prelamin A isoforms at the nuclear lamina triggers an ATM- and NEMO-dependent signaling pathway that leads to NF-κB activation and secretion of high levels of proinflammatory cytokines in two different mouse models of accelerated aging (Zmpste24−/− and LmnaG609G/G609G mice). Causal involvement of NF-κB in accelerated aging was demonstrated by the fact that both genetic and pharmacological inhibition of NF-κB signaling prevents age-associated features in these animal models, significantly extending their longevity. Our findings provide in vivo proof of principle for the feasibility of pharmacological modulation of the NF-κB pathway to slow down the progression of physiological and pathological aging. PMID:23019125

Species diversity and toxigenic potential of Fusarium graminearum complex isolates from maize fields in northwest Argentina

USDA-ARS?s Scientific Manuscript database

Members of the Fusarium graminearum species complex (Fg complex) are the causal agents of ear rot in maize and Fusarium head blight of wheat and other small grain cereals. The potential of these pathogens to contaminate cereals with trichothecene mycotoxins is a health risk for both humans and anima...

Exploring complex causal pathways between urban renewal, health and health inequality using a theory-driven realist approach.

PubMed

Mehdipanah, Roshanak; Manzano, Ana; Borrell, Carme; Malmusi, Davide; Rodriguez-Sanz, Maica; Greenhalgh, Joanne; Muntaner, Carles; Pawson, Ray

2015-01-01

Urban populations are growing and to accommodate these numbers, cities are becoming more involved in urban renewal programs to improve the physical, social and economic conditions in different areas. This paper explores some of the complexities surrounding the link between urban renewal, health and health inequalities using a theory-driven approach. We focus on an urban renewal initiative implemented in Barcelona, the Neighbourhoods Law, targeting Barcelona's (Spain) most deprived neighbourhoods. We present evidence from two studies on the health evaluation of the Neighbourhoods Law, while drawing from recent urban renewal literature, to follow a four-step process to develop a program theory. We then use two specific urban renewal interventions, the construction of a large central plaza and the repair of streets and sidewalks, to further examine this link. In order for urban renewal programs to affect health and health inequality, neighbours must use and adapt to the changes produced by the intervention. However, there exist barriers that can result in negative outcomes including factors such as accessibility, safety and security. This paper provides a different perspective to the field that is largely dominated by traditional quantitative studies that are not always able to address the complexities such interventions provide. Furthermore, the framework and discussions serve as a guide for future research, policy development and evaluation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identification of chemical components of combustion emissions that affect pro-atherosclerotic vascular responses in mice

PubMed Central

Seilkop, Steven K.; Campen, Matthew J.; Lund, Amie K.; McDonald, Jacob D.; Mauderly, Joe L.

2012-01-01

Combustion emissions cause pro-atherosclerotic responses in apolipoprotein E-deficient (ApoE/−) mice, but the causal components of these complex mixtures are unresolved. In studies previously reported, ApoE−/− mice were exposed by inhalation 6 h/day for 50 consecutive days to multiple dilutions of diesel or gasoline exhaust, wood smoke, or simulated “downwind” coal emissions. In this study, the analysis of the combined four-study database using the Multiple Additive Regression Trees (MART) data mining approach to determine putative causal exposure components regardless of combustion source is reported. Over 700 physical–chemical components were grouped into 45 predictor variables. Response variables measured in aorta included endothelin-1, vascular endothelin growth factor, three matrix metalloproteinases (3, 7, 9), metalloproteinase inhibitor 2, heme-oxygenase-1, and thiobarbituric acid reactive substances. Two or three predictors typically explained most of the variation in response among the experimental groups. Overall, sulfur dioxide, ammonia, nitrogen oxides, and carbon monoxide were most highly predictive of responses, although their rankings differed among the responses. Consistent with the earlier finding that filtration of particles had little effect on responses, particulate components ranked third to seventh in predictive importance for the eight response variables. MART proved useful for identifying putative causal components, although the small number of pollution mixtures (4) can provide only suggestive evidence of causality. The potential independent causal contributions of these gases to the vascular responses, as well as possible interactions among them and other components of complex pollutant mixtures, warrant further evaluation. PMID:22486345

Identification of chemical components of combustion emissions that affect pro-atherosclerotic vascular responses in mice.

PubMed

Seilkop, Steven K; Campen, Matthew J; Lund, Amie K; McDonald, Jacob D; Mauderly, Joe L

2012-04-01

Combustion emissions cause pro-atherosclerotic responses in apolipoprotein E-deficient (ApoE/⁻) mice, but the causal components of these complex mixtures are unresolved. In studies previously reported, ApoE⁻/⁻ mice were exposed by inhalation 6 h/day for 50 consecutive days to multiple dilutions of diesel or gasoline exhaust, wood smoke, or simulated "downwind" coal emissions. In this study, the analysis of the combined four-study database using the Multiple Additive Regression Trees (MART) data mining approach to determine putative causal exposure components regardless of combustion source is reported. Over 700 physical-chemical components were grouped into 45 predictor variables. Response variables measured in aorta included endothelin-1, vascular endothelin growth factor, three matrix metalloproteinases (3, 7, 9), metalloproteinase inhibitor 2, heme-oxygenase-1, and thiobarbituric acid reactive substances. Two or three predictors typically explained most of the variation in response among the experimental groups. Overall, sulfur dioxide, ammonia, nitrogen oxides, and carbon monoxide were most highly predictive of responses, although their rankings differed among the responses. Consistent with the earlier finding that filtration of particles had little effect on responses, particulate components ranked third to seventh in predictive importance for the eight response variables. MART proved useful for identifying putative causal components, although the small number of pollution mixtures (4) can provide only suggestive evidence of causality. The potential independent causal contributions of these gases to the vascular responses, as well as possible interactions among them and other components of complex pollutant mixtures, warrant further evaluation.

Beyond a series of security nets: Applying STAMP & STPA to port security

DOE PAGES

Williams, Adam D.

2015-11-17

Port security is an increasing concern considering the significant role of ports in global commerce and today’s increasingly complex threat environment. Current approaches to port security mirror traditional models of accident causality -- ‘a series of security nets’ based on component reliability and probabilistic assumptions. Traditional port security frameworks result in isolated and inconsistent improvement strategies. Recent work in engineered safety combines the ideas of hierarchy, emergence, control and communication into a new paradigm for understanding port security as an emergent complex system property. The ‘System-Theoretic Accident Model and Process (STAMP)’ is a new model of causality based on systemsmore » and control theory. The associated analysis process -- System Theoretic Process Analysis (STPA) -- identifies specific technical or procedural security requirements designed to work in coordination with (and be traceable to) overall port objectives. This process yields port security design specifications that can mitigate (if not eliminate) port security vulnerabilities related to an emphasis on component reliability, lack of coordination between port security stakeholders or economic pressures endemic in the maritime industry. As a result, this article aims to demonstrate how STAMP’s broader view of causality and complexity can better address the dynamic and interactive behaviors of social, organizational and technical components of port security.« less

GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits.

PubMed

Huang, Dandan; Yi, Xianfu; Zhang, Shijie; Zheng, Zhanye; Wang, Panwen; Xuan, Chenghao; Sham, Pak Chung; Wang, Junwen; Li, Mulin Jun

2018-05-16

Genome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.

Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Directed Cyclic Graph and Joint Probability Distribution.

PubMed

Zhang, Qin

2015-07-01

Probabilistic graphical models (PGMs) such as Bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning. Dynamic uncertain causality graph (DUCG) is a newly presented model of PGMs, which can be applied to fault diagnosis of large and complex industrial systems, disease diagnosis, and so on. The basic methodology of DUCG has been previously presented, in which only the directed acyclic graph (DAG) was addressed. However, the mathematical meaning of DUCG was not discussed. In this paper, the DUCG with directed cyclic graphs (DCGs) is addressed. In contrast, BN does not allow DCGs, as otherwise the conditional independence will not be satisfied. The inference algorithm for the DUCG with DCGs is presented, which not only extends the capabilities of DUCG from DAGs to DCGs but also enables users to decompose a large and complex DUCG into a set of small, simple sub-DUCGs, so that a large and complex knowledge base can be easily constructed, understood, and maintained. The basic mathematical definition of a complete DUCG with or without DCGs is proved to be a joint probability distribution (JPD) over a set of random variables. The incomplete DUCG as a part of a complete DUCG may represent a part of JPD. Examples are provided to illustrate the methodology.

Beyond a series of security nets: Applying STAMP & STPA to port security

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Adam D.

Port security is an increasing concern considering the significant role of ports in global commerce and today’s increasingly complex threat environment. Current approaches to port security mirror traditional models of accident causality -- ‘a series of security nets’ based on component reliability and probabilistic assumptions. Traditional port security frameworks result in isolated and inconsistent improvement strategies. Recent work in engineered safety combines the ideas of hierarchy, emergence, control and communication into a new paradigm for understanding port security as an emergent complex system property. The ‘System-Theoretic Accident Model and Process (STAMP)’ is a new model of causality based on systemsmore » and control theory. The associated analysis process -- System Theoretic Process Analysis (STPA) -- identifies specific technical or procedural security requirements designed to work in coordination with (and be traceable to) overall port objectives. This process yields port security design specifications that can mitigate (if not eliminate) port security vulnerabilities related to an emphasis on component reliability, lack of coordination between port security stakeholders or economic pressures endemic in the maritime industry. As a result, this article aims to demonstrate how STAMP’s broader view of causality and complexity can better address the dynamic and interactive behaviors of social, organizational and technical components of port security.« less

Inhalation Exposure and Lung Dose Analysis of Multi-mode Complex Ambient Aerosols

EPA Science Inventory

Rationale: Ambient aerosols are complex mixture of particles with different size, shape and chemical composition. Although they are known to cause health hazard, it is not fully understood about causal mechanisms and specific attributes of particles causing the effects. Internal ...

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free

PubMed Central

Bianconi, Ginestra; Rahmede, Christoph

2015-01-01

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension . We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the -faces of the -dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the -faces. PMID:26356079

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free.

PubMed

Bianconi, Ginestra; Rahmede, Christoph

2015-09-10

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension d. We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the δ-faces of the d-dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the δ-faces.

An fMRI study of neural pathways following acupuncture in mild cognitive impairment patients

NASA Astrophysics Data System (ADS)

Feng, Yuanyuan; Bai, Lijun; Wang, Hu; Zhong, Chongguang; You, Youbo; Zhang, Wensheng; Tian, Jie

2012-03-01

While the use of acupuncture as a complementary therapeutic method for treating MCI is popular in certain parts of the world, the underlying mechanism is still elusive. In the current study, we adopted multivariate Granger causality analysis (mGCA) to explore the causal interactions of brain networks involving acupuncture in mild cognitive impairment (MCI) patients compared to healthy controls (HC). The fMRI experiment was performed with two different paradigms: namely, deep acupuncture (DA) and superficial acupuncture (SA) at acupoint KI3. Results demonstrated that deep acupuncture could modulate the abnormal regions in MCI group. These regions are implicated in memory encoding and retrieving. This may relate to the purported therapeutically beneficial effects of acupuncture for the treatment of MCI. However, the most significant causal interactions were found in the sensorimotor regions in HC group. This may because acupuncture has a greater modulatory effect on patients with a pathological imbalance. This paper provides the preliminary neurophysiological evidence for the potential efficacy effect of acupuncture on MCI.

Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system.

PubMed

Gao, Xiangyun; Huang, Shupei; Sun, Xiaoqi; Hao, Xiaoqing; An, Feng

2018-03-01

Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion.

Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system

PubMed Central

Huang, Shupei; Sun, Xiaoqi; Hao, Xiaoqing; An, Feng

2018-01-01

Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion. PMID:29657804

Architecture of Explanatory Inference in the Human Prefrontal Cortex

PubMed Central

Barbey, Aron K.; Patterson, Richard

2011-01-01

Causal reasoning is a ubiquitous feature of human cognition. We continuously seek to understand, at least implicitly and often explicitly, the causal scenarios in which we live, so that we may anticipate what will come next, plan a potential response and envision its outcome, decide among possible courses of action in light of their probable outcomes, make midstream adjustments in our goal-related activities as our situation changes, and so on. A considerable body of research shows that the lateral prefrontal cortex (PFC) is crucial for causal reasoning, but also that there are significant differences in the manner in which ventrolateral PFC, dorsolateral PFC, and anterolateral PFC support causal reasoning. We propose, on the basis of research on the evolution, architecture, and functional organization of the lateral PFC, a general framework for understanding its roles in the many and varied sorts of causal reasoning carried out by human beings. Specifically, the ventrolateral PFC supports the generation of basic causal explanations and inferences; dorsolateral PFC supports the evaluation of these scenarios in light of some given normative standard (e.g., of plausibility or correctness in light of real or imagined causal interventions); and anterolateral PFC supports explanation and inference at an even higher level of complexity, coordinating the processes of generation and evaluation with further cognitive processes, and especially with computations of hedonic value and emotional implications of possible behavioral scenarios – considerations that are often critical both for understanding situations causally and for deciding about our own courses of action. PMID:21845182

Behavior systems and reinforcement: an integrative approach.

PubMed Central

Timberlake, W

1993-01-01

Most traditional conceptions of reinforcement are based on a simple causal model in which responding is strengthened by the presentation of a reinforcer. I argue that reinforcement is better viewed as the outcome of constraint of a functioning causal system comprised of multiple interrelated causal sequences, complex linkages between causes and effects, and a set of initial conditions. Using a simplified system conception of the reinforcement situation, I review the similarities and drawbacks of traditional reinforcement models and analyze the recent contributions of cognitive, regulatory, and ecological approaches. Finally, I show how the concept of behavior systems can begin to incorporate both traditional and recent conceptions of reinforcement in an integrative approach. PMID:8354963

Causal inferences on the effectiveness of complex social programs: Navigating assumptions, sources of complexity and evaluation design challenges.

PubMed

Chatterji, Madhabi

2016-12-01

This paper explores avenues for navigating evaluation design challenges posed by complex social programs (CSPs) and their environments when conducting studies that call for generalizable, causal inferences on the intervention's effectiveness. A definition is provided of a CSP drawing on examples from different fields, and an evaluation case is analyzed in depth to derive seven (7) major sources of complexity that typify CSPs, threatening assumptions of textbook-recommended experimental designs for performing impact evaluations. Theoretically-supported, alternative methodological strategies are discussed to navigate assumptions and counter the design challenges posed by the complex configurations and ecology of CSPs. Specific recommendations include: sequential refinement of the evaluation design through systems thinking, systems-informed logic modeling; and use of extended term, mixed methods (ETMM) approaches with exploratory and confirmatory phases of the evaluation. In the proposed approach, logic models are refined through direct induction and interactions with stakeholders. To better guide assumption evaluation, question-framing, and selection of appropriate methodological strategies, a multiphase evaluation design is recommended. Copyright © 2016 Elsevier Ltd. All rights reserved.

Why pleiotropic interventions are needed for Alzheimer's disease.

PubMed

Frautschy, Sally A; Cole, Greg M

2010-06-01

Alzheimer's disease (AD) involves a complex pathological cascade thought to be initially triggered by the accumulation of beta-amyloid (Abeta) peptide aggregates or aberrant amyloid precursor protein (APP) processing. Much is known of the factors initiating the disease process decades prior to the onset of cognitive deficits, but an unclear understanding of events immediately preceding and precipitating cognitive decline is a major factor limiting the rapid development of adequate prevention and treatment strategies. Multiple pathways are known to contribute to cognitive deficits by disruption of neuronal signal transduction pathways involved in memory. These pathways are altered by aberrant signaling, inflammation, oxidative damage, tau pathology, neuron loss, and synapse loss. We need to develop stage-specific interventions that not only block causal events in pathogenesis (aberrant tau phosphorylation, Abeta production and accumulation, and oxidative damage), but also address damage from these pathways that will not be reversed by targeting prodromal pathways. This approach would not only focus on blocking early events in pathogenesis, but also adequately correct for loss of synapses, substrates for neuroprotective pathways (e.g., docosahexaenoic acid), defects in energy metabolism, and adverse consequences of inappropriate compensatory responses (aberrant sprouting). Monotherapy targeting early single steps in this complicated cascade may explain disappointments in trials with agents inhibiting production, clearance, or aggregation of the initiating Abeta peptide or its aggregates. Both plaque and tangle pathogenesis have already reached AD levels in the more vulnerable brain regions during the "prodromal" period prior to conversion to "mild cognitive impairment (MCI)." Furthermore, many of the pathological events are no longer proceeding in series, but are going on in parallel. By the MCI stage, we stand a greater chance of success by considering pleiotropic drugs or cocktails that can independently limit the parallel steps of the AD cascade at all stages, but that do not completely inhibit the constitutive normal functions of these pathways. Based on this hypothesis, efforts in our laboratories have focused on the pleiotropic activities of omega-3 fatty acids and the anti-inflammatory, antioxidant, and anti-amyloid activity of curcumin in multiple models that cover many steps of the AD pathogenic cascade (Cole and Frautschy, Alzheimers Dement 2:284-286, 2006).

A method for gene-based pathway analysis using genomewide association study summary statistics reveals nine new type 1 diabetes associations.

PubMed

Evangelou, Marina; Smyth, Deborah J; Fortune, Mary D; Burren, Oliver S; Walker, Neil M; Guo, Hui; Onengut-Gumuscu, Suna; Chen, Wei-Min; Concannon, Patrick; Rich, Stephen S; Todd, John A; Wallace, Chris

2014-12-01

Pathway analysis can complement point-wise single nucleotide polymorphism (SNP) analysis in exploring genomewide association study (GWAS) data to identify specific disease-associated genes that can be candidate causal genes. We propose a straightforward methodology that can be used for conducting a gene-based pathway analysis using summary GWAS statistics in combination with widely available reference genotype data. We used this method to perform a gene-based pathway analysis of a type 1 diabetes (T1D) meta-analysis GWAS (of 7,514 cases and 9,045 controls). An important feature of the conducted analysis is the removal of the major histocompatibility complex gene region, the major genetic risk factor for T1D. Thirty-one of the 1,583 (2%) tested pathways were identified to be enriched for association with T1D at a 5% false discovery rate. We analyzed these 31 pathways and their genes to identify SNPs in or near these pathway genes that showed potentially novel association with T1D and attempted to replicate the association of 22 SNPs in additional samples. Replication P-values were skewed (P=9.85×10-11) with 12 of the 22 SNPs showing P<0.05. Support, including replication evidence, was obtained for nine T1D associated variants in genes ITGB7 (rs11170466, P=7.86×10-9), NRP1 (rs722988, 4.88×10-8), BAD (rs694739, 2.37×10-7), CTSB (rs1296023, 2.79×10-7), FYN (rs11964650, P=5.60×10-7), UBE2G1 (rs9906760, 5.08×10-7), MAP3K14 (rs17759555, 9.67×10-7), ITGB1 (rs1557150, 1.93×10-6), and IL7R (rs1445898, 2.76×10-6). The proposed methodology can be applied to other GWAS datasets for which only summary level data are available. © 2014 The Authors. ** Genetic Epidemiology published by Wiley Periodicals, Inc.

Mouse chronic social stress increases blood and brain kynurenine pathway activity and fear behaviour: Both effects are reversed by inhibition of indoleamine 2,3-dioxygenase.

PubMed

Fuertig, René; Azzinnari, Damiano; Bergamini, Giorgio; Cathomas, Flurin; Sigrist, Hannes; Seifritz, Erich; Vavassori, Stefano; Luippold, Andreas; Hengerer, Bastian; Ceci, Angelo; Pryce, Christopher R

2016-05-01

Psychosocial stress is a major risk factor for mood and anxiety disorders, in which excessive reactivity to aversive events/stimuli is a major psychopathology. In terms of pathophysiology, immune-inflammation is an important candidate, including high blood and brain levels of metabolites belonging to the kynurenine pathway. Animal models are needed to study causality between psychosocial stress, immune-inflammation and hyper-reactivity to aversive stimuli. The present mouse study investigated effects of psychosocial stress as chronic social defeat (CSD) versus control-handling (CON) on: Pavlovian tone-shock fear conditioning, activation of the kynurenine pathway, and efficacy of a specific inhibitor (IDOInh) of the tryptophan-kynurenine catabolising enzyme indoleamine 2,3-dioxygenase (IDO1), in reversing CSD effects on the kynurenine pathway and fear. CSD led to excessive fear learning and memory, whilst repeated oral escitalopram (antidepressant and anxiolytic) reversed excessive fear memory, indicating predictive validity of the model. CSD led to higher blood levels of TNF-α, IFN-γ, kynurenine (KYN), 3-hydroxykynurenine (3-HK) and kynurenic acid, and higher KYN and 3-HK in amygdala and hippocampus. CSD was without effect on IDO1 gene or protein expression in spleen, ileum and liver, whilst increasing liver TDO2 gene expression. Nonetheless, oral IDOInh reduced blood and brain levels of KYN and 3-HK in CSD mice to CON levels, and we therefore infer that CSD increases IDO1 activity by increasing its post-translational activation. Furthermore, repeated oral IDOInh reversed excessive fear memory in CSD mice to CON levels. IDOInh reversal of CSD-induced hyper-activity in the kynurenine pathway and fear system contributes significantly to the evidence for a causal pathway between psychosocial stress, immune-inflammation and the excessive fearfulness that is a major psychopathology in stress-related neuropsychiatric disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Computer modelling of epilepsy.

PubMed

Lytton, William W

2008-08-01

Epilepsy is a complex set of disorders that can involve many areas of the cortex, as well as underlying deep-brain systems. The myriad manifestations of seizures, which can be as varied as déjà vu and olfactory hallucination, can therefore give researchers insights into regional functions and relations. Epilepsy is also complex genetically and pathophysiologically: it involves microscopic (on the scale of ion channels and synaptic proteins), macroscopic (on the scale of brain trauma and rewiring) and intermediate changes in a complex interplay of causality. It has long been recognized that computer modelling will be required to disentangle causality, to better understand seizure spread and to understand and eventually predict treatment efficacy. Over the past few years, substantial progress has been made in modelling epilepsy at levels ranging from the molecular to the socioeconomic. We review these efforts and connect them to the medical goals of understanding and treating the disorder.

Big data to smart data in Alzheimer's disease: Real-world examples of advanced modeling and simulation.

PubMed

Haas, Magali; Stephenson, Diane; Romero, Klaus; Gordon, Mark Forrest; Zach, Neta; Geerts, Hugo

2016-09-01

Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair.

PubMed

Day, Felix R; Ruth, Katherine S; Thompson, Deborah J; Lunetta, Kathryn L; Pervjakova, Natalia; Chasman, Daniel I; Stolk, Lisette; Finucane, Hilary K; Sulem, Patrick; Bulik-Sullivan, Brendan; Esko, Tõnu; Johnson, Andrew D; Elks, Cathy E; Franceschini, Nora; He, Chunyan; Altmaier, Elisabeth; Brody, Jennifer A; Franke, Lude L; Huffman, Jennifer E; Keller, Margaux F; McArdle, Patrick F; Nutile, Teresa; Porcu, Eleonora; Robino, Antonietta; Rose, Lynda M; Schick, Ursula M; Smith, Jennifer A; Teumer, Alexander; Traglia, Michela; Vuckovic, Dragana; Yao, Jie; Zhao, Wei; Albrecht, Eva; Amin, Najaf; Corre, Tanguy; Hottenga, Jouke-Jan; Mangino, Massimo; Smith, Albert V; Tanaka, Toshiko; Abecasis, Goncalo; Andrulis, Irene L; Anton-Culver, Hoda; Antoniou, Antonis C; Arndt, Volker; Arnold, Alice M; Barbieri, Caterina; Beckmann, Matthias W; Beeghly-Fadiel, Alicia; Benitez, Javier; Bernstein, Leslie; Bielinski, Suzette J; Blomqvist, Carl; Boerwinkle, Eric; Bogdanova, Natalia V; Bojesen, Stig E; Bolla, Manjeet K; Borresen-Dale, Anne-Lise; Boutin, Thibaud S; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Campbell, Archie; Campbell, Harry; Chanock, Stephen J; Chapman, J Ross; Chen, Yii-Der Ida; Chenevix-Trench, Georgia; Couch, Fergus J; Coviello, Andrea D; Cox, Angela; Czene, Kamila; Darabi, Hatef; De Vivo, Immaculata; Demerath, Ellen W; Dennis, Joe; Devilee, Peter; Dörk, Thilo; Dos-Santos-Silva, Isabel; Dunning, Alison M; Eicher, John D; Fasching, Peter A; Faul, Jessica D; Figueroa, Jonine; Flesch-Janys, Dieter; Gandin, Ilaria; Garcia, Melissa E; García-Closas, Montserrat; Giles, Graham G; Girotto, Giorgia G; Goldberg, Mark S; González-Neira, Anna; Goodarzi, Mark O; Grove, Megan L; Gudbjartsson, Daniel F; Guénel, Pascal; Guo, Xiuqing; Haiman, Christopher A; Hall, Per; Hamann, Ute; Henderson, Brian E; Hocking, Lynne J; Hofman, Albert; Homuth, Georg; Hooning, Maartje J; Hopper, John L; Hu, Frank B; Huang, Jinyan; Humphreys, Keith; Hunter, David J; Jakubowska, Anna; Jones, Samuel E; Kabisch, Maria; Karasik, David; Knight, Julia A; Kolcic, Ivana; Kooperberg, Charles; Kosma, Veli-Matti; Kriebel, Jennifer; Kristensen, Vessela; Lambrechts, Diether; Langenberg, Claudia; Li, Jingmei; Li, Xin; Lindström, Sara; Liu, Yongmei; Luan, Jian'an; Lubinski, Jan; Mägi, Reedik; Mannermaa, Arto; Manz, Judith; Margolin, Sara; Marten, Jonathan; Martin, Nicholas G; Masciullo, Corrado; Meindl, Alfons; Michailidou, Kyriaki; Mihailov, Evelin; Milani, Lili; Milne, Roger L; Müller-Nurasyid, Martina; Nalls, Michael; Neale, Ben M; Nevanlinna, Heli; Neven, Patrick; Newman, Anne B; Nordestgaard, Børge G; Olson, Janet E; Padmanabhan, Sandosh; Peterlongo, Paolo; Peters, Ulrike; Petersmann, Astrid; Peto, Julian; Pharoah, Paul D P; Pirastu, Nicola N; Pirie, Ailith; Pistis, Giorgio; Polasek, Ozren; Porteous, David; Psaty, Bruce M; Pylkäs, Katri; Radice, Paolo; Raffel, Leslie J; Rivadeneira, Fernando; Rudan, Igor; Rudolph, Anja; Ruggiero, Daniela; Sala, Cinzia F; Sanna, Serena; Sawyer, Elinor J; Schlessinger, David; Schmidt, Marjanka K; Schmidt, Frank; Schmutzler, Rita K; Schoemaker, Minouk J; Scott, Robert A; Seynaeve, Caroline M; Simard, Jacques; Sorice, Rossella; Southey, Melissa C; Stöckl, Doris; Strauch, Konstantin; Swerdlow, Anthony; Taylor, Kent D; Thorsteinsdottir, Unnur; Toland, Amanda E; Tomlinson, Ian; Truong, Thérèse; Tryggvadottir, Laufey; Turner, Stephen T; Vozzi, Diego; Wang, Qin; Wellons, Melissa; Willemsen, Gonneke; Wilson, James F; Winqvist, Robert; Wolffenbuttel, Bruce B H R; Wright, Alan F; Yannoukakos, Drakoulis; Zemunik, Tatijana; Zheng, Wei; Zygmunt, Marek; Bergmann, Sven; Boomsma, Dorret I; Buring, Julie E; Ferrucci, Luigi; Montgomery, Grant W; Gudnason, Vilmundur; Spector, Tim D; van Duijn, Cornelia M; Alizadeh, Behrooz Z; Ciullo, Marina; Crisponi, Laura; Easton, Douglas F; Gasparini, Paolo P; Gieger, Christian; Harris, Tamara B; Hayward, Caroline; Kardia, Sharon L R; Kraft, Peter; McKnight, Barbara; Metspalu, Andres; Morrison, Alanna C; Reiner, Alex P; Ridker, Paul M; Rotter, Jerome I; Toniolo, Daniela; Uitterlinden, André G; Ulivi, Sheila; Völzke, Henry; Wareham, Nicholas J; Weir, David R; Yerges-Armstrong, Laura M; Price, Alkes L; Stefansson, Kari; Visser, Jenny A; Ong, Ken K; Chang-Claude, Jenny; Murabito, Joanne M; Perry, John R B; Murray, Anna

2015-11-01

Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ∼70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two regions harboring additional rare missense alleles of large effect. We found enrichment of signals in or near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses identified major association with DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal effect of later ANM on breast cancer risk (∼6% increase in risk per year; P = 3 × 10(-14)), likely mediated by prolonged sex hormone exposure rather than DDR mechanisms.

Large-scale genomic analyses link reproductive ageing to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair

PubMed Central

Lunetta, Kathryn L.; Pervjakova, Natalia; Chasman, Daniel I.; Stolk, Lisette; Finucane, Hilary K.; Sulem, Patrick; Bulik-Sullivan, Brendan; Esko, Tõnu; Johnson, Andrew D.; Elks, Cathy E.; Franceschini, Nora; He, Chunyan; Altmaier, Elisabeth; Brody, Jennifer A.; Franke, Lude L.; Huffman, Jennifer E.; Keller, Margaux F.; McArdle, Patrick F.; Nutile, Teresa; Porcu, Eleonora; Robino, Antonietta; Rose, Lynda M.; Schick, Ursula M.; Smith, Jennifer A.; Teumer, Alexander; Traglia, Michela; Vuckovic, Dragana; Yao, Jie; Zhao, Wei; Albrecht, Eva; Amin, Najaf; Corre, Tanguy; Hottenga, Jouke-Jan; Mangino, Massimo; Smith, Albert V.; Tanaka, Toshiko; Abecasis, Goncalo; Andrulis, Irene L.; Anton-Culver, Hoda; Antoniou, Antonis C.; Arndt, Volker; Arnold, Alice M.; Barbieri, Caterina; Beckmann, Matthias W.; Beeghly-Fadiel, Alicia; Benitez, Javier; Bernstein, Leslie; Bielinski, Suzette J.; Blomqvist, Carl; Boerwinkle, Eric; Bogdanova, Natalia V.; Bojesen, Stig E.; Bolla, Manjeet K.; Borresen-Dale, Anne-Lise; Boutin, Thibaud S; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Campbell, Archie; Campbell, Harry; Chanock, Stephen J.; Chapman, J. Ross; Chen, Yii-Der Ida; Chenevix-Trench, Georgia; Couch, Fergus J.; Coviello, Andrea D.; Cox, Angela; Czene, Kamila; Darabi, Hatef; De Vivo, Immaculata; Demerath, Ellen W.; Dennis, Joe; Devilee, Peter; Dörk, Thilo; dos-Santos-Silva, Isabel; Dunning, Alison M.; Eicher, John D.; Fasching, Peter A.; Faul, Jessica D.; Figueroa, Jonine; Flesch-Janys, Dieter; Gandin, Ilaria; Garcia, Melissa E.; García-Closas, Montserrat; Giles, Graham G.; Girotto, Giorgia G.; Goldberg, Mark S.; González-Neira, Anna; Goodarzi, Mark O.; Grove, Megan L.; Gudbjartsson, Daniel F.; Guénel, Pascal; Guo, Xiuqing; Haiman, Christopher A.; Hall, Per; Hamann, Ute; Henderson, Brian E.; Hocking, Lynne J.; Hofman, Albert; Homuth, Georg; Hooning, Maartje J.; Hopper, John L.; Hu, Frank B.; Huang, Jinyan; Humphreys, Keith; Hunter, David J.; Jakubowska, Anna; Jones, Samuel E.; Kabisch, Maria; Karasik, David; Knight, Julia A.; Kolcic, Ivana; Kooperberg, Charles; Kosma, Veli-Matti; Kriebel, Jennifer; Kristensen, Vessela; Lambrechts, Diether; Langenberg, Claudia; Li, Jingmei; Li, Xin; Lindström, Sara; Liu, Yongmei; Luan, Jian’an; Lubinski, Jan; Mägi, Reedik; Mannermaa, Arto; Manz, Judith; Margolin, Sara; Marten, Jonathan; Martin, Nicholas G.; Masciullo, Corrado; Meindl, Alfons; Michailidou, Kyriaki; Mihailov, Evelin; Milani, Lili; Milne, Roger L.; Müller-Nurasyid, Martina; Nalls, Michael; Neale, Ben M.; Nevanlinna, Heli; Neven, Patrick; Newman, Anne B.; Nordestgaard, Børge G.; Olson, Janet E.; Padmanabhan, Sandosh; Peterlongo, Paolo; Peters, Ulrike; Petersmann, Astrid; Peto, Julian; Pharoah, Paul D.P.; Pirastu, Nicola N.; Pirie, Ailith; Pistis, Giorgio; Polasek, Ozren; Porteous, David; Psaty, Bruce M.; Pylkäs, Katri; Radice, Paolo; Raffel, Leslie J.; Rivadeneira, Fernando; Rudan, Igor; Rudolph, Anja; Ruggiero, Daniela; Sala, Cinzia F.; Sanna, Serena; Sawyer, Elinor J.; Schlessinger, David; Schmidt, Marjanka K.; Schmidt, Frank; Schmutzler, Rita K.; Schoemaker, Minouk J.; Scott, Robert A.; Seynaeve, Caroline M.; Simard, Jacques; Sorice, Rossella; Southey, Melissa C.; Stöckl, Doris; Strauch, Konstantin; Swerdlow, Anthony; Taylor, Kent D.; Thorsteinsdottir, Unnur; Toland, Amanda E.; Tomlinson, Ian; Truong, Thérèse; Tryggvadottir, Laufey; Turner, Stephen T.; Vozzi, Diego; Wang, Qin; Wellons, Melissa; Willemsen, Gonneke; Wilson, James F.; Winqvist, Robert; Wolffenbuttel, Bruce B.H.R.; Wright, Alan F.; Yannoukakos, Drakoulis; Zemunik, Tatijana; Zheng, Wei; Zygmunt, Marek; Bergmann, Sven; Boomsma, Dorret I.; Buring, Julie E.; Ferrucci, Luigi; Montgomery, Grant W.; Gudnason, Vilmundur; Spector, Tim D.; van Duijn, Cornelia M; Alizadeh, Behrooz Z.; Ciullo, Marina; Crisponi, Laura; Easton, Douglas F.; Gasparini, Paolo P.; Gieger, Christian; Harris, Tamara B.; Hayward, Caroline; Kardia, Sharon L.R.; Kraft, Peter; McKnight, Barbara; Metspalu, Andres; Morrison, Alanna C.; Reiner, Alex P.; Ridker, Paul M.; Rotter, Jerome I.; Toniolo, Daniela; Uitterlinden, André G.; Ulivi, Sheila; Völzke, Henry; Wareham, Nicholas J.; Weir, David R.; Yerges-Armstrong, Laura M.; Price, Alkes L.; Stefansson, Kari; Visser, Jenny A.; Ong, Ken K.; Chang-Claude, Jenny; Murabito, Joanne M.; Perry, John R.B.; Murray, Anna

2015-01-01

Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ~70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two harbouring additional rare missense alleles of large effect. We found enrichment of signals in/near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses revealed a major association with DNA damage-response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomisation analyses supported a causal effect of later ANM on breast cancer risk (~6% risk increase per-year, P=3×10−14), likely mediated by prolonged sex hormone exposure, rather than DDR mechanisms. PMID:26414677

From deindustrialization to individual health-related quality of life: multilevel evidence of contextual predictors, mediators and modulators across French regions, 2003.

PubMed

Audureau, Etienne; Rican, Stéphane; Coste, Joël

2013-07-01

Although small area effects on health-related quality of life (HRQoL) have been extensively studied, less is known at the regional level, particularly in France where no multilevel evidence is available. Using data from a large representative cross-sectional survey conducted in 2003 (N=16 732), this study explores individual and regional determinants of the SF-36 Physical Functioning and Mental Health subscales. We considered a causal pathway leading from deindustrialization to HRQoL and assessed the roles of net migratory flows, deprivation, and the social and physical environments. Worse HRQoL results were found in regions most affected by deindustrialization, with evidence for mediating effects of migration, voter abstention rate and individual health-related behaviors. Cross-level interactions and intraregional heterogeneity were also found, confirming the complexity of individual-area relationships and the need for carefully conceptualized multilevel analyses to guide health policies effectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

The impact of education on sexual behavior in sub-Saharan Africa: a review of the evidence.

PubMed

Zuilkowski, Stephanie Simmons; Jukes, Matthew C H

2012-01-01

Many studies have attempted to determine the relationship between education and HIV status. However, a complete and causal understanding of this relationship requires analysis of its mediating pathways, focusing on sexual behaviors. We developed a series of hypotheses based on the differential effect of educational attainment on three sexual behaviors. We tested our predictions in a systematic literature review including 65 articles reporting associations between three specific sexual behaviors -- sexual initiation, number of partners, and condom use -- and educational attainment or school enrollment in sub-Saharan Africa. The patterns of associations varied by behavior. The findings for condom use were particularly convergent; none of the 44 studies using educational attainment as a predictor reviewed found that more educated people were significantly less likely to use condoms. Findings for sexual initiation and number of partners were more complex. The contrast between findings for condom use on the one hand and sexual initiation and number of partners on the other supports predictions based on our theoretical framework.

Gene signature critical to cancer phenotype as a paradigm for anti-cancer drug discovery

PubMed Central

Sampson, Erik R.; McMurray, Helene R.; Hassane, Duane C.; Newman, Laurel; Salzman, Peter; Jordan, Craig T.; Land, Hartmut

2013-01-01

Malignant cell transformation commonly results in the deregulation of thousands of cellular genes, an observation that suggests a complex biological process and an inherently challenging scenario for the development of effective cancer interventions. To better define the genes/pathways essential to regulating the malignant phenotype, we recently described a novel strategy based on the cooperative nature of carcinogenesis that focuses on genes synergistically deregulated in response to cooperating oncogenic mutations. These so-called “cooperation response genes” (CRGs) are highly enriched for genes critical for the cancer phenotype, thereby suggesting their causal role in the malignant state. Here we show that CRGs play an essential role in drug-mediated anti-cancer activity and that anti-cancer agents can be identified through their ability to antagonize the CRG expression profile. These findings provide proof-of-concept for the use of the CRG signature as a novel means of drug discovery with relevance to underlying anti-cancer drug mechanisms. PMID:22964631

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

NASA Technical Reports Server (NTRS)

Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Multiple Causal Links Between Magnocellular-Dorsal Pathway Deficit and Developmental Dyslexia.

PubMed

Gori, Simone; Seitz, Aaron R; Ronconi, Luca; Franceschini, Sandro; Facoetti, Andrea

2016-10-17

Although impaired auditory-phonological processing is the most popular explanation of developmental dyslexia (DD), the literature shows that the combination of several causes rather than a single factor contributes to DD. Functioning of the visual magnocellular-dorsal (MD) pathway, which plays a key role in motion perception, is a much debated, but heavily suspected factor contributing to DD. Here, we employ a comprehensive approach that incorporates all the accepted methods required to test the relationship between the MD pathway dysfunction and DD. The results of 4 experiments show that (1) Motion perception is impaired in children with dyslexia in comparison both with age-match and with reading-level controls; (2) pre-reading visual motion perception-independently from auditory-phonological skill-predicts future reading development, and (3) targeted MD trainings-not involving any auditory-phonological stimulation-leads to improved reading skill in children and adults with DD. Our findings demonstrate, for the first time, a causal relationship between MD deficits and DD, virtually closing a 30-year long debate. Since MD dysfunction can be diagnosed much earlier than reading and language disorders, our findings pave the way for low resource-intensive, early prevention programs that could drastically reduce the incidence of DD. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A micro-level model of employment relations and health inequalities.

PubMed

Benach, Joan; Solar, Orielle; Santana, Vilma; Castedo, Antía; Chung, Haejoo; Muntaner, Carles

2010-01-01

Theoretical models are a way of visualizing, in context, the many factors that contribute to inequalities in health. This article presents a model showing the micro-level pathways relating employment and working conditions to health inequalities. A first important (indirect) pathway runs through the unequal distribution of harmful working conditions. Both employment and working conditions tend to be unequally distributed along the same social axes: social class, gender, ethnicity/race, immigration/migration status, territory, and so forth. Underlying mechanisms are exploitation, domination, and discrimination. Material deprivation and economic inequalities constitute a second direct pathway linking (nonstandard) employment conditions to health inequalities. In a third pathway, employment conditions may have an important effect on health inequalities via several psychosocial, behavioral, and physiopathological pathways. Although these several pathways are separated for analytical purposes, they are largely intertwined and, ideally, should be studied in an integrated way. The theoretical model presented in this article serves three main purposes: providing analytical clarity for organizing scientific data, encouraging further observation and causal testing, and identifying policy entry points.

Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

NASA Astrophysics Data System (ADS)

Xu, Chuan-Ming; Yan, Yan; Zhu, Xiao-Wu; Li, Xiao-Teng; Chen, Xiao-Song

2013-11-01

The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.

The center for causal discovery of biomedical knowledge from big data.

PubMed

Cooper, Gregory F; Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-11-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Fengbin, E-mail: fblu@amss.ac.cn

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relationsmore » evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.« less

Confounding factors in determining causal soil moisture-precipitation feedback

NASA Astrophysics Data System (ADS)

Tuttle, Samuel E.; Salvucci, Guido D.

2017-07-01

Identification of causal links in the land-atmosphere system is important for construction and testing of land surface and general circulation models. However, the land and atmosphere are highly coupled and linked by a vast number of complex, interdependent processes. Statistical methods, such as Granger causality, can help to identify feedbacks from observational data, independent of the different parameterizations of physical processes and spatiotemporal resolution effects that influence feedbacks in models. However, statistical causal identification methods can easily be misapplied, leading to erroneous conclusions about feedback strength and sign. Here, we discuss three factors that must be accounted for in determination of causal soil moisture-precipitation feedback in observations and model output: seasonal and interannual variability, precipitation persistence, and endogeneity. The effect of neglecting these factors is demonstrated in simulated and observational data. The results show that long-timescale variability and precipitation persistence can have a substantial effect on detected soil moisture-precipitation feedback strength, while endogeneity has a smaller effect that is often masked by measurement error and thus is more likely to be an issue when analyzing model data or highly accurate observational data.

Do New Caledonian crows solve physical problems through causal reasoning?

PubMed Central

Taylor, A.H.; Hunt, G.R.; Medina, F.S.; Gray, R.D.

2008-01-01

The extent to which animals other than humans can reason about physical problems is contentious. The benchmark test for this ability has been the trap-tube task. We presented New Caledonian crows with a series of two-trap versions of this problem. Three out of six crows solved the initial trap-tube. These crows continued to avoid the trap when the arbitrary features that had previously been associated with successful performances were removed. However, they did not avoid the trap when a hole and a functional trap were in the tube. In contrast to a recent primate study, the three crows then solved a causally equivalent but visually distinct problem—the trap-table task. The performance of the three crows across the four transfers made explanations based on chance, associative learning, visual and tactile generalization, and previous dispositions unlikely. Our findings suggest that New Caledonian crows can solve complex physical problems by reasoning both causally and analogically about causal relations. Causal and analogical reasoning may form the basis of the New Caledonian crow's exceptional tool skills. PMID:18796393

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

PubMed

Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze

2017-01-01

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright © 2016 Elsevier Ltd. All rights reserved.

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

PubMed Central

van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V.

2013-01-01

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor. PMID:23359524

Outward foreign direct investments and home country's economic growth

NASA Astrophysics Data System (ADS)

Ciesielska, Dorota; Kołtuniak, Marcin

2017-09-01

The study examines the time stability of the causality direction and cross-correlations between the home country's economic growth and pace of growth of its outward foreign direct investment (OFDI) stocks within the complex system of the Polish national economy. The research has been performed in order to verify, using both the time and frequency domains time series analyses, if economic agents' long term decisions on outward foreign direct investments, leading to cross-border value chains and production fragmentation processes, are of adaptive or predictive character. Consequently, the aim was to check if the home country's economic growth leads the internationalization processes of domestic enterprises, which stays in line with Dunning's Investment Development Path (IDP) paradigm, or if these complex processes, thanks to entrepreneurs' ability to formulate relevant rational expectations, precede the home country's economic growth, which would be supported with the introduction of the policy on reinforcing the internationalization processes of domestic enterprises. The presence of the unidirectional economic growth-led internationalization, consistent with the IDP concept's base assumptions, has been ascertained by the results of the short term Granger causality tests. Nevertheless, the results of the wavelet analyses, supported with the results of the econometric block exogeneity long term causality Wald tests, have revealed that in the long term the OFDI stocks' growth permanently precedes the home country's economic growth, which stays in the unequivocal contrast with the IDP paradigm's premises, as well as with the indicated above short term Granger causality tests' outcomes and indicates that economic agents' choices are not strictly of adaptive but also of predictive character, which influences the current state of knowledge on economic complex systems' characteristics. Such a result is of a great importance in the light of the existence of the significant and still unexploited internationalization potential of Polish enterprises.

You Need to Know: There Is a Causal Relationship between Structural Knowledge and Control Performance in Complex Problem Solving Tasks

ERIC Educational Resources Information Center

Goode, Natassia; Beckmann, Jens F.

2010-01-01

This study investigates the relationships between structural knowledge, control performance and fluid intelligence in a complex problem solving (CPS) task. 75 participants received either complete, partial or no information regarding the underlying structure of a complex problem solving task, and controlled the task to reach specific goals.…

Retrieving hydrological connectivity from empirical causality in karst systems

NASA Astrophysics Data System (ADS)

Delforge, Damien; Vanclooster, Marnik; Van Camp, Michel; Poulain, Amaël; Watlet, Arnaud; Hallet, Vincent; Kaufmann, Olivier; Francis, Olivier

2017-04-01

Because of their complexity, karst systems exhibit nonlinear dynamics. Moreover, if one attempts to model a karst, the hidden behavior complicates the choice of the most suitable model. Therefore, both intense investigation methods and nonlinear data analysis are needed to reveal the underlying hydrological connectivity as a prior for a consistent physically based modelling approach. Convergent Cross Mapping (CCM), a recent method, promises to identify causal relationships between time series belonging to the same dynamical systems. The method is based on phase space reconstruction and is suitable for nonlinear dynamics. As an empirical causation detection method, it could be used to highlight the hidden complexity of a karst system by revealing its inner hydrological and dynamical connectivity. Hence, if one can link causal relationships to physical processes, the method should show great potential to support physically based model structure selection. We present the results of numerical experiments using karst model blocks combined in different structures to generate time series from actual rainfall series. CCM is applied between the time series to investigate if the empirical causation detection is consistent with the hydrological connectivity suggested by the karst model.

Status Report on Speech Research. A Report on the Status and Progress of Studies on the Nature of Speech, Instrumentation for Its Investigation, and Practical Applications.

DTIC Science & Technology

1984-08-01

6, 391-395. Abbs, J. H., & Gracco, V. L. (in press). Control of complex motor gestures 0 and orofacial muscle responses to load perturbations of the...E2, and E, are on the same world line where %,! E. is causally constrained by E2 and E. is causally constrained by El. You take pains to note that the

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

PubMed

Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan

2017-05-01

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls

PubMed Central

Demirci, Oguz; Stevens, Michael C.; Andreasen, Nancy C.; Michael, Andrew; Liu, Jingyu; White, Tonya; Pearlson, Godfrey D.; Clark, Vincent P.; Calhoun, Vince D.

2009-01-01

Functional network connectivity (FNC) is an approach that examines the relationships between brain networks (as opposed to functional connectivity (FC) that focuses upon the relationships between single voxels). FNC may help explain the complex relationships between distributed cerebral sites in the brain and possibly provide new understanding of neurological and psychiatric disorders such as schizophrenia. In this paper, we use independent component analysis (ICA) to extract the time courses of spatially independent components and then use these in Granger causality test (GCT) to investigate causal relationships between brain activation networks. We present results using both simulations and fMRI data of 155 subjects obtained during two different tasks. Unlike previous research, causal relationships are presented over different portions of the frequency spectrum in order to differentiate high and low frequency effects and not merged in a scalar. The results obtained using Sternberg item recognition paradigm (SIRP) and auditory oddball (AOD) tasks showed FNC differentiations between schizophrenia and control groups, and explained how the two groups differed during these tasks. During the SIRP task, secondary visual and cerebellum activation networks served as hubs and included most complex relationships between the activated regions. Secondary visual and temporal lobe activations replaced these components during the AOD task. PMID:19245841

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*

PubMed Central

Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan

2017-01-01

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111

Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy

PubMed Central

Abdul Razak, Fatimah; Jensen, Henrik Jeldtoft

2014-01-01

‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets. PMID:24955766

Causal Inferences from Mining ToxCast Data and the Biomedical Literature for Molecular Pathways and Cellular Processes in Cleft Palate (SOT)

EPA Science Inventory

Sixty-five chemicals in the ToxCast high-throughput screening (HTS) dataset have been linked to cleft palate based on data from ToxRefDB (rat or rabbit prenatal developmental toxicity studies) or from literature reports. These compounds are structurally diverse and thus likely to...

Do Paternal Arrest and Imprisonment Lead to Child Behaviour Problems and Substance Use? A Longitudinal Analysis

ERIC Educational Resources Information Center

Kinner, Stuart A.; Alati, Rosa; Najman, Jake M.; Williams, Gail M.

2007-01-01

Background: Children of prisoners are at increased risk of impaired health, behavioural problems and substance misuse; however, the causal pathways to these problems are unclear. Under some circumstances, parental imprisonment may result in improved outcomes for the child. This study investigates the impact of paternal arrest and imprisonment on…

Eco-Health Linkages: Assessing the Role of Ecosystem Goods and Services on Human Health Using Causal Criteria Analysis

EPA Science Inventory

Objectives In the last decade, we saw an upsurge of studies evaluating the role of ecosystem goods and services (EGS) on human health (Eco-Health). Most of this work consists of observational research of intermediate processes and few address the full pathways from ecosystem to E...

Isolating causal pathways between flow and fish in the regulated river hierarchy

Treesearch

Ryan McManamay; Donald J. Orth; Charles A. Dolloff; David C. Mathews

2015-01-01

Unregulated river systems are organized in a hierarchy in which large scale factors (i.e. landscape and segment scales) influence local habitats (i.e. reach, meso- and microhabitat scales), and both differentially exert selective pressures on biota. Dams, however, create discontinua in these processes and change the hierarchical structure. We examined the relative...

Thousand cankers pathway assessment: movement of Geosmithia sp. and Pityophthorus juglandis Blackman from the western into the eastern United States

Treesearch

Leslie Newton; Glenn Fowler; Alison Neeley; Robert Schall; Yu Takeuchi; Scott. Pfister

2011-01-01

A newly recognized fungal canker disease of walnut, identified by state cooperators, may threaten the native range of eastern black walnut, Juglans nigra. The causal agent is a Geosmithia fungus (proposed name Geosmithia morbida) and the only known vector is the walnut twig beetle, Pityophthorus...

Disruption of SF3B1 results in deregulated expression and splicing of key genes and pathways in myelodysplastic syndrome hematopoietic stem and progenitor cells.

PubMed

Dolatshad, H; Pellagatti, A; Fernandez-Mercado, M; Yip, B H; Malcovati, L; Attwood, M; Przychodzen, B; Sahgal, N; Kanapin, A A; Lockstone, H; Scifo, L; Vandenberghe, P; Papaemmanuil, E; Smith, C W J; Campbell, P J; Ogawa, S; Maciejewski, J P; Cazzola, M; Savage, K I; Boultwood, J

2015-05-01

The splicing factor SF3B1 is the most commonly mutated gene in the myelodysplastic syndrome (MDS), particularly in patients with refractory anemia with ring sideroblasts (RARS). We investigated the functional effects of SF3B1 disruption in myeloid cell lines: SF3B1 knockdown resulted in growth inhibition, cell cycle arrest and impaired erythroid differentiation and deregulation of many genes and pathways, including cell cycle regulation and RNA processing. MDS is a disorder of the hematopoietic stem cell and we thus studied the transcriptome of CD34(+) cells from MDS patients with SF3B1 mutations using RNA sequencing. Genes significantly differentially expressed at the transcript and/or exon level in SF3B1 mutant compared with wild-type cases include genes that are involved in MDS pathogenesis (ASXL1 and CBL), iron homeostasis and mitochondrial metabolism (ALAS2, ABCB7 and SLC25A37) and RNA splicing/processing (PRPF8 and HNRNPD). Many genes regulated by a DNA damage-induced BRCA1-BCLAF1-SF3B1 protein complex showed differential expression/splicing in SF3B1 mutant cases. This is the first study to determine the target genes of SF3B1 mutation in MDS CD34(+) cells. Our data indicate that SF3B1 has a critical role in MDS by affecting the expression and splicing of genes involved in specific cellular processes/pathways, many of which are relevant to the known RARS pathophysiology, suggesting a causal link.

Ceramide and Its Related Neurochemical Networks as Targets for Some Brain Disorder Therapies.

PubMed

Brodowicz, Justyna; Przegaliński, Edmund; Müller, Christian P; Filip, Malgorzata

2018-02-01

Correlational and causal comparative research link ceramide (Cer), the precursor of complex sphingolipids, to some psychiatric (e.g., depression, schizophrenia (SZ), alcohol use disorder, and morphine antinociceptive tolerance) and neurological (e.g., Alzheimer's disease (AD), Parkinson disease (PD)) disorders. Cer generation can occur through the de novo synthesis pathway, the sphingomyelinase pathways, and the salvage pathway. The discoveries that plasma Cer concentration increase during depressive episodes in patients and that tricyclic and tetracyclic antidepressants functionally inhibit acid sphingomyelinase (ASM), the enzyme that catalyzes the degradation of sphingomyelin to Cer, have initiated a series of studies on the role of the ASM-Cer system in depressive disorder. Disturbances in the metabolism of Cer or SM are associated with the occurrence of SZ and PD. In both PD and SZ patients, the elevated levels of Cer or SM in the brain regions were associated with the disease. AD patients showed also an abnormal metabolism of brain Cer at early stages of the disease which may suggest Cer as an AD biomarker. In plasma of AD patients and in AD transgenic mice, ASM activity was increased. In contrast, partial ASM inhibition of Aβ deposition improved memory deficits. Furthermore, in clinical and preclinical research, ethanol enhanced activation of ASM followed by Cer production. Limited data have shown that Cer plays an important role in the development of morphine antinociceptive tolerance. In summary, clinical and preclinical findings provide evidence that targeting the Cer system should be considered as an innovative translational strategy for some brain disorders.

Political Attitudes Develop Independently of Personality Traits

PubMed Central

Hatemi, Peter K.; Verhulst, Brad

2015-01-01

The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not significant or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, latent genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data. Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question. We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them. Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area. PMID:25734580

Refined mapping of autoimmune disease associated genetic variants with gene expression suggests an important role for non-coding RNAs.

PubMed

Ricaño-Ponce, Isis; Zhernakova, Daria V; Deelen, Patrick; Luo, Oscar; Li, Xingwang; Isaacs, Aaron; Karjalainen, Juha; Di Tommaso, Jennifer; Borek, Zuzanna Agnieszka; Zorro, Maria M; Gutierrez-Achury, Javier; Uitterlinden, Andre G; Hofman, Albert; van Meurs, Joyce; Netea, Mihai G; Jonkers, Iris H; Withoff, Sebo; van Duijn, Cornelia M; Li, Yang; Ruan, Yijun; Franke, Lude; Wijmenga, Cisca; Kumar, Vinod

2016-04-01

Genome-wide association and fine-mapping studies in 14 autoimmune diseases (AID) have implicated more than 250 loci in one or more of these diseases. As more than 90% of AID-associated SNPs are intergenic or intronic, pinpointing the causal genes is challenging. We performed a systematic analysis to link 460 SNPs that are associated with 14 AID to causal genes using transcriptomic data from 629 blood samples. We were able to link 71 (39%) of the AID-SNPs to two or more nearby genes, providing evidence that for part of the AID loci multiple causal genes exist. While 54 of the AID loci are shared by one or more AID, 17% of them do not share candidate causal genes. In addition to finding novel genes such as ULK3, we also implicate novel disease mechanisms and pathways like autophagy in celiac disease pathogenesis. Furthermore, 42 of the AID SNPs specifically affected the expression of 53 non-coding RNA genes. To further understand how the non-coding genome contributes to AID, the SNPs were linked to functional regulatory elements, which suggest a model where AID genes are regulated by network of chromatin looping/non-coding RNAs interactions. The looping model also explains how a causal candidate gene is not necessarily the gene closest to the AID SNP, which was the case in nearly 50% of cases. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Political attitudes develop independently of personality traits.

PubMed

Hatemi, Peter K; Verhulst, Brad

2015-01-01

The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not significant or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, latent genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data. Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question. We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them. Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area.

Researchers and stakeholders shape advances in management of tree and vine trunk-disease complexes

USDA-ARS?s Scientific Manuscript database

The grapevine trunk-disease complex limits grape production and vineyard longevity worldwide. Every vineyard in California eventually is infected by one or more trunk diseases. The causal fungi, which are taxonomically unrelated Ascomycetes, infect and then degrade the permanent woody structure of t...

Functional ability loss in sensory impaired and sensory unimpaired very old adults: analyzing causal relations with positive affect across four years.

PubMed

Wahl, Hans-Werner; Drapaniotis, Philipp M; Heyl, Vera

2014-11-01

This paper focuses on the relationship between functional ability (FA) and positive affect (PA), a major component of well-being, in sensory impaired very old adults (SI) compared with sensory unimpaired individuals (UI). Previous research mostly suggests a robust causal impact of FA on PA. However, some research, drawing from Fredrickson's broaden-and-build theory, also points to the possibility of an inverse causality between FA and PA. We examine in this paper both of these causal directions in SI as well as UI individuals across a 4year observation period. Additionally, we checked for the role of negative affect (NA). The T1-T2 sample comprised 81 out of 237 SI individuals (visually or hearing impaired) assessed at T1, with a mean age at T1 of 81.8years, and 87 UI individuals out of 150 assessed at T1, with a mean age at T1 of 81.5years. Established scales were used to assess FA, PA, and NA. Using cross-lagged panel analysis to examine the direction of causality, our findings indicate that FA has significant impact on PA in both the SI and the UI group, whereas the alternative causal pathway was not confirmed. Both cross-lagged relationships between FA and NA were non-significant. No group differences in path strengths between SI and UI were present. Our study provides evidence that FA is a key competence for successful emotional aging in vulnerable groups of very old adults such as SI as well as in UI adults in advanced old age. Copyright © 2014 Elsevier Inc. All rights reserved.

Early Identification and Treatment of Antisocial Behavior.

PubMed

Frick, Paul J

2016-10-01

Severe and persistent antisocial behavior is a prevalent, serious, and costly mental health problem. Individuals who are most likely to show persistent antisocial behavior through adolescence and into adulthood often show patterns of severe and varied conduct problems early in childhood. Treatments that intervene early in the development of these problems are most effective and least costly. Furthermore, there appear to be several common causal pathways that differ in their genetic, emotional, cognitive, and contextual characteristics. These pathways are differentiated by the level of callous-unemotional traits displayed by the individual. Copyright © 2016 Elsevier Inc. All rights reserved.

Information flow and causality as rigorous notions ab initio

NASA Astrophysics Data System (ADS)

Liang, X. San

2016-11-01

Information flow or information transfer the widely applicable general physics notion can be rigorously derived from first principles, rather than axiomatically proposed as an ansatz. Its logical association with causality is firmly rooted in the dynamical system that lies beneath. The principle of nil causality that reads, an event is not causal to another if the evolution of the latter is independent of the former, which transfer entropy analysis and Granger causality test fail to verify in many situations, turns out to be a proven theorem here. Established in this study are the information flows among the components of time-discrete mappings and time-continuous dynamical systems, both deterministic and stochastic. They have been obtained explicitly in closed form, and put to applications with the benchmark systems such as the Kaplan-Yorke map, Rössler system, baker transformation, Hénon map, and stochastic potential flow. Besides unraveling the causal relations as expected from the respective systems, some of the applications show that the information flow structure underlying a complex trajectory pattern could be tractable. For linear systems, the resulting remarkably concise formula asserts analytically that causation implies correlation, while correlation does not imply causation, providing a mathematical basis for the long-standing philosophical debate over causation versus correlation.

Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

PubMed Central

Lin, Fa-Hsuan; Hara, Keiko; Solo, Victor; Vangel, Mark; Belliveau, John W.; Stufflebeam, Steven M.; Hamalainen, Matti S.

2010-01-01

A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using Structural Equation Modeling and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain. PMID:19378280

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

PubMed Central

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K.; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C.; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com PMID:25887162

Pathway analysis of high-throughput biological data within a Bayesian network framework.

PubMed

Isci, Senol; Ozturk, Cengizhan; Jones, Jon; Otu, Hasan H

2011-06-15

Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC. Accompanying BPA software (BPAS) package is freely available for academic use at http://bumil.boun.edu.tr/bpa.

A new pressure ulcer conceptual framework.

PubMed

Coleman, Susanne; Nixon, Jane; Keen, Justin; Wilson, Lyn; McGinnis, Elizabeth; Dealey, Carol; Stubbs, Nikki; Farrin, Amanda; Dowding, Dawn; Schols, Jos M G A; Cuddigan, Janet; Berlowitz, Dan; Jude, Edward; Vowden, Peter; Schoonhoven, Lisette; Bader, Dan L; Gefen, Amit; Oomens, Cees W J; Nelson, E Andrea

2014-10-01

This paper discusses the critical determinants of pressure ulcer development and proposes a new pressure ulcer conceptual framework. Recent work to develop and validate a new evidence-based pressure ulcer risk assessment framework was undertaken. This formed part of a Pressure UlceR Programme Of reSEarch (RP-PG-0407-10056), funded by the National Institute for Health Research. The foundation for the risk assessment component incorporated a systematic review and a consensus study that highlighted the need to propose a new conceptual framework. Discussion Paper. The new conceptual framework links evidence from biomechanical, physiological and epidemiological evidence, through use of data from a systematic review (search conducted March 2010), a consensus study (conducted December 2010-2011) and an international expert group meeting (conducted December 2011). A new pressure ulcer conceptual framework incorporating key physiological and biomechanical components and their impact on internal strains, stresses and damage thresholds is proposed. Direct and key indirect causal factors suggested in a theoretical causal pathway are mapped to the physiological and biomechanical components of the framework. The new proposed conceptual framework provides the basis for understanding the critical determinants of pressure ulcer development and has the potential to influence risk assessment guidance and practice. It could also be used to underpin future research to explore the role of individual risk factors conceptually and operationally. By integrating existing knowledge from epidemiological, physiological and biomechanical evidence, a theoretical causal pathway and new conceptual framework are proposed with potential implications for practice and research. © 2014 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

A new pressure ulcer conceptual framework

PubMed Central

Coleman, Susanne; Nixon, Jane; Keen, Justin; Wilson, Lyn; McGinnis, Elizabeth; Dealey, Carol; Stubbs, Nikki; Farrin, Amanda; Dowding, Dawn; Schols, Jos MGA; Cuddigan, Janet; Berlowitz, Dan; Jude, Edward; Vowden, Peter; Schoonhoven, Lisette; Bader, Dan L; Gefen, Amit; Oomens, Cees WJ; Nelson, E Andrea

2014-01-01

Aim This paper discusses the critical determinants of pressure ulcer development and proposes a new pressure ulcer conceptual framework. Background Recent work to develop and validate a new evidence-based pressure ulcer risk assessment framework was undertaken. This formed part of a Pressure UlceR Programme Of reSEarch (RP-PG-0407-10056), funded by the National Institute for Health Research. The foundation for the risk assessment component incorporated a systematic review and a consensus study that highlighted the need to propose a new conceptual framework. Design Discussion Paper. Data Sources The new conceptual framework links evidence from biomechanical, physiological and epidemiological evidence, through use of data from a systematic review (search conducted March 2010), a consensus study (conducted December 2010–2011) and an international expert group meeting (conducted December 2011). Implications for Nursing A new pressure ulcer conceptual framework incorporating key physiological and biomechanical components and their impact on internal strains, stresses and damage thresholds is proposed. Direct and key indirect causal factors suggested in a theoretical causal pathway are mapped to the physiological and biomechanical components of the framework. The new proposed conceptual framework provides the basis for understanding the critical determinants of pressure ulcer development and has the potential to influence risk assessment guidance and practice. It could also be used to underpin future research to explore the role of individual risk factors conceptually and operationally. Conclusion By integrating existing knowledge from epidemiological, physiological and biomechanical evidence, a theoretical causal pathway and new conceptual framework are proposed with potential implications for practice and research. PMID:24684197

Bridging the Gap: Using Simultaneous Multi-Patient Simulations to Improve Nursing Competency and Transition to Practice: A Causal-Comparative Inquiry

ERIC Educational Resources Information Center

Fomenko, Julie Ann Schwein

2017-01-01

Twenty-first-century healthcare is a complex and demanding arena. Today's hospital environment is more complex than in previous years while patients move through the system at a much faster pace. Newly graduated nurses are challenged in their first year with the healthcare needs of complex patients. Nurse educators and nurse leaders differ in…

Genome-wide meta-analysis in alopecia areata resolves HLA associations and reveals two new susceptibility loci.

PubMed

Betz, Regina C; Petukhova, Lynn; Ripke, Stephan; Huang, Hailiang; Menelaou, Androniki; Redler, Silke; Becker, Tim; Heilmann, Stefanie; Yamany, Tarek; Duvic, Madeliene; Hordinsky, Maria; Norris, David; Price, Vera H; Mackay-Wiggan, Julian; de Jong, Annemieke; DeStefano, Gina M; Moebus, Susanne; Böhm, Markus; Blume-Peytavi, Ulrike; Wolff, Hans; Lutz, Gerhard; Kruse, Roland; Bian, Li; Amos, Christopher I; Lee, Annette; Gregersen, Peter K; Blaumeiser, Bettina; Altshuler, David; Clynes, Raphael; de Bakker, Paul I W; Nöthen, Markus M; Daly, Mark J; Christiano, Angela M

2015-01-22

Alopecia areata (AA) is a prevalent autoimmune disease with 10 known susceptibility loci. Here we perform the first meta-analysis of research on AA by combining data from two genome-wide association studies (GWAS), and replication with supplemented ImmunoChip data for a total of 3,253 cases and 7,543 controls. The strongest region of association is the major histocompatibility complex, where we fine-map four independent effects, all implicating human leukocyte antigen-DR as a key aetiologic driver. Outside the major histocompatibility complex, we identify two novel loci that exceed the threshold of statistical significance, containing ACOXL/BCL2L11(BIM) (2q13); GARP (LRRC32) (11q13.5), as well as a third nominally significant region SH2B3(LNK)/ATXN2 (12q24.12). Candidate susceptibility gene expression analysis in these regions demonstrates expression in relevant immune cells and the hair follicle. We integrate our results with data from seven other autoimmune diseases and provide insight into the alignment of AA within these disorders. Our findings uncover new molecular pathways disrupted in AA, including autophagy/apoptosis, transforming growth factor beta/Tregs and JAK kinase signalling, and support the causal role of aberrant immune processes in AA.

Integrating Genomic Analysis with the Genetic Basis of Gene Expression: Preliminary Evidence of the Identification of Causal Genes for Cardiovascular and Metabolic Traits Related to Nutrition in Mexicans123

PubMed Central

Bastarrachea, Raúl A.; Gallegos-Cabriales, Esther C.; Nava-González, Edna J.; Haack, Karin; Voruganti, V. Saroja; Charlesworth, Jac; Laviada-Molina, Hugo A.; Veloz-Garza, Rosa A.; Cardenas-Villarreal, Velia Margarita; Valdovinos-Chavez, Salvador B.; Gomez-Aguilar, Patricia; Meléndez, Guillermo; López-Alvarenga, Juan Carlos; Göring, Harald H. H.; Cole, Shelley A.; Blangero, John; Comuzzie, Anthony G.; Kent, Jack W.

2012-01-01

Whole-transcriptome expression profiling provides novel phenotypes for analysis of complex traits. Gene expression measurements reflect quantitative variation in transcript-specific messenger RNA levels and represent phenotypes lying close to the action of genes. Understanding the genetic basis of gene expression will provide insight into the processes that connect genotype to clinically significant traits representing a central tenet of system biology. Synchronous in vivo expression profiles of lymphocytes, muscle, and subcutaneous fat were obtained from healthy Mexican men. Most genes were expressed at detectable levels in multiple tissues, and RNA levels were correlated between tissue types. A subset of transcripts with high reliability of expression across tissues (estimated by intraclass correlation coefficients) was enriched for cis-regulated genes, suggesting that proximal sequence variants may influence expression similarly in different cellular environments. This integrative global gene expression profiling approach is proving extremely useful for identifying genes and pathways that contribute to complex clinical traits. Clearly, the coincidence of clinical trait quantitative trait loci and expression quantitative trait loci can help in the prioritization of positional candidate genes. Such data will be crucial for the formal integration of positional and transcriptomic information characterized as genetical genomics. PMID:22797999

Empirical evaluation of the conceptual model underpinning a regional aquatic long-term monitoring program using causal modelling

USGS Publications Warehouse

Irvine, Kathryn M.; Miller, Scott; Al-Chokhachy, Robert K.; Archer, Erik; Roper, Brett B.; Kershner, Jeffrey L.

2015-01-01

Conceptual models are an integral facet of long-term monitoring programs. Proposed linkages between drivers, stressors, and ecological indicators are identified within the conceptual model of most mandated programs. We empirically evaluate a conceptual model developed for a regional aquatic and riparian monitoring program using causal models (i.e., Bayesian path analysis). We assess whether data gathered for regional status and trend estimation can also provide insights on why a stream may deviate from reference conditions. We target the hypothesized causal pathways for how anthropogenic drivers of road density, percent grazing, and percent forest within a catchment affect instream biological condition. We found instream temperature and fine sediments in arid sites and only fine sediments in mesic sites accounted for a significant portion of the maximum possible variation explainable in biological condition among managed sites. However, the biological significance of the direct effects of anthropogenic drivers on instream temperature and fine sediments were minimal or not detected. Consequently, there was weak to no biological support for causal pathways related to anthropogenic drivers’ impact on biological condition. With weak biological and statistical effect sizes, ignoring environmental contextual variables and covariates that explain natural heterogeneity would have resulted in no evidence of human impacts on biological integrity in some instances. For programs targeting the effects of anthropogenic activities, it is imperative to identify both land use practices and mechanisms that have led to degraded conditions (i.e., moving beyond simple status and trend estimation). Our empirical evaluation of the conceptual model underpinning the long-term monitoring program provided an opportunity for learning and, consequently, we discuss survey design elements that require modification to achieve question driven monitoring, a necessary step in the practice of adaptive monitoring. We suspect our situation is not unique and many programs may suffer from the same inferential disconnect. Commonly, the survey design is optimized for robust estimates of regional status and trend detection and not necessarily to provide statistical inferences on the causal mechanisms outlined in the conceptual model, even though these relationships are typically used to justify and promote the long-term monitoring of a chosen ecological indicator. Our application demonstrates a process for empirical evaluation of conceptual models and exemplifies the need for such interim assessments in order for programs to evolve and persist.

Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

PubMed

Stramaglia, Sebastiano; Angelini, Leonardo; Wu, Guorong; Cortes, Jesus M; Faes, Luca; Marinazzo, Daniele

2016-12-01

We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.

Complexities in understanding the role of compensation-related factors on recovery from whiplash-associated disorders: discussion paper 2.

PubMed

Carroll, Linda J; Connelly, Luke B; Spearing, Natalie M; Côté, Pierre; Buitenhuis, Jan; Kenardy, Justin

2011-12-01

Focused discussion. To present some of the complexities in conducting research on the role of compensation and compensation-related factors in recovery from whiplash-associated disorders (WAD) and to suggest directions for future research. There is divergence of opinion, primary research findings, and systematic reviews on the role of compensation and/or compensation-related factors in WAD recovery. The topic of research of compensation/compensation-related factors was discussed at an international summit meeting of 21 researchers from diverse fields of scientific enquiry. This article summarizes the main points raised in that discussion. Traffic injury compensation is a complex sociopolitical construct, which varies widely across jurisdictions. This leads to conceptual and methodological challenges in conducting and interpreting research in this area. It is important that researchers and their audiences be clear about what aspect of the compensation system is being addressed, what compensation-related variables are being studied, and what social/economic environment the compensation system exists in. In addition, summit participants also recommended that nontraditional, sophisticated study designs and analysis strategies be employed to clarify the complex causal pathways and mechanisms of effects. Care must be taken by both researchers and their audiences not to overgeneralize or confuse different aspects of WAD compensation. In considering the role of compensation/compensation-related factors on WAD and WAD recovery, it is important to retain a broad-based conceptualization of the range of biological, psychological, social, and economic factors that combine and interact to define and determine how people recover from WAD.

Causal relationships between milk quality and coagulation properties in Italian Holstein-Friesian dairy cattle.

PubMed

Tiezzi, Francesco; Valente, Bruno D; Cassandro, Martino; Maltecca, Christian

2015-05-13

Recently, selection for milk technological traits was initiated in the Italian dairy cattle industry based on direct measures of milk coagulation properties (MCP) such as rennet coagulation time (RCT) and curd firmness 30 min after rennet addition (a30) and on some traditional milk quality traits that are used as predictors, such as somatic cell score (SCS) and casein percentage (CAS). The aim of this study was to shed light on the causal relationships between traditional milk quality traits and MCP. Different structural equation models that included causal effects of SCS and CAS on RCT and a30 and of RCT on a30 were implemented in a Bayesian framework. Our results indicate a non-zero magnitude of the causal relationships between the traits studied. Causal effects of SCS and CAS on RCT and a30 were observed, which suggests that the relationship between milk coagulation ability and traditional milk quality traits depends more on phenotypic causal pathways than directly on common genetic influence. While RCT does not seem to be largely controlled by SCS and CAS, some of the variation in a30 depends on the phenotypes of these traits. However, a30 depends heavily on coagulation time. Our results also indicate that, when direct effects of SCS, CAS and RCT are considered simultaneously, most of the overall genetic variability of a30 is mediated by other traits. This study suggests that selection for RCT and a30 should not be performed on correlated traits such as SCS or CAS but on direct measures because the ability of milk to coagulate is improved through the causal effect that the former play on the latter, rather than from a common source of genetic variation. Breaking the causal link (e.g. standardizing SCS or CAS before the milk is processed into cheese) would reduce the impact of the improvement due to selective breeding. Since a30 depends heavily on RCT, the relative emphasis that is put on this trait should be reconsidered and weighted for the fact that the pure measure of a30 almost double-counts RCT.

A Framework for Estimating Causal Effects in Latent Class Analysis: Is There a Causal Link Between Early Sex and Subsequent Profiles of Delinquency?

PubMed Central

Lanza, Stephanie T.; Coffman, Donna L.

2013-01-01

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p=0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p=0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work. PMID:23839479

A framework for estimating causal effects in latent class analysis: is there a causal link between early sex and subsequent profiles of delinquency?

PubMed

Butera, Nicole M; Lanza, Stephanie T; Coffman, Donna L

2014-06-01

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p = 0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p = 0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work.

Computer-Based Assessment of Complex Problem Solving: Concept, Implementation, and Application

ERIC Educational Resources Information Center

Greiff, Samuel; Wustenberg, Sascha; Holt, Daniel V.; Goldhammer, Frank; Funke, Joachim

2013-01-01

Complex Problem Solving (CPS) skills are essential to successfully deal with environments that change dynamically and involve a large number of interconnected and partially unknown causal influences. The increasing importance of such skills in the 21st century requires appropriate assessment and intervention methods, which in turn rely on adequate…

Gene flow in complex landscapes: Testing multiple hypotheses with causal modeling

Treesearch

Samuel A. Cushman; Kevin S. McKelvey; Jim Hayden; Michael K. Schwartz

2006-01-01

Predicting population-level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears...

Spatial Patterns of Airborne Pesticides in the Alpine Habitat of a Declining Calfornia Amphibian, The Mountain Yellow-Legged Frog

EPA Science Inventory

The mountain yellow-legged frog complex (Rana muscosa complex) has disappeared from most of its historic localities in the Sierra Nevada of California, and airborne pesticides from the Central Valley have been implicated as a causal agent. To determine the distributions and conce...

Spatial Patterns of Airborne Pesticides in the Alpine Habitat of a Declining California Amphibian, The Mountain Yellow-Legged Frog

EPA Science Inventory

The mountain yellow-legged frog complex (Rana muscosa complex) has disappeared from most of its historic localities in the Sierra Nevada of California, and airborne pesticides from the Central Valley have been implicated as a causal agent. To determine the distributions and conce...

The association between substantiated childhood maltreatment, asthma and lung function: A prospective investigation.

PubMed

Abajobir, Amanuel Alemu; Kisely, Steve; Williams, Gail; Strathearn, Lane; Suresh, Sadasivam; Najman, Jake Moses

2017-10-01

Asthma reflects multiple and likely complex causal pathways. We investigate the possibility that childhood maltreatment is one such causal pathway. Childhood maltreatment can be interpreted as a form of early life adversity and like other life adversities may predict a range of negative health outcomes, including asthma. A total of 3762 young adults (52.63% female) from the Mater Hospital-University of Queensland Study of Pregnancy (MUSP) participated in this study. MUSP is a prospective Australian birth cohort study of mothers consecutively recruited during their first antenatal clinic visit at Brisbane's Mater Hospital from 1981 to 1983. The study followed both mother-child dyads to the age of 21years after birth. Participants reported whether they had been diagnosed by a physician with asthma by the 21-year follow-up. Trained research assistants also performed gender- and height-standardized lung function tests using a Spirobank G spirometer system attached to a laptop computer. We linked this dataset with data obtained from the child protection services and which comprised all substantiated cases of childhood maltreatment in the MUSP cohort. Substantiations of childhood maltreatment included children in an age range of 0-14years. The experience of any childhood maltreatment, particularly emotional abuse, was independently associated with self-reported physician-diagnosed asthma by the 21-year follow-up. The association was no longer significant after adjustment for a range of confounders and covariates in neglected children. Childhood maltreatment, including multiple events, was not associated with lung function in adjusted models. Childhood maltreatment, including emotional abuse, was associated with lifetime ever asthma. This was in contrast to the absence of an association with objective measures of lung function. More research is indicated on the effect of childhood maltreatment on lung function using objective measures. In the meantime, there should be a greater awareness of the potential impact of childhood maltreatment on the potential to develop asthma, as well as of the possibility that asthma in adulthood may precede childhood maltreatment. Copyright © 2017 Elsevier Inc. All rights reserved.

Theorising and testing environmental pathways to behaviour change: natural experimental study of the perception and use of new infrastructure to promote walking and cycling in local communities

PubMed Central

Panter, Jenna; Ogilvie, David

2015-01-01

Objective Some studies have assessed the effectiveness of environmental interventions to promote physical activity, but few have examined how such interventions work. We investigated the environmental mechanisms linking an infrastructural intervention with behaviour change. Design Natural experimental study. Setting Three UK municipalities (Southampton, Cardiff and Kenilworth). Participants Adults living within 5 km of new walking and cycling infrastructure. Intervention Construction or improvement of walking and cycling routes. Exposure to the intervention was defined in terms of residential proximity. Outcome measures Questionnaires at baseline and 2-year follow-up assessed perceptions of the supportiveness of the environment, use of the new infrastructure, and walking and cycling behaviours. Analysis proceeded via factor analysis of perceptions of the physical environment (step 1) and regression analysis to identify plausible pathways involving physical and social environmental mediators and refine the intervention theory (step 2) to a final path analysis to test the model (step 3). Results Participants who lived near and used the new routes reported improvements in their perceptions of provision and safety. However, path analysis (step 3, n=967) showed that the effects of the intervention on changes in time spent walking and cycling were largely (90%) explained by a simple causal pathway involving use of the new routes, and other pathways involving changes in environmental cognitions explained only a small proportion of the effect. Conclusions Physical improvement of the environment itself was the key to the effectiveness of the intervention, and seeking to change people's perceptions may be of limited value. Studies of how interventions lead to population behaviour change should complement those concerned with estimating their effects in supporting valid causal inference. PMID:26338837

Diabetes and mitochondrial function: Role of hyperglycemia and oxidative stress

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rolo, Anabela P.; Palmeira, Carlos M.

2006-04-15

Hyperglycemia resulting from uncontrolled glucose regulation is widely recognized as the causal link between diabetes and diabetic complications. Four major molecular mechanisms have been implicated in hyperglycemia-induced tissue damage: activation of protein kinase C (PKC) isoforms via de novo synthesis of the lipid second messenger diacylglycerol (DAG), increased hexosamine pathway flux, increased advanced glycation end product (AGE) formation, and increased polyol pathway flux. Hyperglycemia-induced overproduction of superoxide is the causal link between high glucose and the pathways responsible for hyperglycemic damage. In fact, diabetes is typically accompanied by increased production of free radicals and/or impaired antioxidant defense capabilities, indicating amore » central contribution for reactive oxygen species (ROS) in the onset, progression, and pathological consequences of diabetes. Besides oxidative stress, a growing body of evidence has demonstrated a link between various disturbances in mitochondrial functioning and type 2 diabetes. Mutations in mitochondrial DNA (mtDNA) and decreases in mtDNA copy number have been linked to the pathogenesis of type 2 diabetes. The study of the relationship of mtDNA to type 2 diabetes has revealed the influence of the mitochondria on nuclear-encoded glucose transporters, glucose-stimulated insulin secretion, and nuclear-encoded uncoupling proteins (UCPs) in {beta}-cell glucose toxicity. This review focuses on a range of mitochondrial factors important in the pathogenesis of diabetes. We review the published literature regarding the direct effects of hyperglycemia on mitochondrial function and suggest the possibility of regulation of mitochondrial function at a transcriptional level in response to hyperglycemia. The main goal of this review is to include a fresh consideration of pathways involved in hyperglycemia-induced diabetic complications.« less

Pubertal Development and Sexual Intercourse among Adolescent Girls: An Examination of Direct, Mediated, and Spurious Pathways

ERIC Educational Resources Information Center

Savolainen, Jukka; Mason, W. Alex; Hughes, Lorine A.; Ebeling, Hanna; Hurtig, Tuula M.; Taanila, Anja M.

2015-01-01

There are strong reasons to assume that early onset of puberty accelerates coital debut among adolescent girls. Although many studies support this assumption, evidence regarding the putative causal processes is limited and inconclusive. In this research, longitudinal data from the 1986 Northern Finland Birth Cohort Study (N = 2,596) were used to…

Human Capital, Poverty, Educational Access and Exclusion: The Case of Ghana 1991-2006. CREATE Pathways to Access. Research Monograph No. 22

ERIC Educational Resources Information Center

Rolleston, Caine

2009-01-01

The period since 1991 has seen a general improvement both in terms of household welfare and schooling participation in Ghana. This monograph explores the patterns among descriptive indicators and uses regression analysis to examine possible causal relationships with special reference to the role of education in determining welfare and its…

Developmental Phenotypes and Causal Pathways in Attention Deficit/Hyperactivity Disorder: Potential Targets for Early Intervention?

ERIC Educational Resources Information Center

Sonuga-Barke, Edmund J. S.; Halperin, Jeffrey M.

2010-01-01

Early intervention approaches have rarely been implemented for the prevention of attention deficit/hyperactivity disorder (ADHD). In this paper we explore whether such an approach may represent an important new direction for therapeutic innovation. We propose that such an approach is most likely to be of value when grounded in and informed by…

Detecting the chaotic nature in a transitional boundary layer using symbolic information-theory quantifiers.

PubMed

Zhang, Wen; Liu, Peiqing; Guo, Hao; Wang, Jinjun

2017-11-01

The permutation entropy and the statistical complexity are employed to study the boundary-layer transition induced by the surface roughness. The velocity signals measured in the transition process are analyzed with these symbolic quantifiers, as well as the complexity-entropy causality plane, and the chaotic nature of the instability fluctuations is identified. The frequency of the dominant fluctuations has been found according to the time scales corresponding to the extreme values of the symbolic quantifiers. The laminar-turbulent transition process is accompanied by the evolution in the degree of organization of the complex eddy motions, which is also characterized with the growing smaller and flatter circles in the complexity-entropy causality plane. With the help of the permutation entropy and the statistical complexity, the differences between the chaotic fluctuations detected in the experiments and the classical Tollmien-Schlichting wave are shown and discussed. It is also found that the chaotic features of the instability fluctuations can be approximated with a number of regular sine waves superimposed on the fluctuations of the undisturbed laminar boundary layer. This result is related to the physical mechanism in the generation of the instability fluctuations, which is the noise-induced chaos.

Detecting the chaotic nature in a transitional boundary layer using symbolic information-theory quantifiers

NASA Astrophysics Data System (ADS)

Zhang, Wen; Liu, Peiqing; Guo, Hao; Wang, Jinjun

2017-11-01

The permutation entropy and the statistical complexity are employed to study the boundary-layer transition induced by the surface roughness. The velocity signals measured in the transition process are analyzed with these symbolic quantifiers, as well as the complexity-entropy causality plane, and the chaotic nature of the instability fluctuations is identified. The frequency of the dominant fluctuations has been found according to the time scales corresponding to the extreme values of the symbolic quantifiers. The laminar-turbulent transition process is accompanied by the evolution in the degree of organization of the complex eddy motions, which is also characterized with the growing smaller and flatter circles in the complexity-entropy causality plane. With the help of the permutation entropy and the statistical complexity, the differences between the chaotic fluctuations detected in the experiments and the classical Tollmien-Schlichting wave are shown and discussed. It is also found that the chaotic features of the instability fluctuations can be approximated with a number of regular sine waves superimposed on the fluctuations of the undisturbed laminar boundary layer. This result is related to the physical mechanism in the generation of the instability fluctuations, which is the noise-induced chaos.

Causal models of attention-deficit/hyperactivity disorder: from common simple deficits to multiple developmental pathways.

PubMed

Sonuga-Barke, Edmund J S

2005-06-01

Until recently, causal models of attention-deficit/hyperactivity disorder (ADHD) have tended to focus on the role of common, simple, core deficits. One such model highlights the role of executive dysfunction due to deficient inhibitory control resulting from disturbances in the frontodorsal striatal circuit and associated mesocortical dopaminergic branches. An alternative model presents ADHD as resulting from impaired signaling of delayed rewards arising from disturbances in motivational processes, involving frontoventral striatal reward circuits and mesolimbic branches terminating in the ventral striatum, particularly the nucleus accumbens. In the present article, these models are elaborated in two ways. First, they are each placed within their developmental context by consideration of the role of person x environment correlation and interaction and individual adaptation to developmental constraint. Second, their relationship to one another is reviewed in the light of recent data suggesting that delay aversion and executive functions might each make distinctive contributions to the development of the disorder. This provides an impetus for theoretical models built around the idea of multiple neurodevelopmental pathways. The possibility of neuropathologic heterogeneity in ADHD is likely to have important implications for the clinical management of the condition, potentially impacting on both diagnostic strategies and treatment options.

Targeting the link between loneliness and paranoia via an interventionist-causal model framework.

PubMed

Gollwitzer, Anton; Wilczynska, Magdalena; Jaya, Edo S

2018-05-01

Targeting the antecedents of paranoia may be one potential method to reduce or prevent paranoia. For instance, targeting a potential antecedent of paranoia - loneliness - may reduce paranoia. Our first research question was whether loneliness heightens subclinical paranoia and whether negative affect may mediate this effect. Second, we wondered whether this potential effect could be targeted via two interventionist pathways in line with an interventionist-causal model approach: (1) decreasing loneliness, and (2) intervening on the potential mediator - negative affect. In Study 1 (N = 222), recollecting an experience of companionship reduced paranoia in participants high in pre-manipulation paranoia but not in participants low in pre-manipulation paranoia. Participants recollecting an experience of loneliness, on the other hand, exhibited increased paranoia, and this effect was mediated by negative affect. In Study 2 (N = 196), participants who utilized an emotion-regulation strategy, cognitive reappraisal, to regulate the negative affect associated with loneliness successfully attenuated the effect of loneliness on paranoia. Targeting the effect of loneliness on paranoia by identifying interventionist pathways may be one promising route for reducing and preventing subclinical paranoia. Copyright © 2018 Elsevier B.V. All rights reserved.

Occam’s Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel

NASA Astrophysics Data System (ADS)

Mahoney, John R.; Aghamohammadi, Cina; Crutchfield, James P.

2016-02-01

A stochastic process’ statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process’ cryptic order-a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.

Occam's Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel.

PubMed

Mahoney, John R; Aghamohammadi, Cina; Crutchfield, James P

2016-02-15

A stochastic process' statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process' cryptic order--a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.

Quantum computational complexity, Einstein's equations and accelerated expansion of the Universe

NASA Astrophysics Data System (ADS)

Ge, Xian-Hui; Wang, Bin

2018-02-01

We study the relation between quantum computational complexity and general relativity. The quantum computational complexity is proposed to be quantified by the shortest length of geodesic quantum curves. We examine the complexity/volume duality in a geodesic causal ball in the framework of Fermi normal coordinates and derive the full non-linear Einstein equation. Using insights from the complexity/action duality, we argue that the accelerated expansion of the universe could be driven by the quantum complexity and free from coincidence and fine-tunning problems.

Causal tapestries for psychology and physics.

PubMed

Sulis, William H

2012-04-01

Archetypal dynamics is a formal approach to the modeling of information flow in complex systems used to study emergence. It is grounded in the Fundamental Triad of realisation (system), interpretation (archetype) and representation (formal model). Tapestries play a fundamental role in the framework of archetypal dynamics as a formal representational system. They represent information flow by means of multi layered, recursive, interlinked graphical structures that express both geometry (form or sign) and logic (semantics). This paper presents a detailed mathematical description of a specific tapestry model, the causal tapestry, selected for use in describing behaving systems such as appear in psychology and physics from the standpoint of Process Theory. Causal tapestries express an explicit Lorentz invariant transient now generated by means of a reality game. Observables are represented by tapestry informons while subjective or hidden components (for example intellectual and emotional processes) are incorporated into the reality game that determines the tapestry dynamics. As a specific example, we formulate a random graphical dynamical system using causal tapestries.

Assessing causality in drug policy analyses: How useful are the Bradford Hill criteria in analysing take-home naloxone programs?

PubMed

Olsen, Anna; McDonald, David; Lenton, Simon; Dietze, Paul M

2018-05-01

The Bradford Hill criteria for assessing causality are useful in assembling evidence, including within complex policy analyses. In this paper, we argue that the implementation of take-home naloxone (THN) programs in Australia and elsewhere reflects sensible, evidence-based public health policy, despite the absence of randomised controlled trials. However, we also acknowledge that the debate around expanding access to THN would benefit from a careful consideration of causal inference and health policy impact of THN program implementation. Given the continued debate around expanding access to THN, and the relatively recent access to new data from implementation studies, two research groups independently conducted Bradford Hill analyses in order to carefully consider causal inference and health policy impact. Hill's criteria offer a useful analytical tool for interpreting current evidence on THN programs and making decisions about the (un)certainty of THN program safety and effectiveness. © 2017 Australasian Professional Society on Alcohol and other Drugs.

Detecting switching and intermittent causalities in time series

NASA Astrophysics Data System (ADS)

Zanin, Massimiliano; Papo, David

2017-04-01

During the last decade, complex network representations have emerged as a powerful instrument for describing the cross-talk between different brain regions both at rest and as subjects are carrying out cognitive tasks, in healthy brains and neurological pathologies. The transient nature of such cross-talk has nevertheless by and large been neglected, mainly due to the inherent limitations of some metrics, e.g., causality ones, which require a long time series in order to yield statistically significant results. Here, we present a methodology to account for intermittent causal coupling in neural activity, based on the identification of non-overlapping windows within the original time series in which the causality is strongest. The result is a less coarse-grained assessment of the time-varying properties of brain interactions, which can be used to create a high temporal resolution time-varying network. We apply the proposed methodology to the analysis of the brain activity of control subjects and alcoholic patients performing an image recognition task. Our results show that short-lived, intermittent, local-scale causality is better at discriminating both groups than global network metrics. These results highlight the importance of the transient nature of brain activity, at least under some pathological conditions.

Causal inference and longitudinal data: a case study of religion and mental health.

PubMed

VanderWeele, Tyler J; Jackson, John W; Li, Shanshan

2016-11-01

We provide an introduction to causal inference with longitudinal data and discuss the complexities of analysis and interpretation when exposures can vary over time. We consider what types of causal questions can be addressed with the standard regression-based analyses and what types of covariate control and control for the prior values of outcome and exposure must be made to reason about causal effects. We also consider newer classes of causal models, including marginal structural models, that can assess questions of the joint effects of time-varying exposures and can take into account feedback between the exposure and outcome over time. Such feedback renders cross-sectional data ineffective for drawing inferences about causation. The challenges are illustrated by analyses concerning potential effects of religious service attendance on depression, in which there may in fact be effects in both directions with service attendance preventing the subsequent depression, but depression itself leading to lower levels of the subsequent religious service attendance. Longitudinal designs, with careful control for prior exposures, outcomes, and confounders, and suitable methodology, will strengthen research on mental health, religion and health, and in the biomedical and social sciences generally.

Comparison of six methods for the detection of causality in a bivariate time series

NASA Astrophysics Data System (ADS)

Krakovská, Anna; Jakubík, Jozef; Chvosteková, Martina; Coufal, David; Jajcay, Nikola; Paluš, Milan

2018-04-01

In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20 000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.

Designing Studies to Test Causal Questions About Early Math: The Development of Making Pre-K Count.

PubMed

Mattera, Shira K; Morris, Pamela A; Jacob, Robin; Maier, Michelle; Rojas, Natalia

2017-01-01

A growing literature has demonstrated that early math skills are associated with later outcomes for children. This research has generated interest in improving children's early math competencies as a pathway to improved outcomes for children in elementary school. The Making Pre-K Count study was designed to test the effects of an early math intervention for preschoolers. Its design was unique in that, in addition to causally testing the effects of early math skills, it also allowed for the examination of a number of additional questions about scale-up, the influence of contextual factors and the counterfactual environment, the mechanism of long-term fade-out, and the role of measurement in early childhood intervention findings. This chapter outlines some of the design considerations and decisions put in place to create a rigorous test of the causal effects of early math skills that is also able to answer these questions in early childhood mathematics and intervention. The study serves as a potential model for how to advance science in the fields of preschool intervention and early mathematics. © 2017 Elsevier Inc. All rights reserved.

Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

PubMed

Seok, J-W; Park, M-S; Sohn, J-H

2016-09-01

Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

Mechanisms Underlying the Influence of Disruptive Child Behavior on Interparental Communication

PubMed Central

Wymbs, Brian T.

2012-01-01

Prospective and experimental manipulations of child behavior have demonstrated that disruptive child behavior causes interparental discord. However, research has yet to test for mechanisms underlying this causal pathway. There is reason to suspect parent affect and parenting behavior explain child effects on interparental relations. To investigate this hypothesis, parent couples of 9- to 12-year-old boys and girls with attention-deficit/hyperactivity disorder (ADHD; n=51) and without ADHD (n=39) were randomly assigned to interact with a confederate child exhibiting “disruptive” or “typical” behavior. Parents rated their own affect as well as the quality of their partner's parenting and communication immediately following the interaction. Observers also coded the quality of parenting and communication behaviors parents exhibited during the interaction. Parents who interacted with disruptive confederates reported lower positive affect and higher negative affect than those who interacted with typical confederates. Parents were also noted by their partners and observers to parent disruptive confederates more negatively than typical confederates. Multilevel mediation models with observational coding and partner ratings both found that negative parenting explained the causal pathway between disruptive child behavior and negative communication. Exploratory analyses revealed that the strength of this pathway did not differ between parents of children with and without ADHD. Parent affect was not found to explain child effects on interparental communication. Though methodological issues limit the generalizability of these findings, results indicate that negative parenting may be one mechanism through which disruptive children cause interparental discord. PMID:21875193

Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis

PubMed Central

Zhao, Linjie; Sun, Tanlin; Pei, Jianfeng; Ouyang, Qi

2015-01-01

It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant–wild-type and 16 matched SNP—wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation. PMID:26170328

Ionotropic GABA receptor antagonism-induced adverse outcome pathways for potential neurotoxicity biomarkers.

PubMed

Gong, Ping; Hong, Huixiao; Perkins, Edward J

2015-01-01

Antagonism of ionotropic GABA receptors (iGABARs) can occur at three distinct types of receptor binding sites causing chemically induced epileptic seizures. Here we review three adverse outcome pathways, each characterized by a specific molecular initiating event where an antagonist competitively binds to active sites, negatively modulates allosteric sites or noncompetitively blocks ion channel on the iGABAR. This leads to decreased chloride conductance, followed by depolarization of affected neurons, epilepsy-related death and ultimately decreased population. Supporting evidence for causal linkages from the molecular to population levels is presented and differential sensitivity to iGABAR antagonists in different GABA receptors and organisms discussed. Adverse outcome pathways are poised to become important tools for linking mechanism-based biomarkers to regulated outcomes in next-generation risk assessment.

Mutation screening of 75 candidate genes in 152 complex I deficiency cases identifies pathogenic variants in 16 genes including NDUFB9.

PubMed

Haack, Tobias B; Madignier, Florence; Herzer, Martina; Lamantea, Eleonora; Danhauser, Katharina; Invernizzi, Federica; Koch, Johannes; Freitag, Martin; Drost, Rene; Hillier, Ingo; Haberberger, Birgit; Mayr, Johannes A; Ahting, Uwe; Tiranti, Valeria; Rötig, Agnes; Iuso, Arcangela; Horvath, Rita; Tesarova, Marketa; Baric, Ivo; Uziel, Graziella; Rolinski, Boris; Sperl, Wolfgang; Meitinger, Thomas; Zeviani, Massimo; Freisinger, Peter; Prokisch, Holger

2012-02-01

Mitochondrial complex I deficiency is the most common cause of mitochondrial disease in childhood. Identification of the molecular basis is difficult given the clinical and genetic heterogeneity. Most patients lack a molecular definition in routine diagnostics. A large-scale mutation screen of 75 candidate genes in 152 patients with complex I deficiency was performed by high-resolution melting curve analysis and Sanger sequencing. The causal role of a new disease allele was confirmed by functional complementation assays. The clinical phenotype of patients carrying mutations was documented using a standardised questionnaire. Causative mutations were detected in 16 genes, 15 of which had previously been associated with complex I deficiency: three mitochondrial DNA genes encoding complex I subunits, two mitochondrial tRNA genes and nuclear DNA genes encoding six complex I subunits and four assembly factors. For the first time, a causal mutation is described in NDUFB9, coding for a complex I subunit, resulting in reduction in NDUFB9 protein and both amount and activity of complex I. These features were rescued by expression of wild-type NDUFB9 in patient-derived fibroblasts. Mutant NDUFB9 is a new cause of complex I deficiency. A molecular diagnosis related to complex I deficiency was established in 18% of patients. However, most patients are likely to carry mutations in genes so far not associated with complex I function. The authors conclude that the high degree of genetic heterogeneity in complex I disorders warrants the implementation of unbiased genome-wide strategies for the complete molecular dissection of mitochondrial complex I deficiency.

Evaluating data-driven causal inference techniques in noisy physical and ecological systems

NASA Astrophysics Data System (ADS)

Tennant, C.; Larsen, L.

2016-12-01

Causal inference from observational time series challenges traditional approaches for understanding processes and offers exciting opportunities to gain new understanding of complex systems where nonlinearity, delayed forcing, and emergent behavior are common. We present a formal evaluation of the performance of convergent cross-mapping (CCM) and transfer entropy (TE) for data-driven causal inference under real-world conditions. CCM is based on nonlinear state-space reconstruction, and causality is determined by the convergence of prediction skill with an increasing number of observations of the system. TE is the uncertainty reduction based on transition probabilities of a pair of time-lagged variables. With TE, causal inference is based on asymmetry in information flow between the variables. Observational data and numerical simulations from a number of classical physical and ecological systems: atmospheric convection (the Lorenz system), species competition (patch-tournaments), and long-term climate change (Vostok ice core) were used to evaluate the ability of CCM and TE to infer causal-relationships as data series become increasingly corrupted by observational (instrument-driven) or process (model-or -stochastic-driven) noise. While both techniques show promise for causal inference, TE appears to be applicable to a wider range of systems, especially when the data series are of sufficient length to reliably estimate transition probabilities of system components. Both techniques also show a clear effect of observational noise on causal inference. For example, CCM exhibits a negative logarithmic decline in prediction skill as the noise level of the system increases. Changes in TE strongly depend on noise type and which variable the noise was added to. The ability of CCM and TE to detect driving influences suggest that their application to physical and ecological systems could be transformative for understanding driving mechanisms as Earth systems undergo change.

Thinking or feeling? An exploratory study of maternal scaffolding, child mental state talk, and emotion understanding in language-impaired and typically developing school-aged children.

PubMed

Yuill, Nicola; Little, Sarah

2018-06-01

Mother-child mental state talk (MST) supports children's developing social-emotional understanding. In typically developing (TD) children, family conversations about emotion, cognition, and causes have been linked to children's emotion understanding. Specific language impairment (SLI) may compromise developing emotion understanding and adjustment. We investigated emotion understanding in children with SLI and TD, in relation to mother-child conversation. Specifically, is cognitive, emotion, or causal MST more important for child emotion understanding and how might maternal scaffolding support this? Nine 5- to 9-year-old children with SLI and nine age-matched typically developing (TD) children, and their mothers. We assessed children's language, emotion understanding and reported behavioural adjustment. Mother-child conversations were coded for MST, including emotion, cognition, and causal talk, and for scaffolding of causal talk. Children with SLI scored lower than TD children on emotion understanding and adjustment. Mothers in each group provided similar amounts of cognitive, emotion, and causal talk, but SLI children used proportionally less cognitive and causal talk than TD children did, and more such child talk predicted better child emotion understanding. Child emotion talk did not differ between groups and did not predict emotion understanding. Both groups participated in maternal-scaffolded causal talk, but causal talk about emotion was more frequent in TD children, and such talk predicted higher emotion understanding. Cognitive and causal language scaffolded by mothers provides tools for articulating increasingly complex ideas about emotion, predicting children's emotion understanding. Our study provides a robust method for studying scaffolding processes for understanding causes of emotion. © 2017 The British Psychological Society.

Private schooling and admission to medicine: a case study using matched samples and causal mediation analysis.

PubMed

Houston, Muir; Osborne, Michael; Rimmer, Russell

2015-08-20

Are applicants from private schools advantaged in gaining entry to degrees in medicine? This is of international significance and there is continuing research in a range of nations including the USA, the UK, other English-speaking nations and EU countries. Our purpose is to seek causal explanations using a quantitative approach. We took as a case study admission to medicine in the UK and drew samples of those who attended private schools and those who did not, with sample members matched on background characteristics. Unlike other studies in the area, causal mediation analysis was applied to resolve private-school influence into direct and indirect effects. In so doing, we sought a benchmark, using data for 2004, against which the effectiveness of policies adopted over the past decade can be assessed. Private schooling improved admission likelihood. This did not occur indirectly via the effect of school type on academic performance; but arose directly from attending private schools. A sensitivity analysis suggests this finding is unlikely to be eliminated by the influence of an unobserved variable. Academic excellence is not a certain pathway into medicine at university; yet applying with good grades after attending private school is more certain. The results of our paper differ from those in an earlier observational study and find support in a later study. Consideration of sources of difference from the earlier observational study suggest the causal approach offers substantial benefits and the consequences in the causal study for gender, ethnicity, socio-economic classification and region of residence provide a benchmark for assessing policy in future research.

Narrative persuasion, causality, complex integration, and support for obesity policy.

PubMed

Niederdeppe, Jeff; Shapiro, Michael A; Kim, Hye Kyung; Bartolo, Danielle; Porticella, Norman

2014-01-01

Narrative messages have the potential to convey causal attribution information about complex social issues. This study examined attributions about obesity, an issue characterized by interrelated biological, behavioral, and environmental causes. Participants were randomly assigned to read one of three narratives emphasizing societal causes and solutions for obesity or an unrelated story that served as the control condition. The three narratives varied in the extent to which the character in the story acknowledged personal responsibility (high, moderate, and none) for controlling her weight. Stories that featured no acknowledgment and moderate acknowledgment of personal responsibility, while emphasizing environmental causes and solutions, were successful at increasing societal cause attributions about obesity and, among conservatives, increasing support for obesity-related policies relative to the control group. The extent to which respondents were able to make connections between individual and environmental causes of obesity (complex integration) mediated the relationship between the moderate acknowledgment condition and societal cause attributions. We conclude with a discussion of the implications of this work for narrative persuasion theory and health communication campaigns.

Fair Inference on Outcomes

PubMed Central

Nabi, Razieh; Shpitser, Ilya

2017-01-01

In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are “sensitive,” in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.

From what might have been to what must have been: counterfactual thinking creates meaning.

PubMed

Kray, Laura J; George, Linda G; Liljenquist, Katie A; Galinsky, Adam D; Tetlock, Philip E; Roese, Neal J

2010-01-01

Four experiments explored whether 2 uniquely human characteristics-counterfactual thinking (imagining alternatives to the past) and the fundamental drive to create meaning in life-are causally related. Rather than implying a random quality to life, the authors hypothesized and found that counterfactual thinking heightens the meaningfulness of key life experiences. Reflecting on alternative pathways to pivotal turning points even produced greater meaning than directly reflecting on the meaning of the event itself. Fate perceptions ("it was meant to be") and benefit-finding (recognition of positive consequences) were identified as independent causal links between counterfactual thinking and the construction of meaning. Through counterfactual reflection, the upsides to reality are identified, a belief in fate emerges, and ultimately more meaning is derived from important life events.

Toward improved public health outcomes from urban nature.

PubMed

Shanahan, Danielle F; Lin, Brenda B; Bush, Robert; Gaston, Kevin J; Dean, Julie H; Barber, Elizabeth; Fuller, Richard A

2015-03-01

There is mounting concern for the health of urban populations as cities expand at an unprecedented rate. Urban green spaces provide settings for a remarkable range of physical and mental health benefits, and pioneering health policy is recognizing nature as a cost-effective tool for planning healthy cities. Despite this, limited information on how specific elements of nature deliver health outcomes restricts its use for enhancing population health. We articulate a framework for identifying direct and indirect causal pathways through which nature delivers health benefits, and highlight current evidence. We see a need for a bold new research agenda founded on testing causality that transcends disciplinary boundaries between ecology and health. This will lead to cost-effective and tailored solutions that could enhance population health and reduce health inequalities.

Toward Improved Public Health Outcomes From Urban Nature

PubMed Central

Bush, Robert; Gaston, Kevin J.; Dean, Julie H.; Barber, Elizabeth; Fuller, Richard A.

2015-01-01

There is mounting concern for the health of urban populations as cities expand at an unprecedented rate. Urban green spaces provide settings for a remarkable range of physical and mental health benefits, and pioneering health policy is recognizing nature as a cost-effective tool for planning healthy cities. Despite this, limited information on how specific elements of nature deliver health outcomes restricts its use for enhancing population health. We articulate a framework for identifying direct and indirect causal pathways through which nature delivers health benefits, and highlight current evidence. We see a need for a bold new research agenda founded on testing causality that transcends disciplinary boundaries between ecology and health. This will lead to cost-effective and tailored solutions that could enhance population health and reduce health inequalities. PMID:25602866

Detecting dynamic causal inference in nonlinear two-phase fracture flow

NASA Astrophysics Data System (ADS)

Faybishenko, Boris

2017-08-01

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

Cancer metabolism: fatty acid oxidation in the limelight

PubMed Central

Carracedo, Arkaitz; Cantley, Lewis C.; Pandolfi, Pier Paolo

2013-01-01

Warburg suggested that the alterations in metabolism that he observed in cancer cells were due to the malfunction of mitochondria. In the past decade, we have revisited this idea and reached a better understanding of the ‘metabolic switch’ in cancer cells, including the intimate and causal relationship between cancer genes and metabolic alterations, and their potential to be targeted for cancer treatment. However, the vast majority of the research into cancer metabolism has been limited to a handful of metabolic pathways, while other pathways have remained in the dark. This Progress article brings to light the important contribution of fatty acid oxidation to cancer cell function. PMID:23446547

Teacher Stress: Complex Model Building with LISREL. Pedagogical Reports, No. 16.

ERIC Educational Resources Information Center

Tellenback, Sten

This paper presents a complex causal model of teacher stress based on data received from the responses of 1,466 teachers from Malmo, Sweden to a questionnaire. Also presented is a method for treating the model variables as higher-order factors or higher-order theoretical constructs. The paper's introduction presents a brief review of teacher…

The Effect of Contextualized Conversational Feedback in a Complex Open-Ended Learning Environment

ERIC Educational Resources Information Center

Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

2013-01-01

Betty's Brain is an open-ended learning environment in which students learn about science topics by teaching a virtual agent named Betty through the construction of a visual causal map that represents the relevant science phenomena. The task is complex, and success requires the use of metacognitive strategies that support knowledge acquisition,…

An Examination of Text Complexity as Characterized by Readability and Cohesion

ERIC Educational Resources Information Center

Reed, Deborah K.; Kershaw-Herrera, Sarah

2016-01-01

To better understand dimensions of text complexity and their effect on the comprehension of adolescents, 103 high school seniors were randomly assigned to 4 groups. Each group read versions of the same 2 informational passages and answered comprehension test items targeting factual recall and inferences of causal content. Group A passages had a…

A Measure of Systems Engineering Effectiveness in Government Acquisition of Complex Information Systems: A Bayesian Belief Network-Based Approach

ERIC Educational Resources Information Center

Doskey, Steven Craig

2014-01-01

This research presents an innovative means of gauging Systems Engineering effectiveness through a Systems Engineering Relative Effectiveness Index (SE REI) model. The SE REI model uses a Bayesian Belief Network to map causal relationships in government acquisitions of Complex Information Systems (CIS), enabling practitioners to identify and…

Motivation and burnout among top amateur rugby players.

PubMed

Cresswell, Scott L; Eklund, Robert C

2005-03-01

Self-determination theory has proven to be a useful theoretical explanation of the occurrence of ill-being on a variety of accounts. Self-determination theory may also provide a useful explanation of the occurrence of athlete burnout. To date, limited evidence exists to support links between motivation and burnout. To examine relationships and potential causal directions among burnout and types of motivation differing in degree of self-determination. Data were collected on burnout using the Athlete Burnout Questionnaire and Sport Motivation Scale from 392 top amateur male rugby players. Structural equation modeling procedures were employed to evaluate a measurement model and three conceptually grounded structural models. One conceptual model specified concomitant (noncausal) relationships between burnout and motivations varying in self-determination. The other conceptual models specified causal pathways between burnout and the three motivation variables considered in the investigation (i.e., intrinsic motivation, external regulation, and amotivation). Within the models, amotivation, the least self-determined type of motivation, had a large positive association with burnout. Externally regulated motivation had trivial and nonsignificant relationships with burnout. Self-determined forms of motivation (i.e., intrinsic motivation) exhibited significant negative associations with burnout. Overall the results support the potential utility of a self-determination theory explanation of burnout. As all models displayed reasonable and comparable fits, further research is required to establish the nature (concomitant vs directional causal vs reciprocal causal) of the relationship between burnout and motivation.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

PubMed

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.

Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model.

PubMed

Nalluri, Joseph J; Rana, Pratip; Barh, Debmalya; Azevedo, Vasco; Dinh, Thang N; Vladimirov, Vladimir; Ghosh, Preetam

2017-08-15

In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the regulation of various pathophysiological conditions, signaling pathways and different types of cancers. Studying miRNA-disease associations has been an extensive area of research; however deciphering miRNA-miRNA network regulatory patterns in several diseases remains a challenge. In this study, we use information diffusion theory to quantify the influence diffusion in a miRNA-miRNA regulation network across multiple disease categories. Our proposed methodology determines the critical disease specific miRNAs which play a causal role in their signaling cascade and hence may regulate disease progression. We extensively validate our framework using existing computational tools from the literature. Furthermore, we implement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify the causal miRNAs for alcohol-dependency in patients which were validated by the phase-shift in their expression scores towards the early stages of the disease. Finally, our computational framework for identifying causal miRNAs implicated in diseases is available as a free online tool for the greater scientific community.

Why cachexia kills: examining the causality of poor outcomes in wasting conditions.

PubMed

Kalantar-Zadeh, Kamyar; Rhee, Connie; Sim, John J; Stenvinkel, Peter; Anker, Stefan D; Kovesdy, Csaba P

2013-06-01

Weight loss is the hallmark of any progressive acute or chronic disease state. In its extreme form of significant lean body mass (including skeletal muscle) and fat loss, it is referred to as cachexia. It has been known for millennia that muscle and fat wasting leads to poor outcomes including death. On one hand, conditions and risk factors that lead to cachexia and inadequate nutrition may independently lead to increased mortality. Additionaly, cachexia per se, withdrawal of nutritional support in progressive cachexia, and advanced age may lead to death via cachexia-specific pathways. Despite the strong and consistent association of cachexia with mortality, no unifying mechanism has yet been suggested as to why wasting conditions are associated with an exceptionally high mortality risk. Hence, the causality of the cachexia-death association, even though it is biologically plausible, is widely unknown. This century-long uncertainty may have played a role as to why the field of cachexia treatment development has not shown major advances over the past decades. We suggest that cachexia-associated relative thrombocytosis and platelet activation may play a causal role in cachexia-related death, while other mechanisms may also contribute including arrhythmia-associated sudden deaths, endocrine disorders such as hypothyroidism, and immune system compromise leading to infectious events and deaths. Multidimensional research including examining biologically plausible models is urgently needed to investigate the causality of the cachexia-death association.

Nonsyndromic cleft lip with or without cleft palate: Increased burden of rare variants within Gremlin-1, a component of the bone morphogenetic protein 4 pathway.

PubMed

Al Chawa, Taofik; Ludwig, Kerstin U; Fier, Heide; Pötzsch, Bernd; Reich, Rudolf H; Schmidt, Gül; Braumann, Bert; Daratsianos, Nikolaos; Böhmer, Anne C; Schuencke, Hannah; Alblas, Margrieta; Fricker, Nadine; Hoffmann, Per; Knapp, Michael; Lange, Christoph; Nöthen, Markus M; Mangold, Elisabeth

2014-06-01

The genes Gremlin-1 (GREM1) and Noggin (NOG) are components of the bone morphogenetic protein 4 pathway, which has been implicated in craniofacial development. Both genes map to recently identified susceptibility loci (chromosomal region 15q13, 17q22) for nonsyndromic cleft lip with or without cleft palate (nsCL/P). The aim of the present study was to determine whether rare variants in either gene are implicated in nsCL/P etiology. The complete coding regions, untranslated regions, and splice sites of GREM1 and NOG were sequenced in 96 nsCL/P patients and 96 controls of Central European ethnicity. Three burden and four nonburden tests were performed. Statistically significant results were followed up in a second case-control sample (n = 96, respectively). For rare variants observed in cases, segregation analyses were performed. In NOG, four rare sequence variants (minor allele frequency < 1%) were identified. Here, burden and nonburden analyses generated nonsignificant results. In GREM1, 33 variants were identified, 15 of which were rare. Of these, five were novel. Significant p-values were generated in three nonburden analyses. Segregation analyses revealed incomplete penetrance for all variants investigated. Our study did not provide support for NOG being the causal gene at 17q22. However, the observation of a significant excess of rare variants in GREM1 supports the hypothesis that this is the causal gene at chr. 15q13. Because no single causal variant was identified, future sequencing analyses of GREM1 should involve larger samples and the investigation of regulatory elements. © 2014 Wiley Periodicals, Inc.

Healthier students are better learners: a missing link in school reforms to close the achievement gap.

PubMed

Basch, Charles E

2011-10-01

This article provides an introduction to the October 2011 special issue of the Journal of School Health on "Healthier Students Are Better Learners." Literature was reviewed and synthesized to identify health problems affecting school-aged youth that are highly prevalent, disproportionately affect urban minority youth, directly and indirectly causally affect academic achievement, and can be feasibly and effectively addressed through school health programs and services. Based on these criteria, 7 educationally relevant health disparities were selected as strategic priorities to help close the achievement gap: (1) vision, (2) asthma, (3) teen pregnancy, (4) aggression and violence, (5) physical activity, (6) breakfast, and (7) inattention and hyperactivity. Research clearly shows that these health problems influence students' motivation and ability to learn. Disparities among urban minority youth are outlined, along with the causal pathways through which each adversely affects academic achievement, including sensory perceptions, cognition, school connectedness, absenteeism, and dropping out. Evidence-based approaches that schools can implement to address these problems are presented. These health problems and the causal pathways they influence have interactive and a synergistic effect, which is why they must be addressed collectively using a coordinated approach. No matter how well teachers are prepared to teach, no matter what accountability measures are put in place, no matter what governing structures are established for schools, educational progress will be profoundly limited if students are not motivated and able to learn. Particular health problems play a major role in limiting the motivation and ability to learn of urban minority youth. This is why reducing these disparities through a coordinated approach warrants validation as a cohesive school improvement initiative to close the achievement gap. Local, state, and national policies for implementing this recommendation are suggested. © 2011, American School Health Association.

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

PubMed Central

Courellis, Hristos; Mullen, Tim; Poizner, Howard; Cauwenberghs, Gert; Iversen, John R.

2017-01-01

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI. PMID:28566997

[Causal analysis approaches in epidemiology].

PubMed

Dumas, O; Siroux, V; Le Moual, N; Varraso, R

2014-02-01

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the formulation of causal hypotheses, which will be a basis for all methodological choices. Beyond this step, statistical analysis tools recently developed offer new possibilities to delineate complex relationships, in particular in life course epidemiology. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification

PubMed Central

Faye, Laura L.; Machiela, Mitchell J.; Kraft, Peter; Bull, Shelley B.; Sun, Lei

2013-01-01

Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website. PMID:23950724

Assessing the causal role of adiposity on disordered eating in childhood, adolescence, and adulthood: a Mendelian randomization analysis

PubMed Central

2017-01-01

Background: Observational studies have shown that higher body mass index (BMI) is associated with increased risk of developing disordered eating patterns. However, the causal direction of this relation remains ambiguous. Objective: We used Mendelian randomization (MR) to infer the direction of causality between BMI and disordered eating in childhood, adolescence, and adulthood. Design: MR analyses were conducted with a genetic score as an instrumental variable for BMI to assess the causal effect of BMI at age 7 y on disordered eating patterns at age 13 y with the use of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 4473). To examine causality in the reverse direction, MR analyses were used to estimate the effect of the same disordered eating patterns at age 13 y on BMI at age 17 y via a split-sample approach in the ALSPAC. We also investigated the causal direction of the association between BMI and eating disorders (EDs) in adults via a two-sample MR approach and publically available genome-wide association study data. Results: MR results indicated that higher BMI at age 7 y likely causes higher levels of binge eating and overeating, weight and shape concerns, and weight-control behavior patterns in both males and females and food restriction in males at age 13 y. Furthermore, results suggested that higher levels of binge eating and overeating in males at age 13 y likely cause higher BMI at age 17 y. We showed no evidence of causality between BMI and EDs in adulthood in either direction. Conclusions: This study provides evidence to suggest a causal effect of higher BMI in childhood and increased risk of disordered eating at age 13 y. Furthermore, higher levels of binge eating and overeating may cause higher BMI in later life. These results encourage an exploration of the ways to break the causal chain between these complex phenotypes, which could inform and prevent disordered eating problems in adolescence. PMID:28747331

Assessing the causal role of adiposity on disordered eating in childhood, adolescence, and adulthood: a Mendelian randomization analysis.

PubMed

Reed, Zoe E; Micali, Nadia; Bulik, Cynthia M; Davey Smith, George; Wade, Kaitlin H

2017-09-01

Background: Observational studies have shown that higher body mass index (BMI) is associated with increased risk of developing disordered eating patterns. However, the causal direction of this relation remains ambiguous. Objective: We used Mendelian randomization (MR) to infer the direction of causality between BMI and disordered eating in childhood, adolescence, and adulthood. Design: MR analyses were conducted with a genetic score as an instrumental variable for BMI to assess the causal effect of BMI at age 7 y on disordered eating patterns at age 13 y with the use of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) ( n = 4473). To examine causality in the reverse direction, MR analyses were used to estimate the effect of the same disordered eating patterns at age 13 y on BMI at age 17 y via a split-sample approach in the ALSPAC. We also investigated the causal direction of the association between BMI and eating disorders (EDs) in adults via a two-sample MR approach and publically available genome-wide association study data. Results: MR results indicated that higher BMI at age 7 y likely causes higher levels of binge eating and overeating, weight and shape concerns, and weight-control behavior patterns in both males and females and food restriction in males at age 13 y. Furthermore, results suggested that higher levels of binge eating and overeating in males at age 13 y likely cause higher BMI at age 17 y. We showed no evidence of causality between BMI and EDs in adulthood in either direction. Conclusions: This study provides evidence to suggest a causal effect of higher BMI in childhood and increased risk of disordered eating at age 13 y. Furthermore, higher levels of binge eating and overeating may cause higher BMI in later life. These results encourage an exploration of the ways to break the causal chain between these complex phenotypes, which could inform and prevent disordered eating problems in adolescence.

Indices of insulin resistance and glucotoxicity are not associated with bipolar disorder or major depressive disorder, but are differently associated with inflammatory, oxidative and nitrosative biomarkers.

PubMed

Landucci Bonifácio, Kamila; Sabbatini Barbosa, Décio; Gastaldello Moreira, Estefânia; de Farias, Carine Coneglian; Higachi, Luciana; Camargo, Alissana Ester Iakmiu; Favaro Soares, Janaina; Odebrecht Vargas, Heber; Nunes, Sandra Odebrecht Vargas; Berk, Michael; Dodd, Seetal; Maes, Michael

2017-11-01

Insulin resistance (IR) is a key factor in diabetes mellitus, metabolic syndrome (MetS) and obesity and may occur in mood disorders and tobacco use disorder (TUD), where disturbances of immune-inflammatory, oxidative and nitrosative stress (IO&NS) pathways are important shared pathophysiological pathways. This study aimed to a) examine IR and β-cell function as measured by the homeostasis model assessment of insulin resistance (HOMA-IR) and insulin sensitivity and β cell function (HOMA-B) and glucotoxicity (conceptualized as increased glucose levels versus lowered HOMA-B values) in 74 participants with major depressive disorder (MDD) and bipolar disorder, with and or without MetS and TUD, versus 46 healthy controls, and b) whether IR is associated with IO&NS biomarkers, including nitric oxide metabolites (NOx), lipid hydroperoxides (LOOH), plasma advanced oxidation protein products (AOPP), C-reactive protein (CRP), haptoglobin (Hp) and uric acid. Mood disorders are not associated with changes in IR or glucotoxicity, although the number of mood episodes may increase IR. 47.8% of the variance in HOMA-IR is explained by AOPP and body mass index (BMI, both positively) and NOx, Hp and TUD (all inversely). 43.2% of the variance in HOMA-B is explained by NOx, Hp and age (all inversely associated) and higher BMI and sex. The glucotoxic index is strongly associated with NOx, Hp and BMI (positively), male gender and lower education. This is a cross-sectional study and therefore we cannot draw firm conclusions on causal associations. Activated IO&NS pathways (especially increased Hp and NOx) increase glucotoxicity and exert very complex effects modulating IR. Mood disorders are not associated with increased IR. Copyright © 2017. Published by Elsevier B.V.

Dissecting the Molecular Pathway Involved in PLK2 Kinase-mediated α-Synuclein-selective Autophagic Degradation.

PubMed

Dahmene, Manel; Bérard, Morgan; Oueslati, Abid

2017-03-03

Increasing lines of evidence support the causal link between α-synuclein (α-syn) accumulation in the brain and Parkinson's disease (PD) pathogenesis. Therefore, lowering α-syn protein levels may represent a viable therapeutic strategy for the treatment of PD and related disorders. We recently described a novel selective α-syn degradation pathway, catalyzed by the activity of the Polo-like kinase 2 (PLK2), capable of reducing α-syn protein expression and suppressing its toxicity in vivo However, the exact molecular mechanisms underlying this degradation route remain elusive. In the present study we report that among PLK family members, PLK3 is also able to catalyze α-syn phosphorylation and degradation in living cells. Using pharmacological and genetic approaches, we confirmed the implication of the macroautophagy on PLK2-mediated α-syn turnover, and our observations suggest a concomitant co-degradation of these two proteins. Moreover, we showed that the N-terminal region of α-syn is important for PLK2-mediated α-syn phosphorylation and degradation and is implicated in the physical interaction between the two proteins. We also demonstrated that PLK2 polyubiquitination is important for PLK2·α-syn protein complex degradation, and we hypothesize that this post-translational modification may act as a signal for the selective recognition by the macroautophagy machinery. Finally, we observed that the PD-linked mutation E46K enhances PLK2-mediated α-syn degradation, suggesting that this mutated form is a bona fide substrate of this degradation pathway. In conclusion, our study provides a detailed description of the new degradation route of α-syn and offers new opportunities for the development of therapeutic strategies aiming to reduce α-syn protein accumulation and toxicity. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Implications of the causality principle for ultra chiral metamaterials

PubMed Central

Gorkunov, Maxim V.; Dmitrienko, Vladimir E.; Ezhov, Alexander A.; Artemov, Vladimir V.; Rogov, Oleg Y.

2015-01-01

Chiral metamaterials – artificial subwavelength structures with broken mirror symmetry – demonstrate outstanding degree of optical chirality that exhibits sophisticated spectral behavior and can eventually reach extreme values. Based on the fundamental causality principle we show how one can unambiguously relate the metamaterial circular dichroism and optical activity by the generalized Kramers-Kronig relations. Contrary to the conventional relations, the generalized ones provide a unique opportunity of extracting information on material-dependent zeroes of transmission coefficient in the upper half plane of complex frequency. We illustrate the merit of the formulated relations by applying them to the observed ultra chiral optical transmission spectra of subwavelength arrays of chiral holes in silver films. Apart from the possibility of precise verification of experimental data, the relations enable resolving complex eigenfrequencies of metamaterial intrinsic modes and resonances. PMID:25787007

Problem solving in great apes (Pan paniscus, Pan troglodytes, Gorilla gorilla, and Pongo abelii): the effect of visual feedback.

PubMed

Völter, Christoph J; Call, Josep

2012-09-01

What kind of information animals use when solving problems is a controversial topic. Previous research suggests that, in some situations, great apes prefer to use causally relevant cues over arbitrary ones. To further examine to what extent great apes are able to use information about causal relations, we presented three different puzzle box problems to the four nonhuman great ape species. Of primary interest here was a comparison between one group of apes that received visual access to the functional mechanisms of the puzzle boxes and one group that did not. Apes' performance in the first two, less complex puzzle boxes revealed that they are able to solve such problems by means of trial-and-error learning, requiring no information about the causal structure of the problem. However, visual inspection of the functional mechanisms of the puzzle boxes reduced the amount of time needed to solve the problems. In the case of the most complex problem, which required the use of a crank, visual feedback about what happened when the handle of the crank was turned was necessary for the apes to solve the task. Once the solution was acquired, however, visual feedback was no longer required. We conclude that visual feedback about the consequences of their actions helps great apes to solve complex problems. As the crank task matches the basic requirements of vertical string pulling in birds, the present results are discussed in light of recent findings with corvids.

The Blood Exposome and Its Role in Discovering Causes of Disease

PubMed Central

Barupal, Dinesh K.; Wishart, David; Vineis, Paolo; Scalbert, Augustin

2014-01-01

Background: Since 2001, researchers have examined the human genome (G) mainly to discover causes of disease, despite evidence that G explains relatively little risk. We posit that unexplained disease risks are caused by the exposome (E; representing all exposures) and G × E interactions. Thus, etiologic research has been hampered by scientists’ continuing reliance on low-tech methods to characterize E compared with high-tech omics for characterizing G. Objectives: Because exposures are inherently chemical in nature and arise from both endogenous and exogenous sources, blood specimens can be used to characterize exposomes. To explore the “blood exposome” and its connection to disease, we sought human blood concentrations of many chemicals, along with their sources, evidence of chronic-disease risks, and numbers of metabolic pathways. Methods: From the literature we obtained human blood concentrations of 1,561 small molecules and metals derived from foods, drugs, pollutants, and endogenous processes. We mapped chemical similarities after weighting by blood concentrations, disease-risk citations, and numbers of human metabolic pathways. Results: Blood concentrations spanned 11 orders of magnitude and were indistinguishable for endogenous and food chemicals and drugs, whereas those of pollutants were 1,000 times lower. Chemical similarities mapped by disease risks were equally distributed by source categories, but those mapped by metabolic pathways were dominated by endogenous molecules and essential nutrients. Conclusions: For studies of disease etiology, the complexity of human exposures motivates characterization of the blood exposome, which includes all biologically active chemicals. Because most small molecules in blood are not human metabolites, investigations of causal pathways should expand beyond the endogenous metabolome. Citation: Rappaport SM, Barupal DK, Wishart D, Vineis P, Scalbert A. 2014. The blood exposome and its role in discovering causes of disease. Environ Health Perspect 122:769–774; http://dx.doi.org/10.1289/ehp.1308015 PMID:24659601

A first insight into the involvement of phytohormones pathways in coffee resistance and susceptibility to Colletotrichum kahawae.

PubMed

Diniz, Inês; Figueiredo, Andreia; Loureiro, Andreia; Batista, Dora; Azinheira, Helena; Várzea, Vítor; Pereira, Ana Paula; Gichuru, Elijah; Moncada, Pilar; Guerra-Guimarães, Leonor; Oliveira, Helena; Silva, Maria do Céu

2017-01-01

Understanding the molecular mechanisms underlying coffee-pathogen interactions are of key importance to aid disease resistance breeding efforts. In this work the expression of genes involved in salicylic acid (SA), jasmonic acid (JA) and ethylene (ET) pathways were studied in hypocotyls of two coffee varieties challenged with the hemibiotrophic fungus Colletotrichum kahawae, the causal agent of Coffee Berry Disease. Based on a cytological analysis, key time-points of the infection process were selected and qPCR was used to evaluate the expression of phytohormones biosynthesis, reception and responsive-related genes. The resistance to C. kahawae was characterized by restricted fungal growth associated with early accumulation of phenolic compounds in the cell walls and cytoplasmic contents, and deployment of hypersensitive reaction. Similar responses were detected in the susceptible variety, but in a significantly lower percentage of infection sites and with no apparent effect on disease development. Gene expression analysis suggests a more relevant involvement of JA and ET phytohormones than SA in this pathosystem. An earlier and stronger activation of the JA pathway observed in the resistant variety, when compared with the susceptible one, seems to be responsible for the successful activation of defense responses and inhibition of fungal growth. For the ET pathway, the down or non-regulation of ET receptors in the resistant variety, together with a moderate expression of the responsive-related gene ERF1, indicates that this phytohormone may be related with other functions besides the resistance response. However, in the susceptible variety, the stronger activation of ERF1 gene at the beginning of the necrotrophic phase, suggests the involvement of ET in tissue senescence. As far as we know, this is the first attempt to unveil the role of phytohormones in coffee-C. kahawae interactions, thus contributing to deepen our understanding on the complex mechanisms of plant signaling and defense.

A first insight into the involvement of phytohormones pathways in coffee resistance and susceptibility to Colletotrichum kahawae

PubMed Central

Figueiredo, Andreia; Loureiro, Andreia; Batista, Dora; Azinheira, Helena; Várzea, Vítor; Pereira, Ana Paula; Gichuru, Elijah; Moncada, Pilar; Guerra-Guimarães, Leonor; Oliveira, Helena; Silva, Maria do Céu

2017-01-01

Understanding the molecular mechanisms underlying coffee-pathogen interactions are of key importance to aid disease resistance breeding efforts. In this work the expression of genes involved in salicylic acid (SA), jasmonic acid (JA) and ethylene (ET) pathways were studied in hypocotyls of two coffee varieties challenged with the hemibiotrophic fungus Colletotrichum kahawae, the causal agent of Coffee Berry Disease. Based on a cytological analysis, key time-points of the infection process were selected and qPCR was used to evaluate the expression of phytohormones biosynthesis, reception and responsive-related genes. The resistance to C. kahawae was characterized by restricted fungal growth associated with early accumulation of phenolic compounds in the cell walls and cytoplasmic contents, and deployment of hypersensitive reaction. Similar responses were detected in the susceptible variety, but in a significantly lower percentage of infection sites and with no apparent effect on disease development. Gene expression analysis suggests a more relevant involvement of JA and ET phytohormones than SA in this pathosystem. An earlier and stronger activation of the JA pathway observed in the resistant variety, when compared with the susceptible one, seems to be responsible for the successful activation of defense responses and inhibition of fungal growth. For the ET pathway, the down or non-regulation of ET receptors in the resistant variety, together with a moderate expression of the responsive-related gene ERF1, indicates that this phytohormone may be related with other functions besides the resistance response. However, in the susceptible variety, the stronger activation of ERF1 gene at the beginning of the necrotrophic phase, suggests the involvement of ET in tissue senescence. As far as we know, this is the first attempt to unveil the role of phytohormones in coffee-C. kahawae interactions, thus contributing to deepen our understanding on the complex mechanisms of plant signaling and defense. PMID:28542545

Coevolution of landesque capital intensive agriculture and sociopolitical hierarchy

PubMed Central

Sheehan, Oliver; Gray, Russell D.; Atkinson, Quentin D.

2018-01-01

One of the defining trends of the Holocene has been the emergence of complex societies. Two essential features of complex societies are intensive resource use and sociopolitical hierarchy. Although it is widely agreed that these two phenomena are associated cross-culturally and have both contributed to the rise of complex societies, the causality underlying their relationship has been the subject of longstanding debate. Materialist theories of cultural evolution tend to view resource intensification as driving the development of hierarchy, but the reverse order of causation has also been advocated, along with a range of intermediate views. Phylogenetic methods have the potential to test between these different causal models. Here we report the results of a phylogenetic study that modeled the coevolution of one type of resource intensification—the development of landesque capital intensive agriculture—with political complexity and social stratification in a sample of 155 Austronesian-speaking societies. We found support for the coevolution of landesque capital with both political complexity and social stratification, but the contingent and nondeterministic nature of both of these relationships was clear. There was no indication that intensification was the “prime mover” in either relationship. Instead, the relationship between intensification and social stratification was broadly reciprocal, whereas political complexity was more of a driver than a result of intensification. These results challenge the materialist view and emphasize the importance of both material and social factors in the evolution of complex societies, as well as the complex and multifactorial nature of cultural evolution. PMID:29555760

Transfer Entropy as a Log-Likelihood Ratio

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Bossomaier, Terry

2012-09-01

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Transfer entropy as a log-likelihood ratio.

PubMed

Barnett, Lionel; Bossomaier, Terry

2012-09-28

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Model robustness as a confirmatory virtue: The case of climate science.

PubMed

Lloyd, Elisabeth A

2015-02-01

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Linkages between Maternal Education and Childhood Immunization in India

PubMed Central

Vikram, Kriti; Vanneman, Reeve; Desai, Sonalde

2012-01-01

While correlations between maternal education and child health have been observed in diverse parts of the world, the causal pathways explaining how maternal education improves child health remain far from clear. Using data from the nationally representative India Human Development Survey of 2004-5, this analysis examines four possible pathways that may mediate the influence of maternal education on childhood immunization: greater human, social, and cultural capitals and more autonomy within the household. Data from 5287 households in India show the familiar positive relationship between maternal education and childhood immunization even after extensive controls for socio-demographic characteristics and village- and neighborhood-fixed effects. Two pathways are important: human capital (health knowledge) is an especially important advantage for mothers with primary education, and cultural capital (communication skills) is important for mothers with some secondary education and beyond. PMID:22531572

The effect of obesity, weight gain, and weight loss on asthma inception and control.

PubMed

Forno, Erick; Celedón, Juan C

2017-04-01

There is ample and growing evidence that obesity increases the risk of asthma and morbidity from asthma. Here, we review recent clinical evidence supporting a causal link between obesity and asthma, and the mechanisms that may lead to 'obese asthma'. Although in some children obesity and asthma simply co-occur, those with 'obese asthma' have increased asthma severity, lower quality of life, and reduced medication response. Underlying mechanistic pathways may include anatomical changes of the airways such as obstruction and dysanapsis, systemic inflammation, production of adipokines, impaired glucose-insulin metabolism, altered nutrient levels, genetic and epigenetic changes, and alterations in the airway and/or gut microbiome. A few small studies have shown that weight loss interventions may lead to improvements in asthma outcomes, but thus far research on therapeutic interventions for these children has been limited. Obesity increases the risk of asthma - and worsens asthma severity or control - via multiple mechanisms. 'Obese asthma' is a complex, multifactorial phenotype in children. Obesity and its complications must be managed as part of the treatment of asthma in obese children.

A de novo FOXP1 variant in a patient with autism, intellectual disability and severe speech and language impairment.

PubMed

Lozano, Reymundo; Vino, Arianna; Lozano, Cristina; Fisher, Simon E; Deriziotis, Pelagia

2015-12-01

FOXP1 (forkhead box protein P1) is a transcription factor involved in the development of several tissues, including the brain. An emerging phenotype of patients with protein-disrupting FOXP1 variants includes global developmental delay, intellectual disability and mild to severe speech/language deficits. We report on a female child with a history of severe hypotonia, autism spectrum disorder and mild intellectual disability with severe speech/language impairment. Clinical exome sequencing identified a heterozygous de novo FOXP1 variant c.1267_1268delGT (p.V423Hfs*37). Functional analyses using cellular models show that the variant disrupts multiple aspects of FOXP1 activity, including subcellular localization and transcriptional repression properties. Our findings highlight the importance of performing functional characterization to help uncover the biological significance of variants identified by genomics approaches, thereby providing insight into pathways underlying complex neurodevelopmental disorders. Moreover, our data support the hypothesis that de novo variants represent significant causal factors in severe sporadic disorders and extend the phenotype seen in individuals with FOXP1 haploinsufficiency.

Hallmarks of progeroid syndromes: lessons from mice and reprogrammed cells

PubMed Central

López-Otín, Carlos

2016-01-01

ABSTRACT Ageing is a process that inevitably affects most living organisms and involves the accumulation of macromolecular damage, genomic instability and loss of heterochromatin. Together, these alterations lead to a decline in stem cell function and to a reduced capability to regenerate tissue. In recent years, several genetic pathways and biochemical mechanisms that contribute to physiological ageing have been described, but further research is needed to better characterize this complex biological process. Because premature ageing (progeroid) syndromes, including progeria, mimic many of the characteristics of human ageing, research into these conditions has proven to be very useful not only to identify the underlying causal mechanisms and identify treatments for these pathologies, but also for the study of physiological ageing. In this Review, we summarize the main cellular and animal models used in progeria research, with an emphasis on patient-derived induced pluripotent stem cell models, and define a series of molecular and cellular hallmarks that characterize progeroid syndromes and parallel physiological ageing. Finally, we describe the therapeutic strategies being investigated for the treatment of progeroid syndromes, and their main limitations. PMID:27482812

Drosophila and experimental neurology in the post-genomic era.

PubMed

Shulman, Joshua M

2015-12-01

For decades, the fruit fly, Drosophila melanogaster, has been among the premiere genetic model systems for probing fundamental neurobiology, including elucidation of mechanisms responsible for human neurologic disorders. Flies continue to offer virtually unparalleled versatility and speed for genetic manipulation, strong genomic conservation, and a nervous system that recapitulates a range of cellular and network properties relevant to human disease. I focus here on four critical challenges emerging from recent advances in our understanding of the genomic basis of human neurologic disorders where innovative experimental strategies are urgently needed: (1) pinpointing causal genes from associated genomic loci; (2) confirming the functional impact of allelic variants; (3) elucidating nervous system roles for novel or poorly studied genes; and (4) probing network interactions within implicated regulatory pathways. Drosophila genetic approaches are ideally suited to address each of these potential translational roadblocks, and will therefore contribute to mechanistic insights and potential breakthrough therapies for complex genetic disorders in the coming years. Strategic collaboration between neurologists, human geneticists, and the Drosophila research community holds great promise to accelerate progress in the post-genomic era. Copyright © 2015 Elsevier Inc. All rights reserved.

Necroinflammation in Kidney Disease.

PubMed

Mulay, Shrikant R; Linkermann, Andreas; Anders, Hans-Joachim

2016-01-01

The bidirectional causality between kidney injury and inflammation remains an area of unexpected discoveries. The last decade unraveled the molecular mechanisms of sterile inflammation, which established danger signaling via pattern recognition receptors as a new concept of kidney injury-related inflammation. In contrast, renal cell necrosis remained considered a passive process executed either by the complement-related membrane attack complex, exotoxins, or cytotoxic T cells. Accumulating data now suggest that renal cell necrosis is a genetically determined and regulated process involving specific outside-in signaling pathways. These findings support a unifying theory in which kidney injury and inflammation are reciprocally enhanced in an autoamplification loop, referred to here as necroinflammation. This integrated concept is of potential clinical importance because it offers numerous innovative molecular targets for limiting kidney injury by blocking cell death, inflammation, or both. Here, the contribution of necroinflammation to AKI is discussed in thrombotic microangiopathies, necrotizing and crescentic GN, acute tubular necrosis, and infective pyelonephritis or sepsis. Potential new avenues are further discussed for abrogating necroinflammation-related kidney injury, and questions and strategies are listed for further exploration in this evolving field. Copyright © 2016 by the American Society of Nephrology.

Environment, power, and society. [stressing energy language and energy analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Odum, H.T.

Studies of the energetics of ecological systems suggest general means for applying basic laws of energy and matter to the complex systems of nature and man. In this book, energy language is used to consider the pressing problem of survival in our time--the partnership of man in nature. An effort is made to show that energy analysis can help answer many of the questions of economics, law, and religion. Models for the analysis of a system are made by recognizing major divisions whose causal relationships are indicated by the pathways of interchange of energy and work. Then simulation allows themore » model's performance to be tested against the performance of the real system. Ideal energy flows are illustrated with ecological systems and then applied to all kinds of situations from very small biochemical processes to the large overall systems of man and the biosphere. Energy diagraming is included to consider the great problems of power, pollution, population, food, and war. This account also attempts to introduce ecology through the energy language.« less

Human FAN1 promotes strand incision in 5'-flapped DNA complexed with RPA.

PubMed

Takahashi, Daisuke; Sato, Koichi; Hirayama, Emiko; Takata, Minoru; Kurumizaka, Hitoshi

2015-09-01

Fanconi anaemia (FA) is a human infantile recessive disorder. Seventeen FA causal proteins cooperatively function in the DNA interstrand crosslink (ICL) repair pathway. Dual DNA strand incisions around the crosslink are critical steps in ICL repair. FA-associated nuclease 1 (FAN1) is a DNA structure-specific endonuclease that is considered to be involved in DNA incision at the stalled replication fork. Replication protein A (RPA) rapidly assembles on the single-stranded DNA region of the stalled fork. However, the effect of RPA on the FAN1-mediated DNA incision has not been determined. In this study, we purified human FAN1, as a bacterially expressed recombinant protein. FAN1 exhibited robust endonuclease activity with 5'-flapped DNA, which is formed at the stalled replication fork. We found that FAN1 efficiently promoted DNA incision at the proper site of RPA-coated 5'-flapped DNA. Therefore, FAN1 possesses the ability to promote the ICL repair of 5'-flapped DNA covered by RPA. © The Authors 2015. Published by Oxford University Press on behalf of the Japanese Biochemical Society. All rights reserved.

Complex self-management interventions in chronic disease unravelled: a review of lessons learned from an individual patient data meta-analysis.

PubMed

Jonkman, Nini H; Groenwold, Rolf H H; Trappenburg, Jaap C A; Hoes, Arno W; Schuurmans, Marieke J

2017-03-01

Meta-analyses using individual patient data (IPD) rather than aggregated data are increasingly applied to analyze sources of heterogeneity between trials and have only recently been applied to unravel multicomponent, complex interventions. This study reflects on methodological challenges encountered in two IPD meta-analyses on self-management interventions in patients with heart failure or chronic obstructive pulmonary disease. Critical reflection on prior IPD meta-analyses and discussion of literature. Experience from two IPD meta-analyses illustrates methodological challenges. Despite close collaboration with principal investigators, assessing the effect of characteristics of complex interventions on the outcomes of trials is compromised by lack of sufficient details on intervention characteristics and limited data on fidelity and adherence. Furthermore, trials collected baseline variables in a highly diverse way, limiting the possibilities to study subgroups of patients in a consistent manner. Possible solutions are proposed based on lessons learnt from the methodological challenges. Future researchers of complex interventions should pay considerable attention to the causal mechanism underlying the intervention and conducting process evaluations. Future researchers on IPD meta-analyses of complex interventions should carefully consider their own causal assumptions and availability of required data in eligible trials before undertaking such resource-intensive IPD meta-analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

Quantum computation with indefinite causal structures

NASA Astrophysics Data System (ADS)

Araújo, Mateus; Guérin, Philippe Allard; Baumeler, ńmin

2017-11-01

One way to study the physical plausibility of closed timelike curves (CTCs) is to examine their computational power. This has been done for Deutschian CTCs (D-CTCs) and postselection CTCs (P-CTCs), with the result that they allow for the efficient solution of problems in PSPACE and PP, respectively. Since these are extremely powerful complexity classes, which are not expected to be solvable in reality, this can be taken as evidence that these models for CTCs are pathological. This problem is closely related to the nonlinearity of this models, which also allows, for example, cloning quantum states, in the case of D-CTCs, or distinguishing nonorthogonal quantum states, in the case of P-CTCs. In contrast, the process matrix formalism allows one to model indefinite causal structures in a linear way, getting rid of these effects and raising the possibility that its computational power is rather tame. In this paper, we show that process matrices correspond to a linear particular case of P-CTCs, and therefore that its computational power is upperbounded by that of PP. We show, furthermore, a family of processes that can violate causal inequalities but nevertheless can be simulated by a causally ordered quantum circuit with only a constant overhead, showing that indefinite causality is not necessarily hard to simulate.

Monitoring signals for vaccine safety: the assessment of individual adverse event reports by an expert advisory committee. Advisory Committee on Causality Assessment.

PubMed Central

Collet, J. P.; MacDonald, N.; Cashman, N.; Pless, R.

2000-01-01

Monitoring vaccine safety is a complex and shared responsibility. It can be carried out in many ways, one of which is the reporting of individual cases of adverse reactions thought to be due to vaccination. The task is difficult because ascribing causality to an individual case report is fraught with challenges. A standardized evaluation instrument--known as the causality assessment form--was therefore developed for use by an expert advisory committee to facilitate the process. By following the several sections in this form, the members of the committee are taken through a series of points to establish causality. These points include the basic criteria for causation such as biological plausibility, the time elapsed between the vaccine administration and the onset of the adverse event, and whether other factors (drugs, chemicals or underlying disease) could account for the adverse symptoms. The form concludes with a consensus assessment of causality, a commentary about the assessment, and advice for further study or follow-up. This method of assessing the more serious cases of adverse reaction reported to vaccination has proven useful in evaluating ongoing safety of vaccines in Canada. Through analyses such as this, new signals can be identified and investigated further. PMID:10743282

Inferring Causalities in Landscape Genetics: An Extension of Wright's Causal Modeling to Distance Matrices.

PubMed

Fourtune, Lisa; Prunier, Jérôme G; Paz-Vinas, Ivan; Loot, Géraldine; Veyssière, Charlotte; Blanchet, Simon

2018-04-01

Identifying landscape features that affect functional connectivity among populations is a major challenge in fundamental and applied sciences. Landscape genetics combines landscape and genetic data to address this issue, with the main objective of disentangling direct and indirect relationships among an intricate set of variables. Causal modeling has strong potential to address the complex nature of landscape genetic data sets. However, this statistical approach was not initially developed to address the pairwise distance matrices commonly used in landscape genetics. Here, we aimed to extend the applicability of two causal modeling methods-that is, maximum-likelihood path analysis and the directional separation test-by developing statistical approaches aimed at handling distance matrices and improving functional connectivity inference. Using simulations, we showed that these approaches greatly improved the robustness of the absolute (using a frequentist approach) and relative (using an information-theoretic approach) fits of the tested models. We used an empirical data set combining genetic information on a freshwater fish species (Gobio occitaniae) and detailed landscape descriptors to demonstrate the usefulness of causal modeling to identify functional connectivity in wild populations. Specifically, we demonstrated how direct and indirect relationships involving altitude, temperature, and oxygen concentration influenced within- and between-population genetic diversity of G. occitaniae.

Design of fuzzy cognitive maps using neural networks for predicting chaotic time series.

PubMed

Song, H J; Miao, C Y; Shen, Z Q; Roel, W; Maja, D H; Francky, C

2010-12-01

As a powerful paradigm for knowledge representation and a simulation mechanism applicable to numerous research and application fields, Fuzzy Cognitive Maps (FCMs) have attracted a great deal of attention from various research communities. However, the traditional FCMs do not provide efficient methods to determine the states of the investigated system and to quantify causalities which are the very foundation of the FCM theory. Therefore in many cases, constructing FCMs for complex causal systems greatly depends on expert knowledge. The manually developed models have a substantial shortcoming due to model subjectivity and difficulties with accessing its reliability. In this paper, we propose a fuzzy neural network to enhance the learning ability of FCMs so that the automatic determination of membership functions and quantification of causalities can be incorporated with the inference mechanism of conventional FCMs. In this manner, FCM models of the investigated systems can be automatically constructed from data, and therefore are independent of the experts. Furthermore, we employ mutual subsethood to define and describe the causalities in FCMs. It provides more explicit interpretation for causalities in FCMs and makes the inference process easier to understand. To validate the performance, the proposed approach is tested in predicting chaotic time series. The simulation studies show the effectiveness of the proposed approach. Copyright © 2010 Elsevier Ltd. All rights reserved.

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster

PubMed Central

Edwards, Stefan M.; Sørensen, Izel F.; Sarup, Pernille; Mackay, Trudy F. C.; Sørensen, Peter

2016-01-01

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits. PMID:27235308

A review of human-automation interaction and lessons learned

DOT National Transportation Integrated Search

2006-10-01

This report reviews 37 accidents in aviation, other vehicles, process control and other complex systems where human-automation interaction is involved. Implications about causality with respect to design, procedures, management and training are drawn...

Causal Mathematical Logic as a guiding framework for the prediction of "Intelligence Signals" in brain simulations

NASA Astrophysics Data System (ADS)

Lanzalaco, Felix; Pissanetzky, Sergio

2013-12-01

A recent theory of physical information based on the fundamental principles of causality and thermodynamics has proposed that a large number of observable life and intelligence signals can be described in terms of the Causal Mathematical Logic (CML), which is proposed to encode the natural principles of intelligence across any physical domain and substrate. We attempt to expound the current definition of CML, the "Action functional" as a theory in terms of its ability to possess a superior explanatory power for the current neuroscientific data we use to measure the mammalian brains "intelligence" processes at its most general biophysical level. Brain simulation projects define their success partly in terms of the emergence of "non-explicitly programmed" complex biophysical signals such as self-oscillation and spreading cortical waves. Here we propose to extend the causal theory to predict and guide the understanding of these more complex emergent "intelligence Signals". To achieve this we review whether causal logic is consistent with, can explain and predict the function of complete perceptual processes associated with intelligence. Primarily those are defined as the range of Event Related Potentials (ERP) which include their primary subcomponents; Event Related Desynchronization (ERD) and Event Related Synchronization (ERS). This approach is aiming for a universal and predictive logic for neurosimulation and AGi. The result of this investigation has produced a general "Information Engine" model from translation of the ERD and ERS. The CML algorithm run in terms of action cost predicts ERP signal contents and is consistent with the fundamental laws of thermodynamics. A working substrate independent natural information logic would be a major asset. An information theory consistent with fundamental physics can be an AGi. It can also operate within genetic information space and provides a roadmap to understand the live biophysical operation of the phenotype

Effect of Age on Complexity and Causality of the Cardiovascular Control: Comparison between Model-Based and Model-Free Approaches

PubMed Central

Porta, Alberto; Faes, Luca; Bari, Vlasta; Marchi, Andrea; Bassani, Tito; Nollo, Giandomenico; Perseguini, Natália Maria; Milan, Juliana; Minatel, Vinícius; Borghi-Silva, Audrey; Takahashi, Anielle C. M.; Catai, Aparecida M.

2014-01-01

The proposed approach evaluates complexity of the cardiovascular control and causality among cardiovascular regulatory mechanisms from spontaneous variability of heart period (HP), systolic arterial pressure (SAP) and respiration (RESP). It relies on construction of a multivariate embedding space, optimization of the embedding dimension and a procedure allowing the selection of the components most suitable to form the multivariate embedding space. Moreover, it allows the comparison between linear model-based (MB) and nonlinear model-free (MF) techniques and between MF approaches exploiting local predictability (LP) and conditional entropy (CE). The framework was applied to study age-related modifications of complexity and causality in healthy humans in supine resting (REST) and during standing (STAND). We found that: 1) MF approaches are more efficient than the MB method when nonlinear components are present, while the reverse situation holds in presence of high dimensional embedding spaces; 2) the CE method is the least powerful in detecting age-related trends; 3) the association of HP complexity on age suggests an impairment of cardiac regulation and response to STAND; 4) the relation of SAP complexity on age indicates a gradual increase of sympathetic activity and a reduced responsiveness of vasomotor control to STAND; 5) the association from SAP to HP on age during STAND reveals a progressive inefficiency of baroreflex; 6) the reduced connection from HP to SAP with age might be linked to the progressive exploitation of Frank-Starling mechanism at REST and to the progressive increase of peripheral resistances during STAND; 7) at REST the diminished association from RESP to HP with age suggests a vagal withdrawal and a gradual uncoupling between respiratory activity and heart; 8) the weakened connection from RESP to SAP with age might be related to the progressive increase of left ventricular thickness and vascular stiffness and to the gradual decrease of respiratory sinus arrhythmia. PMID:24586796

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis.

PubMed

Ishikawa, Akira

2017-11-27

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

Quantifying causal emergence shows that macro can beat micro.

PubMed

Hoel, Erik P; Albantakis, Larissa; Tononi, Giulio

2013-12-03

Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system's mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system's possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence--the gain in EI when moving from a micro to a macro level of analysis.

Assessing the complexity of interventions within systematic reviews: development, content and use of a new tool (iCAT_SR).

PubMed

Lewin, Simon; Hendry, Maggie; Chandler, Jackie; Oxman, Andrew D; Michie, Susan; Shepperd, Sasha; Reeves, Barnaby C; Tugwell, Peter; Hannes, Karin; Rehfuess, Eva A; Welch, Vivien; Mckenzie, Joanne E; Burford, Belinda; Petkovic, Jennifer; Anderson, Laurie M; Harris, Janet; Noyes, Jane

2017-04-26

Health interventions fall along a spectrum from simple to more complex. There is wide interest in methods for reviewing 'complex interventions', but few transparent approaches for assessing intervention complexity in systematic reviews. Such assessments may assist review authors in, for example, systematically describing interventions and developing logic models. This paper describes the development and application of the intervention Complexity Assessment Tool for Systematic Reviews (iCAT_SR), a new tool to assess and categorise levels of intervention complexity in systematic reviews. We developed the iCAT_SR by adapting and extending an existing complexity assessment tool for randomized trials. We undertook this adaptation using a consensus approach in which possible complexity dimensions were circulated for feedback to a panel of methodologists with expertise in complex interventions and systematic reviews. Based on these inputs, we developed a draft version of the tool. We then invited a second round of feedback from the panel and a wider group of systematic reviewers. This informed further refinement of the tool. The tool comprises ten dimensions: (1) the number of active components in the intervention; (2) the number of behaviours of recipients to which the intervention is directed; (3) the range and number of organizational levels targeted by the intervention; (4) the degree of tailoring intended or flexibility permitted across sites or individuals in applying or implementing the intervention; (5) the level of skill required by those delivering the intervention; (6) the level of skill required by those receiving the intervention; (7) the degree of interaction between intervention components; (8) the degree to which the effects of the intervention are context dependent; (9) the degree to which the effects of the interventions are changed by recipient or provider factors; (10) and the nature of the causal pathway between intervention and outcome. Dimensions 1-6 are considered 'core' dimensions. Dimensions 7-10 are optional and may not be useful for all interventions. The iCAT_SR tool facilitates more in-depth, systematic assessment of the complexity of interventions in systematic reviews and can assist in undertaking reviews and interpreting review findings. Further testing of the tool is now needed.

Genetic Modifiers and Oligogenic Inheritance

PubMed Central

Kousi, Maria; Katsanis, Nicholas

2015-01-01

Despite remarkable progress in the identification of mutations that drive genetic disorders, progress in understanding the effect of genetic background on the penetrance and expressivity of causal alleles has been modest, in part because of the methodological challenges in identifying genetic modifiers. Nonetheless, the progressive discovery of modifier alleles has improved both our interpretative ability and our analytical tools to dissect such phenomena. In this review, we analyze the genetic properties and behaviors of modifiers as derived from studies in patient populations and model organisms and we highlight conceptual and technological tools used to overcome some of the challenges inherent in modifier mapping and cloning. Finally, we discuss how the identification of these modifiers has facilitated the elucidation of biological pathways and holds the potential to improve the clinical predictive value of primary causal mutations and to develop novel drug targets. PMID:26033081

Leaving the Faith: How Religious Switching Changes Pathways to Adulthood among Conservative Protestant Youth.

PubMed

Glass, Jennifer L; Sutton, April; Fitzgerald, Scott T

2015-06-01

Research revealing associations between conservative Protestantism and lower socioeconomic status is bedeviled by questions of causal inference. Religious switching offers another way to understand the causal ordering of religious participation and demographic markers of class position. In this paper, we look at adolescents who change their religious affiliation across four waves of data from the National Longitudinal Study of Adolescent Health (Add Health) and then observe their transition to adulthood using four crucial markers - completed educational attainment, age at first marriage, age at first birth, and income at the final wave. Results show that switching out of a conservative Protestant denomination in adolescence can alter some, but not all, of the negative consequences associated with growing up in a conservative Protestant household. Specifically, family formation is delayed among switchers, but early cessation of education is not.

How to and how not to develop a theory of change to evaluate a complex intervention: reflections on an experience in the Democratic Republic of Congo.

PubMed

Maini, Rishma; Mounier-Jack, Sandra; Borghi, Josephine

2018-01-01

Theories of change (ToCs) describe how interventions can bring about long-term outcomes through a logical sequence of intermediate outcomes and have been used to design and measure the impact of public health programmes in several countries. In recognition of their capacity to provide a framework for monitoring and evaluation, they are being increasingly employed in the development sector. The construction of a ToC typically occurs through a consultative process, requiring stakeholders to reflect on how their programmes can bring about change. ToCs help make explicit any underlying assumptions, acknowledge the role of context and provide evidence to justify the chain of causal pathways. However, while much literature exists on how to develop a ToC with respect to interventions in theory, there is comparatively little reflection on applying it in practice to complex interventions in the health sector. This paper describes the initial process of developing a ToC to inform the design of an evaluation of a complex intervention aiming to improve government payments to health workers in the Democratic Republic of Congo. Lessons learnt include: the need for the ToC to understand how the intervention produces effects on the wider system and having broad stakeholder engagement at the outset to maximise chances of the intervention's success and ensure ownership. Power relationships between stakeholders may also affect the ToC discourse but can be minimised by having an independent facilitator. We hope these insights are of use to other global public health practitioners using this approach to evaluate complex interventions.

Does sufficient evidence exist to support a causal association between vitamin D status and cardiovascular disease risk? An assessment using Hill's criteria for causality.

PubMed

Weyland, Patricia G; Grant, William B; Howie-Esquivel, Jill

2014-09-02

Serum 25-hydroxyvitamin D (25(OH)D) levels have been found to be inversely associated with both prevalent and incident cardiovascular disease (CVD) risk factors; dyslipidemia, hypertension and diabetes mellitus. This review looks for evidence of a causal association between low 25(OH)D levels and increased CVD risk. We evaluated journal articles in light of Hill's criteria for causality in a biological system. The results of our assessment are as follows. Strength of association: many randomized controlled trials (RCTs), prospective and cross-sectional studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors. Consistency of observed association: most studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors in various populations, locations and circumstances. Temporality of association: many RCTs and prospective studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors. Biological gradient (dose-response curve): most studies assessing 25(OH)D levels and CVD risk found an inverse association exhibiting a linear biological gradient. Plausibility of biology: several plausible cellular-level causative mechanisms and biological pathways may lead from a low 25(OH)D level to increased risk for CVD with mediators, such as dyslipidemia, hypertension and diabetes mellitus. Experimental evidence: some well-designed RCTs found increased CVD risk factors with decreasing 25(OH)D levels. Analogy: the association between serum 25(OH)D levels and CVD risk is analogous to that between 25(OH)D levels and the risk of overall cancer, periodontal disease, multiple sclerosis and breast cancer. all relevant Hill criteria for a causal association in a biological system are satisfied to indicate a low 25(OH)D level as a CVD risk factor.

Incorporating Functional Genomic Information in Genetic Association Studies Using an Empirical Bayes Approach.

PubMed

Spencer, Amy V; Cox, Angela; Lin, Wei-Yu; Easton, Douglas F; Michailidou, Kyriaki; Walters, Kevin

2016-04-01

There is a large amount of functional genetic data available, which can be used to inform fine-mapping association studies (in diseases with well-characterised disease pathways). Single nucleotide polymorphism (SNP) prioritization via Bayes factors is attractive because prior information can inform the effect size or the prior probability of causal association. This approach requires the specification of the effect size. If the information needed to estimate a priori the probability density for the effect sizes for causal SNPs in a genomic region isn't consistent or isn't available, then specifying a prior variance for the effect sizes is challenging. We propose both an empirical method to estimate this prior variance, and a coherent approach to using SNP-level functional data, to inform the prior probability of causal association. Through simulation we show that when ranking SNPs by our empirical Bayes factor in a fine-mapping study, the causal SNP rank is generally as high or higher than the rank using Bayes factors with other plausible values of the prior variance. Importantly, we also show that assigning SNP-specific prior probabilities of association based on expert prior functional knowledge of the disease mechanism can lead to improved causal SNPs ranks compared to ranking with identical prior probabilities of association. We demonstrate the use of our methods by applying the methods to the fine mapping of the CASP8 region of chromosome 2 using genotype data from the Collaborative Oncological Gene-Environment Study (COGS) Consortium. The data we analysed included approximately 46,000 breast cancer case and 43,000 healthy control samples. © 2016 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

Multivariate causal attribution and cost-effectiveness of a national mass media campaign in the Philippines.

PubMed

Kincaid, D Lawrence; Do, Mai Phuong

2006-01-01

Cost-effectiveness analysis is based on a simple formula. A dollar estimate of the total cost to conduct a program is divided by the number of people estimated to have been affected by it in terms of some intended outcome. The direct, total costs of most communication campaigns are usually available. Estimating the amount of effect that can be attributed to the communication alone, however is problematical in full-coverage, mass media campaigns where the randomized control group design is not feasible. Single-equation, multiple regression analysis controls for confounding variables but does not adequately address the issue of causal attribution. In this article, multivariate causal attribution (MCA) methods are applied to data from a sample survey of 1,516 married women in the Philippines to obtain a valid measure of the number of new adopters of modern contraceptives that can be causally attributed to a national mass media campaign and to calculate its cost-effectiveness. The MCA analysis uses structural equation modeling to test the causal pathways and to test for endogeneity, biprobit analysis to test for direct effects of the campaign and endogeneity, and propensity score matching to create a statistically equivalent, matched control group that approximates the results that would have been obtained from a randomized control group design. The MCA results support the conclusion that the observed, 6.4 percentage point increase in modern contraceptive use can be attributed to the national mass media campaign and to its indirect effects on attitudes toward contraceptives. This net increase represented 348,695 new adopters in the population of married women at a cost of U.S. $1.57 per new adopter.

Modelling possible causality in the associations between unemployment, cannabis use, and alcohol misuse.

PubMed

Boden, Joseph M; Lee, Jungeun Olivia; Horwood, L John; Grest, Carolina Villamil; McLeod, Geraldine F H

2017-02-01

There has been considerable interest in the extent to which substance use and unemployment may be related, particularly the causal pathways that may be involved in these associations. It has been argued that these associations may reflect social causation, in which unemployment influences substance use, or that they may reflect social selection, in which substance use increases the risk of becoming and remaining unemployed. The present study sought to test these competing explanations. Data from the Christchurch Health and Development Study, featuring a longitudinal birth cohort, were used to model the associations between unemployment and both cannabis and alcohol. Data on patterns of unemployment, involvement with cannabis, and symptoms of alcohol use disorder were examined from ages 18-35 years. The associations between unemployment and both cannabis dependence and alcohol use disorder (AUD) were modelled using conditional fixed-effects regression models, augmented by time-dynamic covariate factors. The analyses showed evidence of possible reciprocal causal processes in the association between unemployment and cannabis dependence, in which unemployment of at least three months' duration significantly (p < 0.0001) increased the risk of cannabis dependence, and cannabis dependence significantly (p < 0.0001) increased the risk of being unemployed. Similar evidence was found for the associations between unemployment and AUD, although these associations were smaller in magnitude. The present findings support both social causation and social selection arguments, by indicating that unemployment plays a causal role in substance misuse, and that it is also likely that a reverse causal process whereby substance misuse increases the risk of unemployment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Contours of a causal feedback mechanism between adaptive personality and psychosocial function in patients with personality disorders: a secondary analysis from a randomized clinical trial.

PubMed

Klungsøyr, Ole; Antonsen, Bjørnar; Wilberg, Theresa

2017-06-05

Patients with personality disorders commonly exhibit impairment in psychosocial function that persists over time even with diagnostic remission. Further causal knowledge may help to identify and assess factors with a potential to alleviate this impairment. Psychosocial function is associated with personality functioning which describes personality disorder severity in DSM-5 (section III) and which can reportedly be improved by therapy. The reciprocal association between personality functioning and psychosocial function was assessed, in 113 patients with different personality disorders, in a secondary longitudinal analysis of data from a randomized clinical trial, over six years. Personality functioning was represented by three domains of the Severity Indices of Personality Problems: Relational Capacity, Identity Integration, and Self-control. Psychosocial function was measured by Global Assessment of Functioning. The marginal structural model was used for estimation of causal effects of the three personality functioning domains on psychosocial function, and vice versa. The attractiveness of this model lies in the ability to assess an effect of a time - varying exposure on an outcome, while adjusting for time - varying confounding. Strong causal effects were found. A hypothetical intervention to increase Relational Capacity by one standard deviation, both at one and two time-points prior to assessment of psychosocial function, would increase psychosocial function by 3.5 standard deviations (95% CI: 2.0, 4.96). Significant effects of Identity Integration and Self-control on psychosocial function, and from psychosocial function on all three domains of personality functioning, although weaker, were also found. This study indicates that persistent impairment in psychosocial function can be addressed through a causal pathway of personality functioning, with interventions of at least 18 months duration.

Biological sex and the risk of cerebral palsy in Victoria, Australia.

PubMed

Reid, Susan M; Meehan, Elaine; Gibson, Catherine S; Scott, Heather; Delacy, Michael J

2016-02-01

Males typically outnumber females in cerebral palsy (CP) cohorts. To better understand this 'male disadvantage' and provide insight into causal pathways to CP, this study used 1983 to 2009 Australian CP and population birth cohorts to identify associations and trends with respect to biological sex and CP. Within birth gestation groups, sex ratios were calculated to evaluate any male excess in the CP cohort compared with livebirths, neonatal deaths, neonatal mortality and survival rates, neonatal survivors, and CP rates in survivors. Sex- and gestation-specific trends in neonatal mortality, CP rates, and CP sex ratios were assessed by plotting their annual frequencies and fitting quadratic curves. Over-representation of males in preterm live births partly explained the male excess in the CP cohort after preterm birth, especially at 28 to 31 weeks. Higher CP rates in male neonatal survivors also contributed to the male excess in CP, particularly at <28 and 37+ weeks. Higher neonatal mortality rates in males at all gestations had little impact on the CP sex ratio. There was no clearly discernible change over time in the CP sex ratio. Gestation-specific associations between sex and CP provide insight into causal pathways to CP and suggest sex-specific differences in response to neuroprotective strategies. © 2016 The Authors. Developmental Medicine & Child Neurology © 2016 Mac Keith Press.

Can attribution theory explain carers' propensity to help men with intellectual disabilities who display inappropriate sexual behaviour?

PubMed

Willner, P; Smith, M

2008-01-01

This study examined the responses of care managers and direct care staff to vignettes of inappropriate sexual behaviour by a man with an intellectual disability. The aim was to test the theory that helping behaviour is determined by emotional responses (positive and negative emotional reactions, and optimism), which in turn are determined by causal attributions (respectively: controllability and stability of the incident depicted in the vignette). The vignettes varied in response topography and the age of the victim. Regression analysis was used to examine the relationships between causal attributions, emotional responses, and willingness to invest extra time and effort in the service user's care. No support was found for the pathway: low controllability --> increased sympathy and/or decreased negative emotions --> increased helping. However, strong support was found for the pathway: low stability --> high optimism --> increased helping, particularly in direct care staff. High levels of sympathy were also associated with increased helping, the effect again being mediated by feelings of optimism. The data provide support for one (but not the other) strand of attribution theory as applied to inappropriate sexual behaviour. The discussion considers the discrepancy between the present data and the far less encouraging literature on attribution theory as applied to challenging behaviour.

Genetic Alterations in Glioma

PubMed Central

Bralten, Linda B. C.; French, Pim J.

2011-01-01

Gliomas are the most common type of primary brain tumor and have a dismal prognosis. Understanding the genetic alterations that drive glioma formation and progression may help improve patient prognosis by identification of novel treatment targets. Recently, two major studies have performed in-depth mutation analysis of glioblastomas (the most common and aggressive subtype of glioma). This systematic approach revealed three major pathways that are affected in glioblastomas: The receptor tyrosine kinase signaling pathway, the TP53 pathway and the pRB pathway. Apart from frequent mutations in the IDH1/2 gene, much less is known about the causal genetic changes of grade II and III (anaplastic) gliomas. Exceptions include TP53 mutations and fusion genes involving the BRAF gene in astrocytic and pilocytic glioma subtypes, respectively. In this review, we provide an update on all common events involved in the initiation and/or progression across the different subtypes of glioma and provide future directions for research into the genetic changes. PMID:24212656

The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology.

PubMed

Krieger, Nancy; Davey Smith, George

2016-12-01

'Causal inference', in 21st century epidemiology, has notably come to stand for a specific approach, one focused primarily on counterfactual and potential outcome reasoning and using particular representations, such as directed acyclic graphs (DAGs) and Bayesian causal nets. In this essay, we suggest that in epidemiology no one causal approach should drive the questions asked or delimit what counts as useful evidence. Robust causal inference instead comprises a complex narrative, created by scientists appraising, from diverse perspectives, different strands of evidence produced by myriad methods. DAGs can of course be useful, but should not alone wag the causal tale. To make our case, we first address key conceptual issues, after which we offer several concrete examples illustrating how the newly favoured methods, despite their strengths, can also: (i) limit who and what may be deemed a 'cause', thereby narrowing the scope of the field; and (ii) lead to erroneous causal inference, especially if key biological and social assumptions about parameters are poorly conceived, thereby potentially causing harm. As an alternative, we propose that the field of epidemiology consider judicious use of the broad and flexible framework of 'inference to the best explanation', an approach perhaps best developed by Peter Lipton, a philosopher of science who frequently employed epidemiologically relevant examples. This stance requires not only that we be open to being pluralists about both causation and evidence but also that we rise to the challenge of forging explanations that, in Lipton's words, aspire to 'scope, precision, mechanism, unification and simplicity'. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Loss of the co-repressor GPS2 sensitizes macrophage activation upon metabolic stress induced by obesity and type 2 diabetes.

PubMed

Fan, Rongrong; Toubal, Amine; Goñi, Saioa; Drareni, Karima; Huang, Zhiqiang; Alzaid, Fawaz; Ballaire, Raphaelle; Ancel, Patricia; Liang, Ning; Damdimopoulos, Anastasios; Hainault, Isabelle; Soprani, Antoine; Aron-Wisnewsky, Judith; Foufelle, Fabienne; Lawrence, Toby; Gautier, Jean-Francois; Venteclef, Nicolas; Treuter, Eckardt

2016-07-01

Humans with obesity differ in their susceptibility to developing insulin resistance and type 2 diabetes (T2D). This variation may relate to the extent of adipose tissue (AT) inflammation that develops as their obesity progresses. The state of macrophage activation has a central role in determining the degree of AT inflammation and thus its dysfunction, and these states are driven by epigenomic alterations linked to gene expression. The underlying mechanisms that regulate these alterations, however, are poorly defined. Here we demonstrate that a co-repressor complex containing G protein pathway suppressor 2 (GPS2) crucially controls the macrophage epigenome during activation by metabolic stress. The study of AT from humans with and without obesity revealed correlations between reduced GPS2 expression in macrophages, elevated systemic and AT inflammation, and diabetic status. The causality of this relationship was confirmed by using macrophage-specific Gps2-knockout (KO) mice, in which inappropriate co-repressor complex function caused enhancer activation, pro-inflammatory gene expression and hypersensitivity toward metabolic-stress signals. By contrast, transplantation of GPS2-overexpressing bone marrow into two mouse models of obesity (ob/ob and diet-induced obesity) reduced inflammation and improved insulin sensitivity. Thus, our data reveal a potentially reversible disease mechanism that links co-repressor-dependent epigenomic alterations in macrophages to AT inflammation and the development of T2D.

Xenopus as a Model Organism for Birth Defects – Congenital Heart Disease and Heterotaxy

PubMed Central

Duncan, Anna R.; Khokha, Mustafa K.

2016-01-01

Congenital heart disease is the leading cause of birth defects, affecting 9 out of 1000 newborns each year. A particularly severe form of congenital heart disease is heterotaxy, a disorder of left-right development. Despite aggressive surgical management, patients with heterotaxy have poor survival rates and severe morbidity due to their complex congenital heart disease. Recent genetic analysis of affected patients has found novel candidate genes for heterotaxy although their underlying mechanisms remain unknown. In this review, we discuss the importance and challenges of birth defects research including high locus heterogeneity and few second alleles that make defining disease causality difficult. A powerful strategy moving forward is to analyze these candidate genes in a high-throughput human disease model. Xenopus is ideal for these studies. We present multiple examples demonstrating the power of Xenopus in discovery new biology from the analysis of candidate heterotaxy genes such as GALNT11, NEK2 and BCOR. These genes have diverse roles in embryos and have led to a greater understanding of complex signaling pathways and basic developmental biology. It is our hope that the mechanistic analysis of these candidate genes in Xenopus enabled by next generation sequencing of patients will provide clinicians with a greater understanding of patient pathophysiology allowing more precise and personalized medicine, to help them more effectively in the future. PMID:26910255