Do Emotions Expressed Online Correlate with Actual Changes in Decision-Making?: The Case of Stock Day Traders

PubMed Central

Liu, Bin; Govindan, Ramesh; Uzzi, Brian

2016-01-01

Emotions are increasingly inferred linguistically from online data with a goal of predicting off-line behavior. Yet, it is unknown whether emotions inferred linguistically from online communications correlate with actual changes in off-line activity. We analyzed all 886,000 trading decisions and 1,234,822 instant messages of 30 professional day traders over a continuous 2 year period. Linguistically inferring the traders’ emotional states from instant messages, we find that emotions expressed in online communications reflect the same distributions of emotions found in controlled experiments done on traders. Further, we find that expressed online emotions predict the profitability of actual trading behavior. Relative to their baselines, traders who expressed little emotion or traders that expressed high levels of emotion made relatively unprofitable trades. Conversely, traders expressing moderate levels of emotional activation made relatively profitable trades. PMID:26765539

Category Label Effects on Chinese Children's Inductive Inferences: Modulation by Perceptual Detail and Category Specificity

ERIC Educational Resources Information Center

Long, Changquan; Lu, Xiaoying; Zhang, Li; Li, Hong; Deak, Gedeon O.

2012-01-01

Inductive generalization of novel properties to same-category or similar-looking objects was studied in Chinese preschool children. The effects of category labels on generalizations were investigated by comparing basic-level labels, superordinate-level labels, and a control phrase applied to three kinds of stimulus materials: colored photographs…

Inconsistencies in spontaneous and intentional trait inferences.

PubMed

Ma, Ning; Vandekerckhove, Marie; Baetens, Kris; Van Overwalle, Frank; Seurinck, Ruth; Fias, Wim

2012-11-01

This study explores the fMRI correlates of observers making trait inferences about other people under conflicting social cues. Participants were presented with several behavioral descriptions involving an agent that implied a particular trait. The last behavior was either consistent or inconsistent with the previously implied trait. This was done under instructions that elicited either spontaneous trait inferences ('read carefully') or intentional trait inferences ('infer a trait'). The results revealed that when the behavioral descriptions violated earlier trait implications, regardless of instruction, the medial prefrontal cortex (mPFC) was more strongly recruited as well as the domain-general conflict network including the posterior medial frontal cortex (pmFC) and the right prefrontal cortex (rPFC). These latter two areas were more strongly activated under intentional than spontaneous instructions. These findings suggest that when trait-relevant behavioral information is inconsistent, not only is activity increased in the mentalizing network responsible for trait processing, but control is also passed to a higher level conflict monitoring network in order to detect and resolve the contradiction.

Association between job characteristics and plasma fibrinogen in a normal working population: a cross sectional analysis in referents of the SHEEP Study. Stockholm Heart Epidemiology Program

PubMed Central

Tsutsumi, A.; Theorell, T.; Hallqvist, J.; Reuterwall, C.; de Faire, U.

1999-01-01

STUDY OBJECTIVE: To explore the association between job characteristics and plasma fibrinogen concentrations. DESIGN: Cross sectional design. SETTING: The Greater Stockholm area. SUBJECTS: A total of 1018 men and 490 women aged 45-70 who were randomly selected from the general population during 1992-1994. They were all employed and had no history of myocardial infarction. MAIN RESULTS: The self reported job characteristics were measured by a Swedish version of the Karasek demand-control questionnaire. For inferred scoring of job characteristics, psychosocial exposure categories (job control and psychological demands) were assigned by linking each subject's occupational history with a work organisation exposure matrix. Job strain was defined as the ratio between demands and control. In univariate analyses, expected linear trends were found in three of four tests of association between high plasma fibrinogen and low control (the self reported score for women and the inferred score for both sexes), in one of four tests of association between high plasma fibrinogen and high demands (the inferred score for women) and in two of four tests of association between high plasma fibrinogen and job strain (the inferred score for both sexes). Multiple logistic regression analyses showed that men in the inferred job strain group have an increased risk of falling into the increased plasma fibrinogen concentration group (above median level of the distribution) (odds ratio (OR) 1.2; 95% CI 1.0, 1.5) after adjustment for the variables that were associated with plasma fibrinogen in the univariate analyses. In women, low self reported control, high inferred demand, and inferred job strain were significantly associated with increased plasma fibrinogen concentration (OR 1.3; 95% CI 1.0, 1.8, OR 1.5; 95% CI 1.0, 2.2, OR 1.5; 95% CI 1.1, 2.2, respectively). CONCLUSIONS: These results indicate that adverse job characteristics may be related to plasma fibrinogen concentrations and this relation is more relevant in female workers. The clearest evidence for psychosocial effects on plasma fibrinogen seems to be with job control and the associations are clearer for the objective than for the self report variables.   PMID:10396481

Comparison of a non-stationary voxelation-corrected cluster-size test with TFCE for group-Level MRI inference.

PubMed

Li, Huanjie; Nickerson, Lisa D; Nichols, Thomas E; Gao, Jia-Hong

2017-03-01

Two powerful methods for statistical inference on MRI brain images have been proposed recently, a non-stationary voxelation-corrected cluster-size test (CST) based on random field theory and threshold-free cluster enhancement (TFCE) based on calculating the level of local support for a cluster, then using permutation testing for inference. Unlike other statistical approaches, these two methods do not rest on the assumptions of a uniform and high degree of spatial smoothness of the statistic image. Thus, they are strongly recommended for group-level fMRI analysis compared to other statistical methods. In this work, the non-stationary voxelation-corrected CST and TFCE methods for group-level analysis were evaluated for both stationary and non-stationary images under varying smoothness levels, degrees of freedom and signal to noise ratios. Our results suggest that, both methods provide adequate control for the number of voxel-wise statistical tests being performed during inference on fMRI data and they are both superior to current CSTs implemented in popular MRI data analysis software packages. However, TFCE is more sensitive and stable for group-level analysis of VBM data. Thus, the voxelation-corrected CST approach may confer some advantages by being computationally less demanding for fMRI data analysis than TFCE with permutation testing and by also being applicable for single-subject fMRI analyses, while the TFCE approach is advantageous for VBM data. Hum Brain Mapp 38:1269-1280, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Multi-agent based control of large-scale complex systems employing distributed dynamic inference engine

NASA Astrophysics Data System (ADS)

Zhang, Daili

Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algorithm in MSDBNs makes state estimations robust to partial damage in the whole system. Combining the distributed control architecture design and the distributed inference engine design leads to a process of control system design for a general large-scale complex system. As applications of the proposed methodology, the control system design of a simplified ship chilled water system and a notional ship chilled water system have been demonstrated step by step. Simulation results not only show that the proposed methodology gives a clear guideline for control system design for general large-scale complex systems with dynamic and uncertain environment, but also indicate that the combination of MSDBNs and HyMABC can provide excellent performance for controlling general large-scale complex systems.

Using SPM 12’s Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users

PubMed Central

Han, Hyemin; Park, Joonsuk

2018-01-01

Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely P < 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parameters for Bayesian inference and how to interpret the results, such as Bayes factors, from the inference. We also discuss the practical and philosophical benefits of Bayesian inference and directions for future research. PMID:29456498

How to talk about protein‐level false discovery rates in shotgun proteomics

PubMed Central

The, Matthew; Tasnim, Ayesha

2016-01-01

A frequently sought output from a shotgun proteomics experiment is a list of proteins that we believe to have been present in the analyzed sample before proteolytic digestion. The standard technique to control for errors in such lists is to enforce a preset threshold for the false discovery rate (FDR). Many consider protein‐level FDRs a difficult and vague concept, as the measurement entities, spectra, are manifestations of peptides and not proteins. Here, we argue that this confusion is unnecessary and provide a framework on how to think about protein‐level FDRs, starting from its basic principle: the null hypothesis. Specifically, we point out that two competing null hypotheses are used concurrently in today's protein inference methods, which has gone unnoticed by many. Using simulations of a shotgun proteomics experiment, we show how confusing one null hypothesis for the other can lead to serious discrepancies in the FDR. Furthermore, we demonstrate how the same simulations can be used to verify FDR estimates of protein inference methods. In particular, we show that, for a simple protein inference method, decoy models can be used to accurately estimate protein‐level FDRs for both competing null hypotheses. PMID:27503675

Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control

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

Delgoshaei, Parastoo; Austin, Mark A.; Pertzborn, Amanda J.

State-of-the-art building simulation control methods incorporate physical constraints into their mathematical models, but omit implicit constraints associated with policies of operation and dependency relationships among rules representing those constraints. To overcome these shortcomings, there is a recent trend in enabling the control strategies with inference-based rule checking capabilities. One solution is to exploit semantic web technologies in building simulation control. Such approaches provide the tools for semantic modeling of domains, and the ability to deduce new information based on the models through use of Description Logic (DL). In a step toward enabling this capability, this paper presents a cross-disciplinary data-drivenmore » control strategy for building energy management simulation that integrates semantic modeling and formal rule checking mechanisms into a Model Predictive Control (MPC) formulation. The results show that MPC provides superior levels of performance when initial conditions and inputs are derived from inference-based rules.« less

Active Inference, homeostatic regulation and adaptive behavioural control

PubMed Central

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-01-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. PMID:26365173

Thermomechanical controls on magma supply and volcanic deformation: application to Aira caldera, Japan.

PubMed

Hickey, James; Gottsmann, Joachim; Nakamichi, Haruhisa; Iguchi, Masato

2016-09-13

Ground deformation often precedes volcanic eruptions, and results from complex interactions between source processes and the thermomechanical behaviour of surrounding rocks. Previous models aiming to constrain source processes were unable to include realistic mechanical and thermal rock properties, and the role of thermomechanical heterogeneity in magma accumulation was unclear. Here we show how spatio-temporal deformation and magma reservoir evolution are fundamentally controlled by three-dimensional thermomechanical heterogeneity. Using the example of continued inflation at Aira caldera, Japan, we demonstrate that magma is accumulating faster than it can be erupted, and the current uplift is approaching the level inferred prior to the violent 1914 Plinian eruption. Magma storage conditions coincide with estimates for the caldera-forming reservoir ~29,000 years ago, and the inferred magma supply rate indicates a ~130-year timeframe to amass enough magma to feed a future 1914-sized eruption. These new inferences are important for eruption forecasting and risk mitigation, and have significant implications for the interpretations of volcanic deformation worldwide.

Thermomechanical controls on magma supply and volcanic deformation: application to Aira caldera, Japan

PubMed Central

Hickey, James; Gottsmann, Joachim; Nakamichi, Haruhisa; Iguchi, Masato

2016-01-01

Ground deformation often precedes volcanic eruptions, and results from complex interactions between source processes and the thermomechanical behaviour of surrounding rocks. Previous models aiming to constrain source processes were unable to include realistic mechanical and thermal rock properties, and the role of thermomechanical heterogeneity in magma accumulation was unclear. Here we show how spatio-temporal deformation and magma reservoir evolution are fundamentally controlled by three-dimensional thermomechanical heterogeneity. Using the example of continued inflation at Aira caldera, Japan, we demonstrate that magma is accumulating faster than it can be erupted, and the current uplift is approaching the level inferred prior to the violent 1914 Plinian eruption. Magma storage conditions coincide with estimates for the caldera-forming reservoir ~29,000 years ago, and the inferred magma supply rate indicates a ~130-year timeframe to amass enough magma to feed a future 1914-sized eruption. These new inferences are important for eruption forecasting and risk mitigation, and have significant implications for the interpretations of volcanic deformation worldwide. PMID:27619897

Double jeopardy in inferring cognitive processes

PubMed Central

Fific, Mario

2014-01-01

Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2n. In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs. PMID:25374545

Statistical Inference at Work: Statistical Process Control as an Example

ERIC Educational Resources Information Center

Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia

2008-01-01

To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…

Alexithymia, empathy, emotion identification and social inference in anorexia nervosa: A case-control study.

PubMed

Gramaglia, Carla; Ressico, Francesca; Gambaro, Eleonora; Palazzolo, Anna; Mazzarino, Massimiliano; Bert, Fabrizio; Siliquini, Roberta; Zeppegno, Patrizia

2016-08-01

Alexithymia, difficulties in facial emotion recognition, poor socio-relational skills are typical of anorexia nervosa (AN). We assessed patients with AN and healthy controls (HCs) with mixed stimuli: questionnaires (Toronto Alexithymia Scale-TAS, Interpersonal Reactivity Index-IRI), photographs (Facial Emotion Identification Test-FEIT) and dynamic images (The Awareness of Social Inference Test-TASIT). TAS and IRI Personal Distress (PD) were higher in AN than HCs. Few or no differences emerged at the FEIT and TASIT, respectively. Larger effect sizes were found for the TAS results. Despite higher levels of alexithymia, patients with AN seem to properly acknowledge others' emotions while being inhibited in the expression of their own. Copyright © 2016 Elsevier Ltd. All rights reserved.

Counterfactual cognitive deficit in persons with Parkinson's disease

PubMed Central

McNamara, P; Durso, R; Brown, A; Lynch, A

2003-01-01

Background: Counterfactuals are mental representations of alternatives to past events. Recent research has shown them to be important for other cognitive processes, such as planning, causal reasoning, problem solving, and decision making—all processes independently linked to the frontal lobes. Objective: To test the hypothesis that counterfactual thinking is impaired in some patients with Parkinson's disease and is linked to frontal dysfunction in these patients. Methods. Measures of counterfactual processing and frontal lobe functioning were administered to 24 persons with Parkinson's disease and 15 age matched healthy controls. Results. Patients with Parkinson's disease spontaneously generated significantly fewer counterfactuals than controls despite showing no differences from controls on a semantic fluency test; they also performed at chance levels on a counterfactual inference test, while age matched controls performed above chance levels on this test. Performance on both the counterfactual generation and inference tests correlated significantly with performance on two tests traditionally linked to frontal lobe functioning (Stroop colour–word interference and Tower of London planning tasks) and one test of pragmatic social communication skills. Conclusions: Counterfactual thinking is impaired in Parkinson's disease. This impairment may be related to frontal lobe dysfunction. PMID:12876235

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

PubMed

Chen, Daizhuo; Fraiberger, Samuel P; Moakler, Robert; Provost, Foster

2017-09-01

Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from "Likes" on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the "cloaking device"-a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users.

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals

PubMed Central

Chen, Daizhuo; Fraiberger, Samuel P.; Moakler, Robert; Provost, Foster

2017-01-01

Abstract Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from “Likes” on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the “cloaking device”—a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users. PMID:28933942

Active Inference, homeostatic regulation and adaptive behavioural control.

PubMed

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-11-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

How to talk about protein-level false discovery rates in shotgun proteomics.

PubMed

The, Matthew; Tasnim, Ayesha; Käll, Lukas

2016-09-01

A frequently sought output from a shotgun proteomics experiment is a list of proteins that we believe to have been present in the analyzed sample before proteolytic digestion. The standard technique to control for errors in such lists is to enforce a preset threshold for the false discovery rate (FDR). Many consider protein-level FDRs a difficult and vague concept, as the measurement entities, spectra, are manifestations of peptides and not proteins. Here, we argue that this confusion is unnecessary and provide a framework on how to think about protein-level FDRs, starting from its basic principle: the null hypothesis. Specifically, we point out that two competing null hypotheses are used concurrently in today's protein inference methods, which has gone unnoticed by many. Using simulations of a shotgun proteomics experiment, we show how confusing one null hypothesis for the other can lead to serious discrepancies in the FDR. Furthermore, we demonstrate how the same simulations can be used to verify FDR estimates of protein inference methods. In particular, we show that, for a simple protein inference method, decoy models can be used to accurately estimate protein-level FDRs for both competing null hypotheses. © 2016 The Authors. Proteomics Published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Analyzing spacecraft configurations through specialization and default reasoning

NASA Technical Reports Server (NTRS)

Barry, Matthew R.; Lowe, Carlyle M.

1990-01-01

For an intelligent system to describe a real-world situation using as few statements as possible, it is necessary to make inferences based on observed data and to incorporate general knowledge of the reasoning domain into the description. These reasoning processes must reduce several levels of specific descriptions into only those few that most precisely describe the situation. Moreover, the system must be able to generate descriptions in the absence of data, as instructed by certain rules of inference. The deductions applied by the system, then, generate a high-level description from the low-level evidence provided by the real and default data sources. An implementation of these ideas in a real-world situation is described. The application concerns evaluation of Space Shuttle electromechanical system configurations by console operators in the Mission Control Center. A production system provides the reasoning mechanism through which the default assignments and specializations occur. Examples are provided within this domain for each type of inference, and the suitability is discussed of each toward achieving the goal of describing a situation in the fewest statements possible. Finally, several enhancements are suggested that will further increase the intelligence of similar spacecraft monitoring applications.

Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: A proof of concept study.

PubMed

Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam

2016-05-01

Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations. Published by Elsevier Ltd.

Causal Inference in Retrospective Studies.

ERIC Educational Resources Information Center

Holland, Paul W.; Rubin, Donald B.

1988-01-01

The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…

Riparian forest effects on nitrogen export to an agricultural stream inferred from experimental data and a model

EPA Science Inventory

The effects of riparian vegetation on the reduction of agricultural nitrogen export to streams have been well described experimentally, but a clear understanding of process-level hydrological and biogeochemical controls can be difficult to ascertain from data alone. We apply a ne...

ANUBIS: artificial neuromodulation using a Bayesian inference system.

PubMed

Smith, Benjamin J H; Saaj, Chakravarthini M; Allouis, Elie

2013-01-01

Gain tuning is a crucial part of controller design and depends not only on an accurate understanding of the system in question, but also on the designer's ability to predict what disturbances and other perturbations the system will encounter throughout its operation. This letter presents ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatically tuning controller parameters in real time. ANUBIS is based on the Bayesian brain concept and modifies it by incorporating a model of the neuromodulatory system comprising four artificial neuromodulators. It has been applied to the controller of EchinoBot, a prototype walking rover for Martian exploration. ANUBIS has been implemented at three levels of the controller; gait generation, foot trajectory planning using Bézier curves, and foot trajectory tracking using a terminal sliding mode controller. We compare the results to a similar system that has been tuned using a multilayer perceptron. The use of Bayesian inference means that the system retains mathematical interpretability, unlike other intelligent tuning techniques, which use neural networks, fuzzy logic, or evolutionary algorithms. The simulation results show that ANUBIS provides significant improvements in efficiency and adaptability of the three controller components; it allows the robot to react to obstacles and uncertainties faster than the system tuned with the MLP, while maintaining stability and accuracy. As well as advancing rover autonomy, ANUBIS could also be applied to other situations where operating conditions are likely to change or cannot be accurately modeled in advance, such as process control. In addition, it demonstrates one way in which neuromodulation could fit into the Bayesian brain framework.

Dopamine, Affordance and Active Inference

PubMed Central

Friston, Karl J.; Shiner, Tamara; FitzGerald, Thomas; Galea, Joseph M.; Adams, Rick; Brown, Harriet; Dolan, Raymond J.; Moran, Rosalyn; Stephan, Klaas Enno; Bestmann, Sven

2012-01-01

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level. PMID:22241972

Protective and risk factors in amateur equestrians and description of injury patterns: A retrospective data analysis and a case - control survey

PubMed Central

2011-01-01

Background In Switzerland there are about 150,000 equestrians. Horse related injuries, including head and spinal injuries, are frequently treated at our level I trauma centre. Objectives To analyse injury patterns, protective factors, and risk factors related to horse riding, and to define groups of safer riders and those at greater risk Methods We present a retrospective and a case-control survey at conducted a tertiary trauma centre in Bern, Switzerland. Injured equestrians from July 2000 - June 2006 were retrospectively classified by injury pattern and neurological symptoms. Injured equestrians from July-December 2008 were prospectively collected using a questionnaire with 17 variables. The same questionnaire was applied in non-injured controls. Multiple logistic regression was performed, and combined risk factors were calculated using inference trees. Results Retrospective survey A total of 528 injuries occured in 365 patients. The injury pattern revealed as follows: extremities (32%: upper 17%, lower 15%), head (24%), spine (14%), thorax (9%), face (9%), pelvis (7%) and abdomen (2%). Two injuries were fatal. One case resulted in quadriplegia, one in paraplegia. Case-control survey 61 patients and 102 controls (patients: 72% female, 28% male; controls: 63% female, 37% male) were included. Falls were most frequent (65%), followed by horse kicks (19%) and horse bites (2%). Variables statistically significant for the controls were: Older age (p = 0.015), male gender (p = 0.04) and holding a diploma in horse riding (p = 0.004). Inference trees revealed typical groups less and more likely to suffer injury. Conclusions Experience with riding and having passed a diploma in horse riding seem to be protective factors. Educational levels and injury risk should be graded within an educational level-injury risk index. PMID:21294862

Using Alien Coins to Test Whether Simple Inference Is Bayesian

ERIC Educational Resources Information Center

Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.

2016-01-01

Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…

Adaptive Critic-based Neurofuzzy Controller for the Steam Generator Water Level

NASA Astrophysics Data System (ADS)

Fakhrazari, Amin; Boroushaki, Mehrdad

2008-06-01

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.

Physical activity alters limb bone structure but not entheseal morphology.

PubMed

Wallace, Ian J; Winchester, Julia M; Su, Anne; Boyer, Doug M; Konow, Nicolai

2017-06-01

Studies of ancient human skeletal remains frequently proceed from the assumption that individuals with robust limb bones and/or rugose, hypertrophic entheses can be inferred to have been highly physically active during life. Here, we experimentally test this assumption by measuring the effects of exercise on limb bone structure and entheseal morphology in turkeys. Growing females were either treated with a treadmill-running regimen for 10 weeks or served as controls. After the experiment, femoral cortical and trabecular bone structure were quantified with μCT in the mid-diaphysis and distal epiphysis, respectively, and entheseal morphology was quantified in the lateral epicondyle. The results indicate that elevated levels of physical activity affect limb bone structure but not entheseal morphology. Specifically, animals subjected to exercise displayed enhanced diaphyseal and trabecular bone architecture relative to controls, but no significant difference was detected between experimental groups in entheseal surface topography. These findings suggest that diaphyseal and trabecular structure are more reliable proxies than entheseal morphology for inferring ancient human physical activity levels from skeletal remains. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural correlates of pragmatic language comprehension in autism spectrum disorders.

PubMed

Tesink, C M J Y; Buitelaar, J K; Petersson, K M; van der Gaag, R J; Kan, C C; Tendolkar, I; Hagoort, P

2009-07-01

Difficulties with pragmatic aspects of communication are universal across individuals with autism spectrum disorders (ASDs). Here we focused on an aspect of pragmatic language comprehension that is relevant to social interaction in daily life: the integration of speaker characteristics inferred from the voice with the content of a message. Using functional magnetic resonance imaging (fMRI), we examined the neural correlates of the integration of voice-based inferences about the speaker's age, gender or social background, and sentence content in adults with ASD and matched control participants. Relative to the control group, the ASD group showed increased activation in right inferior frontal gyrus (RIFG; Brodmann area 47) for speaker-incongruent sentences compared to speaker-congruent sentences. Given that both groups performed behaviourally at a similar level on a debriefing interview outside the scanner, the increased activation in RIFG for the ASD group was interpreted as being compensatory in nature. It presumably reflects spill-over processing from the language dominant left hemisphere due to higher task demands faced by the participants with ASD when integrating speaker characteristics and the content of a spoken sentence. Furthermore, only the control group showed decreased activation for speaker-incongruent relative to speaker-congruent sentences in right ventral medial prefrontal cortex (vMPFC; Brodmann area 10), including right anterior cingulate cortex (ACC; Brodmann area 24/32). Since vMPFC is involved in self-referential processing related to judgments and inferences about self and others, the absence of such a modulation in vMPFC activation in the ASD group possibly points to atypical default self-referential mental activity in ASD. Our results show that in ASD compensatory mechanisms are necessary in implicit, low-level inferential processes in spoken language understanding. This indicates that pragmatic language problems in ASD are not restricted to high-level inferential processes, but encompass the most basic aspects of pragmatic language processing.

Inferring molecular interactions pathways from eQTL data

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

Rashid, Imran; McDermott, Jason E.; Samudrala, Ram

Analysis of expression quantitative trait loci (eQTL) helps elucidate the connection between genotype, gene expression levels, and phenotype. However, standard statistical genetics can only attribute changes in expression levels to loci on the genome, not specific genes. Each locus can contain many genes, making it very difficult to discover which gene is controlling the expression levels of other genes. Furthermore, it is even more difficult to find a pathway of molecular interactions responsible for controlling the expression levels. Here we describe a series of techniques for finding explanatory pathways by exploring graphs of molecular interactions. We show several simple methodsmore » can find complete pathways the explain the mechanism of differential expression in eQTL data.« less

Using Virtual Reality in the Inference-Based Treatment of Compulsive Hoarding

PubMed Central

St-Pierre-Delorme, Marie-Eve; O’Connor, Kieron

2016-01-01

The present study evaluated the efficacy of adding a virtual reality (VR) component to the treatment of compulsive hoarding (CH), following inference-based therapy (IBT). Participants were randomly assigned to either an experimental or a control condition. Seven participants received the experimental and seven received the control condition. Five sessions of 1 h were administered weekly. A significant difference indicated that the level of clutter in the bedroom tended to diminish more in the experimental group as compared to the control group F(2,24) = 2.28, p = 0.10. In addition, the results demonstrated that both groups were immersed and present in the environment. The results on posttreatment measures of CH (Saving Inventory revised, Saving Cognition Inventory and Clutter Image Rating scale) demonstrate the efficacy of IBT in terms of symptom reduction. Overall, these results suggest that the creation of a virtual environment may be effective in the treatment of CH by helping the compulsive hoarders take action over their clutter. PMID:27486574

Language processing and executive functions in early treated adults with phenylketonuria (PKU).

PubMed

De Felice, Sara; Romani, Cristina; Geberhiwot, Tarekegn; MacDonald, Anita; Palermo, Liana

We provide an in-depth analysis of language functions in early-treated adults with phenylketonuria (AwPKUs, N = 15-33), as compared to age- and education-matched controls (N = 24-32; N varying across tasks), through: a. narrative production (the Cinderella story), b. language pragmatics comprehension (humour, metaphors, inferred meaning), c. prosody discrimination d. lexical inhibitory control and planning (Blocked Cyclic Naming; Hayling Sentence Completion Test, Burgess & Shallice, 1997). AwPKUs exhibited intact basic language processing (lexical retrieval, phonology/articulation, sentence construction). Instead, deficits emerged in planning and reasoning abilities. Compared to controls, AwPKUs were: less informative in narrative production (lower rate of Correct Information Units); slower in metaphorical understanding and inferred meaning; less accurate in focused lexical-search (Hayling test). These results suggest that i) executive deficits in PKU cannot be explained by an accumulation of lower-order deficits and/or general speed impairments, ii) executive functions engage dedicated neurophysiological resources, rather than simply being an emergent property of lower-level systems.

Industrial solutions trends for the control of HiRes spectrograph@E-ELT

NASA Astrophysics Data System (ADS)

Di Marcantonio, P.; Baldini, V.; Calderone, G.; Cirami, R.; Coretti, I.; Cristiani, S.

Starting a few years ago, ESO initiated a number of projects aiming to explore the possible adoption of industrial standards and commercial off-the-shelf components (COTS) for the control of future VLT and E-ELT instrumentations. In this context, ESPRESSO, the next generation high-stability spectrograph for the VLT and to a certain extent, a precursor of HiRes, has adopted since the preliminary design phase those solutions. Based on the ESPRESSO experience and taking into account the requirements inferred from the preliminary Hi-Res studies in terms of both high-level operations as well as low-level control, I will present in this paper the current proposal for the HiRes hardware architecture.

Packet Randomized Experiments for Eliminating Classes of Confounders

PubMed Central

Pavela, Greg; Wiener, Howard; Fontaine, Kevin R.; Fields, David A.; Voss, Jameson D.; Allison, David B.

2014-01-01

Background Although randomization is considered essential for causal inference, it is often not possible to randomize in nutrition and obesity research. To address this, we develop a framework for an experimental design—packet randomized experiments (PREs), which improves causal inferences when randomization on a single treatment variable is not possible. This situation arises when subjects are randomly assigned to a condition (such as a new roommate) which varies in one characteristic of interest (such as weight), but also varies across many others. There has been no general discussion of this experimental design, including its strengths, limitations, and statistical properties. As such, researchers are left to develop and apply PREs on an ad hoc basis, limiting its potential to improve causal inferences among nutrition and obesity researchers. Methods We introduce PREs as an intermediary design between randomized controlled trials and observational studies. We review previous research that used the PRE design and describe its application in obesity-related research, including random roommate assignments, heterochronic parabiosis, and the quasi-random assignment of subjects to geographic areas. We then provide a statistical framework to control for potential packet-level confounders not accounted for by randomization. Results PREs have successfully been used to improve causal estimates of the effect of roommates, altitude, and breastfeeding on weight outcomes. When certain assumptions are met, PREs can asymptotically control for packet-level characteristics. This has the potential to statistically estimate the effect of a single treatment even when randomization to a single treatment did not occur. Conclusions Applying PREs to obesity-related research will improve decisions about clinical, public health, and policy actions insofar as it offers researchers new insight into cause and effect relationships among variables. PMID:25444088

Online inferential and textual processing by adolescents with attention-deficit/hyperactivity disorder during reading comprehension: Evidence from a probing method.

PubMed

Yeari, Menahem; Avramovich, Adi; Schiff, Rachel

2017-06-01

Previous studies have demonstrated that students with attention-deficit/hyperactivity disorder (ADHD) struggle particularly with grasping the implicit, inferential level of narratives that is crucial for story comprehension. However, these studies used offline tasks (i.e., after story presentation), used indirect measurements (e.g., identifying main ideas), and/or yielded inconclusive results using think-aloud techniques. Moreover, most studies were conducted with preschool or elementary school children with ADHD, using listening or televised story comprehension. In this study, we were interested in examining the spontaneous, immediate activation and/or suppression of forward-predictive inferences, backward-explanatory inferences, and inference-evoking textual information, as they occur online during reading comprehension by adolescents with ADHD. Participants with and without ADHD read short narrative texts, each of which included a predictive sentence, a bridging sentence that referred back to the predictive sentence via actualization of the predicted event, and two intervening sentences positioned between the predictive and bridging sentences that introduced a temporary transition from the main (predictive) episode. Activation and suppression of inferential and/or textual information were assessed using naming times of word probes that were implied by the preceding text, explicitly mentioned in it, or neither when following control texts. In some cases, a true-false inferential or textual question followed the probe. Naming facilitations were observed for the control but not for the ADHD group, in responding to inference probes that followed the predictive and bridging sentences, and to text probes that followed the predictive sentences. Participants with ADHD were accurate, albeit slower, than controls in answering the true-false questions. Adolescents with ADHD have difficulties in generating predictive and explanatory inferences and in retaining relevant textual information in working memory while reading, although they can answer questions after reading when texts are relatively short. These findings are discussed with regard to development of comprehension strategies for individuals with ADHD.

Gene regulatory network inference using fused LASSO on multiple data sets

PubMed Central

Omranian, Nooshin; Eloundou-Mbebi, Jeanne M. O.; Mueller-Roeber, Bernd; Nikoloski, Zoran

2016-01-01

Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions. PMID:26864687

Inferring Network Controls from Topology Using the Chomp Database

DTIC Science & Technology

2015-12-03

AFRL-AFOSR-VA-TR-2016-0033 INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE John Harer DUKE UNIVERSITY Final Report 12/03/2015...INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-10-1-0436 5c. PROGRAM ELEMENT NUMBER 6...area of Topological Data Analysis (TDA) and it’s application to dynamical systems. The role of this work in the Complex Networks program is based on

A Fuzzy Reasoning Design for Fault Detection and Diagnosis of a Computer-Controlled System

PubMed Central

Ting, Y.; Lu, W.B.; Chen, C.H.; Wang, G.K.

2008-01-01

A Fuzzy Reasoning and Verification Petri Nets (FRVPNs) model is established for an error detection and diagnosis mechanism (EDDM) applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree (FRDT) and a Petri nets (PNs) technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstratrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program. PMID:19255619

A rational inference approach to group and individual-level sentence comprehension performance in aphasia.

PubMed

Warren, Tessa; Dickey, Michael Walsh; Liburd, Teljer L

2017-07-01

The rational inference, or noisy channel, account of language comprehension predicts that comprehenders are sensitive to the probabilities of different interpretations for a given sentence and adapt as these probabilities change (Gibson, Bergen & Piantadosi, 2013). This account provides an important new perspective on aphasic sentence comprehension: aphasia may increase the likelihood of sentence distortion, leading people with aphasia (PWA) to rely more on the prior probability of an interpretation and less on the form or structure of the sentence (Gibson, Sandberg, Fedorenko, Bergen & Kiran, 2015). We report the results of a sentence-picture matching experiment that tested the predictions of the rational inference account and other current models of aphasic sentence comprehension across a variety of sentence structures. Consistent with the rational inference account, PWA showed similar sensitivity to the probability of particular kinds of form distortions as age-matched controls, yet overall their interpretations relied more on prior probability and less on sentence form. As predicted by rational inference, but not by other models of sentence comprehension in aphasia, PWA's interpretations were more faithful to the form for active and passive sentences than for direct object and prepositional object sentences. However contra rational inference, there was no evidence that individual PWA's severity of syntactic or semantic impairment predicted their sensitivity to form versus the prior probability of a sentence, as cued by semantics. These findings confirm and extend previous findings that suggest the rational inference account holds promise for explaining aphasic and neurotypical comprehension, but they also raise new challenges for the account. Copyright © 2017 Elsevier Ltd. All rights reserved.

Joint Command and Control: Integration Not Interoperability

DTIC Science & Technology

2013-03-01

separate computer and communication equipment. Besides having to engineer interoperability, the Services also must determine the level of...effects.  Determines force responsiveness and allocates resources.5 This thesis argues Joint military operations will never be fully integrated as...processes and systems. Secondly, the limited depth of discussion risks implying (or the reader inferring) the solution is more straightforward than

Effects of Increasing Sound Pressure Level on Lip and Jaw Movement Parameters and Consistency in Young Adults

ERIC Educational Resources Information Center

Huber, Jessica E.; Chandrasekaran, Bharath

2006-01-01

Purpose: Examination of movement parameters and consistency has been used to infer underlying neural control of movement. However, there has been no systematic investigation of whether the way individuals are asked (or cued) to increase loudness alters articulation. This study examined whether different cues to elicit louder speech induce…

The Role of Visuospatial Resources in Generating Predictive and Bridging Inferences

ERIC Educational Resources Information Center

Fincher-Kiefer, Rebecca; D'Agostino, Paul R.

2004-01-01

It has been suggested that predictive and bridging inferences are generated at different levels of text representation: predictive inferences at a reader's situation model and bridging inferences at a reader's propositional textbase (Fincher-Kiefer, 1993, 1996; McDaniel, Schmalhofer, & Keefe, 2001; Schmalhofer, McDaniel, & Keefe, 2002). Recently,…

Training for Fostering Knowledge Co-Construction from Collaborative Inference-Drawing

ERIC Educational Resources Information Center

Deiglmayr, Anne; Spada, Hans

2011-01-01

Groups typically have difficulties drawing inferences that integrate individuals' unique information (collaborative inferences) and thus yield a true assembly bonus. An experiment with 36 dyads of university-level students in four training conditions showed, particularly in untrained dyads, that collaborative inferences were less likely to be…

Exploring bird aerodynamics using radio-controlled models.

PubMed

Hoey, Robert G

2010-12-01

A series of radio-controlled glider models was constructed by duplicating the aerodynamic shape of soaring birds (raven, turkey vulture, seagull and pelican). Controlled tests were conducted to determine the level of longitudinal and lateral-directional static stability, and to identify the characteristics that allowed flight without a vertical tail. The use of tail-tilt for controlling small bank-angle changes, as observed in soaring birds, was verified. Subsequent tests, using wing-tip ailerons, inferred that birds use a three-dimensional flow pattern around the wing tip (wing tip vortices) to control adverse yaw and to create a small amount of forward thrust in gliding flight.

Discovering sparse transcription factor codes for cell states and state transitions during development

PubMed Central

Furchtgott, Leon A; Melton, Samuel; Menon, Vilas; Ramanathan, Sharad

2017-01-01

Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships. DOI: http://dx.doi.org/10.7554/eLife.20488.001 PMID:28296636

Water Quality Control for Shrimp Pond Using Adaptive Neuro Fuzzy Inference System : The First Project

NASA Astrophysics Data System (ADS)

Umam, F.; Budiarto, H.

2018-01-01

Shrimp farming becomes the main commodity of society in Madura Island East Java Indonesia. Because of Madura island has a very extreme weather, farmers have difficulty in keeping the balance of pond water. As a consequence of this condition, there are some farmers experienced losses. In this study an adaptive control system was developed using ANFIS method to control pH balance (7.5-8.5), Temperature (25-31°C), water level (70-120 cm) and Dissolved Oxygen (4-7,5 ppm). Each parameter (pH, temperature, level and DO) is controlled separately but can work together. The output of the control system is in the form of pump activation which provides the antidote to the imbalance that occurs in pond water. The system is built with two modes at once, which are automatic mode and manual mode. The manual control interface based on android which is easy to use.

Evaluation of Second-Level Inference in fMRI Analysis

PubMed Central

Roels, Sanne P.; Loeys, Tom; Moerkerke, Beatrijs

2016-01-01

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference. PMID:26819578

Inferring infection hazard in wildlife populations by linking data across individual and population scales.

PubMed

Pepin, Kim M; Kay, Shannon L; Golas, Ben D; Shriner, Susan S; Gilbert, Amy T; Miller, Ryan S; Graham, Andrea L; Riley, Steven; Cross, Paul C; Samuel, Michael D; Hooten, Mevin B; Hoeting, Jennifer A; Lloyd-Smith, James O; Webb, Colleen T; Buhnerkempe, Michael G

2017-03-01

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease. © 2017 John Wiley & Sons Ltd/CNRS.

Inferring infection hazard in wildlife populations by linking data across individual and population scales

USGS Publications Warehouse

Pepin, Kim M.; Kay, Shannon L.; Golas, Ben D.; Shriner, Susan A.; Gilbert, Amy T.; Miller, Ryan S.; Graham, Andrea L.; Riley, Steven; Cross, Paul C.; Samuel, Michael D.; Hooten, Mevin B.; Hoeting, Jennifer A.; Lloyd-Smith, James O.; Webb, Colleen T.; Buhnerkempe, Michael G.

2017-01-01

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Knowledge, Expectations, and Inductive Reasoning within Conceptual Hierarchies

ERIC Educational Resources Information Center

Coley, John D.; Hayes, Brett; Lawson, Christopher; Moloney, Michelle

2004-01-01

Previous research (e.g. "Cognition" 64 (1997) 73) suggests that the privileged level for inductive inference in a folk biological conceptual hierarchy does not correspond to the ''basic'' level (i.e. the level at which concepts are both informative and distinct). To further explore inductive inference within conceptual hierarchies, we examine…

Reasoning about Informal Statistical Inference: One Statistician's View

ERIC Educational Resources Information Center

Rossman, Allan J.

2008-01-01

This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical…

Inference Processing in Adolescents with Asperger Syndrome: Relationship with Theory of Mind Abilities

ERIC Educational Resources Information Center

Le Sourn-Bissaoui, Sandrine; Caillies, Stephanie; Gierski, Fabien; Motte, Jacques

2009-01-01

The aim of this study was to investigate the role of theory of mind competence in inference processing in adolescents with Asperger syndrome (AS). We sought to pinpoint the level at which AS individuals experience difficulty drawing inferences and identify the factors that account for their inference-drawing problems. We hypothesized that this…

Combinatorial influence of environmental parameters on transcription factor activity.

PubMed

Knijnenburg, T A; Wessels, L F A; Reinders, M J T

2008-07-01

Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. The Matlab code is available from the authors upon request. Supplementary data are available at Bioinformatics online.

Hydrologic-energy balance constraints on the Holocene lake-level history of lake Titicaca, South America

NASA Astrophysics Data System (ADS)

Rowe, H. D.; Dunbar, R. B.

2004-09-01

A basin-scale hydrologic-energy balance model that integrates modern climatological, hydrological, and hypsographic observations was developed for the modern Lake Titicaca watershed (northern Altiplano, South America) and operated under variable conditions to understand controls on post-glacial changes in lake level. The model simulates changes in five environmental variables (air temperature, cloud fraction, precipitation, relative humidity, and land surface albedo). Relatively small changes in three meteorological variables (mean annual precipitation, temperature, and/or cloud fraction) explain the large mid-Holocene lake-level decrease (˜85 m) inferred from seismic reflection profiling and supported by sediment-based paleoproxies from lake sediments. Climatic controls that shape the present-day Altiplano and the sediment-based record of Holocene lake-level change are combined to interpret model-derived lake-level simulations in terms of changes in the mean state of ENSO and its impact on moisture transport to the Altiplano.

Using Eye-Tracking to Measure Lexical Inferences and Its Effects on Reading Rate during EFL Reading

ERIC Educational Resources Information Center

Dolgunsöz, Emrah

2016-01-01

Inferring unknown word meanings by using contextual clues is a common strategy employed by EFL learners during reading. This study aims to (a) investigate the effect of familiarity on lexical inferences in EFL reading; (b) examine inference efficiency among EFL readers with different levels of vocabulary knowledge and reading proficiency; and (c)…

Sensitivity of gas filter correlation instrument to variations in optical balance. [computer program simulated the response of the GFCR to changing pollutant levels

NASA Technical Reports Server (NTRS)

Orr, H. D., III; Campbell, S. A.

1975-01-01

A computer program was used to simulate the response of the Gas Filter Correlation Radiometer (GFCR) to changing pollutant levels of CO, SO2, CH4, and NH3 in two model atmospheres. Positive and negative deviations of tau sub alpha of magnitudes 0.01, 0.1, and 1 percent were imposed upon the simulation and the resulting deviations in inferred concentrations were determined. For the CO, CH4, and the higher pressure cell of the NH3 channel, the deviations are less than + or - 12 percent for deviations in tau sub alpha of + or - 0.1 percent, but increase to significantly higher values for larger deviations. For the lower pressure cell of NH3 and for SO2, the deviations in inferred concentration begin to rise sharply between 0.01 and 0.1 percent deviation in tau sub alpha, suggesting that a tighter control on tau sub alpha may be required for these channels.

Timescales and bottlenecks in miRNA-dependent gene regulation.

PubMed

Hausser, Jean; Syed, Afzal Pasha; Selevsek, Nathalie; van Nimwegen, Erik; Jaskiewicz, Lukasz; Aebersold, Ruedi; Zavolan, Mihaela

2013-12-03

MiRNAs are post-transcriptional regulators that contribute to the establishment and maintenance of gene expression patterns. Although their biogenesis and decay appear to be under complex control, the implications of miRNA expression dynamics for the processes that they regulate are not well understood. We derived a mathematical model of miRNA-mediated gene regulation, inferred its parameters from experimental data sets, and found that the model describes well time-dependent changes in mRNA, protein and ribosome density levels measured upon miRNA transfection and induction. The inferred parameters indicate that the timescale of miRNA-dependent regulation is slower than initially thought. Delays in miRNA loading into Argonaute proteins and the slow decay of proteins relative to mRNAs can explain the typically small changes in protein levels observed upon miRNA transfection. For miRNAs to regulate protein expression on the timescale of a day, as miRNAs involved in cell-cycle regulation do, accelerated miRNA turnover is necessary.

Inference of beliefs and emotions in patients with Alzheimer's disease.

PubMed

Zaitchik, Deborah; Koff, Elissa; Brownell, Hiram; Winner, Ellen; Albert, Marilyn

2006-01-01

The present study compared 20 patients with mild to moderate Alzheimer's disease with 20 older controls (ages 69-94 years) on their ability to make inferences about emotions and beliefs in others. Six tasks tested their ability to make 1st-order and 2nd-order inferences as well as to offer explanations and moral evaluations of human action by appeal to emotions and beliefs. Results showed that the ability to infer emotions and beliefs in 1st-order tasks remains largely intact in patients with mild to moderate Alzheimer's. Patients were able to use mental states in the prediction, explanation, and moral evaluation of behavior. Impairment on 2nd-order tasks involving inference of mental states was equivalent to impairment on control tasks, suggesting that patients' difficulty is secondary to their cognitive impairments. ((c) 2006 APA, all rights reserved).

Setters and Samoyeds: The Emergence of Subordinate Level Categories as a Basis for Inductive Inference in Preschool-Age Children.

ERIC Educational Resources Information Center

Waxman, Sandra R.; Lynch, Elizabeth B.; Casey, K. Lyman; Baer, Leslie

1997-01-01

Three experiments examine how preschoolers partition their basic level categories to form subordinate level categories and whether these have inductive potential. Results suggest that contrastive information promotes the emergence of subordinate categories as a basis of inductive inference and newly established subordinate categories can retain…

Impaired self-agency inferences in schizophrenia: The role of cognitive capacity and causal reasoning style.

PubMed

Prikken, M; van der Weiden, A; Kahn, R S; Aarts, H; van Haren, N E M

2018-01-01

The sense of self-agency, i.e., experiencing oneself as the cause of one's own actions, is impaired in patients with schizophrenia. Normally, inferences of self-agency are enhanced when actual outcomes match with pre-activated outcome information, where this pre-activation can result from explicitly set goals (i.e., goal-based route) or implicitly primed outcome information (i.e., prime-based route). Previous research suggests that patients show specific impairments in the prime-based route, implicating that they do not rely on matches between implicitly available outcome information and actual action-outcomes when inferring self-agency. The question remains: Why? Here, we examine whether neurocognitive functioning and self-serving bias (SSB) may explain abnormalities in patients' agency inferences. Thirty-six patients and 36 healthy controls performed a commonly used agency inference task to measure goal- and prime-based self-agency inferences. Neurocognitive functioning was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS) and the SSB was assessed with the Internal Personal and Situational Attributions Questionnaire. Results showed a substantial smaller effect of primed outcome information on agency experiences in patients compared with healthy controls. Whereas patients and controls differed on BACS and marginally on SSB scores, these differences were not related to patients' impairments in prime-based agency inferences. Patients showed impairments in prime-based agency inferences, thereby replicating previous studies. This finding could not be explained by cognitive dysfunction or SSB. Results are discussed in the context of the recent surge to understand and examine deficits in agency experiences in schizophrenia. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Setters and samoyeds: the emergence of subordinate level categories as a basis for inductive inference in preschool-age children.

PubMed

Waxman, S R; Lynch, E B; Casey, K L; Baer, L

1997-11-01

Basic level categories are a rich source of inductive inference for children and adults. These 3 experiments examine how preschool-age children partition their inductively rich basic level categories to form subordinate level categories and whether these have inductive potential. Children were taught a novel property about an individual member of a familiar basic level category (e.g., a collie). Then, children's extensions of that property to other objects from the same subordinate (e.g., other collies), basic (e.g., other dogs), and superordinate (e.g., other animals) level categories were examined. The results suggest (a) that contrastive information promotes the emergence of subordinate categories as a basis of inductive inference and (b) that newly established subordinate categories can retain their inductive potential in subsequent reasoning over a week's time.

A Comprehensive review of group level model performance in the presence of heteroscedasticity: Can a single model control Type I errors in the presence of outliers?

PubMed Central

Mumford, Jeanette A.

2017-01-01

Even after thorough preprocessing and a careful time series analysis of functional magnetic resonance imaging (fMRI) data, artifact and other issues can lead to violations of the assumption that the variance is constant across subjects in the group level model. This is especially concerning when modeling a continuous covariate at the group level, as the slope is easily biased by outliers. Various models have been proposed to deal with outliers including models that use the first level variance or that use the group level residual magnitude to differentially weight subjects. The most typically used robust regression, implementing a robust estimator of the regression slope, has been previously studied in the context of fMRI studies and was found to perform well in some scenarios, but a loss of Type I error control can occur for some outlier settings. A second type of robust regression using a heteroscedastic autocorrelation consistent (HAC) estimator, which produces robust slope and variance estimates has been shown to perform well, with better Type I error control, but with large sample sizes (500–1000 subjects). The Type I error control with smaller sample sizes has not been studied in this model and has not been compared to other modeling approaches that handle outliers such as FSL’s Flame 1 and FSL’s outlier de-weighting. Focusing on group level inference with a continuous covariate over a range of sample sizes and degree of heteroscedasticity, which can be driven either by the within- or between-subject variability, both styles of robust regression are compared to ordinary least squares (OLS), FSL’s Flame 1, Flame 1 with outlier de-weighting algorithm and Kendall’s Tau. Additionally, subject omission using the Cook’s Distance measure with OLS and nonparametric inference with the OLS statistic are studied. Pros and cons of these models as well as general strategies for detecting outliers in data and taking precaution to avoid inflated Type I error rates are discussed. PMID:28030782

Feature-to-Feature Inference Under Conditions of Cue Restriction and Dimensional Correlation.

PubMed

Lancaster, Matthew E; Homa, Donald

2017-01-01

The present study explored feature-to-feature and label-to-feature inference in a category task for different category structures. In the correlated condition, each of the 4 dimensions comprising the category was positively correlated to each other and to the category label. In the uncorrelated condition, no correlation existed between the 4 dimensions comprising the category, although the dimension to category label correlation matched that of the correlated condition. After learning, participants made inference judgments of a missing feature, given 1, 2, or 3 feature cues; on half the trials, the category label was also included as a cue. The results showed superior inference of features following training on the correlated structure, with accurate inference when only a single feature was presented. In contrast, a single-feature cue resulted in chance levels of inference for the uncorrelated structure. Feature inference systematically improved with number of cues after training on the correlated structure. Surprisingly, a similar outcome was obtained for the uncorrelated structure, an outcome that must have reflected mediation via the category label. A descriptive model is briefly introduced to explain the results, with a suggestion that this paradigm might be profitably extended to hierarchical structures where the levels of feature-to-feature inference might vary with the depth of the hierarchy.

Combinatorial influence of environmental parameters on transcription factor activity

PubMed Central

Knijnenburg, T.A.; Wessels, L.F.A.; Reinders, M.J.T.

2008-01-01

Motivation: Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. Results: We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. Availability: The Matlab code is available from the authors upon request. Contact: t.a.knijnenburg@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18586711

Aberrant link between empathy and social attribution style in borderline personality disorder.

PubMed

Homan, Philipp; Reddan, Marianne C; Brosch, Tobias; Koenigsberg, Harold W; Schiller, Daniela

2017-11-01

In social interactions, we often need to quickly infer why other people do what they do. More often than not, we infer that behavior is a result of personality rather than circumstances. It is unclear how the tendency itself may contribute to psychopathology and interpersonal dysfunction. Borderline personality disorder (BPD) is characterized by severe interpersonal dysfunction. Here, we investigated if this dysfunction is related to the tendency to over-attribute behaviors to personality traits. Healthy controls and patients with BPD judged positive and negative behaviors presented within a situational constraint during functional magnetic resonance imaging. Before the experiment, we measured trait levels of empathy, paranoia, and need for cognition. Behaviorally, we found that empathy levels predicted the tendency to attribute behavior to traits in healthy controls, whereas in patients with BPD this relationship was significantly weakened. Whole brain analysis of group-by-empathy interaction revealed that when participants judged the behavior during the attribution phase, several brain regions implicated in mentalizing distinguished patients from controls: In healthy controls, neural activity scaled negatively with empathy, but this relationship was reversed in BPD patients. Due to the cross-sectional study design we cannot establish a causal link between empathy and social attributions. These findings indicate that the self-reported tendency to feel for others is related to the tendency to integrate situational information beyond personality. In BPD patients, by contrast, the association between empathy and attribution was significantly weaker, rendering empathy less informative in predicting the overall attribution style. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fostering Social Cognition through an Imitation- and Synchronization-Based Dance/Movement Intervention in Adults with Autism Spectrum Disorder: A Controlled Proof-of-Concept Study.

PubMed

Koehne, Svenja; Behrends, Andrea; Fairhurst, Merle T; Dziobek, Isabel

2016-01-01

Since social cognition is impaired in individuals with autism spectrum disorder (ASD), this study aimed at establishing the efficacy of a newly developed imitation- and synchronization-based dance/movement intervention (SI-DMI) in fostering emotion inference and empathic feelings (emotional reaction to feelings of others) in adults with high-functioning ASD. Fifty-five adults with ASD (IQ ≥85) who were blinded to the aim of the study were assigned to receive either 10 weeks of a dance/movement intervention focusing on interpersonal movement imitation and synchronization (SI-DMI, n = 27) or a control movement intervention (CMI, n = 24) focusing on individual motor coordination (2 participants from each group declined before baseline testing). The primary outcome measure was the objective Multifaceted Empathy Test targeting emotion inference and empathic feelings. Secondary outcomes were scores on the self-rated Interpersonal Reactivity Index. The well-established automatic imitation task and synchronization finger-tapping task were used to quantify effects on imitation and synchronization functions, complemented by the more naturalistic Assessment of Spontaneous Interaction in Movement. Intention-to-treat analyses revealed that from baseline to 3 months, patients treated with SI-DMI showed a significantly larger improvement in emotion inference (d = 0.58), but not empathic feelings, than those treated with CMI (d = -0.04). On the close generalization level, SI-DMI increased synchronization skills and imitation tendencies, as well as whole-body imitation/synchronization and movement reciprocity/dialogue, compared to CMI. SI-DMI can be successful in promoting emotion inference in adults with ASD and warrants further investigation. © 2015 S. Karger AG, Basel.

Inferences and metaphoric comprehension in unilaterally implanted children with adequate formal oral language performance.

PubMed

Nicastri, Maria; Filipo, Roberto; Ruoppolo, Giovanni; Viccaro, Marika; Dincer, Hilal; Guerzoni, Letizia; Cuda, Domenico; Bosco, Ersilia; Prosperini, Luca; Mancini, Patrizia

2014-05-01

To assess skills in inferences during conversations and in metaphors comprehension of unilaterally cochlear implanted children with adequate abilities at the formal language tests, comparing them with well-matched hearing peers; to verify the influence of age of implantation on overall skills. The study was designed as a matched case-control study. 31 deaf children, unilateral cochlear implant users, with normal linguistic competence at formal language tests were compared with 31 normal hearing matched peers. Inferences and metaphor comprehension skills were assessed through the Implicit Meaning Comprehension, Situations and Metaphors subtests of the Italian Standardized Battery of "Pragmatic Language Skills MEDEA". Differences between patient and control groups were tested by the Mann-Whitney U test. Correlations between age at implantation and time of implant use with each subtest were investigated by the Spearman rank correlation coefficient. No significant differences between the two groups were found in inferencing skills (p=0.24 and p=0.011 respectively for Situations and Implicit Meaning Comprehension). Regarding figurative language, unilaterally cochlear implanted children performed significantly below their normal hearing peers in Verbal Metaphor comprehension (p=0.001). Performances were related to age at implantation, but not with time of implant use. Unilaterally cochlear implanted children with normal language level showed responses similar to NH children in discourse inferences, but not in figurative language comprehension. Metaphors still remains a challenge for unilateral implant users and above all when they have not any reference, as demonstrated by the significant difference in verbal rather than figurative metaphors comprehension. Older age at implantation was related to worse performance for all items. These aspects, until now less investigated, had to receive more attention to deeply understand specific mechanisms involved and possible effects of different levels of figurative language complexity (presence or absence of contextual input, degree of transparency and syntactic frozenness). New insight is needed to orient programs in early intervention settings in considering and adequately responding to all these complex communicative need of children with hearing loss. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Inferring Emotional Reactions in Social Situations: Differences in Children with Language Impairment.

ERIC Educational Resources Information Center

Ford, Janet A.; Milosky, Linda M.

2003-01-01

Kindergarten children with language impairment (LI) and age-matched controls were asked to label facial expressions depicting various emotions and then to infer emotional reactions from stories presented either verbally, visually, or combined. Results suggest that inference errors made by children with LI during early stages of social processing…

Genetic network inference as a series of discrimination tasks.

PubMed

Kimura, Shuhei; Nakayama, Satoshi; Hatakeyama, Mariko

2009-04-01

Genetic network inference methods based on sets of differential equations generally require a great deal of time, as the equations must be solved many times. To reduce the computational cost, researchers have proposed other methods for inferring genetic networks by solving sets of differential equations only a few times, or even without solving them at all. When we try to obtain reasonable network models using these methods, however, we must estimate the time derivatives of the gene expression levels with great precision. In this study, we propose a new method to overcome the drawbacks of inference methods based on sets of differential equations. Our method infers genetic networks by obtaining classifiers capable of predicting the signs of the derivatives of the gene expression levels. For this purpose, we defined a genetic network inference problem as a series of discrimination tasks, then solved the defined series of discrimination tasks with a linear programming machine. Our experimental results demonstrated that the proposed method is capable of correctly inferring genetic networks, and doing so more than 500 times faster than the other inference methods based on sets of differential equations. Next, we applied our method to actual expression data of the bacterial SOS DNA repair system. And finally, we demonstrated that our approach relates to the inference method based on the S-system model. Though our method provides no estimation of the kinetic parameters, it should be useful for researchers interested only in the network structure of a target system. Supplementary data are available at Bioinformatics online.

Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference

ERIC Educational Resources Information Center

Schochet, Peter Z.

2013-01-01

This article examines the estimation of two-stage clustered designs for education randomized control trials (RCTs) using the nonparametric Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for…

Social Dynamics Modeling and Inference

DTIC Science & Technology

2018-03-29

AFRL-AFOSR-JP-TR-2018-0027 Social Dynamics Modeling and Inference Kwang-Cheng Chen NATIONAL TAIWAN UNIVERSITY Final Report 03/29/2018 DISTRIBUTION A...DATES COVERED (From - To)      14 May 2014 to 13 May 2017 4.  TITLE AND SUBTITLE Social Dynamics Modeling and Inference 5a.  CONTRACT NUMBER 5b.  GRANT...behavior in human society, to set up the foundation of future possible inference and even control of social collective behavior. Two primary

Understanding behavior under nonverbal transitive-inference procedures: Stimulus-control-topography analyses.

PubMed

Galizio, Ann; Doughty, Adam H; Williams, Dean C; Saunders, Kathryn J

2017-07-01

Following training with verbal stimulus relations involving A is greater than B and B is greater than C, verbally-competent individuals reliably select A>C when asked "which is greater, A or C?" (i.e., verbal transitive inference). This result is easy to interpret. Nonhuman animals and humans with and without intellectual disabilities have been exposed to nonverbal transitive-inference procedures involving trained arbitrary stimulus relations. Following the training of A+B-, B+C-, C+D-, and D+E-, B reliably is selected over D (i.e., nonverbal transitive inference). Such findings are more challenging to interpret. The present research explored accounts of nonverbal transitive inference based in transitive inference per se, reinforcement, such as value-transfer theory, and operant stimulus control. In Experiment 1, college students selected B>G following the training of A+B-, B+C-, C+D-///E+F-, F+G-, and G+H- (where///signifies the omission of D+E-). In Experiment 2, college students selected B>G following the training of A+B-, B+C-, C+D-///E+F-, F+G-, and G+X- (where X refers to 10 stimuli that alternated across trials). In Experiment 3, college students selected G>B following the training of Y+B-, B+C-, C+D-///E+F-, F+G-, and G+X- (where Y and X refer to 10 stimuli, respectively, that alternated across trials). These findings are discussed in the context of operant stimulus control by offering an approach based in stimulus B typically acquiring only a select stimulus control topography. Copyright © 2017 Elsevier B.V. All rights reserved.

The role of familiarity in binary choice inferences.

PubMed

Honda, Hidehito; Abe, Keiga; Matsuka, Toshihiko; Yamagishi, Kimihiko

2011-07-01

In research on the recognition heuristic (Goldstein & Gigerenzer, Psychological Review, 109, 75-90, 2002), knowledge of recognized objects has been categorized as "recognized" or "unrecognized" without regard to the degree of familiarity of the recognized object. In the present article, we propose a new inference model--familiarity-based inference. We hypothesize that when subjective knowledge levels (familiarity) of recognized objects differ, the degree of familiarity of recognized objects will influence inferences. Specifically, people are predicted to infer that the more familiar object in a pair of two objects has a higher criterion value on the to-be-judged dimension. In two experiments, using a binary choice task, we examined inferences about populations in a pair of two cities. Results support predictions of familiarity-based inference. Participants inferred that the more familiar city in a pair was more populous. Statistical modeling showed that individual differences in familiarity-based inference lie in the sensitivity to differences in familiarity. In addition, we found that familiarity-based inference can be generally regarded as an ecologically rational inference. Furthermore, when cue knowledge about the inference criterion was available, participants made inferences based on the cue knowledge about population instead of familiarity. Implications of the role of familiarity in psychological processes are discussed.

Brain Activity Associated with Logical Inferences in Geometry: Focusing on Students with Different Levels of Ability

ERIC Educational Resources Information Center

Waisman, Ilana; Leikin, Mark; Leikin, Roza

2016-01-01

Mathematical processing associated with solving short geometry problems requiring logical inference was examined among students who differ in their levels of general giftedness (G) and excellence in mathematics (EM) using ERP research methodology. Sixty-seven male adolescents formed four major research groups designed according to various…

Higher-level phylogeny of paraneopteran insects inferred from mitochondrial genome sequences

PubMed Central

Li, Hu; Shao, Renfu; Song, Nan; Song, Fan; Jiang, Pei; Li, Zhihong; Cai, Wanzhi

2015-01-01

Mitochondrial (mt) genome data have been proven to be informative for animal phylogenetic studies but may also suffer from systematic errors, due to the effects of accelerated substitution rate and compositional heterogeneity. We analyzed the mt genomes of 25 insect species from the four paraneopteran orders, aiming to better understand how accelerated substitution rate and compositional heterogeneity affect the inferences of the higher-level phylogeny of this diverse group of hemimetabolous insects. We found substantial heterogeneity in base composition and contrasting rates in nucleotide substitution among these paraneopteran insects, which complicate the inference of higher-level phylogeny. The phylogenies inferred with concatenated sequences of mt genes using maximum likelihood and Bayesian methods and homogeneous models failed to recover Psocodea and Hemiptera as monophyletic groups but grouped, instead, the taxa that had accelerated substitution rates together, including Sternorrhyncha (a suborder of Hemiptera), Thysanoptera, Phthiraptera and Liposcelididae (a family of Psocoptera). Bayesian inference with nucleotide sequences and heterogeneous models (CAT and CAT + GTR), however, recovered Psocodea, Thysanoptera and Hemiptera each as a monophyletic group. Within Psocodea, Liposcelididae is more closely related to Phthiraptera than to other species of Psocoptera. Furthermore, Thysanoptera was recovered as the sister group to Hemiptera. PMID:25704094

Abnormal agency experiences in schizophrenia patients: Examining the role of psychotic symptoms and familial risk.

PubMed

Prikken, Merel; van der Weiden, Anouk; Renes, Robert A; Koevoets, Martijn G J C; Heering, Henriette D; Kahn, René S; Aarts, Henk; van Haren, Neeltje E M

2017-04-01

Experiencing self-agency over one's own action outcomes is essential for social functioning. Recent research revealed that patients with schizophrenia do not use implicitly available information about their action-outcomes (i.e., prime-based agency inference) to arrive at self-agency experiences. Here, we examined whether this is related to symptoms and/or familial risk to develop the disease. Fifty-four patients, 54 controls, and 19 unaffected (and unrelated) siblings performed an agency inference task, in which experienced agency was measured over action-outcomes that matched or mismatched outcome-primes that were presented before action performance. The Positive and Negative Syndrome Scale (PANSS) and Comprehensive Assessment of Symptoms and History (CASH) were administered to assess psychopathology. Impairments in prime-based inferences did not differ between patients with symptoms of over- and underattribution. However, patients with agency underattribution symptoms reported significantly lower overall self-agency experiences. Siblings displayed stronger prime-based agency inferences than patients, but weaker prime-based inferences than healthy controls. However, these differences were not statistically significant. Findings suggest that impairments in prime-based agency inferences may be a trait characteristic of schizophrenia. Moreover, this study may stimulate further research on the familial basis and the clinical relevance of impairments in implicit agency inferences. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effect of posttraumatic stress on study time in a task measuring four component processes underlying text-level reading.

PubMed

Sullivan, Michael P; Griffiths, Gina G; Moore Sohlberg, Mckay

2014-10-01

To investigate the effect of combat-related posttraumatic stress disorder (PTSD) on 4 components underlying text-level reading comprehension. A group of 17 veterans with PTSD and 17 matched control participants took part. An experimental task required participants to read and study 3-sentence paragraphs describing semantic features associated with real and unreal objects. Each paragraph was followed by true-false statements that assessed knowledge access, text memory, inference, and integration. The results revealed that the PTSD group took significantly longer than the control group to study the paragraphs. Although there was no group difference in test statement accuracy, the PTSD group also took significantly longer to respond to the test statements. Overall, the results provide evidence for the control theory of attention but suggest that more direct measures of task-irrelevant processing during text-level reading are needed. More important, the results begin to lay a foundation for developing not only diagnostic but also intervention strategies.

Linguistic Summarization of Video for Fall Detection Using Voxel Person and Fuzzy Logic

PubMed Central

Anderson, Derek; Luke, Robert H.; Keller, James M.; Skubic, Marjorie; Rantz, Marilyn; Aud, Myra

2009-01-01

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person’s states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. PMID:20046216

An Excel sheet for inferring children's number-knower levels from give-N data.

PubMed

Negen, James; Sarnecka, Barbara W; Lee, Michael D

2012-03-01

Number-knower levels are a series of stages of number concept development in early childhood. A child's number-knower level is typically assessed using the give-N task. Although the task procedure has been highly refined, the standard ways of analyzing give-N data remain somewhat crude. Lee and Sarnecka (Cogn Sci 34:51-67, 2010, in press) have developed a Bayesian model of children's performance on the give-N task that allows knower level to be inferred in a more principled way. However, this model requires considerable expertise and computational effort to implement and apply to data. Here, we present an approximation to the model's inference that can be computed with Microsoft Excel. We demonstrate the accuracy of the approximation and provide instructions for its use. This makes the powerful inferential capabilities of the Bayesian model accessible to developmental researchers interested in estimating knower levels from give-N data.

The role of meta-cognitions and thought control techniques in predisposition to auditory and visual hallucinations.

PubMed

García-Montes, José M; Cangas, Adolfo; Pérez-Alvarez, M; Fidalgo, Angel M; Gutiérrez, Olga

2006-09-01

This study examines the relationship between a predisposition to hallucinations and meta-cognitive variables and thought-control techniques, controlling for the possible effect of anxiety. In order to do so, we start out with the hypothesis that anxiety does not, in itself, explain the association between meta-cognitions and a predisposition to auditory and visual hallucinations. A within-participants correlational design was employed. Four psychometric tests relating to predisposition to hallucinations, anxiety, meta-cognitions and thought-control techniques were administered to 150 participants. It was found that, after controlling for participants' anxiety levels, the 'loss of cognitive confidence' factor predicted the score on the scale of predisposition to both auditory and visual hallucinations. Thought-control strategies based on worry were also found to be predictive of a greater predisposition to hallucinations, regardless of whether or not participants' anxiety level was controlled. Meta-cognitive variables of cognitive confidence and thought control through worry are positively associated with a predisposition to hallucinations. The correlational nature of the design does not allow inferences about causal relationships.

Bayesian inference of physiologically meaningful parameters from body sway measurements.

PubMed

Tietäväinen, A; Gutmann, M U; Keski-Vakkuri, E; Corander, J; Hæggström, E

2017-06-19

The control of the human body sway by the central nervous system, muscles, and conscious brain is of interest since body sway carries information about the physiological status of a person. Several models have been proposed to describe body sway in an upright standing position, however, due to the statistical intractability of the more realistic models, no formal parameter inference has previously been conducted and the expressive power of such models for real human subjects remains unknown. Using the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear control model to posturographic measurements, and we showed that it can accurately predict the sway characteristics of both simulated and real subjects. Our method provides a full statistical characterization of the uncertainty related to all model parameters as quantified by posterior probability density functions, which is useful for comparisons across subjects and test settings. The ability to infer intractable control models from sensor data opens new possibilities for monitoring and predicting body status in health applications.

The Role of Morphological and Contextual Information in L2 Lexical Inference

ERIC Educational Resources Information Center

Hamada, Megumi

2014-01-01

This study investigated the role of morphological and contextual information in inferring the meaning of unknown L2 words during reading. Four groups of college-level ESL students, beginning (n?=?34), intermediate (n?=?27), high-intermediate (n?=?21), and advanced (n?=?25), chose the inferred meanings of 20 pseudo compounds (e.g.,…

Controller certification: The generalized stability margin inference for a large number of MIMO controllers

NASA Astrophysics Data System (ADS)

Park, Jisang

In this dissertation, we investigate MIMO stability margin inference of a large number of controllers using pre-established stability margins of a small number of nu-gap-wise adjacent controllers. The generalized stability margin and the nu-gap metric are inherently able to handle MIMO system analysis without the necessity of repeating multiple channel-by-channel SISO analyses. This research consists of three parts: (i) development of a decision support tool for inference of the stability margin, (ii) computational considerations for yielding the maximal stability margin with the minimal nu-gap metric in a less conservative manner, and (iii) experiment design for estimating the generalized stability margin with an assured error bound. A modern problem from aerospace control involves the certification of a large set of potential controllers with either a single plant or a fleet of potential plant systems, with both plants and controllers being MIMO and, for the moment, linear. Experiments on a limited number of controller/plant pairs should establish the stability and a certain level of margin of the complete set. We consider this certification problem for a set of controllers and provide algorithms for selecting an efficient subset for testing. This is done for a finite set of candidate controllers and, at least for SISO plants, for an infinite set. In doing this, the nu-gap metric will be the main tool. We provide a theorem restricting a radius of a ball in the parameter space so that the controller can guarantee a prescribed level of stability and performance if parameters of the controllers are contained in the ball. Computational examples are given, including one of certification of an aircraft engine controller. The overarching aim is to introduce truly MIMO margin calculations and to understand their efficacy in certifying stability over a set of controllers and in replacing legacy single-loop gain and phase margin calculations. We consider methods for the computation of; maximal MIMO stability margins bP̂,C, minimal nu-gap metrics deltanu , and the maximal difference between these two values, through the use of scaling and weighting functions. We propose simultaneous scaling selections that attempt to maximize the generalized stability margin and minimize the nu-gap. The minimization of the nu-gap by scaling involves a non-convex optimization. We modify the XY-centering algorithm to handle this non-convexity. This is done for applications in controller certification. Estimating the generalized stability margin with an accurate error bound has significant impact on controller certification. We analyze an error bound of the generalized stability margin as the infinity norm of the MIMO empirical transfer function estimate (ETFE). Input signal design to reduce the error on the estimate is also studied. We suggest running the system for a certain amount of time prior to recording of each output data set. The assured upper bound of estimation error can be tuned by the amount of the pre-experiment.

Morphological Instructional Packages as Determinants of Inferring Word Meanings in Reading Comprehension among Secondary School Students

ERIC Educational Resources Information Center

Akinwumi, Julius Olaitan; Olubunmi, Olagundoye Christanah

2017-01-01

This study investigated the effects of morphological instructional packages as determinants of inferring word meanings in reading comprehension among secondary school students in Ekiti State. The study adopted pre-test, post-test and control quasi-experimental research using two experimental groups and one control group with a sample of 270 Senior…

Affective expressions in groups and inferences about members' relational well-being: The effects of socially engaging and disengaging emotions.

PubMed

Rothman, Naomi B; Magee, Joe C

2016-01-01

Our findings draw attention to the interpersonal communication function of a relatively unexplored dimension of emotions-the level of social engagement versus disengagement. In four experiments, regardless of valence and target group gender, observers infer greater relational well-being (more cohesiveness and less conflict) between group members from socially engaging (sadness and appreciation) versus disengaging (anger and pride) emotion expressions. Supporting our argument that social (dis)engagement is a critical dimension communicated by these emotions, we demonstrate (1) that inferences about group members' self-interest mediate the effect of socially engaging emotions on cohesiveness and (2) that the influence of socially disengaging emotion expressions on inferences of conflict is attenuated when groups have collectivistic norms (i.e., members value a high level of social engagement). Furthermore, we show an important downstream consequence of these inferences of relational well-being: Groups that seem less cohesive because of their members' proud (versus appreciative) expressions are also expected to have worse task performance.

On the Analysis of Case-Control Studies in Cluster-correlated Data Settings.

PubMed

Haneuse, Sebastien; Rivera-Rodriguez, Claudia

2018-01-01

In resource-limited settings, long-term evaluation of national antiretroviral treatment (ART) programs often relies on aggregated data, the analysis of which may be subject to ecological bias. As researchers and policy makers consider evaluating individual-level outcomes such as treatment adherence or mortality, the well-known case-control design is appealing in that it provides efficiency gains over random sampling. In the context that motivates this article, valid estimation and inference requires acknowledging any clustering, although, to our knowledge, no statistical methods have been published for the analysis of case-control data for which the underlying population exhibits clustering. Furthermore, in the specific context of an ongoing collaboration in Malawi, rather than performing case-control sampling across all clinics, case-control sampling within clinics has been suggested as a more practical strategy. To our knowledge, although similar outcome-dependent sampling schemes have been described in the literature, a case-control design specific to correlated data settings is new. In this article, we describe this design, discuss balanced versus unbalanced sampling techniques, and provide a general approach to analyzing case-control studies in cluster-correlated settings based on inverse probability-weighted generalized estimating equations. Inference is based on a robust sandwich estimator with correlation parameters estimated to ensure appropriate accounting of the outcome-dependent sampling scheme. We conduct comprehensive simulations, based in part on real data on a sample of N = 78,155 program registrants in Malawi between 2005 and 2007, to evaluate small-sample operating characteristics and potential trade-offs associated with standard case-control sampling or when case-control sampling is performed within clusters.

77 FR 62350 - Practices and Procedures

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-12

... inference against a respondent agency with respect to non- appearance of any employee not under its control... inference in favor of the requesting party with regard to the information sought. The existing regulation...

Salicylate-induced changes in auditory thresholds of adolescent and adult rats.

PubMed

Brennan, J F; Brown, C A; Jastreboff, P J

1996-01-01

Shifts in auditory intensity thresholds after salicylate administration were examined in postweanling and adult pigmented rats at frequencies ranging from 1 to 35 kHz. A total of 132 subjects from both age levels were tested under two-way active avoidance or one-way active avoidance paradigms. Estimated thresholds were inferred from behavioral responses to presentations of descending and ascending series of intensities for each test frequency value. Reliable threshold estimates were found under both avoidance conditioning methods, and compared to controls, subjects at both age levels showed threshold shifts at selective higher frequency values after salicylate injection, and the extent of shifts was related to salicylate dose level.

Shared neural circuits for mentalizing about the self and others.

PubMed

Lombardo, Michael V; Chakrabarti, Bhismadev; Bullmore, Edward T; Wheelwright, Sally J; Sadek, Susan A; Suckling, John; Baron-Cohen, Simon

2010-07-01

Although many examples exist for shared neural representations of self and other, it is unknown how such shared representations interact with the rest of the brain. Furthermore, do high-level inference-based shared mentalizing representations interact with lower level embodied/simulation-based shared representations? We used functional neuroimaging (fMRI) and a functional connectivity approach to assess these questions during high-level inference-based mentalizing. Shared mentalizing representations in ventromedial prefrontal cortex, posterior cingulate/precuneus, and temporo-parietal junction (TPJ) all exhibited identical functional connectivity patterns during mentalizing of both self and other. Connectivity patterns were distributed across low-level embodied neural systems such as the frontal operculum/ventral premotor cortex, the anterior insula, the primary sensorimotor cortex, and the presupplementary motor area. These results demonstrate that identical neural circuits are implementing processes involved in mentalizing of both self and other and that the nature of such processes may be the integration of low-level embodied processes within higher level inference-based mentalizing.

Data fusion in cyber security: first order entity extraction from common cyber data

NASA Astrophysics Data System (ADS)

Giacobe, Nicklaus A.

2012-06-01

The Joint Directors of Labs Data Fusion Process Model (JDL Model) provides a framework for how to handle sensor data to develop higher levels of inference in a complex environment. Beginning from a call to leverage data fusion techniques in intrusion detection, there have been a number of advances in the use of data fusion algorithms in this subdomain of cyber security. While it is tempting to jump directly to situation-level or threat-level refinement (levels 2 and 3) for more exciting inferences, a proper fusion process starts with lower levels of fusion in order to provide a basis for the higher fusion levels. The process begins with first order entity extraction, or the identification of important entities represented in the sensor data stream. Current cyber security operational tools and their associated data are explored for potential exploitation, identifying the first order entities that exist in the data and the properties of these entities that are described by the data. Cyber events that are represented in the data stream are added to the first order entities as their properties. This work explores typical cyber security data and the inferences that can be made at the lower fusion levels (0 and 1) with simple metrics. Depending on the types of events that are expected by the analyst, these relatively simple metrics can provide insight on their own, or could be used in fusion algorithms as a basis for higher levels of inference.

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response.

PubMed

MacGilvray, Matthew E; Shishkova, Evgenia; Chasman, Deborah; Place, Michael; Gitter, Anthony; Coon, Joshua J; Gasch, Audrey P

2018-05-01

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Quantitative assessment of glacial fluctuations in the level of Lake Lisan, Dead Sea rift

NASA Astrophysics Data System (ADS)

Rohling, Eelco J.

2013-06-01

A quantitative understanding of climatic variations in the Levant during the last glacial cycle is needed to support archaeologists in assessing the drivers behind hominin migrations and cultural developments in this key region at the intersection between Africa and Europe. It will also foster a better understanding of the region's natural variability as context to projections of modern climate change. Detailed documentation of variations in the level of Lake Lisan - the lake that occupied the Dead Sea rift during the last glacial cycle - provides crucial climatic information for this region. Existing reconstructions suggest that Lake Lisan highstands during cold intervals of the last glacial cycle represent relatively humid conditions in the region, but these interpretations have remained predominantly qualitative. Here, I evaluate realistic ranges of the key climatological parameters that controlled lake level, based on the observed timing and amplitudes of lake-level variability. I infer that a mean precipitation rate over the wider catchment area of about 500 mm y-1, as proposed in the literature, would be consistent with observed lake levels if there was a concomitant 15-50% increase in wind speed during cold glacial stadials. This lends quantitative support to previous inferences of a notable increase in the intensity of Mediterranean (winter) storms during glacial periods, which tracked eastward into the Levant. In contrast to highstands during ‘regular’ stadials, lake level dropped during Heinrich Events. I demonstrate that this likely indicates a further intensification of the winds during those times.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

PubMed Central

Chasman, Deborah; Walters, Kevin B.; Lopes, Tiago J. S.; Eisfeld, Amie J.; Kawaoka, Yoshihiro; Roy, Sushmita

2016-01-01

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. PMID:27403523

Hierarchy-associated semantic-rule inference framework for classifying indoor scenes

NASA Astrophysics Data System (ADS)

Yu, Dan; Liu, Peng; Ye, Zhipeng; Tang, Xianglong; Zhao, Wei

2016-03-01

Typically, the initial task of classifying indoor scenes is challenging, because the spatial layout and decoration of a scene can vary considerably. Recent efforts at classifying object relationships commonly depend on the results of scene annotation and predefined rules, making classification inflexible. Furthermore, annotation results are easily affected by external factors. Inspired by human cognition, a scene-classification framework was proposed using the empirically based annotation (EBA) and a match-over rule-based (MRB) inference system. The semantic hierarchy of images is exploited by EBA to construct rules empirically for MRB classification. The problem of scene classification is divided into low-level annotation and high-level inference from a macro perspective. Low-level annotation involves detecting the semantic hierarchy and annotating the scene with a deformable-parts model and a bag-of-visual-words model. In high-level inference, hierarchical rules are extracted to train the decision tree for classification. The categories of testing samples are generated from the parts to the whole. Compared with traditional classification strategies, the proposed semantic hierarchy and corresponding rules reduce the effect of a variable background and improve the classification performance. The proposed framework was evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness.

Neural basis for inferring false beliefs and social emotions in others among individuals with schizophrenia and those at ultra-high risk for psychosis.

PubMed

Takano, Yosuke; Aoki, Yuta; Yahata, Noriaki; Kawakubo, Yuki; Inoue, Hideyuki; Iwashiro, Norichika; Natsubori, Tatsunobu; Koike, Shinsuke; Gonoi, Wataru; Sasaki, Hiroki; Takao, Hidemasa; Kasai, Kiyoto; Yamasue, Hidenori

2017-01-30

Inferring beliefs and social emotions of others has different neural substrates and possibly different roles in the pathophysiology of different clinical phases of schizophrenia. The current study investigated the neural basis for inferring others' beliefs and social emotions, as individual concepts, in 17 subjects at ultra-high risk for psychosis (UHR), 16 patients with schizophrenia and 20 healthy controls. Brain activity significantly differed from normal in both the left superior temporal sulcus (STS) and the inferior frontal gyrus (IFG) in the schizophrenia group while inferring others' beliefs, whereas those of UHR group were in the middle of those in the schizophrenia and healthy-control groups. Brain activity during inferring others' social emotions significantly differed in both the left STS and right IFG among individuals at UHR; however, there was no significant difference in the schizophrenia group. In contrast, brain activity differed in the left IFG of those in both the schizophrenia and UHR groups while inferring social emotion. Regarding the difference in direction of the abnormality, both the UHR and schizophrenia groups were characterized by hyper-STS and hypo-IFG activations when inferring others' beliefs and emotions. These findings might reflect different aspects of the same pathophysiological process at different clinical phases of psychosis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Metacognition and abstract reasoning.

PubMed

Markovits, Henry; Thompson, Valerie A; Brisson, Janie

2015-05-01

The nature of people's meta-representations of deductive reasoning is critical to understanding how people control their own reasoning processes. We conducted two studies to examine whether people have a metacognitive representation of abstract validity and whether familiarity alone acts as a separate metacognitive cue. In Study 1, participants were asked to make a series of (1) abstract conditional inferences, (2) concrete conditional inferences with premises having many potential alternative antecedents and thus specifically conducive to the production of responses consistent with conditional logic, or (3) concrete problems with premises having relatively few potential alternative antecedents. Participants gave confidence ratings after each inference. Results show that confidence ratings were positively correlated with logical performance on abstract problems and concrete problems with many potential alternatives, but not with concrete problems with content less conducive to normative responses. Confidence ratings were higher with few alternatives than for abstract content. Study 2 used a generation of contrary-to-fact alternatives task to improve levels of abstract logical performance. The resulting increase in logical performance was mirrored by increases in mean confidence ratings. Results provide evidence for a metacognitive representation based on logical validity, and show that familiarity acts as a separate metacognitive cue.

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.

Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+

NASA Astrophysics Data System (ADS)

Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew

2007-04-01

One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

Mathematical inference and control of molecular networks from perturbation experiments

NASA Astrophysics Data System (ADS)

Mohammed-Rasheed, Mohammed

One of the main challenges facing biologists and mathematicians in the post genomic era is to understand the behavior of molecular networks and harness this understanding into an educated intervention of the cell. The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks (GRNs) or molecular networks. GRNs can be viewed as dynamical systems with inherent properties and mechanisms, such as steady-state equilibriums and stability, that determine the behavior of the cell. The biological relevance of the mathematical concepts are important as they may predict the differentiation of a stem cell, the maintenance of a normal cell, the development of cancer and its aberrant behavior, and the design of drugs and response to therapy. Uncovering the underlying GRN structure from gene/protein expression data, e.g., microarrays or perturbation experiments, is called inference or reverse engineering of the molecular network. Because of the high cost and time consuming nature of biological experiments, the number of available measurements or experiments is very small compared to the number of molecules (genes, RNA and proteins). In addition, the observations are noisy, where the noise is due to the measurements imperfections as well as the inherent stochasticity of genetic expression levels. Intra-cellular activities and extra-cellular environmental attributes are also another source of variability. Thus, the inference of GRNs is, in general, an under-determined problem with a highly noisy set of observations. The ultimate goal of GRN inference and analysis is to be able to intervene within the network, in order to force it away from undesirable cellular states and into desirable ones. However, it remains a major challenge to design optimal intervention strategies in order to affect the time evolution of molecular activity in a desirable manner. In this proposal, we address both the inference and control problems of GRNs. In the first part of the thesis, we consider the control problem. We assume that we are given a general topology network structure, whose dynamics follow a discrete-time Markov chain model. We subsequently develop a comprehensive framework for optimal perturbation control of the network. The aim of the perturbation is to drive the network away from undesirable steady-states and to force it to converge to a unique desirable steady-state. The proposed framework does not make any assumptions about the topology of the initial network (e.g., ergodicity, weak and strong connectivity), and is thus applicable to general topology networks. We define the optimal perturbation as the minimum-energy perturbation measured in terms of the Frobenius norm between the initial and perturbed networks. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state. In the event where the optimal perturbation does not exist, we construct a family of sub-optimal perturbations that approximate the optimal solution arbitrarily closely. In the second part of the thesis, we address the inference problem of GRNs from time series data. We model the dynamics of the molecules using a system of ordinary differential equations corrupted by additive white noise. For large-scale networks, we formulate the inference problem as a constrained maximum likelihood estimation problem. We derive the molecular interactions that maximize the likelihood function while constraining the network to be sparse. We further propose a procedure to recover weak interactions based on the Bayesian information criterion. For small-size networks, we investigated the inference of a globally stable 7-gene melanoma genetic regulatory network from genetic perturbation experiments. We considered five melanoma cell lines, who exhibit different motility/invasion behavior under the same perturbation experiment of gene Wnt5a. The results of the simulations validate both the steady state levels and the experimental data of the perturbation experiments of all five cell lines. The goal of this study is to answer important questions that link the response of the network to perturbations, as measured by the experiments, to its structure, i.e., connectivity. Answers to these questions shed novel insights on the structure of networks and how they react to perturbations.

Fuzzy logic

NASA Technical Reports Server (NTRS)

Zadeh, Lofti A.

1988-01-01

The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.

Correlation of combustor acoustic power levels inferred from internal fluctuating pressure measurements

NASA Technical Reports Server (NTRS)

Vonglahn, U. H.

1978-01-01

Combustion chamber acoustic power levels inferred from internal fluctuating pressure measurements are correlated with operating conditions and chamber geometries over a wide range. The variables include considerations of chamber design (can, annular, and reverse-flow annular) and size, number of fuel nozzles, burner staging and fuel split, airflow and heat release rates, and chamber inlet pressure and temperature levels. The correlated data include those obtained with combustion component development rigs as well as engines.

Hierarchical animal movement models for population-level inference

USGS Publications Warehouse

Hooten, Mevin B.; Buderman, Frances E.; Brost, Brian M.; Hanks, Ephraim M.; Ivans, Jacob S.

2016-01-01

New methods for modeling animal movement based on telemetry data are developed regularly. With advances in telemetry capabilities, animal movement models are becoming increasingly sophisticated. Despite a need for population-level inference, animal movement models are still predominantly developed for individual-level inference. Most efforts to upscale the inference to the population level are either post hoc or complicated enough that only the developer can implement the model. Hierarchical Bayesian models provide an ideal platform for the development of population-level animal movement models but can be challenging to fit due to computational limitations or extensive tuning required. We propose a two-stage procedure for fitting hierarchical animal movement models to telemetry data. The two-stage approach is statistically rigorous and allows one to fit individual-level movement models separately, then resample them using a secondary MCMC algorithm. The primary advantages of the two-stage approach are that the first stage is easily parallelizable and the second stage is completely unsupervised, allowing for an automated fitting procedure in many cases. We demonstrate the two-stage procedure with two applications of animal movement models. The first application involves a spatial point process approach to modeling telemetry data, and the second involves a more complicated continuous-time discrete-space animal movement model. We fit these models to simulated data and real telemetry data arising from a population of monitored Canada lynx in Colorado, USA.

Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach.

PubMed

Elaziz, Mohamed Abd; Hemdan, Ahmed Monem; Hassanien, AboulElla; Oliva, Diego; Xiong, Shengwu

2017-09-07

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

Role of Utility and Inference in the Evolution of Functional Information

PubMed Central

Sharov, Alexei A.

2009-01-01

Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and the function (phenotype) and determines the shape of the fitness landscape. The utility factor operates at multiple time scales: short-term selection drives evolution towards higher survival and reproduction rate within a given fitness landscape, and long-term selection favors those fitness landscapes that support adaptability and lead to evolutionary expansion of certain lineages. Inference rules make short-term selection possible by shaping the fitness landscape and defining possible directions of evolution, but they are under control of the long-term selection of lineages. Communication normally occurs within a set of agents with compatible interpretation systems, which I call communication system. Functional information cannot be directly transferred between communication systems with incompatible inference rules. Each biological species is a genetic communication system that carries unique functional information together with inference rules that determine evolutionary directions and constraints. This view of the relation between utility and inference can resolve the conflict between realism/positivism and pragmatism. Realism overemphasizes the role of inference in evolution of human knowledge because it assumes that logic is embedded in reality. Pragmatism substitutes usefulness for truth and therefore ignores the advantage of inference. The proposed concept of evolutionary pragmatism rejects the idea that logic is embedded in reality; instead, inference rules are constructed within each communication system to represent reality and they evolve towards higher adaptability on a long time scale. PMID:20160960

Using permutation tests to enhance causal inference in interrupted time series analysis.

PubMed

Linden, Ariel

2018-06-01

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied serially over time and the intervention is expected to "interrupt" the level and/or trend of that outcome. The internal validity is strengthened considerably when the treated unit is contrasted with a comparable control group. In this paper, we introduce a robustness check based on permutation tests to further improve causal inference. We evaluate the effect of California's Proposition 99 for reducing cigarette sales by iteratively casting each nontreated state into the role of "treated," creating a comparable control group using the ITSAMATCH package in Stata, and then evaluating treatment effects using ITSA regression. If statistically significant "treatment effects" are estimated for pseudotreated states, then any significant changes in the outcome of the actual treatment unit (California) cannot be attributed to the intervention. We perform these analyses setting the cutpoint significance level to P > .40 for identifying balanced matches (the highest threshold possible for which controls could still be found for California) and use the difference in differences of trends as the treatment effect estimator. Only California attained a statistically significant treatment effect, strengthening confidence in the conclusion that Proposition 99 reduced cigarette sales. The proposed permutation testing framework provides an additional robustness check to either support or refute a treatment effect identified in for the true treated unit in ITSA. Given its value and ease of implementation, this framework should be considered as a standard robustness test in all multiple group interrupted time series analyses. © 2018 John Wiley & Sons, Ltd.

Past and present cosmic structure in the SDSS DR7 main sample

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

Jasche, J.; Leclercq, F.; Wandelt, B.D., E-mail: jasche@iap.fr, E-mail: florent.leclercq@polytechnique.org, E-mail: wandelt@iap.fr

2015-01-01

We present a chrono-cosmography project, aiming at the inference of the four dimensional formation history of the observed large scale structure from its origin to the present epoch. To do so, we perform a full-scale Bayesian analysis of the northern galactic cap of the Sloan Digital Sky Survey (SDSS) Data Release 7 main galaxy sample, relying on a fully probabilistic, physical model of the non-linearly evolved density field. Besides inferring initial conditions from observations, our methodology naturally and accurately reconstructs non-linear features at the present epoch, such as walls and filaments, corresponding to high-order correlation functions generated by late-time structuremore » formation. Our inference framework self-consistently accounts for typical observational systematic and statistical uncertainties such as noise, survey geometry and selection effects. We further account for luminosity dependent galaxy biases and automatic noise calibration within a fully Bayesian approach. As a result, this analysis provides highly-detailed and accurate reconstructions of the present density field on scales larger than ∼ 3 Mpc/h, constrained by SDSS observations. This approach also leads to the first quantitative inference of plausible formation histories of the dynamic large scale structure underlying the observed galaxy distribution. The results described in this work constitute the first full Bayesian non-linear analysis of the cosmic large scale structure with the demonstrated capability of uncertainty quantification. Some of these results will be made publicly available along with this work. The level of detail of inferred results and the high degree of control on observational uncertainties pave the path towards high precision chrono-cosmography, the subject of simultaneously studying the dynamics and the morphology of the inhomogeneous Universe.« less

Modeling two strains of disease via aggregate-level infectivity curves.

PubMed

Romanescu, Razvan; Deardon, Rob

2016-04-01

Well formulated models of disease spread, and efficient methods to fit them to observed data, are powerful tools for aiding the surveillance and control of infectious diseases. Our project considers the problem of the simultaneous spread of two related strains of disease in a context where spatial location is the key driver of disease spread. We start our modeling work with the individual level models (ILMs) of disease transmission, and extend these models to accommodate the competing spread of the pathogens in a two-tier hierarchical population (whose levels we refer to as 'farm' and 'animal'). The postulated interference mechanism between the two strains is a period of cross-immunity following infection. We also present a framework for speeding up the computationally intensive process of fitting the ILM to data, typically done using Markov chain Monte Carlo (MCMC) in a Bayesian framework, by turning the inference into a two-stage process. First, we approximate the number of animals infected on a farm over time by infectivity curves. These curves are fit to data sampled from farms, using maximum likelihood estimation, then, conditional on the fitted curves, Bayesian MCMC inference proceeds for the remaining parameters. Finally, we use posterior predictive distributions of salient epidemic summary statistics, in order to assess the model fitted.

Locomotor activity influences muscle architecture and bone growth but not muscle attachment site morphology.

PubMed

Rabey, Karyne N; Green, David J; Taylor, Andrea B; Begun, David R; Richmond, Brian G; McFarlin, Shannon C

2015-01-01

The ability to make behavioural inferences from skeletal remains is critical to understanding the lifestyles and activities of past human populations and extinct animals. Muscle attachment site (enthesis) morphology has long been assumed to reflect muscle strength and activity during life, but little experimental evidence exists to directly link activity patterns with muscle development and the morphology of their attachments to the skeleton. We used a mouse model to experimentally test how the level and type of activity influences forelimb muscle architecture of spinodeltoideus, acromiodeltoideus, and superficial pectoralis, bone growth rate and gross morphology of their insertion sites. Over an 11-week period, we collected data on activity levels in one control group and two experimental activity groups (running, climbing) of female wild-type mice. Our results show that both activity type and level increased bone growth rates influenced muscle architecture, including differences in potential muscular excursion (fibre length) and potential force production (physiological cross-sectional area). However, despite significant influences on muscle architecture and bone development, activity had no observable effect on enthesis morphology. These results suggest that the gross morphology of entheses is less reliable than internal bone structure for making inferences about an individual's past behaviour. Copyright © 2014 Elsevier Ltd. All rights reserved.

Locomotor activity influences muscle architecture and bone growth but not muscle attachment site morphology

PubMed Central

Rabey, Karyne N.; Green, David J.; Taylor, Andrea B.; Begun, David R.; Richmond, Brian G.; McFarlin, Shannon C.

2014-01-01

The ability to make behavioural inferences from skeletal remains is critical to understanding the lifestyles and activities of past human populations and extinct animals. Muscle attachment site (enthesis) morphology has long been assumed to reflect muscle strength and activity during life, but little experimental evidence exists to directly link activity patterns with muscle development and the morphology of their attachments to the skeleton. We used a mouse model to experimentally test how the level and type of activity influences forelimb muscle architecture of spinodeltoideus, acromiodeltoideus, and superficial pectoralis, bone growth rate and gross morphology of their insertion sites. Over an 11-week period, we collected data on activity levels in one control group and two experimental activity groups (running, climbing) of female wild-type mice. Our results show that both activity type and level increased bone growth rates influenced muscle architecture, including differences in potential muscular excursion (fibre length) and potential force production (physiological cross-sectional area). However, despite significant influences on muscle architecture and bone development, activity had no observable effect on enthesis morphology. These results suggest that the gross morphology of entheses is less reliable than internal bone structure for making inferences about an individual’s past behaviour. PMID:25467113

Fractal regional myocardial blood flows pattern according to metabolism, not vascular anatomy

PubMed Central

Yipintsoi, Tada; Kroll, Keith

2015-01-01

Regional myocardial blood flows are markedly heterogeneous. Fractal analysis shows strong near-neighbor correlation. In experiments to distinguish control by vascular anatomy vs. local vasomotion, coronary flows were increased in open-chest dogs by stimulating myocardial metabolism (catecholamines + atropine) with and without adenosine. During control states mean left ventricular (LV) myocardial blood flows (microspheres) were 0.5–1 ml·g−1·min−1 and increased to 2–3 ml·g−1·min−1 with catecholamine infusion and to ∼4 ml·g−1·min−1 with adenosine (Ado). Flow heterogeneity was similar in all states: relative dispersion (RD = SD/mean) was ∼25%, using LV pieces 0.1–0.2% of total. During catecholamine infusion local flows increased in proportion to the mean flows in 45% of the LV, “tracking” closely (increased proportionately to mean flow), while ∼40% trended toward the mean. Near-neighbor regional flows remained strongly spatially correlated, with fractal dimension D near 1.2 (Hurst coefficient 0.8). The spatial patterns remain similar at varied levels of metabolic stimulation inferring metabolic dominance. In contrast, adenosine vasodilation increased flows eightfold times control while destroying correlation with the control state. The Ado-induced spatial patterns differed from control but were self-consistent, inferring that with full vasodilation the relaxed arterial anatomy dominates the distribution. We conclude that vascular anatomy governs flow distributions during adenosine vasodilation but that metabolic vasoregulation dominates in normal physiological states. PMID:26589329

Fractal regional myocardial blood flows pattern according to metabolism, not vascular anatomy.

PubMed

Yipintsoi, Tada; Kroll, Keith; Bassingthwaighte, James B

2016-02-01

Regional myocardial blood flows are markedly heterogeneous. Fractal analysis shows strong near-neighbor correlation. In experiments to distinguish control by vascular anatomy vs. local vasomotion, coronary flows were increased in open-chest dogs by stimulating myocardial metabolism (catecholamines + atropine) with and without adenosine. During control states mean left ventricular (LV) myocardial blood flows (microspheres) were 0.5-1 ml·g(-1)·min(-1) and increased to 2-3 ml·g(-1)·min(-1) with catecholamine infusion and to ∼4 ml·g(-1)·min(-1) with adenosine (Ado). Flow heterogeneity was similar in all states: relative dispersion (RD = SD/mean) was ∼25%, using LV pieces 0.1-0.2% of total. During catecholamine infusion local flows increased in proportion to the mean flows in 45% of the LV, "tracking" closely (increased proportionately to mean flow), while ∼40% trended toward the mean. Near-neighbor regional flows remained strongly spatially correlated, with fractal dimension D near 1.2 (Hurst coefficient 0.8). The spatial patterns remain similar at varied levels of metabolic stimulation inferring metabolic dominance. In contrast, adenosine vasodilation increased flows eightfold times control while destroying correlation with the control state. The Ado-induced spatial patterns differed from control but were self-consistent, inferring that with full vasodilation the relaxed arterial anatomy dominates the distribution. We conclude that vascular anatomy governs flow distributions during adenosine vasodilation but that metabolic vasoregulation dominates in normal physiological states. Copyright © 2016 the American Physiological Society.

Quantifying Transmission Heterogeneity Using Both Pathogen Phylogenies and Incidence Time Series

PubMed Central

Li, Lucy M.; Grassly, Nicholas C.; Fraser, Christophe

2017-01-01

Abstract Heterogeneity in individual-level transmissibility can be quantified by the dispersion parameter k of the offspring distribution. Quantifying heterogeneity is important as it affects other parameter estimates, it modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. Aggregated data such as incidence time series are often not sufficiently informative to estimate k. Incorporating phylogenetic analysis can help to estimate k concurrently with other epidemiological parameters. We have developed an inference framework that uses particle Markov Chain Monte Carlo to estimate k and other epidemiological parameters using both incidence time series and the pathogen phylogeny. Using the framework to fit a modified compartmental transmission model that includes the parameter k to simulated data, we found that more accurate and less biased estimates of the reproductive number were obtained by combining epidemiological and phylogenetic analyses. However, k was most accurately estimated using pathogen phylogeny alone. Accurately estimating k was necessary for unbiased estimates of the reproductive number, but it did not affect the accuracy of reporting probability and epidemic start date estimates. We further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. Finally, we used the inference framework to estimate transmission parameters from epidemiological and genetic data collected during a poliovirus outbreak. Despite the large degree of phylogenetic uncertainty, we demonstrated that incorporating phylogenetic data in parameter inference improved the accuracy and precision of estimates. PMID:28981709

Knowledge, expectations, and inductive reasoning within conceptual hierarchies.

PubMed

Coley, John D; Hayes, Brett; Lawson, Christopher; Moloney, Michelle

2004-01-01

Previous research (e.g. Cognition 64 (1997) 73) suggests that the privileged level for inductive inference in a folk biological conceptual hierarchy does not correspond to the "basic" level (i.e. the level at which concepts are both informative and distinct). To further explore inductive inference within conceptual hierarchies, we examine relations between knowledge of concepts at different hierarchical levels, expectations about conceptual coherence, and inductive inference. In Experiments 1 and 2, 5- and 8-year-olds and adults listed features of living kind (Experiments 1 and 2) and artifact (Experiment 2) concepts at different hierarchical levels (e.g. plant, tree, oak, desert oak), and also rated the strength of generalizations to the same concepts. For living kinds, the level that showed a relative advantage on these two tasks differed; the greatest increase in features listed tended to occur at the life-form level (e.g. tree), whereas the greatest increase in inductive strength tended to occur at the folk-generic level (e.g. oak). Knowledge and induction also showed different developmental trajectories. For artifact concepts, the levels at which the greatest gains in knowledge and induction occurred were more varied, and corresponded more closely across tasks. In Experiment 3, adults reported beliefs about within-category similarity for concepts at different levels of animal, plant and artifact hierarchies, and rated inductive strength as before. For living kind concepts, expectations about category coherence predicted patterns of inductions; knowledge did not. For artifact concepts, both knowledge and expectations predicted patterns of induction. Results suggest that beliefs about conceptual coherence play an important role in guiding inductive inference, that this role may be largely independent of specific knowledge of concepts, and that such beliefs are especially important in reasoning about living kinds.

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

PubMed

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

System-Level Insights into the Cellular Interactome of a Non-Model Organism: Inferring, Modelling and Analysing Functional Gene Network of Soybean (Glycine max)

PubMed Central

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN. PMID:25423109

Excellent Patient Care Processes in Poor Hospitals? Why Hospital-Level and Patient-Level Care Quality-Outcome Relationships Can Differ.

PubMed

Finney, John W; Humphreys, Keith; Kivlahan, Daniel R; Harris, Alex H S

2016-04-01

Studies finding weak or nonexistent relationships between hospital performance on providing recommended care and hospital-level clinical outcomes raise questions about the value and validity of process of care performance measures. Such findings may cause clinicians to question the effectiveness of the care process presumably captured by the performance measure. However, one cannot infer from hospital-level results whether patients who received the specified care had comparable, worse or superior outcomes relative to patients not receiving that care. To make such an inference has been labeled the "ecological fallacy," an error that is well known among epidemiologists and sociologists, but less so among health care researchers and policy makers. We discuss such inappropriate inferences in the health care performance measurement field and illustrate how and why process measure-outcome relationships can differ at the patient and hospital levels. We also offer recommendations for appropriate multilevel analyses to evaluate process measure-outcome relationships at the patient and hospital levels and for a more effective role for performance measure bodies and research funding organizations in encouraging such multilevel analyses.

Is Social Categorization the Missing Link Between Weak Central Coherence and Mental State Inference Abilities in Autism? Preliminary Evidence from a General Population Sample.

PubMed

Skorich, Daniel P; May, Adrienne R; Talipski, Louisa A; Hall, Marnie H; Dolstra, Anita J; Gash, Tahlia B; Gunningham, Beth H

2016-03-01

We explore the relationship between the 'theory of mind' (ToM) and 'central coherence' difficulties of autism. We introduce covariation between hierarchically-embedded categories and social information--at the local level, the global level, or at both levels simultaneously--within a category confusion task. We then ask participants to infer the mental state of novel category members, and measure participants' autism-spectrum quotient (AQ). Results reveal a positive relationship between AQ and the degree of local/global social categorization, which in turn predicts the pattern of mental state inferences. These results provide preliminary evidence for a causal relationship between central coherence and ToM abilities. Implications with regard to ToM processes, social categorization, intervention, and the development of a unified account of autism are discussed.

Semisupervised learning using Bayesian interpretation: application to LS-SVM.

PubMed

Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

2011-04-01

Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

Higher-level fusion for military operations based on abductive inference: proof of principle

NASA Astrophysics Data System (ADS)

Pantaleev, Aleksandar V.; Josephson, John

2006-04-01

The ability of contemporary military commanders to estimate and understand complicated situations already suffers from information overload, and the situation can only grow worse. We describe a prototype application that uses abductive inferencing to fuse information from multiple sensors to evaluate the evidence for higher-level hypotheses that are close to the levels of abstraction needed for decision making (approximately JDL levels 2 and 3). Abductive inference (abduction, inference to the best explanation) is a pattern of reasoning that occurs naturally in diverse settings such as medical diagnosis, criminal investigations, scientific theory formation, and military intelligence analysis. Because abduction is part of common-sense reasoning, implementations of it can produce reasoning traces that are very human understandable. Automated abductive inferencing can be deployed to augment human reasoning, taking advantage of computation to process large amounts of information, and to bypass limits to human attention and short-term memory. We illustrate the workings of the prototype system by describing an example of its use for small-unit military operations in an urban setting. Knowledge was encoded as it might be captured prior to engagement from a standard military decision making process (MDMP) and analysis of commander's priority intelligence requirements (PIR). The system is able to reasonably estimate the evidence for higher-level hypotheses based on information from multiple sensors. Its inference processes can be examined closely to verify correctness. Decision makers can override conclusions at any level and changes will propagate appropriately.

Saul: Towards Declarative Learning Based Programming

PubMed Central

Kordjamshidi, Parisa; Roth, Dan; Wu, Hao

2015-01-01

We present Saul, a new probabilistic programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of AI systems. Such languages need to interact with messy, naturally occurring data, to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level, to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and, finally, to support flexible integration of these learning and inference models within an application program. Saul is an object-functional programming language written in Scala that facilitates these by (1) allowing a programmer to learn, name and manipulate named abstractions over relational data; (2) supporting seamless incorporation of trainable (probabilistic or discriminative) components into the program, and (3) providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints. Saul is developed over a declaratively defined relational data model, can use piecewise learned factor graphs with declaratively specified learning and inference objectives, and it supports inference over probabilistic models augmented with declarative knowledge-based constraints. We describe the key constructs of Saul and exemplify its use in developing applications that require relational feature engineering and structured output prediction. PMID:26635465

Saul: Towards Declarative Learning Based Programming.

PubMed

Kordjamshidi, Parisa; Roth, Dan; Wu, Hao

2015-07-01

We present Saul , a new probabilistic programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of AI systems. Such languages need to interact with messy, naturally occurring data, to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level, to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and, finally, to support flexible integration of these learning and inference models within an application program. Saul is an object-functional programming language written in Scala that facilitates these by (1) allowing a programmer to learn, name and manipulate named abstractions over relational data; (2) supporting seamless incorporation of trainable (probabilistic or discriminative) components into the program, and (3) providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints. Saul is developed over a declaratively defined relational data model, can use piecewise learned factor graphs with declaratively specified learning and inference objectives, and it supports inference over probabilistic models augmented with declarative knowledge-based constraints. We describe the key constructs of Saul and exemplify its use in developing applications that require relational feature engineering and structured output prediction.

Analogical and category-based inference: a theoretical integration with Bayesian causal models.

PubMed

Holyoak, Keith J; Lee, Hee Seung; Lu, Hongjing

2010-11-01

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality.

Orbital frontal cortex updates state-induced value change for decision-making.

PubMed

Baltz, Emily T; Yalcinbas, Ege A; Renteria, Rafael; Gremel, Christina M

2018-06-13

Recent hypotheses have posited that orbital frontal cortex (OFC) is important for using inferred consequences to guide behavior. Less clear is OFC's contribution to goal-directed or model-based behavior, where the decision to act is controlled by previous experience with the consequence or outcome. Investigating OFC's role in learning about changed outcomes separate from decision-making is not trivial and often the two are confounded. Here we adapted an incentive learning task to mice, where we investigated processes controlling experience-based outcome updating independent from inferred action control. We found chemogenetic OFC attenuation did not alter the ability to perceive motivational state-induced changes in outcome value but did prevent the experience-based updating of this change. Optogenetic inhibition of OFC excitatory neuron activity selectively when experiencing an outcome change disrupted the ability to update, leaving mice unable to infer the appropriate behavior. Our findings support a role for OFC in learning that controls decision-making. © 2018, Baltz et al.

Moisture variation inferred from a nebkha profile correlates with vegetation changes in the southwestern Mu Us Desert of China over one century.

PubMed

Li, Jinchang; Zhao, Yanfang; Han, Liuyan; Zhang, Guoming; Liu, Rentao

2017-11-15

We inferred moisture variations from the early 1930s to the early 2010s in the southwestern Mu Us Desert of China using Rb/Sr ratio, chemical index of alteration (CIA), and organic matter (OM) content in a nebkha profile. Our results showed that the variations in moisture may have been the main factor that controlled vegetation recovery or degradation, and we believe that gradual vegetation recovery was notable throughout the study area during the past 80years, despite two notable degradation stages during the mid-1950s and the mid-1980s. The Rb/Sr ratio, CIA, and OM content revealed that moisture levels increased during the study period, though with large interannual variations. During the early stage of nebkha formation, the moisture variations were controlled by unusually low precipitation. Thereafter, the precipitation, pan evaporation and temperature determined together moisture variations, but the key factor determining moisture variations was different during different periods. The moisture variations trend revealed in this study may not be restricted to this region as it was similar with the adjacent Mongolian Plateau. Copyright © 2017 Elsevier B.V. All rights reserved.

How providing more or less time to solve a cognitive task interferes with upright stance control; a posturographic analysis on healthy young adults.

PubMed

Rougier, Patrice R; Bonnet, Cédrick T

2016-06-01

Contrasted postural effects have been reported in dual-task protocols associating balance control and cognitive task that could be explained by the nature and the relative difficulty of the cognitive task and the biomechanical significance of the force platform data. To better assess their respective role, eleven healthy young adults were required to stand upright quietly on a force platform while concomitantly solving mental-calculation or mental-navigation cognitive tasks. Various levels of difficulty were applied by adjusting the velocity rate at which the instructions were provided to the subject according to his/her maximal capacities measured beforehand. A condition without any concomitant cognitive task was added to constitute a baseline behavior. Two basic components, the horizontal center-of-gravity movements and the horizontal difference between center-of-gravity and center-of-pressures were computed from the complex center-of-pressure recorded movements. It was hypothesized that increasing the delay should infer less interaction between postural control and task solution. The results indicate that both mental-calculation and mental-navigation tasks induce reduced amplitudes for the center-of-pressure minus center-of-gravity movements, only along the mediolateral axis, whereas center-of-gravity movements were not affected, suggesting that different circuits are involved in the central nervous system to control these two movements. Moreover, increasing the delays task does not infer any effect for both movements. Since center-of-pressure minus center-of-gravity expresses the horizontal acceleration communicated to the center-of-gravity, one may assume that the control of the latter should be facilitated in dual-tasks conditions, inferring reduced center-of-gravity movements, which is not seen in our results. This lack of effect should be thus interpreted as a modification in the control of these center-of-gravity movements. Taken together, these results emphasized how undisturbed upright stance control can be impacted by mental tasks requiring attention, whatever their nature (calculation or navigation) and their relative difficulty. Depending on the provided instructions, i.e. focusing our attention on body movements or on the opposite diverting this attention toward other objectives, the evaluation of upright stance control capacities might be drastically altered. Copyright © 2016. Published by Elsevier B.V.

Counterfactual Reasoning in Non-psychotic First-Degree Relatives of People with Schizophrenia

PubMed Central

Albacete, Auria; Contreras, Fernando; Bosque, Clara; Gilabert, Ester; Albiach, Ángela; Menchón, José M.; Crespo-Facorro, Benedicto; Ayesa-Arriola, Rosa

2016-01-01

Counterfactual thinking (CFT) is a type of conditional reasoning that enables the generation of mental simulations of alternatives to past factual events. Previous research has found this cognitive feature to be disrupted in schizophrenia (Hooker et al., 2000; Contreras et al., 2016). At the same time, the study of cognitive deficits in unaffected relatives of people with schizophrenia has significantly increased, supporting its potential endophenotypic role in this disorder. Using an exploratory approach, the current study examined CFT for the first time in a sample of non-psychotic first-degree relatives of schizophrenia patients (N = 43), in comparison with schizophrenia patients (N = 54) and healthy controls (N = 44). A series of tests that assessed the “causal order effect” in CFT and the ability to generate counterfactual thoughts and counterfactually derive inferences using the Counterfactual Inference Test was completed. Associations with variables of basic and social cognition, levels of schizotypy and psychotic-like experiences in addition to clinical and socio-demographic characteristics were also explored. Findings showed that first-degree relatives generated a lower number of counterfactual thoughts than controls, and were more adept at counterfactually deriving inferences, specifically in the scenarios related to regret and to judgments of avoidance in an unusual situation. No other significant results were found. These preliminary findings suggest that non-psychotic first-degree relatives of schizophrenia patients show a subtle disruption of global counterfactual thinking compared with what is normally expected in the general population. Due to the potential impact of such deficits, new treatments targeting CFT improvement might be considered in future management strategies. PMID:27242583

Counterfactual Reasoning in Non-psychotic First-Degree Relatives of People with Schizophrenia.

PubMed

Albacete, Auria; Contreras, Fernando; Bosque, Clara; Gilabert, Ester; Albiach, Ángela; Menchón, José M; Crespo-Facorro, Benedicto; Ayesa-Arriola, Rosa

2016-01-01

Counterfactual thinking (CFT) is a type of conditional reasoning that enables the generation of mental simulations of alternatives to past factual events. Previous research has found this cognitive feature to be disrupted in schizophrenia (Hooker et al., 2000; Contreras et al., 2016). At the same time, the study of cognitive deficits in unaffected relatives of people with schizophrenia has significantly increased, supporting its potential endophenotypic role in this disorder. Using an exploratory approach, the current study examined CFT for the first time in a sample of non-psychotic first-degree relatives of schizophrenia patients (N = 43), in comparison with schizophrenia patients (N = 54) and healthy controls (N = 44). A series of tests that assessed the "causal order effect" in CFT and the ability to generate counterfactual thoughts and counterfactually derive inferences using the Counterfactual Inference Test was completed. Associations with variables of basic and social cognition, levels of schizotypy and psychotic-like experiences in addition to clinical and socio-demographic characteristics were also explored. Findings showed that first-degree relatives generated a lower number of counterfactual thoughts than controls, and were more adept at counterfactually deriving inferences, specifically in the scenarios related to regret and to judgments of avoidance in an unusual situation. No other significant results were found. These preliminary findings suggest that non-psychotic first-degree relatives of schizophrenia patients show a subtle disruption of global counterfactual thinking compared with what is normally expected in the general population. Due to the potential impact of such deficits, new treatments targeting CFT improvement might be considered in future management strategies.

Deficits in social perception in opioid maintenance patients, abstinent opioid users and non-opioid users.

PubMed

McDonald, Skye; Darke, Shane; Kaye, Sharlene; Torok, Michelle

2013-03-01

This study aimed to compare emotion perception and social inference in opioid maintenance patients with abstinent ex-users and non-heroin-using controls, and determine whether any deficits in could be accounted for by cognitive deficits and/or risk factors for brain damage. Case-control. Sydney, Australia. A total of 125 maintenance patients (MAIN), 50 abstinent opiate users (ABST) and 50 matched controls (CON). The Awareness of Social Inference Test (TASIT) was used to measure emotion perception and social inference. Measures were also taken of executive function, working memory, information processing speed, verbal/non-verbal learning and psychological distress. After adjusting for age, sex, pre-morbid IQ and psychological distress, the MAIN group was impaired relative to CON (β = -0.19, P < 0.05) and ABST (β = -0.19, P < 0.05) on emotion perception and relative to CON (β = -0.25, P < 0.001) and ABST (β = -0.24, P < 0.01) on social inference. In neither case did the CON and ABST groups differ. For both emotion perception (P < 0.001) and social inference (P < 0.001), pre-morbid IQ was a significant independent predictor. Cognitive function was a major predictor of poor emotion perception (β = -0.44, P < 0.001) and social inference (β = -0.48, P < 0.001). Poor emotion recognition was also predicted by number of heroin overdoses (β = -0.14, P < 0.05). Neither time in treatment or type of maintenance medication (methadone or buprenorphine) were related to performance. People in opioid maintenance treatment may have an impaired capacity for emotion perception and ability to make inferences about social situations. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.

The Sense of Confidence during Probabilistic Learning: A Normative Account.

PubMed

Meyniel, Florent; Schlunegger, Daniel; Dehaene, Stanislas

2015-06-01

Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable "feeling of knowing" or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process.

The Sense of Confidence during Probabilistic Learning: A Normative Account

PubMed Central

Meyniel, Florent; Schlunegger, Daniel; Dehaene, Stanislas

2015-01-01

Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable “feeling of knowing” or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process. PMID:26076466

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

PubMed

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance

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

Ahn, Tae-Hyuk; Chai, Juanjuan; Pan, Chongle

Motivation: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis. Results: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic readsmore » to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. In conclusion, the algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains. Availability and Implementation: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org.« less

Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance

DOE PAGES

Ahn, Tae-Hyuk; Chai, Juanjuan; Pan, Chongle

2014-09-29

Motivation: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis. Results: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic readsmore » to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. In conclusion, the algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains. Availability and Implementation: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org.« less

A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests

PubMed Central

Chen, Yong; Liu, Yulun; Ning, Jing; Cormier, Janice; Chu, Haitao

2014-01-01

Systematic reviews of diagnostic tests often involve a mixture of case-control and cohort studies. The standard methods for evaluating diagnostic accuracy only focus on sensitivity and specificity and ignore the information on disease prevalence contained in cohort studies. Consequently, such methods cannot provide estimates of measures related to disease prevalence, such as population averaged or overall positive and negative predictive values, which reflect the clinical utility of a diagnostic test. In this paper, we propose a hybrid approach that jointly models the disease prevalence along with the diagnostic test sensitivity and specificity in cohort studies, and the sensitivity and specificity in case-control studies. In order to overcome the potential computational difficulties in the standard full likelihood inference of the proposed hybrid model, we propose an alternative inference procedure based on the composite likelihood. Such composite likelihood based inference does not suffer computational problems and maintains high relative efficiency. In addition, it is more robust to model mis-specifications compared to the standard full likelihood inference. We apply our approach to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma. PMID:25897179

Permutation inference for the general linear model

PubMed Central

Winkler, Anderson M.; Ridgway, Gerard R.; Webster, Matthew A.; Smith, Stephen M.; Nichols, Thomas E.

2014-01-01

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm. PMID:24530839

[Fractional exhaled nitric oxide in monitoring and therapeutic management of asthma].

PubMed

Melo, Bruno; Costa, Patrício; Afonso, Ariana; Machado, Vânia; Moreira, Carla; Gonçalves, Augusta; Gonçalves, Jean-Pierre

2014-01-01

Asthma is a chronic respiratory disease characterized by hyper-responsiveness and bronchial inflammation. The bronchial inflammation in these patients can be monitored by measuring the fractional exhaled nitric oxide. This study aims to determine fractional exhaled nitric oxide association with peak expiratory flow and with asthma control inferred by the Global Initiative for Asthma. Observational, analytical and cross-sectional study of children with asthma, 6-12 years-old, followed in the Outpatient Respiratory Pathology of Braga Hospital. Sociodemographic and clinical information were collected through a questionnaire. fractional exhaled nitric oxide and peak expiratory flow were determined by portable analyzer Niox Mino® and flow meter, respectively. The sample is constituted by 101 asthmatic children, 63 (62.4%) of males and 38 (37.6%) females. The mean age of participants in the sample is 9.18 (1.99) years. The logistic regression performed with the cutoff value obtained by ROC curve, revealed that fractional exhaled nitric oxide (b(FENO classes) = 0.85; χ(2)Wald (1) = 8.71; OR = 2.33; p = 0.003) has a statistical significant effect on the probability of changing level of asthma control. The odds ratio of going from "controlled" to "partly controlled/uncontrolled" is 2.33 per each level of fractional exhaled nitric oxide. The probability of an asthmatic children change their level of asthma control, from 'controlled' to 'partly controlled/uncontrolled', taking into account a change in their fractional exhaled nitric oxide level, increases 133%.

Adult Attachment Affects Neural Response to Preference-Inferring in Ambiguous Scenarios: Evidence From an fMRI Study

PubMed Central

Zhang, Xing; Ran, Guangming; Xu, Wenjian; Ma, Yuanxiao; Chen, Xu

2018-01-01

Humans are highly social animals, and the ability to cater to the preferences of other individuals is encouraged by society. Preference-inferring is an important aspect of the theory of mind (TOM). Many previous studies have shown that attachment style is closely related to TOM ability. However, little is known about the effects of adult attachment style on preferences inferring under different levels of certainty. Here, we investigated how adult attachment style affects neural activity underlying preferences inferred under different levels of certainty by using functional magnetic resonance imaging (fMRI). The fMRI results demonstrated that adult attachment influenced the activation of anterior insula (AI) and inferior parietal lobule (IPL) in response to ambiguous preference-inferring. More specifically, in the ambiguous preference condition, the avoidant attached groups exhibited a significantly enhanced activation than secure and anxious attached groups in left IPL; the anxious attached groups exhibited a significantly reduced activation secure attached group in left IPL. In addition, the anxious attached groups exhibited a significantly reduced activation than secure and avoidant attached groups in left AI. These results were also further confirmed by the subsequent PPI analysis. The results from current study suggest that, under ambiguous situations, the avoidant attached individuals show lower sensitivity to the preference of other individuals and need to invest more cognitive resources for preference-reasoning; while compared with avoidant attached group, the anxious attached individuals express high tolerance for uncertainty and a higher ToM proficiency. Results from the current study imply that differences in preference-inferring under ambiguous conditions associated with different levels of individual attachment may explain the differences in interpersonal interaction. PMID:29559932

Unravelling the influence of mild traumatic brain injury (MTBI) on cognitive-linguistic processing: a comparative group analysis.

PubMed

Barwood, Caroline H S; Murdoch, Bruce E

2013-06-01

Cognitive-linguistic deficits often accompany traumatic brain injury (TBI) and can negatively impact communicative competency. The linguistic sequelae underpinning mild TBI (MTBI) remain largely unexplored in contemporary literature. The present research methods aim to provide group evidence pertaining to the influence of MTBI on linguistic and higher-level language processing. Extrapolating on the findings of recent case reports, it is hypothesized that performance of the MTBI patients will be significantly reduced compared to normal controls performance on the employed high-level linguistic tasks. Sixteen patients with MTBI and 16 age- and education-matched normal control participants were assessed using a comprehensive battery of cognitive-linguistic assessments. The results demonstrated statistically significant differences between MTBI and normal control group performance across a number of higher-level linguistic, general cognitive and general language tasks. MTBI group performance was significantly lower than the normal control group on tasks requiring complex lexical semantic operations and memory demands, including: Recall, organization, making inferences, naming and perception/discrimination. These outcomes confer that post-MTBI, cognitive, high-level language and isolated general language performance (e.g. naming) is significantly reduced in MTBI patients, compared to normal controls. Furthermore, the detailed cognitive-linguistic profile offered provides a necessary direction for the identification of areas of linguistic decline in MTBI and targets for therapeutic intervention of impaired cognitive-linguistic processes to ultimately improve communicative outcomes in MTBI.

Efficient Bayesian inference for natural time series using ARFIMA processes

NASA Astrophysics Data System (ADS)

Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.

2015-11-01

Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. In this paper we present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators. For CET we also extend our method to seasonal long memory.

Using fuzzy logic to integrate neural networks and knowledge-based systems

NASA Technical Reports Server (NTRS)

Yen, John

1991-01-01

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

Neural Correlates of Bridging Inferences and Coherence Processing

ERIC Educational Resources Information Center

Kim, Sung-il; Yoon, Misun; Kim, Wonsik; Lee, Sunyoung; Kang, Eunjoo

2012-01-01

We explored the neural correlates of bridging inferences and coherence processing during story comprehension using Positron Emission Tomography (PET). Ten healthy right-handed volunteers were visually presented three types of stories (Strong Coherence, Weak Coherence, and Control) consisted of three sentences. The causal connectedness among…

Causal inference from observational data.

PubMed

Listl, Stefan; Jürges, Hendrik; Watt, Richard G

2016-10-01

Randomized controlled trials have long been considered the 'gold standard' for causal inference in clinical research. In the absence of randomized experiments, identification of reliable intervention points to improve oral health is often perceived as a challenge. But other fields of science, such as social science, have always been challenged by ethical constraints to conducting randomized controlled trials. Methods have been established to make causal inference using observational data, and these methods are becoming increasingly relevant in clinical medicine, health policy and public health research. This study provides an overview of state-of-the-art methods specifically designed for causal inference in observational data, including difference-in-differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed-effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records ('big data'). © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Anomalous rise in algal production linked to lakewater calcium decline through food web interactions

PubMed Central

Korosi, Jennifer B.; Burke, Samantha M.; Thienpont, Joshua R.; Smol, John P.

2012-01-01

Increased algal blooms are a threat to aquatic ecosystems worldwide, although the combined effects of multiple stressors make it difficult to determine the underlying causes. We explore whether changes in trophic interactions in response to declining calcium (Ca) concentrations, a water quality issue only recently recognized in Europe and North America, can be linked with unexplained bloom production. Using a palaeolimnological approach analysing the remains of Cladocera (herbivorous grazers) and visual reflectance spectroscopically inferred chlorophyll a from the sediments of a Nova Scotia (Canada) lake, we show that a keystone grazer, Daphnia, declined in the early 1990s and was replaced by a less effective grazer, Bosmina, while inferred chlorophyll a levels tripled at constant total phosphorus (TP) concentrations. The decline in Daphnia cannot be attributed to changes in pH, thermal stratification or predation, but instead is linked to declining lakewater [Ca]. The consistency in the timing of changes in Daphnia and inferred chlorophyll a suggests top-down control on algal production, providing, to our knowledge, the first evidence of a link between lakewater [Ca] decline and elevated algal production mediated through the effects of [Ca] decline on Daphnia. [Ca] decline has severe implications for whole-lake food webs, and presents yet another mechanism for potential increases in algal blooms. PMID:21957138

Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus

NASA Astrophysics Data System (ADS)

Lobo, Daniel; Lobikin, Maria; Levin, Michael

2017-01-01

Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation. We developed a Machine Learning method which inferred a model explaining this complex, stochastic all-or-none dataset. We then used this model to ask how a new phenotype could be generated: animals in which only some of the melanocytes converted. Systematically performing in silico perturbations, the model predicted that a combination of altanserin (5HTR2 inhibitor), reserpine (VMAT inhibitor), and VP16-XlCreb1 (constitutively active CREB) would break the all-or-none concordance. Remarkably, applying the predicted combination of three reagents in vivo revealed precisely the expected novel outcome, resulting in partial conversion of melanocytes within individuals. This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.

Inferring a District-Based Hierarchical Structure of Social Contacts from Census Data

PubMed Central

Yu, Zhiwen; Liu, Jiming; Zhu, Xianjun

2015-01-01

Researchers have recently paid attention to social contact patterns among individuals due to their useful applications in such areas as epidemic evaluation and control, public health decisions, chronic disease research and social network research. Although some studies have estimated social contact patterns from social networks and surveys, few have considered how to infer the hierarchical structure of social contacts directly from census data. In this paper, we focus on inferring an individual’s social contact patterns from detailed census data, and generate various types of social contact patterns such as hierarchical-district-structure-based, cross-district and age-district-based patterns. We evaluate newly generated contact patterns derived from detailed 2011 Hong Kong census data by incorporating them into a model and simulation of the 2009 Hong Kong H1N1 epidemic. We then compare the newly generated social contact patterns with the mixing patterns that are often used in the literature, and draw the following conclusions. First, the generation of social contact patterns based on a hierarchical district structure allows for simulations at different district levels. Second, the newly generated social contact patterns reflect individuals social contacts. Third, the newly generated social contact patterns improve the accuracy of the SEIR-based epidemic model. PMID:25679787

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

PubMed

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

2016-08-18

A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (<7 %) between estimation and ground truth. By applying the topic model-based purification to mass spectrometric data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed that incorporation of scan-level features have the potential to lead to more accurate purification results by alleviating the loss in information as a result of integrating peaks. We believe cancer biomarker discovery studies that use mass spectrometric analysis of human biospecimens can greatly benefit from topic model-based purification of the data prior to statistical and pathway analyses.

Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level

NASA Astrophysics Data System (ADS)

Thakar, Juilee; Albert, Réka

The following sections are included: * Introduction * Boolean Network Concepts and History * Extensions of the Classical Boolean Framework * Boolean Inference Methods and Examples in Biology * Dynamic Boolean Models: Examples in Plant Biology, Developmental Biology and Immunology * Conclusions * References

Preschoolers can infer general rules governing fantastical events in fiction.

PubMed

Van de Vondervoort, Julia W; Friedman, Ori

2014-05-01

Young children are frequently exposed to fantastic fiction. How do they make sense of the unrealistic and impossible events that occur in such fiction? Although children could view such events as isolated episodes, the present experiments suggest that children use such events to infer general fantasy rules. In 2 experiments, 2- to 4-year-olds were shown scenarios in which 2 animals behaved unrealistically (N = 78 in Experiment 1, N = 94 in Experiment 2). When asked to predict how other animals in the fiction would behave, children predicted novel behaviors consistent with the nature of the fiction. These findings suggest that preschoolers can infer the general rules that govern the events and entities in fantastic fiction and can use these rules to predict what events will happen in the fiction. The findings also provide evidence that children may infer fantasy rules at a more superordinate level than the basic level. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Comparing Families of Dynamic Causal Models

PubMed Central

Penny, Will D.; Stephan, Klaas E.; Daunizeau, Jean; Rosa, Maria J.; Friston, Karl J.; Schofield, Thomas M.; Leff, Alex P.

2010-01-01

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data. PMID:20300649

Forward and Backward Inference in Spatial Cognition

PubMed Central

Penny, Will D.; Zeidman, Peter; Burgess, Neil

2013-01-01

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of ‘lower-level’ computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus. PMID:24348230

Accounting for correlation in network meta-analysis with multi-arm trials.

PubMed

Franchini, A J; Dias, S; Ades, A E; Jansen, J P; Welton, N J

2012-06-01

Multi-arm trials (trials with more than two arms) are particularly valuable forms of evidence for network meta-analysis (NMA). Trial results are available either as arm-level summaries, where effect measures are reported for each arm, or as contrast-level summaries, where the differences in effect between arms compare with the control arm chosen for the trial. We show that likelihood-based inference in both contrast-level and arm-level formats is identical if there are only two-arm trials, but that if there are multi-arm trials, results from the contrast-level format will be incorrect unless correlations are accounted for in the likelihood. We review Bayesian and frequentist software for NMA with multi-arm trials that can account for this correlation and give an illustrative example of the difference in estimates that can be introduced if the correlations are not incorporated. We discuss methods of imputing correlations when they cannot be derived from the reported results and urge trialists to report the standard error for the control arm even if only contrast-level summaries are reported. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

Context recognition for a hyperintensional inference machine

NASA Astrophysics Data System (ADS)

Duží, Marie; Fait, Michal; Menšík, Marek

2017-07-01

The goal of this paper is to introduce the algorithm of context recognition in the functional programming language TIL-Script, which is a necessary condition for the implementation of the TIL-Script inference machine. The TIL-Script language is an operationally isomorphic syntactic variant of Tichý's Transparent Intensional Logic (TIL). From the formal point of view, TIL is a hyperintensional, partial, typed λ-calculus with procedural semantics. Hyperintensional, because TIL λ-terms denote procedures (defined as TIL constructions) producing set-theoretic functions rather than the functions themselves; partial, because TIL is a logic of partial functions; and typed, because all the entities of TIL ontology, including constructions, receive a type within a ramified hierarchy of types. These features make it possible to distinguish three levels of abstraction at which TIL constructions operate. At the highest hyperintensional level the object to operate on is a construction (though a higher-order construction is needed to present this lower-order construction as an object of predication). At the middle intensional level the object to operate on is the function presented, or constructed, by a construction, while at the lowest extensional level the object to operate on is the value (if any) of the presented function. Thus a necessary condition for the development of an inference machine for the TIL-Script language is recognizing a context in which a construction occurs, namely extensional, intensional and hyperintensional context, in order to determine the type of an argument at which a given inference rule can be properly applied. As a result, our logic does not flout logical rules of extensional logic, which makes it possible to develop a hyperintensional inference machine for the TIL-Script language.

High level intelligent control of telerobotics systems

NASA Technical Reports Server (NTRS)

Mckee, James

1988-01-01

A high level robot command language is proposed for the autonomous mode of an advanced telerobotics system and a predictive display mechanism for the teleoperational model. It is believed that any such system will involve some mixture of these two modes, since, although artificial intelligence can facilitate significant autonomy, a system that can resort to teleoperation will always have the advantage. The high level command language will allow humans to give the robot instructions in a very natural manner. The robot will then analyze these instructions to infer meaning so that is can translate the task into lower level executable primitives. If, however, the robot is unable to perform the task autonomously, it will switch to the teleoperational mode. The time delay between control movement and actual robot movement has always been a problem in teleoperations. The remote operator may not actually see (via a monitor) the results of high actions for several seconds. A computer generated predictive display system is proposed whereby the operator can see a real-time model of the robot's environment and the delayed video picture on the monitor at the same time.

Inference generation and story comprehension among children with ADHD.

PubMed

Van Neste, Jessica; Hayden, Angela; Lorch, Elizabeth P; Milich, Richard

2015-02-01

Academic difficulties are well-documented among children with ADHD. Exploring these difficulties through story comprehension research has revealed deficits among children with ADHD in making causal connections between events and in using causal structure and thematic importance to guide recall of stories. Important to theories of story comprehension and implied in these deficits is the ability to make inferences. Often, characters' goals are implicit and explanations of events must be inferred. The purpose of the present study was to compare the inferences generated during story comprehension by 23 7- to 11-year-old children with ADHD (16 males) and 35 comparison peers (19 males). Children watched two televised stories, each paused at five points. In the experimental condition, at each pause children told what they were thinking about the story, whereas in the control condition no responses were made during pauses. After viewing, children recalled the story. Several types of inferences and inference plausibility were coded. Children with ADHD generated fewer of the most essential inferences, plausible explanatory inferences, than did comparison children, both during story processing and during story recall. The groups did not differ on production of other types of inferences. Group differences in generating inferences during the think-aloud task significantly mediated group differences in patterns of recall. Both groups recalled more of the most important story information after completing the think-aloud task. Generating fewer explanatory inferences has important implications for story comprehension deficits in children with ADHD.

Statistical methods for the beta-binomial model in teratology.

PubMed Central

Yamamoto, E; Yanagimoto, T

1994-01-01

The beta-binomial model is widely used for analyzing teratological data involving littermates. Recent developments in statistical analyses of teratological data are briefly reviewed with emphasis on the model. For statistical inference of the parameters in the beta-binomial distribution, separation of the likelihood introduces an likelihood inference. This leads to reducing biases of estimators and also to improving accuracy of empirical significance levels of tests. Separate inference of the parameters can be conducted in a unified way. PMID:8187716

Drug target inference through pathway analysis of genomics data

PubMed Central

Ma, Haisu; Zhao, Hongyu

2013-01-01

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Decision generation tools and Bayesian inference

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Wang, Wenjian; Forrester, Thomas; Kostrzewski, Andrew; Veeris, Christian; Nielsen, Thomas

2014-05-01

Digital Decision Generation (DDG) tools are important software sub-systems of Command and Control (C2) systems and technologies. In this paper, we present a special type of DDGs based on Bayesian Inference, related to adverse (hostile) networks, including such important applications as terrorism-related networks and organized crime ones.

Thinking Fast and Slow about Causality: Response to Palinkas

ERIC Educational Resources Information Center

Marsh, Jeanne C.

2014-01-01

Larry Palinkas advances the developing science of social work by providing an explanation of how social science research methods, both qualitative and quantitative, can improve our capacity to draw casual inferences. Understanding causal relations and making causal inferences--with the promise of being able to predict and control outcomes--is…

Reconstruction of Exposure to m-Xylene from Human Biomonitoring Data Using PBPK Modelling, Bayesian Inference, and Markov Chain Monte Carlo Simulation

PubMed Central

McNally, Kevin; Cotton, Richard; Cocker, John; Jones, Kate; Bartels, Mike; Rick, David; Price, Paul; Loizou, George

2012-01-01

There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals. Due to the lack of exposure and kinetic data, the correlation of biomarker levels with exposure concentrations leads to difficulty in utilizing biomonitoring data for biological guidance values. Exposure reconstruction or reverse dosimetry is the retrospective interpretation of external exposure consistent with biomonitoring data. We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene. We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures. We also investigated the importance of model structure and dimensionality with respect to its ability to reconstruct exposure. PMID:22719759

Reverse engineering of gene regulatory networks.

PubMed

Cho, K H; Choo, S M; Jung, S H; Kim, J R; Choi, H S; Kim, J

2007-05-01

Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.

Total meltwater volume since the Last Glacial Maximum and viscosity structure of Earth's mantle inferred from relative sea level changes at Barbados and Bonaparte Gulf and GIA-induced J˙2

NASA Astrophysics Data System (ADS)

Nakada, Masao; Okuno, Jun'ichi; Yokoyama, Yusuke

2016-02-01

Inference of globally averaged eustatic sea level (ESL) rise since the Last Glacial Maximum (LGM) highly depends on the interpretation of relative sea level (RSL) observations at Barbados and Bonaparte Gulf, Australia, which are sensitive to the viscosity structure of Earth's mantle. Here we examine the RSL changes at the LGM for Barbados and Bonaparte Gulf ({{RSL}}_{{L}}^{{{Bar}}} and {{RSL}}_{{L}}^{{{Bon}}}), differential RSL for both sites (Δ {{RSL}}_{{L}}^{{{Bar}},{{Bon}}}) and rate of change of degree-two harmonics of Earth's geopotential due to glacial isostatic adjustment (GIA) process (GIA-induced J˙2) to infer the ESL component and viscosity structure of Earth's mantle. Differential RSL, Δ {{RSL}}_{{L}}^{{{Bar}},{{Bon}}} and GIA-induced J˙2 are dominantly sensitive to the lower-mantle viscosity, and nearly insensitive to the upper-mantle rheological structure and GIA ice models with an ESL component of about (120-130) m. The comparison between the predicted and observationally derived Δ {{RSL}}_{{L}}^{{{Bar}},{{Bon}}} indicates the lower-mantle viscosity higher than ˜2 × 1022 Pa s, and the observationally derived GIA-induced J˙2 of -(6.0-6.5) × 10-11 yr-1 indicates two permissible solutions for the lower mantle, ˜1022 and (5-10) × 1022 Pa s. That is, the effective lower-mantle viscosity inferred from these two observational constraints is (5-10) × 1022 Pa s. The LGM RSL changes at both sites, {{RSL}}_{{L}}^{{{Bar}}} and {{RSL}}_{{L}}^{{{Bon}}}, are also sensitive to the ESL component and upper-mantle viscosity as well as the lower-mantle viscosity. The permissible upper-mantle viscosity increases with decreasing ESL component due to the sensitivity of the LGM sea level at Bonaparte Gulf ({{RSL}}_{{L}}^{{{Bon}}}) to the upper-mantle viscosity, and inferred upper-mantle viscosity for adopted lithospheric thicknesses of 65 and 100 km is (1-3) × 1020 Pa s for ESL˜130 m and (4-10) × 1020 Pa s for ESL˜125 m. The former solution of (1-3) × 1020 Pa s is consistent with the inferences from the postglacial differential RSL changes in the Australian region and also inversion study of far-field sea-level data. The inference of the viscosity structure based on these four observational constraints is, however, relatively insensitive to the viscosity structure of D″ layer.

I Know Who My Friends Are, But Do You? Predictors of Self-reported and Peer-inferred Relationships

PubMed Central

Neal, Jennifer Watling; Neal, Zachary P.; Cappella, Elise

2013-01-01

Using social network data, this study examines which features of social and spatial proximity predict self-reported, or “real,” and peer-reported, or “inferred,” relationships among 2695 pairwise combinations of African American second through fourth grade students (ages 7-11). Relationships were more likely to exist, and more likely to be inferred to exist by peers, between pairs of children who were the same sex, sat near one another, shared a positive academic orientation, or shared athletic ability. Sex similarity had a dramatically larger effect on peers’ inferences about relationships than on self-reported real relationships, suggesting that children overestimate the importance of sex in their inferences about relationships. Results were stable across different grade levels in middle childhood and for boys and girls. PMID:24320155

Stable isotope enrichment in laboratory ant colonies: effects of colony age, metamorphosis, diet, and fat storage

USDA-ARS?s Scientific Manuscript database

Ecologists use stable isotopes to infer diets and trophic levels of animals in food webs, yet some assumptions underlying these inferences have not been thoroughly tested. We used laboratory-reared colonies of Solenopsis invicta Buren (Formicidae: Solenopsidini) to test the effects of metamorphosis,...

Evaluating the Use of Random Distribution Theory to Introduce Statistical Inference Concepts to Business Students

ERIC Educational Resources Information Center

Larwin, Karen H.; Larwin, David A.

2011-01-01

Bootstrapping methods and random distribution methods are increasingly recommended as better approaches for teaching students about statistical inference in introductory-level statistics courses. The authors examined the effect of teaching undergraduate business statistics students using random distribution and bootstrapping simulations. It is the…

The architecture of adaptive neural network based on a fuzzy inference system for implementing intelligent control in photovoltaic systems

NASA Astrophysics Data System (ADS)

Gimazov, R.; Shidlovskiy, S.

2018-05-01

In this paper, we consider the architecture of the algorithm for extreme regulation in the photovoltaic system. An algorithm based on an adaptive neural network with fuzzy inference is proposed. The implementation of such an algorithm not only allows solving a number of problems in existing algorithms for extreme power regulation of photovoltaic systems, but also creates a reserve for the creation of a universal control system for a photovoltaic system.

Mind the gap! The mitochondrial control region and its power as a phylogenetic marker in echinoids.

PubMed

Bronstein, Omri; Kroh, Andreas; Haring, Elisabeth

2018-05-30

In Metazoa, mitochondrial markers are the most commonly used targets for inferring species-level molecular phylogenies due to their extremely low rate of recombination, maternal inheritance, ease of use and fast substitution rate in comparison to nuclear DNA. The mitochondrial control region (CR) is the main non-coding area of the mitochondrial genome and contains the mitochondrial origin of replication and transcription. While sequences of the cytochrome oxidase subunit 1 (COI) and 16S rRNA genes are the prime mitochondrial markers in phylogenetic studies, the highly variable CR is typically ignored and not targeted in such analyses. However, the higher substitution rate of the CR can be harnessed to infer the phylogeny of closely related species, and the use of a non-coding region alleviates biases resulting from both directional and purifying selection. Additionally, complete mitochondrial genome assemblies utilizing next generation sequencing (NGS) data often show exceptionally low coverage at specific regions, including the CR. This can only be resolved by targeted sequencing of this region. Here we provide novel sequence data for the echinoid mitochondrial control region in over 40 species across the echinoid phylogenetic tree. We demonstrate the advantages of directly targeting the CR and adjacent tRNAs to facilitate complementing low coverage NGS data from complete mitochondrial genome assemblies. Finally, we test the performance of this region as a phylogenetic marker both in the lab and in phylogenetic analyses, and demonstrate its superior performance over the other available mitochondrial markers in echinoids. Our target region of the mitochondrial CR (1) facilitates the first thorough investigation of this region across a wide range of echinoid taxa, (2) provides a tool for complementing missing data in NGS experiments, and (3) identifies the CR as a powerful, novel marker for phylogenetic inference in echinoids due to its high variability, lack of selection, and high compatibility across the entire class, outperforming conventional mitochondrial markers.

Conceptual influences on category-based induction

PubMed Central

Gelman, Susan A.; Davidson, Natalie S.

2013-01-01

One important function of categories is to permit rich inductive inferences. Prior work shows that children use category labels to guide their inductive inferences. However, there are competing theories to explain this phenomenon, differing in the roles attributed to conceptual information versus perceptual similarity. Seven experiments with 4- to 5-year-old children and adults (N = 344) test these theories by teaching categories for which category membership and perceptual similarity are in conflict, and varying the conceptual basis of the novel categories. Results indicate that for non-natural kind categories that have little conceptual coherence, children make inferences based on perceptual similarity, whereas adults make inferences based on category membership. In contrast, for basic- and ontological-level categories that have a principled conceptual basis, children and adults alike make use of category membership more than perceptual similarity as the basis of their inferences. These findings provide evidence in favor of the role of conceptual information in preschoolers’ inferences, and further demonstrate that labeled categories are not all equivalent; they differ in their inductive potential. PMID:23517863

Limited utility of residue masking for positive-selection inference.

PubMed

Spielman, Stephanie J; Dawson, Eric T; Wilke, Claus O

2014-09-01

Errors in multiple sequence alignments (MSAs) can reduce accuracy in positive-selection inference. Therefore, it has been suggested to filter MSAs before conducting further analyses. One widely used filter, Guidance, allows users to remove MSA positions aligned with low confidence. However, Guidance's utility in positive-selection inference has been disputed in the literature. We have conducted an extensive simulation-based study to characterize fully how Guidance impacts positive-selection inference, specifically for protein-coding sequences of realistic divergence levels. We also investigated whether novel scoring algorithms, which phylogenetically corrected confidence scores, and a new gap-penalization score-normalization scheme improved Guidance's performance. We found that no filter, including original Guidance, consistently benefitted positive-selection inferences. Moreover, all improvements detected were exceedingly minimal, and in certain circumstances, Guidance-based filters worsened inferences. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Learning new vocabulary in German: the effects of inferring word meanings, type of feedback, and time of test.

PubMed

Carpenter, Shana K; Sachs, Riebana E; Martin, Beth; Schmidt, Kristian; Looft, Ruxandra

2012-02-01

In the present study, introductory-level German students read a simplified story and learned the meanings of new German words by reading English translations in marginal glosses versus trying to infer (i.e., guess) their translations. Students who inferred translations were given feedback in English or in German, or no feedback at all. Although immediate retention of new vocabulary was better for students who used marginal glosses, students who inferred word meanings and then received English feedback forgot fewer translations over time. Plausible but inaccurate inferences (i.e., those that made sense in the context) were more likely to be corrected by students who received English feedback as compared with German feedback, providing support for the beneficial effects of mediating information. Implausible inaccurate inferences, however, were more likely to be corrected on the delayed vocabulary test by students who received German feedback as compared with English feedback, possibly because of the additional contextual support provided by German feedback.

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach

PubMed Central

Tong, Xin; Zeng, Yao

2016-01-01

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people’s incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning, to address whether with high probability, a large fraction of people in a given finite population network can make “good” inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows. PMID:27076691

Performance on a probabilistic inference task in healthy subjects receiving ketamine compared with patients with schizophrenia

PubMed Central

Almahdi, Basil; Sultan, Pervez; Sohanpal, Imrat; Brandner, Brigitta; Collier, Tracey; Shergill, Sukhi S; Cregg, Roman; Averbeck, Bruno B

2012-01-01

Evidence suggests that some aspects of schizophrenia can be induced in healthy volunteers through acute administration of the non-competitive NMDA-receptor antagonist, ketamine. In probabilistic inference tasks, patients with schizophrenia have been shown to ‘jump to conclusions’ (JTC) when asked to make a decision. We aimed to test whether healthy participants receiving ketamine would adopt a JTC response pattern resembling that of patients. The paradigmatic task used to investigate JTC has been the ‘urn’ task, where participants are shown a sequence of beads drawn from one of two ‘urns’, each containing coloured beads in different proportions. Participants make a decision when they think they know the urn from which beads are being drawn. We compared performance on the urn task between controls receiving acute ketamine or placebo with that of patients with schizophrenia and another group of controls matched to the patient group. Patients were shown to exhibit a JTC response pattern relative to their matched controls, whereas JTC was not evident in controls receiving ketamine relative to placebo. Ketamine does not appear to promote JTC in healthy controls, suggesting that ketamine does not affect probabilistic inferences. PMID:22389244

Design and implementation of the tree-based fuzzy logic controller.

PubMed

Liu, B D; Huang, C Y

1997-01-01

In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search spaces of the fuzzy inference process are largely reduced; the operation of the inference process can be simplified as a one-dimensional matrix operation because of the fuzzy tree approach; and the controller has regular and modular properties, so it is easy to be implemented by hardware. Furthermore, the proposed fuzzy tree approach has been applied to design the color reproduction system for verifying the proposed methodology. The color reproduction system is mainly used to obtain a color image through the printer that is identical to the original one. In addition to the software simulation, an FPGA is used to implement the prototype hardware system for real-time application. Experimental results show that the effect of color correction is quite good and that the prototype hardware system can operate correctly under the condition of 30 MHz clock rate.

Efficient Bayesian inference for natural time series using ARFIMA processes

NASA Astrophysics Data System (ADS)

Graves, Timothy; Gramacy, Robert; Franzke, Christian; Watkins, Nicholas

2016-04-01

Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. We present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators [1]. In addition we show how the method can be used to perform joint inference of the stability exponent and the memory parameter when ARFIMA is extended to allow for alpha-stable innovations. Such models can be used to study systems where heavy tails and long range memory coexist. [1] Graves et al, Nonlin. Processes Geophys., 22, 679-700, 2015; doi:10.5194/npg-22-679-2015.

An inference method from multi-layered structure of biomedical data.

PubMed

Kim, Myungjun; Nam, Yonghyun; Shin, Hyunjung

2017-05-18

Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels. To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required. In this study, we develop a semi-supervised learning algorithm that can be applied to the multi-layered complex system. In order to verify the validity of the inference, it was applied to the prediction problem of disease co-occurrence with a two-layered network composed of symptom-layer and disease-layer. The symptom-disease layered network obtained a fairly high value of AUC, 0.74, which is regarded as noticeable improvement when comparing 0.59 AUC of single-layered disease network. If further stretched to whole layered structure of omics, the proposed method is expected to produce more promising results. This research has novelty in that it is a new integrative algorithm that incorporates the vertical structure of omics data, on contrary to other existing methods that integrate the data in parallel fashion. The results can provide enhanced guideline for disease co-occurrence prediction, thereby serve as a valuable tool for inference process of multi-layered biological system.

Performance Optimization Control of ECH using Fuzzy Inference Application

NASA Astrophysics Data System (ADS)

Dubey, Abhay Kumar

Electro-chemical honing (ECH) is a hybrid electrolytic precision micro-finishing technology that, by combining physico-chemical actions of electro-chemical machining and conventional honing processes, provides the controlled functional surfaces-generation and fast material removal capabilities in a single operation. Process multi-performance optimization has become vital for utilizing full potential of manufacturing processes to meet the challenging requirements being placed on the surface quality, size, tolerances and production rate of engineering components in this globally competitive scenario. This paper presents an strategy that integrates the Taguchi matrix experimental design, analysis of variances and fuzzy inference system (FIS) to formulate a robust practical multi-performance optimization methodology for complex manufacturing processes like ECH, which involve several control variables. Two methodologies one using a genetic algorithm tuning of FIS (GA-tuned FIS) and another using an adaptive network based fuzzy inference system (ANFIS) have been evaluated for a multi-performance optimization case study of ECH. The actual experimental results confirm their potential for a wide range of machining conditions employed in ECH.

A caveat regarding diatom-inferred nitrogen concentrations in oligotrophic lakes

USGS Publications Warehouse

Arnett, Heather A.; Saros, Jasmine E.; Mast, M. Alisa

2012-01-01

Atmospheric deposition of reactive nitrogen (Nr) has enriched oligotrophic lakes with nitrogen (N) in many regions of the world and elicited dramatic changes in diatom community structure. The lakewater concentrations of nitrate that cause these community changes remain unclear, raising interest in the development of diatom-based transfer functions to infer nitrate. We developed a diatom calibration set using surface sediment samples from 46 high-elevation lakes across the Rocky Mountains of the western US, a region spanning an N deposition gradient from very low to moderate levels (<1 to 3.2 kg Nr ha−1 year−1 in wet deposition). Out of the fourteen measured environmental variables for these 46 lakes, ordination analysis identified that nitrate, specific conductance, total phosphorus, and hypolimnetic water temperature were related to diatom distributions. A transfer function was developed for nitrate and applied to a sedimentary diatom profile from Heart Lake in the central Rockies. The model coefficient of determination (bootstrapping validation) of 0.61 suggested potential for diatom-inferred reconstructions of lakewater nitrate concentrations over time, but a comparison of observed versus diatom-inferred nitrate values revealed the poor performance of this model at low nitrate concentrations. Resource physiology experiments revealed that nitrogen requirements of two key taxa were opposite to nitrate optima defined in the transfer function. Our data set reveals two underlying ecological constraints that impede the development of nitrate transfer functions in oligotrophic lakes: (1) even in lakes with nitrate concentrations below quantification (<1 μg L−1), diatom assemblages were already dominated by species indicative of moderate N enrichment; (2) N-limited oligotrophic lakes switch to P limitation after receiving only modest inputs of reactive N, shifting the controls on diatom species changes along the length of the nitrate gradient. These constraints suggest that quantitative inferences of nitrate from diatom assemblages will likely require experimental approaches.

BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities

PubMed Central

Chipman, Hugh; Gu, Hong; Bielawski, Joseph P.

2014-01-01

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host's nutrient-based mechanism for bacterial partner selection. PMID:25412107

Functional networks inference from rule-based machine learning models.

PubMed

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html.

Active Inference, Epistemic Value, and Vicarious Trial and Error

ERIC Educational Resources Information Center

Pezzulo, Giovanni; Cartoni, Emilio; Rigoli, Francesco; io-Lopez, Léo; Friston, Karl

2016-01-01

Balancing habitual and deliberate forms of choice entails a comparison of their respective merits--the former being faster but inflexible, and the latter slower but more versatile. Here, we show that arbitration between these two forms of control can be derived from first principles within an Active Inference scheme. We illustrate our arguments…

Direct Evidence for a Dual Process Model of Deductive Inference

ERIC Educational Resources Information Center

Markovits, Henry; Brunet, Marie-Laurence; Thompson, Valerie; Brisson, Janie

2013-01-01

In 2 experiments, we tested a strong version of a dual process theory of conditional inference (cf. Verschueren et al., 2005a, 2005b) that assumes that most reasoners have 2 strategies available, the choice of which is determined by situational variables, cognitive capacity, and metacognitive control. The statistical strategy evaluates inferences…

seXY: a tool for sex inference from genotype arrays.

PubMed

Qian, David C; Busam, Jonathan A; Xiao, Xiangjun; O'Mara, Tracy A; Eeles, Rosalind A; Schumacher, Frederick R; Phelan, Catherine M; Amos, Christopher I

2017-02-15

Checking concordance between reported sex and genotype-inferred sex is a crucial quality control measure in genome-wide association studies (GWAS). However, limited insights exist regarding the true accuracy of software that infer sex from genotype array data. We present seXY, a logistic regression model trained on both X chromosome heterozygosity and Y chromosome missingness, that consistently demonstrated >99.5% sex inference accuracy in cross-validation for 889 males and 5,361 females enrolled in prostate cancer and ovarian cancer GWAS. Compared to PLINK, one of the most popular tools for sex inference in GWAS that assesses only X chromosome heterozygosity, seXY achieved marginally better male classification and 3% more accurate female classification. https://github.com/Christopher-Amos-Lab/seXY. Christopher.I.Amos@dartmouth.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Classification versus inference learning contrasted with real-world categories.

PubMed

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

Gene-network inference by message passing

NASA Astrophysics Data System (ADS)

Braunstein, A.; Pagnani, A.; Weigt, M.; Zecchina, R.

2008-01-01

The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.

From individual to population level effects of toxicants in the tubicifid Branchiura sowerbyi using threshold effect models in a Bayesian framework.

PubMed

Ducrot, Virginie; Billoir, Elise; Péry, Alexandre R R; Garric, Jeanne; Charles, Sandrine

2010-05-01

Effects of zinc were studied in the freshwater worm Branchiura sowerbyi using partial and full life-cycle tests. Only newborn and juveniles were sensitive to zinc, displaying effects on survival, growth, and age at first brood at environmentally relevant concentrations. Threshold effect models were proposed to assess toxic effects on individuals. They were fitted to life-cycle test data using Bayesian inference and adequately described life-history trait data in exposed organisms. The daily asymptotic growth rate of theoretical populations was then simulated with a matrix population model, based upon individual-level outputs. Population-level outputs were in accordance with existing literature for controls. Working in a Bayesian framework allowed incorporating parameter uncertainty in the simulation of the population-level response to zinc exposure, thus increasing the relevance of test results in the context of ecological risk assessment.

Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.

Lower and lower Middle Pennsylvanian fluvial to estuarine deposition, central Appalachian basin: Effects of eustasy, tectonics, and climate

USGS Publications Warehouse

Greb, S.F.; Chesnut, D.R.

1996-01-01

Interpretations of Pennsylvanian sedimentation and peat accumulation commonly use examples from the Appalachian basin because of the excellent outcrops and large reserve of coal (>100 billion metric tons) in the region. Particularly controversial is the origin of Lower and lower Middle Pennsylvanian quartzose sandstones; beach-barrier, marine-bar, tidalstrait, and fluvial models all have been applied to a series of sand bodies along the western outcrop margin of the basin. Inter-pretations of these sandstones and their inferred lateral relationships are critical for understanding the relative degree of eustatic, tectonic, and climatic controls on Early Pennsylvanian sedimentation. Cross sections utilizing >1000 subsurface records and detailed sedimentological analysis of the Livingston Conglomerate, Rockcastle Sandstone, Corbin Sandstone, and Pine Creek sandstone (an informal member) of the Breathitt Group were used to show that each of the principal quartzose sandstones on the margin of the central Appalachian basin contains both fluvial and marginal marine facies. The four sandstones are fluvially dominated and are inferred to represent successive bed-load trunk systems of the Appalachian foreland. Base-level rise and an associated decrease in extra-basinal sediment at the end of each fluvial episode led to the development of local estuaries and marine reworking of the tops of the sand belts. Each of the sand belts is capped locally by a coal, regardless of whether the upper surfaces of the sand belts are of fluvial or estuarine origin, suggesting allocyclic controls on deposition. Peats were controlled by a tropical ever-wet climate, which also influenced sandstone composition through weathering of stored sands in slowly aggrading braidplains. Recurrent stacking of thick, coarse-grained, fluvial deposits with extra-basinal quartz pebbles; dominance of bed-load fluvial-lowstand deposits over mixed-load, estuarine-transgressive deposits; thinning of sand belts around tectonic highs and along faults; cratonward shift and amalgamation of successive sand belts on the margin of the basin; and truncation of successive sand belts toward the fault-bound margin of the basin are interpreted as regional responses to Alleghenian tectonism, inferred to have been the dominant control on accommodation space and sediment flux in the Early Pennsylvanian basin.

Using Literature to Teach Inference across the Curriculum

ERIC Educational Resources Information Center

Bintz, William P.; Moran, Petra Pienkosky; Berndt, Rochelle; Ritz, Elizabeth; Skilton, Julie A.; Bircher, Lisa S.

2012-01-01

Today, increasing numbers of teachers at all grade levels are recognizing that inference is a powerful way of thinking and an important 21st century skill for all students to use and develop across the curriculum. Increasing numbers of teachers are also recognizing that developing and implementing integrative curriculum is important at all grade…

Developing Young Children's Emergent Inferential Practices in Statistics

ERIC Educational Resources Information Center

Makar, Katie

2016-01-01

Informal statistical inference has now been researched at all levels of schooling and initial tertiary study. Work in informal statistical inference is least understood in the early years, where children have had little if any exposure to data handling. A qualitative study in Australia was carried out through a series of teaching experiments with…

Impressions of Milgram's obedient teachers: situational cues inform inferences about motives and traits.

PubMed

Reeder, Glenn D; Monroe, Andrew E; Pryor, John B

2008-07-01

The research investigated impressions formed of a "teacher" who obeyed an experimenter by delivering painful electric shocks to an innocent person (S. Milgram, 1963, 1974). Three findings emerged across different methodologies and different levels of experimenter-induced coercion. First, contrary to conventional wisdom, perceivers both recognized and appreciated situational forces, such as the experimenter's orders that prompted the aggression. Second, perceivers' explanations of the teacher's behavior focused on the motive of obedience (i.e., wanting to appease the experimenter) rather than on hurtful (or evil) motivation. Despite this overall pattern, perceptions of hurtful versus helpful motivation varied as a function of information regarding the level of coercion applied by the experimenter. Finally, theoretically important relationships were revealed among perceptions of situations, motives, and traits. In particular, situational cues (such as aspects of the experimenter's behavior) signaled the nature of the teacher's motives, which in turn informed inferences of the teacher's traits. Overall, the findings pose problems for the lay dispositionism perspective but fit well with multiple inference models of dispositional inference.

Controlling false-negative errors in microarray differential expression analysis: a PRIM approach.

PubMed

Cole, Steve W; Galic, Zoran; Zack, Jerome A

2003-09-22

Theoretical considerations suggest that current microarray screening algorithms may fail to detect many true differences in gene expression (Type II analytic errors). We assessed 'false negative' error rates in differential expression analyses by conventional linear statistical models (e.g. t-test), microarray-adapted variants (e.g. SAM, Cyber-T), and a novel strategy based on hold-out cross-validation. The latter approach employs the machine-learning algorithm Patient Rule Induction Method (PRIM) to infer minimum thresholds for reliable change in gene expression from Boolean conjunctions of fold-induction and raw fluorescence measurements. Monte Carlo analyses based on four empirical data sets show that conventional statistical models and their microarray-adapted variants overlook more than 50% of genes showing significant up-regulation. Conjoint PRIM prediction rules recover approximately twice as many differentially expressed transcripts while maintaining strong control over false-positive (Type I) errors. As a result, experimental replication rates increase and total analytic error rates decline. RT-PCR studies confirm that gene inductions detected by PRIM but overlooked by other methods represent true changes in mRNA levels. PRIM-based conjoint inference rules thus represent an improved strategy for high-sensitivity screening of DNA microarrays. Freestanding JAVA application at http://microarray.crump.ucla.edu/focus

Testosterone and estrogen impact social evaluations and vicarious emotions: A double-blind placebo-controlled study.

PubMed

Olsson, Andreas; Kopsida, Eleni; Sorjonen, Kimmo; Savic, Ivanka

2016-06-01

The abilities to "read" other peoples' intentions and emotions, and to learn from their experiences, are critical to survival. Previous studies have highlighted the role of sex hormones, notably testosterone and estrogen, in these processes. Yet it is unclear how these hormones affect social cognition and emotion using acute hormonal administration. In the present double-blind placebo-controlled study, we administered an acute exogenous dose of testosterone or estrogen to healthy female and male volunteers, respectively, with the aim of investigating the effects of these steroids on social-cognitive and emotional processes. Following hormonal and placebo treatment, participants made (a) facial dominance judgments, (b) mental state inferences (Reading the Mind in the Eyes Test), and (c) learned aversive associations through watching others' emotional responses (observational fear learning [OFL]). Our results showed that testosterone administration to females enhanced ratings of facial dominance but diminished their accuracy in inferring mental states. In men, estrogen administration resulted in an increase in emotional (vicarious) reactivity when watching a distressed other during the OFL task. Taken together, these results suggest that sex hormones affect social-cognitive and emotional functions at several levels, linking our results to neuropsychiatric disorders in which these functions are impaired. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Robust functional regression model for marginal mean and subject-specific inferences.

PubMed

Cao, Chunzheng; Shi, Jian Qing; Lee, Youngjo

2017-01-01

We introduce flexible robust functional regression models, using various heavy-tailed processes, including a Student t-process. We propose efficient algorithms in estimating parameters for the marginal mean inferences and in predicting conditional means as well as interpolation and extrapolation for the subject-specific inferences. We develop bootstrap prediction intervals (PIs) for conditional mean curves. Numerical studies show that the proposed model provides a robust approach against data contamination or distribution misspecification, and the proposed PIs maintain the nominal confidence levels. A real data application is presented as an illustrative example.

Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

PubMed

Fechner, Hanna B; Pachur, Thorsten; Schooler, Lael J; Mehlhorn, Katja; Battal, Ceren; Volz, Kirsten G; Borst, Jelmer P

2016-12-01

How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level. Copyright © 2016 Elsevier B.V. All rights reserved.

Action adaptation during natural unfolding social scenes influences action recognition and inferences made about actor beliefs.

PubMed

Keefe, Bruce D; Wincenciak, Joanna; Jellema, Tjeerd; Ward, James W; Barraclough, Nick E

2016-07-01

When observing another individual's actions, we can both recognize their actions and infer their beliefs concerning the physical and social environment. The extent to which visual adaptation influences action recognition and conceptually later stages of processing involved in deriving the belief state of the actor remains unknown. To explore this we used virtual reality (life-size photorealistic actors presented in stereoscopic three dimensions) to see how visual adaptation influences the perception of individuals in naturally unfolding social scenes at increasingly higher levels of action understanding. We presented scenes in which one actor picked up boxes (of varying number and weight), after which a second actor picked up a single box. Adaptation to the first actor's behavior systematically changed perception of the second actor. Aftereffects increased with the duration of the first actor's behavior, declined exponentially over time, and were independent of view direction. Inferences about the second actor's expectation of box weight were also distorted by adaptation to the first actor. Distortions in action recognition and actor expectations did not, however, extend across different actions, indicating that adaptation is not acting at an action-independent abstract level but rather at an action-dependent level. We conclude that although adaptation influences more complex inferences about belief states of individuals, this is likely to be a result of adaptation at an earlier action recognition stage rather than adaptation operating at a higher, more abstract level in mentalizing or simulation systems.

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar

2016-01-01

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

Prophetic Granger Causality to infer gene regulatory networks.

PubMed

Carlin, Daniel E; Paull, Evan O; Graim, Kiley; Wong, Christopher K; Bivol, Adrian; Ryabinin, Peter; Ellrott, Kyle; Sokolov, Artem; Stuart, Joshua M

2017-01-01

We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.

Prophetic Granger Causality to infer gene regulatory networks

PubMed Central

Carlin, Daniel E.; Paull, Evan O.; Graim, Kiley; Wong, Christopher K.; Bivol, Adrian; Ryabinin, Peter; Ellrott, Kyle; Sokolov, Artem

2017-01-01

We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring. PMID:29211761

Gravity or turbulence? - III. Evidence of pure thermal Jeans fragmentation at ˜0.1 pc scale

NASA Astrophysics Data System (ADS)

Palau, Aina; Ballesteros-Paredes, Javier; Vázquez-Semadeni, Enrique; Sánchez-Monge, Álvaro; Estalella, Robert; Fall, S. Michael; Zapata, Luis A.; Camacho, Vianey; Gómez, Laura; Naranjo-Romero, Raúl; Busquet, Gemma; Fontani, Francesco

2015-11-01

We combine previously published interferometric and single-dish data of relatively nearby massive dense cores that are actively forming stars to test whether their `fragmentation level' is controlled by turbulent or thermal support. We find no clear correlation between the fragmentation level and velocity dispersion, nor between the observed number of fragments and the number of fragments expected when the gravitationally unstable mass is calculated including various prescriptions for `turbulent support'. On the other hand, the best correlation is found for the case of pure thermal Jeans fragmentation, for which we infer a core formation efficiency around 13 per cent, consistent with previous works. We conclude that the dominant factor determining the fragmentation level of star-forming massive dense cores at 0.1 pc scale seems to be thermal Jeans fragmentation.

Vertical land motion controls regional sea level rise patterns on the United States east coast since 1900

NASA Astrophysics Data System (ADS)

Piecuch, C. G.; Huybers, P. J.; Hay, C.; Mitrovica, J. X.; Little, C. M.; Ponte, R. M.; Tingley, M.

2017-12-01

Understanding observed spatial variations in centennial relative sea level trends on the United States east coast has important scientific and societal applications. Past studies based on models and proxies variously suggest roles for crustal displacement, ocean dynamics, and melting of the Greenland ice sheet. Here we perform joint Bayesian inference on regional relative sea level, vertical land motion, and absolute sea level fields based on tide gauge records and GPS data. Posterior solutions show that regional vertical land motion explains most (80% median estimate) of the spatial variance in the large-scale relative sea level trend field on the east coast over 1900-2016. The posterior estimate for coastal absolute sea level rise is remarkably spatially uniform compared to previous studies, with a spatial average of 1.4-2.3 mm/yr (95% credible interval). Results corroborate glacial isostatic adjustment models and reveal that meaningful long-period, large-scale vertical velocity signals can be extracted from short GPS records.

Active inference, evidence accumulation and the urn task

PubMed Central

FitzGerald, Thomas HB; Schwartenbeck, Philipp; Moutoussis, Michael; Dolan, Raymond J; Friston, Karl

2015-01-01

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one which is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical paper, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behaviour – in this case, that they will make informed decisions. Normative decision-making is then modelled using variational Bayes to minimise surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step towards understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference, and may cast light on why this process is disordered in psychopathology. PMID:25514108

Applying a propensity score-based weighting model to interrupted time series data: improving causal inference in programme evaluation.

PubMed

Linden, Ariel; Adams, John L

2011-12-01

Often, when conducting programme evaluations or studying the effects of policy changes, researchers may only have access to aggregated time series data, presented as observations spanning both the pre- and post-intervention periods. The most basic analytic model using these data requires only a single group and models the intervention effect using repeated measurements of the dependent variable. This model controls for regression to the mean and is likely to detect a treatment effect if it is sufficiently large. However, many potential sources of bias still remain. Adding one or more control groups to this model could strengthen causal inference if the groups are comparable on pre-intervention covariates and level and trend of the dependent variable. If this condition is not met, the validity of the study findings could be called into question. In this paper we describe a propensity score-based weighted regression model, which overcomes these limitations by weighting the control groups to represent the average outcome that the treatment group would have exhibited in the absence of the intervention. We illustrate this technique studying cigarette sales in California before and after the passage of Proposition 99 in California in 1989. While our results were similar to those of the Synthetic Control method, the weighting approach has the advantage of being technically less complicated, rooted in regression techniques familiar to most researchers, easy to implement using any basic statistical software, may accommodate any number of treatment units, and allows for greater flexibility in the choice of treatment effect estimators. © 2010 Blackwell Publishing Ltd.

Assessing Student Understanding of the "New Biology": Development and Evaluation of a Criterion-Referenced Genomics and Bioinformatics Assessment

NASA Astrophysics Data System (ADS)

Campbell, Chad Edward

Over the past decade, hundreds of studies have introduced genomics and bioinformatics (GB) curricula and laboratory activities at the undergraduate level. While these publications have facilitated the teaching and learning of cutting-edge content, there has yet to be an evaluation of these assessment tools to determine if they are meeting the quality control benchmarks set forth by the educational research community. An analysis of these assessment tools indicated that <10% referenced any quality control criteria and that none of the assessments met more than one of the quality control benchmarks. In the absence of evidence that these benchmarks had been met, it is unclear whether these assessment tools are capable of generating valid and reliable inferences about student learning. To remedy this situation the development of a robust GB assessment aligned with the quality control benchmarks was undertaken in order to ensure evidence-based evaluation of student learning outcomes. Content validity is a central piece of construct validity, and it must be used to guide instrument and item development. This study reports on: (1) the correspondence of content validity evidence gathered from independent sources; (2) the process of item development using this evidence; (3) the results from a pilot administration of the assessment; (4) the subsequent modification of the assessment based on the pilot administration results and; (5) the results from the second administration of the assessment. Twenty-nine different subtopics within GB (Appendix B: Genomics and Bioinformatics Expert Survey) were developed based on preliminary GB textbook analyses. These subtopics were analyzed using two methods designed to gather content validity evidence: (1) a survey of GB experts (n=61) and (2) a detailed content analyses of GB textbooks (n=6). By including only the subtopics that were shown to have robust support across these sources, 22 GB subtopics were established for inclusion in the assessment. An expert panel subsequently developed, evaluated, and revised two multiple-choice items to align with each of the 22 subtopics, producing a final item pool of 44 items. These items were piloted with student samples of varying content exposure levels. Both Classical Test Theory (CTT) and Item Response Theory (IRT) methodologies were used to evaluate the assessment's validity, reliability and ability inferences, and its ability to differentiate students with different magnitudes of content exposure. A total of 18 items were subsequently modified and reevaluated by an expert panel. The 26 original and 18 modified items were once again piloted with student samples of varying content exposure levels. Both CTT and IRT methodologies were once again used to evaluate student responses in order to evaluate the assessment's validity and reliability inferences as well as its ability to differentiate students with different magnitudes of content exposure. Interviews with students from different content exposure levels were also performed in order to gather convergent validity evidence (external validity evidence) as well as substantive validity evidence. Also included are the limitations of the assessment and a set of guidelines on how the assessment can best be used.

Inductive reasoning and the understanding of intention in schizophrenia.

PubMed

Corcoran, Rhiannon

2003-08-01

The study explored the relationship between the understanding of intention in veiled speech acts and the ability to reason inductively. A total of 39 people with DSM-IV-defined schizophrenia with no behavioural signs and 44 healthy participants performed the Hinting Task, a measure of pragmatic language in which the speaker's intention must be inferred, and a measure of inductive reasoning (Aha! Sentences) in which the meaning of ambiguous nonsocial sentences had to be inferred. The participants also completed measures of general intellectual ability, immediate memory for narrative and social problem-solving ability. A substantial correlation was found between performance on the inductive reasoning task and the Hinting Task in the sample of people with schizophrenia. The same relationship was not seen in the normal control sample. The robust relationship between these two measures in this sample survived when the roles of immediate memory for narrative and intellectual ability were controlled for. Furthermore, the relationship was distinctly more compelling for the patients who were currently ill compared to those in remission. These data suggest that people with schizophrenia use a different strategy to infer the meaning behind pragmatic language than that used by normally functioning adults. It is suggested that a reliance on different, possibly less specialised, skills in this group to perform this simple social inference task underlies their deficient performance on this and other measures of social inference. The fact that the relationship between the tasks in patients in remission is not as robust implies that the use of specialised skills to perform social inference tasks may be compromised most significantly during acute phases.

Exploiting combinatorial cultivation conditions to infer transcriptional regulation

PubMed Central

Knijnenburg, Theo A; de Winde, Johannes H; Daran, Jean-Marc; Daran-Lapujade, Pascale; Pronk, Jack T; Reinders, Marcel JT; Wessels, Lodewyk FA

2007-01-01

Background Regulatory networks often employ the model that attributes changes in gene expression levels, as observed across different cellular conditions, to changes in the activity of transcription factors (TFs). Although the actual conditions that trigger a change in TF activity should form an integral part of the generated regulatory network, they are usually lacking. This is due to the fact that the large heterogeneity in the employed conditions and the continuous changes in environmental parameters in the often used shake-flask cultures, prevent the unambiguous modeling of the cultivation conditions within the computational framework. Results We designed an experimental setup that allows us to explicitly model the cultivation conditions and use these to infer the activity of TFs. The yeast Saccharomyces cerevisiae was cultivated under four different nutrient limitations in both aerobic and anaerobic chemostat cultures. In the chemostats, environmental and growth parameters are accurately controlled. Consequently, the measured transcriptional response can be directly correlated with changes in the limited nutrient or oxygen concentration. We devised a tailor-made computational approach that exploits the systematic setup of the cultivation conditions in order to identify the individual and combined effects of nutrient limitations and oxygen availability on expression behavior and TF activity. Conclusion Incorporating the actual growth conditions when inferring regulatory relationships provides detailed insight in the functionality of the TFs that are triggered by changes in the employed cultivation conditions. For example, our results confirm the established role of TF Hap4 in both aerobic regulation and glucose derepression. Among the numerous inferred condition-specific regulatory associations between gene sets and TFs, also many novel putative regulatory mechanisms, such as the possible role of Tye7 in sulfur metabolism, were identified. PMID:17241460

Exploiting combinatorial cultivation conditions to infer transcriptional regulation.

PubMed

Knijnenburg, Theo A; de Winde, Johannes H; Daran, Jean-Marc; Daran-Lapujade, Pascale; Pronk, Jack T; Reinders, Marcel J T; Wessels, Lodewyk F A

2007-01-22

Regulatory networks often employ the model that attributes changes in gene expression levels, as observed across different cellular conditions, to changes in the activity of transcription factors (TFs). Although the actual conditions that trigger a change in TF activity should form an integral part of the generated regulatory network, they are usually lacking. This is due to the fact that the large heterogeneity in the employed conditions and the continuous changes in environmental parameters in the often used shake-flask cultures, prevent the unambiguous modeling of the cultivation conditions within the computational framework. We designed an experimental setup that allows us to explicitly model the cultivation conditions and use these to infer the activity of TFs. The yeast Saccharomyces cerevisiae was cultivated under four different nutrient limitations in both aerobic and anaerobic chemostat cultures. In the chemostats, environmental and growth parameters are accurately controlled. Consequently, the measured transcriptional response can be directly correlated with changes in the limited nutrient or oxygen concentration. We devised a tailor-made computational approach that exploits the systematic setup of the cultivation conditions in order to identify the individual and combined effects of nutrient limitations and oxygen availability on expression behavior and TF activity. Incorporating the actual growth conditions when inferring regulatory relationships provides detailed insight in the functionality of the TFs that are triggered by changes in the employed cultivation conditions. For example, our results confirm the established role of TF Hap4 in both aerobic regulation and glucose derepression. Among the numerous inferred condition-specific regulatory associations between gene sets and TFs, also many novel putative regulatory mechanisms, such as the possible role of Tye7 in sulfur metabolism, were identified.

Water storage in marine sediment and implications for inferences of past global ice volume

NASA Astrophysics Data System (ADS)

Ferrier, K.; Li, Q.; Pico, T.; Austermann, J.

2017-12-01

Changes in past sea level are of wide interest because they provide information on the sensitivity of ice sheets to climate change, and thus inform predictions of future sea-level change. Sea level changes are influenced by many processes, including the storage of water in sedimentary pore space. Here we use a recent extension of gravitationally self-consistent sea-level models to explore the effects of marine sedimentary water storage on the global seawater balance and inferences of past global ice volume. Our analysis suggests that sedimentary water storage can be a significant component of the global seawater budget over the 105-year timescales associated with glacial-interglacial cycles, and an even larger component over longer timescales. Estimates of global sediment fluxes to the oceans suggest that neglecting marine sedimentary water storage may produce meter-scale errors in estimates of peak global mean sea level equivalent (GMSL) during the Last Interglacial (LIG). These calculations show that marine sedimentary water storage can be a significant contributor to the overall effects of sediment redistribution on sea-level change, and that neglecting sedimentary water storage can lead to substantial errors in inferences of global ice volume at past interglacials. This highlights the importance of accounting for the influences of sediment fluxes and sedimentary water storage on sea-level change over glacial-interglacial timescales.

Inferring heuristic classification hierarchies from natural language input

NASA Technical Reports Server (NTRS)

Hull, Richard; Gomez, Fernando

1993-01-01

A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies.

The Impact of Advanced Vocational Education and Training on Earnings in Sweden

ERIC Educational Resources Information Center

Andersson, Roland; Nabavi, Pardis; Wilhelmsson, Mats

2014-01-01

Researchers have established a relationship between greater education and training and higher earnings but it is difficult to infer that the former causes the latter if those with higher earnings tend to engage in more education and training. The present study attempts to control for ability and family background to see if stronger inferences can…

A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System

PubMed Central

Tang, Yongchuan; Zhou, Deyun

2016-01-01

In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method. PMID:27482707

A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System.

PubMed

Tang, Yongchuan; Zhou, Deyun; Jiang, Wen

2016-01-01

In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method.

A Hybrid Procedural/Deductive Executive for Autonomous Spacecraft

NASA Technical Reports Server (NTRS)

Pell, Barney; Gamble, Edward B.; Gat, Erann; Kessing, Ron; Kurien, James; Millar, William; Nayak, P. Pandurang; Plaunt, Christian; Williams, Brian C.; Lau, Sonie (Technical Monitor)

1998-01-01

The New Millennium Remote Agent (NMRA) will be the first AI system to control an actual spacecraft. The spacecraft domain places a strong premium on autonomy and requires dynamic recoveries and robust concurrent execution, all in the presence of tight real-time deadlines, changing goals, scarce resource constraints, and a wide variety of possible failures. To achieve this level of execution robustness, we have integrated a procedural executive based on generic procedures with a deductive model-based executive. A procedural executive provides sophisticated control constructs such as loops, parallel activity, locks, and synchronization which are used for robust schedule execution, hierarchical task decomposition, and routine configuration management. A deductive executive provides algorithms for sophisticated state inference and optimal failure recover), planning. The integrated executive enables designers to code knowledge via a combination of procedures and declarative models, yielding a rich modeling capability suitable to the challenges of real spacecraft control. The interface between the two executives ensures both that recovery sequences are smoothly merged into high-level schedule execution and that a high degree of reactivity is retained to effectively handle additional failures during recovery.

Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration

PubMed Central

Sanderson, Eleanor; Macdonald-Wallis, Corrie; Davey Smith, George

2018-01-01

Abstract Background Negative control exposure studies are increasingly being used in epidemiological studies to strengthen causal inference regarding an exposure-outcome association when unobserved confounding is thought to be present. Negative control exposure studies contrast the magnitude of association of the negative control, which has no causal effect on the outcome but is associated with the unmeasured confounders in the same way as the exposure, with the magnitude of the association of the exposure with the outcome. A markedly larger effect of the exposure on the outcome than the negative control on the outcome strengthens inference that the exposure has a causal effect on the outcome. Methods We investigate the effect of measurement error in the exposure and negative control variables on the results obtained from a negative control exposure study. We do this in models with continuous and binary exposure and negative control variables using analysis of the bias of the estimated coefficients and Monte Carlo simulations. Results Our results show that measurement error in either the exposure or negative control variables can bias the estimated results from the negative control exposure study. Conclusions Measurement error is common in the variables used in epidemiological studies; these results show that negative control exposure studies cannot be used to precisely determine the size of the effect of the exposure variable, or adequately adjust for unobserved confounding; however, they can be used as part of a body of evidence to aid inference as to whether a causal effect of the exposure on the outcome is present. PMID:29088358

Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration.

PubMed

Sanderson, Eleanor; Macdonald-Wallis, Corrie; Davey Smith, George

2018-04-01

Negative control exposure studies are increasingly being used in epidemiological studies to strengthen causal inference regarding an exposure-outcome association when unobserved confounding is thought to be present. Negative control exposure studies contrast the magnitude of association of the negative control, which has no causal effect on the outcome but is associated with the unmeasured confounders in the same way as the exposure, with the magnitude of the association of the exposure with the outcome. A markedly larger effect of the exposure on the outcome than the negative control on the outcome strengthens inference that the exposure has a causal effect on the outcome. We investigate the effect of measurement error in the exposure and negative control variables on the results obtained from a negative control exposure study. We do this in models with continuous and binary exposure and negative control variables using analysis of the bias of the estimated coefficients and Monte Carlo simulations. Our results show that measurement error in either the exposure or negative control variables can bias the estimated results from the negative control exposure study. Measurement error is common in the variables used in epidemiological studies; these results show that negative control exposure studies cannot be used to precisely determine the size of the effect of the exposure variable, or adequately adjust for unobserved confounding; however, they can be used as part of a body of evidence to aid inference as to whether a causal effect of the exposure on the outcome is present.

Backward renormalization-group inference of cortical dipole sources and neural connectivity efficacy

NASA Astrophysics Data System (ADS)

Amaral, Selene da Rocha; Baccalá, Luiz A.; Barbosa, Leonardo S.; Caticha, Nestor

2017-06-01

Proper neural connectivity inference has become essential for understanding cognitive processes associated with human brain function. Its efficacy is often hampered by the curse of dimensionality. In the electroencephalogram case, which is a noninvasive electrophysiological monitoring technique to record electrical activity of the brain, a possible way around this is to replace multichannel electrode information with dipole reconstructed data. We use a method based on maximum entropy and the renormalization group to infer the position of the sources, whose success hinges on transmitting information from low- to high-resolution representations of the cortex. The performance of this method compares favorably to other available source inference algorithms, which are ranked here in terms of their performance with respect to directed connectivity inference by using artificially generated dynamic data. We examine some representative scenarios comprising different numbers of dynamically connected dipoles over distinct cortical surface positions and under different sensor noise impairment levels. The overall conclusion is that inverse problem solutions do not affect the correct inference of the direction of the flow of information as long as the equivalent dipole sources are correctly found.

The Role of Phonological Decoding in Second Language Word-Meaning Inference

ERIC Educational Resources Information Center

Hamada, Megumi; Koda, Keiko

2010-01-01

Two hypotheses were tested: Similarity between first language (L1) and second language (L2) orthographic processing facilitates L2-decoding efficiency; and L2-decoding efficiency contributes to word-meaning inference to different degrees among L2 learners with diverse L1 orthographic backgrounds. The participants were college-level English as a…

Testosterone, migration distance, and migratory timing in song sparrows Melospiza melodia.

PubMed

Lymburner, Alannah H; Kelly, Tosha R; Hobson, Keith A; MacDougall-Shackleton, Elizabeth A; MacDougall-Shackleton, Scott A

2016-09-01

In seasonally migratory animals, migration distance often varies substantially within populations such that individuals breeding at the same site may overwinter different distances from the breeding grounds. Shorter migration may allow earlier return to the breeding grounds, which may be particularly advantageous to males competing to acquire a breeding territory. However, little is known about potential mechanisms that may mediate migration distance. We investigated naturally-occurring variation in androgen levels at the time of arrival to the breeding site and its relationship to overwintering latitude in male and female song sparrows (Melospiza melodia). We used stable isotope analysis of hydrogen (δ(2)H) in winter-grown claw tissue to infer relative overwintering latitude (migration distance), combined with 14years of capture records from a long-term study population to infer the arrival timing of males versus females. Relative to females, males had higher circulating androgen levels, migrated shorter distances, and were more likely to be caught early in the breeding season. Males that migrate short distances may benefit from early arrival at the breeding grounds, allowing them to establish a breeding territory. Even after controlling for sex and date, androgen levels were highest in individuals that migrated shorter distances. Our findings indicate that androgens and migration distance are correlated traits within and between sexes that may reflect individual variation within an integrated phenotype in which testosterone has correlated effects on behavioral traits such as migration. Copyright © 2016 Elsevier Inc. All rights reserved.

Bayesian power spectrum inference with foreground and target contamination treatment

NASA Astrophysics Data System (ADS)

Jasche, J.; Lavaux, G.

2017-10-01

This work presents a joint and self-consistent Bayesian treatment of various foreground and target contaminations when inferring cosmological power spectra and three-dimensional density fields from galaxy redshift surveys. This is achieved by introducing additional block-sampling procedures for unknown coefficients of foreground and target contamination templates to the previously presented ARES framework for Bayesian large-scale structure analyses. As a result, the method infers jointly and fully self-consistently three-dimensional density fields, cosmological power spectra, luminosity-dependent galaxy biases, noise levels of the respective galaxy distributions, and coefficients for a set of a priori specified foreground templates. In addition, this fully Bayesian approach permits detailed quantification of correlated uncertainties amongst all inferred quantities and correctly marginalizes over observational systematic effects. We demonstrate the validity and efficiency of our approach in obtaining unbiased estimates of power spectra via applications to realistic mock galaxy observations that are subject to stellar contamination and dust extinction. While simultaneously accounting for galaxy biases and unknown noise levels, our method reliably and robustly infers three-dimensional density fields and corresponding cosmological power spectra from deep galaxy surveys. Furthermore, our approach correctly accounts for joint and correlated uncertainties between unknown coefficients of foreground templates and the amplitudes of the power spectrum. This effect amounts to correlations and anti-correlations of up to 10 per cent across wide ranges in Fourier space.

The perception of surface layout during low level flight

NASA Technical Reports Server (NTRS)

Perrone, John A.

1991-01-01

Although it is fairly well established that information about surface layout can be gained from motion cues, it is not so clear as to what information humans can use and what specific information they should be provided. Theoretical analyses tell us that the information is in the stimulus. It will take more experiments to verify that this information can be used by humans to extract surface layout from the 2D velocity flow field. The visual motion factors that can affect the pilot's ability to control an aircraft and to infer the layout of the terrain ahead are discussed.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Specimen-level phylogenetics in paleontology using the Fossilized Birth-Death model with sampled ancestors.

PubMed

Cau, Andrea

2017-01-01

Bayesian phylogenetic methods integrating simultaneously morphological and stratigraphic information have been applied increasingly among paleontologists. Most of these studies have used Bayesian methods as an alternative to the widely-used parsimony analysis, to infer macroevolutionary patterns and relationships among species-level or higher taxa. Among recently introduced Bayesian methodologies, the Fossilized Birth-Death (FBD) model allows incorporation of hypotheses on ancestor-descendant relationships in phylogenetic analyses including fossil taxa. Here, the FBD model is used to infer the relationships among an ingroup formed exclusively by fossil individuals, i.e., dipnoan tooth plates from four localities in the Ain el Guettar Formation of Tunisia. Previous analyses of this sample compared the results of phylogenetic analysis using parsimony with stratigraphic methods, inferred a high diversity (five or more genera) in the Ain el Guettar Formation, and interpreted it as an artifact inflated by depositional factors. In the analysis performed here, the uncertainty on the chronostratigraphic relationships among the specimens was included among the prior settings. The results of the analysis confirm the referral of most of the specimens to the taxa Asiatoceratodus , Equinoxiodus, Lavocatodus and Neoceratodus , but reject those to Ceratodus and Ferganoceratodus . The resulting phylogeny constrained the evolution of the Tunisian sample exclusively in the Early Cretaceous, contrasting with the previous scenario inferred by the stratigraphically-calibrated topology resulting from parsimony analysis. The phylogenetic framework also suggests that (1) the sampled localities are laterally equivalent, (2) but three localities are restricted to the youngest part of the section; both results are in agreement with previous stratigraphic analyses of these localities. The FBD model of specimen-level units provides a novel tool for phylogenetic inference among fossils but also for independent tests of stratigraphic scenarios.

Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly.

PubMed

Moura, Lidia Mvr; Westover, M Brandon; Kwasnik, David; Cole, Andrew J; Hsu, John

2017-01-01

The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer's disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions.

Divide et impera: subgoaling reduces the complexity of probabilistic inference and problem solving

PubMed Central

Maisto, Domenico; Donnarumma, Francesco; Pezzulo, Giovanni

2015-01-01

It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabilistic inference: subgoals are selected using a sampling method that considers the descriptive complexity of the resulting sub-problems. We validate the proposed method using a standard reinforcement learning benchmark (four-rooms scenario) and show that the proposed method requires less inferential steps and permits selecting more compact control programs compared to an equivalent procedure without subgoaling. Furthermore, we show that the proposed method offers a mechanistic explanation of the neuronal dynamics found in the prefrontal cortex of monkeys that solve planning problems. Our computational framework provides a novel integrative perspective on subgoaling and its adaptive advantages for planning, control and learning, such as for example lowering cognitive effort and working memory load. PMID:25652466

Mobile Context Provider for Social Networking

NASA Astrophysics Data System (ADS)

Santos, André C.; Cardoso, João M. P.; Ferreira, Diogo R.; Diniz, Pedro C.

The ability to infer user context based on a mobile device together with a set of external sensors opens up the way to new context-aware services and applications. In this paper, we describe a mobile context provider that makes use of sensors available in a smartphone as well as sensors externally connected via bluetooth. We describe the system architecture from sensor data acquisition to feature extraction, context inference and the publication of context information to well-known social networking services such as Twitter and Hi5. In the current prototype, context inference is based on decision trees, but the middleware allows the integration of other inference engines. Experimental results suggest that the proposed solution is a promising approach to provide user context to both local and network-level services.

Can circular inference relate the neuropathological and behavioral aspects of schizophrenia?

PubMed

Leptourgos, Pantelis; Denève, Sophie; Jardri, Renaud

2017-10-01

Schizophrenia is a complex and heterogeneous mental disorder, and researchers have only recently begun to understand its neuropathology. However, since the time of Kraepelin and Bleuler, much information has been accumulated regarding the behavioral abnormalities usually encountered in patients suffering from schizophrenia. Despite recent progress, how the latter are caused by the former is still debated. Here, we argue that circular inference, a computational framework proposed as a potential explanation for various schizophrenia symptoms, could help end this debate. Based on Marr's three levels of analysis, we discuss how impairments in local and more global neural circuits could generate aberrant beliefs, with far-ranging consequences from probabilistic decision making to high-level visual perception in conditions of ambiguity. Interestingly, the circular inference framework appears to be compatible with a variety of pathophysiological theories of schizophrenia while simulating the behavioral symptoms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Localization of the lumbar discs using machine learning and exact probabilistic inference.

PubMed

Oktay, Ayse Betul; Akgul, Yusuf Sinan

2011-01-01

We propose a novel fully automatic approach to localize the lumbar intervertebral discs in MR images with PHOG based SVM and a probabilistic graphical model. At the local level, our method assigns a score to each pixel in target image that indicates whether it is a disc center or not. At the global level, we define a chain-like graphical model that represents the lumbar intervertebral discs and we use an exact inference algorithm to localize the discs. Our main contributions are the employment of the SVM with the PHOG based descriptor which is robust against variations of the discs and a graphical model that reflects the linear nature of the vertebral column. Our inference algorithm runs in polynomial time and produces globally optimal results. The developed system is validated on a real spine MRI dataset and the final localization results are favorable compared to the results reported in the literature.

The role of counterfactual theory in causal reasoning.

PubMed

Maldonado, George

2016-10-01

In this commentary I review the fundamentals of counterfactual theory and its role in causal reasoning in epidemiology. I consider if counterfactual theory dictates that causal questions must be framed in terms of well-defined interventions. I conclude that it does not. I hypothesize that the interventionist approach to causal inference in epidemiology stems from elevating the randomized trial design to the gold standard for thinking about causal inference. I suggest that instead the gold standard we should use for thinking about causal inference in epidemiology is the thought experiment that, for example, compares an actual disease frequency under one exposure level with a counterfactual disease frequency under a different exposure level (as discussed in Greenland and Robins (1986) and Maldonado and Greenland (2002)). I also remind us that no method should be termed "causal" unless it addresses the effect of other biases in addition to the problem of confounding. Copyright © 2016 Elsevier Inc. All rights reserved.

Asian and European American cultural values and communication styles among Asian American and European American college students.

PubMed

Park, Yong S; Kim, Bryan S K

2008-01-01

The present study examined the relationships between adherence to Asian and European cultural values and communication styles among 210 Asian American and 136 European American college students. A principal components analysis revealed that, for both Asian Americans and European Americans, the contentious, dramatic, precise, and open styles loaded onto the first component suggesting low context communication, and interpersonal sensitivity and inferring meaning styles loaded onto the second component suggesting high context communication. Higher adherence to emotional self-control and lower adherence to European American values explained Asian Americans' higher use of the indirect communication, while higher emotional self-control explained why Asian Americans use a less open communication style than their European American counterparts. When differences between sex and race were controlled, adherence to humility was inversely related to contentious and dramatic communication styles but directly related to inferring meaning style, adherence to European American values was positively associated with precise communication and inferring meaning styles, and collectivism was positively related to interpersonal sensitivity style. 2008 APA

Do the Eyes Have It? Inferring Mental States from Animated Faces in Autism

ERIC Educational Resources Information Center

Back, Elisa; Ropar, Danielle; Mitchell, Peter

2007-01-01

The ability of individuals with autistic spectrum disorders (ASD) to infer mental states from dynamic and static facial stimuli was investigated. In Experiment 1, individuals with ASD (10- to 14-year olds; N = 18) performed above chance but not as well as controls. Accuracy scores for mental states did not differ between dynamic and static faces.…

False belief and counterfactual reasoning in a social environment.

PubMed

Van Hoeck, Nicole; Begtas, Elizabet; Steen, Johan; Kestemont, Jenny; Vandekerckhove, Marie; Van Overwalle, Frank

2014-04-15

Behavioral studies indicate that theory of mind and counterfactual reasoning are strongly related cognitive processes. In a neuroimaging study, we explored the common and distinct regions underlying these inference processes. We directly compared false belief reasoning (inferring an agent's false belief about an object's location or content) and counterfactual reasoning (inferring what the object's location or content would be if an agent had acted differently), both in contrast with a baseline condition of conditional reasoning (inferring what the true location or content of an object is). Results indicate that these three types of reasoning about social scenarios are supported by activations in the mentalizing network (left temporo-parietal junction and precuneus) and the executive control network (bilateral prefrontal cortex [PFC] and right inferior parietal lobule). In addition, representing a false belief or counterfactual state (both not directly observable in the external world) recruits additional activity in the executive control network (left dorsolateral PFC and parietal lobe). The results further suggest that counterfactual reasoning is a more complex cognitive process than false belief reasoning, showing stronger activation of the dorsomedial, left dorsolateral PFC, cerebellum and left temporal cortex. Copyright © 2013 Elsevier Inc. All rights reserved.

Effects of gliadin addition on the rheological, microscopic and thermal characteristics of wheat gluten.

PubMed

Khatkar, B S; Barak, Sheweta; Mudgil, Deepak

2013-02-01

In the present study, micro-structural, thermal and rheological changes in the gluten network upon addition of gliadins at 5% and 10% levels were investigated using scanning electron microscopy (SEM), thermo gravimetric analysis (TGA), differential scanning calorimetry (DSC) and dynamic rheometry. The addition of gliadins decreased the peak dough height inferring decrease in dough strength. The dough stability also decreased from 3.20 to 1.40 min upon addition of 10% gliadin to the base flour. The TGA profile and the glass transition behavior of the control gluten and gluten obtained from dough with gliadin added at 5% and 10% levels showed decrease in thermal stability. The SEM micrograph of the control gluten showed foam like protein matrix. As the gliadin percentage in the gluten was increased, the compactness of the gluten structure reduced considerably leading to the formation of a more open weak gluten network. Copyright © 2012 Elsevier B.V. All rights reserved.

Protein and gene model inference based on statistical modeling in k-partite graphs.

PubMed

Gerster, Sarah; Qeli, Ermir; Ahrens, Christian H; Bühlmann, Peter

2010-07-06

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference. We present Markovian Inference of Proteins and Gene Models (MIPGEM), a statistical model based on clearly stated assumptions to address the problem of protein and gene model inference for shotgun proteomics data. In particular, we are dealing with dependencies among peptides and proteins using a Markovian assumption on k-partite graphs. We are also addressing the problems of shared peptides and ambiguous proteins by scoring the encoding gene models. Empirical results on two control datasets with synthetic mixtures of proteins and on complex protein samples of Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana suggest that the results with MIPGEM are competitive with existing tools for protein inference.

Rethinking fast and slow based on a critique of reaction-time reverse inference

PubMed Central

Krajbich, Ian; Bartling, Björn; Hare, Todd; Fehr, Ernst

2015-01-01

Do people intuitively favour certain actions over others? In some dual-process research, reaction-time (RT) data have been used to infer that certain choices are intuitive. However, the use of behavioural or biological measures to infer mental function, popularly known as ‘reverse inference', is problematic because it does not take into account other sources of variability in the data, such as discriminability of the choice options. Here we use two example data sets obtained from value-based choice experiments to demonstrate that, after controlling for discriminability (that is, strength-of-preference), there is no evidence that one type of choice is systematically faster than the other. Moreover, using specific variations of a prominent value-based choice experiment, we are able to predictably replicate, eliminate or reverse previously reported correlations between RT and selfishness. Thus, our findings shed crucial light on the use of RT in inferring mental processes and strongly caution against using RT differences as evidence favouring dual-process accounts. PMID:26135809

Rethinking fast and slow based on a critique of reaction-time reverse inference.

PubMed

Krajbich, Ian; Bartling, Björn; Hare, Todd; Fehr, Ernst

2015-07-02

Do people intuitively favour certain actions over others? In some dual-process research, reaction-time (RT) data have been used to infer that certain choices are intuitive. However, the use of behavioural or biological measures to infer mental function, popularly known as 'reverse inference', is problematic because it does not take into account other sources of variability in the data, such as discriminability of the choice options. Here we use two example data sets obtained from value-based choice experiments to demonstrate that, after controlling for discriminability (that is, strength-of-preference), there is no evidence that one type of choice is systematically faster than the other. Moreover, using specific variations of a prominent value-based choice experiment, we are able to predictably replicate, eliminate or reverse previously reported correlations between RT and selfishness. Thus, our findings shed crucial light on the use of RT in inferring mental processes and strongly caution against using RT differences as evidence favouring dual-process accounts.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

Characterizing novel endogenous retroviruses from genetic variation inferred from short sequence reads

PubMed Central

Mourier, Tobias; Mollerup, Sarah; Vinner, Lasse; Hansen, Thomas Arn; Kjartansdóttir, Kristín Rós; Guldberg Frøslev, Tobias; Snogdal Boutrup, Torsten; Nielsen, Lars Peter; Willerslev, Eske; Hansen, Anders J.

2015-01-01

From Illumina sequencing of DNA from brain and liver tissue from the lion, Panthera leo, and tumor samples from the pike-perch, Sander lucioperca, we obtained two assembled sequence contigs with similarity to known retroviruses. Phylogenetic analyses suggest that the pike-perch retrovirus belongs to the epsilonretroviruses, and the lion retrovirus to the gammaretroviruses. To determine if these novel retroviral sequences originate from an endogenous retrovirus or from a recently integrated exogenous retrovirus, we assessed the genetic diversity of the parental sequences from which the short Illumina reads are derived. First, we showed by simulations that we can robustly infer the level of genetic diversity from short sequence reads. Second, we find that the measures of nucleotide diversity inferred from our retroviral sequences significantly exceed the level observed from Human Immunodeficiency Virus infections, prompting us to conclude that the novel retroviruses are both of endogenous origin. Through further simulations, we rule out the possibility that the observed elevated levels of nucleotide diversity are the result of co-infection with two closely related exogenous retroviruses. PMID:26493184

Algorithmic methods to infer the evolutionary trajectories in cancer progression

PubMed Central

Graudenzi, Alex; Ramazzotti, Daniele; Sanz-Pamplona, Rebeca; De Sano, Luca; Mauri, Giancarlo; Moreno, Victor; Antoniotti, Marco; Mishra, Bud

2016-01-01

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the “selective advantage” relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc’s ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses. PMID:27357673

Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

DOE PAGES

Alamaniotis, Miltiadis; Agarwal, Vivek

2014-04-01

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, artificially intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. Our proposed methodology implements an anticipatorymore » system aiming at controlling energy systems in a robust way. Initially a set of support vector regressors is adopted for making predictions over critical system parameters. Furthermore, the predicted values are fed into a two stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions into a single one at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.« less

Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression.

PubMed

Houpt, Joseph W; Bittner, Jennifer L

2018-07-01

Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences. Copyright © 2018 Elsevier Ltd. All rights reserved.

Performance analysis of a fault inferring nonlinear detection system algorithm with integrated avionics flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

1985-01-01

This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.

Top-Down CO Emissions Based On IASI Observations and Hemispheric Constraints on OH Levels

NASA Astrophysics Data System (ADS)

Müller, J.-F.; Stavrakou, T.; Bauwens, M.; George, M.; Hurtmans, D.; Coheur, P.-F.; Clerbaux, C.; Sweeney, C.

2018-02-01

Assessments of carbon monoxide emissions through inverse modeling are dependent on the modeled abundance of the hydroxyl radical (OH) which controls both the primary sink of CO and its photochemical source through hydrocarbon oxidation. However, most chemistry transport models (CTMs) fall short of reproducing constraints on hemispherically averaged OH levels derived from methylchloroform (MCF) observations. Here we construct five different OH fields compatible with MCF-based analyses, and we prescribe those fields in a global CTM to infer CO fluxes based on Infrared Atmospheric Sounding Interferometer (IASI) CO columns. Each OH field leads to a different set of optimized emissions. Comparisons with independent data (surface, ground-based remotely sensed, aircraft) indicate that the inversion adopting the lowest average OH level in the Northern Hemisphere (7.8 × 105 molec cm-3, ˜18% lower than the best estimate based on MCF measurements) provides the best overall agreement with all tested observation data sets.

Transient Plume Model Testing Using LADEE Spacecraft Attitude Control System Operations

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

Woronowicz, M. S.

2011-05-20

The Lunar Atmosphere Dust Environment Explorer (LADEE) spacecraft is being designed for a mission featuring low altitude orbits of the Moon to take relevant ambient measurements before that environment becomes altered by future exploration activities. Instruments include a neutral mass spectrometer capable of measuring ambient species density levels below 100 molecules/cm{sup 3}. Coincidentally, with a favorable combination of spacecraft orientations, it is also possible to measure plume gases from LADEE attitude control system thruster operations as they are reflected from the daytime lunar surface and subsequently intercepted by the spacecraft as it orbits overhead. Under such circumstances, it may bemore » possible to test a variety of properties and assumptions associated with various transient plume models or to infer certain aspects regarding lunar surface properties.« less

The use of a Nintendo Wii remote control in physics experiments

NASA Astrophysics Data System (ADS)

Abellán, F. J.; Arenas, A.; Núñez, M. J.; Victoria, L.

2013-09-01

In this paper we describe how a Nintendo Wii remote control (known as the Wiimote) can be used in the design and implementation of several undergraduate-level experiments in a physics laboratory class. An experimental setup composed of a Wiimote and a conveniently located IR LED allows the trajectory of one or several moving objects to be tracked and recorded accurately, in both long and short displacement. The authors have developed a user interface program to configure the operation of the acquisition system of such data. The two experiments included in this work are the free fall of a body with magnetic braking and the simple pendulum, but other physics experiments could have been chosen. The treatment of the data was performed using Bayesian inference.

Transient Plume Model Testing Using LADEE Spacecraft Attitude Control System Operations

NASA Technical Reports Server (NTRS)

Woronowicz, M. S.

2010-01-01

The Lunar Atmosphere Dust Environment Explorer (LADEE) spacecraft is being designed for a mission featuring low altitude orbits of the Moon to take relevant ambient measurements before that environment becomes altered by future exploration activities. Instruments include a neutral mass spectrometer capable of measuring ambient species density levels below 100 molecules/cu cm. Coincidentally, with a favorable combination of spacecraft orientations, it is also possible to measure plume gases from LADEE attitude control system thruster operations as they are reflected from the daytime lunar surface and subsequently intercepted by the spacecraft as it orbits overhead. Under such circumstances, it may be possible to test a variety of properties and assumptions associated with various transient plume models or to infer certain aspects regarding lunar surface properties.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

The influence of high viscosity slabs on post-glacial sea-level change: the case of Barbados

NASA Astrophysics Data System (ADS)

Austermann, Jacqueline; Mitrovica, Jerry X.; Latychev, Konstantin

2013-04-01

The coral record at Barbados is one of the best available measures of relative sea level during the last glacial cycle and has been widely used to reconstruct ice volume (or, equivalently, eustatic sea-level, ESL) changes during the last deglaciation phase of the ice age. However, to estimate ESL variations from the local relative sea level (RSL) history at Barbados, one has to account for the contaminating effect of glacial isostatic adjustment (GIA). In previous work, the GIA signal at this site has been corrected for by assuming a spherically symmetric (i.e., 1-D) viscoelastic Earth. Since Barbados is located at the margin of the South American - Caribbean subduction zone, this assumption may introduce a significant error in inferences of ice volumes. To address this issue, we use a finite-volume numerical code to model GIA in the Caribbean region including the effects of a lithosphere with variable elastic thickness, plate boundaries, lateral variations in lower mantle viscosity, and a high viscosity slab within the upper mantle. The geometry of the subducted slab is inferred from local seismicity. We find that predictions of relative sea-level change since the Last Glacial Maximum (LGM) in the Caribbean region are diminished by ~10 m, relative to 1-D calculations, which suggests that previous studies have underestimated post-LGM ESL change by the same amount. This perturbation, which largely reflects the impact of the high viscosity slab, is nearly twice the total GIA-induced departure from eustasy predicted at Barbados using the 1-D Earth model. Our calculations imply an excess ice-volume equivalent to ~130 m ESL at the LGM, which brings the Barbados-based estimate into agreement with inferences based on other far-field RSL histories, such as at Bonaparte Gulf. This inference, together with recent studies that have substantially lowered estimates of Antarctic Ice Sheet mass at LGM, suggest that a significant amount of ice remains unaccounted for in sea-level based ice sheet reconstructions. In addition, we conclude that inference of ice age ice volumes derived from RSL histories at sites in proximity to subduction zones must incorporate slab structure into the numerical predictions of the GIA process.

Statistical inference on censored data for targeted clinical trials under enrichment design.

PubMed

Chen, Chen-Fang; Lin, Jr-Rung; Liu, Jen-Pei

2013-01-01

For the traditional clinical trials, inclusion and exclusion criteria are usually based on some clinical endpoints; the genetic or genomic variability of the trial participants are not totally utilized in the criteria. After completion of the human genome project, the disease targets at the molecular level can be identified and can be utilized for the treatment of diseases. However, the accuracy of diagnostic devices for identification of such molecular targets is usually not perfect. Some of the patients enrolled in targeted clinical trials with a positive result for the molecular target might not have the specific molecular targets. As a result, the treatment effect may be underestimated in the patient population truly with the molecular target. To resolve this issue, under the exponential distribution, we develop inferential procedures for the treatment effects of the targeted drug based on the censored endpoints in the patients truly with the molecular targets. Under an enrichment design, we propose using the expectation-maximization algorithm in conjunction with the bootstrap technique to incorporate the inaccuracy of the diagnostic device for detection of the molecular targets on the inference of the treatment effects. A simulation study was conducted to empirically investigate the performance of the proposed methods. Simulation results demonstrate that under the exponential distribution, the proposed estimator is nearly unbiased with adequate precision, and the confidence interval can provide adequate coverage probability. In addition, the proposed testing procedure can adequately control the size with sufficient power. On the other hand, when the proportional hazard assumption is violated, additional simulation studies show that the type I error rate is not controlled at the nominal level and is an increasing function of the positive predictive value. A numerical example illustrates the proposed procedures. Copyright © 2013 John Wiley & Sons, Ltd.

Reinforcement and inference in cross-situational word learning.

PubMed

Tilles, Paulo F C; Fontanari, José F

2013-01-01

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

Inference of Vohradský's Models of Genetic Networks by Solving Two-Dimensional Function Optimization Problems

PubMed Central

Kimura, Shuhei; Sato, Masanao; Okada-Hatakeyama, Mariko

2013-01-01

The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations. PMID:24386175

Data-driven inference for the spatial scan statistic.

PubMed

Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C

2011-08-02

Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

Comparison of Urban Human Movements Inferring from Multi-Source Spatial-Temporal Data

NASA Astrophysics Data System (ADS)

Cao, Rui; Tu, Wei; Cao, Jinzhou; Li, Qingquan

2016-06-01

The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

Inferring topologies via driving-based generalized synchronization of two-layer networks

NASA Astrophysics Data System (ADS)

Wang, Yingfei; Wu, Xiaoqun; Feng, Hui; Lu, Jun-an; Xu, Yuhua

2016-05-01

The interaction topology among the constituents of a complex network plays a crucial role in the network’s evolutionary mechanisms and functional behaviors. However, some network topologies are usually unknown or uncertain. Meanwhile, coupling delays are ubiquitous in various man-made and natural networks. Hence, it is necessary to gain knowledge of the whole or partial topology of a complex dynamical network by taking into consideration communication delay. In this paper, topology identification of complex dynamical networks is investigated via generalized synchronization of a two-layer network. Particularly, based on the LaSalle-type invariance principle of stochastic differential delay equations, an adaptive control technique is proposed by constructing an auxiliary layer and designing proper control input and updating laws so that the unknown topology can be recovered upon successful generalized synchronization. Numerical simulations are provided to illustrate the effectiveness of the proposed method. The technique provides a certain theoretical basis for topology inference of complex networks. In particular, when the considered network is composed of systems with high-dimension or complicated dynamics, a simpler response layer can be constructed, which is conducive to circuit design. Moreover, it is practical to take into consideration perturbations caused by control input. Finally, the method is applicable to infer topology of a subnetwork embedded within a complex system and locate hidden sources. We hope the results can provide basic insight into further research endeavors on understanding practical and economical topology inference of networks.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

PubMed Central

Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

2012-01-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

Towards the unification of inference structures in medical diagnostic tasks.

PubMed

Mira, J; Rives, J; Delgado, A E; Martínez, R

1998-01-01

The central purpose of artificial intelligence applied to medicine is to develop models for diagnosis and therapy planning at the knowledge level, in the Newell sense, and software environments to facilitate the reduction of these models to the symbol level. The usual methodology (KADS, Common-KADS, GAMES, HELIOS, Protégé, etc) has been to develop libraries of generic tasks and reusable problem-solving methods with explicit ontologies. The principal problem which clinicians have with these methodological developments concerns the diversity and complexity of new terms whose meaning is not sufficiently clear, precise, unambiguous and consensual for them to be accessible in the daily clinical environment. As a contribution to the solution of this problem, we develop in this article the conjecture that one inference structure is enough to describe the set of analysis tasks associated with medical diagnoses. To this end, we first propose a modification of the systematic diagnostic inference scheme to obtain an analysis generic task and then compare it with the monitoring and the heuristic classification task inference schemes using as comparison criteria the compatibility of domain roles (data structures), the similarity in the inferences, and the commonality in the set of assumptions which underlie the functionally equivalent models. The equivalences proposed are illustrated with several examples. Note that though our ongoing work aims to simplify the methodology and to increase the precision of the terms used, the proposal presented here should be viewed more in the nature of a conjecture.

A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis

PubMed Central

Ni, Ying; Aghamirzaie, Delasa; Elmarakeby, Haitham; Collakova, Eva; Li, Song; Grene, Ruth; Heath, Lenwood S.

2016-01-01

Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git. PMID:28066488

Comparative genomics of metabolic capacities of regulons controlled by cis-regulatory RNA motifs in bacteria.

PubMed

Sun, Eric I; Leyn, Semen A; Kazanov, Marat D; Saier, Milton H; Novichkov, Pavel S; Rodionov, Dmitry A

2013-09-02

In silico comparative genomics approaches have been efficiently used for functional prediction and reconstruction of metabolic and regulatory networks. Riboswitches are metabolite-sensing structures often found in bacterial mRNA leaders controlling gene expression on transcriptional or translational levels.An increasing number of riboswitches and other cis-regulatory RNAs have been recently classified into numerous RNA families in the Rfam database. High conservation of these RNA motifs provides a unique advantage for their genomic identification and comparative analysis. A comparative genomics approach implemented in the RegPredict tool was used for reconstruction and functional annotation of regulons controlled by RNAs from 43 Rfam families in diverse taxonomic groups of Bacteria. The inferred regulons include ~5200 cis-regulatory RNAs and more than 12000 target genes in 255 microbial genomes. All predicted RNA-regulated genes were classified into specific and overall functional categories. Analysis of taxonomic distribution of these categories allowed us to establish major functional preferences for each analyzed cis-regulatory RNA motif family. Overall, most RNA motif regulons showed predictable functional content in accordance with their experimentally established effector ligands. Our results suggest that some RNA motifs (including thiamin pyrophosphate and cobalamin riboswitches that control the cofactor metabolism) are widespread and likely originated from the last common ancestor of all bacteria. However, many more analyzed RNA motifs are restricted to a narrow taxonomic group of bacteria and likely represent more recent evolutionary innovations. The reconstructed regulatory networks for major known RNA motifs substantially expand the existing knowledge of transcriptional regulation in bacteria. The inferred regulons can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. The obtained genome-wide collection of reference RNA motif regulons is available in the RegPrecise database (http://regprecise.lbl.gov/).

Tectonic imprints upon inferences of eustatic sea level history: the Pliocene warm period and the Orangeburg Scarp

NASA Astrophysics Data System (ADS)

Chandan, D.; Peltier, W. R.

2013-12-01

The issue of tectonic contamination of geological inferences of relative sea level history is an important one. The issue arises on timescales that range from the 21-26 kyrs that have passed since the Last Glacial Maximum, to the most recent time when periods as warm as the present are expected to have existed, such as the mid-Pliocene. The coral based record from Barbados, for example, is known to be contaminated by continuing tectonic uplift of the island at a rate of approximately 0.34 mm/yr. For the Pliocene warm period at ~3 Myr, records from geological sites, such as the Orangeburg Scarp in North Carolina, have played a prominent role in arguments underpinning the design of the ongoing international PlioMIP program. In connection with the latter site, Rowley et al (2013) have recently argued that this record is contaminated by a tectonic imprint sufficiently strong to suggest that the usual inferences of Pliocene eustatic sea level based upon it (eg. Miller et al, 2012) must be seen as highly suspect. Here we employ a tomographically constrained model of the mantle convection process to revisit the issue of the tectonic imprint on relative sea level at the Orangeburg site, as well as other similar locations. Our analysis is based upon the inferred time dependence of dynamic topography forced by the mantle's internal density heterogeneities delivered by the S20RTS seismic tomography model. We begin by comparing the static, present day dynamic topography predicted by the (linear) internal loading theory based on the formalism of Pari and Peltier (2000) with that predicted using using a full three dimensional version of the nonlinear time-dependent mantle convection model of Shahnas and Peltier (2010, 2011). We demonstrate first that these two methodologies produce extremely similar results for the static field. We then proceed to run the nonlinear convection model in data assimilation mode while continuously nudging the internal density field back towards the structure inferred from tomography. Following a transient shock associated with the assimilation process, the model makes rather stable predictions for the time dependence of dynamic topography at a number of important locations from which data have been selected for the purpose of inferring the mid-Pliocene eustatic sea level. At Orangeburg where the inferred rates of tectonic uplift have ranged from 0.005 to 0.02 mm/yr (Dowsett and Cronin (1990), Soller (1989)) our model predicts an uplift rate of 0.024 mm/yr. This is sufficiently high to leave little room for any significant increase in eustatic sea level beyond what is expected to have existed as a consequence of the fact that the Greenland ice sheet had yet to fully form. Dowsett and Cronin (1990), Geology, 18, 435-438 Miller et al (2012), Geology, 40, 407-410 Pari and Peltier (2000), J. Geophys. Res., 105, 5635-5662 Rowley et al (2013), Science, 340, 1560-1563 Shahnas and Peltier (2010), J. Geophys. Res., 115, B11 Shahnas and Peltier (2011), J. Geophys. Res., 116, B8 Soller (1989), USGS professional paper, 1466-A

Active Wiki Knowledge Repository

DTIC Science & Technology

2012-10-01

data using SPARQL queries or RESTful web-services; ‘gardening’ tools for examining the semantically tagged content in the wiki; high-level language tool...Tagging & RDF triple-store Fusion and inferences for collaboration Tools for Consuming Data SPARQL queries or RESTful WS Inference & Gardening tools...other stores using AW SPARQL queries and rendering templates; and 4) Interactively share maps and other content using annotation tools to post notes

INFERENCE BUILDING BLOCKS

DTIC Science & Technology

2018-02-15

address the problem that probabilistic inference algorithms are diÿcult and tedious to implement, by expressing them in terms of a small number of...building blocks, which are automatic transformations on probabilistic programs. On one hand, our curation of these building blocks reflects the way human...reasoning with low-level computational optimization, so the speed and accuracy of the generated solvers are competitive with state-of-the-art systems. 15

Supra dietary levels of vitamins C and E enhance antibody production and immune memory in juvenile milkfish, Chanos chanos (Forsskal) to formalin-killed Vibrio vulnificus.

PubMed

Azad, I S; Dayal, J Syama; Poornima, M; Ali, S A

2007-07-01

Juveniles of milkfish, Chanos chanos (Forsskal), were fed two independent supra dietary levels of vitamins C (500 and 1500 mg kg(-1) feed, T1 and T2) and E (50 and 150 mg kg(-1), T3 and T4). Milkfish fed diets with supra (in addition to the vitamins present in the control diet) and normal levels (T5 containing 90 and 1.2mg of vitamins C and E, respectively, kg(-1) of feed) of vitamins were immunized (ip) with formalin-killed Vibrio vulnificus (FKVV). Priming and booster antibody responses to the injected bacterin were significantly (P<0.05) better in the milkfish juveniles fed supra dietary levels. Survival response of the experimental fish fed supra dietary levels of vitamins (T1, T2 and T3) was significantly (P<0.01) better than that of the control set. Protective response against virulent bacterial challenge of the vaccinated fish fed vitamin-supplemented diets (T2 and T3) was better than the control (T5) and T1 and T4. Memory factor reflecting immunological memory was superior in the fish fed vitamin-supplemented diets. Diets supplemented with either 1500 mg of Vitamin C or 50mg of Vitamin E kg(-1) produced the best antibody responses, final survival and protective response upon challenge. No conclusive inferences could be drawn on the growth responses from the experiment.

The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions

PubMed Central

Esserman, Denise; Allore, Heather G.; Travison, Thomas G.

2016-01-01

Cluster-randomized clinical trials (CRT) are trials in which the unit of randomization is not a participant but a group (e.g. healthcare systems or community centers). They are suitable when the intervention applies naturally to the cluster (e.g. healthcare policy); when lack of independence among participants may occur (e.g. nursing home hygiene); or when it is most ethical to apply an intervention to all within a group (e.g. school-level immunization). Because participants in the same cluster receive the same intervention, CRT may approximate clinical practice, and may produce generalizable findings. However, when not properly designed or interpreted, CRT may induce biased results. CRT designs have features that add complexity to statistical estimation and inference. Chief among these is the cluster-level correlation in response measurements induced by the randomization. A critical consideration is the experimental unit of inference; often it is desirable to consider intervention effects at the level of the individual rather than the cluster. Finally, given that the number of clusters available may be limited, simple forms of randomization may not achieve balance between intervention and control arms at either the cluster- or participant-level. In non-clustered clinical trials, balance of key factors may be easier to achieve because the sample can be homogenous by exclusion of participants with multiple chronic conditions (MCC). CRTs, which are often pragmatic, may eschew such restrictions. Failure to account for imbalance may induce bias and reducing validity. This article focuses on the complexities of randomization in the design of CRTs, such as the inclusion of patients with MCC, and imbalances in covariate factors across clusters. PMID:27478520

Independence of Hot and Cold Executive Function Deficits in High-Functioning Adults with Autism Spectrum Disorder.

PubMed

Zimmerman, David L; Ownsworth, Tamara; O'Donovan, Analise; Roberts, Jacqueline; Gullo, Matthew J

2016-01-01

Individuals with autistic spectrum disorder (ASD) display diverse deficits in social, cognitive and behavioral functioning. To date, there has been mixed findings on the profile of executive function deficits for high-functioning adults (IQ > 70) with ASD. A conceptual distinction is commonly made between "cold" and "hot" executive functions. Cold executive functions refer to mechanistic higher-order cognitive operations (e.g., working memory), whereas hot executive functions entail cognitive abilities supported by emotional awareness and social perception (e.g., social cognition). This study aimed to determine the independence of deficits in hot and cold executive functions for high-functioning adults with ASD. Forty-two adults with ASD (64% male, aged 18-66 years) and 40 age and gender matched controls were administered The Awareness of Social Inference Test (TASIT; emotion recognition and social inference), Letter Number Sequencing (working memory) and Hayling Sentence Completion Test (response initiation and suppression). Between-group analyses identified that the ASD group performed significantly worse than matched controls on all measures of cold and hot executive functions (d = 0.54 - 1.5). Hierarchical multiple regression analyses revealed that the ASD sample performed more poorly on emotion recognition and social inference tasks than matched controls after controlling for cold executive functions and employment status. The findings also indicated that the ability to recognize emotions and make social inferences was supported by working memory and response initiation and suppression processes. Overall, this study supports the distinction between hot and cold executive function impairments for adults with ASD. Moreover, it advances understanding of higher-order impairments underlying social interaction difficulties for this population which, in turn, may assist with diagnosis and inform intervention programs.

Independence of Hot and Cold Executive Function Deficits in High-Functioning Adults with Autism Spectrum Disorder

PubMed Central

Zimmerman, David L.; Ownsworth, Tamara; O'Donovan, Analise; Roberts, Jacqueline; Gullo, Matthew J.

2016-01-01

Individuals with autistic spectrum disorder (ASD) display diverse deficits in social, cognitive and behavioral functioning. To date, there has been mixed findings on the profile of executive function deficits for high-functioning adults (IQ > 70) with ASD. A conceptual distinction is commonly made between “cold” and “hot” executive functions. Cold executive functions refer to mechanistic higher-order cognitive operations (e.g., working memory), whereas hot executive functions entail cognitive abilities supported by emotional awareness and social perception (e.g., social cognition). This study aimed to determine the independence of deficits in hot and cold executive functions for high-functioning adults with ASD. Forty-two adults with ASD (64% male, aged 18–66 years) and 40 age and gender matched controls were administered The Awareness of Social Inference Test (TASIT; emotion recognition and social inference), Letter Number Sequencing (working memory) and Hayling Sentence Completion Test (response initiation and suppression). Between-group analyses identified that the ASD group performed significantly worse than matched controls on all measures of cold and hot executive functions (d = 0.54 − 1.5). Hierarchical multiple regression analyses revealed that the ASD sample performed more poorly on emotion recognition and social inference tasks than matched controls after controlling for cold executive functions and employment status. The findings also indicated that the ability to recognize emotions and make social inferences was supported by working memory and response initiation and suppression processes. Overall, this study supports the distinction between hot and cold executive function impairments for adults with ASD. Moreover, it advances understanding of higher-order impairments underlying social interaction difficulties for this population which, in turn, may assist with diagnosis and inform intervention programs. PMID:26903836

Cerebellarlike corrective model inference engine for manipulation tasks.

PubMed

Luque, Niceto Rafael; Garrido, Jesús Alberto; Carrillo, Richard Rafael; Coenen, Olivier J-M D; Ros, Eduardo

2011-10-01

This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model inference process takes place within a feedforward control loop based on the cerebellar structure and on how these internal models are built up by means of biologically plausible synaptic adaptation mechanisms. This kind of investigation may provide clues on how biology achieves accurate control of non-stiff-joint robot with low-power actuators which involve controlling systems with high inertial components. This paper studies how a basic temporal-correlation kernel including long-term depression (LTD) and a constant long-term potentiation (LTP) at parallel fiber-Purkinje cell synapses can effectively infer corrective models. We evaluate how this spike-timing-dependent plasticity correlates sensorimotor activity arriving through the parallel fibers with teaching signals (dependent on error estimates) arriving through the climbing fibers from the inferior olive. This paper addresses the study of how these LTD and LTP components need to be well balanced with each other to achieve accurate learning. This is of interest to evaluate the relevant role of homeostatic mechanisms in biological systems where adaptation occurs in a distributed manner. Furthermore, we illustrate how the temporal-correlation kernel can also work in the presence of transmission delays in sensorimotor pathways. We use a cerebellumlike spiking neural network which stores the corrective models as well-structured weight patterns distributed among the parallel fibers to Purkinje cell connections.

Divide et impera: subgoaling reduces the complexity of probabilistic inference and problem solving.

PubMed

Maisto, Domenico; Donnarumma, Francesco; Pezzulo, Giovanni

2015-03-06

It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabilistic inference: subgoals are selected using a sampling method that considers the descriptive complexity of the resulting sub-problems. We validate the proposed method using a standard reinforcement learning benchmark (four-rooms scenario) and show that the proposed method requires less inferential steps and permits selecting more compact control programs compared to an equivalent procedure without subgoaling. Furthermore, we show that the proposed method offers a mechanistic explanation of the neuronal dynamics found in the prefrontal cortex of monkeys that solve planning problems. Our computational framework provides a novel integrative perspective on subgoaling and its adaptive advantages for planning, control and learning, such as for example lowering cognitive effort and working memory load. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Squeezing observational data for better causal inference: Methods and examples for prevention research.

PubMed

Garcia-Huidobro, Diego; Michael Oakes, J

2017-04-01

Randomised controlled trials (RCTs) are typically viewed as the gold standard for causal inference. This is because effects of interest can be identified with the fewest assumptions, especially imbalance in background characteristics. Yet because conducting RCTs are expensive, time consuming and sometimes unethical, observational studies are frequently used to study causal associations. In these studies, imbalance, or confounding, is usually controlled with multiple regression, which entails strong assumptions. The purpose of this manuscript is to describe strengths and weaknesses of several methods to control for confounding in observational studies, and to demonstrate their use in cross-sectional dataset that use patient registration data from the Juan Pablo II Primary Care Clinic in La Pintana-Chile. The dataset contains responses from 5855 families who provided complete information on family socio-demographics, family functioning and health problems among their family members. We employ regression adjustment, stratification, restriction, matching, propensity score matching, standardisation and inverse probability weighting to illustrate the approaches to better causal inference in non-experimental data and compare results. By applying study design and data analysis techniques that control for confounding in different ways than regression adjustment, researchers may strengthen the scientific relevance of observational studies. © 2016 International Union of Psychological Science.

Decision-making in schizophrenia: A predictive-coding perspective.

PubMed

Sterzer, Philipp; Voss, Martin; Schlagenhauf, Florian; Heinz, Andreas

2018-05-31

Dysfunctional decision-making has been implicated in the positive and negative symptoms of schizophrenia. Decision-making can be conceptualized within the framework of hierarchical predictive coding as the result of a Bayesian inference process that uses prior beliefs to infer states of the world. According to this idea, prior beliefs encoded at higher levels in the brain are fed back as predictive signals to lower levels. Whenever these predictions are violated by the incoming sensory data, a prediction error is generated and fed forward to update beliefs encoded at higher levels. Well-documented impairments in cognitive decision-making support the view that these neural inference mechanisms are altered in schizophrenia. There is also extensive evidence relating the symptoms of schizophrenia to aberrant signaling of prediction errors, especially in the domain of reward and value-based decision-making. Moreover, the idea of altered predictive coding is supported by evidence for impaired low-level sensory mechanisms and motor processes. We review behavioral and neural findings from these research areas and provide an integrated view suggesting that schizophrenia may be related to a pervasive alteration in predictive coding at multiple hierarchical levels, including cognitive and value-based decision-making processes as well as sensory and motor systems. We relate these findings to decision-making processes and propose that varying degrees of impairment in the implicated brain areas contribute to the variety of psychotic experiences. Copyright © 2018 Elsevier Inc. All rights reserved.

Normalization of RNA-seq data using factor analysis of control genes or samples

PubMed Central

Risso, Davide; Ngai, John; Speed, Terence P.; Dudoit, Sandrine

2015-01-01

Normalization of RNA-seq data has proven essential to ensure accurate inference of expression levels. Here we show that usual normalization approaches mostly account for sequencing depth and fail to correct for library preparation and other more-complex unwanted effects. We evaluate the performance of the External RNA Control Consortium (ERCC) spike-in controls and investigate the possibility of using them directly for normalization. We show that the spike-ins are not reliable enough to be used in standard global-scaling or regression-based normalization procedures. We propose a normalization strategy, remove unwanted variation (RUV), that adjusts for nuisance technical effects by performing factor analysis on suitable sets of control genes (e.g., ERCC spike-ins) or samples (e.g., replicate libraries). Our approach leads to more-accurate estimates of expression fold-changes and tests of differential expression compared to state-of-the-art normalization methods. In particular, RUV promises to be valuable for large collaborative projects involving multiple labs, technicians, and/or platforms. PMID:25150836

Impact of Prematurity and Perinatal Antibiotics on the Developing Intestinal Microbiota: A Functional Inference Study.

PubMed

Arboleya, Silvia; Sánchez, Borja; Solís, Gonzalo; Fernández, Nuria; Suárez, Marta; Hernández-Barranco, Ana M; Milani, Christian; Margolles, Abelardo; de Los Reyes-Gavilán, Clara G; Ventura, Marco; Gueimonde, Miguel

2016-04-29

The microbial colonization of the neonatal gut provides a critical stimulus for normal maturation and development. This process of early microbiota establishment, known to be affected by several factors, constitutes an important determinant for later health. We studied the establishment of the microbiota in preterm and full-term infants and the impact of perinatal antibiotics upon this process in premature babies. To this end, 16S rRNA gene sequence-based microbiota assessment was performed at phylum level and functional inference analyses were conducted. Moreover, the levels of the main intestinal microbial metabolites, the short-chain fatty acids (SCFA) acetate, propionate and butyrate, were measured by Gas-Chromatography Flame ionization/Mass spectrometry detection. Prematurity affects microbiota composition at phylum level, leading to increases of Proteobacteria and reduction of other intestinal microorganisms. Perinatal antibiotic use further affected the microbiota of the preterm infant. These changes involved a concomitant alteration in the levels of intestinal SCFA. Moreover, functional inference analyses allowed for identifying metabolic pathways potentially affected by prematurity and perinatal antibiotics use. A deficiency or delay in the establishment of normal microbiota function seems to be present in preterm infants. Perinatal antibiotic use, such as intrapartum prophylaxis, affected the early life microbiota establishment in preterm newborns, which may have consequences for later health.

Assessment of PTSD symptoms in emergency room, intensive care unit, and general floor nurses.

PubMed

Kerasiotis, Bernadina; Motta, Robert W

2004-01-01

A total of 125 registered nurses participated in an investigation of Posttraumatic Stress Disorder (PTSD) related symptoms and various levels of nursing care. The sample included 43 emergency room nurses (ER), 51 intensive care unit nurses (ICU), and 31 general floor nurses (GF). All participants were assessed on measures of PTSD, social support, dissociation, anxiety, depression, and demographics. Contrary to expectations, the ER nurses were not found to be uniquely stressed by their work when compared to the other nursing groups. Results indicated that all nurses experienced a significant level of anxiety but were not in the clinically significant range for PTSD, depression, or dissociation. It was inferred that social support played a significant role in helping nurses cope with work-related stress. Nevertheless, the high anxiety levels of all nurses were highlighted as a concern. It was suggested that exposure to numerous traumatic experiences over a lifetime of nursing, and a lack of control over these experiences, contributed to the high anxiety levels seen in all nursing groups.

Ontology-Based High-Level Context Inference for Human Behavior Identification

PubMed Central

Villalonga, Claudia; Razzaq, Muhammad Asif; Khan, Wajahat Ali; Pomares, Hector; Rojas, Ignacio; Lee, Sungyoung; Banos, Oresti

2016-01-01

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users. PMID:27690050

Thorium-230 ages of corals and duration of the last interglacial sea-level high stand on Oahu, Hawaii

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

Szabo, B.J.; Ludwig, K.R.; Muhs, D.R.

1994-10-07

Thorium-230 ages of emergent marine deposits on Oahu, Hawaii, have a uniform distribution of ages from {approximately}114,000 to {approximately}131,000 years, indicating a duration for the last interglacial sea-level high stand of {approximately}17,000 years, in contrast to a duration of {approximately}8000 years inferred from the orbitally tuned marine oxygen isotope record. Sea level on Oahu rose to {>=}1 to 2 meters higher than present by 131,000 years ago or {approximately}6000 years earlier than inferred from the marine record. Although the latter record suggests a shift back to glacial conditions beginning at {approximately}119,000 years ago, the Oahu coral ages indicate a nearmore » present sea level until {approximately}114,000 years ago.« less

Coseismic water level changes induced by two distant earthquakes in multiple wells of the Chinese mainland

NASA Astrophysics Data System (ADS)

Ma, Yuchuan; Huang, Fuqiong

2017-01-01

Coseismic water level oscillations, or step-like rises and step-like drops were recorded in 159 wells throughout the Chinese mainland due to the 2015 Nepal Mw 7.8 earthquake, and 184 wells for the 2011 Japan Mw 9.0 earthquake. The earthquake magnitude, and the associated dynamic stresses, has positive roles in both the sensitivity of water level to earthquake induced change, and the amplitude and duration of resulting coseismic water level changes. Wells whose water levels are sensitive to Earth tides have high potential to response to earthquakes. Polarities of step-like changes (rises or drops) are locally controlled and spatially variable, with artesian wells generally recording water-level rises. Permeability enhancement was assessed as a mechanism responsible for step-like changes by analyzing the tidal phase responses. Permeability variations are inferred for 17 out of 95 wells with step-like changes during the Nepal earthquake and for 32 out of 105 wells following the Japan earthquake; however, only 6 wells have permeability variations after both earthquakes.

Learning and Inductive Inference

DTIC Science & Technology

1982-07-01

a set of graph grammars to describe visual scenes . Other researchers have applied graph grammars to the pattern recognition of handwritten characters...345 1. Issues / 345 2. Mostows’ operationalizer / 350 0. Learning from ezamples / 360 1. Issues / 3t60 2. Learning in control and pattern recognition ...art.icleis on rote learntinig and ailvice- tAik g. K(ennieth Clarkson contributed Ltte article on grmvit atical inference, anid Geoff’ lroiney wrote

Network Model-Assisted Inference from Respondent-Driven Sampling Data

PubMed Central

Gile, Krista J.; Handcock, Mark S.

2015-01-01

Summary Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population. PMID:26640328

Network Model-Assisted Inference from Respondent-Driven Sampling Data.

PubMed

Gile, Krista J; Handcock, Mark S

2015-06-01

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.

Quantum state and mode profile tomography by the overlap

NASA Astrophysics Data System (ADS)

Tiedau, J.; Shchesnovich, V. S.; Mogilevtsev, D.; Ansari, V.; Harder, G.; Bartley, T. J.; Korolkova, N.; Silberhorn, Ch

2018-03-01

Any measurement scheme involving interference of quantum states of the electromagnetic field necessarily mixes information about the spatiotemporal structure of these fields and quantum states in the recorded data. We show that in this case, a trade-off is possible between extracting information about the quantum states and the structure of the underlying fields, with the modal overlap being either a goal or a convenient tool of the reconstruction. We show that varying quantum states in a controlled way allows one to infer temporal profiles of modes. Vice versa, for the known quantum state of the probe and controlled variable overlap, one can infer the quantum state of the signal. We demonstrate this trade-off by performing an experiment using the simplest on-off detection in an unbalanced weak homodyning scheme. For the single-mode case, we demonstrate experimentally inference of the overlap and a few-photon signal state. Moreover, we show theoretically that the same single-detector scheme is sufficient even for arbitrary multi-mode fields.

Decision-theoretic control of EUVE telescope scheduling

NASA Technical Reports Server (NTRS)

Hansson, Othar; Mayer, Andrew

1993-01-01

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Examining the influences of tree-to-tree competition and climate on size-growth relationships in hydric, multi-aged Fraxinus nigra stands

Treesearch

Christopher E. Looney; Anthony W. D' Amato; Shawn Fraver; Brian J. Palik; Michael R. Reinikainen

2016-01-01

Most research on tree-tree competition and size-growth relationship (SGR – a stand-level metric that infers the relative efficiency with which different sized trees utilize available resources) has focused on upland systems. It is unclear if inferences from these studies extend to wetland forests. Moreover, no study to date has thoroughly investigated the relationship...

Consistency of biological networks inferred from microarray and sequencing data.

PubMed

Vinciotti, Veronica; Wit, Ernst C; Jansen, Rick; de Geus, Eco J C N; Penninx, Brenda W J H; Boomsma, Dorret I; 't Hoen, Peter A C

2016-06-24

Sparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples. Our analysis shows that individual node variances can have a remarkable effect on the connectivity of the resulting network. Their inconsistency across platforms and the fact that the variability level of a node may not be linked to its regulatory role mean that, failing to scale the data prior to the network analysis, leads to networks that are not reproducible across different platforms and that may be misleading. Moreover, we show how the reproducibility of networks across different platforms is significantly higher if networks are summarised in terms of enrichment amongst functional groups of interest, such as pathways, rather than at the level of individual edges. Careful pre-processing of transcriptional data and summaries of networks beyond individual edges can improve the consistency of network inference across platforms. However, caution is needed at this stage in the (over)interpretation of gene regulatory networks inferred from biological data.

Sea Level Change due to Time-Dependent Long-Wavelength Dynamic Topography Inferred from Plate Tectonic Reconstructions

NASA Astrophysics Data System (ADS)

Conrad, Clinton P.; Steinberger, Bernhard; Torsvik, Trond H.

2017-04-01

Earth's surface is deflected vertically by stresses associated with convective mantle flow. Although dynamic topography is important for both sea level change and continental uplift and subsidence, the time history of dynamic topography is difficult to constrain because the time-dependence of mantle flow is not known. However, the motions of the tectonic plates contain information about the mantle flow patterns that drive them. In particular, we show that the longest wavelengths of mantle flow are tightly linked to the dipole and quadrupole moments (harmonic degrees 1 and 2) of plate motions. This coupling allows us to infer patterns of long-wavelength mantle flow, and the associated dynamic topography, from tectonic plate motions. After calibrating this linkage using models of present-day mantle flow, we can use reconstructions of global plate motions to infer the basic patterns of long-wavelength dynamic topography back to 250 Ma. We find relatively stable dynamic uplift persists above large-scale mantle upwelling beneath Africa and the Central Pacific. Regions of major downwelling encircled the periphery of these stable upwellings, alternating between primarily east-west and north-south orientations. The amplitude of long-wavelength dynamic topography was likely largest in the Cretaceous, when global plate motions were fastest. Continental motions over this time-evolving dynamic topography predict patterns of continental uplift and subsidence that are confirmed by geological observations of continental surfaces relative to sea level. Net uplift or subsidence of the global seafloor can also induce eustatic sea level changes. We infer that dispersal of the Pangean supercontinent away from stable upwelling beneath Africa may have exposed the seafloor to an increasingly larger area of growing positive dynamic topography during the Mesozoic. This net uplift of the seafloor caused 60 m of sea level rise during the Triassic and Jurassic, ceasing in the Cenozoic once continents fully override degree-2 downwellings. These sea level changes represent a significant component of the estimated 200 m of sea level variations during the Phanerozoic, which exhibit a similar temporal pattern.

Inverse Bayesian inference as a key of consciousness featuring a macroscopic quantum logical structure.

PubMed

Gunji, Yukio-Pegio; Shinohara, Shuji; Haruna, Taichi; Basios, Vasileios

2017-02-01

To overcome the dualism between mind and matter and to implement consciousness in science, a physical entity has to be embedded with a measurement process. Although quantum mechanics have been regarded as a candidate for implementing consciousness, nature at its macroscopic level is inconsistent with quantum mechanics. We propose a measurement-oriented inference system comprising Bayesian and inverse Bayesian inferences. While Bayesian inference contracts probability space, the newly defined inverse one relaxes the space. These two inferences allow an agent to make a decision corresponding to an immediate change in their environment. They generate a particular pattern of joint probability for data and hypotheses, comprising multiple diagonal and noisy matrices. This is expressed as a nondistributive orthomodular lattice equivalent to quantum logic. We also show that an orthomodular lattice can reveal information generated by inverse syllogism as well as the solutions to the frame and symbol-grounding problems. Our model is the first to connect macroscopic cognitive processes with the mathematical structure of quantum mechanics with no additional assumptions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

NASA Technical Reports Server (NTRS)

Cornhill, Dennis; Bharadwaj, Raj; Mylaraswamy, Dinkar

2013-01-01

This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.

IGF-I gene variability is associated with an increased risk for AD.

PubMed

Vargas, Teo; Martinez-Garcia, Ana; Antequera, Desiree; Vilella, Elisabet; Clarimon, Jordi; Mateo, Ignacio; Sanchez-Juan, Pascual; Rodriguez-Rodriguez, Eloy; Frank, Ana; Rosich-Estrago, Marcel; Lleo, Alberto; Molina-Porcel, Laura; Blesa, Rafael; Gomez-Isla, Teresa; Combarros, Onofre; Bermejo-Pareja, Felix; Valdivieso, Fernando; Bullido, Maria Jesus; Carro, Eva

2011-03-01

Insulin-like growth factor I (IGF-I), a neuroprotective factor with a wide spectrum of actions in the adult brain, is involved in the pathogenesis of Alzheimer's disease (AD). Circulating levels of IGF-I change in AD patients and are implicated in the clearance of brain amyloid beta (Aβ) complexes. To investigate this hypothesis, we screened the IGF-I gene for various well known single nucleotide polymorphisms (SNPs) covering % of the gene variability in a population of 2352 individuals. Genetic analysis indicated different distribution of genotypes of 1 single nucleotide polymorphism, and 1 extended haplotype in the AD population compared with healthy control subjects. In particular, the frequency of rs972936 GG genotype was significantly greater in AD patients than in control subjects (63% vs. 55%). The rs972936 GG genotype was associated with an increased risk for disease, independently of apolipoprotein E genotype, and with enhanced circulating levels of IGF-I. These findings suggest that polymorphisms within the IGF-I gene could infer greater risk for AD through their effect on IGF-I levels, and confirm the physiological role IGF-I in the pathogenesis of AD. Copyright © 2011 IBRO. Published by Elsevier Inc. All rights reserved.

Control of paleoshorelines by trench forebulge uplift, Loyalty Islands

NASA Astrophysics Data System (ADS)

Dickinson, William R.

2013-07-01

Unlike most tropical Pacific islands, which lie along island arcs or hotspot chains, the Loyalty Islands between New Caledonia and Vanuatu owe their existence and morphology to the uplift of pre-existing atolls on the flexural forebulge of the New Hebrides Trench. The configuration and topography of each island is a function of distance from the crest of the uplifted forebulge. Both Maré and Lifou are fully emergent paleoatolls upon which ancient barrier reefs form highstanding annular ridges that enclose interior plateaus representing paleolagoon floors, whereas the partially emergent Ouvea paleoatoll rim flanks a drowned remnant lagoon. Emergent paleoshoreline features exposed by island uplift include paleoreef flats constructed as ancient fringing reefs built to past low tide levels and emergent tidal notches incised at past high tide levels. Present paleoshoreline elevations record uplift rates of the islands since last-interglacial and mid-Holocene highstands in global and regional sea levels, respectively, and paleoreef stratigraphy reflects net Quaternary island emergence. The empirical uplift rates vary in harmony with theoretical uplift rates inferred from the different positions of the islands in transit across the trench forebulge at the trench subduction rate. The Loyalty Islands provide a case study of island environments controlled primarily by neotectonics.

A quantum probability framework for human probabilistic inference.

PubMed

Trueblood, Jennifer S; Yearsley, James M; Pothos, Emmanuel M

2017-09-01

There is considerable variety in human inference (e.g., a doctor inferring the presence of a disease, a juror inferring the guilt of a defendant, or someone inferring future weight loss based on diet and exercise). As such, people display a wide range of behaviors when making inference judgments. Sometimes, people's judgments appear Bayesian (i.e., normative), but in other cases, judgments deviate from the normative prescription of classical probability theory. How can we combine both Bayesian and non-Bayesian influences in a principled way? We propose a unified explanation of human inference using quantum probability theory. In our approach, we postulate a hierarchy of mental representations, from 'fully' quantum to 'fully' classical, which could be adopted in different situations. In our hierarchy of models, moving from the lowest level to the highest involves changing assumptions about compatibility (i.e., how joint events are represented). Using results from 3 experiments, we show that our modeling approach explains 5 key phenomena in human inference including order effects, reciprocity (i.e., the inverse fallacy), memorylessness, violations of the Markov condition, and antidiscounting. As far as we are aware, no existing theory or model can explain all 5 phenomena. We also explore transitions in our hierarchy, examining how representations change from more quantum to more classical. We show that classical representations provide a better account of data as individuals gain familiarity with a task. We also show that representations vary between individuals, in a way that relates to a simple measure of cognitive style, the Cognitive Reflection Test. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Salivary alpha-amylase and cortisol responsiveness following electrical stimulation stress in patients with the generalized type of social anxiety disorder.

PubMed

Tamura, A; Maruyama, Y; Ishitobi, Y; Kawano, A; Ando, T; Ikeda, R; Inoue, A; Imanaga, J; Okamoto, S; Kanehisa, M; Ninomiya, T; Tanaka, Y; Tsuru, J; Akiyoshi, J

2013-11-01

Social anxiety disorder is believed to be a stress-induced disease. Although it can be inferred from the symptoms during attacks that there exists some abnormality of autonomic nervous system in any of the stress systems in social anxiety disorder, little evidence has been reported. This study focused on comparing the reactivity of 2 stress systems, the autonomic nervous system (ANS) and the hypothalamic-pituitary-adrenal (HPA) axis in patients with social anxiety disorder. 32 patients with the generalized type of social anxiety disorder were compared with 80 age- and gender-matched controls. We collected saliva samples from patients and controls before and after electrical stimulation to measure the concentrations of salivary alpha-amylase (sAA) and salivary cortisol. Profile of Mood State (POMS) and State-Trait Anxiety Inventory (STAI) scores and Heart Rate Variability (HRV) were also determined following stimulation. SAA in patients displayed a significantly higher level at baseline and a significantly larger response to electrical stimulation as compared to controls, whereas no group differences were seen in any HRV. Neither within-subject nor group differences were seen in salivary cortisol levels. These results suggest that SAD patients displayed enhanced ANS (but not HPA axis) activity vs. healthy controls. © Georg Thieme Verlag KG Stuttgart · New York.

A synthesis of the basal thermal state of the Greenland Ice Sheet

PubMed Central

MacGregor, Joseph A.; Fahnestock, Mark A.; Catania, Ginny A.; Aschwanden, Andy; Clow, Gary D.; Colgan, William T.; Gogineni, S. Prasad; Morlighem, Mathieu; Nowicki, Sophie M. J.; Paden, John D.; Price, Stephen F.; Seroussi, Hélène

2017-01-01

The basal thermal state of an ice sheet (frozen or thawed) is an important control upon its evolution, dynamics and response to external forcings. However, this state can only be observed directly within sparse boreholes or inferred conclusively from the presence of subglacial lakes. Here we synthesize spatially extensive inferences of the basal thermal state of the Greenland Ice Sheet to better constrain this state. Existing inferences include outputs from the eight thermomechanical ice-flow models included in the SeaRISE effort. New remote-sensing inferences of the basal thermal state are derived from Holocene radiostratigraphy, modern surface velocity and MODIS imagery. Both thermomechanical modeling and remote inferences generally agree that the Northeast Greenland Ice Stream and large portions of the southwestern ice-drainage systems are thawed at the bed, whereas the bed beneath the central ice divides, particularly their west-facing slopes, is frozen. Elsewhere, there is poor agreement regarding the basal thermal state. Both models and remote inferences rarely represent the borehole-observed basal thermal state accurately near NorthGRIP and DYE-3. This synthesis identifies a large portion of the Greenland Ice Sheet (about one third by area) where additional observations would most improve knowledge of its overall basal thermal state. PMID:28163988

A synthesis of the basal thermal state of the Greenland Ice Sheet.

PubMed

MacGregor, Joseph A; Fahnestock, Mark A; Catania, Ginny A; Aschwanden, Andy; Clow, Gary D; Colgan, William T; Gogineni, S Prasad; Morlighem, Mathieu; Nowicki, Sophie M J; Paden, John D; Price, Stephen F; Seroussi, Hélène

2016-08-10

The basal thermal state of an ice sheet (frozen or thawed) is an important control upon its evolution, dynamics and response to external forcings. However, this state can only be observed directly within sparse boreholes or inferred conclusively from the presence of subglacial lakes. Here we synthesize spatially extensive inferences of the basal thermal state of the Greenland Ice Sheet to better constrain this state. Existing inferences include outputs from the eight thermomechanical ice-flow models included in the SeaRISE effort. New remote-sensing inferences of the basal thermal state are derived from Holocene radiostratigraphy, modern surface velocity and MODIS imagery. Both thermomechanical modeling and remote inferences generally agree that the Northeast Greenland Ice Stream and large portions of the southwestern ice-drainage systems are thawed at the bed, whereas the bed beneath the central ice divides, particularly their west-facing slopes, is frozen. Elsewhere, there is poor agreement regarding the basal thermal state. Both models and remote inferences rarely represent the borehole-observed basal thermal state accurately near NorthGRIP and DYE-3. This synthesis identifies a large portion of the Greenland Ice Sheet (about one third by area) where additional observations would most improve knowledge of its overall basal thermal state.

A synthesis of the basal thermal state of the Greenland Ice Sheet

USGS Publications Warehouse

MacGregor, Joseph A; Fahnestock, Mark A; Catania, Ginny A; Aschwanden, Andy; Clow, Gary D.; Colgan, William T.; Gogineni, Prasad S.; Morlighem, Mathieu; Nowicki, Sophie M .J.; Paden, John D; Price, Stephen F.; Seroussi, Helene

2016-01-01

The basal thermal state of an ice sheet (frozen or thawed) is an important control upon its evolution, dynamics and response to external forcings. However, this state can only be observed directly within sparse boreholes or inferred conclusively from the presence of subglacial lakes. Here we synthesize spatially extensive inferences of the basal thermal state of the Greenland Ice Sheet to better constrain this state. Existing inferences include outputs from the eight thermomechanical ice-flow models included in the SeaRISE effort. New remote-sensing inferences of the basal thermal state are derived from Holocene radiostratigraphy, modern surface velocity and MODIS imagery. Both thermomechanical modeling and remote inferences generally agree that the Northeast Greenland Ice Stream and large portions of the southwestern ice-drainage systems are thawed at the bed, whereas the bed beneath the central ice divides, particularly their west-facing slopes, is frozen. Elsewhere, there is poor agreement regarding the basal thermal state. Both models and remote inferences rarely represent the borehole-observed basal thermal state accurately near NorthGRIP and DYE-3. This synthesis identifies a large portion of the Greenland Ice Sheet (about one third by area) where additional observations would most improve knowledge of its overall basal thermal state.

A Causal Inference Analysis of the Effect of Wildland Fire ...

EPA Pesticide Factsheets

Wildfire smoke is a major contributor to ambient air pollution levels. In this talk, we develop a spatio-temporal model to estimate the contribution of fire smoke to overall air pollution in different regions of the country. We combine numerical model output with observational data within a causal inference framework. Our methods account for aggregation and potential bias of the numerical model simulation, and address uncertainty in the causal estimates. We apply the proposed method to estimation of ozone and fine particulate matter from wildland fires and the impact on health burden assessment. We develop a causal inference framework to assess contributions of fire to ambient PM in the presence of spatial interference.

A Kinect based intelligent e-rehabilitation system in physical therapy.

PubMed

Gal, Norbert; Andrei, Diana; Nemeş, Dan Ion; Nădăşan, Emanuela; Stoicu-Tivadar, Vasile

2015-01-01

This paper presents an intelligent Kinect and fuzzy inference system based e-rehabilitation system. The Kinect can detect the posture and motion of the patients while the fuzzy inference system can interpret the acquired data on the cognitive level. The system is capable to assess the initial posture and motion ranges of 20 joints. Using angles to describe the motion of the joints, exercise patterns can be developed for each patient. Using the exercise descriptors the fuzzy inference system can track the patient and deliver real-time feedback to maximize the efficiency of the rehabilitation. The first laboratory tests confirm the utility of this system for the initial posture detection, motion range and exercise tracking.

Category inference as a function of correlational structure, category discriminability, and number of available cues.

PubMed

Lancaster, Matthew E; Shelhamer, Ryan; Homa, Donald

2013-04-01

Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.

Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control

NASA Astrophysics Data System (ADS)

Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

2007-12-01

The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.

Applying Statistical Process Control to Clinical Data: An Illustration.

ERIC Educational Resources Information Center

Pfadt, Al; And Others

1992-01-01

Principles of statistical process control are applied to a clinical setting through the use of control charts to detect changes, as part of treatment planning and clinical decision-making processes. The logic of control chart analysis is derived from principles of statistical inference. Sample charts offer examples of evaluating baselines and…

An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

NASA Astrophysics Data System (ADS)

Ajay Kumar, M.; Srikanth, N. V.

2014-03-01

In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

Inhibitory control gains from higher-order cognitive strategy training.

PubMed

Motes, Michael A; Gamino, Jacquelyn F; Chapman, Sandra B; Rao, Neena K; Maguire, Mandy J; Brier, Matthew R; Kraut, Michael A; Hart, John

2014-02-01

The present study examined the transfer of higher-order cognitive strategy training to inhibitory control. Middle school students enrolled in a comprehension- and reasoning-focused cognitive strategy training program and passive controls participated. The training program taught students a set of steps for inferring essential gist or themes from materials. Both before and after training or a comparable duration in the case of the passive controls, participants completed a semantically cued Go/No-Go task that was designed to assess the effects of depth of semantic processing on response inhibition and components of event-related potentials (ERP) related to response inhibition. Depth of semantic processing was manipulated by varying the level of semantic categorization required for response selection and inhibition. The SMART-trained group showed inhibitory control gains and changes in fronto-central P3 ERP amplitudes on inhibition trials; whereas, the control group did not. The results provide evidence of the transfer of higher-order cognitive strategy training to inhibitory control and modulation of ERPs associated with semantically cued inhibitory control. The findings are discussed in terms of implications for cognitive strategy training, models of cognitive abilities, and education. Published by Elsevier Inc.

Deductive and inductive reasoning in Parkinson's disease patients and normal controls: review and experimental evidence.

PubMed

Natsopoulos, D; Katsarou, Z; Alevriadou, A; Grouios, G; Bostantzopoulou, S; Mentenopoulos, G

1997-09-01

In the present study, fifty-four subjects were tested; twenty-seven with idiopathic Parkinson's disease and twenty-seven normal controls matched in age, education, verbal ability, level of depression, sex and socio-economic status. The subjects were tested on eight tasks. Five of the tasks were the classic deductive reasoning syllogisms, modus ponens, modus tollendo tollens, affirming the consequent, denying the antecedent and three-term series problems phrased in a factual context (brief scripts). Three of the tasks were inductive reasoning, including logical inferences, metaphors and similes. All tasks were presented to subjects in a multiple choice format. The results, overall, have shown nonsignificant differences between the two groups in deductive and inductive reasoning, an ability traditionally associated with frontal lobes involvement. Of the comparisons performed between subgroups of the patients and normal controls concerning disease duration, disease onset and predominant involvement of the left and/or right hemisphere, significant differences were found between patients with earlier disease onset and normal controls and between bilaterally affected patients and normal controls, demonstrating an additive effect of lateralization to reasoning ability.

Evaluating the inverse reasoning account of object discovery.

PubMed

Carroll, Christopher D; Kemp, Charles

2015-06-01

People routinely make inferences about unobserved objects. A hotel guest with welts on his arms, for example, will often worry about bed bugs. The discovery of unobserved objects almost always involves a backward inference from some observed effects (e.g., welts) to unobserved causes (e.g., bed bugs). The inverse reasoning account, which is typically formalized as Bayesian inference, posits that the strength of a backward inference is closely connected to the strength of the corresponding forward inference from the unobserved causes to the observed effects. We evaluated the inverse reasoning account of object discovery in three experiments where participants were asked to discover the unobserved "attractors" and "repellers" that controlled a "particle" moving within an arena. Experiments 1 and 2 showed that participants often failed to provide the best explanations for various particle motions, even when the best explanations were simple and when participants enthusiastically endorsed these explanations when presented with them. This failure demonstrates that object discovery is critically dependent on the processes that support hypothesis generation-processes that the inverse reasoning account does not explain. Experiment 3 demonstrated that people sometimes generate explanations that are invalid even according to their own forward inferences, suggesting that the psychological processes that support forward and backward inference are less intertwined than the inverse reasoning account suggests. The experimental findings support an alternative account of object discovery in which people rely on heuristics to generate possible explanations. Copyright © 2015 Elsevier B.V. All rights reserved.

The Feedback-related Negativity Codes Components of Abstract Inference during Reward-based Decision-making.

PubMed

Reiter, Andrea M F; Koch, Stefan P; Schröger, Erich; Hinrichs, Hermann; Heinze, Hans-Jochen; Deserno, Lorenz; Schlagenhauf, Florian

2016-08-01

Behavioral control is influenced not only by learning from the choices made and the rewards obtained but also by "what might have happened," that is, inference about unchosen options and their fictive outcomes. Substantial progress has been made in understanding the neural signatures of direct learning from choices that are actually made and their associated rewards via reward prediction errors (RPEs). However, electrophysiological correlates of abstract inference in decision-making are less clear. One seminal theory suggests that the so-called feedback-related negativity (FRN), an ERP peaking 200-300 msec after a feedback stimulus at frontocentral sites of the scalp, codes RPEs. Hitherto, the FRN has been predominantly related to a so-called "model-free" RPE: The difference between the observed outcome and what had been expected. Here, by means of computational modeling of choice behavior, we show that individuals employ abstract, "double-update" inference on the task structure by concurrently tracking values of chosen stimuli (associated with observed outcomes) and unchosen stimuli (linked to fictive outcomes). In a parametric analysis, model-free RPEs as well as their modification because of abstract inference were regressed against single-trial FRN amplitudes. We demonstrate that components related to abstract inference uniquely explain variance in the FRN beyond model-free RPEs. These findings advance our understanding of the FRN and its role in behavioral adaptation. This might further the investigation of disturbed abstract inference, as proposed, for example, for psychiatric disorders, and its underlying neural correlates.

STATISTICAL RELATIONAL LEARNING AND SCRIPT INDUCTION FOR TEXTUAL INFERENCE

DTIC Science & Technology

2017-12-01

E 23rd St Austin , TX 78712 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) Air Force Research ...Processing (EMNLP), Austin , TX , 2016. 
 Pichotta, K. and Mooney, R.J., “Using Sentence-Level LSTM Language Models for Script Inference,” Proceedings of the...on Uphill Battles in Language Processing, Austin , TX , 2016. 
 Rajani, N., and Mooney, R. J., “Stacked Ensembles of Information Extractors for

Health Systems' Responsiveness and Its Characteristics: A Cross-Country Comparative Analysis

PubMed Central

Robone, Silvana; Rice, Nigel; Smith, Peter C

2011-01-01

Objectives Responsiveness has been identified as one of the intrinsic goals of health care systems. Little is known, however, about its determinants. Our objective is to investigate the potential country-level drivers of health system responsiveness. Data Source Data on responsiveness are taken from the World Health Survey. Information on country-level characteristics is obtained from a variety of sources including the United Nations Development Program (UNDP). Study Design A two-step procedure. First, using survey data we derive a country-level measure of system responsiveness purged of differences in individual reporting behavior. Secondly, we run cross-sectional country-level regressions of responsiveness on potential drivers. Principal Findings Health care expenditures per capita are positively associated with responsiveness, after controlling for the influence of potential confounding factors. Aspects of responsiveness are also associated with public sector spending (negatively) and educational development (positively). Conclusions From a policy perspective, improvements in responsiveness may require higher spending levels. The expansion of nonpublic sector provision, perhaps in the form of increased patient choice, may also serve to improve responsiveness. However, these inferences are tentative and require further study. PMID:21762144

Multiple independent origins of mitochondrial control region duplications in the order Psittaciformes

PubMed Central

Schirtzinger, Erin E.; Tavares, Erika S.; Gonzales, Lauren A.; Eberhard, Jessica R.; Miyaki, Cristina Y.; Sanchez, Juan J.; Hernandez, Alexis; Müeller, Heinrich; Graves, Gary R.; Fleischer, Robert C.; Wright, Timothy F.

2012-01-01

Mitochondrial genomes are generally thought to be under selection for compactness, due to their small size, consistent gene content, and a lack of introns or intergenic spacers. As more animal mitochondrial genomes are fully sequenced, rearrangements and partial duplications are being identified with increasing frequency, particularly in birds (Class Aves). In this study, we investigate the evolutionary history of mitochondrial control region states within the avian order Psittaciformes (parrots and cockatoos). To this aim, we reconstructed a comprehensive multi-locus phylogeny of parrots, used PCR of three diagnostic fragments to classify the mitochondrial control region state as single or duplicated, and mapped these states onto the phylogeny. We further sequenced 44 selected species to validate these inferences of control region state. Ancestral state reconstruction using a range of weighting schemes identified six independent origins of mitochondrial control region duplications within Psittaciformes. Analysis of sequence data showed that varying levels of mitochondrial gene and tRNA homology and degradation were present within a given clade exhibiting duplications. Levels of divergence between control regions within an individual varied from 0–10.9% with the differences occurring mainly between 51 and 225 nucleotides 3′ of the goose hairpin in domain I. Further investigations into the fates of duplicated mitochondrial genes, the potential costs and benefits of having a second control region, and the complex relationship between evolutionary rates, selection, and time since duplication are needed to fully explain these patterns in the mitochondrial genome. PMID:22543055

Usefulness of near-infrared spectroscopy to detect brain dysfunction in children with autism spectrum disorder when inferring the mental state of others.

PubMed

Iwanaga, Ryoichiro; Tanaka, Goro; Nakane, Hideyuki; Honda, Sumihisa; Imamura, Akira; Ozawa, Hiroki

2013-05-01

The purpose of this study was to examine the usefulness of near-infrared spectroscopy (NIRS) for identifying abnormalities in prefrontal brain activity in children with autism spectrum disorders (ASD) as they inferred the mental states of others. The subjects were 16 children with ASD aged between 8 and 14 years and 16 age-matched healthy control children. Oxygenated hemoglobin concentration was measured in the subject's prefrontal brain region on NIRS during tasks expressing a person's mental state (MS task) and expressing an object's characteristics (OC task). There was a significant main effect of group (ASD vs control), with the control group having more activity than the ASD group. But there was no significant main effect of task (MS task vs OC task) or hemisphere (right vs left). Significant interactions of task and group were found, with the control group showing more activity than the ASD group during the MS task relative to the OC task. NIRS showed that there was lower activity in the prefrontal brain area when children with ASD performed MS tasks. Therefore, clinicians might be able to use NIRS and these tasks for conveniently detecting brain dysfunction in children with ASD related to inferring mental states, in the clinical setting. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

Childhood adversity and cognitive function in schizophrenia spectrum disorders and healthy controls: evidence for an association between neglect and social cognition.

PubMed

Kilian, S; Asmal, L; Chiliza, B; Olivier, M R; Phahladira, L; Scheffler, F; Seedat, S; Marder, S R; Green, M F; Emsley, R

2017-12-22

Childhood adversity is associated with cognitive impairments in schizophrenia. However, findings to date are inconsistent and little is known about the relationship between social cognition and childhood trauma. We investigated the relationship between childhood abuse and neglect and cognitive function in patients with a first-episode of schizophrenia or schizophreniform disorder (n = 56) and matched healthy controls (n = 52). To the best of our knowledge, this is the first study assessing this relationship in patients and controls exposed to similarly high levels of trauma. Pearson correlational coefficients were used to assess correlations between Childhood Trauma Questionnaire abuse and neglect scores and cognition. For the MCCB domains displaying significant (p < 0.05) correlations, within group hierarchical linear regression, was done to assess whether abuse and neglect were significant predictors of cognition after controlling for the effect of education. Patients and controls reported similarly high levels of abuse and neglect. Cognitive performance was poorer for patients compared with controls for all cognitive domains except working memory and social cognition. After controlling for education, exposure to childhood neglect remained a significant predictor of impairment in social cognition in both patients and controls. Neglect was also a significant predictor of poorer verbal learning in patients and of attention/vigilance in controls. However, childhood abuse did not significantly predict cognitive impairments in either patients or controls. These findings are cross sectional and do not infer causality. Nonetheless, they indicate that associations between one type of childhood adversity (i.e. neglect) and social cognition are present and are not illness-specific.

Silica exposure during construction activities: statistical modeling of task-based measurements from the literature.

PubMed

Sauvé, Jean-François; Beaudry, Charles; Bégin, Denis; Dion, Chantal; Gérin, Michel; Lavoué, Jérôme

2013-05-01

Many construction activities can put workers at risk of breathing silica containing dusts, and there is an important body of literature documenting exposure levels using a task-based strategy. In this study, statistical modeling was used to analyze a data set containing 1466 task-based, personal respirable crystalline silica (RCS) measurements gathered from 46 sources to estimate exposure levels during construction tasks and the effects of determinants of exposure. Monte-Carlo simulation was used to recreate individual exposures from summary parameters, and the statistical modeling involved multimodel inference with Tobit models containing combinations of the following exposure variables: sampling year, sampling duration, construction sector, project type, workspace, ventilation, and controls. Exposure levels by task were predicted based on the median reported duration by activity, the year 1998, absence of source control methods, and an equal distribution of the other determinants of exposure. The model containing all the variables explained 60% of the variability and was identified as the best approximating model. Of the 27 tasks contained in the data set, abrasive blasting, masonry chipping, scabbling concrete, tuck pointing, and tunnel boring had estimated geometric means above 0.1mg m(-3) based on the exposure scenario developed. Water-fed tools and local exhaust ventilation were associated with a reduction of 71 and 69% in exposure levels compared with no controls, respectively. The predictive model developed can be used to estimate RCS concentrations for many construction activities in a wide range of circumstances.

An algebra-based method for inferring gene regulatory networks.

PubMed

Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

2014-03-26

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network. Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.

40 CFR 312.10 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

.... Institutional controls means: non-engineered instruments, such as administrative and/or legal controls, that... be presumed to be deserted, or an intent to relinquish possession or control can be inferred from the..., tribe, or U.S. territory (or the Commonwealth of Puerto Rico) and have the equivalent of three (3) years...

40 CFR 312.10 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

.... Institutional controls means: non-engineered instruments, such as administrative and/or legal controls, that... be presumed to be deserted, or an intent to relinquish possession or control can be inferred from the..., tribe, or U.S. territory (or the Commonwealth of Puerto Rico) and have the equivalent of three (3) years...

40 CFR 312.10 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... Institutional controls means: non-engineered instruments, such as administrative and/or legal controls, that... be presumed to be deserted, or an intent to relinquish possession or control can be inferred from the..., tribe, or U.S. territory (or the Commonwealth of Puerto Rico) and have the equivalent of three (3) years...

A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.

PubMed

Nasejje, Justine B; Mwambi, Henry; Dheda, Keertan; Lesosky, Maia

2017-07-28

Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.

Inferential control -- Part 1: Crude unit advanced controls pass accuracy and repeatability tests

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

San, Y.P.; Landells, K.C.; Mackay, D.C.

1994-11-28

An inferential model is one that provides a quality for which an analyzer is not available. This type of model uses readily available physical measurements -- such as temperatures, pressures, and flow rates -- to infer a quality such as kerosine flash point. The No. 2 crude distillation unit (CDU-2) at Singapore Refining Co. Pte. Ltd.'s Pulau Merlimau refinery has a nominal 130,000 b/d capacity. It produces naphtha, kerosine, diesel, and residue products from a wide range of crude blends. Over the past 12 months, extensive advanced control applications have been implemented on the unit. This first of two articlesmore » will describe the control system and its implementation. The second will outline the project's achievements, including reduced quality giveaway and increased profits. The paper describes background of the company and unit, the process, project implementation, the Infer model, model tuning, closed-loop control, feed rate maximization, and economic monitoring.« less

Children Reading to Dogs: A Systematic Review of the Literature.

PubMed

Hall, Sophie Susannah; Gee, Nancy R; Mills, Daniel Simon

2016-01-01

Despite growing interest in the value of human-animal interactions (HAI) to human mental and physical health the quality of the evidence on which postulated benefits from animals to human psychological health are based is often unclear. To date there exist no systematic reviews on the effects of HAI in educational settings specifically focussing on the perceived benefits to children of reading to dogs. With rising popularity and implementation of these programmes in schools, it is essential that the evidence base exploring the pedagogic value of these initiatives is well documented. Using PRISMA guidelines we systematically investigated the literature reporting the pedagogic effects of reading to dogs. Because research in this area is in the early stages of scientific enquiry we adopted broad inclusion criteria, accepting all reports which discussed measurable effects related to the topic that were written in English. Multiple online databases were searched during January-March 2015; grey literature searches were also conducted. The search results which met the inclusion criteria were evaluated, and discussed, in relation to the Oxford Centre for Evidence Based Medicine levels of evidence; 27 papers were classified as Level 5, 13 as Level 4, 7 as Level 2c and 1 as Level 2b. The evidence suggests that reading to a dog may have a beneficial effect on a number of behavioural processes which contribute to a positive effect on the environment in which reading is practiced, leading to improved reading performance. However, the evidence base on which these inferences are made is of low quality. There is a clear need for the use of higher quality research methodologies and the inclusion of appropriate controls in order to draw causal inferences on whether or how reading to dogs may benefit children's reading practices. The mechanisms for any effect remain a matter of conjecture.

Children Reading to Dogs: A Systematic Review of the Literature

PubMed Central

Hall, Sophie Susannah; Gee, Nancy R.; Mills, Daniel Simon

2016-01-01

Background Despite growing interest in the value of human-animal interactions (HAI) to human mental and physical health the quality of the evidence on which postulated benefits from animals to human psychological health are based is often unclear. To date there exist no systematic reviews on the effects of HAI in educational settings specifically focussing on the perceived benefits to children of reading to dogs. With rising popularity and implementation of these programmes in schools, it is essential that the evidence base exploring the pedagogic value of these initiatives is well documented. Methods Using PRISMA guidelines we systematically investigated the literature reporting the pedagogic effects of reading to dogs. Because research in this area is in the early stages of scientific enquiry we adopted broad inclusion criteria, accepting all reports which discussed measurable effects related to the topic that were written in English. Multiple online databases were searched during January-March 2015; grey literature searches were also conducted. The search results which met the inclusion criteria were evaluated, and discussed, in relation to the Oxford Centre for Evidence Based Medicine levels of evidence; 27 papers were classified as Level 5, 13 as Level 4, 7 as Level 2c and 1 as Level 2b. Conclusion The evidence suggests that reading to a dog may have a beneficial effect on a number of behavioural processes which contribute to a positive effect on the environment in which reading is practiced, leading to improved reading performance. However, the evidence base on which these inferences are made is of low quality. There is a clear need for the use of higher quality research methodologies and the inclusion of appropriate controls in order to draw causal inferences on whether or how reading to dogs may benefit children’s reading practices. The mechanisms for any effect remain a matter of conjecture. PMID:26901412

Consumer understanding, interpretation and perceived levels of personal responsibility in relation to satiety-related claims.

PubMed

Bilman, Els M; Kleef, Ellen van; Mela, David J; Hulshof, Toine; van Trijp, Hans C M

2012-12-01

The aim of this study was to explore (a) whether and how consumers may (over-) interpret satiety claims, and (b) whether and to what extent consumers recognize that personal efforts are required to realize possible satiety-related or weight loss benefits. Following means-end chain theory, we explored for a number of satiety claims the extent of inference-making to higher-level benefits than actually stated in the claim, using internet-based questions and tasks. Respondents (N=1504) in U.K., France, Italy and Germany participated in the study. The majority of these respondents correctly interpret satiety-related claims; i.e. they largely limit their interpretation to what was actually stated. They do not expect a "magic bullet" effect, but understand that personal efforts are required to translate product attributes into potential weight control benefits. Less-restrained eaters were at lower risk for over-interpreting satiety-related claims, whilst respondents with a stronger belief that their weight is something that they can control accept more personal responsibility, and better understand that personal efforts are required to be effective in weight control. Overall, these results indicate there is likely to be a relatively low level of consumer misinterpretation of satiety-related claims on food products. Copyright © 2012 International Life Sciences Institute. Published by Elsevier Ltd.. All rights reserved.

Assessment of test methods for evaluating effectiveness of cleaning flexible endoscopes.

PubMed

Washburn, Rebecca E; Pietsch, Jennifer J

2018-06-01

Strict adherence to each step of reprocessing is imperative to removing potentially infectious agents. Multiple methods for verifying proper reprocessing exist; however, each presents challenges and limitations, and best practice within the industry has not been established. Our goal was to evaluate endoscope cleaning verification tests with particular interest in the evaluation of the manual cleaning step. The results of the cleaning verification tests were compared with microbial culturing to see if a positive cleaning verification test would be predictive of microbial growth. This study was conducted at 2 high-volume endoscopy units within a multisite health care system. Each of the 90 endoscopes were tested for adenosine triphosphate, protein, microbial growth via agar plate, and rapid gram-negative culture via assay. The endoscopes were tested in 3 locations: the instrument channel, control knob, and elevator mechanism. This analysis showed substantial level of agreement between protein detection postmanual cleaning and protein detection post-high-level disinfection at the control head for scopes sampled sequentially. This study suggests that if protein is detected postmanual cleaning, there is a significant likelihood that protein will also be detected post-high-level disinfection. It also infers that a cleaning verification test is not predictive of microbial growth. Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Social inequalities in residential exposure to road traffic noise: an environmental justice analysis based on the RECORD Cohort Study.

PubMed

Havard, Sabrina; Reich, Brian J; Bean, Kathy; Chaix, Basile

2011-05-01

To explore social inequalities in residential exposure to road traffic noise in an urban area. Environmental injustice in road traffic noise exposure was investigated in Paris, France, using the RECORD Cohort Study (n = 2130) and modelled noise data. Associations were assessed by estimating noise exposure within the local area around participants' residence, considering various socioeconomic variables defined at both individual and neighbourhood level, and comparing different regression models attempting or not to control for spatial autocorrelation in noise levels. After individual-level adjustment, participants' noise exposure increased with neighbourhood educational level and dwelling value but also with proportion of non-French citizens, suggesting seemingly contradictory findings. However, when country of citizenship was defined according to its human development level, noise exposure in fact increased and decreased with the proportions of citizens from advantaged and disadvantaged countries, respectively. These findings were consistent with those reported for the other socioeconomic characteristics, suggesting higher road traffic noise exposure in advantaged neighbourhoods. Substantial collinearity between neighbourhood explanatory variables and spatial random effects caused identifiability problems that prevented successful control for spatial autocorrelation. Contrary to previous literature, this study shows that people living in advantaged neighbourhoods were more exposed to road traffic noise in their residential environment than their deprived counterparts. This case study demonstrates the need to systematically perform sensitivity analyses with multiple socioeconomic characteristics to avoid incorrect inferences about an environmental injustice situation and the complexity of effectively controlling for spatial autocorrelation when fixed and random components of the model are correlated.

Computational Psychosomatics and Computational Psychiatry: Toward a Joint Framework for Differential Diagnosis.

PubMed

Petzschner, Frederike H; Weber, Lilian A E; Gard, Tim; Stephan, Klaas E

2017-09-15

This article outlines how a core concept from theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differential diagnosis in computational psychiatry and computational psychosomatics. In particular, we discuss 1) how conceptualizing perception and action as inference-control loops yields a joint computational perspective on brain-world and brain-body interactions and 2) how the concrete formulation of this loop as a hierarchical Bayesian model points to key computational quantities that inform a taxonomy of potential disease mechanisms. We consider the utility of this perspective for differential diagnosis in concrete clinical applications. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Bayesian statistical inference enhances the interpretation of contemporary randomized controlled trials.

PubMed

Wijeysundera, Duminda N; Austin, Peter C; Hux, Janet E; Beattie, W Scott; Laupacis, Andreas

2009-01-01

Randomized trials generally use "frequentist" statistics based on P-values and 95% confidence intervals. Frequentist methods have limitations that might be overcome, in part, by Bayesian inference. To illustrate these advantages, we re-analyzed randomized trials published in four general medical journals during 2004. We used Medline to identify randomized superiority trials with two parallel arms, individual-level randomization and dichotomous or time-to-event primary outcomes. Studies with P<0.05 in favor of the intervention were deemed "positive"; otherwise, they were "negative." We used several prior distributions and exact conjugate analyses to calculate Bayesian posterior probabilities for clinically relevant effects. Of 88 included studies, 39 were positive using a frequentist analysis. Although the Bayesian posterior probabilities of any benefit (relative risk or hazard ratio<1) were high in positive studies, these probabilities were lower and variable for larger benefits. The positive studies had only moderate probabilities for exceeding the effects that were assumed for calculating the sample size. By comparison, there were moderate probabilities of any benefit in negative studies. Bayesian and frequentist analyses complement each other when interpreting the results of randomized trials. Future reports of randomized trials should include both.

Characterizing Behavioral and Brain Changes Associated with Practicing Reasoning Skills

PubMed Central

Mackey, Allyson P.; Miller Singley, Alison T.; Wendelken, Carter; Bunge, Silvia A.

2015-01-01

We have reported previously that intensive preparation for a standardized test that taxes reasoning leads to changes in structural and functional connectivity within the frontoparietal network. Here, we investigated whether reasoning instruction transfers to improvement on unpracticed tests of reasoning, and whether these improvements are associated with changes in neural recruitment during reasoning task performance. We found behavioral evidence for transfer to a transitive inference task, but no evidence for transfer to a rule generation task. Across both tasks, we observed reduced lateral prefrontal activation in the trained group relative to the control group, consistent with other studies of practice-related changes in brain activation. In the transitive inference task, we observed enhanced suppression of task-negative, or default-mode, regions, consistent with work suggesting that better cognitive skills are associated with more efficient switching between networks. In the rule generation task, we found a pattern consistent with a training-related shift in the balance between phonological and visuospatial processing. Broadly, we discuss general methodological considerations related to the analysis and interpretation of training-related changes in brain activation. In summary, we present preliminary evidence for changes in brain activation associated with practice of high-level cognitive skills. PMID:26368278

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

PubMed

Chen, Chen; Tong, Hanghang; Xie, Lei; Ying, Lei; He, Qing

2017-08-01

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.

Phylogeographic History and Gene Flow Among Giant Galápagos Tortoises on Southern Isabela Island

PubMed Central

Ciofi, Claudio; Wilson, Gregory A.; Beheregaray, Luciano B.; Marquez, Cruz; Gibbs, James P.; Tapia, Washington; Snell, Howard L.; Caccone, Adalgisa; Powell, Jeffrey R.

2006-01-01

Volcanic islands represent excellent models with which to study the effect of vicariance on colonization and dispersal, particularly when the evolution of genetic diversity mirrors the sequence of geological events that led to island formation. Phylogeographic inference, however, can be particularly challenging for recent dispersal events within islands, where the antagonistic effects of land bridge formation and vicariance can affect movements of organisms with limited dispersal ability. We investigated levels of genetic divergence and recovered signatures of dispersal events for 631 Galápagos giant tortoises across the volcanoes of Sierra Negra and Cerro Azul on the island of Isabela. These volcanoes are among the most recent formations in the Galápagos (<0.7 million years), and previous studies based on genetic and morphological data could not recover a consistent pattern of lineage sorting. We integrated nested clade analysis of mitochondrial DNA control region sequences, to infer historical patterns of colonization, and a novel Bayesian multilocus genotyping method for recovering evidence of recent migration across volcanoes using eleven microsatellite loci. These genetic studies illuminate taxonomic distinctions as well as provide guidance to possible repatriation programs aimed at countering the rapid population declines of these spectacular animals. PMID:16387883

Phylogeographic history and gene flow among giant Galápagos tortoises on southern Isabela Island.

PubMed

Ciofi, Claudio; Wilson, Gregory A; Beheregaray, Luciano B; Marquez, Cruz; Gibbs, James P; Tapia, Washington; Snell, Howard L; Caccone, Adalgisa; Powell, Jeffrey R

2006-03-01

Volcanic islands represent excellent models with which to study the effect of vicariance on colonization and dispersal, particularly when the evolution of genetic diversity mirrors the sequence of geological events that led to island formation. Phylogeographic inference, however, can be particularly challenging for recent dispersal events within islands, where the antagonistic effects of land bridge formation and vicariance can affect movements of organisms with limited dispersal ability. We investigated levels of genetic divergence and recovered signatures of dispersal events for 631 Galápagos giant tortoises across the volcanoes of Sierra Negra and Cerro Azul on the island of Isabela. These volcanoes are among the most recent formations in the Galápagos (<0.7 million years), and previous studies based on genetic and morphological data could not recover a consistent pattern of lineage sorting. We integrated nested clade analysis of mitochondrial DNA control region sequences, to infer historical patterns of colonization, and a novel Bayesian multilocus genotyping method for recovering evidence of recent migration across volcanoes using eleven microsatellite loci. These genetic studies illuminate taxonomic distinctions as well as provide guidance to possible repatriation programs aimed at countering the rapid population declines of these spectacular animals.

Direct elicitation of template concentration from quantification cycle (Cq) distributions in digital PCR.

PubMed

Mojtahedi, Mitra; Fouquier d'Hérouël, Aymeric; Huang, Sui

2014-01-01

Digital PCR (dPCR) exploits limiting dilution of a template into an array of PCR reactions. From this array the number of reactions that contain at least one (as opposed to zero) initial template is determined, allowing inferring the original template concentration. Here we present a novel protocol to efficiently infer the concentration of a sample and its optimal dilution for dPCR from few targeted qPCR assays. By taking advantage of the real-time amplification feature of qPCR as opposed to relying on endpoint PCR assessment as in standard dPCR prior knowledge of template concentration is not necessary. This eliminates the need for serial dilutions in a separate titration and reduces the number of necessary reactions. We describe the theory underlying our approach and discuss experimental moments that contribute to uncertainty. We present data from a controlled experiment where the initial template concentration is known as proof of principle and apply our method on directly monitoring transcript level change during cell differentiation as well as gauging amplicon numbers in cDNA samples after pre-amplification. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

PubMed Central

Hamey, Fiona K.; Nestorowa, Sonia; Kinston, Sarah J.; Kent, David G.; Wilson, Nicola K.

2017-01-01

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte–erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships. PMID:28584094

Transcriptome inference and systems approaches to polypharmacology and drug discovery in herbal medicine.

PubMed

Li, Peng; Chen, Jianxin; Zhang, Wuxia; Fu, Bangze; Wang, Wei

2017-01-04

Herbal medicine is a concoction of numerous chemical ingredients, and it exhibits polypharmacological effects to act on multiple pharmacological targets, regulating different biological mechanisms and treating a variety of diseases. Thus, this complexity is impossible to deconvolute by the reductionist method of extracting one active ingredient acting on one biological target. To dissect the polypharmacological effects of herbal medicines and their underling pharmacological targets as well as their corresponding active ingredients. We propose a system-biology strategy that combines omics and bioinformatical methodologies for exploring the polypharmacology of herbal mixtures. The myocardial ischemia model was induced by Ameroid constriction of the left anterior descending coronary in Ba-Ma miniature pigs. RNA-seq analysis was utilized to find the differential genes induced by myocardial ischemia in pigs treated with formula QSKL. A transcriptome-based inference method was used to find the landmark drugs with similar mechanisms to QSKL. Gene-level analysis of RNA-seq data in QSKL-treated cases versus control animals yields 279 differential genes. Transcriptome-based inference methods identified 80 landmark drugs that covered nearly all drug classes. Then, based on the landmark drugs, 155 potential pharmacological targets and 57 indications were identified for QSKL. Our results demonstrate the power of a combined approach for exploring the pharmacological target and chemical space of herbal medicines. We hope that our method could enhance our understanding of the molecular mechanisms of herbal systems and further accelerate the exploration of the value of traditional herbal medicine systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Straightforward Inference of Ancestry and Admixture Proportions through Ancestry-Informative Insertion Deletion Multiplexing

PubMed Central

Pereira, Rui; Phillips, Christopher; Pinto, Nádia; Santos, Carla; dos Santos, Sidney Emanuel Batista; Amorim, António; Carracedo, Ángel; Gusmão, Leonor

2012-01-01

Ancestry-informative markers (AIMs) show high allele frequency divergence between different ancestral or geographically distant populations. These genetic markers are especially useful in inferring the likely ancestral origin of an individual or estimating the apportionment of ancestry components in admixed individuals or populations. The study of AIMs is of great interest in clinical genetics research, particularly to detect and correct for population substructure effects in case-control association studies, but also in population and forensic genetics studies. This work presents a set of 46 ancestry-informative insertion deletion polymorphisms selected to efficiently measure population admixture proportions of four different origins (African, European, East Asian and Native American). All markers are analyzed in short fragments (under 230 basepairs) through a single PCR followed by capillary electrophoresis (CE) allowing a very simple one tube PCR-to-CE approach. HGDP-CEPH diversity panel samples from the four groups, together with Oceanians, were genotyped to evaluate the efficiency of the assay in clustering populations from different continental origins and to establish reference databases. In addition, other populations from diverse geographic origins were tested using the HGDP-CEPH samples as reference data. The results revealed that the AIM-INDEL set developed is highly efficient at inferring the ancestry of individuals and provides good estimates of ancestry proportions at the population level. In conclusion, we have optimized the multiplexed genotyping of 46 AIM-INDELs in a simple and informative assay, enabling a more straightforward alternative to the commonly available AIM-SNP typing methods dependent on complex, multi-step protocols or implementation of large-scale genotyping technologies. PMID:22272242

Miocene-Pliocene ice-volcano interactions at monogenetic volcanoes near Hobbs Coast, Marie Byrd Land, Antarctica

USGS Publications Warehouse

Wilch, T.I.; McIntosh, W.C.

2007-01-01

Ar geochronology of seven eroded monogenetic volcanoes near the Hobbs Coast, Marie Byrd Land, West Antarctica provide proxy records of WAIS paleo-ice-levels in Miocene-Pliocene times. Interpretations, based on lithofacies analysis, indicate whether the volcanoes erupted below, near, or above the level of the ice sheet. Our interpretations differ significantly from previous interpretations as they highlight the abundant evidence for ice-volcano interactions at emergent paleoenvironments but limited evidence of higher-than-present syn-eruptive ice-levels. Evidence for subglacial volcanic paleoenvironments is limited to Kennel Peak, a ~8 Ma volcano where a pillow lava sequence extending 25 m above current ice level overlies an inferred glacial till and unconformity. A major complication in the Hobbs Coast region is that the volcanism occurred on interfluves between regions of fast-flowing ice. Such a setting precludes establishing precise regional paleo-ice-levels although the presence or absence of ice at times of eruptions can be inferred.

Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm. | Office of Cancer Genomics

Cancer.gov

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined.

Reinforce: An Ensemble Approach for Inferring PPI Network from AP-MS Data.

PubMed

Tian, Bo; Duan, Qiong; Zhao, Can; Teng, Ben; He, Zengyou

2017-05-17

Affinity Purification-Mass Spectrometry (AP-MS) is one of the most important technologies for constructing protein-protein interaction (PPI) networks. In this paper, we propose an ensemble method, Reinforce, for inferring PPI network from AP-MS data set. The new algorithm named Reinforce is based on rank aggregation and false discovery rate control. Under the null hypothesis that the interaction scores from different scoring methods are randomly generated, Reinforce follows three steps to integrate multiple ranking results from different algorithms or different data sets. The experimental results show that Reinforce can get more stable and accurate inference results than existing algorithms. The source codes of Reinforce and data sets used in the experiments are available at: https://sourceforge.net/projects/reinforce/.

Statistics for nuclear engineers and scientists. Part 1. Basic statistical inference

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

Beggs, W.J.

1981-02-01

This report is intended for the use of engineers and scientists working in the nuclear industry, especially at the Bettis Atomic Power Laboratory. It serves as the basis for several Bettis in-house statistics courses. The objectives of the report are to introduce the reader to the language and concepts of statistics and to provide a basic set of techniques to apply to problems of the collection and analysis of data. Part 1 covers subjects of basic inference. The subjects include: descriptive statistics; probability; simple inference for normally distributed populations, and for non-normal populations as well; comparison of two populations; themore » analysis of variance; quality control procedures; and linear regression analysis.« less

Heat and Freshwater Convergence Anomalies in the Atlantic Ocean Inferred from Observations

NASA Astrophysics Data System (ADS)

Kelly, K. A.; Drushka, K.; Thompson, L.

2015-12-01

Observations of thermosteric and halosteric sea level from hydrographic data, ocean mass from GRACE and altimetric sea surface height are used to infer meridional heat transport (MHT) and freshwater convergence (FWC) anomalies for the Atlantic Ocean. An "unknown control" version of a Kalman filter in each of eight regions extracts smooth estimates of heat transport convergence (HTC) and FWC from discrepancies between the sea level response to monthly surface heat and freshwater fluxes and observed heat and freshwater content. The model is run for 1993-2014. Estimates of MHT anomalies are derived by summing the HTC from north to south and adding a spatially uniform, time-varying MHT derived from updated MHT estimates at 41N (Willis 2010). Estimated anomalies in MHT are comparable to those recently observed at the RAPID/MOCHA line at 26.5N. MHT estimates are relatively insensitive to the choice of heat flux products and are highly coherent spatially. MHT anomalies at 35S resemble estimates of Agulhas Leakage derived from altimeter (LeBars et al 2014) suggesting that the Indian Ocean is the source of the anomalous heat inflow. FWC estimates in the Atlantic Ocean (67N to 35S) resemble estimates of Atlantic river inflow (de Couet and Maurer, GRDC 2009). Increasing values of FWC after 2002 at a time when MHT was decreasing may indicate a feedback between the Atlantic Meridional Overturning Circulation and FWC that would accelerate the AMOC slowdown.

Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation

NASA Astrophysics Data System (ADS)

Carreau, J.; Naveau, P.; Neppel, L.

2017-05-01

The French Mediterranean is subject to intense precipitation events occurring mostly in autumn. These can potentially cause flash floods, the main natural danger in the area. The distribution of these events follows specific spatial patterns, i.e., some sites are more likely to be affected than others. The peaks-over-threshold approach consists in modeling extremes, such as heavy precipitation, by the generalized Pareto (GP) distribution. The shape parameter of the GP controls the probability of extreme events and can be related to the hazard level of a given site. When interpolating across a region, the shape parameter should reproduce the observed spatial patterns of the probability of heavy precipitation. However, the shape parameter estimators have high uncertainty which might hide the underlying spatial variability. As a compromise, we choose to let the shape parameter vary in a moderate fashion. More precisely, we assume that the region of interest can be partitioned into subregions with constant hazard level. We formalize the model as a conditional mixture of GP distributions. We develop a two-step inference strategy based on probability weighted moments and put forward a cross-validation procedure to select the number of subregions. A synthetic data study reveals that the inference strategy is consistent and not very sensitive to the selected number of subregions. An application on daily precipitation data from the French Mediterranean shows that the conditional mixture of GPs outperforms two interpolation approaches (with constant or smoothly varying shape parameter).

Statistical modeling of software reliability

NASA Technical Reports Server (NTRS)

Miller, Douglas R.

1992-01-01

This working paper discusses the statistical simulation part of a controlled software development experiment being conducted under the direction of the System Validation Methods Branch, Information Systems Division, NASA Langley Research Center. The experiment uses guidance and control software (GCS) aboard a fictitious planetary landing spacecraft: real-time control software operating on a transient mission. Software execution is simulated to study the statistical aspects of reliability and other failure characteristics of the software during development, testing, and random usage. Quantification of software reliability is a major goal. Various reliability concepts are discussed. Experiments are described for performing simulations and collecting appropriate simulated software performance and failure data. This data is then used to make statistical inferences about the quality of the software development and verification processes as well as inferences about the reliability of software versions and reliability growth under random testing and debugging.

Active interoceptive inference and the emotional brain

PubMed Central

Friston, Karl J.

2016-01-01

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’. PMID:28080966

A method for inferring regional origins of neurodegeneration.

PubMed

Torok, Justin; Maia, Pedro D; Powell, Fon; Pandya, Sneha; Raj, Ashish

2018-02-02

Alzheimer's disease, the most common form of dementia, is characterized by the emergence and spread of senile plaques and neurofibrillary tangles, causing widespread neurodegeneration. Though the progression of Alzheimer's disease is considered to be stereotyped, the significant variability within clinical populations obscures this interpretation on the individual level. Of particular clinical importance is understanding where exactly pathology, e.g. tau, emerges in each patient and how the incipient atrophy pattern relates to future spread of disease. Here we demonstrate a newly developed graph theoretical method of inferring prior disease states in patients with Alzheimer's disease and mild cognitive impairment using an established network diffusion model and an L1-penalized optimization algorithm. Although the 'seeds' of origin using our inference method successfully reproduce known trends in Alzheimer's disease staging on a population level, we observed that the high degree of heterogeneity between patients at baseline is also reflected in their seeds. Additionally, the individualized seeds are significantly more predictive of future atrophy than a single seed placed at the hippocampus. Our findings illustrate that understanding where disease originates in individuals is critical to determining how it progresses and that our method allows us to infer early stages of disease from atrophy patterns observed at diagnosis. © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Construction of regulatory networks using expression time-series data of a genotyped population.

PubMed

Yeung, Ka Yee; Dombek, Kenneth M; Lo, Kenneth; Mittler, John E; Zhu, Jun; Schadt, Eric E; Bumgarner, Roger E; Raftery, Adrian E

2011-11-29

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Young children's racial awareness and affect and their perceptions about mothers' racial affect in a multiracial context.

PubMed

Lam, Virginia; Guerrero, Silvia; Damree, Natasha; Enesco, Ileana

2011-11-01

There is a substantial literature documenting pre-schoolers' racial awareness and affect from multiracial societies in North America and a fast-growing body of work from societies that are or were once more racially homogeneous. However, studies in Britain, a racially diverse society, on this developmental period have been curiously rare. This study examined racial awareness and affect of 125 White, Black, and Asian 3--to 5-year-olds in London. Children were tested on cognitive level, person description and classification, race labelling and matching, self-categorization and asked about their racial preference and rejection and inferences about their mothers' preference and rejection. Children were least likely to use race versus other categorical cues to spontaneously describe or classify others, even though the majority correctly sorted others by race labels, matched them to drawings, and categorized themselves by race. With age and increasing cognitive level, children described and categorized others by race more and improved in race matching. White children from age 4 preferred White peers and inferred that their mothers would prefer White children at age 5. Children's own preference and inference about mothers are related. Children did not show race-based rejection, but boys inferred that their mothers would prefer White children and reject Black children. The findings are discussed in relation to racial salience between contexts, previous research, and theories. ©2010 The British Psychological Society.

TRACING CO-REGULATORY NETWORK DYNAMICS IN NOISY, SINGLE-CELL TRANSCRIPTOME TRAJECTORIES.

PubMed

Cordero, Pablo; Stuart, Joshua M

2017-01-01

The availability of gene expression data at the single cell level makes it possible to probe the molecular underpinnings of complex biological processes such as differentiation and oncogenesis. Promising new methods have emerged for reconstructing a progression 'trajectory' from static single-cell transcriptome measurements. However, it remains unclear how to adequately model the appreciable level of noise in these data to elucidate gene regulatory network rewiring. Here, we present a framework called Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR) that infers progressions from static single-cell transcriptomes by employing a continuous parametrization of Gaussian mixtures in high-dimensional curves. SCIMITAR yields rich models from the data that highlight genes with expression and co-expression patterns that are associated with the inferred progression. Further, SCIMITAR extracts regulatory states from the implicated trajectory-evolvingco-expression networks. We benchmark the method on simulated data to show that it yields accurate cell ordering and gene network inferences. Applied to the interpretation of a single-cell human fetal neuron dataset, SCIMITAR finds progression-associated genes in cornerstone neural differentiation pathways missed by standard differential expression tests. Finally, by leveraging the rewiring of gene-gene co-expression relations across the progression, the method reveals the rise and fall of co-regulatory states and trajectory-dependent gene modules. These analyses implicate new transcription factors in neural differentiation including putative co-factors for the multi-functional NFAT pathway.

The Importance of Form in Skinner's Analysis of Verbal Behavior and a Further Step

PubMed Central

Vargas, E. A.

2013-01-01

A series of quotes from B. F. Skinner illustrates the importance of form in his analysis of verbal behavior. In that analysis, form plays an important part in contingency control. Form and function complement each other. Function, the array of variables that control a verbal utterance, dictates the meaning of a specified form; form, as stipulated by a verbal community, indicates that meaning. The mediational actions that shape verbal utterances do not necessarily encounter their controlling variables. These are inferred from the form of the verbal utterance. Form carries the burden of implied meaning and underscores the importance of the verbal community in the expression of all the forms of language. Skinner's analysis of verbal behavior and the importance of form within that analysis provides the foundation by which to investigate language. But a further step needs to be undertaken to examine and to explain the abstractions of language as an outcome of action at an aggregate level. PMID:23814376

Graphical explanation in an expert system for Space Station Freedom rack integration

NASA Technical Reports Server (NTRS)

Craig, F. G.; Cutts, D. E.; Fennel, T. R.; Purves, B.

1990-01-01

The rationale and methodology used to incorporate graphics into explanations provided by an expert system for Space Station Freedom rack integration is examined. The rack integration task is typical of a class of constraint satisfaction problems for large programs where expertise from several areas is required. Graphically oriented approaches are used to explain the conclusions made by the system, the knowledge base content, and even at more abstract levels the control strategies employed by the system. The implemented architecture combines hypermedia and inference engine capabilities. The advantages of this architecture include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. The graphical techniques employed range from simple statis presentation of schematics to dynamic creation of a series of pictures presented motion picture style. User models control the type, amount, and order of information presented.

Modeling Post-death Transmission of Ebola: Challenges for Inference and Opportunities for Control

NASA Astrophysics Data System (ADS)

Weitz, Joshua S.; Dushoff, Jonathan

2015-03-01

Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa. These models include consideration of counter-measures meant to slow and, eventually, stop the spread of the disease. Here, we examine one component of Ebola dynamics that is of ongoing concern - the transmission of Ebola from the dead to the living. We do so by applying the toolkit of mathematical epidemiology to analyze the consequences of post-death transmission. We show that underlying disease parameters cannot be inferred with confidence from early-stage incidence data (that is, they are not ``identifiable'') because different parameter combinations can produce virtually the same epidemic trajectory. Despite this identifiability problem, we find robustly that inferences that don't account for post-death transmission tend to underestimate the basic reproductive number - thus, given the observed rate of epidemic growth, larger amounts of post-death transmission imply larger reproductive numbers. From a control perspective, we explain how improvements in reducing post-death transmission of Ebola may reduce the overall epidemic spread and scope substantially. Increased attention to the proportion of post-death transmission has the potential to aid both in projecting the course of the epidemic and in evaluating a portfolio of control strategies.

Emotional speech comprehension in children and adolescents with autism spectrum disorders.

PubMed

Le Sourn-Bissaoui, Sandrine; Aguert, Marc; Girard, Pauline; Chevreuil, Claire; Laval, Virginie

2013-01-01

We examined the understanding of emotional speech by children and adolescents with autism spectrum disorders (ASD). We predicted that they would have difficulty understanding emotional speech, not because of an emotional prosody processing impairment but because of problems drawing appropriate inferences, especially in multiple-cue environments. Twenty-six children and adolescents with ASD and 26 typically developing controls performed a computerized task featuring emotional prosody, either embedded in a discrepant context or without any context at all. They must identify the speaker's feeling. When the prosody was the sole cue, participants with ASD performed just as well as controls, relying on this cue to infer the speaker's intention. When the prosody was embedded in a discrepant context, both ASD and TD participants exhibited a contextual bias and a negativity bias. However ASD participants relied less on the emotional prosody than the controls when it was positive. We discuss these findings with respect to executive function and intermodal processing. After reading this article, the reader should be able to (1) describe the ASD participants pragmatic impairments, (2) explain why ASD participants did not have an emotional prosody processing impairment, and (3) explain why ASD participants had difficulty inferring the speaker's intention from emotional prosody in a discrepant situation. Copyright © 2013 Elsevier Inc. All rights reserved.

The anatomy of choice: active inference and agency.

PubMed

Friston, Karl; Schwartenbeck, Philipp; Fitzgerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

Research participant compensation: A matter of statistical inference as well as ethics.

PubMed

Swanson, David M; Betensky, Rebecca A

2015-11-01

The ethics of compensation of research subjects for participation in clinical trials has been debated for years. One ethical issue of concern is variation among subjects in the level of compensation for identical treatments. Surprisingly, the impact of variation on the statistical inferences made from trial results has not been examined. We seek to identify how variation in compensation may influence any existing dependent censoring in clinical trials, thereby also influencing inference about the survival curve, hazard ratio, or other measures of treatment efficacy. In simulation studies, we consider a model for how compensation structure may influence the censoring model. Under existing dependent censoring, we estimate survival curves under different compensation structures and observe how these structures induce variability in the estimates. We show through this model that if the compensation structure affects the censoring model and dependent censoring is present, then variation in that structure induces variation in the estimates and affects the accuracy of estimation and inference on treatment efficacy. From the perspectives of both ethics and statistical inference, standardization and transparency in the compensation of participants in clinical trials are warranted. Copyright © 2015 Elsevier Inc. All rights reserved.

Prosocial preferences do not explain human cooperation in public-goods games

PubMed Central

Burton-Chellew, Maxwell N.; West, Stuart A.

2013-01-01

It has become an accepted paradigm that humans have “prosocial preferences” that lead to higher levels of cooperation than those that would maximize their personal financial gain. However, the existence of prosocial preferences has been inferred post hoc from the results of economic games, rather than with direct experimental tests. Here, we test how behavior in a public-goods game is influenced by knowledge of the consequences of actions for other players. We found that (i) individuals cooperate at similar levels, even when they are not informed that their behavior benefits others; (ii) an increased awareness of how cooperation benefits others leads to a reduction, rather than an increase, in the level of cooperation; and (iii) cooperation can be either lower or higher than expected, depending on experimental design. Overall, these results contradict the suggested role of the prosocial preferences hypothesis and show how the complexity of human behavior can lead to misleading conclusions from controlled laboratory experiments. PMID:23248298

Prosocial preferences do not explain human cooperation in public-goods games.

PubMed

Burton-Chellew, Maxwell N; West, Stuart A

2013-01-02

It has become an accepted paradigm that humans have "prosocial preferences" that lead to higher levels of cooperation than those that would maximize their personal financial gain. However, the existence of prosocial preferences has been inferred post hoc from the results of economic games, rather than with direct experimental tests. Here, we test how behavior in a public-goods game is influenced by knowledge of the consequences of actions for other players. We found that (i) individuals cooperate at similar levels, even when they are not informed that their behavior benefits others; (ii) an increased awareness of how cooperation benefits others leads to a reduction, rather than an increase, in the level of cooperation; and (iii) cooperation can be either lower or higher than expected, depending on experimental design. Overall, these results contradict the suggested role of the prosocial preferences hypothesis and show how the complexity of human behavior can lead to misleading conclusions from controlled laboratory experiments.

Interstellar clouds containing optically thin H2

NASA Technical Reports Server (NTRS)

Jura, M.

1975-01-01

The theory of Black and Delgarno that the relative populations of the excited rotational levels of H2 can be understood in terms of cascading following absorption in the Lyman and Werner bands is employed to infer the gas densities and radiation fields within diffuse interstellar clouds containing H2 that is optically thin in those bands. The procedure is described for computing the populations of the different rotation levels, the relative distribution among the different rotation levels of newly formed H2 is determined on the basis of five simplified models, and the rate of H2 formation is estimated. The results are applied to delta Ori, two components of iota Ori, the second components of rho Leo and zeta Ori, tau Sco, gamma Vel, and zeta Pup. The inferred parameters are summarized for each cloud.

Adversarial reasoning: challenges and approaches

NASA Astrophysics Data System (ADS)

Kott, Alexander; Ownby, Michael

2005-05-01

This paper defines adversarial reasoning as computational approaches to inferring and anticipating an enemy's perceptions, intents and actions. It argues that adversarial reasoning transcends the boundaries of game theory and must also leverage such disciplines as cognitive modeling, control theory, AI planning and others. To illustrate the challenges of applying adversarial reasoning to real-world problems, the paper explores the lessons learned in the CADET -- a battle planning system that focuses on brigade-level ground operations and involves adversarial reasoning. From this example of current capabilities, the paper proceeds to describe RAID -- a DARPA program that aims to build capabilities in adversarial reasoning, and how such capabilities would address practical requirements in Defense and other application areas.

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Luchinsky, Dmitry Georgievich; Hafiychuk, Vasyl Nmn; Osipov, Viatcheslav V.; Patterson-Hine, F. Ann

2010-01-01

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Use of Fatty Acid Analysis to Determine Dispersal of Caspian Terns in the Columbia River Basin, U.S.A.

USGS Publications Warehouse

Maranto, C.J.; Parrish, J.K.; Herman, D.P.; Punt, A.E.; Olden, J.D.; Brett, M.T.; Roby, D.D.

2011-01-01

Lethal control, which has been used to reduce local abundances of animals in conflict with humans or with endangered species, may not achieve management goals if animal movement is not considered. In populations with emigration and immigration, lethal control may induce compensatory immigration, if the source of attraction remains unchanged. Within the Columbia River Basin (Washington, U.S.A.), avian predators forage at dams because dams tend to reduce rates of emigration of juvenile salmonids (Oncorhynchus spp.), artificially concentrating these prey. We used differences in fatty acid profiles between Caspian Terns (Hydroprogne caspia) at coastal and inland breeding colonies and terns culled by a lethal control program at a mid-Columbia River dam to infer dispersal patterns. We modeled the rate of loss of fatty acid biomarkers, which are fatty acids that can be traced to a single prey species or groups of species, to infer whether and when terns foraging at dams had emigrated from the coast. Nonmetric multidimensional scaling showed that coastal terns had high levels of C20 and C22 monounsaturated fatty acids, whereas fatty acids of inland breeders were high in C18:3n3, C20:4n6, and C22:5n3. Models of the rate of loss of fatty acid showed that approximately 60% of the terns collected at Rock Island Dam were unlikely to have bred successfully at local (inland) sites, suggesting that terns foraging at dams come from an extensive area. Fatty acid biomarkers may provide accurate information about patterns of dispersal in animal populations and may be extremely valuable in cases where populations differ demonstrably in prey base. ??2011 Society for Conservation Biology.

Use of fatty acid analysis to determine dispersal of caspian terns in the Columbia River Basin, USA.

PubMed

Maranto, Christina J; Parrish, Julia K; Herman, David P; Punt, André E; Olden, Julian D; Brett, Michael T; Roby, Daniel D

2011-08-01

Lethal control, which has been used to reduce local abundances of animals in conflict with humans or with endangered species, may not achieve management goals if animal movement is not considered. In populations with emigration and immigration, lethal control may induce compensatory immigration, if the source of attraction remains unchanged. Within the Columbia River Basin (Washington, U.S.A.), avian predators forage at dams because dams tend to reduce rates of emigration of juvenile salmonids (Oncorhynchus spp.), artificially concentrating these prey. We used differences in fatty acid profiles between Caspian Terns (Hydroprogne caspia) at coastal and inland breeding colonies and terns culled by a lethal control program at a mid-Columbia River dam to infer dispersal patterns. We modeled the rate of loss of fatty acid biomarkers, which are fatty acids that can be traced to a single prey species or groups of species, to infer whether and when terns foraging at dams had emigrated from the coast. Nonmetric multidimensional scaling showed that coastal terns had high levels of C(20) and C(22) monounsaturated fatty acids, whereas fatty acids of inland breeders were high in C18:3n3, C20:4n6, and C22:5n3. Models of the rate of loss of fatty acid showed that approximately 60% of the terns collected at Rock Island Dam were unlikely to have bred successfully at local (inland) sites, suggesting that terns foraging at dams come from an extensive area. Fatty acid biomarkers may provide accurate information about patterns of dispersal in animal populations and may be extremely valuable in cases where populations differ demonstrably in prey base. © 2011 Society for Conservation Biology.

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

NASA Astrophysics Data System (ADS)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated a model of the SIP component as implemented in ns-2.

Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagation.

PubMed

Zeng, Jia; Hannenhalli, Sridhar

2013-01-01

Gene duplication, followed by functional evolution of duplicate genes, is a primary engine of evolutionary innovation. In turn, gene expression evolution is a critical component of overall functional evolution of paralogs. Inferring evolutionary history of gene expression among paralogs is therefore a problem of considerable interest. It also represents significant challenges. The standard approaches of evolutionary reconstruction assume that at an internal node of the duplication tree, the two duplicates evolve independently. However, because of various selection pressures functional evolution of the two paralogs may be coupled. The coupling of paralog evolution corresponds to three major fates of gene duplicates: subfunctionalization (SF), conserved function (CF) or neofunctionalization (NF). Quantitative analysis of these fates is of great interest and clearly influences evolutionary inference of expression. These two interrelated problems of inferring gene expression and evolutionary fates of gene duplicates have not been studied together previously and motivate the present study. Here we propose a novel probabilistic framework and algorithm to simultaneously infer (i) ancestral gene expression and (ii) the likely fate (SF, NF, CF) at each duplication event during the evolution of gene family. Using tissue-specific gene expression data, we develop a nonparametric belief propagation (NBP) algorithm to predict the ancestral expression level as a proxy for function, and describe a novel probabilistic model that relates the predicted and known expression levels to the possible evolutionary fates. We validate our model using simulation and then apply it to a genome-wide set of gene duplicates in human. Our results suggest that SF tends to be more frequent at the earlier stage of gene family expansion, while NF occurs more frequently later on.

An Empirical Test of Causal Inference Between Role Perceptions, Satisfaction with Work, Performance and Organizational Level

ERIC Educational Resources Information Center

Szilagyi, Andrew D.

1977-01-01

Attempts to empirically verify the causal source and direction of causal influence between role ambiguity, role conflict and job satisfaction and performance for three organizational levels in a hospital environment. (Author/RK)

Statistical modeling of crystalline silica exposure by trade in the construction industry using a database compiled from the literature.

PubMed

Sauvé, Jean-François; Beaudry, Charles; Bégin, Denis; Dion, Chantal; Gérin, Michel; Lavoué, Jérôme

2012-09-01

A quantitative determinants-of-exposure analysis of respirable crystalline silica (RCS) levels in the construction industry was performed using a database compiled from an extensive literature review. Statistical models were developed to predict work-shift exposure levels by trade. Monte Carlo simulation was used to recreate exposures derived from summarized measurements which were combined with single measurements for analysis. Modeling was performed using Tobit models within a multimodel inference framework, with year, sampling duration, type of environment, project purpose, project type, sampling strategy and use of exposure controls as potential predictors. 1346 RCS measurements were included in the analysis, of which 318 were non-detects and 228 were simulated from summary statistics. The model containing all the variables explained 22% of total variability. Apart from trade, sampling duration, year and strategy were the most influential predictors of RCS levels. The use of exposure controls was associated with an average decrease of 19% in exposure levels compared to none, and increased concentrations were found for industrial, demolition and renovation projects. Predicted geometric means for year 1999 were the highest for drilling rig operators (0.238 mg m(-3)) and tunnel construction workers (0.224 mg m(-3)), while the estimated exceedance fraction of the ACGIH TLV by trade ranged from 47% to 91%. The predicted geometric means in this study indicated important overexposure compared to the TLV. However, the low proportion of variability explained by the models suggests that the construction trade is only a moderate predictor of work-shift exposure levels. The impact of the different tasks performed during a work shift should also be assessed to provide better management and control of RCS exposure levels on construction sites.

Effect of Preschool Working Memory, Language, and Narrative Abilities on Inferential Comprehension at School-Age in Children with Spina Bifida Myelomeningocele and Typically Developing Children

PubMed Central

Pike, Meredith; Swank, Paul; Taylor, Heather; Landry, Susan; Barnes, Marcia A.

2014-01-01

Children with spina bifida myelomeningocele (SBM) are more likely to display a pattern of good-decoding/poor comprehension than their neurologically intact peers. The goals of the current study were to (1) examine the cognitive origins of one of the component skills of comprehension, bridging inferences, from a developmental perspective and (2) to test the effects of those relations on reading comprehension achievement. Data from a sample of children with SBM and a control group (n = 78) who participated in a longitudinal study were taken from age 36-month and 9.5-year time points. A multiple mediation model provided evidence that three preschool cognitive abilities (working memory/inhibitory control, oral comprehension, narrative recall), could partially explain the relation between group and bridging inference skill. A second mediation model supported that each of the 36-month abilities had an indirect effect on reading comprehension through bridging inference skill. Findings contribute to an understanding of both typical and atypical comprehension development, blending theories from the developmental, cognitive, and neuropsychological literature. PMID:23388065

Effect of preschool working memory, language, and narrative abilities on inferential comprehension at school-age in children with spina bifida myelomeningocele and typically developing children.

PubMed

Pike, Meredith; Swank, Paul; Taylor, Heather; Landry, Susan; Barnes, Marcia A

2013-04-01

Children with spina bifida myelomeningocele (SBM) are more likely to display a pattern of good-decoding/poor comprehension than their neurologically intact peers. The goals of the current study were to (1) examine the cognitive origins of one of the component skills of comprehension, bridging inferences, from a developmental perspective and (2) to test the effects of those relations on reading comprehension achievement. Data from a sample of children with SBM and a control group (n = 78) who participated in a longitudinal study were taken from age 36-month and 9.5-year time points. A multiple mediation model provided evidence that three preschool cognitive abilities (working memory/inhibitory control, oral comprehension, narrative recall), could partially explain the relation between group and bridging inference skill. A second mediation model supported that each of the 36-month abilities had an indirect effect on reading comprehension through bridging inference skill. Findings contribute to an understanding of both typical and atypical comprehension development, blending theories from the developmental, cognitive, and neuropsychological literature.

The influence of performance on action-effect integration in sense of agency.

PubMed

Wen, Wen; Yamashita, Atsushi; Asama, Hajime

2017-08-01

Sense of agency refers to the subjective feeling of being able to control an outcome through one's own actions or will. Prior studies have shown that both sensory processing (e.g., comparisons between sensory feedbacks and predictions basing on one's motor intentions) and high-level cognitive/constructive processes (e.g., inferences based on one's performance or the consequences of one's actions) contribute to judgments of sense of agency. However, it remains unclear how these two types of processes interact, which is important for clarifying the mechanisms underlying sense of agency. Thus, we examined whether performance-based inferences influence action-effect integration in sense of agency using a delay detection paradigm in two experiments. In both experiments, participants pressed left and right arrow keys to control the direction in which a moving dot was travelling. The dot's response delay was manipulated randomly on 7 levels (0-480ms) between the trials; for each trial, participants were asked to judge whether the dot response was delayed and to rate their level of agency over the dot. In Experiment 1, participants tried to direct the dot to reach a destination on the screen as quickly as possible. Furthermore, the computer assisted participants by ignoring erroneous commands for half of the trials (assisted condition), while in the other half, all of the participants' commands were executed (self-control condition). In Experiment 2, participants directed the dot as they pleased (without a specific goal), but, in half of the trials, the computer randomly ignored 32% of their commands (disturbed condition) rather than assisted them. The results from the two experiments showed that performance enhanced action-effect integration. Specifically, when task performance was improved through the computer's assistance in Experiment 1, delay detection was reduced in the 480-ms delay condition, despite the fact that 32% of participants' commands were ignored. Conversely, when no feedback on task performance was given (as in Experiment 2), the participants reported greater delay when some of their commands were randomly ignored. Furthermore, the results of a logistic regression analysis showed that the threshold of delay detection was greater in the assisted condition than in the self-control condition in Experiment 1, which suggests a wider time window for action-effect integration. A multivariate analysis also revealed that assistance was related to reduced delay detection via task performance, while reduced delay detection was directly correlated with a better sense of agency. These results indicate an association between the implicit and explicit aspects of sense of agency. Copyright © 2017 Elsevier Inc. All rights reserved.

Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration

PubMed Central

Lobo, Daniel; Levin, Michael

2015-01-01

Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form. PMID:26042810

Phylogeography above the species level for perennial species in a composite genus

PubMed Central

Tremetsberger, Karin; Ortiz, María Ángeles; Terrab, Anass; Balao, Francisco; Casimiro-Soriguer, Ramón; Talavera, María; Talavera, Salvador

2016-01-01

In phylogeography, DNA sequence and fingerprint data at the population level are used to infer evolutionary histories of species. Phylogeography above the species level is concerned with the genealogical aspects of divergent lineages. Here, we present a phylogeographic study to examine the evolutionary history of a western Mediterranean composite, focusing on the perennial species of Helminthotheca (Asteraceae, Cichorieae). We used molecular markers (amplified fragment length polymorphism (AFLP), internal transcribed spacer and plastid DNA sequences) to infer relationships among populations throughout the distributional range of the group. Interpretation is aided by biogeographic and molecular clock analyses. Four coherent entities are revealed by Bayesian mixture clustering of AFLP data, which correspond to taxa previously recognized at the rank of subspecies. The origin of the group was in western North Africa, from where it expanded across the Strait of Gibraltar to the Iberian Peninsula and across the Strait of Sicily to Sicily. Pleistocene lineage divergence is inferred within western North Africa as well as within the western Iberian region. The existence of the four entities as discrete evolutionary lineages suggests that they should be elevated to the rank of species, yielding H. aculeata, H. comosa, H. maroccana and H. spinosa, whereby the latter two necessitate new combinations. PMID:26644340

Inference of epidemiological parameters from household stratified data

PubMed Central

Walker, James N.; Ross, Joshua V.

2017-01-01

We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters—governing within-household transmission, recovery, and between-household transmission—from data of the day upon which each individual became infectious and the household in which each infection occurred, as might be available from First Few Hundred studies. Each method is a form of Bayesian Markov Chain Monte Carlo that allows us to calculate a joint posterior distribution for all parameters and hence the household reproduction number and the early growth rate of the epidemic. The first method performs exact Bayesian inference using a standard data-augmentation approach; the second performs approximate Bayesian inference based on a likelihood approximation derived from branching processes. These methods are compared for computational efficiency and posteriors from each are compared. The branching process is shown to be a good approximation and remains computationally efficient as the amount of data is increased. PMID:29045456

Ontological Constraints in Children's Inductive Inferences: Evidence From a Comparison of Inferences Within Animals and Vehicles.

PubMed

Tarlowski, Andrzej

2018-01-01

There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.

Inferring the emotions of friends versus strangers: the role of culture and self-construal.

PubMed

Ma-Kellams, Christine; Blascovich, Jim

2012-07-01

Three studies examined cross-cultural differences in empathic accuracy (the ability to correctly infer another's emotional experience) within the context of different relationships. East-West cultural differences in self-construal were hypothesized to differentiate levels of empathic accuracy across relationship types. In contrast to the independent self prevalent among members of Western cultures, members of Eastern cultures generally view the self as interdependent with those with whom they have a relationship. Easterners, relative to Westerners, are more concerned with the thoughts or feelings of close others and less concerned with the thoughts or feelings of those with whom they have no relational link (i.e., strangers). Across three studies, the authors found that East Asians, compared with European Americans, made more accurate inferences regarding the emotions of close others (i.e., friends), but less accurate inferences regarding the emotions of strangers. Furthermore, individual differences in interdependent self-construal among East Asians predicted the degree of empathic accuracy.

Ontological Constraints in Children's Inductive Inferences: Evidence From a Comparison of Inferences Within Animals and Vehicles

PubMed Central

Tarlowski, Andrzej

2018-01-01

There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects. PMID:29760669

Regulatory effect of acetyl-l-carnitine on expression of lenticular antioxidant and apoptotic genes in selenite-induced cataract.

PubMed

Elanchezhian, R; Sakthivel, M; Geraldine, P; Thomas, P A

2010-03-30

Differential expression of apoptotic genes has been demonstrated in selenite-induced cataract. Acetyl-l-carnitine (ALCAR) has been shown to prevent selenite cataractogenesis by maintaining lenticular antioxidant enzyme and redox system components at near normal levels and also by inhibiting lenticular calpain activity. The aim of the present experiment was to investigate the possibility that ALCAR also prevents selenite-induced cataractogenesis by regulating the expression of antioxidant (catalase) and apoptotic [caspase-3, early growth response protein-1 (EGR-1) and cytochrome c oxidase subunit I (COX-I)] genes. The experiment was conducted on 9-day-old Wistar rat pups, which were divided into normal, cataract-untreated and cataract-treated groups. Putative changes in gene expression in whole lenses removed from the rats were determined by measuring mRNA transcript levels of the four genes by RT-PCR analysis, using glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as an internal control. The expression of lenticular caspase-3 and EGR-1 genes appeared to be upregulated, as inferred by detecting increased mRNA transcript levels, while that of COX-I and catalase genes appeared to be downregulated (lowered mRNA transcript levels) in the lenses of cataract-untreated rats. However, in rats treated with ALCAR, the lenticular mRNA transcript levels were maintained at near normal (control) levels. These results suggest that ALCAR may prevent selenite-induced cataractogenesis by preventing abnormal expression of lenticular genes governing apoptosis.

Protection of LLC-PK1 cells against hydrogen peroxide-induced cell death by modulation of ceramide level.

PubMed

Yoo, Jae-Myung; Lee, Youn-Sun; Choi, Heon-Kyo; Lee, Yong-Moon; Hong, Jin-Tae; Yun, Yeo-Pyo; Oh, Seikwan; Yoo, Hwan-Soo

2005-03-01

Oxidative stress has been reported to elevate ceramide level during cell death. The purpose of the present study was to modulate cell death in relation to cellular glutathione (GSH) level and GST (glutathione S-transferase) expression by regulating the sphingolipid metabolism. LLC-PK1 cells were treated with H2O2 in the absence of serum to induce cell death. Subsequent to exposure to H2O2, LLC-PK1 cells were treated with desipramine, sphingomyelinase inhibitor, and N-acetylcysteine (NAC), GSH substrate. Based on comparative visual observation with H2O2-treated control cells, it was observed that 0.5 microM of desipramine and 25 mM of NAC exhibited about 90 and 95% of cytoprotection, respectively, against H2O2-induced cell death. Desipramine and NAC lowered the release of LDH activity by 36 and 3%, respectively, when compared to 71% in H2O2-exposed cells. Cellular glutathione level in 500 microM H2O2-treated cells was reduced to 890 pmol as compared to control level of 1198 pmol per mg protein. GST P1-1 expression was decreased in H2O2-treated cells compared to healthy normal cells. In conclusion, it has been inferred that H2O2-induced cell death is closely related to cellular GSH level and GST P1-1 expression in LLC-PK1 cells and occurs via ceramide elevation by sphingomyelinase activation.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

PubMed

Zhang, Jian-Hua; Xia, Jia-Jun; Garibaldi, Jonathan M; Groumpos, Petros P; Wang, Ru-Bin

2017-06-01

In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller. Copyright © 2017 Elsevier B.V. All rights reserved.

Free will in Bayesian and inverse Bayesian inference-driven endo-consciousness.

PubMed

Gunji, Yukio-Pegio; Minoura, Mai; Kojima, Kei; Horry, Yoichi

2017-12-01

How can we link challenging issues related to consciousness and/or qualia with natural science? The introduction of endo-perspective, instead of exo-perspective, as proposed by Matsuno, Rössler, and Gunji, is considered one of the most promising candidate approaches. Here, we distinguish the endo-from the exo-perspective in terms of whether the external is or is not directly operated. In the endo-perspective, the external can be neither perceived nor recognized directly; rather, one can only indirectly summon something outside of the perspective, which can be illustrated by a causation-reversal pair. On one hand, causation logically proceeds from the cause to the effect. On the other hand, a reversal from the effect to the cause is non-logical and is equipped with a metaphorical structure. We argue that the differences in exo- and endo-perspectives result not from the difference between Western and Eastern cultures, but from differences between modernism and animism. Here, a causation-reversal pair described using a pair of upward (from premise to consequence) and downward (from consequence to premise) causation and a pair of Bayesian and inverse Bayesian inference (BIB inference). Accordingly, the notion of endo-consciousness is proposed as an agent equipped with BIB inference. We also argue that BIB inference can yield both highly efficient computations through Bayesian interference and robust computations through inverse Bayesian inference. By adapting a logical model of the free will theorem to the BIB inference, we show that endo-consciousness can explain free will as a regression of the controllability of voluntary action. Copyright © 2017. Published by Elsevier Ltd.

Visual representation of statistical information improves diagnostic inferences in doctors and their patients.

PubMed

Garcia-Retamero, Rocio; Hoffrage, Ulrich

2013-04-01

Doctors and patients have difficulty inferring the predictive value of a medical test from information about the prevalence of a disease and the sensitivity and false-positive rate of the test. Previous research has established that communicating such information in a format the human mind is adapted to-namely natural frequencies-as compared to probabilities, boosts accuracy of diagnostic inferences. In a study, we investigated to what extent these inferences can be improved-beyond the effect of natural frequencies-by providing visual aids. Participants were 81 doctors and 81 patients who made diagnostic inferences about three medical tests on the basis of information about prevalence of a disease, and the sensitivity and false-positive rate of the tests. Half of the participants received the information in natural frequencies, while the other half received the information in probabilities. Half of the participants only received numerical information, while the other half additionally received a visual aid representing the numerical information. In addition, participants completed a numeracy scale. Our study showed three important findings: (1) doctors and patients made more accurate inferences when information was communicated in natural frequencies as compared to probabilities; (2) visual aids boosted accuracy even when the information was provided in natural frequencies; and (3) doctors were more accurate in their diagnostic inferences than patients, though differences in accuracy disappeared when differences in numerical skills were controlled for. Our findings have important implications for medical practice as they suggest suitable ways to communicate quantitative medical data. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Systematic Bayesian Integration of Epidemiological and Genetic Data

PubMed Central

Lau, Max S. Y.; Marion, Glenn; Streftaris, George; Gibson, Gavin

2015-01-01

Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process. PMID:26599399

Hierarchical models of animal abundance and occurrence

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, R.M.

2006-01-01

Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.

Empirical evidence for acceleration-dependent amplification factors

USGS Publications Warehouse

Borcherdt, R.D.

2002-01-01

Site-specific amplification factors, Fa and Fv, used in current U.S. building codes decrease with increasing base acceleration level as implied by the Loma Prieta earthquake at 0.1g and extrapolated using numerical models and laboratory results. The Northridge earthquake recordings of 17 January 1994 and subsequent geotechnical data permit empirical estimates of amplification at base acceleration levels up to 0.5g. Distance measures and normalization procedures used to infer amplification ratios from soil-rock pairs in predetermined azimuth-distance bins significantly influence the dependence of amplification estimates on base acceleration. Factors inferred using a hypocentral distance norm do not show a statistically significant dependence on base acceleration. Factors inferred using norms implied by the attenuation functions of Abrahamson and Silva show a statistically significant decrease with increasing base acceleration. The decrease is statistically more significant for stiff clay and sandy soil (site class D) sites than for stiffer sites underlain by gravely soils and soft rock (site class C). The decrease in amplification with increasing base acceleration is more pronounced for the short-period amplification factor, Fa, than for the midperiod factor, Fv.

Real-time fuzzy inference based robot path planning

NASA Technical Reports Server (NTRS)

Pacini, Peter J.; Teichrow, Jon S.

1990-01-01

This project addresses the problem of adaptive trajectory generation for a robot arm. Conventional trajectory generation involves computing a path in real time to minimize a performance measure such as expended energy. This method can be computationally intensive, and it may yield poor results if the trajectory is weakly constrained. Typically some implicit constraints are known, but cannot be encoded analytically. The alternative approach used here is to formulate domain-specific knowledge, including implicit and ill-defined constraints, in terms of fuzzy rules. These rules utilize linguistic terms to relate input variables to output variables. Since the fuzzy rulebase is determined off-line, only high-level, computationally light processing is required in real time. Potential applications for adaptive trajectory generation include missile guidance and various sophisticated robot control tasks, such as automotive assembly, high speed electrical parts insertion, stepper alignment, and motion control for high speed parcel transfer systems.

A new method for locating changes in a tree reveals distinct nucleotide polymorphism vs. divergence patterns in mouse mitochondrial control region.

PubMed

Galtier, N; Boursot, P

2000-03-01

A new, model-based method was devised to locate nucleotide changes in a given phylogenetic tree. For each site, the posterior probability of any possible change in each branch of the tree is computed. This probabilistic method is a valuable alternative to the maximum parsimony method when base composition is skewed (i.e., different from 25% A, 25% C, 25% G, 25% T): computer simulations showed that parsimony misses more rare --> common than common --> rare changes, resulting in biased inferred change matrices, whereas the new method appeared unbiased. The probabilistic method was applied to the analysis of the mutation and substitution processes in the mitochondrial control region of mouse. Distinct change patterns were found at the polymorphism (within species) and divergence (between species) levels, rejecting the hypothesis of a neutral evolution of base composition in mitochondrial DNA.

Controlling rabies through a multidisciplinary, public health system in Trujillo, La Libertad, Peru

PubMed Central

Seneschall, Charlotte; Luna-Farro, Maria

2013-01-01

Rabies remains endemic in Peru. In 1983, Latin America and the Caribbean promised to eliminate canine-transmitted rabies from the continent. This led to Peru introducing a multidisciplinary public health system for controlling and managing rabies across the country. The system consists of mass canine vaccination campaigns, post exposure prophylaxis and monitoring aggressor animals for signs of rabies. The Peruvian city of Trujillo, La Libertad, is an urban area where dogs are the principal reservoir for rabies. The disease burden of rabies in Trujillo, La Libertad is currently minimal, with no rabies cases in humans for over 10 years, and only three canine cases. No human deaths due to rabies have occurred for several decades. From this it can be inferred that antirabies systems such as this do have real effects in reducing cases of human rabies at a grass roots level. PMID:24392679

Pathway analysis from lists of microRNAs: common pitfalls and alternative strategy

PubMed Central

Godard, Patrice; van Eyll, Jonathan

2015-01-01

MicroRNAs (miRNAs) are involved in the regulation of gene expression at a post-transcriptional level. As such, monitoring miRNA expression has been increasingly used to assess their role in regulatory mechanisms of biological processes. In large scale studies, once miRNAs of interest have been identified, the target genes they regulate are often inferred using algorithms or databases. A pathway analysis is then often performed in order to generate hypotheses about the relevant biological functions controlled by the miRNA signature. Here we show that the method widely used in scientific literature to identify these pathways is biased and leads to inaccurate results. In addition to describing the bias and its origin we present an alternative strategy to identify potential biological functions specifically impacted by a miRNA signature. More generally, our study exemplifies the crucial need of relevant negative controls when developing, and using, bioinformatics methods. PMID:25800743

Inference of R 0 and Transmission Heterogeneity from the Size Distribution of Stuttering Chains

PubMed Central

Blumberg, Seth; Lloyd-Smith, James O.

2013-01-01

For many infectious disease processes such as emerging zoonoses and vaccine-preventable diseases, and infections occur as self-limited stuttering transmission chains. A mechanistic understanding of transmission is essential for characterizing the risk of emerging diseases and monitoring spatio-temporal dynamics. Thus methods for inferring and the degree of heterogeneity in transmission from stuttering chain data have important applications in disease surveillance and management. Previous researchers have used chain size distributions to infer , but estimation of the degree of individual-level variation in infectiousness (as quantified by the dispersion parameter, ) has typically required contact tracing data. Utilizing branching process theory along with a negative binomial offspring distribution, we demonstrate how maximum likelihood estimation can be applied to chain size data to infer both and the dispersion parameter that characterizes heterogeneity. While the maximum likelihood value for is a simple function of the average chain size, the associated confidence intervals are dependent on the inferred degree of transmission heterogeneity. As demonstrated for monkeypox data from the Democratic Republic of Congo, this impacts when a statistically significant change in is detectable. In addition, by allowing for superspreading events, inference of shifts the threshold above which a transmission chain should be considered anomalously large for a given value of (thus reducing the probability of false alarms about pathogen adaptation). Our analysis of monkeypox also clarifies the various ways that imperfect observation can impact inference of transmission parameters, and highlights the need to quantitatively evaluate whether observation is likely to significantly bias results. PMID:23658504

Historical Sea Level in the South Pacific from Rescued Archives, Geodetic Measurements, and Satellite Altimetry

NASA Astrophysics Data System (ADS)

Aucan, J.; Merrifield, M. A.; Pouvreau, N.

2017-10-01

Automatic sea-level measurements in Nouméa, South Pacific, started in 1957 for the International Geophysical year. Data from this location exist in paper record for the 1957-1967 period, and in two distinct electronic records for the 1967-2005 and 2005-2015 period. In this study, we digitize the early record, and established a link between the two electronic records to create a unique, nearly 60 year-long instrumental sea-level record. This work creates one of the longest instrumental sea-level records in the Pacific Islands. These data are critical for the study of regional and interannual variations of sea level. This new data set is then used to infer rates of vertical movements by comparing it to (1) the entire satellite altimetric record (1993-2013) and (2) a global sea-level reconstruction (1957-2010). These inferred rates show an uplift of 1.3-1.4 mm/year, opposite to the currently accepted values of subsidence found in the geological and geodetic literature, and underlie the importance of systematic geodetic measurements at, over very near tide gauges.

Link-state-estimation-based transmission power control in wireless body area networks.

PubMed

Kim, Seungku; Eom, Doo-Seop

2014-07-01

This paper presents a novel transmission power control protocol to extend the lifetime of sensor nodes and to increase the link reliability in wireless body area networks (WBANs). We first experimentally investigate the properties of the link states using the received signal strength indicator (RSSI). We then propose a practical transmission power control protocol based on both short- and long-term link-state estimations. Both the short- and long-term link-state estimations enable the transceiver to adapt the transmission power level and target the RSSI threshold range, respectively, to simultaneously satisfy the requirements of energy efficiency and link reliability. Finally, the performance of the proposed protocol is experimentally evaluated in two experimental scenarios-body posture change and dynamic body motion-and compared with the typical WBAN transmission power control protocols, a real-time reactive scheme, and a dynamic postural position inference mechanism. From the experimental results, it is found that the proposed protocol increases the lifetime of the sensor nodes by a maximum of 9.86% and enhances the link reliability by reducing the packet loss by a maximum of 3.02%.

Using a network modularity analysis to inform management of a rare endemic plant in the northern Great Plains, USA

USGS Publications Warehouse

Larson, Diane L.; Droege, Sam; Rabie, Paul A.; Larson, Jennifer L.; Devalez, Jelle; Haar, Milton; McDermott-Kubeczko, Margaret

2014-01-01

1. Analyses of flower-visitor interaction networks allow application of community-level information to conservation problems, but management recommendations that ensue from such analyses are not well characterized. Results of modularity analyses, which detect groups of species (modules) that interact more with each other than with species outside their module, may be particularly applicable to management concerns. 2. We conducted modularity analyses of networks surrounding a rare endemic annual plant, Eriogonum visheri, at Badlands National Park, USA, in 2010 and 2011. Plant species visited were determined by pollen on insect bodies and by flower species upon which insects were captured. Roles within modules (network hub, module hub, connector and peripheral, in decreasing order of network structural importance) were determined for each species. 3. Relationships demonstrated by the modularity analysis, in concert with knowledge of pollen species carried by insects, allowed us to infer effects of two invasive species on E. visheri. Sharing a module increased risk of interspecific pollen transfer to E. visheri. Control of invasive Salsola tragus, which shared a module with E. visheri, is therefore a prudent management objective, but lack of control of invasive Melilotus officinalis, which occupied a different module, is unlikely to negatively affect pollination of E. visheri. Eriogonum pauciflorum may occupy a key position in this network, supporting insects from the E. visheri module when E. visheri is less abundant. 4. Year-to-year variation in species' roles suggests management decisions must be based on observations over several years. Information on pollen deposition on stigmas would greatly strengthen inferences made from the modularity analysis. 5. Synthesis and applications: Assessing the consequences of pollination, whether at the community or individual level, is inherently time-consuming. A trade-off exists: rather than an estimate of fitness effects, the network approach provides a broad understanding of the relationships among insect visitors and other plant species that may affect the focal rare plant. Knowledge of such relationships allows managers to detect, target and prioritize control of only the important subset of invasive species present and identify other species that may augment a rare species' population stability, such as E. pauciflorum in our study.

Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

PubMed

Gong, Wuming; Koyano-Nakagawa, Naoko; Li, Tongbin; Garry, Daniel J

2015-03-07

Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion. We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes. The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p <10(-100)), suggesting that a high LR score is a reliable indicator for functional TF binding sites. Our network inference model identified a region with an elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions. We report a novel method to systematically integrate multi-dimensional -omics data and reconstruct the gene regulatory networks. This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli.

PubMed

Lemmens, Karen; De Bie, Tijl; Dhollander, Thomas; De Keersmaecker, Sigrid C; Thijs, Inge M; Schoofs, Geert; De Weerdt, Ami; De Moor, Bart; Vanderleyden, Jos; Collado-Vides, Julio; Engelen, Kristof; Marchal, Kathleen

2009-01-01

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

Social cognitive conflict resolution: Contributions of domain general and domain specific neural systems

PubMed Central

Zaki, Jamil; Hennigan, Kelly; Weber, Jochen; Ochsner, Kevin N.

2010-01-01

Cognitive control mechanisms allow individuals to behave adaptively in the face of complex and sometimes conflicting information. While the neural bases of these control mechanisms have been examined in many contexts, almost no attention has been paid to their role in resolving conflicts between competing social cues, which is surprising, given that cognitive conflicts are part of many social interactions. Evidence about the neural processing of social information suggests that two systems—the mirror neuron system (MNS) and mental state attribution system (MSAS)—are specialized for processing nonverbal and contextual social cues, respectively. This could support a model of social cognitive conflict resolution in which competition between social cues would recruit domain-general cognitive control mechanisms, which in turn would bias processing towards the MNS or MSAS. Such biasing could also alter social behaviors, such as inferences made about the internal states of others. We tested this model by scanning participants using fMRI while they drew inferences about social targets' emotional states based on congruent or incongruent nonverbal and contextual social cues. Conflicts between social cues recruited the anterior cingulate and lateral prefrontal cortex, brain areas associated with domain-general control processes. This activation was accompanied by biasing of neural activity towards areas in the MNS or MSAS, which tracked, respectively, with perceivers' behavioral reliance on nonverbal or contextual cues when drawing inferences about targets' emotions. Together, these data provide evidence about both domain general and domain specific mechanisms involved in resolving social cognitive conflicts. PMID:20573895

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

1991-01-01

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

Can training improve the quality of inferences made by raters in competency modeling? A quasi-experiment.

PubMed

Lievens, Filip; Sanchez, Juan I

2007-05-01

A quasi-experiment was conducted to investigate the effects of frame-of-reference training on the quality of competency modeling ratings made by consultants. Human resources consultants from a large consulting firm were randomly assigned to either a training or a control condition. The discriminant validity, interrater reliability, and accuracy of the competency ratings were significantly higher in the training group than in the control group. Further, the discriminant validity and interrater reliability of competency inferences were highest among an additional group of trained consultants who also had competency modeling experience. Together, these results suggest that procedural interventions such as rater training can significantly enhance the quality of competency modeling. 2007 APA, all rights reserved

What do we mean by prediction in language comprehension?

PubMed Central

Kuperberg, Gina R.; Jaeger, T. Florian

2016-01-01

We consider several key aspects of prediction in language comprehension: its computational nature, the representational level(s) at which we predict, whether we use higher level representations to predictively pre-activate lower level representations, and whether we ‘commit’ in any way to our predictions, beyond pre-activation. We argue that the bulk of behavioral and neural evidence suggests that we predict probabilistically and at multiple levels and grains of representation. We also argue that we can, in principle, use higher level inferences to predictively pre-activate information at multiple lower representational levels. We also suggest that the degree and level of predictive pre-activation might be a function of the expected utility of prediction, which, in turn, may depend on comprehenders’ goals and their estimates of the relative reliability of their prior knowledge and the bottom-up input. Finally, we argue that all these properties of language understanding can be naturally explained and productively explored within a multi-representational hierarchical actively generative architecture whose goal is to infer the message intended by the producer, and in which predictions play a crucial role in explaining the bottom-up input. PMID:27135040

Real-time value-driven diagnosis

NASA Technical Reports Server (NTRS)

Dambrosio, Bruce

1995-01-01

Diagnosis is often thought of as an isolated task in theoretical reasoning (reasoning with the goal of updating our beliefs about the world). We present a decision-theoretic interpretation of diagnosis as a task in practical reasoning (reasoning with the goal of acting in the world), and sketch components of our approach to this task. These components include an abstract problem description, a decision-theoretic model of the basic task, a set of inference methods suitable for evaluating the decision representation in real-time, and a control architecture to provide the needed continuing coordination between the agent and its environment. A principal contribution of this work is the representation and inference methods we have developed, which extend previously available probabilistic inference methods and narrow, somewhat, the gap between probabilistic and logical models of diagnosis.

The design and application of a Transportable Inference Engine (TIE1)

NASA Technical Reports Server (NTRS)

Mclean, David R.

1986-01-01

A Transportable Inference Engine (TIE1) system has been developed by the author as part of the Interactive Experimenter Planning System (IEPS) task which is involved with developing expert systems in support of the Spacecraft Control Programs Branch at Goddard Space Flight Center in Greenbelt, Maryland. Unlike traditional inference engines, TIE1 is written in the C programming language. In the TIE1 system, knowledge is represented by a hierarchical network of objects which have rule frames. The TIE1 search algorithm uses a set of strategies, including backward chaining, to obtain the values of goals. The application of TIE1 to a spacecraft scheduling problem is described. This application involves the development of a strategies interpreter which uses TIE1 to do constraint checking.

Semantic message oriented middleware for publish/subscribe networks

NASA Astrophysics Data System (ADS)

Li, Han; Jiang, Guofei

2004-09-01

The publish/subscribe paradigm of Message Oriented Middleware provides a loosely coupled communication model between distributed applications. Traditional publish/subscribe middleware uses keywords to match advertisements and subscriptions and does not support deep semantic matching. To this end, we designed and implemented a Semantic Message Oriented Middleware system to provide such capabilities for semantic description and matching. We adopted the DARPA Agent Markup Language and Ontology Inference Layer, a formal knowledge representation language for expressing sophisticated classifications and enabling automated inference, as the topic description language in our middleware system. A simple description logic inference system was implemented to handle the matching process between the subscriptions of subscribers and the advertisements of publishers. Moreover our middleware system also has a security architecture to support secure communication and user privilege control.

``Carbon Credits'' for Resource-Bounded Computations Using Amortised Analysis

NASA Astrophysics Data System (ADS)

Jost, Steffen; Loidl, Hans-Wolfgang; Hammond, Kevin; Scaife, Norman; Hofmann, Martin

Bounding resource usage is important for a number of areas, notably real-time embedded systems and safety-critical systems. In this paper, we present a fully automatic static type-based analysis for inferring upper bounds on resource usage for programs involving general algebraic datatypes and full recursion. Our method can easily be used to bound any countable resource, without needing to revisit proofs. We apply the analysis to the important metrics of worst-case execution time, stack- and heap-space usage. Our results from several realistic embedded control applications demonstrate good matches between our inferred bounds and measured worst-case costs for heap and stack usage. For time usage we infer good bounds for one application. Where we obtain less tight bounds, this is due to the use of software floating-point libraries.

Effects of task and age on the magnitude and structure of force fluctuations: insights into underlying neuro-behavioral processes.

PubMed

Vieluf, Solveig; Temprado, Jean-Jacques; Berton, Eric; Jirsa, Viktor K; Sleimen-Malkoun, Rita

2015-03-13

The present study aimed at characterizing the effects of increasing (relative) force level and aging on isometric force control. To achieve this objective and to infer changes in the underlying control mechanisms, measures of information transmission, as well as magnitude and time-frequency structure of behavioral variability were applied to force-time-series. Older adults were found to be weaker, more variable, and less efficient than young participants. As a function of force level, efficiency followed an inverted-U shape in both groups, suggesting a similar organization of the force control system. The time-frequency structure of force output fluctuations was only significantly affected by task conditions. Specifically, a narrower spectral distribution with more long-range correlations and an inverted-U pattern of complexity changes were observed with increasing force level. Although not significant older participants displayed on average a less complex behavior for low and intermediate force levels. The changes in force signal's regularity presented a strong dependence on time-scales, which significantly interacted with age and condition. An inverted-U profile was only observed for the time-scale relevant to the sensorimotor control process. However, in both groups the peak was not aligned with the optimum of efficiency. Our results support the view that behavioral variability, in terms of magnitude and structure, has a functional meaning and affords non-invasive markers of the adaptations of the sensorimotor control system to various constraints. The measures of efficiency and variability ought to be considered as complementary since they convey specific information on the organization of control processes. The reported weak age effect on variability and complexity measures suggests that the behavioral expression of the loss of complexity hypothesis is not as straightforward as conventionally admitted. However, group differences did not completely vanish, which suggests that age differences can be more or less apparent depending on task properties and whether difficulty is scaled in relative or absolute terms.

Structural Correlates of Reading the Mind in the Eyes in Autism Spectrum Disorder.

PubMed

Sato, Wataru; Uono, Shota; Kochiyama, Takanori; Yoshimura, Sayaka; Sawada, Reiko; Kubota, Yasutaka; Sakihama, Morimitsu; Toichi, Motomi

2017-01-01

Behavioral studies have shown that individuals with autism spectrum disorder (ASD) have impaired ability to read the mind in the eyes. Although this impairment is central to their social malfunctioning, its structural neural correlates remain unclear. To investigate this issue, we assessed Reading the Mind in the Eyes Test, revised version (Eyes Test) and acquired structural magnetic resonance images in adults with high-functioning ASD ( n = 19) and age-, sex- and intelligence quotient-matched typically developing (TD) controls ( n = 19). On the behavioral level, the Eyes Test scores were lower in the ASD group than in the control group. On the neural level, an interaction between group and Eyes Test score was found in the left temporoparietal junction (TPJ). A positive association between the Eyes Test score and gray matter volume of this region was evident in the control group, but not in the ASD group. This finding suggests that the failure to develop appropriate structural neural representations in the TPJ may underlie the impaired ability of individuals with ASD to read the mind in the eyes. These behavioral and neural findings provide support for the theories that impairments in processing eyes and the ability to infer others' mental states are the core symptoms of ASD, and that atypical features in the social brain network underlie such impairments.

An algebra-based method for inferring gene regulatory networks

PubMed Central

2014-01-01

Background The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network. Conclusions Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html. PMID:24669835

Higher-order language dysfunctions as a possible neurolinguistic endophenotype for schizophrenia: Evidence from patients and their unaffected first degree relatives.

PubMed

Pawełczyk, Agnieszka; Łojek, Emila; Żurner, Natalia; Gawłowska-Sawosz, Marta; Pawełczyk, Tomasz

2018-05-31

The purpose of the study was to examine the presence of pragmatic dysfunctions in first episode (FE) subjects and their healthy first degree relatives as a potential endophenotype for schizophrenia. Thirty-four FE patients, 34 parents of the patients (REL) and 32 healthy controls (HC) took part in the study. Pragmatic language functions were evaluated with the Right Hemisphere Language Battery, attention and executive functions were controlled, as well as age and education level. The parents differed from HC but not from their FE offspring with regard to overall level of language and communication and the general knowledge component of language processing. The FE participants differed from HC in comprehension of inferred meaning, emotional prosody, discourse dimensions, overall level of language and communication, language processing with regard to general knowledge and communication competences. The FE participants differed from REL regarding discourse dimensions. Our findings suggest that pragmatic dysfunctions may act as vulnerability markers of schizophrenia; their assessment may help in the diagnosis of early stages of the illness and in understanding its pathophysiology. In future research the adoptive and biological parents of schizophrenia patients should be compared to elucidate which language failures reflect genetic vulnerability and which ones environmental factors. Copyright © 2018. Published by Elsevier B.V.

Operator function modeling: Cognitive task analysis, modeling and intelligent aiding in supervisory control systems

NASA Technical Reports Server (NTRS)

Mitchell, Christine M.

1990-01-01

The design, implementation, and empirical evaluation of task-analytic models and intelligent aids for operators in the control of complex dynamic systems, specifically aerospace systems, are studied. Three related activities are included: (1) the models of operator decision making in complex and predominantly automated space systems were used and developed; (2) the Operator Function Model (OFM) was used to represent operator activities; and (3) Operator Function Model Expert System (OFMspert), a stand-alone knowledge-based system was developed, that interacts with a human operator in a manner similar to a human assistant in the control of aerospace systems. OFMspert is an architecture for an operator's assistant that uses the OFM as its system and operator knowledge base and a blackboard paradigm of problem solving to dynamically generate expectations about upcoming operator activities and interpreting actual operator actions. An experiment validated the OFMspert's intent inferencing capability and showed that it inferred the intentions of operators in ways comparable to both a human expert and operators themselves. OFMspert was also augmented with control capabilities. An interface allowed the operator to interact with OFMspert, delegating as much or as little control responsibility as the operator chose. With its design based on the OFM, OFMspert's control capabilities were available at multiple levels of abstraction and allowed the operator a great deal of discretion over the amount and level of delegated control. An experiment showed that overall system performance was comparable for teams consisting of two human operators versus a human operator and OFMspert team.

Dominant controls of transpiration along a hillslope transect inferred from ecohydrological measurements and thermodynamic limits

NASA Astrophysics Data System (ADS)

Renner, Maik; Hassler, Sibylle K.; Blume, Theresa; Weiler, Markus; Hildebrandt, Anke; Guderle, Marcus; Schymanski, Stanislaus J.; Kleidon, Axel

2016-05-01

We combine ecohydrological observations of sap flow and soil moisture with thermodynamically constrained estimates of atmospheric evaporative demand to infer the dominant controls of forest transpiration in complex terrain. We hypothesize that daily variations in transpiration are dominated by variations in atmospheric demand, while site-specific controls, including limiting soil moisture, act on longer timescales. We test these hypotheses with data of a measurement setup consisting of five sites along a valley cross section in Luxembourg. Both hillslopes are covered by forest dominated by European beech (Fagus sylvatica L.). Two independent measurements are used to estimate stand transpiration: (i) sap flow and (ii) diurnal variations in soil moisture, which were used to estimate the daily root water uptake. Atmospheric evaporative demand is estimated through thermodynamically constrained evaporation, which only requires absorbed solar radiation and temperature as input data without any empirical parameters. Both transpiration estimates are strongly correlated to atmospheric demand at the daily timescale. We find that neither vapor pressure deficit nor wind speed add to the explained variance, supporting the idea that they are dependent variables on land-atmosphere exchange and the surface energy budget. Estimated stand transpiration was in a similar range at the north-facing and the south-facing hillslopes despite the different aspect and the largely different stand composition. We identified an inverse relationship between sap flux density and the site-average sapwood area per tree as estimated by the site forest inventories. This suggests that tree hydraulic adaptation can compensate for heterogeneous conditions. However, during dry summer periods differences in topographic factors and stand structure can cause spatially variable transpiration rates. We conclude that absorption of solar radiation at the surface forms a dominant control for turbulent heat and mass exchange and that vegetation across the hillslope adjusts to this constraint at the tree and stand level. These findings should help to improve the description of land-surface-atmosphere exchange at regional scales.

Distinguishing between statistical significance and practical/clinical meaningfulness using statistical inference.

PubMed

Wilkinson, Michael

2014-03-01

Decisions about support for predictions of theories in light of data are made using statistical inference. The dominant approach in sport and exercise science is the Neyman-Pearson (N-P) significance-testing approach. When applied correctly it provides a reliable procedure for making dichotomous decisions for accepting or rejecting zero-effect null hypotheses with known and controlled long-run error rates. Type I and type II error rates must be specified in advance and the latter controlled by conducting an a priori sample size calculation. The N-P approach does not provide the probability of hypotheses or indicate the strength of support for hypotheses in light of data, yet many scientists believe it does. Outcomes of analyses allow conclusions only about the existence of non-zero effects, and provide no information about the likely size of true effects or their practical/clinical value. Bayesian inference can show how much support data provide for different hypotheses, and how personal convictions should be altered in light of data, but the approach is complicated by formulating probability distributions about prior subjective estimates of population effects. A pragmatic solution is magnitude-based inference, which allows scientists to estimate the true magnitude of population effects and how likely they are to exceed an effect magnitude of practical/clinical importance, thereby integrating elements of subjective Bayesian-style thinking. While this approach is gaining acceptance, progress might be hastened if scientists appreciate the shortcomings of traditional N-P null hypothesis significance testing.

The anatomy of choice: active inference and agency

PubMed Central

Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J.

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback–Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action—constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution—that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control. PMID:24093015

Valid statistical inference methods for a case-control study with missing data.

PubMed

Tian, Guo-Liang; Zhang, Chi; Jiang, Xuejun

2018-04-01

The main objective of this paper is to derive the valid sampling distribution of the observed counts in a case-control study with missing data under the assumption of missing at random by employing the conditional sampling method and the mechanism augmentation method. The proposed sampling distribution, called the case-control sampling distribution, can be used to calculate the standard errors of the maximum likelihood estimates of parameters via the Fisher information matrix and to generate independent samples for constructing small-sample bootstrap confidence intervals. Theoretical comparisons of the new case-control sampling distribution with two existing sampling distributions exhibit a large difference. Simulations are conducted to investigate the influence of the three different sampling distributions on statistical inferences. One finding is that the conclusion by the Wald test for testing independency under the two existing sampling distributions could be completely different (even contradictory) from the Wald test for testing the equality of the success probabilities in control/case groups under the proposed distribution. A real cervical cancer data set is used to illustrate the proposed statistical methods.

Inferential modeling and predictive feedback control in real-time motion compensation using the treatment couch during radiotherapy

NASA Astrophysics Data System (ADS)

Qiu, Peng; D'Souza, Warren D.; McAvoy, Thomas J.; Liu, K. J. Ray

2007-09-01

Tumor motion induced by respiration presents a challenge to the reliable delivery of conformal radiation treatments. Real-time motion compensation represents the technologically most challenging clinical solution but has the potential to overcome the limitations of existing methods. The performance of a real-time couch-based motion compensation system is mainly dependent on two aspects: the ability to infer the internal anatomical position and the performance of the feedback control system. In this paper, we propose two novel methods for the two aspects respectively, and then combine the proposed methods into one system. To accurately estimate the internal tumor position, we present partial-least squares (PLS) regression to predict the position of the diaphragm using skin-based motion surrogates. Four radio-opaque markers were placed on the abdomen of patients who underwent fluoroscopic imaging of the diaphragm. The coordinates of the markers served as input variables and the position of the diaphragm served as the output variable. PLS resulted in lower prediction errors compared with standard multiple linear regression (MLR). The performance of the feedback control system depends on the system dynamics and dead time (delay between the initiation and execution of the control action). While the dynamics of the system can be inverted in a feedback control system, the dead time cannot be inverted. To overcome the dead time of the system, we propose a predictive feedback control system by incorporating forward prediction using least-mean-square (LMS) and recursive least square (RLS) filtering into the couch-based control system. Motion data were obtained using a skin-based marker. The proposed predictive feedback control system was benchmarked against pure feedback control (no forward prediction) and resulted in a significant performance gain. Finally, we combined the PLS inference model and the predictive feedback control to evaluate the overall performance of the feedback control system. Our results show that, with the tumor motion unknown but inferred by skin-based markers through the PLS model, the predictive feedback control system was able to effectively compensate intra-fraction motion.

Arm Activity During Daily Life in Individuals With Chronic Obstructive Pulmonary Disease.

PubMed

Janaudis-Ferreira, Tania; Mathur, Sunita; Romano, Julia Marie; Goldstein, Roger Samuel; Brooks, Dina

2016-01-01

To determine whether individuals with chronic obstructive pulmonary disease (COPD) have decreased arm activity during daily life compared with healthy controls and explore the relationships between arm activity during daily life and arm functional measures in individuals with COPD. This was a prospective cross-sectional study that included 30 people with COPD and 14 healthy controls. Subjects attended a single assessment session in which measurements of arm exercise capacity, arm functional performance, self-perception of performance during activities of daily living (ADL), shoulder and elbow flexion force and biceps and triceps thickness were performed. On completion of this session, participants were issued a wrist actigraph and asked to wear the device on the dominant arm for 24 hours for 7 consecutive days. Compared with healthy controls, patients with COPD presented decreased total activity level in daily life (P = .001). When corrected for walking, the level of arm activity did not differ between individuals with COPD and healthy controls (P = .62). No correlations were found between arm activity and arm exercise capacity, arm functional performance, upper limb muscle strength, and self-perception of performance during ADL (r =-0.20 to 0.14; all P ≥ .10). Arm activity intensity in individuals with COPD did not differ from that of healthy controls when measured by a wrist actigraph. Moreover, arm activity was not associated with other clinical outcomes of arm function. Disability during ADL is multifactorial, and only limited inferences of function can be made from accelerometer data.

Teacher Effectiveness and Causal Inference in Observational Studies

ERIC Educational Resources Information Center

Rose, Roderick A.

2013-01-01

An important target of education policy is to improve overall teacher effectiveness using evidence-based policies. Randomized control trials (RCTs), which randomly assign study participants or groups of participants to treatment and control conditions, are not always practical or possible and observational studies using rigorous quasi-experimental…

Climate-induced changes in lake ecosystem structure inferred from coupled neo- and paleoecological approaches

USGS Publications Warehouse

Saros, Jasmine E.; Stone, Jeffery R.; Pederson, Gregory T.; Slemmons, Krista; Spanbauer, Trisha; Schliep, Anna; Cahl, Douglas; Williamson, Craig E.; Engstrom, Daniel R.

2015-01-01

Over the 20th century, surface water temperatures have increased in many lake ecosystems around the world, but long-term trends in the vertical thermal structure of lakes remain unclear, despite the strong control that thermal stratification exerts on the biological response of lakes to climate change. Here we used both neo- and paleoecological approaches to develop a fossil-based inference model for lake mixing depths and thereby refine understanding of lake thermal structure change. We focused on three common planktonic diatom taxa, the distributions of which previous research suggests might be affected by mixing depth. Comparative lake surveys and growth rate experiments revealed that these species respond to lake thermal structure when nitrogen is sufficient, with species optima ranging from shallower to deeper mixing depths. The diatom-based mixing depth model was applied to sedimentary diatom profiles extending back to 1750 AD in two lakes with moderate nitrate concentrations but differing climate settings. Thermal reconstructions were consistent with expected changes, with shallower mixing depths inferred for an alpine lake where treeline has advanced, and deeper mixing depths inferred for a boreal lake where wind strength has increased. The inference model developed here provides a new tool to expand and refine understanding of climate-induced changes in lake ecosystems.

Factorial Structure of the Family Values Scale from a Multilevel-Multicultural Perspective

ERIC Educational Resources Information Center

Byrne, Barbara M.; van de Vijver, Fons J. R.

2014-01-01

In cross-cultural research, there is a tendency for researchers to draw inferences at the country level based on individual-level data. Such action implicitly and often mistakenly assumes that both the measuring instrument and its underlying construct(s) are operating equivalently across both levels. Based on responses from 5,482 college students…

A Bayesian account of ‘hysteria’

PubMed Central

Adams, Rick A.; Brown, Harriet; Pareés, Isabel; Friston, Karl J.

2012-01-01

This article provides a neurobiological account of symptoms that have been called ‘hysterical’, ‘psychogenic’ or ‘medically unexplained’, which we will call functional motor and sensory symptoms. We use a neurobiologically informed model of hierarchical Bayesian inference in the brain to explain functional motor and sensory symptoms in terms of perception and action arising from inference based on prior beliefs and sensory information. This explanation exploits the key balance between prior beliefs and sensory evidence that is mediated by (body focused) attention, symptom expectations, physical and emotional experiences and beliefs about illness. Crucially, this furnishes an explanation at three different levels: (i) underlying neuromodulatory (synaptic) mechanisms; (ii) cognitive and experiential processes (attention and attribution of agency); and (iii) formal computations that underlie perceptual inference (representation of uncertainty or precision). Our explanation involves primary and secondary failures of inference; the primary failure is the (autonomous) emergence of a percept or belief that is held with undue certainty (precision) following top-down attentional modulation of synaptic gain. This belief can constitute a sensory percept (or its absence) or induce movement (or its absence). The secondary failure of inference is when the ensuing percept (and any somatosensory consequences) is falsely inferred to be a symptom to explain why its content was not predicted by the source of attentional modulation. This account accommodates several fundamental observations about functional motor and sensory symptoms, including: (i) their induction and maintenance by attention; (ii) their modification by expectation, prior experience and cultural beliefs and (iii) their involuntary and symptomatic nature. PMID:22641838

Social cognition in anorexia nervosa: Specific difficulties in decoding emotional but not nonemotional mental states.

PubMed

Brockmeyer, Timo; Pellegrino, Judith; Münch, Hannah; Herzog, Wolfgang; Dziobek, Isabell; Friederich, Hans-Christoph

2016-09-01

Building on recent models of anorexia nervosa (AN) that emphasize the importance of impaired social cognition in the development and maintenance of the disorder, the present study aimed at examining whether women with AN have more difficulties with inferring other people's emotional and nonemotional mental states than healthy women. Social cognition was assessed in 25 adult women with AN and 25 age-matched healthy women. To overcome limitations of previous research on social cognition in AN, the processing of social information was examined in a more complex and ecologically valid manner. The Movie for the Assessment of Social Cognition (MASC) reflects complex real-life social interaction and allows for disentangling emotional and non-emotional mental state inference as well as different types of errors in mentalizing. Women with AN showed poorer emotional mental state inference, whereas non-emotional mental state inference was largely intact. Groups did not differ in undermentalizing (overly simplistic theory of mind) and overmentalizing (overly complex or over-interpretative mental state reasoning). Performance in the MASC was independent of levels of eating disorder psychopathology and symptoms of depression and anxiety. The findings suggest that AN is associated with specific difficulties in emotional mental state inference despite largely intact nonemotional mental state inference. Upon replication in larger samples, these findings advocate a stronger emphasis on socio-emotional processing in AN treatment. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2016; 49:883-890). © 2016 Wiley Periodicals, Inc.

Computational statistics using the Bayesian Inference Engine

NASA Astrophysics Data System (ADS)

Weinberg, Martin D.

2013-09-01

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU General Public License.

Inverse methods-based estimation of plate coupling in a plate motion model governed by mantle flow

NASA Astrophysics Data System (ADS)

Ratnaswamy, V.; Stadler, G.; Gurnis, M.

2013-12-01

Plate motion is primarily controlled by buoyancy (slab pull) which occurs at convergent plate margins where oceanic plates undergo deformation near the seismogenic zone. Yielding within subducting plates, lateral variations in viscosity, and the strength of seismic coupling between plate margins likely have an important control on plate motion. Here, we wish to infer the inter-plate coupling for different subduction zones, and develop a method for inferring it as a PDE-constrained optimization problem, where the cost functional is the misfit in plate velocities and is constrained by the nonlinear Stokes equation. The inverse models have well resolved slabs, plates, and plate margins in addition to a power law rheology with yielding in the upper mantle. Additionally, a Newton method is used to solve the nonlinear Stokes equation with viscosity bounds. We infer plate boundary strength using an inexact Gauss-Newton method with line search for backtracking. Each inverse model is applied to two simple 2-D scenarios (each with three subduction zones), one with back-arc spreading and one without. For each case we examine the sensitivity of the inversion to the amount of surface velocity used: 1) full surface velocity data and 2) surface velocity data simplified using a single scalar average (2-D equivalent to an Euler pole) for each plate. We can recover plate boundary strength in each case, even in the presence of highly nonlinear flow with extreme variations in viscosity. Additionally, we ascribe an uncertainty in each plate's velocity and perform an uncertainty quantification (UQ) through the Hessian of the misfit in plate velocities. We find that as plate boundaries become strongly coupled, the uncertainty in the inferred plate boundary strength decreases. For very weak, uncoupled subduction zones, the uncertainty of inferred plate margin strength increases since there is little sensitivity between plate margin strength and plate velocity. This result is significant because it implies we can infer which plate boundaries are more coupled (seismically) for a realistic dynamic model of plates and mantle flow.

Sea level change since 2005: importance of salinity

NASA Astrophysics Data System (ADS)

Llovel, W.; Purkey, S.; Meyssignac, B.; Kolodziejczyk, N.; Blazquez, A.; Bamber, J. L.

2017-12-01

Sea level rise is one of the most important consequences of the actual global warming. Global mean sea level has been rising at a faster rate since 1993 (over the satellite altimetry era) than previous decades. This rise is expected to accelerate over the coming decades and century. At global scale, sea level rise is caused by a combination of freshwater increase from land ice melting and land water changes (mass component) and ocean warming (thermal expansion). Estimating the causes is of great interest not only to understand the past sea level changes but also to validate projections based on climate models. In this study, we investigate the global mass contribution to recent sea level changes with an alternative approach by estimating the global ocean freshening. For that purpose, we consider the unprecedented amount of salinity measurements from Argo floats for the past decade (2005-2015). We compare our results to the ocean mass inferred by GRACE data and based on a sea level budget approach. Our results bring new constrains on the global water cycle (ocean freshening) and energy budget (ocean warming) as well as on the global ocean mass directly inferred from GRACE data.

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.

Towards a more molecular taxonomy of disease.

PubMed

Park, Jisoo; Hescott, Benjamin J; Slonim, Donna K

2017-07-27

Disease taxonomies have been designed for many applications, but they tend not to fully incorporate the growing amount of molecular-level knowledge of disease processes, inhibiting research efforts. Understanding the degree to which we can infer disease relationships from molecular data alone may yield insights into how to ultimately construct more modern taxonomies that integrate both physiological and molecular information. We introduce a new technique we call Parent Promotion to infer hierarchical relationships between disease terms using disease-gene data. We compare this technique with both an established ontology inference method (CliXO) and a minimum weight spanning tree approach. Because there is no gold standard molecular disease taxonomy available, we compare our inferred hierarchies to both the Medical Subject Headings (MeSH) category C forest of diseases and to subnetworks of the Disease Ontology (DO). This comparison provides insights about the inference algorithms, choices of evaluation metrics, and the existing molecular content of various subnetworks of MeSH and the DO. Our results suggest that the Parent Promotion method performs well in most cases. Performance across MeSH trees is also correlated between inference methods. Specifically, inferred relationships are more consistent with those in smaller MeSH disease trees than larger ones, but there are some notable exceptions that may correlate with higher molecular content in MeSH. Our experiments provide insights about learning relationships between diseases from disease genes alone. Future work should explore the prospect of disease term discovery from molecular data and how best to integrate molecular data with anatomical and clinical knowledge. This study nonetheless suggests that disease gene information has the potential to form an important part of the foundation for future representations of the disease landscape.

Assessing children's inference generation: what do tests of reading comprehension measure?

PubMed

Bowyer-Crane, Claudine; Snowling, Margaret J

2005-06-01

Previous research suggests that children with specific comprehension difficulties have problems with the generation of inferences. This raises important questions as to whether poor comprehenders have poor comprehension skills generally, or whether their problems are confined to specific inference types. The main aims of the study were (a) using two commonly used tests of reading comprehension to classify the questions requiring the generation of inferences, and (b) to investigate the relative performance of skilled and less-skilled comprehenders on questions tapping different inference types. The performance of 10 poor comprehenders (mean age 110.06 months) was compared with the performance of 10 normal readers (mean age 112.78 months) on two tests of reading comprehension. A qualitative analysis of the NARA II (form 1) and the WORD comprehension subtest was carried out. Participants were then administered the NARA II, WORD comprehension subtest and a test of non-word reading. The NARA II was heavily reliant on the generation of knowledge-based inferences, while the WORD comprehension subtest was biased towards the retention of literal information. Children identified by the NARA II as having comprehension difficulties performed in the normal range on the WORD comprehension subtests. Further, children with comprehension difficulties performed poorly on questions requiring the generation of knowledge-based and elaborative inferences. However, they were able to answer questions requiring attention to literal information or use of cohesive devices at a level comparable to normal readers. Different reading tests tap different types of inferencing skills. Lessskilled comprehenders have particular difficulty applying real-world knowledge to a text during reading, and this has implications for the formulation of effective intervention strategies.

Open Platform for Limit Protection with Carefree Maneuver Applications

NASA Technical Reports Server (NTRS)

Jeram, Geoffrey J.

2004-01-01

This Open Platform for Limit Protection guides the open design of maneuver limit protection systems in general, and manned, rotorcraft, aerospace applications in particular. The platform uses three stages of limit protection modules: limit cue creation, limit cue arbitration, and control system interface. A common set of limit cue modules provides commands that can include constraints, alerts, transfer functions, and friction. An arbitration module selects the "best" limit protection cues and distributes them to the most appropriate control path interface. This platform adopts a holistic approach to limit protection whereby it considers all potential interface points, including the pilot's visual, aural, and tactile displays; and automatic command restraint shaping for autonomous limit protection. For each functional module, this thesis guides the control system designer through the design choices and information interfaces among the modules. Limit cue module design choices include type of prediction, prediction mechanism, method of critical control calculation, and type of limit cue. Special consideration is given to the nature of the limit, particularly the level of knowledge about it, and the ramifications for limit protection design, especially with respect to intelligent control methods such as fuzzy inference systems and neural networks.

Using a Novel Wireless-Networked Decentralized Control Scheme under Unpredictable Environmental Conditions

PubMed Central

Chang, Chung-Liang; Huang, Yi-Ming; Hong, Guo-Fong

2015-01-01

The direction of sunshine or the installation sites of environmental control facilities in the greenhouse result in different temperature and humidity levels in the various zones of the greenhouse, and thus, the production quality of crop is inconsistent. This study proposed a wireless-networked decentralized fuzzy control scheme to regulate the environmental parameters of various culture zones within a greenhouse. The proposed scheme can create different environmental conditions for cultivating different crops in various zones and achieve diversification or standardization of crop production. A star-type wireless sensor network is utilized to communicate with each sensing node, actuator node, and control node in various zones within the greenhouse. The fuzzy rule-based inference system is used to regulate the environmental parameters for temperature and humidity based on real-time data of plant growth response provided by a growth stage selector. The growth stage selector defines the control ranges of temperature and humidity of the various culture zones according to the leaf area of the plant, the number of leaves, and the cumulative amount of light. The experimental results show that the proposed scheme is stable and robust and provides basis for future greenhouse applications. PMID:26569264

A Level-of-Question Effect on ESL Reading Comprehension.

ERIC Educational Resources Information Center

Perkins, Kyle; And Others

A sample of 150 Japanese English-as-a-Second-Language students at Southern Illinois University, Carbondale was given a reading comprehension test containing three levels of questions: factual, generalization, and inference, to measure comprehension effects at different proficiency strata. The results indicated that there were significant…

Procedural Guidelines for Ecological Risk Assessments at U.S. Army Sites. Volume 1.

DTIC Science & Technology

1994-12-01

altered chemically or physically. Stressors may also result from management practices such as harvesting of fishery or forest resources, or cultivation ...Inferring population-level significance from individual- level effects: An extrapolation from fisheries science to ecotoxicology . p. 289-300. In G.W

Multi-scale habitat selection modeling: A review and outlook

Treesearch

Kevin McGarigal; Ho Yi Wan; Kathy A. Zeller; Brad C. Timm; Samuel A. Cushman

2016-01-01

Scale is the lens that focuses ecological relationships. Organisms select habitat at multiple hierarchical levels and at different spatial and/or temporal scales within each level. Failure to properly address scale dependence can result in incorrect inferences in multi-scale habitat selection modeling studies.

New paleomagnetic data from the Wadi Abyad crustal section and their implications for the rotation history of the Oman ophiolite

NASA Astrophysics Data System (ADS)

Meyer, Matthew; Morris, Antony; Anderson, Mark; MacLeod, Chris

2015-04-01

The Oman ophiolite is an important natural laboratory for understanding the construction of oceanic crust at fast spreading axes and its subsequent tectonic evolution. Previous paleomagnetic research in lavas of the northern ophiolitic blocks (Perrin et al., 2000) has demonstrated substantial clockwise intraoceanic tectonic rotations. Paleomagnetic data from lower crustal sequences in the southern blocks, however, have been more equivocal due to complications arising from remagnetization, and have been used to infer that clockwise rotations seen in the north are internal to the ophiolite rather than regionally significant (Weiler, 2000). Here we demonstrate the importance and advantages of sampling crustal transects in the ophiolite in order to understand the nature and variability in magnetization directions. By systematically sampling the lower crustal sequence exposed in Wadi Abyad (Rustaq block) we resolve for the first time in a single section a pattern of remagnetized lowermost gabbros and retention of earlier magnetizations by uppermost gabbros and the overlying dyke-rooting zone. Results are supported by a positive fold test that shows that remagnetization of lower gabbros occurred prior to the Campanian structural disruption of the Moho. NW-directed remagnetized remanences in the lower units are consistent with those used by Weiler (2000) to infer lack of significant rotation of the southern blocks and to argue, therefore, that rotation of the northern blocks was internal to the ophiolite. In contrast, E/ENE-directed remanences in the uppermost levels of Wadi Abyad imply large, clockwise rotation of the Rustaq block, of a sense and magnitude consistent with intraoceanic rotations inferred from extrusive sections in the northern blocks. We conclude that without the control provided by systematic crustal sampling, the potential for different remanence directions being acquired at different times may lead to erroneous tectonic interpretation.

Heart rate variability and serum level of insulin-like growth factor-1 are correlated with symptoms of emotional disorders in patients suffering a mild traumatic brain injury.

PubMed

Sung, Chih-Wei; Chen, Kai-Yun; Chiang, Yung-Hsiao; Chiu, Wen-Ta; Ou, Ju-Chi; Lee, Hsin-Chien; Tsai, Shin-Han; Lin, Jia-Wei; Yang, Che-Ming; Tsai, Yan-Rou; Liao, Kuo-Hsing; Chen, Gunng-Shinng; Li, Wei-Jiun; Wang, Jia-Yi

2016-02-01

Patients who have experienced a mild traumatic brain injury (mTBI) are susceptible to symptoms of anxiety or depression. To explore the potential biomarkers for emotional disorders in mTBI patients, we analyzed the frequency domain of heart rate variability (HRV) and serum concentrations of four neurohormones. We assessed mTBI patients on their first visit and follow-up. Symptoms were evaluated by the Beck Anxiety Inventory and the Beck Depression Inventory, respectively. Serum levels of adrenocorticotropic hormone (ACTH), melatonin, cortisol, and insulin-like growth factor (IGF)-1 and HRV follow-ups were measured and compared. mTBI patients were more vulnerable to symptoms of anxiety or depression than healthy controls. Reduced HRV was noted in mTBI patients compared to healthy controls. The mTBI patients demonstrated higher serum levels of ACTH, lower IGF-1 compared to healthy controls. In correlation analysis, only IGF-1 was positively correlated with HRV in mTBI patients. Both HRV and IGF-1 were correlated with symptom of depression while only HRV was correlated with symptom of anxiety in mTBI patients. We infer that HRV may be more significantly correlated with emotional disorders than is IGF-1 in mTBI patients. The study is relevant for specific diagnostic markers in mTBI patients. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Temperature controls on the basal emission rate of isoprene in a tropical tree Ficus septica: exploring molecular regulatory mechanisms.

PubMed

Mutanda, Ishmael; Inafuku, Masashi; Saitoh, Seikoh; Iwasaki, Hironori; Fukuta, Masakazu; Watanabe, Keiichi; Oku, Hirosuke

2016-10-01

Isoprene emission from plants is very sensitive to environmental temperature both at short-term and long-term scales. Our previous study demonstrated suppression of isoprene emission by cold temperatures in a high emitting tropical tree Ficus septica and revealed a strong correlation of emission to isoprene synthase (IspS) protein levels. When challenged with decreasing daily temperatures from 30 to 12 °C, F. septica completely stopped isoprene emission at 12 °C, only to recover on the second day after re-exposure to 30 °C. Here, we explored this regulation of isoprene emission in response to environmental temperature by a comprehensive analysis of transcriptome data, gene expressions and metabolite pools of the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway. MEP pathway genes and metabolites dynamics did not support substrate-level limitations as major control over observed basal emission, but transcriptome data, network inferences and putative regulatory elements on IspS promoter suggested transcriptional regulation of IspS gene through circadian rhythm and phytohormone signalling processes. Expression levels of 29 genes involved in these pathways were examined by quantitative real-time PCR. We propose that temperature controls over basal isoprene emission at a time-scale of hours to few days are regulated by phytohormone-mediated transcriptional modulation of IspS gene under synchronization by the circadian clock. © 2016 John Wiley & Sons Ltd.

Cashew nut meal in the feeding of meat quails.

PubMed

Fernandes, Danilo Rodrigues; Freitas, Ednardo Rodrigues; Watanabe, Pedro Henrique; Filgueira, Thales Marcel Bezerra; Cruz, Carlos Eduardo Braga; do Nascimento, Germano Augusto Jerônimo; Aguiar, Geovana Costa; Nascimento, Etho Robério Medeiros

2016-04-01

A study was aimed to evaluate the effects of cashew nut meal inclusion (CNM) on nutrient digestibility, performance and carcass characteristics of meat quails. A total of 432 meat quails with 7 days of age, were distributed in a completely randomized design with six treatments and nine replicates of eight birds each. Treatments were obtained with inclusion of CNM at levels of 0, 50, 100, 150, 200, and 250 g/kg. According to regression analysis, the inclusion of CNM, at levels above 50 g/kg, provided a linear reduction in digestibility of dry matter and metabolizable energy of diets, linear increase in feed intake and an increase in feed conversion ratio, not influencing weight gain and carcass characteristics. Comparing the results obtained with the different inclusion levels compared to those obtained with the diet without CNM (control group), it was noted that diets with 200 g/kg of CNM inclusion, the dry matter digestibility and metabolizable energy of diet were lower and the level of 250 g/kg provided higher feed intake. Considering the results, it can be inferred that cashew nut meal can be used as a feedstuff in meat quail's diets at levels up to 250 g/kg.

Health systems' responsiveness and its characteristics: a cross-country comparative analysis.

PubMed

Robone, Silvana; Rice, Nigel; Smith, Peter C

2011-12-01

OBJECTIVES. Responsiveness has been identified as one of the intrinsic goals of health care systems. Little is known, however, about its determinants. Our objective is to investigate the potential country-level drivers of health system responsiveness. DATA SOURCE. Data on responsiveness are taken from the World Health Survey. Information on country-level characteristics is obtained from a variety of sources including the United Nations Development Program (UNDP). STUDY DESIGN. A two-step procedure. First, using survey data we derive a country-level measure of system responsiveness purged of differences in individual reporting behavior. Secondly, we run cross-sectional country-level regressions of responsiveness on potential drivers. PRINCIPAL FINDINGS. Health care expenditures per capita are positively associated with responsiveness, after controlling for the influence of potential confounding factors. Aspects of responsiveness are also associated with public sector spending (negatively) and educational development (positively). CONCLUSIONS. From a policy perspective, improvements in responsiveness may require higher spending levels. The expansion of nonpublic sector provision, perhaps in the form of increased patient choice, may also serve to improve responsiveness. However, these inferences are tentative and require further study. © Health Research and Educational Trust.

Testing AGN unification via inference from large catalogs

NASA Astrophysics Data System (ADS)

Nikutta, Robert; Ivezic, Zeljko; Elitzur, Moshe; Nenkova, Maia

2018-01-01

Source orientation and clumpiness of the central dust are the main factors in AGN classification. Type-1 QSOs are easy to observe and large samples are available (e.g. in SDSS), but obscured type-2 AGN are dimmer and redder as our line of sight is more obscured, making it difficult to obtain a complete sample. WISE has found up to a million QSOs. With only 4 bands and a relatively small aperture the analysis of individual sources is challenging, but the large sample allows inference of bulk properties at a very significant level.CLUMPY (www.clumpy.org) is arguably the most popular database of AGN torus SEDs. We model the ensemble properties of the entire WISE AGN content using regularized linear regression, with orientation-dependent CLUMPY color-color-magnitude (CCM) tracks as basis functions. We can reproduce the observed number counts per CCM bin with percent-level accuracy, and simultaneously infer the probability distributions of all torus parameters, redshifts, additional SED components, and identify type-1/2 AGN populations through their IR properties alone. We increase the statistical power of our AGN unification tests even further, by adding other datasets as axes in the regression problem. To this end, we make use of the NOAO Data Lab (datalab.noao.edu), which hosts several high-level large datasets and provides very powerful tools for handling large data, e.g. cross-matched catalogs, fast remote queries, etc.

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways *

PubMed Central

Erdem, Cemal; Nagle, Alison M.; Casa, Angelo J.; Litzenburger, Beate C.; Wang, Yu-fen; Taylor, D. Lansing; Lee, Adrian V.; Lezon, Timothy R.

2016-01-01

Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro. PMID:27364358

Shear wave prediction using committee fuzzy model constrained by lithofacies, Zagros basin, SW Iran

NASA Astrophysics Data System (ADS)

Shiroodi, Sadjad Kazem; Ghafoori, Mohammad; Ansari, Hamid Reza; Lashkaripour, Golamreza; Ghanadian, Mostafa

2017-02-01

The main purpose of this study is to introduce the geological controlling factors in improving an intelligence-based model to estimate shear wave velocity from seismic attributes. The proposed method includes three main steps in the framework of geological events in a complex sedimentary succession located in the Persian Gulf. First, the best attributes were selected from extracted seismic data. Second, these attributes were transformed into shear wave velocity using fuzzy inference systems (FIS) such as Sugeno's fuzzy inference (SFIS), adaptive neuro-fuzzy inference (ANFIS) and optimized fuzzy inference (OFIS). Finally, a committee fuzzy machine (CFM) based on bat-inspired algorithm (BA) optimization was applied to combine previous predictions into an enhanced solution. In order to show the geological effect on improving the prediction, the main classes of predominate lithofacies in the reservoir of interest including shale, sand, and carbonate were selected and then the proposed algorithm was performed with and without lithofacies constraint. The results showed a good agreement between real and predicted shear wave velocity in the lithofacies-based model compared to the model without lithofacies especially in sand and carbonate.

Children's and Adults' Judgments of the Controllability of Cognitive Activities

ERIC Educational Resources Information Center

Pillow, Bradford H.; Pearson, RaeAnne M.

2015-01-01

Two experiments investigated 1st-, 3rd-, and 5th-grade children's and adults' judgments related to the controllability of cognitive activities, including object recognition, inferential reasoning, counting, and pretending. In Experiment 1, fifth-grade children and adults rated transitive inference and interpretation of ambiguous pictures as more…

Implantable, Ingestible Electronic Thermometer

NASA Technical Reports Server (NTRS)

Kleinberg, Leonard

1987-01-01

Small quartz-crystal-controlled oscillator swallowed or surgically implanted provides continuous monitoring of patient's internal temperature. Receiver placed near patient measures oscillator frequency, and temperature inferred from previously determined variation of frequency with temperature. Frequency of crystal-controlled oscillator varies with temperature. Circuit made very small and implanted or ingested to measure internal body temperature.

Nonlinear Decoupling Control With ANFIS-Based Unmodeled Dynamics Compensation for a Class of Complex Industrial Processes.

PubMed

Zhang, Yajun; Chai, Tianyou; Wang, Hong; Wang, Dianhui; Chen, Xinkai

2018-06-01

Complex industrial processes are multivariable and generally exhibit strong coupling among their control loops with heavy nonlinear nature. These make it very difficult to obtain an accurate model. As a result, the conventional and data-driven control methods are difficult to apply. Using a twin-tank level control system as an example, a novel multivariable decoupling control algorithm with adaptive neural-fuzzy inference system (ANFIS)-based unmodeled dynamics (UD) compensation is proposed in this paper for a class of complex industrial processes. At first, a nonlinear multivariable decoupling controller with UD compensation is introduced. Different from the existing methods, the decomposition estimation algorithm using ANFIS is employed to estimate the UD, and the desired estimating and decoupling control effects are achieved. Second, the proposed method does not require the complicated switching mechanism which has been commonly used in the literature. This significantly simplifies the obtained decoupling algorithm and its realization. Third, based on some new lemmas and theorems, the conditions on the stability and convergence of the closed-loop system are analyzed to show the uniform boundedness of all the variables. This is then followed by the summary on experimental tests on a heavily coupled nonlinear twin-tank system that demonstrates the effectiveness and the practicability of the proposed method.

Evaluating Effects of H2O and overhead O3 on Global Mean Tropospheric OH Concentration

NASA Technical Reports Server (NTRS)

Nicely, Julie M.; Salawitch, R.J.; Canty, T.; Lang, Chang; Duncan, Bryan; Liang, Qing; Oman, Luke David; Stolarski, Richard S.; Waugh, Darryn

2012-01-01

The oxidizing capacity of the troposphere is controlled, to a large extent, by the abundance of hydroxyl radical (OH). The global mean concentration of OH, [OH]GLOBAL, inferred from measurements of methyl chloroform, has remained relatively constant during the past several decades, despite rising levels of CH4 that should have led to a steady decline. Here we examine other factors that may have affected [OH]GLOBAL, such as the overhead burden of stratospheric O3 and tropospheric H2O, using global OH fields from the GEOS-CHEM Chemistry-Climate Model. Our analysis suggests these factors may have contributed a positive trend to [OH]GLOBAL large enough to counter the decrease due to CH4.

Sexual selection targets cetacean pelvic bones

PubMed Central

Dines, J. P.; Otárola-Castillo, E.; Ralph, P.; Alas, J.; Daley, T.; Smith, A. D.; Dean, M. D.

2014-01-01

Male genitalia evolve rapidly, probably as a result of sexual selection. Whether this pattern extends to the internal infrastructure that influences genital movements remains unknown. Cetaceans (whales and dolphins) offer a unique opportunity to test this hypothesis: since evolving from land-dwelling ancestors, they lost external hind limbs and evolved a highly reduced pelvis which seems to serve no other function except to anchor muscles that maneuver the penis. Here we create a novel morphometric pipeline to analyze the size and shape evolution of pelvic bones from 130 individuals (29 species) in the context of inferred mating system. We present two main findings: 1) males from species with relatively intense sexual selection (inferred by relative testes size) have evolved relatively large penises and pelvic bones compared to their body size, and 2) pelvic bone shape diverges more quickly in species pairs that have diverged in inferred mating system. Neither pattern was observed in the anterior-most pair of vertebral ribs, which served as a negative control. This study provides evidence that sexual selection can affect internal anatomy that controls male genitalia. These important functions may explain why cetacean pelvic bones have not been lost through evolutionary time. PMID:25186496

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics

PubMed Central

2017-01-01

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration. PMID:29065473

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.

PubMed

Probst, Dimitri; Petrovici, Mihai A; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.

Modeling Protein Expression and Protein Signaling Pathways

PubMed Central

Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

2015-01-01

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons

PubMed Central

Probst, Dimitri; Petrovici, Mihai A.; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems. PMID:25729361

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics.

PubMed

Sotelo Monge, Marco Antonio; Maestre Vidal, Jorge; García Villalba, Luis Javier

2017-10-21

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration.

Measure of librarian pressure using fuzzy inference system: A case study in Longyan University

NASA Astrophysics Data System (ADS)

Huang, Jian-Jing

2014-10-01

As the hierarchy of middle managers in college's librarian. They may own much work pressure from their mind. How to adapt psychological problem, control the emotion and keep a good relationship in their work place, it becomes an important issue. Especially, they work in China mainland environment. How estimate the librarians work pressure and improve the quality of service in college libraries. Those are another serious issues. In this article, the authors would like discuss how can we use fuzzy inference to test librarian work pressure.

Signal Processing Expert Code (SPEC)

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

Ames, H.S.

1985-12-01

The purpose of this paper is to describe a prototype expert system called SPEC which was developed to demonstrate the utility of providing an intelligent interface for users of SIG, a general purpose signal processing code. The expert system is written in NIL, runs on a VAX 11/750 and consists of a backward chaining inference engine and an English-like parser. The inference engine uses knowledge encoded as rules about the formats of SIG commands and about how to perform frequency analyses using SIG. The system demonstrated that expert system can be used to control existing codes.

Visual Inference Programming

NASA Technical Reports Server (NTRS)

Wheeler, Kevin; Timucin, Dogan; Rabbette, Maura; Curry, Charles; Allan, Mark; Lvov, Nikolay; Clanton, Sam; Pilewskie, Peter

2002-01-01

The goal of visual inference programming is to develop a software framework data analysis and to provide machine learning algorithms for inter-active data exploration and visualization. The topics include: 1) Intelligent Data Understanding (IDU) framework; 2) Challenge problems; 3) What's new here; 4) Framework features; 5) Wiring diagram; 6) Generated script; 7) Results of script; 8) Initial algorithms; 9) Independent Component Analysis for instrument diagnosis; 10) Output sensory mapping virtual joystick; 11) Output sensory mapping typing; 12) Closed-loop feedback mu-rhythm control; 13) Closed-loop training; 14) Data sources; and 15) Algorithms. This paper is in viewgraph form.

The hydro-climatic history of the Gulf of Cadiz throughout the last ~570 kyr from IODP 339 Site U1386

NASA Astrophysics Data System (ADS)

Kaboth, Stefanie; de Boer, Bas; Bahr, Andrè; Zeeden, Christian; Lourens, Lucas

2016-04-01

Here we present the first continuous and high-resolution (~ 1 kyr) benthic δ18O record from IODP Site U1386 (Gulf of Cadiz, IODP Exp. 339) representing the last 570 kyr. We find distinct and periodic δ18O enrichment events overimposed on patterns of global ice volume change as inferred from the global mean δ18O signal (i.e. LR04). These events occur most prominently during glacials, and are characterized by a relative increase of up to 1‰. The observed glacial δ18O enrichment represents a striking difference to deep-sea benthic δ18O records worldwide but is not without precedent as similar δ18O variability can be observed in the planktic signal of the Red Sea (Sites KL11 and KL23). There, similar glacial δ18O enrichment events have not raised particular interest since their occurrence was attributed to sea level induced salinity increase within this virtually landlocked basin. Our results suggest that the glacial δ18O enrichment at Site U1386 present salinity and/or temperature variability related to changes in the position of the frontal zone between subpolar and subtropical water masses within the Gulf of Cadiz. Interestingly, the δ18O enrichments at Site U1386 strongly reflect precession and semi-precession patterns. Since similar patterns can be observed in the Red Sea isotopic records, we argue that part of the inferred sea level reconstructions are biased by a regional and precession controlled mechanism.

Studies on thermophysical properties at New Jersey Shallow Shelf (IODP Expedition 313)

NASA Astrophysics Data System (ADS)

Fehr, A.; Pechnig, R.; Inwood, J.; LOFI, J.; Bosch, F. P.; Clauser, C.

2011-12-01

The IODP drilling expedition 313 New Jersey Shallow Shelf was proposed for obtaining deep sub-seafloor samples and downhole logging measurements in the crucial inner shelf region.The inner to central shelf off-shore New Jersey is an ideal location for studying the history of sea-level changes and its relationship to sequence stratigraphy and onshore/offshore groundwater flows. The region features rapid depositional rates, tectonic stability, and well-preserved, cosmopolitan age control fossils suitable for characterizing the sediments of this margin throughout the time interval of interest. Past sea-level rise and fall is documented in sedimentary layers deposited during Earth's history. In addition, the inner shelf is characterised by relatively fresh pore water intervals alternating vertically with saltier intervals (Mountain et al., 2010). Therefore, three boreholes were drilled in the so-called New Jersey/Mid-Atlantic transect during IODP Expedition 313 New Jersey Shallow Shelf. Numerous questions have arisen concerning the age and origin of the brackish waters recovered offshore at depth. Here we present an analysis of thermophysical properties to be used as input parameters in constructing numerical models for future groundwater flow simulations. Our study is based mainly on Nuclear Magnetic Resonance (NMR) measurements for inferring porosity and permeability, and thermal conductivity. We performed NMR measurements on samples from boreholes M0027A, M0028A and M0029A and thermal conductivity measurements on the whole round cores prior to the Onshore Party. These results are compared with data from alternative laboratory measurements and with petrophysical properties inferred from downhole logging data.

The unique contributions of perceiver and target characteristics in person perception.

PubMed

Hehman, Eric; Sutherland, Clare A M; Flake, Jessica K; Slepian, Michael L

2017-10-01

Models of person perception have long asserted that our impressions of others are guided by characteristics of both the target and perceiver. However, research has not yet quantified to what extent perceivers and targets contribute to different impressions. This quantification is theoretically critical, as it addresses how much an impression arises from "our minds" versus "others' faces." Here, we apply cross-classified random effects models to address this fundamental question in social cognition, using approximately 700,000 ratings of faces. With this approach, we demonstrate that (a) different trait impressions have unique causal processes, meaning that some impressions are largely informed by perceiver-level characteristics whereas others are driven more by physical target-level characteristics; (b) modeling of perceiver- and target-variance in impressions informs fundamental models of social perception; (c) Perceiver × Target interactions explain a substantial portion of variance in impressions; (d) greater emotional intensity in stimuli decreases the influence of the perceiver; and (e) more variable, naturalistic stimuli increases variation across perceivers. Important overarching patterns emerged. Broadly, traits and dimensions representing inferences of character (e.g., dominance) are driven more by perceiver characteristics than those representing appearance-based appraisals (e.g., youthful-attractiveness). Moreover, inferences made of more ambiguous traits (e.g., creative) or displays (e.g., faces with less extreme emotions, less-controlled stimuli) are similarly driven more by perceiver than target characteristics. Together, results highlight the large role that perceiver and target variability play in trait impressions, and develop a new topography of trait impressions that considers the source of the impression. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Subtle changes in myelination due to childhood experiences: label-free microscopy to infer nerve fibers morphology and myelination in brain (Conference Presentation)

NASA Astrophysics Data System (ADS)

Gasecka, Alicja; Tanti, Arnaud; Lutz, Pierre-Eric; Mechawar, Naguib; Cote, Daniel C.

2017-02-01

Adverse childhood experiences have lasting detrimental effects on mental health and are strongly associated with impaired cognition and increased risk of developing psychopathologies. Preclinical and neuroimaging studies have suggested that traumatic events during brain development can affect cerebral myelination particularly in areas and tracts implicated in mood and emotion. Although current neuroimaging techniques are quite powerful, they lack the resolution to infer myelin integrity at the cellular level. Recently demonstrated coherent Raman microscopy has accomplished cellular level imaging of myelin sheaths in the nervous system. However, a quantitative morphometric analysis of nerve fibers still remains a challenge. In particular, in brain, where fibres exhibit small diameters and varying local orientation. In this work, we developed an automated myelin identification and analysis method that is capable of providing a complete picture of axonal myelination and morphology in brain samples. This method performs three main procedures 1) detects molecular anisotropy of membrane phospholipids based on polarization resolved coherent Raman microscopy, 2) identifies regions of different molecular organization, 3) calculates morphometric features of myelinated axons (e.g. myelin thickness, g-ratio). We applied this method to monitor white matter areas from suicides adults that suffered from early live adversity and depression compared to depressed suicides adults and psychiatrically healthy controls. We demonstrate that our method allows for the rapid acquisition and automated analysis of neuronal networks morphology and myelination. This is especially useful for clinical and comparative studies, and may greatly enhance the understanding of processes underlying the neurobiological and psychopathological consequences of child abuse.

Segmentation of plate coupling, fate of subduction fluids, and modes of arc magmatism in Cascadia, inferred from magnetotelluric resistivity

USGS Publications Warehouse

Wannamaker, Philip E.; Evans, Rob L.; Bedrosian, Paul A.; Unsworth, Martyn J.; Maris, Virginie; McGary, R. Shane

2014-01-01

Five magnetotelluric (MT) profiles have been acquired across the Cascadia subduction system and transformed using 2-D and 3-D nonlinear inversion to yield electrical resistivity cross sections to depths of ∼200 km. Distinct changes in plate coupling, subduction fluid evolution, and modes of arc magmatism along the length of Cascadia are clearly expressed in the resistivity structure. Relatively high resistivities under the coasts of northern and southern Cascadia correlate with elevated degrees of inferred plate locking, and suggest fluid- and sediment-deficient conditions. In contrast, the north-central Oregon coastal structure is quite conductive from the plate interface to shallow depths offshore, correlating with poor plate locking and the possible presence of subducted sediments. Low-resistivity fluidized zones develop at slab depths of 35–40 km starting ∼100 km west of the arc on all profiles, and are interpreted to represent prograde metamorphic fluid release from the subducting slab. The fluids rise to forearc Moho levels, and sometimes shallower, as the arc is approached. The zones begin close to clusters of low-frequency earthquakes, suggesting fluid controls on the transition to steady sliding. Under the northern and southern Cascadia arc segments, low upper mantle resistivities are consistent with flux melting above the slab plus possible deep convective backarc upwelling toward the arc. In central Cascadia, extensional deformation is interpreted to segregate upper mantle melts leading to underplating and low resistivities at Moho to lower crustal levels below the arc and nearby backarc. The low- to high-temperature mantle wedge transition lies slightly trenchward of the arc.

Research designs and making causal inferences from health care studies.

PubMed

Flannelly, Kevin J; Jankowski, Katherine R B

2014-01-01

This article summarizes the major types of research designs used in healthcare research, including experimental, quasi-experimental, and observational studies. Observational studies are divided into survey studies (descriptive and correlational studies), case-studies and analytic studies, the last of which are commonly used in epidemiology: case-control, retrospective cohort, and prospective cohort studies. Similarities and differences among the research designs are described and the relative strength of evidence they provide is discussed. Emphasis is placed on five criteria for drawing causal inferences that are derived from the writings of the philosopher John Stuart Mill, especially his methods or canons. The application of the criteria to experimentation is explained. Particular attention is given to the degree to which different designs meet the five criteria for making causal inferences. Examples of specific studies that have used various designs in chaplaincy research are provided.

The application of a linear algebra to the analysis of mutation rates.

PubMed

Jones, M E; Thomas, S M; Clarke, K

1999-07-07

Cells and bacteria growing in culture are subject to mutation, and as this mutation is the ultimate substrate for selection and evolution, the factors controlling the mutation rate are of some interest. The mutational event is not observed directly, but is inferred from the phenotype of the original mutant or of its descendants; the rate of mutation is inferred from the number of such mutant phenotypes. Such inference presumes a knowledge of the probability distribution for the size of a clone arising from a single mutation. We develop a mathematical formulation that assists in the design and analysis of experiments which investigate mutation rates and mutant clone size distribution, and we use it to analyse data for which the classical Luria-Delbrück clone-size distribution must be rejected. Copyright 1999 Academic Press.

Inference by Exclusion in Goffin Cockatoos (Cacatua goffini)

PubMed Central

O’Hara, Mark; Auersperg, Alice M. I.; Bugnyar, Thomas; Huber, Ludwig

2015-01-01

Inference by exclusion, the ability to base choices on the systematic exclusion of alternatives, has been studied in many nonhuman species over the past decade. However, the majority of methodologies employed so far are hard to integrate into a comparative framework as they rarely use controls for the effect of neophilia. Here, we present an improved approach that takes neophilia into account, using an abstract two-choice task on a touch screen, which is equally feasible for a large variety of species. To test this approach we chose Goffin cockatoos (Cacatua goffini), a highly explorative Indonesian parrot species, which have recently been reported to have sophisticated cognitive skills in the technical domain. Our results indicate that Goffin cockatoos are able to solve such abstract two-choice tasks employing inference by exclusion but also highlight the importance of other response strategies. PMID:26244692

A Fuzzy Logic Based Controller for the Automated Alignment of a Laser-beam-smoothing Spatial Filter

NASA Technical Reports Server (NTRS)

Krasowski, M. J.; Dickens, D. E.

1992-01-01

A fuzzy logic based controller for a laser-beam-smoothing spatial filter is described. It is demonstrated that a human operator's alignment actions can easily be described by a system of fuzzy rules of inference. The final configuration uses inexpensive, off-the-shelf hardware and allows for a compact, readily implemented embedded control system.

Ontological knowledge structure of intuitive biology

NASA Astrophysics Data System (ADS)

Martin, Suzanne Michele

It has become increasingly important for individuals to understand infections disease, as there has been a tremendous rise in viral and bacterial disease. This research examines systematic misconceptions regarding the characteristics of viruses and bacteria present in individuals previously educated in biological sciences at a college level. 90 pre-nursing students were administered the Knowledge Acquisition Device (KAD) which consists of 100 True/False items that included statements about the possible attributes of four entities: bacteria, virus, amoeba, and protein. Thirty pre-nursing students, who incorrectly stated that viruses were alive, were randomly assigned to three conditions. (1) exposed to information about the ontological nature of viruses, (2) Information about viruses, (3) control. In the condition that addressed the ontological nature of a virus, all of those participants were able to classify viruses correctly as not alive; however any items that required inferences, such as viruses come in male and female forms or viruses breed with each other to make baby viruses were still incorrectly answered by all conditions in the posttest. It appears that functional knowledge, ex. If a virus is alive or dead, or how it is structured, is not enough for an individual to have a full and accurate understanding of viruses. Ontological knowledge information may alter the functional knowledge but underlying inferences remain systematically incorrect.

A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

NASA Technical Reports Server (NTRS)

Turso, James A.; Litt, Jonathan S.

2004-01-01

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

Bayesian inference reveals positive but subtle effects of experimental fishery closures on marine predator demographics

PubMed Central

Barham, Barbara J.; Barham, Peter J.; Campbell, Kate J.; Crawford, Robert J. M.; Grigg, Jennifer; Horswill, Cat; Morris, Taryn L.; Pichegru, Lorien; Steinfurth, Antje; Weller, Florian; Winker, Henning

2018-01-01

Global forage-fish landings are increasing, with potentially grave consequences for marine ecosystems. Predators of forage fish may be influenced by this harvest, but the nature of these effects is contentious. Experimental fishery manipulations offer the best solution to quantify population-level impacts, but are rare. We used Bayesian inference to examine changes in chick survival, body condition and population growth rate of endangered African penguins Spheniscus demersus in response to 8 years of alternating time–area closures around two pairs of colonies. Our results demonstrate that fishing closures improved chick survival and condition, after controlling for changing prey availability. However, this effect was inconsistent across sites and years, highlighting the difficultly of assessing management interventions in marine ecosystems. Nevertheless, modelled increases in population growth rates exceeded 1% at one colony; i.e. the threshold considered biologically meaningful by fisheries management in South Africa. Fishing closures evidently can improve the population trend of a forage-fish-dependent predator—we therefore recommend they continue in South Africa and support their application elsewhere. However, detecting demographic gains for mobile marine predators from small no-take zones requires experimental time frames and scales that will often exceed those desired by decision makers. PMID:29343602

A Power Analysis and Recommended Study Design to Directly Detect Population Level Consequences of Acoustic Disturbance

DTIC Science & Technology

2015-09-30

Detect Population-Level Consequences of Acoustic Disturbance Jeffrey Moore NOAA Southwest Fisheries Science Center 8901 La Jolla Shores Drive...inferences about the population. WORK COMPLETED Investigators met for an all-day planning meeting (Aug 28, 2015) at Southwest Fisheries Science

A Holistic Model to Infer Mathematics Performance: The Interrelated Impact of Student, Family and School Context Variables

ERIC Educational Resources Information Center

Zhao, Ningning; Valcke, Martin; Desoete, Annemie; Zhu, Chang; Sang, Guoyuan; Verhaeghe, JeanPierre

2014-01-01

The present study aims at exploring predictors influencing mathematics performance. In particular, the study focuses on internal students' characteristics (gender, age, metacognitive experience, mathematics self-efficacy) and external contextual factors (GDP of school location, parents' educational level, teachers' educational level, and teacher…

Closed-Loop Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals.

PubMed

Fukuma, Ryohei; Yanagisawa, Takufumi; Yorifuji, Shiro; Kato, Ryu; Yokoi, Hiroshi; Hirata, Masayuki; Saitoh, Youichi; Kishima, Haruhiko; Kamitani, Yukiyasu; Yoshimine, Toshiki

2015-01-01

A neuroprosthesis using a brain-machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects' ability to control a neuroprosthesis. Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition. The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student's t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition. Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.

Closed-Loop Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals

PubMed Central

Fukuma, Ryohei; Yanagisawa, Takufumi; Yorifuji, Shiro; Kato, Ryu; Yokoi, Hiroshi; Hirata, Masayuki; Saitoh, Youichi; Kishima, Haruhiko; Kamitani, Yukiyasu; Yoshimine, Toshiki

2015-01-01

Objective A neuroprosthesis using a brain–machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects’ ability to control a neuroprosthesis. Method Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition. Results The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student’s t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition. Conclusions Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI. PMID:26134845

INfORM: Inference of NetwOrk Response Modules.

PubMed

Marwah, Veer Singh; Kinaret, Pia Anneli Sofia; Serra, Angela; Scala, Giovanni; Lauerma, Antti; Fortino, Vittorio; Greco, Dario

2018-06-15

Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps. INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM. Supplementary data are available at Bioinformatics online.

Testing adaptive toolbox models: a Bayesian hierarchical approach.

PubMed

Scheibehenne, Benjamin; Rieskamp, Jörg; Wagenmakers, Eric-Jan

2013-01-01

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.

PubMed

Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad

2013-05-01

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.

A Quantitative Systems Pharmacology Approach to Infer Pathways Involved in Complex Disease Phenotypes.

PubMed

Schurdak, Mark E; Pei, Fen; Lezon, Timothy R; Carlisle, Diane; Friedlander, Robert; Taylor, D Lansing; Stern, Andrew M

2018-01-01

Designing effective therapeutic strategies for complex diseases such as cancer and neurodegeneration that involve tissue context-specific interactions among multiple gene products presents a major challenge for precision medicine. Safe and selective pharmacological modulation of individual molecular entities associated with a disease often fails to provide efficacy in the clinic. Thus, development of optimized therapeutic strategies for individual patients with complex diseases requires a more comprehensive, systems-level understanding of disease progression. Quantitative systems pharmacology (QSP) is an approach to drug discovery that integrates computational and experimental methods to understand the molecular pathogenesis of a disease at the systems level more completely. Described here is the chemogenomic component of QSP for the inference of biological pathways involved in the modulation of the disease phenotype. The approach involves testing sets of compounds of diverse mechanisms of action in a disease-relevant phenotypic assay, and using the mechanistic information known for the active compounds, to infer pathways and networks associated with the phenotype. The example used here is for monogenic Huntington's disease (HD), which due to the pleiotropic nature of the mutant phenotype has a complex pathogenesis. The overall approach, however, is applicable to any complex disease.

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli

PubMed Central

Lemmens, Karen; De Bie, Tijl; Dhollander, Thomas; De Keersmaecker, Sigrid C; Thijs, Inge M; Schoofs, Geert; De Weerdt, Ami; De Moor, Bart; Vanderleyden, Jos; Collado-Vides, Julio; Engelen, Kristof; Marchal, Kathleen

2009-01-01

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity. PMID:19265557

Evaluation of an Adaptive Game that Uses EEG Measures Validated during the Design Process as Inputs to a Biocybernetic Loop.

PubMed

Ewing, Kate C; Fairclough, Stephen H; Gilleade, Kiel

2016-01-01

Biocybernetic adaptation is a form of physiological computing whereby real-time data streaming from the brain and body is used by a negative control loop to adapt the user interface. This article describes the development of an adaptive game system that is designed to maximize player engagement by utilizing changes in real-time electroencephalography (EEG) to adjust the level of game demand. The research consists of four main stages: (1) the development of a conceptual framework upon which to model the interaction between person and system; (2) the validation of the psychophysiological inference underpinning the loop; (3) the construction of a working prototype; and (4) an evaluation of the adaptive game. Two studies are reported. The first demonstrates the sensitivity of EEG power in the (frontal) theta and (parietal) alpha bands to changing levels of game demand. These variables were then reformulated within the working biocybernetic control loop designed to maximize player engagement. The second study evaluated the performance of an adaptive game of Tetris with respect to system behavior and user experience. Important issues for the design and evaluation of closed-loop interfaces are discussed.

Evaluation of an Adaptive Game that Uses EEG Measures Validated during the Design Process as Inputs to a Biocybernetic Loop

PubMed Central

Ewing, Kate C.; Fairclough, Stephen H.; Gilleade, Kiel

2016-01-01

Biocybernetic adaptation is a form of physiological computing whereby real-time data streaming from the brain and body is used by a negative control loop to adapt the user interface. This article describes the development of an adaptive game system that is designed to maximize player engagement by utilizing changes in real-time electroencephalography (EEG) to adjust the level of game demand. The research consists of four main stages: (1) the development of a conceptual framework upon which to model the interaction between person and system; (2) the validation of the psychophysiological inference underpinning the loop; (3) the construction of a working prototype; and (4) an evaluation of the adaptive game. Two studies are reported. The first demonstrates the sensitivity of EEG power in the (frontal) theta and (parietal) alpha bands to changing levels of game demand. These variables were then reformulated within the working biocybernetic control loop designed to maximize player engagement. The second study evaluated the performance of an adaptive game of Tetris with respect to system behavior and user experience. Important issues for the design and evaluation of closed-loop interfaces are discussed. PMID:27242486

Velocity-based movement modeling for individual and population level inference

USGS Publications Warehouse

Hanks, Ephraim M.; Hooten, Mevin B.; Johnson, Devin S.; Sterling, Jeremy T.

2011-01-01

Understanding animal movement and resource selection provides important information about the ecology of the animal, but an animal's movement and behavior are not typically constant in time. We present a velocity-based approach for modeling animal movement in space and time that allows for temporal heterogeneity in an animal's response to the environment, allows for temporal irregularity in telemetry data, and accounts for the uncertainty in the location information. Population-level inference on movement patterns and resource selection can then be made through cluster analysis of the parameters related to movement and behavior. We illustrate this approach through a study of northern fur seal (Callorhinus ursinus) movement in the Bering Sea, Alaska, USA. Results show sex differentiation, with female northern fur seals exhibiting stronger response to environmental variables.

Velocity-Based Movement Modeling for Individual and Population Level Inference

PubMed Central

Hanks, Ephraim M.; Hooten, Mevin B.; Johnson, Devin S.; Sterling, Jeremy T.

2011-01-01

Understanding animal movement and resource selection provides important information about the ecology of the animal, but an animal's movement and behavior are not typically constant in time. We present a velocity-based approach for modeling animal movement in space and time that allows for temporal heterogeneity in an animal's response to the environment, allows for temporal irregularity in telemetry data, and accounts for the uncertainty in the location information. Population-level inference on movement patterns and resource selection can then be made through cluster analysis of the parameters related to movement and behavior. We illustrate this approach through a study of northern fur seal (Callorhinus ursinus) movement in the Bering Sea, Alaska, USA. Results show sex differentiation, with female northern fur seals exhibiting stronger response to environmental variables. PMID:21931584

An expert system shell for inferring vegetation characteristics: Atmospheric techniques (Task G)

NASA Technical Reports Server (NTRS)

Harrison, P. Ann; Harrison, Patrick R.

1993-01-01

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG Subgoals have been reorganized into categories. A new subgoal category 'Atmospheric Techniques' containing two new subgoals has been implemented. The subgoal Atmospheric Passes allows the scientist to take reflectance data measured at ground level and predict what the reflectance values would be if the data were measured at a different atmospheric height. The subgoal Atmospheric Corrections allows atmospheric corrections to be made to data collected from an aircraft or by a satellite to determine what the equivalent reflectance values would be if the data were measured at ground level. The report describes the implementation and testing of the basic framework and interface for the Atmospheric Techniques Subgoals.

On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models.

PubMed

Tang, Yongqiang

2017-12-01

Control-based pattern mixture models (PMM) and delta-adjusted PMMs are commonly used as sensitivity analyses in clinical trials with non-ignorable dropout. These PMMs assume that the statistical behavior of outcomes varies by pattern in the experimental arm in the imputation procedure, but the imputed data are typically analyzed by a standard method such as the primary analysis model. In the multiple imputation (MI) inference, Rubin's variance estimator is generally biased when the imputation and analysis models are uncongenial. One objective of the article is to quantify the bias of Rubin's variance estimator in the control-based and delta-adjusted PMMs for longitudinal continuous outcomes. These PMMs assume the same observed data distribution as the mixed effects model for repeated measures (MMRM). We derive analytic expressions for the MI treatment effect estimator and the associated Rubin's variance in these PMMs and MMRM as functions of the maximum likelihood estimator from the MMRM analysis and the observed proportion of subjects in each dropout pattern when the number of imputations is infinite. The asymptotic bias is generally small or negligible in the delta-adjusted PMM, but can be sizable in the control-based PMM. This indicates that the inference based on Rubin's rule is approximately valid in the delta-adjusted PMM. A simple variance estimator is proposed to ensure asymptotically valid MI inferences in these PMMs, and compared with the bootstrap variance. The proposed method is illustrated by the analysis of an antidepressant trial, and its performance is further evaluated via a simulation study. © 2017, The International Biometric Society.

Divided Attention and Processes Underlying Sense of Agency

PubMed Central

Wen, Wen; Yamashita, Atsushi; Asama, Hajime

2016-01-01

Sense of agency refers to the subjective feeling of controlling events through one’s behavior or will. Sense of agency results from matching predictions of one’s own actions with actual feedback regarding the action. Furthermore, when an action involves a cued goal, performance-based inference contributes to sense of agency. That is, if people achieve their goal, they would believe themselves to be in control. Previous studies have shown that both action-effect comparison and performance-based inference contribute to sense of agency; however, the dominance of one process over the other may shift based on task conditions such as the presence or absence of specific goals. In this study, we examined the influence of divided attention on these two processes underlying sense of agency in two conditions. In the experimental task, participants continuously controlled a moving dot for 10 s while maintaining a string of three or seven digits in working memory. We found that when there was no cued goal (no-cued-goal condition), sense of agency was impaired by high cognitive load. Contrastingly, when participants controlled the dot based on a cued goal (cued-goal-directed condition), their sense of agency was lower than in the no-cued-goal condition and was not affected by cognitive load. The results suggest that the action-effect comparison process underlying sense of agency requires attention. On the other hand, the weaker influence of divided attention in the cued-goal-directed condition could be attributed to the dominance of performance-based inference, which is probably automatic. PMID:26858680

Is Social Categorization the Missing Link between Weak Central Coherence and Mental State Inference Abilities in Autism? Preliminary Evidence from a General Population Sample

ERIC Educational Resources Information Center

Skorich, Daniel P.; May, Adrienne R.; Talipski, Louisa A.; Hall, Marnie H.; Dolstra, Anita J.; Gash, Tahlia B.; Gunningham, Beth H.

2016-01-01

We explore the relationship between the "theory of mind" (ToM) and "central coherence" difficulties of autism. We introduce covariation between hierarchically-embedded categories and social information--at the local level, the global level, or at both levels simultaneously--within a category confusion task. We then ask…

Evaluation of the serum zinc level in erosive and non-erosive oral lichen planus.

PubMed

Gholizadeh, N; Mehdipour, M; Najafi, Sh; Bahramian, A; Garjani, Sh; Khoeini Poorfar, H

2014-06-01

Lichen planus is a chronic inflammatory immunologic-based disease involving skin and mucosa. This disease is generally divided into two categories: erosive and non-erosive. Many etiologic factors are deliberated regarding the disease; however, the disorders of immune system and the role of cytotoxic T-lymphocytes and monocytes are more highlighted. Zinc is an imperative element for the growth of epithelium and its deficiency induces the cytotoxic activity of T-helper2 cells, which seems to be associated with lichen planus. This study was aimed to evaluate the levels of serum zinc in erosive and non-erosive oral lichen planus (OLP) and to compare it with the healthy control group to find out any feasible inference. A total of 22 patients with erosive oral lichen planus, 22 patients with non erosive OLP and 44 healthy individuals as the control group were recruited in this descriptive-comparative study. All the participants were selected from the referees to the department of oral medicine, school of dentistry, Tabriz University of Medical Sciences. Serum zinc level was examined for all the individuals with liquid-stat kit (Beckman Instruments Inc.; Carlsbad, CA). Data were analyzed by adopting the ANOVA and Tukey tests, using SPSS 16 statistical software. The mean age of patients with erosive and non-erosive LP was 41.7 and 41.3 years, respectively. The mean age of the healthy control group was 34.4 years .The mean serum zinc levels in the erosive and non erosive lichen planus groups and control groups were 8.3 (1.15), 11.15 (0.92) and 15.74 (1.75) μg/dl respectively. The difference was statistically significant (p< 0.05). The serum zinc levels were decreased in patients with erosive oral lichen planus. This finding may probably indicate the promising role of zinc in development of oral lichen planus.

In search of functional association from time-series microarray data based on the change trend and level of gene expression

PubMed Central

He, Feng; Zeng, An-Ping

2006-01-01

Background The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. Results In this work we present a new method based on extracting main features of the change trend and level of gene expression between consecutive time points. The method, termed as trend correlation (TC), includes two major steps: 1, calculating a maximal local alignment of change trend score by dynamic programming and a change trend correlation coefficient between the maximal matched change levels of each gene pair; 2, inferring relationships of gene pairs based on two statistical extraction procedures. The new method considers time shifts and inverted relationships in a similar way as the local clustering (LC) method but the latter is merely based on a point-to-point comparison. The TC method is demonstrated with data from yeast cell cycle and compared with the LC method and the widely used Pearson correlation coefficient (PCC) based clustering method. The biological significance of the gene pairs is examined with several large-scale yeast databases. Although the TC method predicts an overall lower number of gene pairs than the other two methods at a same p-value threshold, the additional number of gene pairs inferred by the TC method is considerable: e.g. 20.5% compared with the LC method and 49.6% with the PCC method for a p-value threshold of 2.7E-3. Moreover, the percentage of the inferred gene pairs consistent with databases by our method is generally higher than the LC method and similar to the PCC method. A significant number of the gene pairs only inferred by the TC method are process-identity or function-similarity pairs or have well-documented biological interactions, including 443 known protein interactions and some known cell cycle related regulatory interactions. It should be emphasized that the overlapping of gene pairs detected by the three methods is normally not very high, indicating a necessity of combining the different methods in search of functional association of genes from time-series data. For a p-value threshold of 1E-5 the percentage of process-identity and function-similarity gene pairs among the shared part of the three methods reaches 60.2% and 55.6% respectively, building a good basis for further experimental and functional study. Furthermore, the combined use of methods is important to infer more complete regulatory circuits and network as exemplified in this study. Conclusion The TC method can significantly augment the current major methods to infer functional linkages and biological network and is well suitable for exploring temporal relationships of gene expression in time-series data. PMID:16478547

Direct evidence for a dual process model of deductive inference.

PubMed

Markovits, Henry; Brunet, Marie-Laurence; Thompson, Valerie; Brisson, Janie

2013-07-01

In 2 experiments, we tested a strong version of a dual process theory of conditional inference (cf. Verschueren et al., 2005a, 2005b) that assumes that most reasoners have 2 strategies available, the choice of which is determined by situational variables, cognitive capacity, and metacognitive control. The statistical strategy evaluates inferences probabilistically, accepting those with high conditional probability. The counterexample strategy rejects inferences when a counterexample shows the inference to be invalid. To discriminate strategy use, we presented reasoners with conditional statements (if p, then q) and explicit statistical information about the relative frequency of the probability of p/q (50% vs. 90%). A statistical strategy would accept the more probable inferences more frequently, whereas the counterexample one would reject both. In Experiment 1, reasoners under time pressure used the statistical strategy more, but switched to the counterexample strategy when time constraints were removed; the former took less time than the latter. These data are consistent with the hypothesis that the statistical strategy is the default heuristic. Under a free-time condition, reasoners preferred the counterexample strategy and kept it when put under time pressure. Thus, it is not simply a lack of capacity that produces a statistical strategy; instead, it seems that time pressure disrupts the ability to make good metacognitive choices. In line with this conclusion, in a 2nd experiment, we measured reasoners' confidence in their performance; those under time pressure were less confident in the statistical than the counterexample strategy and more likely to switch strategies under free-time conditions. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Assessment of contrast gain signature in inferred magnocellular and parvocellular pathways in patients with glaucoma

PubMed Central

Sun, Hao; Swanson, William H.; Arvidson, Brian; Dul, Mitchell W.

2010-01-01

PURPOSE Contrast gain signatures of inferred magnocellular and parvocellular postreceptoral pathways were assessed for patients with glaucoma using a contrast discrimination paradigm developed by Pokorny and Smith. The potential causes for changes in contrast gain signature were investigated using model simulations of ganglion cell contrast responses. METHODS Foveal contrast discrimination thresholds were measured with a pedestal-Δ-pedestal paradigm developed by Pokorny and Smith (1997). Stimuli were 27 msec luminance increments superimposed on 227 msec pulsed Δ-pedestals. Contrast thresholds and contrast gain signatures mediated by the inferred magnocellular (MC) and parvocellular (PC) pathways were assessed using linear fits to contrast discrimination thresholds at either lower or higher Δ-pedestal contrasts, respectively. Twenty-seven patients with glaucoma were tested, as well as 16 age-similar control subjects free of eye disease. RESULTS Contrast sensitivity and contrast gain signature mediated by the inferred MC pathway were lower for the glaucoma group, and reduced contrast gain signature was correlated with reduced contrast sensitivity (r2=45%, p<0.0005). These two parameters mediated by the inferred PC pathway were little affected for the glaucoma group. Model simulations suggest that the reduced contrast sensitivity and contrast gain signature were consistent with the hypothesis that reduced MC ganglion cell dendritic complexity can lead to reduced effective retinal illuminance, and hence increased semi-saturation contrast of the ganglion cell contrast response functions. CONCLUSIONS The contrast sensitivity and contrast gain signature of the inferred MC pathway were reduced in patients with glaucoma. The results were consistent with a model of ganglion cell dysfunction due to reduced synaptic density. PMID:18501947

RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS

PubMed Central

Purcell, Braden A.; Palmeri, Thomas J.

2016-01-01

Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated accumulator model dynamics while systematically varying model parameters. In some cases, decision- making mechanisms can be directly inferred from dynamics, allowing us to distinguish between models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception. PMID:28392584

Social networks help to infer causality in the tumor microenvironment.

PubMed

Crespo, Isaac; Doucey, Marie-Agnès; Xenarios, Ioannis

2016-03-15

Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems--such as the tumor microenvironment--where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies. In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature. This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments.

Identifying student difficulties with basic scientific reasoning skills: An example from control of variables

NASA Astrophysics Data System (ADS)

Boudreaux, Andrew

2006-05-01

Current national and local standards for the science learning of K-12 students emphasize both basic concepts (such as density) and fundamental reasoning skills (such as proportional reasoning, the interpretation of graphs, and the use of control of variables). At Western Washington University (WWU) and the University of Washington (UW), an effort is underway to examine the ability of university students to apply these same concepts and skills. Populations include students in liberal arts physics courses, introductory calculus-based physics courses, and special courses for the preparation of teachers. One focus of the research has been on the idea of control of variables. This topic is studied by students at all levels, from the primary grades, in which the notion of a ``fair test,'' is sometimes used, to university courses. This talk will discuss research tasks in which students are expected to infer from experimental data whether a particular variable influences (i.e., affects) or by itself determines (i.e., predicts) a given result. Student responses will be presented to identify specific difficulties.

Prefrontal N-acetylaspartate is strongly associated with memory performance in (abstinent) ecstasy users: preliminary report.

PubMed

Reneman, L; Majoie, C B; Schmand, B; van den Brink, W; den Heeten, G J

2001-10-01

3,4-methylenedioxymethamphetamine (MDMA or "Ecstasy") is known to damage brain serotonin neurons in animals and possibly humans. Because serotonergic damage may adversely affect memory, we compared verbal memory function between MDMA users and MDMA-naïve control subjects and evaluated the relationship between verbal memory function and neuronal dysfunction in the MDMA users. An auditory verbal memory task (Rey Auditory Verbal Learning Test) was used to study eight abstinent MDMA users and seven control subjects. In addition 1H-MRS was used in different brain regions of all MDMA users to measure N-acetylaspartate/creatine (NAA/Cr) ratios, a marker for neuronal viability. The MDMA users recalled significantly fewer words than control subjects on delayed (p =.03) but not immediate recall (p =.08). In MDMA users, delayed memory function was strongly associated with NAA/Cr only in the prefrontal cortex (R(2) =.76, p =.01). Greater decrements in memory function predicted lower NAA/Cr levels-and by inference greater neuronal dysfunction-in the prefrontal cortex of MDMA users.

Reasoning about Multiple Variables: Control of Variables Is Not the Only Challenge

ERIC Educational Resources Information Center

Kuhn, Deanna

2007-01-01

Thirty fourth-grade students participated in an extended intervention previously successful in fostering skills of scientific investigation and inference, notably control of variables (COV). The intervention was similarly successful for a majority of students in the present study, enabling them to isolate the three causal and two noncausal…