Inferring Social Influence of Anti-Tobacco Mass Media Campaign.

PubMed

Zhan, Qianyi; Zhang, Jiawei; Yu, Philip S; Emery, Sherry; Xie, Junyuan

2017-07-01

Anti-tobacco mass media campaigns are designed to influence tobacco users. It has been proved that campaigns will produce users' changes in awareness, knowledge, and attitudes, and also produce meaningful behavior change of audience. Anti-smoking television advertising is the most important part in the campaign. Meanwhile, nowadays, successful online social networks are creating new media environment, however, little is known about the relation between social conversations and anti-tobacco campaigns. This paper aims to infer social influence of these campaigns, and the problem is formally referred to as the Social Influence inference of anti-Tobacco mass mEdia campaigns (Site) problem. To address the Site problem, a novel influence inference framework, TV advertising social influence estimation (Asie), is proposed based on our analysis of two real anti-tobacco campaigns. Asie divides audience attitudes toward TV ads into three distinct stages: 1) cognitive; 2) affective; and 3) conative. Audience online reactions at each of these three stages are depicted by Asie with specific probabilistic models based on the synergistic influences from both online social friends and offline TV ads. Extensive experiments demonstrate the effectiveness of Asie.

From Mere Coincidences to Meaningful Discoveries

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2007-01-01

People's reactions to coincidences are often cited as an illustration of the irrationality of human reasoning about chance. We argue that coincidences may be better understood in terms of rational statistical inference, based on their functional role in processes of causal discovery and theory revision. We present a formal definition of…

Both sides now: the chemistry of clouds

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

Schwartz, S.E.

1984-01-01

Two complementary approaches to the study of the oxidation of SO/sub 2/ and NO/sub x/ to sulfuric and nitric acids as it occurs in liquid-water clouds are presented. The first approach relies upon laboratory determination of fundamental physical and chemical properties and evaluation of rates of dissolution and reaction for representative reagent concentrations and physical situations. The second approach consists of measuring concentrations of relevant reagent and product species and other pertinent quantities in and about clouds and of drawing inferences from these measurements about the rate and extent of processes responsible for establishing cloudwater composition. Based on laboratory studiesmore » the following inferences may be drawn: aqueous-phase oxidation of SO/sub 2/ by H/sub 2/O/sub 2/ or O/sub 3/ is sufficiently rapid to contribute to cloudwater acidity for representative concentrations of these oxidants; however, the rate of the O/sub 3/ reaction decreases strongly with decreasing pH and is of relatively little importance below about pH 4.5. Despite strong thermochemical driving force for oxidation of NO/sub 2/ to nitric acid in cloudwater, this reaction appears to be negligibly slow for representative concentrations of NO/sub 2/, largely because of the low Henry's law solubility of this species in water. These inferences gain support from field measurements of the composition of liquid water stratiform clouds at various locations in the eastern United States, which indicate that the fractional uptake of SO/sub 2/ (as SO/sub 4//sup =/) by cloudwater is frequently high, whereas a corresponding high fractional uptake of NO/sub x/ as NO/sub 3//sup -/ is never observed. A mutual exclusivity of gaseous SO/sub 2/ and dissolved H/sub 2/O/sub 2/ in clouds supports the inference that reaction of these species in clouds is rapid and represents a major process for cloudwater acidification. 143 references, 35 figures, 2 tables.« less

Effects of Inferred Motive on Evaluations of Nonaccommodative Communication

ERIC Educational Resources Information Center

Gasiorek, Jessica; Giles, Howard

2012-01-01

In two studies, we propose, refine, and test a new model of inferred motive predicting of individuals' reactions to nonaccommodation, defined as communicative behavior that is inappropriately adjusted for participants in an interaction. Inferring a negative motive for others' problematic behavior resulted in significantly less positive evaluations…

Electron capture rates in stars studied with heavy ion charge exchange reactions

NASA Astrophysics Data System (ADS)

Bertulani, C. A.

2018-01-01

Indirect methods using nucleus-nucleus reactions at high energies (here, high energies mean ~ 50 MeV/nucleon and higher) are now routinely used to extract information of interest for nuclear astrophysics. This is of extreme relevance as many of the nuclei involved in stellar evolution are short-lived. Therefore, indirect methods became the focus of recent studies carried out in major nuclear physics facilities. Among such methods, heavy ion charge exchange is thought to be a useful tool to infer Gamow-Teller matrix elements needed to describe electron capture rates in stars and also double beta-decay experiments. In this short review, I provide a theoretical guidance based on a simple reaction model for charge exchange reactions.

Metabolic PathFinding: inferring relevant pathways in biochemical networks.

PubMed

Croes, Didier; Couche, Fabian; Wodak, Shoshana J; van Helden, Jacques

2005-07-01

Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).

Rational Inference of Beliefs and Desires From Emotional Expressions.

PubMed

Wu, Yang; Baker, Chris L; Tenenbaum, Joshua B; Schulz, Laura E

2018-04-01

We investigated people's ability to infer others' mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents' desires throughout. When the agent observed, but did not cause the outcome, participants' ability to recover the agent's beliefs depended on the evidence they got (i.e., her reaction only to the actual outcome or to both the expected and actual outcomes; Experiments 1 and 2). When the agent caused the event, participants' judgments also depended on the probability of the action (Experiments 3 and 4); when actions were improbable given the mental states, people failed to recover the agent's beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout (rs ≥ .95), consistent with the proposal that people rationally integrate information about others' actions and emotional reactions to infer their unobservable mental states. Copyright © 2017 Cognitive Science Society, Inc.

Inference of reaction rate parameters based on summary statistics from experiments

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

Khalil, Mohammad; Chowdhary, Kamaljit Singh; Safta, Cosmin

Here, we present the results of an application of Bayesian inference and maximum entropy methods for the estimation of the joint probability density for the Arrhenius rate para meters of the rate coefficient of the H 2/O 2-mechanism chain branching reaction H + O 2 → OH + O. Available published data is in the form of summary statistics in terms of nominal values and error bars of the rate coefficient of this reaction at a number of temperature values obtained from shock-tube experiments. Our approach relies on generating data, in this case OH concentration profiles, consistent with the givenmore » summary statistics, using Approximate Bayesian Computation methods and a Markov Chain Monte Carlo procedure. The approach permits the forward propagation of parametric uncertainty through the computational model in a manner that is consistent with the published statistics. A consensus joint posterior on the parameters is obtained by pooling the posterior parameter densities given each consistent data set. To expedite this process, we construct efficient surrogates for the OH concentration using a combination of Pad'e and polynomial approximants. These surrogate models adequately represent forward model observables and their dependence on input parameters and are computationally efficient to allow their use in the Bayesian inference procedure. We also utilize Gauss-Hermite quadrature with Gaussian proposal probability density functions for moment computation resulting in orders of magnitude speedup in data likelihood evaluation. Despite the strong non-linearity in the model, the consistent data sets all res ult in nearly Gaussian conditional parameter probability density functions. The technique also accounts for nuisance parameters in the form of Arrhenius parameters of other rate coefficients with prescribed uncertainty. The resulting pooled parameter probability density function is propagated through stoichiometric hydrogen-air auto-ignition computations to illustrate the need to account for correlation among the Arrhenius rate parameters of one reaction and across rate parameters of different reactions.« less

Inference of reaction rate parameters based on summary statistics from experiments

DOE PAGES

Khalil, Mohammad; Chowdhary, Kamaljit Singh; Safta, Cosmin; ...

2016-10-15

Here, we present the results of an application of Bayesian inference and maximum entropy methods for the estimation of the joint probability density for the Arrhenius rate para meters of the rate coefficient of the H 2/O 2-mechanism chain branching reaction H + O 2 → OH + O. Available published data is in the form of summary statistics in terms of nominal values and error bars of the rate coefficient of this reaction at a number of temperature values obtained from shock-tube experiments. Our approach relies on generating data, in this case OH concentration profiles, consistent with the givenmore » summary statistics, using Approximate Bayesian Computation methods and a Markov Chain Monte Carlo procedure. The approach permits the forward propagation of parametric uncertainty through the computational model in a manner that is consistent with the published statistics. A consensus joint posterior on the parameters is obtained by pooling the posterior parameter densities given each consistent data set. To expedite this process, we construct efficient surrogates for the OH concentration using a combination of Pad'e and polynomial approximants. These surrogate models adequately represent forward model observables and their dependence on input parameters and are computationally efficient to allow their use in the Bayesian inference procedure. We also utilize Gauss-Hermite quadrature with Gaussian proposal probability density functions for moment computation resulting in orders of magnitude speedup in data likelihood evaluation. Despite the strong non-linearity in the model, the consistent data sets all res ult in nearly Gaussian conditional parameter probability density functions. The technique also accounts for nuisance parameters in the form of Arrhenius parameters of other rate coefficients with prescribed uncertainty. The resulting pooled parameter probability density function is propagated through stoichiometric hydrogen-air auto-ignition computations to illustrate the need to account for correlation among the Arrhenius rate parameters of one reaction and across rate parameters of different reactions.« less

Another expert system rule inference based on DNA molecule logic gates

NASA Astrophysics Data System (ADS)

WÄ siewicz, Piotr

2013-10-01

With the help of silicon industry microfluidic processors were invented utilizing nano membrane valves, pumps and microreactors. These so called lab-on-a-chips combined together with molecular computing create molecular-systems-ona- chips. This work presents a new approach to implementation of molecular inference systems. It requires the unique representation of signals by DNA molecules. The main part of this work includes the concept of logic gates based on typical genetic engineering reactions. The presented method allows for constructing logic gates with many inputs and for executing them at the same quantity of elementary operations, regardless of a number of input signals. Every microreactor of the lab-on-a-chip performs one unique operation on input molecules and can be connected by dataflow output-input connections to other ones.

Modelling Chemical Reasoning to Predict and Invent Reactions.

PubMed

Segler, Marwin H S; Waller, Mark P

2017-05-02

The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

The mechanism for enhanced oxidation degradation of dioxin-like PCBs (PCB-77) in the atmosphere by the solvation effect.

PubMed

Xin, Mei-Ling; Yang, Jia-Wen; Li, Yu

2017-07-11

The reaction pathways of PCB-77 in the atmosphere with ·OH, O 2 , NO x , and 1 O 2 were inferred based on density functional theory calculations with the 6-31G* basis set. The structures the reactants, transition states, intermediates, and products were optimized. The energy barriers and reaction heats were obtained to determine the energetically favorable reaction pathways. To study the solvation effect, the energy barriers and reaction rates for PCB-77 with different polar and nonpolar solvents (cyclohexane, benzene, carbon tetrachloride, chloroform, acetone, dichloromethane, ethanol, methanol, acetonitrile, dimethylsulfoxide, and water) were calculated. The results showed that ·OH preferentially added to the C5 atom of PCB-77, which has no Cl atom substituent, to generate the intermediate IM5. This intermediate subsequently reacted with O 2 via pathway A to generate IM5a, with an energy barrier of 7.27 kcal/mol and total reaction rate of 8.45 × 10 -8  cm 3 /molecule s. Pathway B involved direct dehydrogenation of IM5 to produce the OH-PCBs intermediate IM5b, with an energy barrier of 28.49 kcal/mol and total reaction rate of 1.15 × 10 -5  cm 3 /molecule s. The most likely degradation pathway of PCB-77 in the atmosphere is pathway A to produce IM5a. The solvation effect results showed that cyclohexane, carbon tetrachloride, and benzene could reduce the reaction energy barrier of pathway A. Among these solvents, the solvation effect of benzene was the largest, and could reduce the total reaction energy barrier by 25%. Cyclohexane, carbon tetrachloride, benzene, dichloromethane, acetone, and ethanol could increase the total reaction rate of pathway A. The increase in the reaction rate of pathway A with benzene was 8%. The effect of solvents on oxidative degradation of PCB-77 in the atmosphere is important. Graphical abstract The reaction pathways of PCB-77 in the atmosphere with •OH, O2, NOx, and 1O2 were inferred based on density functional theory calculations with the 6-31G* basis set. Different polar and nonpolar solvents: cyclohexane, benzene, carbon tetrachloride, chloroform, acetone, dichloromethane, ethanol, methanol, acetonitrile, dimethylsulfoxide, and water were selected to study the solvation effect on the favorable reaction pathways. The investigated results showed what kind of pathway was most likely to occur and the solvent effect on the reaction pathway.

Inferring Perspective Versus Getting Perspective: Underestimating the Value of Being in Another Person's Shoes.

PubMed

Zhou, Haotian; Majka, Elizabeth A; Epley, Nicholas

2017-04-01

People use at least two strategies to solve the challenge of understanding another person's mind: inferring that person's perspective by reading his or her behavior (theorization) and getting that person's perspective by experiencing his or her situation (simulation). The five experiments reported here demonstrate a strong tendency for people to underestimate the value of simulation. Predictors estimated a stranger's emotional reactions toward 50 pictures. They could either infer the stranger's perspective by reading his or her facial expressions or simulate the stranger's perspective by watching the pictures he or she viewed. Predictors were substantially more accurate when they got perspective through simulation, but overestimated the accuracy they had achieved by inferring perspective. Predictors' miscalibrated confidence stemmed from overestimating the information revealed through facial expressions and underestimating the similarity in people's reactions to a given situation. People seem to underappreciate a useful strategy for understanding the minds of others, even after they gain firsthand experience with both strategies.

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

PubMed Central

Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi

2008-01-01

Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550

Reconstructing biochemical pathways from time course data.

PubMed

Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago

2007-03-01

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

Cox process representation and inference for stochastic reaction-diffusion processes

NASA Astrophysics Data System (ADS)

Schnoerr, David; Grima, Ramon; Sanguinetti, Guido

2016-05-01

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.

Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

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

Cannon, William; Zucker, Jeremy; Baxter, Douglas

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ODE-based optimization approach based on Marcelin’s 1910 mass action equation is used to obtain the maximum entropy distribution, (2) the predicted metabolite concentrations are compared to those generally expected from experiment using a loss function from which post-translational regulation of enzymes is inferred, (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrationsmore » and reaction fluxes, and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1, 6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.« less

ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study.

PubMed

Heddam, Salim; Bermad, Abdelmalek; Dechemi, Noureddine

2012-04-01

Coagulation is the most important stage in drinking water treatment processes for the maintenance of acceptable treated water quality and economic plant operation, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, pH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Traditionally, jar tests are used to determine the optimum coagulant dosage. However, this is expensive and time-consuming and does not enable responses to changes in raw water quality in real time. Modelling can be used to overcome these limitations. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modelling of coagulant dosage in drinking water treatment plant of Boudouaou, Algeria. Six on-line variables of raw water quality including turbidity, conductivity, temperature, dissolved oxygen, ultraviolet absorbance, and the pH of water, and alum dosage were used to build the coagulant dosage model. Two ANFIS-based Neuro-fuzzy systems are presented. The two Neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based (FIS), named ANFIS-SUB. The low root mean square error and high correlation coefficient values were obtained with ANFIS-SUB method of a first-order Sugeno type inference. This study demonstrates that ANFIS-SUB outperforms ANFIS-GRID due to its simplicity in parameter selection and its fitness in the target problem.

Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration.

PubMed

Chen, Yunjin; Pock, Thomas

2017-06-01

Image restoration is a long-standing problem in low-level computer vision with many interesting applications. We describe a flexible learning framework based on the concept of nonlinear reaction diffusion models for various image restoration problems. By embodying recent improvements in nonlinear diffusion models, we propose a dynamic nonlinear reaction diffusion model with time-dependent parameters (i.e., linear filters and influence functions). In contrast to previous nonlinear diffusion models, all the parameters, including the filters and the influence functions, are simultaneously learned from training data through a loss based approach. We call this approach TNRD-Trainable Nonlinear Reaction Diffusion. The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force. We demonstrate its capabilities with three representative applications, Gaussian image denoising, single image super resolution and JPEG deblocking. Experiments show that our trained nonlinear diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for the tested applications. Our trained models preserve the structural simplicity of diffusion models and take only a small number of diffusion steps, thus are highly efficient. Moreover, they are also well-suited for parallel computation on GPUs, which makes the inference procedure extremely fast.

Human Inferences about Sequences: A Minimal Transition Probability Model

PubMed Central

2016-01-01

The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge. PMID:28030543

Is the P-Value Really Dead? Assessing Inference Learning Outcomes for Social Science Students in an Introductory Statistics Course

ERIC Educational Resources Information Center

Lane-Getaz, Sharon

2017-01-01

In reaction to misuses and misinterpretations of p-values and confidence intervals, a social science journal editor banned p-values from its pages. This study aimed to show that education could address misuse and abuse. This study examines inference-related learning outcomes for social science students in an introductory course supplemented with…

A Reaction Time Advantage for Calculating Beliefs over Public Representations Signals Domain Specificity for "Theory of Mind"

ERIC Educational Resources Information Center

Cohen, Adam S.; German, Tamsin C.

2010-01-01

In a task where participants' overt task was to track the location of an object across a sequence of events, reaction times to unpredictable probes requiring an inference about a social agent's beliefs about the location of that object were obtained. Reaction times to false belief situations were faster than responses about the (false) contents of…

Pressure effects on enzyme-catalyzed quantum tunneling events arise from protein-specific structural and dynamic changes.

PubMed

Hay, Sam; Johannissen, Linus O; Hothi, Parvinder; Sutcliffe, Michael J; Scrutton, Nigel S

2012-06-13

The rate and kinetic isotope effect (KIE) on proton transfer during the aromatic amine dehydrogenase-catalyzed reaction with phenylethylamine shows complex pressure and temperature dependences. We are able to rationalize these effects within an environmentally coupled tunneling model based on constant pressure molecular dynamics (MD) simulations. As pressure appears to act anisotropically on the enzyme, perturbation of the reaction coordinate (donor-acceptor compression) is, in this case, marginal. Therefore, while we have previously demonstrated that pressure and temperature dependences can be used to infer H-tunneling and the involvement of promoting vibrations, these effects should not be used in the absence of atomistic insight, as they can vary greatly for different enzymes. We show that a pressure-dependent KIE is not a definitive hallmark of quantum mechanical H-tunneling during an enzyme-catalyzed reaction and that pressure-independent KIEs cannot be used to exclude tunneling contributions or a role for promoting vibrations in the enzyme-catalyzed reaction. We conclude that coupling of MD calculations with experimental rate and KIE studies is required to provide atomistic understanding of pressure effects in enzyme-catalyzed reactions.

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models.

PubMed

Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-05-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed. Copyright © 2016 Cognitive Science Society, Inc.

Peripheral elastic and inelastic scattering of 17,18O on light targets at 12 MeV/nucleon

NASA Astrophysics Data System (ADS)

Carstoiu, F.; Al-Abdullah, T.; Gagliardi, C. A.; Trache, L.

2015-02-01

The elastic and inelastic scattering of 17,18O with light targets has been undertaken at 12 MeV/nucleon in order to determine the optical potentials needed for the transfer reaction 13C (17O ,18O )12C . Optical potentials in both incoming and outgoing channels have been determined in a single experiment. This transfer reaction was used to infer the direct capture rate to the 17F ( p ,γ)18Ne which is essential to estimate the production of 18F at stellar energies in ONe novae. We demonstrate the stability of the ANC method and OMP results using good quality elastic and inelastic scattering data with stable beams. The peripherality of our reaction is inferred from a semiclassical decomposition of the total scattering amplitude into barrier and internal barrier components. Comparison between elastic scattering of 17O , 18O and 16O projectiles is made.

Inference of Antibiotic Resistance and Virulence Among Diverse Group A Streptococcus Strains Using emm Sequencing and Multilocus Genotyping Methods

DTIC Science & Technology

2009-09-04

apparent GAS-associated conditions were sampled by oropharyn- geal swab. Swabs were streaked on blood agar plates using Table 3. Isolate properties by...testing, samples were re-streaked on blood agar plates (5% sheep blood in TSA base) (Hardy Diagnostics, Santa Maria, CA), and incubated at 35–37uC with 5–10...sensitivity (A-disk method, Hardy Diagnostics) and positive GAS latex agglutination reaction (Hardy Diagnostics). Confirmed GAS isolates were then

One-electron oxidation reactions of purine and pyrimidine bases in cellular DNA

PubMed Central

Cadet, Jean; Wagner, J. Richard; Shafirovich, Vladimir; Geacintov, Nicholas E.

2014-01-01

Purpose The aim of this survey is to critically review the available information on one-electron oxidation reactions of nucleobases in cellular DNA with emphasis on damage induced through the transient generation of purine and pyrimidine radical cations. Since the indirect effect of ionizing radiation mediated by hydroxyl radical is predominant in cells, efforts have been made to selectively ionize bases using suitable one-electron oxidants that consist among others of high intensity UVC laser pulses. Thus, the main oxidation product in cellular DNA was found to be 8-oxo-7,8-dihydroguanine as a result of direct bi-photonic ionization of guanine bases and indirect formation of guanine radical cations through hole transfer reactions from other base radical cations. The formation of 8-oxo-7,8-dihydroguanine and other purine and pyrimidine degradation products was rationalized in terms of the initial generation of related radical cations followed by either hydration or deprotonation reactions in agreement with mechanistic pathways inferred from detailed mechanistic studies. The guanine radical cation has been shown to be implicated in three other nucleophilic additions that give rise to DNA-protein and DNA-DNA cross-links in model systems. Evidence was recently provided for the occurrence of these three reactions in cellular DNA. Conclusion There is growing evidence that one-electron oxidation reactions of nucleobases whose mechanisms have been characterized in model studies involving aqueous solutions take place in a similar way in cells. It may also be pointed out that the above cross-linked lesions are only produced from the guanine radical cation and may be considered as diagnostic products of the direct effect of ionizing radiation. PMID:24369822

One-electron oxidation reactions of purine and pyrimidine bases in cellular DNA.

PubMed

Cadet, Jean; Wagner, J Richard; Shafirovich, Vladimir; Geacintov, Nicholas E

2014-06-01

The aim of this survey is to critically review the available information on one-electron oxidation reactions of nucleobases in cellular DNA with emphasis on damage induced through the transient generation of purine and pyrimidine radical cations. Since the indirect effect of ionizing radiation mediated by hydroxyl radical is predominant in cells, efforts have been made to selectively ionize bases using suitable one-electron oxidants that consist among others of high intensity UVC laser pulses. Thus, the main oxidation product in cellular DNA was found to be 8-oxo-7,8-dihydroguanine as a result of direct bi-photonic ionization of guanine bases and indirect formation of guanine radical cations through hole transfer reactions from other base radical cations. The formation of 8-oxo-7,8-dihydroguanine and other purine and pyrimidine degradation products was rationalized in terms of the initial generation of related radical cations followed by either hydration or deprotonation reactions in agreement with mechanistic pathways inferred from detailed mechanistic studies. The guanine radical cation has been shown to be implicated in three other nucleophilic additions that give rise to DNA-protein and DNA-DNA cross-links in model systems. Evidence was recently provided for the occurrence of these three reactions in cellular DNA. There is growing evidence that one-electron oxidation reactions of nucleobases whose mechanisms have been characterized in model studies involving aqueous solutions take place in a similar way in cells. It may also be pointed out that the above cross-linked lesions are only produced from the guanine radical cation and may be considered as diagnostic products of the direct effect of ionizing radiation.

Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria

PubMed Central

Wienecke, Sarah; Ishwarbhai, Alka; Tsipa, Argyro; Aw, Rochelle; Kylilis, Nicolas; Bell, David J.; McClymont, David W.; Jensen, Kirsten; Biedendieck, Rebekka

2018-01-01

Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications. PMID:29666238

Simulating the Snowball Stratosphere and Its Influence On CO2 Inference

NASA Astrophysics Data System (ADS)

Graham, R. J.; Shaw, T.; Abbot, D. S.

2017-12-01

According to the snowball Earth hypothesis, a large quantity of CO2 must build up during an event in order to cause eventual deglaciation. One prediction of this model is a depletion in atmospheric oxygen-17 as a result of stratospheric chemical reactions, which has been observed in preserved barite minerals. This represents one of the most dramatic and compelling pieces of evidence in support of the snowball Earth hypothesis. The inference of anomalous atmospheric oxygen-17 based on measurements of barite minerals, however, was made by assuming that the stratosphere-troposphere mass exchange rate and mixing within the stratosphere were the same as the present. In this contribution we test these assumptions with simulations of modern and snowball atmospheric conditions using the global climate model ECHAM5. Our simulations are still running, but we will have results to report by December.

Streptococcus massiliensis in the human mouth: a phylogenetic approach for the inference of bacterial habitats.

PubMed

Póntigo, F; Silva, C; Moraga, M; Flores, S V

2015-12-29

Streptococcus is a diverse bacterial lineage. Species of this genus occupy a myriad of environments inside humans and other animals. Despite the elucidation of several of these habitats, many remain to be identified. Here, we explore a methodological approach to reveal unknown bacterial environments. Specifically, we inferred the phylogeny of the Mitis group by analyzing the sequences of eight genes. In addition, information regarding habitat use of species belonging to this group was obtained from the scientific literature. The oral cavity emerged as a potential, previously unknown, environment of Streptococcus massiliensis. This phylogeny-based prediction was confirmed by species-specific polymerase chain reaction (PCR) amplification. We propose employing a similar approach, i.e., use of bibliographic data and molecular phylogenetics as predictive methods, and species-specific PCR as confirmation, in order to reveal other unknown habitats in further bacterial taxa.

Emotions as within or between people? Cultural variation in lay theories of emotion expression and inference.

PubMed

Uchida, Yukiko; Townsend, Sarah S M; Rose Markus, Hazel; Bergsieker, Hilary B

2009-11-01

Four studies using open-ended and experimental methods test the hypothesis that in Japanese contexts, emotions are understood as between people, whereas in American contexts, emotions are understood as primarily within people. Study 1 analyzed television interviews of Olympic athletes. When asked about their relationships, Japanese athletes used significantly more emotion words than American athletes. This difference was not significant when questions asked directly about athletes' feelings. In Study 2, when describing an athlete's emotional reaction to winning, Japanese participants implicated others more often than American participants. After reading an athlete's self-description, Japanese participants inferred more emotions when the athlete mentioned relationships, whereas American participants inferred more emotions when the athlete focused only on herself (Study 3). Finally, when viewing images of athletes, Japanese participants inferred more emotions for athletes pictured with teammates, whereas American participants inferred more emotions for athletes pictured alone (Studies 4a and 4b).

Functional Alignment of Metabolic Networks.

PubMed

Mazza, Arnon; Wagner, Allon; Ruppin, Eytan; Sharan, Roded

2016-05-01

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction, and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.

Reaction Time in Grade 5: Data Collection within the Practice of Statistics

ERIC Educational Resources Information Center

Watson, Jane; English, Lyn

2017-01-01

This study reports on a classroom activity for Grade 5 students investigating their reaction times. The investigation was part of a 3-year research project introducing students to informal inference and giving them experience carrying out the practice of statistics. For this activity the focus within the practice of statistics was on introducing…

Palliative effects of H2 on SOFCs operating with carbon containing fuels

NASA Astrophysics Data System (ADS)

Reeping, Kyle W.; Bohn, Jessie M.; Walker, Robert A.

2017-12-01

Chlorine can accelerate degradation of solid oxide fuel cell (SOFC) Ni-based anodes operating on carbon containing fuels through several different mechanisms. However, supplementing the fuel with a small percentage of excess molecular hydrogen effectively masks the degradation to the catalytic activity of the Ni and carbon fuel cracking reaction reactions. Experiments described in this work explore the chemistry behind the "palliative" effect of hydrogen on SOFCs operating with chlorine-contaminated, carbon-containing fuels using a suite of independent, complementary techniques. Operando Raman spectroscopy is used to monitor carbon accumulation and, by inference, Ni catalytic activity while electrochemical techniques including electrochemical impedance spectroscopy and voltammetry are used to monitor overall cell performance. Briefly, hydrogen not only completely hides degradation observed with chlorine-contaminated carbon-containing fuels, but also actively removes adsorbed chlorine from the surface of the Ni, allowing for the methane cracking reaction to continue, albeit at a slower rate. When hydrogen is removed from the fuel stream the cell fails immediately due to chlorine occupation of methane/biogas reaction sites.

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.

Everyday conversation requires cognitive inference: neural bases of comprehending implicated meanings in conversations.

PubMed

Jang, Gijeong; Yoon, Shin-ae; Lee, Sung-Eun; Park, Haeil; Kim, Joohan; Ko, Jeong Hoon; Park, Hae-Jeong

2013-11-01

In ordinary conversations, literal meanings of an utterance are often quite different from implicated meanings and the inference about implicated meanings is essentially required for successful comprehension of the speaker's utterances. Inference of finding implicated meanings is based on the listener's assumption that the conversational partner says only relevant matters according to the maxim of relevance in Grice's theory of conversational implicature. To investigate the neural correlates of comprehending implicated meanings under the maxim of relevance, a total of 23 participants underwent an fMRI task with a series of conversational pairs, each consisting of a question and an answer. The experimental paradigm was composed of three conditions: explicit answers, moderately implicit answers, and highly implicit answers. Participants were asked to decide whether the answer to the Yes/No question meant 'Yes' or 'No'. Longer reaction time was required for the highly implicit answers than for the moderately implicit answers without affecting the accuracy. The fMRI results show that the left anterior temporal lobe, left angular gyrus, and left posterior middle temporal gyrus had stronger activation in both moderately and highly implicit conditions than in the explicit condition. Comprehension of highly implicit answers had increased activations in additional regions including the left inferior frontal gyrus, left medial prefrontal cortex, left posterior cingulate cortex and right anterior temporal lobe. The activation results indicate involvement of these regions in the inference process to build coherence between literally irrelevant but pragmatically associated utterances under the maxim of relevance. Especially, the left anterior temporal lobe showed high sensitivity to the level of implicitness and showed increased activation for highly versus moderately implicit conditions, which imply its central role in inference such as semantic integration. The right hemisphere activation, uniquely found in the anterior temporal lobe for highly implicit utterances, suggests its competence for integrating distant concepts in implied utterances under the relevance principle. Copyright © 2013 Elsevier Inc. All rights reserved.

Subsurface conditions in hydrothermal vents inferred from diffuse flow composition, and models of reaction and transport

NASA Astrophysics Data System (ADS)

Larson, B. I.; Houghton, J. L.; Lowell, R. P.; Farough, A.; Meile, C. D.

2015-08-01

Chemical gradients in the subsurface of mid-ocean ridge hydrothermal systems create an environment where minerals precipitate and dissolve and where chemosynthetic organisms thrive. However, owing to the lack of easy access to the subsurface, robust knowledge of the nature and extent of chemical transformations remains elusive. Here, we combine measurements of vent fluid chemistry with geochemical and transport modeling to give new insights into the under-sampled subsurface. Temperature-composition relationships from a geochemical mixing model are superimposed on the subsurface temperature distribution determined using a heat flow model to estimate the spatial distribution of fluid composition. We then estimate the distribution of Gibb's free energies of reaction beneath mid oceanic ridges and by combining flow simulations with speciation calculations estimate anhydrite deposition rates. Applied to vent endmembers observed at the fast spreading ridge at the East Pacific Rise, our results suggest that sealing times due to anhydrite formation are longer than the typical time between tectonic and magmatic events. The chemical composition of the neighboring low temperature flow indicates relatively uniform energetically favorable conditions for commonly inferred microbial processes such as methanogenesis, sulfate reduction and numerous oxidation reactions, suggesting that factors other than energy availability may control subsurface microbial biomass distribution. Thus, these model simulations complement fluid-sample datasets from surface venting and help infer the chemical distribution and transformations in subsurface flow.

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.

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models

ERIC Educational Resources Information Center

Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-01-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in…

Peripheral elastic and inelastic scattering of O17,18 on light targets at 12 MeV/nucleon

NASA Astrophysics Data System (ADS)

Al-Abdullah, T.; Carstoiu, F.; Gagliardi, C. A.; Tabacaru, G.; Trache, L.; Tribble, R. E.

2014-06-01

A study of interaction of neutron-rich oxygen isotopes O17,18 with light targets has been undertaken in order to determine the optical potentials needed for the transfer reaction C13(O17,O18)C12. Optical potentials in both incoming and outgoing channels have been determined in a single experiment. This transfer reaction was used to infer the direct capture rate to the F17(p,γ)Ne18 which is essential to estimate the production of F18 at stellar energies in ONe novae. The success of the asymptotic normalization coefficient (ANC) as indirect method for astrophysics is guaranteed if the reaction mechanism is peripheral and the distorted wave Born approximation cross-section calculations are warranted and stable against the optical model potential (OMP) used. We demonstrate the stability of the ANC method and the OMP results by using good-quality elastic and inelastic-scattering data with stable beams before extending the procedures to rare-ion beams. The peripherality of our reaction is inferred from a semiclassical decomposition of the total-scattering amplitude into barrier and internal barrier components. Comparison between elastic scattering of O17, O18, and O16 projectiles is made.

Water catalysis and anticatalysis in photochemical reactions: observation of a delayed threshold effect in the reaction quantum yield.

PubMed

Kramer, Zeb C; Takahashi, Kaito; Skodje, Rex T

2010-11-03

The possible catalysis of photochemical reactions by water molecules is considered. Using theoretical simulations, we investigate the HF-elimination reaction of fluoromethanol in small water clusters initiated by the overtone excitation of the hydroxyl group. The reaction occurs in competition with the process of water evaporation that dissipates the excitation and quenches the reaction. Although the transition state barrier is stabilized by over 20 kcal/mol through hydrogen bonding with water, the quantum yield versus energy shows a pronounced delayed threshold that effectively eliminates the catalytic effect. It is concluded that the quantum chemistry calculations of barrier lowering are not sufficient to infer water catalysis in some photochemical reactions, which instead require dynamical modeling.

Progress in obtaining an absolute calibration of a total deuterium-tritium neutron yield diagnostic based on copper activationa)

NASA Astrophysics Data System (ADS)

Ruiz, C. L.; Chandler, G. A.; Cooper, G. W.; Fehl, D. L.; Hahn, K. D.; Leeper, R. J.; McWatters, B. R.; Nelson, A. J.; Smelser, R. M.; Snow, C. S.; Torres, J. A.

2012-10-01

The 350-keV Cockroft-Walton accelerator at Sandia National laboratory's Ion Beam facility is being used to calibrate absolutely a total DT neutron yield diagnostic based on the 63Cu(n,2n)62Cu(β+) reaction. These investigations have led to first-order uncertainties approaching 5% or better. The experiments employ the associated-particle technique. Deuterons at 175 keV impinge a 2.6 μm thick erbium tritide target producing 14.1 MeV neutrons from the T(d,n)4He reaction. The alpha particles emitted are measured at two angles relative to the beam direction and used to infer the neutron flux on a copper sample. The induced 62Cu activity is then measured and related to the neutron flux. This method is known as the F-factor technique. Description of the associated-particle method, copper sample geometries employed, and the present estimates of the uncertainties to the F-factor obtained are given.

Progress in obtaining an absolute calibration of a total deuterium-tritium neutron yield diagnostic based on copper activation.

PubMed

Ruiz, C L; Chandler, G A; Cooper, G W; Fehl, D L; Hahn, K D; Leeper, R J; McWatters, B R; Nelson, A J; Smelser, R M; Snow, C S; Torres, J A

2012-10-01

The 350-keV Cockroft-Walton accelerator at Sandia National laboratory's Ion Beam facility is being used to calibrate absolutely a total DT neutron yield diagnostic based on the (63)Cu(n,2n)(62)Cu(β+) reaction. These investigations have led to first-order uncertainties approaching 5% or better. The experiments employ the associated-particle technique. Deuterons at 175 keV impinge a 2.6 μm thick erbium tritide target producing 14.1 MeV neutrons from the T(d,n)(4)He reaction. The alpha particles emitted are measured at two angles relative to the beam direction and used to infer the neutron flux on a copper sample. The induced (62)Cu activity is then measured and related to the neutron flux. This method is known as the F-factor technique. Description of the associated-particle method, copper sample geometries employed, and the present estimates of the uncertainties to the F-factor obtained are given.

Unconscious relational inference recruits the hippocampus.

PubMed

Reber, Thomas P; Luechinger, Roger; Boesiger, Peter; Henke, Katharina

2012-05-02

Relational inference denotes the capacity to encode, flexibly retrieve, and integrate multiple memories to combine past experiences to update knowledge and improve decision-making in new situations. Although relational inference is thought to depend on the hippocampus and consciousness, we now show in young, healthy men that it may occur outside consciousness but still recruits the hippocampus. In temporally distinct and unique subliminal episodes, we presented word pairs that either overlapped ("winter-red", "red-computer") or not. Effects of unconscious relational inference emerged in reaction times recorded during unconscious encoding and in the outcome of decisions made 1 min later at test, when participants judged the semantic relatedness of two supraliminal words. These words were either episodically related through a common word ("winter-computer" related through "red") or unrelated. Hippocampal activity increased during the unconscious encoding of overlapping versus nonoverlapping word pairs and during the unconscious retrieval of episodically related versus unrelated words. Furthermore, hippocampal activity during unconscious encoding predicted the outcome of decisions made at test. Hence, unconscious inference may influence decision-making in new situations.

Design-based and model-based inference in surveys of freshwater mollusks

USGS Publications Warehouse

Dorazio, R.M.

1999-01-01

Well-known concepts in statistical inference and sampling theory are used to develop recommendations for planning and analyzing the results of quantitative surveys of freshwater mollusks. Two methods of inference commonly used in survey sampling (design-based and model-based) are described and illustrated using examples relevant in surveys of freshwater mollusks. The particular objectives of a survey and the type of information observed in each unit of sampling can be used to help select the sampling design and the method of inference. For example, the mean density of a sparsely distributed population of mollusks can be estimated with higher precision by using model-based inference or by using design-based inference with adaptive cluster sampling than by using design-based inference with conventional sampling. More experience with quantitative surveys of natural assemblages of freshwater mollusks is needed to determine the actual benefits of different sampling designs and inferential procedures.

Children's Category-Based Inferences Affect Classification

ERIC Educational Resources Information Center

Ross, Brian H.; Gelman, Susan A.; Rosengren, Karl S.

2005-01-01

Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category-based inferences during category learning influence category knowledge…

Archean geochemistry of formaldehyde and cyanide and the oligomerization of cyanohydrin

NASA Technical Reports Server (NTRS)

Arrhenius, T.; Arrhenius, G.; Paplawsky, W.

1994-01-01

The sources and speciation of reduced carbon and nitrogen inferred for the early Archean are reviewed in terms of current observations and models, and known chemical reactions. Within this framework hydrogen cyanide and cyanide ion in significant concentration would have been eliminated by reaction with excess formaldehyde to form cyanohydrin (glycolonitrile), and with ferrous ion to formferrocyanide. Natural reactions of these molecules would under such conditions deserve special consideration in modeling of primordial organochemical processes. As a step in this direction, transformation reactions have been investigated involving glycolonitrile in the presence of water. We find that glycolonitrile, formed from formaldehyde and hydrogen cyanide or cyanide ion, spontaneously cyclodimerizes to 4-amino-2-hydroxymethyloxazole. The crystalline dimer is the major product at low temperatue (approximately 0 C); the yield diminishes with increasing temperature at the expense of polymerization and hydrolysis products. Hydrolysis of glycolamide and of oxazole yields a number of simpler organic molecules, including ammonia and glycolamide. The spontaneous polymerization of glycolonitrile and its dimer gives rise to soluble, cationic oligomers of as yet unknown structure, and, unless arrested, to a viscous liquid, insoluble in water. A loss of cyanide by reaction with formaldehyde, inferred for the early terrestrial hydrosphere and cryosphere would present a dilemma for hypotheses invoking cyanide and related compounds as concentrated reactants capable of forming biomolecular precursor species. Attempts to escape from its horns may take advantage of the efficient concentration and separation of cyanide as solid ferriferrocyanide, and most directly of reactions of glycolonitrile and its derivatives.

Adults with Dyslexia Exhibit Large Effects of Crowding, Increased Dependence on Cues, and Detrimental Effects of Distractors in Visual Search Tasks

ERIC Educational Resources Information Center

Moores, Elisabeth; Cassim, Rizan; Talcott, Joel B.

2011-01-01

Difficulties in visual attention are increasingly being linked to dyslexia. To date, the majority of studies have inferred functionality of attention from response times to stimuli presented for an indefinite duration. However, in paradigms that use reaction times to investigate the ability to orient attention, a delayed reaction time could also…

Electrochemical Impedance Imaging via the Distribution of Diffusion Times

NASA Astrophysics Data System (ADS)

Song, Juhyun; Bazant, Martin Z.

2018-03-01

We develop a mathematical framework to analyze electrochemical impedance spectra in terms of a distribution of diffusion times (DDT) for a parallel array of random finite-length Warburg (diffusion) or Gerischer (reaction-diffusion) circuit elements. A robust DDT inversion method is presented based on complex nonlinear least squares regression with Tikhonov regularization and illustrated for three cases of nanostructured electrodes for energy conversion: (i) a carbon nanotube supercapacitor, (ii) a silicon nanowire Li-ion battery, and (iii) a porous-carbon vanadium flow battery. The results demonstrate the feasibility of nondestructive "impedance imaging" to infer microstructural statistics of random, heterogeneous materials.

Mechanism of Ferric Oxalate Photolysis

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

Mangiante, David. M.; Schaller, Richard D.; Zarzycki, Piotr

Iron(III) oxalate, Fe 3+(C 2O 4) 3 3–, is a photoactive metal organic complex found in natural systems and used to quantify photon flux as a result of its high absorbance and reaction quantum yield. It also serves as a model complex to understand metal carboxylate complex photolysis because the mechanism of photolysis and eventual production of CO 2 is not well understood for any system. Here, we employed pump/probe mid-infrared transient absorption spectroscopy to study the photolysis reaction of the iron(III) oxalate ion in D 2O and H 2O up to 3 ns following photoexcitation. We find that intramolecularmore » electron transfer from oxalate to iron occurs on a sub-picosecond time scale, creating iron(II) complexed by one oxidized and two spectator oxalate ligands. Within 40 ps following electron transfer, the oxidized oxalate molecule dissociates to form free solvated CO 2(aq) and a species inferred to be CO 2 •– based on the appearance of a new vibrational absorption band and ab initio simulation. Our work provides direct spectroscopic evidence for the first mechanistic steps in the photolysis reaction and presents a technique to analyze other environmentally relevant metal carboxylate photolysis reactions.« less

Mechanism of Ferric Oxalate Photolysis

DOE PAGES

Mangiante, David. M.; Schaller, Richard D.; Zarzycki, Piotr; ...

2017-06-08

Iron(III) oxalate, Fe 3+(C 2O 4) 3 3–, is a photoactive metal organic complex found in natural systems and used to quantify photon flux as a result of its high absorbance and reaction quantum yield. It also serves as a model complex to understand metal carboxylate complex photolysis because the mechanism of photolysis and eventual production of CO 2 is not well understood for any system. Here, we employed pump/probe mid-infrared transient absorption spectroscopy to study the photolysis reaction of the iron(III) oxalate ion in D 2O and H 2O up to 3 ns following photoexcitation. We find that intramolecularmore » electron transfer from oxalate to iron occurs on a sub-picosecond time scale, creating iron(II) complexed by one oxidized and two spectator oxalate ligands. Within 40 ps following electron transfer, the oxidized oxalate molecule dissociates to form free solvated CO 2(aq) and a species inferred to be CO 2 •– based on the appearance of a new vibrational absorption band and ab initio simulation. Our work provides direct spectroscopic evidence for the first mechanistic steps in the photolysis reaction and presents a technique to analyze other environmentally relevant metal carboxylate photolysis reactions.« less

Single-particle detection of products from atomic and molecular reactions in a cryogenic ion storage ring

NASA Astrophysics Data System (ADS)

Krantz, C.; Novotný, O.; Becker, A.; George, S.; Grieser, M.; Hahn, R. von; Meyer, C.; Schippers, S.; Spruck, K.; Vogel, S.; Wolf, A.

2017-04-01

We have used a single-particle detector system, based on secondary electron emission, for counting low-energetic (∼keV/u) massive products originating from atomic and molecular ion reactions in the electrostatic Cryogenic Storage Ring (CSR). The detector is movable within the cryogenic vacuum chamber of CSR, and was used to measure production rates of a variety of charged and neutral daughter particles. In operation at a temperature of ∼ 6 K , the detector is characterised by a high dynamic range, combining a low dark event rate with good high-rate particle counting capability. On-line measurement of the pulse height distributions proved to be an important monitor of the detector response at low temperature. Statistical pulse-height analysis allows to infer the particle detection efficiency of the detector, which has been found to be close to unity also in cryogenic operation at 6 K.

Measurements of Deuterium-Tritium Fuel Fractionation from Kinetic Effects in Ignition-Relevant Direct-Drive Cryogenic Implosions

NASA Astrophysics Data System (ADS)

Forrest, C.; Glebov, V. Yu.; Knauer, J. P.; Radha, P. B.; Regan, S. P.; Sangster, T. C.; Stoeckl, C.

2016-10-01

Measurements of DT and DD reaction yields have been studied using ignition-relevant, cryogenically cooled deuterium-tritium gas-filled cryogenic DT targets in inertial confinement fusion (ICF) implosions. In these experiments, carried out at the Omega Laser Facility, highresolution time-of-flight spectroscopy was used to measure the primary neutron peak distribution required to infer the DT and DD reaction yields. From these measurements, it will be shown that the yield ratio has a χ2/per degree of freedom of 0.67 as compared with the measured fraction of the target fuel composition. This observation indicates that kinetic effects leading to species separation are insignificant in ICF ignition-relevant DT implosions on OMEGA. This material is based upon work supported by the Department Of Energy National Nuclear Security Administration under Award Number DE-NA0001944.

13C metabolic flux analysis at a genome-scale.

PubMed

Gopalakrishnan, Saratram; Maranas, Costas D

2015-11-01

Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Inferred ranges for 81% of the reactions in the genome-scale metabolic (GSM) model varied less than one-tenth of the basis glucose uptake rate (95% confidence test). This is because as many as 411 reactions in the GSM are growth coupled meaning that the single measurement of biomass formation rate locks the reaction flux values. This implies that accurate biomass formation rate and composition are critical for resolving metabolic fluxes away from central metabolism and suggests the importance of biomass composition (re)assessment under different genetic and environmental backgrounds. In addition, the loss of information associated with mapping fluxes from MFA on a core model to a GSM model is quantified. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

How to select combination operators for fuzzy expert systems using CRI

NASA Technical Reports Server (NTRS)

Turksen, I. B.; Tian, Y.

1992-01-01

A method to select combination operators for fuzzy expert systems using the Compositional Rule of Inference (CRI) is proposed. First, fuzzy inference processes based on CRI are classified into three categories in terms of their inference results: the Expansion Type Inference, the Reduction Type Inference, and Other Type Inferences. Further, implication operators under Sup-T composition are classified as the Expansion Type Operator, the Reduction Type Operator, and the Other Type Operators. Finally, the combination of rules or their consequences is investigated for inference processes based on CRI.

A self-organized ensemble of fluorescent 3-hydroxyflavone-Al (III) complex as sensor for fluoride and acetate ions.

PubMed

Sathish, Sai; Narayan, Govindh; Rao, Nageswara; Janardhana, Chelli

2007-01-01

Aluminum chloride addition results in a self-organized TURN-ON fluorescence of 3-hydroxyflavone (3HF) by a complexation reaction in MeOH and subsequent ligand exchange reaction with fluoride or acetate ions causes a fluorescence TURN-OFF of this complex, delivering a quantitative estimation route for fluoride and acetate ions. The ternary complex of 3HF with Al (III), a hard acid provides for a sensitive signalling system for fluoride ion, a hard base in the concentration range from 6 muM to 50 mM by a concerted co-ordination of fluoride ion involving an intermediate mechanistic pathway, while the complex is sensitive to acetate addition between 0-68 muM. The ligand exchange reaction of Al (3HF)(2) complex by fluoride or acetate ion, without interference from other common anions, has been investigated by UV-visible and fluorescence spetroscopies. The structure of the in-situ intermediate isolated at higher Al (3HF)(2) complex and acetate concentrations was inferred from the FT-IR spectrum and ESI-MS of the sample.

Revealing networks from dynamics: an introduction

NASA Astrophysics Data System (ADS)

Timme, Marc; Casadiego, Jose

2014-08-01

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.

An integrated open framework for thermodynamics of reactions that combines accuracy and coverage.

PubMed

Noor, Elad; Bar-Even, Arren; Flamholz, Avi; Lubling, Yaniv; Davidi, Dan; Milo, Ron

2012-08-01

The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions. Both methods account for changes in cellular conditions (pH, ionic strength, etc.) by using linear regression over the ΔG(○) of pseudoisomers and reactions. The Pseudoisomeric Reactant Contribution method systematically infers compound formation energies using measured K' and pK(a) data. The Pseudoisomeric Group Contribution method extends the group contribution method and achieves a high coverage of unmeasured reactions. We define a continuous index that predicts the reversibility of a reaction under a given physiological concentration range. In the characteristic physiological range 3μM-3mM, we find that roughly half of the reactions in Escherichia coli's metabolism are reversible. These new tools can increase the accuracy of thermodynamic-based models, especially in non-standard pH and ionic strengths. The reversibility index can help modelers decide which reactions are reversible in physiological conditions. Freely available on the web at: http://equilibrator.weizmann.ac.il. Website implemented in Python, MySQL, Apache and Django, with all major browsers supported. The framework is open-source (code.google.com/p/milo-lab), implemented in pure Python and tested mainly on Linux. ron.milo@weizmann.ac.il Supplementary data are available at Bioinformatics online.

An integrated open framework for thermodynamics of reactions that combines accuracy and coverage

PubMed Central

Noor, Elad; Bar-Even, Arren; Flamholz, Avi; Lubling, Yaniv; Davidi, Dan; Milo, Ron

2012-01-01

Motivation: The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions. Results: Both methods account for changes in cellular conditions (pH, ionic strength, etc.) by using linear regression over the ΔG○ of pseudoisomers and reactions. The Pseudoisomeric Reactant Contribution method systematically infers compound formation energies using measured K′ and pKa data. The Pseudoisomeric Group Contribution method extends the group contribution method and achieves a high coverage of unmeasured reactions. We define a continuous index that predicts the reversibility of a reaction under a given physiological concentration range. In the characteristic physiological range 3μM–3mM, we find that roughly half of the reactions in Escherichia coli's metabolism are reversible. These new tools can increase the accuracy of thermodynamic-based models, especially in non-standard pH and ionic strengths. The reversibility index can help modelers decide which reactions are reversible in physiological conditions. Availability: Freely available on the web at: http://equilibrator.weizmann.ac.il. Website implemented in Python, MySQL, Apache and Django, with all major browsers supported. The framework is open-source (code.google.com/p/milo-lab), implemented in pure Python and tested mainly on Linux. Contact: ron.milo@weizmann.ac.il Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:22645166

An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

NASA Astrophysics Data System (ADS)

Zhang, Guannan; Lu, Dan; Ye, Ming; Gunzburger, Max; Webster, Clayton

2013-10-01

Bayesian analysis has become vital to uncertainty quantification in groundwater modeling, but its application has been hindered by the computational cost associated with numerous model executions required by exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, a new approach is developed to improve the computational efficiency of Bayesian inference by constructing a surrogate of the PPDF, using an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, this paper utilizes a compactly supported higher-order hierarchical basis to construct the surrogate system, resulting in a significant reduction in the number of required model executions. In addition, using the hierarchical surplus as an error indicator allows locally adaptive refinement of sparse grids in the parameter space, which further improves computational efficiency. To efficiently build the surrogate system for the PPDF with multiple significant modes, optimization techniques are used to identify the modes, for which high-probability regions are defined and components of the aSG-hSC approximation are constructed. After the surrogate is determined, the PPDF can be evaluated by sampling the surrogate system directly without model execution, resulting in improved efficiency of the surrogate-based MCMC compared with conventional MCMC. The developed method is evaluated using two synthetic groundwater reactive transport models. The first example involves coupled linear reactions and demonstrates the accuracy of our high-order hierarchical basis approach in approximating high-dimensional posteriori distribution. The second example is highly nonlinear because of the reactions of uranium surface complexation, and demonstrates how the iterative aSG-hSC method is able to capture multimodal and non-Gaussian features of PPDF caused by model nonlinearity. Both experiments show that aSG-hSC is an effective and efficient tool for Bayesian inference.

A prior-based integrative framework for functional transcriptional regulatory network inference

PubMed Central

Siahpirani, Alireza F.

2017-01-01

Abstract Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization. PMID:27794550

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

PubMed

Kiparissides, A; Hatzimanikatis, V

2017-01-01

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Electrochemical Impedance Imaging via the Distribution of Diffusion Times.

PubMed

Song, Juhyun; Bazant, Martin Z

2018-03-16

We develop a mathematical framework to analyze electrochemical impedance spectra in terms of a distribution of diffusion times (DDT) for a parallel array of random finite-length Warburg (diffusion) or Gerischer (reaction-diffusion) circuit elements. A robust DDT inversion method is presented based on complex nonlinear least squares regression with Tikhonov regularization and illustrated for three cases of nanostructured electrodes for energy conversion: (i) a carbon nanotube supercapacitor, (ii) a silicon nanowire Li-ion battery, and (iii) a porous-carbon vanadium flow battery. The results demonstrate the feasibility of nondestructive "impedance imaging" to infer microstructural statistics of random, heterogeneous materials.

Scalar dissipation rates in non-conservative transport systems

PubMed Central

Engdahl, Nicholas B.; Ginn, Timothy R.; Fogg, Graham E.

2014-01-01

This work considers how the inferred mixing state of diffusive and advective-diffusive systems will vary over time when the solute masses are not constant over time. We develop a number of tools that allow the scalar dissipation rate to be used as a mixing measure in these systems without calculating local concentration gradients. The behavior of dissipation rates are investigated for single and multi-component kinetic reactions and a commonly studied equilibrium reaction. The scalar dissipation rate of a tracer experiencing first order decay can be determined exactly from the decay constant and the dissipation rate of a passive tracer, and the mixing rate of a conservative component is not the superposition of the solute specific mixing rates. We then show how the behavior of the scalar dissipation rate can be determined from a limited subset of an infinite domain. Corrections are derived for constant and time dependent limits of integration the latter is used to approximate dissipation rates in advective-diffusive systems. Several of the corrections exhibit similarities to the previous work on mixing, including non-Fickian mixing. This illustrates the importance of accounting for the effects that reaction systems or limited monitoring areas may have on the inferred mixing state. PMID:23584457

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.

Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate-Product Pairs.

PubMed

Moriya, Yuki; Yamada, Takuji; Okuda, Shujiro; Nakagawa, Zenichi; Kotera, Masaaki; Tokimatsu, Toshiaki; Kanehisa, Minoru; Goto, Susumu

2016-03-28

Although there are several databases that contain data on many metabolites and reactions in biochemical pathways, there is still a big gap in the numbers between experimentally identified enzymes and metabolites. It is supposed that many catalytic enzyme genes are still unknown. Although there are previous studies that estimate the number of candidate enzyme genes, these studies required some additional information aside from the structures of metabolites such as gene expression and order in the genome. In this study, we developed a novel method to identify a candidate enzyme gene of a reaction using the chemical structures of the substrate-product pair (reactant pair). The proposed method is based on a search for similar reactant pairs in a reference database and offers ortholog groups that possibly mediate the given reaction. We applied the proposed method to two experimentally validated reactions. As a result, we confirmed that the histidine transaminase was correctly identified. Although our method could not directly identify the asparagine oxo-acid transaminase, we successfully found the paralog gene most similar to the correct enzyme gene. We also applied our method to infer candidate enzyme genes in the mesaconate pathway. The advantage of our method lies in the prediction of possible genes for orphan enzyme reactions where any associated gene sequences are not determined yet. We believe that this approach will facilitate experimental identification of genes for orphan enzymes.

Rate of precipitation of calcium phosphate on heated surfaces.

PubMed

Barton, K P; Chapman, T W; Lund, D

1985-03-01

Fouling of a heated stainless steel surface by calcium phosphate precipitation has been studied in an annular flow apparatus, instrumented to provide a constant heat flux while measuring local metal-surface temperatures. Models of the heat and mass-transfer boundary layers are used to estimate interfacial temperatures and concentrations, from which the heterogeneous reaction rate is inferred. The analysis indicates that the reaction rate is a function of both chemical kinetics and mass transfer limitations.

Estimating reaction rate coefficients within a travel-time modeling framework.

PubMed

Gong, R; Lu, C; Wu, W-M; Cheng, H; Gu, B; Watson, D; Jardine, P M; Brooks, S C; Criddle, C S; Kitanidis, P K; Luo, J

2011-01-01

A generalized, efficient, and practical approach based on the travel-time modeling framework is developed to estimate in situ reaction rate coefficients for groundwater remediation in heterogeneous aquifers. The required information for this approach can be obtained by conducting tracer tests with injection of a mixture of conservative and reactive tracers and measurements of both breakthrough curves (BTCs). The conservative BTC is used to infer the travel-time distribution from the injection point to the observation point. For advection-dominant reactive transport with well-mixed reactive species and a constant travel-time distribution, the reactive BTC is obtained by integrating the solutions to advective-reactive transport over the entire travel-time distribution, and then is used in optimization to determine the in situ reaction rate coefficients. By directly working on the conservative and reactive BTCs, this approach avoids costly aquifer characterization and improves the estimation for transport in heterogeneous aquifers which may not be sufficiently described by traditional mechanistic transport models with constant transport parameters. Simplified schemes are proposed for reactive transport with zero-, first-, nth-order, and Michaelis-Menten reactions. The proposed approach is validated by a reactive transport case in a two-dimensional synthetic heterogeneous aquifer and a field-scale bioremediation experiment conducted at Oak Ridge, Tennessee. The field application indicates that ethanol degradation for U(VI)-bioremediation is better approximated by zero-order reaction kinetics than first-order reaction kinetics. Copyright © 2010 The Author(s). Journal compilation © 2010 National Ground Water Association.

Estimating Reaction Rate Coefficients Within a Travel-Time Modeling Framework

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

Gong, R; Lu, C; Luo, Jian

A generalized, efficient, and practical approach based on the travel-time modeling framework is developed to estimate in situ reaction rate coefficients for groundwater remediation in heterogeneous aquifers. The required information for this approach can be obtained by conducting tracer tests with injection of a mixture of conservative and reactive tracers and measurements of both breakthrough curves (BTCs). The conservative BTC is used to infer the travel-time distribution from the injection point to the observation point. For advection-dominant reactive transport with well-mixed reactive species and a constant travel-time distribution, the reactive BTC is obtained by integrating the solutions to advective-reactive transportmore » over the entire travel-time distribution, and then is used in optimization to determine the in situ reaction rate coefficients. By directly working on the conservative and reactive BTCs, this approach avoids costly aquifer characterization and improves the estimation for transport in heterogeneous aquifers which may not be sufficiently described by traditional mechanistic transport models with constant transport parameters. Simplified schemes are proposed for reactive transport with zero-, first-, nth-order, and Michaelis-Menten reactions. The proposed approach is validated by a reactive transport case in a two-dimensional synthetic heterogeneous aquifer and a field-scale bioremediation experiment conducted at Oak Ridge, Tennessee. The field application indicates that ethanol degradation for U(VI)-bioremediation is better approximated by zero-order reaction kinetics than first-order reaction kinetics.« less

Analogical and Category-Based Inference: A Theoretical Integration with Bayesian Causal Models

ERIC Educational Resources Information Center

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

2010-01-01

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…

Alterations to the orientation of the ground reaction force vector affect sprint acceleration performance in team sports athletes.

PubMed

Bezodis, Neil E; North, Jamie S; Razavet, Jane L

2017-09-01

A more horizontally oriented ground reaction force vector is related to higher levels of sprint acceleration performance across a range of athletes. However, the effects of acute experimental alterations to the force vector orientation within athletes are unknown. Fifteen male team sports athletes completed maximal effort 10-m accelerations in three conditions following different verbal instructions intended to manipulate the force vector orientation. Ground reaction forces (GRFs) were collected from the step nearest 5-m and stance leg kinematics at touchdown were also analysed to understand specific kinematic features of touchdown technique which may influence the consequent force vector orientation. Magnitude-based inferences were used to compare findings between conditions. There was a likely more horizontally oriented ground reaction force vector and a likely lower peak vertical force in the control condition compared with the experimental conditions. 10-m sprint time was very likely quickest in the control condition which confirmed the importance of force vector orientation for acceleration performance on a within-athlete basis. The stance leg kinematics revealed that a more horizontally oriented force vector during stance was preceded at touchdown by a likely more dorsiflexed ankle, a likely more flexed knee, and a possibly or likely greater hip extension velocity.

Training Inference Making Skills Using a Situation Model Approach Improves Reading Comprehension

PubMed Central

Bos, Lisanne T.; De Koning, Bjorn B.; Wassenburg, Stephanie I.; van der Schoot, Menno

2016-01-01

This study aimed to enhance third and fourth graders’ text comprehension at the situation model level. Therefore, we tested a reading strategy training developed to target inference making skills, which are widely considered to be pivotal to situation model construction. The training was grounded in contemporary literature on situation model-based inference making and addressed the source (text-based versus knowledge-based), type (necessary versus unnecessary for (re-)establishing coherence), and depth of an inference (making single lexical inferences versus combining multiple lexical inferences), as well as the type of searching strategy (forward versus backward). Results indicated that, compared to a control group (n = 51), children who followed the experimental training (n = 67) improved their inference making skills supportive to situation model construction. Importantly, our training also resulted in increased levels of general reading comprehension and motivation. In sum, this study showed that a ‘level of text representation’-approach can provide a useful framework to teach inference making skills to third and fourth graders. PMID:26913014

Processes of Formation of Spheroidal Concretions and Inferences for "Blueberries" in Meridiani Planum Sediments

NASA Astrophysics Data System (ADS)

Coleman, M. L.

2005-03-01

Formation of spheroidal concretions on Earth results generally from reactions of organic matter in oxidized sediments. Had organic matter been present in Merididani Planum it could have produced a reduced iron mineral phase later oxidized to hematite.

Infant Visual Expectations: Advances and Issues.

ERIC Educational Resources Information Center

Haith, Marshall M.; Wass, Tara S.; Adler, Scott A.

1997-01-01

Speculates on underlying processes for the reaction time variance and age differences in anticipation latency using the Visual Expectation Paradigm. Discusses the dichotomization of reactive and anticipatory behavior, limitations of longitudinal designs, drawbacks in using standard procedures and materials, and inferences that can be made…

Oxygen isotopic ratios of primordial water in carbonaceous chondrites

NASA Astrophysics Data System (ADS)

Fujiya, Wataru

2018-01-01

In this work, I estimate the δ18 O and δ17 O values of primordial water in CM chondrites to be 55 ± 13 and 35 ± 9‰, respectively, based on whole-rock O and H data. Also, I found that the O and/or H data of Antarctic meteorites are biased, which is attributed to terrestrial weathering. This characteristic O isotopic ratio of water together with corresponding water abundances in CM chondrites are consistent with the origin of water as ice processed by photochemical reactions at the outer regions of the solar nebula, where mass-independent O isotopic fractionation and water destruction may have occurred. Another possible mechanism to produce the inferred O isotopic ratio of water would be O isotopic fractionation between water vapor and ice, which likely occurred near the condensation front of H2O (snow line) in the solar nebula. The inferred O isotopic ratio of water suggests that carbonate in CM chondrites formed at low temperatures of <150 °C. The O isotopic ratios of primordial water in chondrites other than CM chondrites are not well constrained.

Quantitative Proteomics via High Resolution MS Quantification: Capabilities and Limitations

PubMed Central

Higgs, Richard E.; Butler, Jon P.; Han, Bomie; Knierman, Michael D.

2013-01-01

Recent improvements in the mass accuracy and resolution of mass spectrometers have led to renewed interest in label-free quantification using data from the primary mass spectrum (MS1) acquired from data-dependent proteomics experiments. The capacity for higher specificity quantification of peptides from samples enriched for proteins of biological interest offers distinct advantages for hypothesis generating experiments relative to immunoassay detection methods or prespecified peptide ions measured by multiple reaction monitoring (MRM) approaches. Here we describe an evaluation of different methods to post-process peptide level quantification information to support protein level inference. We characterize the methods by examining their ability to recover a known dilution of a standard protein in background matrices of varying complexity. Additionally, the MS1 quantification results are compared to a standard, targeted, MRM approach on the same samples under equivalent instrument conditions. We show the existence of multiple peptides with MS1 quantification sensitivity similar to the best MRM peptides for each of the background matrices studied. Based on these results we provide recommendations on preferred approaches to leveraging quantitative measurements of multiple peptides to improve protein level inference. PMID:23710359

Algorithm Optimally Orders Forward-Chaining Inference Rules

NASA Technical Reports Server (NTRS)

James, Mark

2008-01-01

People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.

Phylogenic inference using alignment-free methods for applications in microbial community surveys using 16s rRNA gene

PubMed Central

2017-01-01

The diversity of microbiota is best explored by understanding the phylogenetic structure of the microbial communities. Traditionally, sequence alignment has been used for phylogenetic inference. However, alignment-based approaches come with significant challenges and limitations when massive amounts of data are analyzed. In the recent decade, alignment-free approaches have enabled genome-scale phylogenetic inference. Here we evaluate three alignment-free methods: ACS, CVTree, and Kr for phylogenetic inference with 16s rRNA gene data. We use a taxonomic gold standard to compare the accuracy of alignment-free phylogenetic inference with that of common microbiome-wide phylogenetic inference pipelines based on PyNAST and MUSCLE alignments with FastTree and RAxML. We re-simulate fecal communities from Human Microbiome Project data to evaluate the performance of the methods on datasets with properties of real data. Our comparisons show that alignment-free methods are not inferior to alignment-based methods in giving accurate and robust phylogenic trees. Moreover, consensus ensembles of alignment-free phylogenies are superior to those built from alignment-based methods in their ability to highlight community differences in low power settings. In addition, the overall running times of alignment-based and alignment-free phylogenetic inference are comparable. Taken together our empirical results suggest that alignment-free methods provide a viable approach for microbiome-wide phylogenetic inference. PMID:29136663

Will water act as a photocatalyst for cluster phase chemical reactions? Vibrational overtone-induced dehydration reaction of methanediol

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

Kramer, Zeb C.; Takahashi, Kaito; Skodje, Rex T.

2012-04-28

The possibility of water catalysis in the vibrational overtone-induced dehydration reaction of methanediol is investigated using ab initio dynamical simulations of small methanediol-water clusters. Quantum chemistry calculations employing clusters with one or two water molecules reveal that the barrier to dehydration is lowered by over 20 kcal/mol because of hydrogen-bonding at the transition state. Nevertheless, the simulations of the reaction dynamics following OH-stretch excitation show little catalytic effect of water and, in some cases, even show an anticatalytic effect. The quantum yield for the dehydration reaction exhibits a delayed threshold effect where reaction does not occur until the photon energymore » is far above the barrier energy. Unlike thermally induced reactions, it is argued that competition between reaction and the irreversible dissipation of photon energy may be expected to raise the dynamical threshold for the reaction above the transition state energy. It is concluded that quantum chemistry calculations showing barrier lowering are not sufficient to infer water catalysis in photochemical reactions, which instead require dynamical modeling.« less

Anchoring quartet-based phylogenetic distances and applications to species tree reconstruction.

PubMed

Sayyari, Erfan; Mirarab, Siavash

2016-11-11

Inferring species trees from gene trees using the coalescent-based summary methods has been the subject of much attention, yet new scalable and accurate methods are needed. We introduce DISTIQUE, a new statistically consistent summary method for inferring species trees from gene trees under the coalescent model. We generalize our results to arbitrary phylogenetic inference problems; we show that two arbitrarily chosen leaves, called anchors, can be used to estimate relative distances between all other pairs of leaves by inferring relevant quartet trees. This results in a family of distance-based tree inference methods, with running times ranging between quadratic to quartic in the number of leaves. We show in simulated studies that DISTIQUE has comparable accuracy to leading coalescent-based summary methods and reduced running times.

Comparisons of mental clocks.

PubMed

Paivio, A

1978-02-01

Subjects in three experiments were presented with pairs of clock times and were required to choose the one in which the hour and minute hand formed the smaller angle. In Experiments 1 and 2, the times were presented digitally, necessitating a transformation into symbolic representations from which the angular size difference could be inferred. The results revealed orderly symbolic distance effects so that comparison reaction time increased as the angular size difference decreased. Moreover, subjects generally reported using imagery to make the judgment, and subjects scoring high on test of imagery ability were faster than those scoring low on such tests. Experiment 3 added a direct perceptual condition in which subjects compared angles between pairs of hands on two drawn (analog) clocks, as well as a mixed condition involving one digital and one analog clock time. The results showed comparable distance effects for all conditions. In addition, reaction time increased from the perceptual, to the mixed, to the pure-digital condition. These results are consistent with predictions from an image-based dual-coding theory.

Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions.

PubMed

La, Mary K; Sedykh, Alexander; Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

2018-06-06

Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug-target-effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug-target-effect paradigm, can be integrated to facilitate the inference of relationships between these entities. This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature. Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C-T-E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson's paradigm to form C-T-E triangles. Missing C-E edges were then inferred as C-E relationships. Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C-E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale. The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.

Inferring silicate weathering rates over recent timescales (less than 100 years) in crystalline aquifers by calibrating lumped parameters models with atmospheric tracers

NASA Astrophysics Data System (ADS)

Marçais, J.; Labasque, T.; Gauvain, A.; De Dreuzy, J. R.; Aquilina, L.; Abbott, B. W.

2016-12-01

Silicate minerals (e.g. feldspars, micas and olivines) are ubiquitous in crystalline rocks such as granite and schist. Groundwater dissolves some of this silica via weathering processes as it passes through the catchment, increasing silica concentration with residence time. However, quantifying weathering rates is complicated by the fact that groundwater residence time distributions (RTD) are typically unknown. Batch experiments can characterize weathering reaction type and provide estimates of dissolution rates, but weathering timescales in the field are far greater than what can be simulated in the laboratory (White and Brantley, 2003). Here we implement a novel approach coupling chlorofluorocarbons (CFC) and dissolved silica concentrations to infer timescales of silica weathering processes at the watershed scale. We investigated 6 crystalline aquifers in Brittany with contrasting lithology. We quantified silicate weathering at the watershed scale based on individual measurements from multiple wells, assuming first-order reaction kinetics. For each well, we used a lumped parameter model to determined RTD with inverse gaussian distributions, which allow two degrees of freedom. Production rate and initial silicate concentration were then optimized at the watershed scale with the calibrated model. Weathering rates were relatively similar among watersheds, varying for most sites from 0.16 to 0.42 mg/L/yr (SD = 0.09 mg/L/yr), and estimates of weathering rates were not significantly influenced by single well measurements. This work demonstrates how atmospheric tracers can be used with dissolved silica concentration to inform both RTD and first order kinetics of weathering reactions. Together these results suggest that dissolved silica could be a robust and cheap groundwater age proxy for recent timescales (less than 100 years). ------------------ White, Art F, and Susan L Brantley. 2003. « The effect of time on the weathering of silicate minerals: why do weathering rates differ in the laboratory and field? » Chemical Geology, Controls on Chemical Weathering, 202 (3-4): 479-506. doi:10.1016/j.chemgeo.2003.03.001.

Standard magnetic resonance-based measurements of the Pi→ATP rate do not index the rate of oxidative phosphorylation in cardiac and skeletal muscles

PubMed Central

Ugurbil, Kamil

2011-01-01

Magnetic resonance spectroscopy-based magnetization transfer techniques (MT) are commonly used to assess the rate of oxidative (i.e., mitochondrial) ATP synthesis in intact tissues. Physiologically appropriate interpretation of MT rate data depends on accurate appraisal of the biochemical events that contribute to a specific MT rate measurement. The relative contributions of the specific enzymatic reactions that can contribute to a MT Pi→ATP rate measurement are tissue dependent; nonrecognition of this fact can bias the interpretation of MT Pi→ATP rate data. The complexities of MT-based measurements of mitochondrial ATP synthesis rates made in striated muscle and other tissues are reviewed, following which, the adverse impacts of erroneous Pi→ATP rate data analyses on the physiological inferences presented in selected published studies of cardiac and skeletal muscle are considered. PMID:21368294

Meta-learning framework applied in bioinformatics inference system design.

PubMed

Arredondo, Tomás; Ormazábal, Wladimir

2015-01-01

This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

Consumer psychology: categorization, inferences, affect, and persuasion.

PubMed

Loken, Barbara

2006-01-01

This chapter reviews research on consumer psychology with emphasis on the topics of categorization, inferences, affect, and persuasion. The chapter reviews theory-based empirical research during the period 1994-2004. Research on categorization includes empirical research on brand categories, goals as organizing frameworks and motivational bases for judgments, and self-based processing. Research on inferences includes numerous types of inferences that are cognitively and/or experienced based. Research on affect includes the effects of mood on processing and cognitive and noncognitive bases for attitudes and intentions. Research on persuasion focuses heavily on the moderating role of elaboration and dual-process models, and includes research on attitude strength responses, advertising responses, and negative versus positive evaluative dimensions.

Concordant Chemical Reaction Networks and the Species-Reaction Graph

PubMed Central

Shinar, Guy; Feinberg, Martin

2015-01-01

In a recent paper it was shown that, for chemical reaction networks possessing a subtle structural property called concordance, dynamical behavior of a very circumscribed (and largely stable) kind is enforced, so long as the kinetics lies within the very broad and natural weakly monotonic class. In particular, multiple equilibria are precluded, as are degenerate positive equilibria. Moreover, under certain circumstances, also related to concordance, all real eigenvalues associated with a positive equilibrium are negative. Although concordance of a reaction network can be decided by readily available computational means, we show here that, when a nondegenerate network’s Species-Reaction Graph satisfies certain mild conditions, concordance and its dynamical consequences are ensured. These conditions are weaker than earlier ones invoked to establish kinetic system injectivity, which, in turn, is just one ramification of network concordance. Because the Species-Reaction Graph resembles pathway depictions often drawn by biochemists, results here expand the possibility of inferring significant dynamical information directly from standard biochemical reaction diagrams. PMID:22940368

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.

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.

"Magnitude-based inference": a statistical review.

PubMed

Welsh, Alan H; Knight, Emma J

2015-04-01

We consider "magnitude-based inference" and its interpretation by examining in detail its use in the problem of comparing two means. We extract from the spreadsheets, which are provided to users of the analysis (http://www.sportsci.org/), a precise description of how "magnitude-based inference" is implemented. We compare the implemented version of the method with general descriptions of it and interpret the method in familiar statistical terms. We show that "magnitude-based inference" is not a progressive improvement on modern statistics. The additional probabilities introduced are not directly related to the confidence interval but, rather, are interpretable either as P values for two different nonstandard tests (for different null hypotheses) or as approximate Bayesian calculations, which also lead to a type of test. We also discuss sample size calculations associated with "magnitude-based inference" and show that the substantial reduction in sample sizes claimed for the method (30% of the sample size obtained from standard frequentist calculations) is not justifiable so the sample size calculations should not be used. Rather than using "magnitude-based inference," a better solution is to be realistic about the limitations of the data and use either confidence intervals or a fully Bayesian analysis.

Inference of missing data and chemical model parameters using experimental statistics

NASA Astrophysics Data System (ADS)

Casey, Tiernan; Najm, Habib

2017-11-01

A method for determining the joint parameter density of Arrhenius rate expressions through the inference of missing experimental data is presented. This approach proposes noisy hypothetical data sets from target experiments and accepts those which agree with the reported statistics, in the form of nominal parameter values and their associated uncertainties. The data exploration procedure is formalized using Bayesian inference, employing maximum entropy and approximate Bayesian computation methods to arrive at a joint density on data and parameters. The method is demonstrated in the context of reactions in the H2-O2 system for predictive modeling of combustion systems of interest. Work supported by the US DOE BES CSGB. Sandia National Labs is a multimission lab managed and operated by Nat. Technology and Eng'g Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell Intl, for the US DOE NCSA under contract DE-NA-0003525.

Quantifying enzymatic lysis: estimating the combined effects of chemistry, physiology and physics.

PubMed

Mitchell, Gabriel J; Nelson, Daniel C; Weitz, Joshua S

2010-10-04

The number of microbial pathogens resistant to antibiotics continues to increase even as the rate of discovery and approval of new antibiotic therapeutics steadily decreases. Many researchers have begun to investigate the therapeutic potential of naturally occurring lytic enzymes as an alternative to traditional antibiotics. However, direct characterization of lytic enzymes using techniques based on synthetic substrates is often difficult because lytic enzymes bind to the complex superstructure of intact cell walls. Here we present a new standard for the analysis of lytic enzymes based on turbidity assays which allow us to probe the dynamics of lysis without preparing a synthetic substrate. The challenge in the analysis of these assays is to infer the microscopic details of lysis from macroscopic turbidity data. We propose a model of enzymatic lysis that integrates the chemistry responsible for bond cleavage with the physical mechanisms leading to cell wall failure. We then present a solution to an inverse problem in which we estimate reaction rate constants and the heterogeneous susceptibility to lysis among target cells. We validate our model given simulated and experimental turbidity assays. The ability to estimate reaction rate constants for lytic enzymes will facilitate their biochemical characterization and development as antimicrobial therapeutics.

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.

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.

Direct measurement of the 7Be(n, α)4 He reaction cross sections for the cosmological Li problem

NASA Astrophysics Data System (ADS)

Kawabata, Takahiro; Fujikawa, Yuki; Furuno, Tatsuya; Goto, Tatsuya; Hashimoto, Toshikazu; Ichikawa, Masaya; Itoh, Makoto; Iwasa, Naohito; Kanada-En'yo, Yoshiko; Koshikawa, Ami; Kubono, Shigeru; Miyawaki, Eisuke; Mizuno, Masatoshi; Mizutani, Keigo; Morimoto, Takahiro; Murata, Motoki; Nanamura, Takuya; Nishimura, Shunji; Nanamura, Takuya; Okamoto, Shintaro; Sakaguchi, Yuichi; Sakata, Itsushi; Sakaue, Akane; Sawada, Ryo; Shikata, Yuki; Takahashi, Yu; Takechi, Daiki; Takeda, Tomoya; Takimoto, Chisato; Tsumura, Miho; Watanabe, Ken; Yoshida, Sota

2017-11-01

The cross sections of the 7Be(n, α)4He reaction for p-wave neutrons were experimentally determined at Ec.m. = 0.20-0.81 MeV close to the Big Bang nucleosynthesis (BBN) energy window for the first time on the basis of the detailed balance principle by measuring the time-reverse reaction. The obtained cross sections are much larger than the cross sections for s-wave neutrons inferred from the recent measurement at the n_TOF facility in CERN, but significantly smaller than the theoretical estimation widely used in the BBN calculations. The present results suggest the 7Be(n, α)4 He reaction rate is not large enough to solve the cosmological lithium problem

Assisted Sonication vs Conventional Transesterification Numerical Simulation and Sensitivity Study

NASA Astrophysics Data System (ADS)

Janajreh, Isam; Noorul Hussain, Mohammed; El Samad, Tala

2015-10-01

Transeterification is known as slow reaction that can take over several hours to complete as the two immiscible liquid reactants combine to form biodiesel and the less favorable glycerol. The quest of finding the perfect catalyst, optimal operational conditions, and reactor configuration to accelerate the reaction in mere few minutes that ensures high quality biodiesel, in economically viable way is coming along with sonication. This drastic reduction is a key enabler for the development of a continuous processing that otherwise is fairly costly and low throughput using conventional method. The reaction kinetics of sonication assisted as inferred by several authors is several time faster and this work implements these rates in a high fidelity numerical simulation model. This flow model is based on Navier-Stokes equations coupled with energy equation for non-isothermal flow and the transport equations of the multiple reactive species. The model is initially validated against experimental data from previous work of the authors using an annular reactor configuration. Following the validation, comparison of the reaction rate is shown to gain more insight to the distribution of the reaction and its attained rates. The two models (conventional and sonication) then compared on the basis of their sensitivity to the methane to oil molar ratio as the most pronounced process parameter. Both the exit reactor yield and the distribution of the species are evaluated with favorable yield under sonication process. These results pave the way to build a more robust process intensified reactor having an integrated selective heterogeneous catalyst to steer the reaction. This can avoid the downstream cleaning processes, cutting reaction time, and render economic benefit to the process.

Load Induced Blindness

ERIC Educational Resources Information Center

Macdonald, James S. P.; Lavie, Nilli

2008-01-01

Although the perceptual load theory of attention has stimulated a great deal of research, evidence for the role of perceptual load in determining perception has typically relied on indirect measures that infer perception from distractor effects on reaction times or neural activity (see N. Lavie, 2005, for a review). Here we varied the level of…

Metacontrast Inferred from Reaction Time and Verbal Report: Replication and Comments on the Feher-Biederman Experiment

ERIC Educational Resources Information Center

Amundson, Vickie E.; Bernstein, Ira H.

1973-01-01

Authors note that Fehrer and Biederman's two statistical tests were not of equal power and that their conclusion could be a statistical artifact of both the lesser power of the verbal report comparison and the insensitivity of their particular verbal report indicator. (Editor)

The Role of Probability-Based Inference in an Intelligent Tutoring System.

ERIC Educational Resources Information Center

Mislevy, Robert J.; Gitomer, Drew H.

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Ensemble stacking mitigates biases in inference of synaptic connectivity.

PubMed

Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

2018-01-01

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

Phylogenetic position of parabasalid symbionts from the termite Calotermes flavicollis based on small subunit rRNA sequences.

PubMed

Gerbod, D; Edgcomb, V P; Noël, C; Delgado-Viscogliosi, P; Viscogliosi, E

2000-09-01

Small subunit rDNA genes were amplified by polymerase chain reaction using specific primers from mixed-population DNA obtained from the whole hindgut of the termite Calotermes flavicollis. Comparative sequence analysis of the clones revealed two kinds of sequences that were both from parabasalid symbionts. In a molecular tree inferred by distance, parsimony and likelihood methods, and including 27 parabasalid sequences retrieved from the data bases, the sequences of the group II (clones Cf5 and Cf6) were closely related to the Devescovinidae/Calonymphidae species and thus were assigned to the Devescovinidae Foaina. The sequence of the group I (clone Cf1) emerged within the Trichomonadinae and strongly clustered with Tetratrichomonas gallinarum. On the basis of morphological data, the Monocercomonadidae Hexamastix termitis might be the most likely origin of this sequence.

Vocal reaction times to unilaterally presented concrete and abstract words: towards a theory of differential right hemispheric semantic processing.

PubMed

Rastatter, M; Dell, C W; McGuire, R A; Loren, C

1987-03-01

Previous studies investigating hemispheric organization for processing concrete and abstract nouns have provided conflicting results. Using manual reaction time tasks some studies have shown that the right hemisphere is capable of analyzing concrete words but not abstract. Others, however, have inferred that the left hemisphere is the sole analyzer of both types of lexicon. The present study tested these issues further by measuring vocal reaction times of normal subjects to unilaterally presented concrete and abstract items. Results were consistent with a model of functional localization which suggests that the minor hemisphere is capable of differentially processing both types of lexicon in the presence of a dominant left hemisphere.

Dynamical simulation priors for human motion tracking.

PubMed

Vondrak, Marek; Sigal, Leonid; Jenkins, Odest Chadwicke

2013-01-01

We propose a simulation-based dynamical motion prior for tracking human motion from video in presence of physical ground-person interactions. Most tracking approaches to date have focused on efficient inference algorithms and/or learning of prior kinematic motion models; however, few can explicitly account for the physical plausibility of recovered motion. Here, we aim to recover physically plausible motion of a single articulated human subject. Toward this end, we propose a full-body 3D physical simulation-based prior that explicitly incorporates a model of human dynamics into the Bayesian filtering framework. We consider the motion of the subject to be generated by a feedback “control loop” in which Newtonian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of interaction forces, motor forces, and gravity. Interaction forces prevent physically impossible hypotheses, enable more appropriate reactions to the environment (e.g., ground contacts), and are produced from detected human-environment collisions. Motor forces actuate the body, ensure that proposed pose transitions are physically feasible, and are generated using a motion controller. For efficient inference in the resulting high-dimensional state space, we utilize an exemplar-based control strategy that reduces the effective search space of motor forces. As a result, we are able to recover physically plausible motion of human subjects from monocular and multiview video. We show, both quantitatively and qualitatively, that our approach performs favorably with respect to Bayesian filtering methods with standard motion priors.

Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

PubMed

Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

2016-01-01

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

Action understanding as inverse planning.

PubMed

Baker, Chris L; Saxe, Rebecca; Tenenbaum, Joshua B

2009-12-01

Humans are adept at inferring the mental states underlying other agents' actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents' behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent's behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an "intentional stance" [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a "teleological stance" [Gergely, G., Nádasdy, Z., Csibra, G., & Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165-193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.

Differentiable cortical networks for inferences concerning people’s intentions versus physical causality

PubMed Central

Mason, Robert A.; Just, Marcel Adam

2010-01-01

Cortical activity associated with generating an inference was measured using fMRI. Participants read three-sentence passages that differed in whether or not an inference needed to be drawn to understand them. The inference was based on either a protagonist’s intention or a physical consequence of a character’s action. Activation was expected in Theory of Mind brain regions for the passages based on protagonists’ intentions but not for the physical consequence passages. The activation measured in the right temporo-parietal junction was greater in the intentional passages than in the consequence passages, consistent with predictions from a Theory of Mind perspective. In contrast, there was increased occipital activation in the physical inference passages. For both types of passage, the cortical activity related to the reading of the critical inference sentence demonstrated a recruitment of a common inference cortical network. This general inference-related activation appeared bilaterally in the language processing areas (the inferior frontal gyrus, the temporal gyrus, and the angular gyrus), as well as in the medial to superior frontal gyrus, which has been found to be active in Theory of Mind tasks. These findings are consistent with the hypothesis that component areas of the discourse processing network are recruited as needed based on the nature of the inference. A Protagonist monitoring and synthesis network is proposed as a more accurate account for Theory of Mind activation during narrative comprehension. PMID:21229617

Vibrationally Excited Carbon Monoxide Produced via a Chemical Reaction Between Carbon Vapor and Oxygen

NASA Astrophysics Data System (ADS)

Jans, Elijah R.; Eckert, Zakari; Frederickson, Kraig; Rich, Bill; Adamovich, Igor V.

2017-06-01

Measurements of the vibrational distribution function of carbon monoxide produced via a reaction between carbon vapor and molecular oxygen has shown a total population inversion on vibrational levels 4-7. Carbon vapor, produced using an arc discharge to sublimate graphite, is mixed with an argon oxygen flow. The excited carbon monoxide is vibrationally populated up to level v=14, at low temperatures, T=400-450 K, in a collision-dominated environment, 15-20 Torr, with total population inversions between v=4-7. The average vibrational energy per CO molecule formed by the reaction is 0.6-1.2 eV/molecule, which corresponds to 10-20% of the reaction enthalpy. Kinetic modeling of the flow reactor, including state specific vibrational processes, was performed to infer the vibrational distribution of the products of the reaction. The results show viability of developing of a new chemical CO laser from the reaction of carbon vapor and oxygen.

Transcript abundance on its own cannot be used to infer fluxes in central metabolism

DOE PAGES

Schwender, Jorg; Konig, Christina; Klapperstuck, Matthias; ...

2014-11-28

An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism,more » some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. Lastly, this limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.« less

ICF Gamma-Ray measurements on the NIF

NASA Astrophysics Data System (ADS)

Herrmann, Hans; Kim, Y.; Hoffman, N. M.; Batha, S. H.; Stoeffl, W.; Church, J. A.; Sayre, D. B.; Liebman, J. A.; Cerjan, C. J.; Carpenter, A. C.; Grafil, E. M.; Khater, H. Y.; Horsfield, C. J.; Rubery, M.

2013-10-01

The primary objective of the NIF Gamma Reaction History (GRH) diagnostic is to provide bang time and burn width information in order to constrain implosion simulation parameters such as shell velocity and confinement time. This is accomplished by measuring DT fusion gamma-rays with energy-thresholded Gas Cherenkov detectors that convert MeV gamma-rays into UV/visible photons for high-bandwidth optical detection. Burn-weighted CH ablator areal density is also inferred based on measurement of the 12C(n,n') gammas emitted at 4.44 MeV from DT neutrons inelastically scattering off carbon nuclei as they pass through the plastic ablator. This requires that the four independent GRH gas cells be set to differing Cherenkov thresholds (e.g., 2.9, 4.5, 8 & 10 MeV) in order to be able to unfold the primary spectral components predicted to be in the gamma ray energy spectrum (i.e., DT γ 27Al & 28Si (n,n') γ from the thermo-mechanical package (TMP); and 12C(n,n' γ from the ablator). The GRH response to 12C(n,n') γ is calibrated in-situ by placing a known areal density of carbon in the form of a puck placed ~6 cm from a DT exploding pusher implosion. Comparisons between inferred gamma fluences and simulations based on the nuclear cross sections databases will be presented. Supported by US DOE NNSA.

Effects of Storage Time on Glycolysis in Donated Human Blood Units

PubMed Central

Qi, Zhen; Roback, John D.; Voit, Eberhard O.

2017-01-01

Background: Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms accounting for such biochemical changes, qualitatively and quantitatively; Study Design and Methods: Based on the integration of metabolic time series data, kinetic models, and a stoichiometric model of the glycolytic pathway, a customized inference method was developed and used to quantify the dynamic changes in glycolytic fluxes during the storage of donated blood units. The method provides a proof of principle for the feasibility of inferences regarding flux characteristics from metabolomics data; Results: Several glycolytic reaction steps change substantially during storage time and vary among different fluxes and donors. The quantification of these storage time effects, which are possibly irreversible, allows for predictions of the transfusion outcome of individual blood units; Conclusion: The improved mechanistic understanding of blood storage, obtained from this computational study, may aid the identification of blood units that age quickly or more slowly during storage, and may ultimately improve transfusion management in clinics. PMID:28353627

Effects of Storage Time on Glycolysis in Donated Human Blood Units.

PubMed

Qi, Zhen; Roback, John D; Voit, Eberhard O

2017-03-29

Background : Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms accounting for such biochemical changes, qualitatively and quantitatively; Study Design and Methods : Based on the integration of metabolic time series data, kinetic models, and a stoichiometric model of the glycolytic pathway, a customized inference method was developed and used to quantify the dynamic changes in glycolytic fluxes during the storage of donated blood units. The method provides a proof of principle for the feasibility of inferences regarding flux characteristics from metabolomics data; Results : Several glycolytic reaction steps change substantially during storage time and vary among different fluxes and donors. The quantification of these storage time effects, which are possibly irreversible, allows for predictions of the transfusion outcome of individual blood units; Conclusion : The improved mechanistic understanding of blood storage, obtained from this computational study, may aid the identification of blood units that age quickly or more slowly during storage, and may ultimately improve transfusion management in clinics.

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

New developments of a knowledge based system (VEG) for inferring vegetation characteristics

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Harrison, P. A.; Harrison, P. R.

1992-01-01

An extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

PubMed

Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

2018-06-01

This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves

PubMed Central

Bogart, Eli; Myers, Christopher R.

2016-01-01

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, we suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data, and we demonstrate that our method predicts fluxes that achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. PMID:26990967

Adult Age Differences in Categorization and Multiple-Cue Judgment

ERIC Educational Resources Information Center

Mata, Rui; von Helversen, Bettina; Karlsson, Linnea; Cupper, Lutz

2012-01-01

We often need to infer unknown properties of objects from observable ones, just like detectives must infer guilt from observable clues and behavior. But how do inferential processes change with age? We examined young and older adults' reliance on rule-based and similarity-based processes in an inference task that can be considered either a…

Relationships among North American and Japanese Laetiporus isolates inferred from molecular phylogenetics and single-spore incompatibility reactions

Treesearch

Mark T. Banik; Daniel L. Lindner; Yuko Ota; Tsutomu Hattori

2010-01-01

Relationships were investigated among North American and Japanese isolates of Laetiporus using phylogenetic analysis of ITS sequences and single-spore isolate incompatibility. Single-spore isolate pairings revealed no significant compatibility between North American and Japanese isolates. ITS analysis revealed 12 clades within the core ...

The Social Validity of "Acceptability of Behavioral Interventions Used in Classrooms": Inferences from Longitudinal Evidence

ERIC Educational Resources Information Center

Elliott, Stephen N.

2017-01-01

In this retrospective commentary on "Acceptability of Behavioral Interventions Used in Classrooms: The Influence of Amount of Teacher Time, Severity of Behavior Problem, and Type of Intervention," I first examine the concept of social validity and related measurement challenges per Wolf's concerns about consumers' subjective reactions to…

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.

PubMed

Bender, Andreas; Scheiber, Josef; Glick, Meir; Davies, John W; Azzaoui, Kamal; Hamon, Jacques; Urban, Laszlo; Whitebread, Steven; Jenkins, Jeremy L

2007-06-01

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

Self-similar structure and experimental signatures of suprathermal ion distribution in inertial confinement fusion implosions

DOE PAGES

Kagan, Grigory; Svyatskiy, D.; Rinderknecht, H. G.; ...

2015-09-03

The distribution function of suprathermal ions is found to be self-similar under conditions relevant to inertial confinement fusion hot spots. By utilizing this feature, interference between the hydrodynamic instabilities and kinetic effects is for the first time assessed quantitatively to find that the instabilities substantially aggravate the fusion reactivity reduction. Thus, the ion tail depletion is also shown to lower the experimentally inferred ion temperature, a novel kinetic effect that may explain the discrepancy between the exploding pusher experiments and rad-hydro simulations and contribute to the observation that temperature inferred from DD reaction products is lower than from DT atmore » the National Ignition Facility.« less

Self-Similar Structure and Experimental Signatures of Suprathermal Ion Distribution in Inertial Confinement Fusion Implosions

NASA Astrophysics Data System (ADS)

Kagan, Grigory; Svyatskiy, D.; Rinderknecht, H. G.; Rosenberg, M. J.; Zylstra, A. B.; Huang, C.-K.; McDevitt, C. J.

2015-09-01

The distribution function of suprathermal ions is found to be self-similar under conditions relevant to inertial confinement fusion hot spots. By utilizing this feature, interference between the hydrodynamic instabilities and kinetic effects is for the first time assessed quantitatively to find that the instabilities substantially aggravate the fusion reactivity reduction. The ion tail depletion is also shown to lower the experimentally inferred ion temperature, a novel kinetic effect that may explain the discrepancy between the exploding pusher experiments and rad-hydro simulations and contribute to the observation that temperature inferred from DD reaction products is lower than from DT at the National Ignition Facility.

Fundamentals and Recent Developments in Approximate Bayesian Computation

PubMed Central

Lintusaari, Jarno; Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka

2017-01-01

Abstract Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.] PMID:28175922

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

High-dynamic-range neutron time-of-flight detector used to infer the D(t,n){sup 4}He and D(d,n){sup 3}He reaction yield and ion temperature on OMEGA

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

Forrest, C. J., E-mail: cforrest@lle.rochester.edu; Glebov, V. Yu.; Goncharov, V. N.

Upgraded microchannel-plate–based photomultiplier tubes (MCP-PMT’s) with increased stability to signal-shape linearity have been implemented on the 13.4-m neutron time-of-flight (nTOF) detector at the Omega Laser Facility. This diagnostic uses oxygenated xylene doped with diphenyloxazole C{sub 15}H{sub 11}NO + p-bis-(o-methylstyryl)-benzene (PPO + bis-MSB) wavelength shifting dyes and is coupled through four viewing ports to fast-gating MCP-PMT’s, each with a different gain to allow one to measure the light output over a dynamic range of 1 × 10{sup 6}. With these enhancements, the 13.4-m nTOF can measure the D(t,n){sup 4}He and D(d,n){sup 3}He reaction yields and average ion temperatures in a singlemore » line of sight. Once calibrated for absolute neutron sensitivity, the nTOF detectors can be used to measure the neutron yield from 1 × 10{sup 9} to 1 × 10{sup 14} and the ion temperature with an accuracy approaching 5% for both the D(t,n){sup 4}He and D(d,n){sup 3}He reactions.« less

Step-to-step spatiotemporal variables and ground reaction forces of intra-individual fastest sprinting in a single session.

PubMed

Nagahara, Ryu; Mizutani, Mirai; Matsuo, Akifumi; Kanehisa, Hiroaki; Fukunaga, Tetsuo

2018-06-01

We aimed to investigate the step-to-step spatiotemporal variables and ground reaction forces during the acceleration phase for characterising intra-individual fastest sprinting within a single session. Step-to-step spatiotemporal variables and ground reaction forces produced by 15 male athletes were measured over a 50-m distance during repeated (three to five) 60-m sprints using a long force platform system. Differences in measured variables between the fastest and slowest trials were examined at each step until the 22nd step using a magnitude-based inferences approach. There were possibly-most likely higher running speed and step frequency (2nd to 22nd steps) and shorter support time (all steps) in the fastest trial than in the slowest trial. Moreover, for the fastest trial there were likely-very likely greater mean propulsive force during the initial four steps and possibly-very likely larger mean net anterior-posterior force until the 17th step. The current results demonstrate that better sprinting performance within a single session is probably achieved by 1) a high step frequency (except the initial step) with short support time at all steps, 2) exerting a greater mean propulsive force during initial acceleration, and 3) producing a greater mean net anterior-posterior force during initial and middle acceleration.

Fabrication and Characterization of Nanoenergetic Hollow Spherical Hexanitrostibene (HNS) Derivatives.

PubMed

Cao, Xiong; Deng, Peng; Hu, Shuangqi; Ren, Lijun; Li, Xiaoxia; Xiao, Peng; Liu, Yu

2018-05-16

The spherization of nanoenergetic materials is the best way to improve the sensitivity and increase loading densities and detonation properties for weapons and ammunition, but the preparation of spherical nanoenergetic materials with high regularization, uniform size and monodispersity is still a challenge. In this paper, nanoenergetic hollow spherical hexanitrostibene (HNS) derivatives were fabricated via a one-pot copolymerization strategy, which is based on the reaction of HNS and piperazine in acetonitrile solution. Characterization results indicated the as-prepared reaction nanoenergetic products were HNS-derived oligomers, where a free radical copolymerization reaction process was inferred. The hollow sphere structure of the HNS derivatives was characterized by scanning electron microscopy (SEM), transmission electron microscope (TEM), and synchrotron radiation X-ray imaging technology. The properties of the nanoenergetic hollow spherical derivatives, including thermal decomposition and sensitivity are discussed in detail. Sensitivity studies showed that the nanoenergetic derivatives exhibited lower impact, friction and spark sensitivity than raw HNS. Thermogravimetric-differential scanning calorimeter (TG-DSC) results showed that continuous exothermic decomposition occurred in the whole temperature range, which indicated that nanoenergetic derivatives have a unique role in thermal applications. Therefore, nanoenergetic hollow spherical HNS derivatives could provide a new way to modify the properties of certain energetic compounds and fabricate spherical nanomaterials to improve the charge configuration.

Short-lived chlorine-36 in a Ca- and Al-rich inclusion from the Ningqiang carbonaceous chondrite

PubMed Central

Lin, Yangting; Guan, Yunbin; Leshin, Laurie A.; Ouyang, Ziyuan; Wang, Daode

2005-01-01

Excesses of sulfur-36 in sodalite, a chlorine-rich mineral, in a calcium- and aluminum-rich inclusion from the Ningqiang carbonaceous chondrite linearly correlate with chorine/sulfur ratios, providing direct evidence for the presence of short-lived chlorine-36 (with a half-life of 0.3 million years) in the early solar system. The best inferred (36Cl/35Cl)o ratios of the sodalite are ≈5 × 10-6. Different from other short-lived radionuclides, chlorine-36 was introduced into the inclusion by solid-gas reaction during secondary alteration. The alteration reaction probably took place at least 1.5 million years after the first formation of the inclusion, based on the correlated study of the 26Al-26Mg systems of the relict primary minerals and the alteration assemblages, from which we inferred an initial ratio of (36Cl/35Cl)o > 1.6 × 10-4 at the time when calcium- and aluminum-rich inclusions formed. This discovery supports a supernova origin of short-lived nuclides [Cameron, A. G. W., Hoeflich, P., Myers, P. C. & Clayton, D. D. (1995) Astrophys. J. 447, L53; Wasserburg, G. J., Gallino, R. & Busso, M. (1998) Astrophys. J. 500, L189–L193], but presents a serious challenge for local irradiation models [Shu, F. H., Shang, H., Glassgold, A. E. & Lee, T. (1997) Science 277, 1475–1479; Gounelle, M., Shu, F. H., Shang, H., Glassgold, A. E., Rehm, K. E. & Lee, T. (2001) Astrophys. J. 548, 1051–1070]. Furthermore, the short-lived 36Cl may serve as a unique fine-scale chronometer for volatile-rock interaction in the early solar system because of its close association with aqueous and/or anhydrous alteration processes. PMID:15671168

Causal learning with local computations.

PubMed

Fernbach, Philip M; Sloman, Steven A

2009-05-01

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

Statistical inference for extended or shortened phase II studies based on Simon's two-stage designs.

PubMed

Zhao, Junjun; Yu, Menggang; Feng, Xi-Ping

2015-06-07

Simon's two-stage designs are popular choices for conducting phase II clinical trials, especially in the oncology trials to reduce the number of patients placed on ineffective experimental therapies. Recently Koyama and Chen (2008) discussed how to conduct proper inference for such studies because they found that inference procedures used with Simon's designs almost always ignore the actual sampling plan used. In particular, they proposed an inference method for studies when the actual second stage sample sizes differ from planned ones. We consider an alternative inference method based on likelihood ratio. In particular, we order permissible sample paths under Simon's two-stage designs using their corresponding conditional likelihood. In this way, we can calculate p-values using the common definition: the probability of obtaining a test statistic value at least as extreme as that observed under the null hypothesis. In addition to providing inference for a couple of scenarios where Koyama and Chen's method can be difficult to apply, the resulting estimate based on our method appears to have certain advantage in terms of inference properties in many numerical simulations. It generally led to smaller biases and narrower confidence intervals while maintaining similar coverages. We also illustrated the two methods in a real data setting. Inference procedures used with Simon's designs almost always ignore the actual sampling plan. Reported P-values, point estimates and confidence intervals for the response rate are not usually adjusted for the design's adaptiveness. Proper statistical inference procedures should be used.

Differentiable cortical networks for inferences concerning people's intentions versus physical causality.

PubMed

Mason, Robert A; Just, Marcel Adam

2011-02-01

Cortical activity associated with generating an inference was measured using fMRI. Participants read three-sentence passages that differed in whether or not an inference needed to be drawn to understand them. The inference was based on either a protagonist's intention or a physical consequence of a character's action. Activation was expected in Theory of Mind brain regions for the passages based on protagonists' intentions but not for the physical consequence passages. The activation measured in the right temporo-parietal junction was greater in the intentional passages than in the consequence passages, consistent with predictions from a Theory of Mind perspective. In contrast, there was increased occipital activation in the physical inference passages. For both types of passage, the cortical activity related to the reading of the critical inference sentence demonstrated a recruitment of a common inference cortical network. This general inference-related activation appeared bilaterally in the language processing areas (the inferior frontal gyrus, the temporal gyrus, and the angular gyrus), as well as in the medial to superior frontal gyrus, which has been found to be active in Theory of Mind tasks. These findings are consistent with the hypothesis that component areas of the discourse processing network are recruited as needed based on the nature of the inference. A Protagonist monitoring and synthesis network is proposed as a more accurate account for Theory of Mind activation during narrative comprehension. Copyright © 2010 Wiley-Liss, Inc.

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens Using Proteomic Data from a Field Biostimulation Experiment

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

Fang, Yilin; Wilkins, Michael J.; Yabusaki, Steven B.

2012-12-12

Biomass and shotgun global proteomics data that reflected relative protein abundances from samples collected during the 2008 experiment at the U.S. Department of Energy Integrated Field-Scale Subsurface Research Challenge site in Rifle, Colorado, provided an unprecedented opportunity to validate a genome-scale metabolic model of Geobacter metallireducens and assess its performance with respect to prediction of metal reduction, biomass yield, and growth rate under dynamic field conditions. Reconstructed from annotated genomic sequence, biochemical, and physiological data, the constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes.more » Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low fluxes through amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.« less

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.

Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Kalamangalam, Giridhar; Tandon, Nitin; Aazhang, Behnaam

2016-10-01

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. Directed information, an information theoretic quantity, is a general metric to infer causal connectivity between time-series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferred using model-based and data-driven DI estimators respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step towards developing novel non-surgical treatments for epilepsy.

In defence of model-based inference in phylogeography

PubMed Central

Beaumont, Mark A.; Nielsen, Rasmus; Robert, Christian; Hey, Jody; Gaggiotti, Oscar; Knowles, Lacey; Estoup, Arnaud; Panchal, Mahesh; Corander, Jukka; Hickerson, Mike; Sisson, Scott A.; Fagundes, Nelson; Chikhi, Lounès; Beerli, Peter; Vitalis, Renaud; Cornuet, Jean-Marie; Huelsenbeck, John; Foll, Matthieu; Yang, Ziheng; Rousset, Francois; Balding, David; Excoffier, Laurent

2017-01-01

Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics. PMID:29284924

On the transmission of partial information: inferences from movement-related brain potentials

NASA Technical Reports Server (NTRS)

Osman, A.; Bashore, T. R.; Coles, M. G.; Donchin, E.; Meyer, D. E.

1992-01-01

Results are reported from a new paradigm that uses movement-related brain potentials to detect response preparation based on partial information. The paradigm uses a hybrid choice-reaction go/nogo procedure in which decisions about response hand and whether to respond are based on separate stimulus attributes. A lateral asymmetry in the movement-related brain potential was found on nogo trials without overt movement. The direction of this asymmetry depended primarily on the signaled response hand rather than on properties of the stimulus. When the asymmetry first appeared was influenced by the time required to select the signaled hand, and when it began to differ on go and nogo trials was influenced by the time to decide whether to respond. These findings indicate that both stimulus attributes were processed in parallel and that the asymmetry reflected preparation of the response hand that began before the go/nogo decision was completed.

Improving Microbial Genome Annotations in an Integrated Database Context

PubMed Central

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; Anderson, Iain; Mavromatis, Konstantinos; Kyrpides, Nikos C.; Ivanova, Natalia N.

2013-01-01

Effective comparative analysis of microbial genomes requires a consistent and complete view of biological data. Consistency regards the biological coherence of annotations, while completeness regards the extent and coverage of functional characterization for genomes. We have developed tools that allow scientists to assess and improve the consistency and completeness of microbial genome annotations in the context of the Integrated Microbial Genomes (IMG) family of systems. All publicly available microbial genomes are characterized in IMG using different functional annotation and pathway resources, thus providing a comprehensive framework for identifying and resolving annotation discrepancies. A rule based system for predicting phenotypes in IMG provides a powerful mechanism for validating functional annotations, whereby the phenotypic traits of an organism are inferred based on the presence of certain metabolic reactions and pathways and compared to experimentally observed phenotypes. The IMG family of systems are available at http://img.jgi.doe.gov/. PMID:23424620

Laboratory Measurements for Deuterated Astrochemistry

NASA Astrophysics Data System (ADS)

Hillenbrand, Pierre-Michel; Bowen, Kyle Patrick; Miller, Kenneth A.; De Ruette, Nathalie; Urbain, Xavier; Savin, Daniel Wolf

2017-06-01

Deuterated molecules are powerful probes of the cold interstellar medium (ISM). Observations of D-bearing molecules are used to infer the chemistry of the ISM and to trace out physical conditions such as density, ionization fraction, and thermal history. The chemistry of the cold ISM results from a complicated interplay between gas-phase processes, reactions on dust grain surfaces, and chemistry occurring both in and on the icy mantles of dust grains. Our focus here is on an improved understanding of the relevant deuterated gas-phase chemistry. At the low temperatures and densities typical of the cold ISM, much of this chemistry is driven by binary ion-neutral reactions, which are typically barrierless and exoergic (as compared to neutral-neutral reactions which often have significant activation energies).One of the biggest challenges in generating a reliable deuterated gas-phase astrochemical network is the uncertainty of the necessary rate coefficients. The vast majority of available chemical kinetic data are for fully hydrogenated species. For those D-bearing reactions where no laboratory data are available, two approaches have been adopted for converting the fully hydrogenated data into partial- and fully-deuterated species. The first approach simply “clones” the H-bearing reactions into D-bearing reactions and assumes that the rate coefficients are the same. The second approach uses a simple mass scaling relationship based on the Langevin formalism.We have initiated a series of laboratory measurements aimed at resolving this issue. For this we use our novel dual-source, merged fast-beams apparatus, which enables us to study reactions of neutral atoms and charged molecules. Using co-propagating beams enables us to achieve collision energies corresponding to temperatures as low as 25 K, limited only by the divergences of the two beams. Recently we have measured the reaction C + H2+(D2+) forming CH+(CD+) + H(D). We are now studying D + H3+(D2H+) forming H2D+(D3+) + H. Here we report on these results and discuss their astrochemical implications.

Stepwise Catalytic Mechanism via Short-Lived Intermediate Inferred from Combined QM/MM MERP and PES Calculations on Retaining Glycosyltransferase ppGalNAcT2

PubMed Central

Trnka, Tomáš; Kozmon, Stanislav; Tvaroška, Igor; Koča, Jaroslav

2015-01-01

The glycosylation of cell surface proteins plays a crucial role in a multitude of biological processes, such as cell adhesion and recognition. To understand the process of protein glycosylation, the reaction mechanisms of the participating enzymes need to be known. However, the reaction mechanism of retaining glycosyltransferases has not yet been sufficiently explained. Here we investigated the catalytic mechanism of human isoform 2 of the retaining glycosyltransferase polypeptide UDP-GalNAc transferase by coupling two different QM/MM-based approaches, namely a potential energy surface scan in two distance difference dimensions and a minimum energy reaction path optimisation using the Nudged Elastic Band method. Potential energy scan studies often suffer from inadequate sampling of reactive processes due to a predefined scan coordinate system. At the same time, path optimisation methods enable the sampling of a virtually unlimited number of dimensions, but their results cannot be unambiguously interpreted without knowledge of the potential energy surface. By combining these methods, we have been able to eliminate the most significant sources of potential errors inherent to each of these approaches. The structural model is based on the crystal structure of human isoform 2. In the QM/MM method, the QM region consists of 275 atoms, the remaining 5776 atoms were in the MM region. We found that ppGalNAcT2 catalyzes a same-face nucleophilic substitution with internal return (SNi). The optimized transition state for the reaction is 13.8 kcal/mol higher in energy than the reactant while the energy of the product complex is 6.7 kcal/mol lower. During the process of nucleophilic attack, a proton is synchronously transferred to the leaving phosphate. The presence of a short-lived metastable oxocarbenium intermediate is likely, as indicated by the reaction energy profiles obtained using high-level density functionals. PMID:25849117

Stepwise catalytic mechanism via short-lived intermediate inferred from combined QM/MM MERP and PES calculations on retaining glycosyltransferase ppGalNAcT2.

PubMed

Trnka, Tomáš; Kozmon, Stanislav; Tvaroška, Igor; Koča, Jaroslav

2015-04-01

The glycosylation of cell surface proteins plays a crucial role in a multitude of biological processes, such as cell adhesion and recognition. To understand the process of protein glycosylation, the reaction mechanisms of the participating enzymes need to be known. However, the reaction mechanism of retaining glycosyltransferases has not yet been sufficiently explained. Here we investigated the catalytic mechanism of human isoform 2 of the retaining glycosyltransferase polypeptide UDP-GalNAc transferase by coupling two different QM/MM-based approaches, namely a potential energy surface scan in two distance difference dimensions and a minimum energy reaction path optimisation using the Nudged Elastic Band method. Potential energy scan studies often suffer from inadequate sampling of reactive processes due to a predefined scan coordinate system. At the same time, path optimisation methods enable the sampling of a virtually unlimited number of dimensions, but their results cannot be unambiguously interpreted without knowledge of the potential energy surface. By combining these methods, we have been able to eliminate the most significant sources of potential errors inherent to each of these approaches. The structural model is based on the crystal structure of human isoform 2. In the QM/MM method, the QM region consists of 275 atoms, the remaining 5776 atoms were in the MM region. We found that ppGalNAcT2 catalyzes a same-face nucleophilic substitution with internal return (SNi). The optimized transition state for the reaction is 13.8 kcal/mol higher in energy than the reactant while the energy of the product complex is 6.7 kcal/mol lower. During the process of nucleophilic attack, a proton is synchronously transferred to the leaving phosphate. The presence of a short-lived metastable oxocarbenium intermediate is likely, as indicated by the reaction energy profiles obtained using high-level density functionals.

A proposal for teaching undergraduate chemistry students carbohydrate biochemistry by problem-based learning activities.

PubMed

Figueira, Angela C M; Rocha, Joao B T

2014-01-01

This article presents a problem-based learning (PBL) approach to teaching elementary biochemistry to undergraduate students. The activity was based on "the foods we eat." It was used to engage students' curiosity and to initiate learning about a subject that could be used by the future teachers in the high school. The experimental activities (8-12 hours) were related to the questions: (i) what does the Benedict's Reagent detect? and (ii) What is determined by glucose oxidase (GOD)? We also ask the students to compare the results with those obtained with the Lugol reagent, which detects starch. Usually, students inferred that the Benedict reagent detects reducing sugars, while GOD could be used to detect glucose. However, in GOD assay, an open question was left, because the results could be due to contamination of the sugars (particularly galactose) with glucose. Though not stressed, GOD does not oxidize the carbohydrates tested and all the positive results are due to contamination. The activities presented here can be easily done in the high school, because they are simple and non-expensive. Furthermore, in the case of Benedict reaction, it is possible to follow the reduction of Cu (II) "macroscopically" by following the formation of the brick-orange precipitate. The concrete observation of a chemical reaction can motivate and facilitate students understanding about chemistry of life. Copyright © 2013 by The International Union of Biochemistry and Molecular Biology.

Affective theory of mind inferences contextually influence the recognition of emotional facial expressions.

PubMed

Stewart, Suzanne L K; Schepman, Astrid; Haigh, Matthew; McHugh, Rhian; Stewart, Andrew J

2018-03-14

The recognition of emotional facial expressions is often subject to contextual influence, particularly when the face and the context convey similar emotions. We investigated whether spontaneous, incidental affective theory of mind inferences made while reading vignettes describing social situations would produce context effects on the identification of same-valenced emotions (Experiment 1) as well as differently-valenced emotions (Experiment 2) conveyed by subsequently presented faces. Crucially, we found an effect of context on reaction times in both experiments while, in line with previous work, we found evidence for a context effect on accuracy only in Experiment 1. This demonstrates that affective theory of mind inferences made at the pragmatic level of a text can automatically, contextually influence the perceptual processing of emotional facial expressions in a separate task even when those emotions are of a distinctive valence. Thus, our novel findings suggest that language acts as a contextual influence to the recognition of emotional facial expressions for both same and different valences.

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

PubMed Central

Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

2012-01-01

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

Reasoning in explanation-based decision making.

PubMed

Pennington, N; Hastie, R

1993-01-01

A general theory of explanation-based decision making is outlined and the multiple roles of inference processes in the theory are indicated. A typology of formal and informal inference forms, originally proposed by Collins (1978a, 1978b), is introduced as an appropriate framework to represent inferences that occur in the overarching explanation-based process. Results from the analysis of verbal reports of decision processes are presented to demonstrate the centrality and systematic character of reasoning in a representative legal decision-making task.

Thermal ageing and short-range ordering of Alloy 690 between 350 and 550 °C

NASA Astrophysics Data System (ADS)

Mouginot, Roman; Sarikka, Teemu; Heikkilä, Mikko; Ivanchenko, Mykola; Ehrnstén, Ulla; Kim, Young Suk; Kim, Sung Soo; Hänninen, Hannu

2017-03-01

Thermal ageing of Alloy 690 triggers an intergranular (IG) carbide precipitation and is known to promote an ordering reaction causing lattice contraction. It may affect the long-term primary water stress corrosion cracking (PWSCC) resistance of pressurized water reactor (PWR) components. Four conditions of Alloy 690 (solution annealed, cold-rolled and/or heat-treated) were aged between 350 and 550 °C for 10 000 h and characterized. Although no direct observation of ordering was made, variations in hardness and lattice parameter were attributed to the formation of short-range ordering (SRO) in all conditions with a peak level at 420 °C, consistent with the literature. Prior heat treatment induced ordering before thermal ageing. At higher temperatures, stress relaxation, recrystallization and α-Cr precipitation were observed in the cold-worked samples, while a disordering reaction was inferred in all samples based on a decrease in hardness. IG precipitation of M23C6 carbides increased with increasing ageing temperature in all conditions, as well as diffusion-induced grain boundary migration (DIGM).

Mantle hydration along outer-rise faults inferred from serpentinite permeability.

PubMed

Hatakeyama, Kohei; Katayama, Ikuo; Hirauchi, Ken-Ichi; Michibayashi, Katsuyoshi

2017-10-24

Recent geophysical surveys indicate that hydration (serpentinization) of oceanic mantle is related to outer-rise faulting prior to subduction. The serpentinization of oceanic mantle influences the generation of intermediate-depth earthquakes and subduction water flux, thereby promoting arc volcanism. Since the chemical reactions that produce serpentinite are geologically rapid at low temperatures, the flux of water delivery to the reaction front appears to control the lateral extent of serpentinization. In this study, we measured the permeability of low-temperature serpentinites composed of lizardite and chrysotile, and calculated the lateral extent of serpentinization along an outer-rise fault based on Darcy's law. The experimental results indicate that serpentinization extends to a region several hundred meters wide in the direction normal to the outer-rise fault in the uppermost oceanic mantle. We calculated the global water flux carried by serpentinized oceanic mantle ranging from 1.7 × 10 11 to 2.4 × 10 12  kg/year, which is comparable or even higher than the water flux of hydrated oceanic crust.

Understanding Emotions in Context: The Effects of Language Impairment on Children's Ability to Infer Emotional Reactions

ERIC Educational Resources Information Center

Spackman, Matthew P.; Fujiki, Martin; Brinton, Bonnie

2006-01-01

Background: Research indicates children with language impairment (LI) may experience social deficits extending beyond those expected due to their language deficits. In particular, it has been found that children with LI have difficulty with various aspects of emotional competence. One aspect of emotional competence is emotion understanding, which…

Some Considerations on the Fundamentals of Chemical Kinetics: Steady State, Quasi-Equilibrium, and Transition State Theory

ERIC Educational Resources Information Center

Perez-Benito, Joaquin F.

2017-01-01

The elementary reaction sequence A ? I ? Products is the simplest mechanism for which the steady-state and quasi-equilibrium kinetic approximations can be applied. The exact integrated solutions for this chemical system allow inferring the conditions that must fulfill the rate constants for the different approximations to hold. A graphical…

Quantitative Analysis of Homogeneous Electrocatalytic Reactions at IDA Electrodes: The Example of [Ni(PPh2NBn2)2]2+

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

Liu, Fei; Parkinson, B. A.; Divan, Ralu

Interdigitated array (IDA) electrodes have been applied to study the EC’ (electron transfer reaction followed by a catalytic reaction) reactions and a new method of quantitative analysis of IDA results was developed. In this new method, currents on IDA generator and collector electrodes for an EC’ mechanism are derived from the number of redox cycles and the contribution of non-catalytic current. And the fractions of bipotential recycling species and catalytic-active species are calculated, which helps understanding the catalytic reaction mechanism. The homogeneous hydrogen evolution reaction catalyzed by [Ni(PPh2NBn2)2]2+ (where PPh2NBn2 is 1,5-dibenzyl-3,7-diphenyl-1,5-diaza-3,7-diphosphacyclooctane) electrocatalyst was examined and analyzed with IDA electrodes.more » Besides, the existence of reaction intermediates in the catalytic cycle is inferred from the electrochemical behavior of a glassy carbon disk electrodes and carbon IDA electrodes. This quantitative analysis of IDA electrode cyclic voltammetry currents can be used as a simple and straightforward method for determining reaction mechanism in other catalytic systems as well.« less

Functional analysis of (4 S)-limonene synthase mutants reveals determinants of catalytic outcome in a model monoterpene synthase

DOE PAGES

Srividya, Narayanan; Davis, Edward M.; Croteau, Rodney B.; ...

2015-03-02

We used crystal structural data for (4S)-limonene synthase [(4S)-LS] of spearmint (Mentha spicata L.) to infer which amino acid residues are in close proximity to the substrate and carbocation intermediates of the enzymatic reaction. Alanine-scanning mutagenesis of 48 amino acids combined with enzyme fidelity analysis [percentage of (-)-limonene produced] indicated which residues are most likely to constitute the active site. Furthermore, the mutation of residues W324 and H579 caused a significant drop in enzyme activity and formation of products (myrcene, linalool, and terpineol) characteristic of a premature termination of the reaction. A double mutant (W324A/H579A) had no detectable enzyme activity,more » indicating that either substrate binding or the terminating reaction was impaired. Exchanges to other aromatic residues (W324H, W324F, W324Y, H579F, H579Y, and H579W) resulted in enzyme catalysts with significantly reduced activity. Sequence comparisons across the angiosperm lineage provided evidence that W324 is a conserved residue, whereas the position equivalent to H579 is occupied by aromatic residues (H, F, or Y). Our results are consistent with a critical role of W324 and H579 in the stabilization of carbocation intermediates. Finally, the potential of these residues to serve as the catalytic base facilitating the terminal deprotonation reaction is discussed.« less

Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search

PubMed Central

Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier; Knapp, Merrill; Talcott, Carolyn

2017-01-01

In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing, which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation. PMID:28191459

Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search.

PubMed

Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier; Knapp, Merrill; Santos-García, Gustavo; Talcott, Carolyn

2017-01-01

In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing , which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation.

A System Computational Model of Implicit Emotional Learning

PubMed Central

Puviani, Luca; Rama, Sidita

2016-01-01

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation. PMID:27378898

A System Computational Model of Implicit Emotional Learning.

PubMed

Puviani, Luca; Rama, Sidita

2016-01-01

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.

Inferring Facts From Fiction: Reading Correct and Incorrect Information Affects Memory for Related Information

PubMed Central

Butler, Andrew C.; Dennis, Nancy A.; Marsh, Elizabeth J.

2012-01-01

People can acquire both true and false knowledge about the world from fictional stories (Marsh & Fazio, 2007). The present study explored whether the benefits and costs of learning about the world from fictional stories extend beyond memory for directly stated pieces of information. Of interest was whether readers would use correct and incorrect story references to make deductive inferences about related information in the story, and then integrate those inferences into their knowledge bases. Subjects read stories containing correct, neutral, and misleading references to facts about the world; each reference could be combined with another reference that occurred in a later sentence to make a deductive inference. Later, they answered general knowledge questions that tested for these deductive inferences. The results showed that subjects generated and retained the deductive inferences regardless of whether the inferences were consistent or inconsistent with world knowledge, and irrespective of whether the references were placed consecutively in the text or separated by many sentences. Readers learn more than what is directly stated in stories; they use references to the real world to make both correct and incorrect inferences that are integrated into their knowledge bases. PMID:22640369

Improvements and important considerations for the 5-choice serial reaction time task-An effective measurement of visual attention in rats.

PubMed

Bhandari, Jayant; Daya, Ritesh; Mishra, Ram K

2016-09-01

The 5-choice serial reaction time task (5-CSRTT) is an automated operant conditioning task that measures rodent attention. The task allows the measurement of several parameters such as response accuracy, speed of processing, motivation, and impulsivity. The task has been widely used to investigate attentional processes in rodents for attention deficit and hyperactivity disorder and has expanded to other illnesses such as Alzheimer's disease, depression, and schizophrenia. The 5-CSRTT is accompanied with two significant caveats: a time intensive training period and largely varied individual rat capability to learn and perform the task. Here we provide a regimented acquisition protocol to enhance training for the 5-CSRTT and discuss important considerations for researchers using the 5-CSRTT. We offer guidelines to ensure that inferences on performance in the 5-CSRTT are in fact a result of experimental manipulation rather than training differences, or individual animal capability. According to our findings only rats that have been trained successfully within a limited time frame should be used for the remainder of the study. Currently the 5-CSRTT employs a training period of variable duration and procedure, and its inferences on attention must overcome heterogeneous innate animal differences. The 5-CSRTT offers valuable and valid insights on various rodent attentional processes and their translation to the underpinnings of illnesses such as schizophrenia. The recommendations made here provide important criteria to ensure inferences made from this task are in fact relevant to the attentional processes being measured. Copyright © 2016 Elsevier B.V. All rights reserved.

DNA-Catalyzed DNA Cleavage by a Radical Pathway with Well-Defined Products.

PubMed

Lee, Yujeong; Klauser, Paul C; Brandsen, Benjamin M; Zhou, Cong; Li, Xinyi; Silverman, Scott K

2017-01-11

We describe an unprecedented DNA-catalyzed DNA cleavage process in which a radical-based reaction pathway cleanly results in excision of most atoms of a specific guanosine nucleoside. Two new deoxyribozymes (DNA enzymes) were identified by in vitro selection from N 40 or N 100 random pools initially seeking amide bond hydrolysis, although they both cleave simple single-stranded DNA oligonucleotides. Each deoxyribozyme generates both superoxide (O 2 -• or HOO • ) and hydrogen peroxide (H 2 O 2 ) and leads to the same set of products (3'-phosphoglycolate, 5'-phosphate, and base propenal) as formed by the natural product bleomycin, with product assignments by mass spectrometry and colorimetric assay. We infer the same mechanistic pathway, involving formation of the C4' radical of the guanosine nucleoside that is subsequently excised. Consistent with a radical pathway, glutathione fully suppresses catalysis. Conversely, adding either superoxide or H 2 O 2 from the outset strongly enhances catalysis. The mechanism of generation and involvement of superoxide and H 2 O 2 by the deoxyribozymes is not yet defined. The deoxyribozymes do not require redox-active metal ions and function with a combination of Zn 2+ and Mg 2+ , although including Mn 2+ increases the activity, and Mn 2+ alone also supports catalysis. In contrast to all of these observations, unrelated DNA-catalyzed radical DNA cleavage reactions require redox-active metals and lead to mixtures of products. This study reports an intriguing example of a well-defined, DNA-catalyzed, radical reaction process that cleaves single-stranded DNA and requires only redox-inactive metal ions.

Probing the Carbonyl Functionality of a Petroleum Resin and Asphaltene through Oximation and Schiff Base Formation in Conjunction with N-15 NMR

PubMed Central

Thorn, Kevin A.; Cox, Larry G.

2015-01-01

Despite recent advances in spectroscopic techniques, there is uncertainty regarding the nature of the carbonyl groups in the asphaltene and resin fractions of crude oil, information necessary for an understanding of the physical properties and environmental fate of these materials. Carbonyl and hydroxyl group functionalities are not observed in natural abundance 13C nuclear magnetic resonance (NMR) spectra of asphaltenes and resins and therefore require spin labeling techniques for detection. In this study, the carbonyl functionalities of the resin and asphaltene fractions from a light aliphatic crude oil that is the source of groundwater contamination at the long term USGS study site near Bemidji, Minnesota, have been examined through reaction with 15N-labeled hydroxylamine and aniline in conjunction with analysis by solid and liquid state 15N NMR. Ketone groups were revealed through 15N NMR detection of their oxime and Schiff base derivatives, and esters through their hydroxamic acid derivatives. Anilinohydroquinone adducts provided evidence for quinones. Some possible configurations of the ketone groups in the resin and asphaltene fractions can be inferred from a consideration of the likely reactions that lead to heterocyclic condensation products with aniline and to the Beckmann reaction products from the initially formed oximes. These include aromatic ketones and ketones adjacent to quaternary carbon centers, β-hydroxyketones, β-diketones, and β-ketoesters. In a solid state cross polarization/magic angle spinning (CP/MAS) 15N NMR spectrum recorded on the underivatized asphaltene as a control, carbazole and pyrrole-like nitrogens were the major naturally abundant nitrogens detected. PMID:26556054

Sensitivity to the Sampling Process Emerges From the Principle of Efficiency.

PubMed

Jara-Ettinger, Julian; Sun, Felix; Schulz, Laura; Tenenbaum, Joshua B

2018-05-01

Humans can seamlessly infer other people's preferences, based on what they do. Broadly, two types of accounts have been proposed to explain different aspects of this ability. The first account focuses on spatial information: Agents' efficient navigation in space reveals what they like. The second account focuses on statistical information: Uncommon choices reveal stronger preferences. Together, these two lines of research suggest that we have two distinct capacities for inferring preferences. Here we propose that this is not the case, and that spatial-based and statistical-based preference inferences can be explained by the assumption that agents are efficient alone. We show that people's sensitivity to spatial and statistical information when they infer preferences is best predicted by a computational model of the principle of efficiency, and that this model outperforms dual-system models, even when the latter are fit to participant judgments. Our results suggest that, as adults, a unified understanding of agency under the principle of efficiency underlies our ability to infer preferences. Copyright © 2018 Cognitive Science Society, Inc.

An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data.

PubMed

Mugzach, Omri; Peleg, Mor; Bagley, Steven C; Guter, Stephen J; Cook, Edwin H; Altman, Russ B

2015-08-01

Our goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects' Autism Diagnostic Interview-Revised (ADI-R) assessment data. Knowledge regarding diagnostic instruments, ASD phenotypes and risk factors was added to augment an existing autism ontology via Ontology Web Language class definitions and semantic web rules. We developed a custom Protégé plugin for enumerating combinatorial OWL axioms to support the many-to-many relations of ADI-R items to diagnostic categories in the DSM. We utilized a reasoner to infer whether 2642 subjects, whose data was obtained from the Simons Foundation Autism Research Initiative, meet DSM-IV-TR (DSM-IV) and DSM-5 diagnostic criteria based on their ADI-R data. We extended the ontology by adding 443 classes and 632 rules that represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. Applying the rules on the data set showed that the method produced accurate results: the true positive and true negative rates for inferring autistic disorder diagnosis according to DSM-IV criteria were 1 and 0.065, respectively; the true positive rate for inferring ASD based on DSM-5 criteria was 0.94. The ontology allows automatic inference of subjects' disease phenotypes and diagnosis with high accuracy. The ontology may benefit future studies by serving as a knowledge base for ASD. In addition, by adding knowledge of related NDDs, commonalities and differences in manifestations and risk factors could be automatically inferred, contributing to the understanding of ASD pathophysiology. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.

PubMed

Kim, Dongchul; Kang, Mingon; Biswas, Ashis; Liu, Chunyu; Gao, Jean

2016-08-10

Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator. We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above. In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

Approximation and inference methods for stochastic biochemical kinetics—a tutorial review

NASA Astrophysics Data System (ADS)

Schnoerr, David; Sanguinetti, Guido; Grima, Ramon

2017-03-01

Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the chemical master equation. Despite its simple structure, no analytic solutions to the chemical master equation are known for most systems. Moreover, stochastic simulations are computationally expensive, making systematic analysis and statistical inference a challenging task. Consequently, significant effort has been spent in recent decades on the development of efficient approximation and inference methods. This article gives an introduction to basic modelling concepts as well as an overview of state of the art methods. First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods. Next, we discuss several approximation methods, including the chemical Langevin equation, the system size expansion, moment closure approximations, time-scale separation approximations and hybrid methods. We discuss their various properties and review recent advances and remaining challenges for these methods. We present a comparison of several of these methods by means of a numerical case study and highlight some of their respective advantages and disadvantages. Finally, we discuss the problem of inference from experimental data in the Bayesian framework and review recent methods developed the literature. In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.

19 F(α,n) thick target yield from 3.5 to 10.0 MeV

DOE PAGES

Norman, E.B.; Chupp, T.E.; Lesko, K.T.; ...

2015-09-01

Using a target of PbF2, the thick-target yield from the 19F(α,n) reaction was measured from Eα=3.5–10 MeV. From these results, we infer the thick-target neutron yields from targets of F2 and UF6 over this same alpha-particle energy range.

Temperature derivatives for fusion reactivity of D-D and D-T

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

Langenbrunner, James R.; Makaruk, Hanna Ewa

Deuterium-tritium (D-T) and deuterium-deuterium (D-D) fusion reaction rates are observable using leakage gamma flux. A direct measurement of γ-rays with equipment that exhibits fast temporal response could be used to infer temperature, if the detector signal is amenable for taking the logarithmic time-derivative, alpha. We consider the temperature dependence for fusion cross section reactivity.

Using multiple secondary fusion products to evaluate fuel ρR, electron temperature, and mix in deuterium-filled implosions at the NIF

DOE PAGES

Rinderknecht, H. G.; Rosenberg, M. J.; Zylstra, A. B.; ...

2015-08-25

In deuterium-filled inertial confinement fusion implosions, the secondary fusion processes D( 3He,p) 4He and D(T,n) 4He occur, as the primary fusion products 3He and T react in flight with thermal deuterons. In implosions with moderate fuel areal density (~ 5–100 mg/cm 2), the secondary D- 3He reaction saturates, while the D-T reaction does not, and the combined information from these secondary products is used to constrain both the areal density and either the plasma electron temperature or changes in the composition due to mix of shell material into the fuel. The underlying theory of this technique is developed and appliedmore » to three classes of implosions on the National Ignition Facility: direct-drive exploding pushers, indirect-drive 1-shock and 2-shock implosions,and polar direct-drive implosions. In the 1- and 2-shock implosions, the electron temperature is inferred to be 0.65 x and 0.33 x the burn-averaged ion temperature, respectively. The inferred mixed mass in the polar direct-drive implosions is in agreement with measurements using alternative techniques.« less

Is awareness necessary for true inference?

PubMed

Leo, Peter D; Greene, Anthony J

2008-09-01

In transitive inference, participants learn a set of context-dependent discriminations that can be organized into a hierarchy that supports inference. Several studies show that inference occurs with or without task awareness. However, some studies assert that without awareness, performance is attributable to pseudoinference. By this account, inference-like performance is achieved by differential stimulus weighting according to the stimuli's proximity to the end items of the hierarchy. We implement an inference task that cannot be based on differential stimulus weighting. The design itself rules out pseudoinference strategies. Success on the task without evidence of deliberative strategies would therefore suggest that true inference can be achieved implicitly. We found that accurate performance on the inference task was not dependent on explicit awareness. The finding is consistent with a growing body of evidence that indicates that forms of learning and memory supporting inference and flexibility do not necessarily depend on task awareness.

Evolutionary relationships of flying foxes (genus Pteropus) in the Philippines inferred from DNA sequences of cytochrome b gene.

PubMed

Bastian, S T; Tanaka, K; Anunciado, R V P; Natural, N G; Sumalde, A C; Namikawa, T

2002-04-01

Six flying fox species, genus Pteropus (four from the Philippines) were investigated using complete cytochrome b gene sequences (1140 bp) to infer their evolutionary relationships. The DNA sequences generated via polymerase chain reaction were analyzed using the neighbor-joining, parsimony, and maximum likelihood methods. We estimated that the first evolutionary event among these Pteropus species occurred approximately 13.90 +/- 1.49 MYA. Within this short period of evolutionary time we further hypothesized that the ancestors of the flying foxes found in the Philippines experienced a subsequent diversification forming two clusters in the topology. The first cluster is composed of P. pumilus (Philippine endemic), P. speciosus (restricted in western Mindanao) with P. scapulatus, while the second one comprised P. vampyrus and P. dasymallus species based on the analysis from first and second codon positions. Consistently, all phylogenetic analyses divulged close association of P. dasymallus with P. vampyrus contradicting the previous report categorizing P. dasymallus under subniger species group with P. pumilus. P. speciosus, and P. hypomelanus. The Philippine endemic species (P. pumilus) is closely linked with P. speciosus. The representative samples of P. vampyrus showed a large genetic distance of 1.87%. The large genetic distance between P. dasymallus and P. hypomelanus, P. pumilus and P. speciosus denotes a distinct species group.

The Harm Done to Reproducibility by the Culture of Null Hypothesis Significance Testing.

PubMed

Lash, Timothy L

2017-09-15

In the last few years, stakeholders in the scientific community have raised alarms about a perceived lack of reproducibility of scientific results. In reaction, guidelines for journals have been promulgated and grant applicants have been asked to address the rigor and reproducibility of their proposed projects. Neither solution addresses a primary culprit, which is the culture of null hypothesis significance testing that dominates statistical analysis and inference. In an innovative research enterprise, selection of results for further evaluation based on null hypothesis significance testing is doomed to yield a low proportion of reproducible results and a high proportion of effects that are initially overestimated. In addition, the culture of null hypothesis significance testing discourages quantitative adjustments to account for systematic errors and quantitative incorporation of prior information. These strategies would otherwise improve reproducibility and have not been previously proposed in the widely cited literature on this topic. Without discarding the culture of null hypothesis significance testing and implementing these alternative methods for statistical analysis and inference, all other strategies for improving reproducibility will yield marginal gains at best. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Empathetic perspective-taking is impaired in schizophrenia: evidence from a study of emotion attribution and theory of mind.

PubMed

Langdon, Robyn; Coltheart, Max; Ward, Philip B

2006-03-01

Schizophrenia and autism are clinically distinct yet both disorders are characterised by theory of mind (ToM) deficits. Autistic individuals fail to appreciate false beliefs, yet understand the causal connections between behavioural events and simple emotions. Findings of this type have promoted the view that ToM deficits in autism reflect a domain-specific difficulty with appreciating the representational nature of epistemic mental states (i.e., beliefs and intentions and not emotions). This study examines whether the same holds true for schizophrenia. A picture-sequencing task assessed capacity to infer false beliefs in patients with schizophrenia and healthy controls. To assess emotion attribution, participants were shown cartoon strips of events likely to elicit strong emotional reactions in story characters. Characters' faces were blanked out. Participants were instructed to think about how the characters would be feeling in order to match up the cards depicting facial affect appropriately. Participants later named emotions depicted in facial affect cards. Patients were as capable as controls of identifying cartoon facial expressions, yet had greater difficulties with: (a) attributing emotions based on circumstances; and (b) inferring false beliefs. Schizophrenia patients, unlike autistic individuals, suffer a domain-general difficulty with empathetic perspective-taking that affects equally their appreciation of other people's beliefs, percepts, and emotions.

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.

PubMed

Fröhlich, Fabian; Thomas, Philipp; Kazeroonian, Atefeh; Theis, Fabian J; Grima, Ramon; Hasenauer, Jan

2016-07-01

Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity.

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

PubMed Central

Thomas, Philipp; Kazeroonian, Atefeh; Theis, Fabian J.; Grima, Ramon; Hasenauer, Jan

2016-01-01

Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity. PMID:27447730

Experimental constraints on the serpentinization rate of fore-arc peridotites: Implications for the upwelling condition of the slab-derived fluid

NASA Astrophysics Data System (ADS)

Nakatani, T.; Nakamura, M.

2016-08-01

To constrain the water circulation in subduction zones, the hydration rates of peridotites were investigated experimentally in fore-arc mantle conditions. Experiments were conducted at 400-580°C and 1.3 and 1.8 GPa, where antigorite is expected to form as a stable serpentine phase. Crushed powders of olivine ± orthopyroxene and orthopyroxene + clinopyroxene were reacted with 15 wt % distilled water for 4-19 days. The synthesized serpentine varieties were lizardite and aluminous lizardite (Al-lizardite) in all experimental conditions except those of 1.8 GPa and 580°C in the olivine + orthopyroxene system, in which antigorite was formed. In the olivine + orthopyroxene system, the reactions were interface-controlled except for the reaction at 400°C, which was transport-controlled. The corresponding reaction rates were 7.0 × 10-12 to 1.5 × 10-11 m s-1 at 500-580°C and 7.5 × 10-16 m2 s-1 at 400°C for the interface and transport-controlled reactions, respectively. Based on a simple reaction-transport model including these hydration rates, we infer that penetration of the slab-derived fluid all the way through a water-unsaturated fore-arc mantle is allowed only when focused flow occurs with a spacing larger than 77-229 km in hot subduction zones such as Nankai and Cascadia. However, the necessary spacing is only 2.3-4.6 m in intermediate-temperature subduction zones such as Kyushu and Costa Rica. These calculations imply that fluid leakage in hot subduction zones may occur after the fore-arc mantle is totally hydrated, whereas in intermediate-temperature subduction zones, leakage through a water-unsaturated fore-arc mantle may be facilitated.

Evaluation of a genome-scale in silico metabolic model for Geobacter metallireducens by using proteomic data from a field biostimulation experiment.

PubMed

Fang, Yilin; Wilkins, Michael J; Yabusaki, Steven B; Lipton, Mary S; Long, Philip E

2012-12-01

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens-specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

Causal learning and inference as a rational process: the new synthesis.

PubMed

Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

Generative Inferences Based on Learned Relations

ERIC Educational Resources Information Center

Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.

2017-01-01

A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

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.

Plasticity in probabilistic reaction norms for maturation in a salmonid fish.

PubMed

Morita, Kentaro; Tsuboi, Jun-ichi; Nagasawa, Toru

2009-10-23

The relationship between body size and the probability of maturing, often referred to as the probabilistic maturation reaction norm (PMRN), has been increasingly used to infer genetic variation in maturation schedule. Despite this trend, few studies have directly evaluated plasticity in the PMRN. A transplant experiment using white-spotted charr demonstrated that the PMRN for precocious males exhibited plasticity. A smaller threshold size at maturity occurred in charr inhabiting narrow streams where more refuges are probably available for small charr, which in turn might enhance the reproductive success of sneaker precocious males. Our findings suggested that plastic effects should clearly be included in investigations of variation in PMRNs.

Exploration of cellular DNA lesion, DNA-binding and biocidal ordeal of novel curcumin based Knoevenagel Schiff base complexes incorporating tryptophan: Synthesis and structural validation

NASA Astrophysics Data System (ADS)

Chandrasekar, Thiravidamani; Raman, Natarajan

2016-07-01

A few novel Schiff base transition metal complexes of general formula [MLCl] (where, L = Schiff base, obtained by the condensation reaction of Knoevenagel condensate of curcumin, L-tryptophan and M = Cu(II), Ni(II), Co(II), and Zn(II)), were prepared by stencil synthesis. They were typified using UV-vis, IR, EPR spectral techniques, micro analytical techniques, magnetic susceptibility and molar conductivity. Geometry of the metal complexes was examined and recognized as square planar. DNA binding and viscosity studies revealed that the metal(II) complexes powerfully bound via an intercalation mechanism with the calf thymus DNA. Gel-electrophoresis technique was used to investigate the DNA cleavage competence of the complexes and they establish to approve the cleavage of pBR322 DNA in presence of oxidant H2O2. This outcome inferred that the synthesized complexes showed better nuclease activity. Moreover, the complexes were monitored for antimicrobial activities. The results exposed that the synthesized compounds were forceful against all the microbes under exploration.

Geochemical constraints on sources of metabolic energy for chemolithoautotrophy in ultramafic-hosted deep-sea hydrothermal systems.

PubMed

McCollom, Thomas M

2007-12-01

Numerical models are employed to investigate sources of chemical energy for autotrophic microbial metabolism that develop during mixing of oxidized seawater with strongly reduced fluids discharged from ultramafic-hosted hydrothermal systems on the seafloor. Hydrothermal fluids in these systems are highly enriched in H(2) and CH(4) as a result of alteration of ultramafic rocks (serpentinization) in the subsurface. Based on the availability of chemical energy sources, inferences are made about the likely metabolic diversity, relative abundance, and spatial distribution of microorganisms within ultramafic-hosted systems. Metabolic reactions involving H(2) and CH(4), particularly hydrogen oxidation, methanotrophy, sulfate reduction, and methanogenesis, represent the predominant sources of chemical energy during fluid mixing. Owing to chemical gradients that develop from fluid mixing, aerobic metabolisms are likely to predominate in low-temperature environments (<20-30 degrees C), while anaerobes will dominate higher-temperature environments. Overall, aerobic metabolic reactions can supply up to approximately 7 kJ of energy per kilogram of hydrothermal fluid, while anaerobic metabolic reactions can supply about 1 kJ, which is sufficient to support a maximum of approximately 120 mg (dry weight) of primary biomass production by aerobic organisms and approximately 20-30 mg biomass by anaerobes. The results indicate that ultramafic-hosted systems are capable of supplying about twice as much chemical energy as analogous deep-sea hydrothermal systems hosted in basaltic rocks.

Principle-Based Inferences in Young Children's Categorization: Revisiting the Impact of Function on the Naming of Artifacts.

ERIC Educational Resources Information Center

Nelson, Deborah G. Kemler

1995-01-01

Three studies investigated the influence of principle-based inferences and unprincipled similarity relations on new category learning by three- to six-year-old children. Results indicated that categorization into newly learned categories may activate self-initiated, principle-based reasoning in young children, suggesting that spontaneous…

Statistical inference for remote sensing-based estimates of net deforestation

Treesearch

Ronald E. McRoberts; Brian F. Walters

2012-01-01

Statistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because...

Contextual Information and Verifying Inferences from Conversations.

ERIC Educational Resources Information Center

Dubitsky, Tony

Research was conducted to investigate the effects of contextual information on the speed and accuracy with which two general classes of inferences were verified by readers. These types of inferences were based on information in conversations that were or were not topically ambiguous, depending upon the amount of available contextual information.…

Optimal inference with suboptimal models: Addiction and active Bayesian inference

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

2015-01-01

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

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

Borg, D.C.; Schaich, K.M.; Forman, A.

Several laboratoreies contend that sometimes reducing free radicals reach directly with H/sub 2/O/sub 2/ to afford OH. in a metal-independent fashion, and others propose that often the strongly electrophilic reaction intermediate is either a metal-oxy complex or a crypto-hydroxyl radical (crypto-OH.) rather than OH., especially when lipid peroxidation is initiated. Our data imply that metal-independent OH. formation is not competitively significant in vivo and that adventitious metals probably were unrecognized in the reactions that prompted others to the contrary conclusion, while the confusing patterns of initiator and inhibitor reactivity that led to inferences of ferryl (or cupryl) intermediation or tomore » the concept of crypto-OH. are explicable by the extremely short reaction radius of OH., which we show can be formed in lipid milieux that are inaccessible to hydrophilic or macromolecular scavengers.« less

Catch and Release: Orbital Symmetry Guided Reaction Dynamics from a Freed “Tension Trapped Transition State”

DOE PAGES

Wang, Junpeng; Ong, Mitchell T.; Kouznetsova, Tatiana B.; ...

2015-08-31

The dynamics of reactions at or in the immediate vicinity of transition states are critical to reaction rates and product distributions, but direct experimental probes of those dynamics are rare. In this paper, s-trans, s-trans 1,3-diradicaloid transition states are trapped by tension along the backbone of purely cis-substituted gem-difluorocyclopropanated polybutadiene using the extensional forces generated by pulsed sonication of dilute polymer solutions. Once released, the branching ratio between symmetry-allowed disrotatory ring closing (of which the trapped diradicaloid structure is the transition state) and symmetry-forbidden conrotatory ring closing (whose transition state is nearby) can be inferred. Finally, net conrotatory ring closingmore » occurred in 5.0 ± 0.5% of the released transition states, in excellent agreement with ab initio molecular dynamics simulations.« less

Organic cofactors participated more frequently than transition metals in redox reactions of primitive proteins.

PubMed

Ji, Hong-Fang; Chen, Lei; Zhang, Hong-Yu

2008-08-01

Protein redox reactions are one of the most basic and important biochemical actions. As amino acids are weak redox mediators, most protein redox functions are undertaken by protein cofactors, which include organic ligands and transition metal ions. Since both kinds of redox cofactors were available in the pre-protein RNA world, it is challenging to explore which one was more involved in redox processes of primitive proteins? In this paper, using an examination of the redox cofactor usage of putative ancient proteins, we infer that organic ligands participated more frequently than transition metals in redox reactions of primitive proteins, at least as protein cofactors. This is further supported by the relative abundance of amino acids in the primordial world. Supplementary material for this article can be found on the BioEssays website. (c) 2008 Wiley Periodicals, Inc.

A reaction time advantage for calculating beliefs over public representations signals domain specificity for 'theory of mind'.

PubMed

Cohen, Adam S; German, Tamsin C

2010-06-01

In a task where participants' overt task was to track the location of an object across a sequence of events, reaction times to unpredictable probes requiring an inference about a social agent's beliefs about the location of that object were obtained. Reaction times to false belief situations were faster than responses about the (false) contents of a map showing the location of the object (Experiment 1) and about the (false) direction of an arrow signaling the location of the object (Experiment 2). These results are consistent with developmental, neuro-imaging and neuropsychological evidence that there exist domain specific mechanisms within human cognition for encoding and reasoning about mental states. Specialization of these mechanisms may arise from either core cognitive architecture or via the accumulation of expertise in the social domain.

The effect of turbulent kinetic energy on inferred ion temperature from neutron spectra

NASA Astrophysics Data System (ADS)

Murphy, T. J.

2014-07-01

Measuring the width of the energy spectrum of fusion-produced neutrons from deuterium (DD) or deuterium-tritium (DT) plasmas is a commonly used method for determining the ion temperature in inertial confinement fusion (ICF) implosions. In a plasma with a Maxwellian distribution of ion energies, the spread in neutron energy arises from the thermal spread in the center-of-mass velocities of reacting pairs of ions. Fluid velocities in ICF are of a similar magnitude as the center-of-mass velocities and can lead to further broadening of the neutron spectrum, leading to erroneous inference of ion temperature. Motion of the reacting plasma will affect DD and DT neutrons differently, leading to disagreement between ion temperatures inferred from the two reactions. This effect may be a contributor to observations over the past decades of ion temperatures higher than expected from simulations, ion temperatures in disagreement with observed yields, and different temperatures measured in the same implosion from DD and DT neutrons. This difference in broadening of DD and DT neutrons also provides a measure of turbulent motion in a fusion plasma.

Karl Pearson and eugenics: personal opinions and scientific rigor.

PubMed

Delzell, Darcie A P; Poliak, Cathy D

2013-09-01

The influence of personal opinions and biases on scientific conclusions is a threat to the advancement of knowledge. Expertise and experience does not render one immune to this temptation. In this work, one of the founding fathers of statistics, Karl Pearson, is used as an illustration of how even the most talented among us can produce misleading results when inferences are made without caution or reference to potential bias and other analysis limitations. A study performed by Pearson on British Jewish schoolchildren is examined in light of ethical and professional statistical practice. The methodology used and inferences made by Pearson and his coauthor are sometimes questionable and offer insight into how Pearson's support of eugenics and his own British nationalism could have potentially influenced his often careless and far-fetched inferences. A short background into Pearson's work and beliefs is provided, along with an in-depth examination of the authors' overall experimental design and statistical practices. In addition, portions of the study regarding intelligence and tuberculosis are discussed in more detail, along with historical reactions to their work.

Medical Rehabilitation Program: 3. Psychological Factors Related to Program Effectiveness

DTIC Science & Technology

1991-08-27

Donaldson’ Craig W. Bischoff Linda K. Hervig’ Cognitive Performance and Psychophysiology Department’ and Physiological Performance and Operational Medicine... Seligman , 1975). The inference that the development of a health problem is the starting point for psychological reactions that culminate in attrition is...mood questionnaire. Psychological Reports 35, 479-484. Seligman , M. E. P. (1975). Helplessness: On depression development and death. San Francisco: WH

Conflict: Operational Realism versus Analytical Rigor in Defense Modeling and Simulation

DTIC Science & Technology

2012-06-14

Campbell, Experimental and Quasi- Eperimental Designs for Generalized Causal Inference, Boston: Houghton Mifflin Company, 2002. [7] R. T. Johnson, G...experimentation? In order for an experiment to be considered rigorous, and the results valid, the experiment should be designed using established...addition to the interview, the pilots were administered a written survey, designed to capture their reactions regarding the level of realism present

Where the wild things are: informal experience and ecological reasoning.

PubMed

Coley, John D

2012-01-01

Category-based induction requires selective use of different relations to guide inferences; this article examines the development of inferences based on ecological relations among living things. Three hundred and forty-six 6-, 8-, and 10-year-old children from rural, suburban, and urban communities projected novel diseases or insides from one species to an ecologically or taxonomically related species; they were also surveyed about hobbies and activities. Frequency of ecological inferences increased with age and with reports of informal exploration of nature, and decreased with population density. By age 10, children preferred taxonomic inferences for insides and ecological inferences for disease, but this pattern emerged earlier among rural children. These results underscore the importance of context by demonstrating effects of both domain-relevant experience and environment on biological reasoning. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes.

PubMed

Schwartenbeck, Philipp; FitzGerald, Thomas H B; Mathys, Christoph; Dolan, Ray; Friston, Karl

2015-10-01

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing. © The Author 2014. Published by Oxford University Press.

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H. B.; Mathys, Christoph; Dolan, Ray; Friston, Karl

2015-01-01

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a “limited offer” game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing. PMID:25056572

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2008-01-01

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

Sowing the seeds of stereotypes: Spontaneous inferences about groups.

PubMed

Hamilton, David L; Chen, Jacqueline M; Ko, Deborah M; Winczewski, Lauren; Banerji, Ishani; Thurston, Joel A

2015-10-01

Although dispositional inferences may be consciously drawn from the trait implications of observed behavior, abundant research has shown that people also spontaneously infer trait dispositions simply in the process of comprehending behavior. These spontaneous trait inferences (STIs) can occur without intention or awareness. All research on STIs has studied STIs based on behaviors of individual persons. Yet important aspects of social life occur in groups, and people regularly perceive groups engaging in coordinated action. We propose that perceivers make spontaneous trait inferences about groups (STIGs), parallel to the STIs formed about individuals. In 5 experiments we showed that (a) perceivers made STIGs comparable with STIs about individuals (based on the same behaviors), (b) a cognitive load manipulation did not affect the occurrence of STIGs, (c) STIGs occurred for groups varying in entitativity, (d) STIGs influenced perceivers' impression ratings of those groups, and (e) STIG-based group impressions generalized to new group members. These experiments provide the first evidence for STIGs, a process that may contribute to the formation of spontaneous group impressions. Implications for stereotype formation are discussed. (c) 2015 APA, all rights reserved).

Direct Reactions at the Facility for Experiments on Nuclear Reactions in Stars (FENRIS)

NASA Astrophysics Data System (ADS)

Longland, Richard; Kelley, John; Marshall, Caleb; Portillo, Federico; Setoodehnia, Kiana

2017-09-01

Nuclear cross sections are a key ingredient in stellar models designed to understand how stars evolve. Determining these cross sections, therefore, is critical for obtaining reliable predictions from stellar models. While many charged-particle reaction cross sections can be measured in the laboratory, the Coulomb barrier means that they cannot always be measured at the low energies relevant to astrophysics. In other cases, radioactive targets make the measurements unfeasible. Radioactive ion beam experiments in inverse kinematics are one solution, but low beam intensities mean that cross sections plague these attempts further. Direct measurements, particularly particle transfer experiments, are one tool in our inventory that provides us with the necessary information to infer reaction cross sections at stellar energies. I will present an overview of one facility: the Facility for Experiments on Nuclear Reactions in Stars (FENRIS), which is dedicated to performing particle transfer measurements for astrophysical cross sections. Over the past few years, FENRIS has been fully upgraded and characterized. I will show highlights of our upgrade activities and current capabilities. I will also highlight our recent experimental results and discuss current upgrade efforts.

Reaction of the mussel Mytilus galloprovincialis (Bivalvia) to Eugymnanthea inquilina (Cnidaria) and Urastoma cyprinae (Turbellaria) concurrent infestation.

PubMed

Mladineo, Ivona; Petrić, Mirela; Hrabar, Jerko; Bočina, Ivana; Peharda, Melita

2012-05-01

In total 480 individuals of Mytilus galloprovincialis were sampled monthly from October 2009 to September 2010, at the shellfish farm in the Mali Ston Bay, south Adriatic Sea (Croatia) in order to assess the extent of pathology imposed by two parasites, Eugymnanthea inquilina (Cnidaria) and Urastoma cyprinae (Turbellaria). Although a deteriorating impact on host reproduction or condition index was lacking, we evidenced ultrastructural and functional alteration in host cells at the attachment site. Ultrastructural changes included hemocytic encapsulation of the turbellarian and cell desquamation in medusoid infestation. Caspase positive reaction inferred by immunohistochemistry (IHC) was triggered in cases of turbellarian infestation, in contrast with hydroids, suggesting that the former exhibits more complex host-parasite interaction, reflected in the persistent attempts of the parasite to survive bivalve reaction. We have evidenced that both organisms trigger specific host reaction that although not costly in terms of host reproductive cycle or growth, results in mild tissue destruction and hemocyte activation. A lower degree of tissue reaction was observed in cases of hydroid infestation, compared to turbellarian. Copyright © 2012 Elsevier Inc. All rights reserved.

Optimization of analytical parameters for inferring relationships among Escherichia coli isolates from repetitive-element PCR by maximizing correspondence with multilocus sequence typing data.

PubMed

Goldberg, Tony L; Gillespie, Thomas R; Singer, Randall S

2006-09-01

Repetitive-element PCR (rep-PCR) is a method for genotyping bacteria based on the selective amplification of repetitive genetic elements dispersed throughout bacterial chromosomes. The method has great potential for large-scale epidemiological studies because of its speed and simplicity; however, objective guidelines for inferring relationships among bacterial isolates from rep-PCR data are lacking. We used multilocus sequence typing (MLST) as a "gold standard" to optimize the analytical parameters for inferring relationships among Escherichia coli isolates from rep-PCR data. We chose 12 isolates from a large database to represent a wide range of pairwise genetic distances, based on the initial evaluation of their rep-PCR fingerprints. We conducted MLST with these same isolates and systematically varied the analytical parameters to maximize the correspondence between the relationships inferred from rep-PCR and those inferred from MLST. Methods that compared the shapes of densitometric profiles ("curve-based" methods) yielded consistently higher correspondence values between data types than did methods that calculated indices of similarity based on shared and different bands (maximum correspondences of 84.5% and 80.3%, respectively). Curve-based methods were also markedly more robust in accommodating variations in user-specified analytical parameter values than were "band-sharing coefficient" methods, and they enhanced the reproducibility of rep-PCR. Phylogenetic analyses of rep-PCR data yielded trees with high topological correspondence to trees based on MLST and high statistical support for major clades. These results indicate that rep-PCR yields accurate information for inferring relationships among E. coli isolates and that accuracy can be enhanced with the use of analytical methods that consider the shapes of densitometric profiles.

Inferring the distribution and demography of an invasive species from sighting data: the red fox incursion into Tasmania.

PubMed

Caley, Peter; Ramsey, David S L; Barry, Simon C

2015-01-01

A recent study has inferred that the red fox (Vulpes vulpes) is now widespread in Tasmania as of 2010, based on the extraction of fox DNA from predator scats. Heuristically, this inference appears at first glance to be at odds with the lack of recent confirmed discoveries of either road-killed foxes--the last of which occurred in 2006, or hunter killed foxes--the most recent in 2001. This paper demonstrates a method to codify this heuristic analysis and produce inferences consistent with assumptions and data. It does this by formalising the analysis in a transparent and repeatable manner to make inference on the past, present and future distribution of an invasive species. It utilizes Approximate Bayesian Computation to make inferences. Importantly, the method is able to inform management of invasive species within realistic time frames, and can be applied widely. We illustrate the technique using the Tasmanian fox data. Based on the pattern of carcass discoveries of foxes in Tasmania, we infer that the population of foxes in Tasmania is most likely extinct, or restricted in distribution and demographically weak as of 2013. It is possible, though unlikely, that that population is widespread and/or demographically robust. This inference is largely at odds with the inference from the predator scat survey data. Our results suggest the chances of successfully eradicating the introduced red fox population in Tasmania may be significantly higher than previously thought.

Inferring the Distribution and Demography of an Invasive Species from Sighting Data: The Red Fox Incursion into Tasmania

PubMed Central

Caley, Peter; Ramsey, David S. L.; Barry, Simon C.

2015-01-01

A recent study has inferred that the red fox (Vulpes vulpes) is now widespread in Tasmania as of 2010, based on the extraction of fox DNA from predator scats. Heuristically, this inference appears at first glance to be at odds with the lack of recent confirmed discoveries of either road-killed foxes—the last of which occurred in 2006, or hunter killed foxes—the most recent in 2001. This paper demonstrates a method to codify this heuristic analysis and produce inferences consistent with assumptions and data. It does this by formalising the analysis in a transparent and repeatable manner to make inference on the past, present and future distribution of an invasive species. It utilizes Approximate Bayesian Computation to make inferences. Importantly, the method is able to inform management of invasive species within realistic time frames, and can be applied widely. We illustrate the technique using the Tasmanian fox data. Based on the pattern of carcass discoveries of foxes in Tasmania, we infer that the population of foxes in Tasmania is most likely extinct, or restricted in distribution and demographically weak as of 2013. It is possible, though unlikely, that that population is widespread and/or demographically robust. This inference is largely at odds with the inference from the predator scat survey data. Our results suggest the chances of successfully eradicating the introduced red fox population in Tasmania may be significantly higher than previously thought. PMID:25602618

Feature inference with uncertain categorization: Re-assessing Anderson's rational model.

PubMed

Konovalova, Elizaveta; Le Mens, Gaël

2017-09-18

A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson's model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson's model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson's model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.

Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network.

PubMed

Chen, Xi; Wang, Qiao-Ling; Zhang, Meng-Hui

2017-10-01

The current study aimed to identify key genes in glaucoma based on a benchmarked dataset and gene regulatory network (GRN). Local and global noise was added to the gene expression dataset to produce a benchmarked dataset. Differentially-expressed genes (DEGs) between patients with glaucoma and normal controls were identified utilizing the Linear Models for Microarray Data (Limma) package based on benchmarked dataset. A total of 5 GRN inference methods, including Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT) and GEne Network Inference with Ensemble of Trees (Genie3) were evaluated using receiver operating characteristic (ROC) and precision and recall (PR) curves. The interference method with the best performance was selected to construct the GRN. Subsequently, topological centrality (degree, closeness and betweenness) was conducted to identify key genes in the GRN of glaucoma. Finally, the key genes were validated by performing reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 176 DEGs were detected from the benchmarked dataset. The ROC and PR curves of the 5 methods were analyzed and it was determined that Genie3 had a clear advantage over the other methods; thus, Genie3 was used to construct the GRN. Following topological centrality analysis, 14 key genes for glaucoma were identified, including IL6 , EPHA2 and GSTT1 and 5 of these 14 key genes were validated by RT-qPCR. Therefore, the current study identified 14 key genes in glaucoma, which may be potential biomarkers to use in the diagnosis of glaucoma and aid in identifying the molecular mechanism of this disease.

What matters? Assessing and developing inquiry and multivariable reasoning skills in high school chemistry

NASA Astrophysics Data System (ADS)

Daftedar Abdelhadi, Raghda Mohamed

Although the Next Generation Science Standards (NGSS) present a detailed set of Science and Engineering Practices, a finer grained representation of the underlying skills is lacking in the standards document. Therefore, it has been reported that teachers are facing challenges deciphering and effectively implementing the standards, especially with regards to the Practices. This analytical study assessed the development of high school chemistry students' (N = 41) inquiry, multivariable causal reasoning skills, and metacognition as a mediator for their development. Inquiry tasks based on concepts of element properties of the periodic table as well as reaction kinetics required students to conduct controlled thought experiments, make inferences, and declare predictions of the level of the outcome variable by coordinating the effects of multiple variables. An embedded mixed methods design was utilized for depth and breadth of understanding. Various sources of data were collected including students' written artifacts, audio recordings of in-depth observational groups and interviews. Data analysis was informed by a conceptual framework formulated around the concepts of coordinating theory and evidence, metacognition, and mental models of multivariable causal reasoning. Results of the study indicated positive change towards conducting controlled experimentation, making valid inferences and justifications. Additionally, significant positive correlation between metastrategic and metacognitive competencies, and sophistication of experimental strategies, signified the central role metacognition played. Finally, lack of consistency in indicating effective variables during the multivariable prediction task pointed towards the fragile mental models of multivariable causal reasoning the students had. Implications for teacher education, science education policy as well as classroom research methods are discussed. Finally, recommendations for developing reform-based chemistry curricula based on the Practices are presented.

A seroepidemiological survey for toxocariasis in apparently healthy residents in Gangwon-do, Korea

PubMed Central

Park, Hyun-Young; Lee, Soo-Ung; Kong, Yoon; Magnaval, Jean-François

2002-01-01

We investigated the sero-prevalence of toxocariasis among healthy Korean adults in 1999. A total of 314 sera from normal inhabitants in Whachon-gun, Gangwon-do, Korea was examined for specific antibody levels against excretory-secretory products of second stage larvae of Toxocara (TES). The presence of cross-reactions with other helminthiases such as cysticercosis, paragonimiasis, sparganosis or clonorchiasis was also checked by specific IgG ELISA. Sera showing positive reaction against TES were also tested by IgG immunoblot and by IgE ELISA. Out of 314 subjects, 16 was found to be positive by TES IgG ELISA and immunoblot, among whom 12 were also positive by TES IgE ELISA. Among the 16 seropositive samples, two sera showed positive reaction against Paragonimus and sparganum antigen, respectively. These results inferred that cross-reactions were negligible between toxocariasis and other helminthiases. Toxocariasis seroprevalence among Korean rural adults was detected to be approximately 5%. PMID:12325440

Time-Reversal Measurement of the p -Wave Cross Sections of the 7Be (n ,α )4He Reaction for the Cosmological Li Problem

NASA Astrophysics Data System (ADS)

Kawabata, T.; Fujikawa, Y.; Furuno, T.; Goto, T.; Hashimoto, T.; Ichikawa, M.; Itoh, M.; Iwasa, N.; Kanada-En'yo, Y.; Koshikawa, A.; Kubono, S.; Miyawaki, E.; Mizuno, M.; Mizutani, K.; Morimoto, T.; Murata, M.; Nanamura, T.; Nishimura, S.; Okamoto, S.; Sakaguchi, Y.; Sakata, I.; Sakaue, A.; Sawada, R.; Shikata, Y.; Takahashi, Y.; Takechi, D.; Takeda, T.; Takimoto, C.; Tsumura, M.; Watanabe, K.; Yoshida, S.

2017-02-01

The cross sections of the 7Be (n ,α )4He reaction for p -wave neutrons were experimentally determined at Ec .m .=0.20 - 0.81 MeV slightly above the big bang nucleosynthesis (BBN) energy window for the first time on the basis of the detailed balance principle by measuring the time-reverse reaction. The obtained cross sections are much larger than the cross sections for s -wave neutrons inferred from the recent measurement at the n_TOF facility in CERN, but significantly smaller than the theoretical estimation widely used in the BBN calculations. The present results suggest the 7Be (n ,α )4He reaction rate is not large enough to solve the cosmological lithium problem, and this conclusion agrees with the recent result from the direct measurement of the s -wave cross sections using a low-energy neutron beam and the evaluated nuclear data library ENDF/B-VII.1.

Chemical Production of Vibrationally Excited Carbon Monoxide from Carbon Vapor and Molecular Oxygen Precursors

NASA Astrophysics Data System (ADS)

Frederickson, Kraig; Musci, Ben; Rich, J. William; Adamovich, Igor

2015-09-01

Recent results demonstrating the formation of vibrationally excited carbon monoxide from carbon vapor and molecular oxygen will be presented. Previous reaction dynamics simulations and crossed molecular beam experiments have shown that gas-phase reaction of carbon atoms and molecular oxygen produces vibrationally excited carbon monoxide. The present work examines the product distribution of this reaction in a collision dominated environment, at a pressure of several Torr. Carbon vapor is produced in an AC arc discharge in argon buffer operated at a voltage of approximately 1 kV and current of 10 A, and mixed with molecular oxygen, which may also be excited by an auxiliary RF discharge, in a flowing chemical reactor. Identification of chemical reaction products and inference of their vibrational populations is performed by comparing infrared emission spectra of the flow in the reactor, taken by a Fourier Transform IR spectrometer, with synthetic spectra. Estimates of vibrationally excited carbon monoxide concentration and relative vibrational level populations will be presented.

Inference of gene regulatory networks from time series by Tsallis entropy

PubMed Central

2011-01-01

Background The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 ≤ q ≤ 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/. PMID:21545720

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

PubMed Central

Hurley, Daniel; Araki, Hiromitsu; Tamada, Yoshinori; Dunmore, Ben; Sanders, Deborah; Humphreys, Sally; Affara, Muna; Imoto, Seiya; Yasuda, Kaori; Tomiyasu, Yuki; Tashiro, Kosuke; Savoie, Christopher; Cho, Vicky; Smith, Stephen; Kuhara, Satoru; Miyano, Satoru; Charnock-Jones, D. Stephen; Crampin, Edmund J.; Print, Cristin G.

2012-01-01

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions. PMID:22121215

Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

PubMed

Guo, Xiaobo; Zhang, Ye; Hu, Wenhao; Tan, Haizhu; Wang, Xueqin

2014-01-01

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation

PubMed Central

Guo, Xiaobo; Zhang, Ye; Hu, Wenhao; Tan, Haizhu; Wang, Xueqin

2014-01-01

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference. PMID:24551058

Ancestral sequence reconstruction in primate mitochondrial DNA: compositional bias and effect on functional inference.

PubMed

Krishnan, Neeraja M; Seligmann, Hervé; Stewart, Caro-Beth; De Koning, A P Jason; Pollock, David D

2004-10-01

Reconstruction of ancestral DNA and amino acid sequences is an important means of inferring information about past evolutionary events. Such reconstructions suggest changes in molecular function and evolutionary processes over the course of evolution and are used to infer adaptation and convergence. Maximum likelihood (ML) is generally thought to provide relatively accurate reconstructed sequences compared to parsimony, but both methods lead to the inference of multiple directional changes in nucleotide frequencies in primate mitochondrial DNA (mtDNA). To better understand this surprising result, as well as to better understand how parsimony and ML differ, we constructed a series of computationally simple "conditional pathway" methods that differed in the number of substitutions allowed per site along each branch, and we also evaluated the entire Bayesian posterior frequency distribution of reconstructed ancestral states. We analyzed primate mitochondrial cytochrome b (Cyt-b) and cytochrome oxidase subunit I (COI) genes and found that ML reconstructs ancestral frequencies that are often more different from tip sequences than are parsimony reconstructions. In contrast, frequency reconstructions based on the posterior ensemble more closely resemble extant nucleotide frequencies. Simulations indicate that these differences in ancestral sequence inference are probably due to deterministic bias caused by high uncertainty in the optimization-based ancestral reconstruction methods (parsimony, ML, Bayesian maximum a posteriori). In contrast, ancestral nucleotide frequencies based on an average of the Bayesian set of credible ancestral sequences are much less biased. The methods involving simpler conditional pathway calculations have slightly reduced likelihood values compared to full likelihood calculations, but they can provide fairly unbiased nucleotide reconstructions and may be useful in more complex phylogenetic analyses than considered here due to their speed and flexibility. To determine whether biased reconstructions using optimization methods might affect inferences of functional properties, ancestral primate mitochondrial tRNA sequences were inferred and helix-forming propensities for conserved pairs were evaluated in silico. For ambiguously reconstructed nucleotides at sites with high base composition variability, ancestral tRNA sequences from Bayesian analyses were more compatible with canonical base pairing than were those inferred by other methods. Thus, nucleotide bias in reconstructed sequences apparently can lead to serious bias and inaccuracies in functional predictions.

Propensity Score-Based Methods versus MTE-Based Methods in Causal Inference: Identification, Estimation, and Application

ERIC Educational Resources Information Center

Zhou, Xiang; Xie, Yu

2016-01-01

Since the seminal introduction of the propensity score (PS) by Rosenbaum and Rubin, PS-based methods have been widely used for drawing causal inferences in the behavioral and social sciences. However, the PS approach depends on the ignorability assumption: there are no unobserved confounders once observed covariates are taken into account. For…

Design-based Sample and Probability Law-Assumed Sample: Their Role in Scientific Investigation.

ERIC Educational Resources Information Center

Ojeda, Mario Miguel; Sahai, Hardeo

2002-01-01

Discusses some key statistical concepts in probabilistic and non-probabilistic sampling to provide an overview for understanding the inference process. Suggests a statistical model constituting the basis of statistical inference and provides a brief review of the finite population descriptive inference and a quota sampling inferential theory.…

Image-Data Compression Using Edge-Optimizing Algorithm for WFA Inference.

ERIC Educational Resources Information Center

Culik, Karel II; Kari, Jarkko

1994-01-01

Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…

The Importance of Statistical Modeling in Data Analysis and Inference

ERIC Educational Resources Information Center

Rollins, Derrick, Sr.

2017-01-01

Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound…

Children's Understanding of Others' Emotional States: Inferences from Extralinguistic or Paralinguistic Cues?

ERIC Educational Resources Information Center

Gil, Sandrine; Aguert, Marc; Bigot, Ludovic Le; Lacroix, Agnès; Laval, Virginie

2014-01-01

The ability to infer the emotional states of others is central to our everyday interactions. These inferences can be drawn from several different sources of information occurring simultaneously in the communication situation. Based on previous studies revealing that children pay more heed to situational context than to emotional prosody when…

Inference Instruction to Support Reading Comprehension for Elementary Students with Learning Disabilities

ERIC Educational Resources Information Center

Hall, Colby; Barnes, Marcia A.

2017-01-01

Making inferences during reading is a critical standards-based skill and is important for reading comprehension. This article supports the improvement of reading comprehension for students with learning disabilities (LD) in upper elementary grades by reviewing what is currently known about inference instruction for students with LD and providing…

Detection of nonauthorized genetically modified organisms using differential quantitative polymerase chain reaction: application to 35S in maize.

PubMed

Cankar, Katarina; Chauvensy-Ancel, Valérie; Fortabat, Marie-Noelle; Gruden, Kristina; Kobilinsky, André; Zel, Jana; Bertheau, Yves

2008-05-15

Detection of nonauthorized genetically modified organisms (GMOs) has always presented an analytical challenge because the complete sequence data needed to detect them are generally unavailable although sequence similarity to known GMOs can be expected. A new approach, differential quantitative polymerase chain reaction (PCR), for detection of nonauthorized GMOs is presented here. This method is based on the presence of several common elements (e.g., promoter, genes of interest) in different GMOs. A statistical model was developed to study the difference between the number of molecules of such a common sequence and the number of molecules identifying the approved GMO (as determined by border-fragment-based PCR) and the donor organism of the common sequence. When this difference differs statistically from zero, the presence of a nonauthorized GMO can be inferred. The interest and scope of such an approach were tested on a case study of different proportions of genetically modified maize events, with the P35S promoter as the Cauliflower Mosaic Virus common sequence. The presence of a nonauthorized GMO was successfully detected in the mixtures analyzed and in the presence of (donor organism of P35S promoter). This method could be easily transposed to other common GMO sequences and other species and is applicable to other detection areas such as microbiology.

Spontaneous evaluative inferences and their relationship to spontaneous trait inferences.

PubMed

Schneid, Erica D; Carlston, Donal E; Skowronski, John J

2015-05-01

Three experiments are reported that explore affectively based spontaneous evaluative impressions (SEIs) of stimulus persons. Experiments 1 and 2 used modified versions of the savings in relearning paradigm (Carlston & Skowronski, 1994) to confirm the occurrence of SEIs, indicating that they are equivalent whether participants are instructed to form trait impressions, evaluative impressions, or neither. These experiments also show that SEIs occur independently of explicit recall for the trait implications of the stimuli. Experiment 3 provides a single dissociation test to distinguish SEIs from spontaneous trait inferences (STIs), showing that disrupting cognitive processing interferes with a trait-based prediction task that presumably reflects STIs, but not with an affectively based social approach task that presumably reflects SEIs. Implications of these findings for the potential independence of spontaneous trait and evaluative inferences, as well as limitations and important steps for future study are discussed. (c) 2015 APA, all rights reserved).

Object-related vs. narcissistic depression: a theoretical and clinical study.

PubMed

Glazer, M W

1979-01-01

This paper has focused on the sense of helplessness as an essential component of a depressive reaction. By inference, a sense of mastery and ability to achieve goals seems essential for a sense of well-being. Both patients presented here revealed infantile fantasies that hampered their exercising this mastery, and the path to well-being was the analysis of these fantasies. The treatment plans differed, though, in the locus of the fantasies. In an object-related depression such as Mr. Janson's, the fantasy involved the inhbition of functioning--that is, the inability to express aggression--and the treatment aimed at removing the inhibition. In a narcissistic depression such as Miss Gaynor's, the helplessness was not due to inhibited functioning per se. Rather, her goals were unrealistic, unattainable, and based on unconscious fantasies. Here the aim of treatment was the development of more reality-adapted and attainable objectives and the concommitant internalization of a more realistic sense of her own worth. Thus the common denominator in both depressive reactions was a sense of helplessness, and the path toward increased self-esteem was by way of the development of a sense of mastery and competence.

Invariantly propagating dissolution fingers in finite-width systems

NASA Astrophysics Data System (ADS)

Dutka, Filip; Szymczak, Piotr

2016-04-01

Dissolution fingers are formed in porous medium due to positive feedback between transport of reactant and chemical reactions [1-4]. We investigate two-dimensional semi-infinite systems, with constant width W in one direction. In numerical simulations we solve the Darcy flow problem combined with advection-dispersion-reaction equation for the solute transport to track the evolving shapes of the fingers and concentration of reactant in the system. We find the stationary, invariantly propagating finger shapes for different widths of the system, flow and reaction rates. Shape of the reaction front, turns out to be controlled by two dimensionless numbers - the (width-based) Péclet number PeW = vW/Dφ0 and Damköhler number DaW = ksW/v, where k is the reaction rate, s - specific reactive surface area, v - characteristic flow rate, D - diffusion coefficient of the solute, and φ0 - initial porosity of the rock matrix. Depending on PeW and DaW stationary shapes can be divided into seperate classes, e.g. parabolic-like and needle-like structures, which can be inferred from theoretical predictions. In addition we determine velocity of propagating fingers in time and concentration of reagent in the system. Our simulations are compared with natural forms (solution pipes). P. Ortoleva, J. Chadam, E. Merino, and A. Sen, Geochemical self-organization II: the reactive-infiltration instability, Am. J. Sci, 287, 1008-1040 (1987). M. L. Hoefner, and H. S. Fogler. Pore evolution and channel formation during flow and reaction in porous media, AIChE Journal 34, 45-54 (1988). C. E. Cohen, D. Ding, M. Quintard, and B. Bazin, From pore scale to wellbore scale: impact of geometry on wormhole growth in carbonate acidization, Chemical Engineering Science 63, 3088-3099 (2008). P. Szymczak and A. J. C. Ladd, Reactive-infiltration nstabilities in rocks. Part II: Dissolution of a porous matrix, J. Fluid Mech. 738, 591-630 (2014).

F-OWL: An Inference Engine for Semantic Web

NASA Technical Reports Server (NTRS)

Zou, Youyong; Finin, Tim; Chen, Harry

2004-01-01

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

Decision Support Systems for Launch and Range Operations Using Jess

NASA Technical Reports Server (NTRS)

Thirumalainambi, Rajkumar

2007-01-01

The virtual test bed for launch and range operations developed at NASA Ames Research Center consists of various independent expert systems advising on weather effects, toxic gas dispersions and human health risk assessment during space-flight operations. An individual dedicated server supports each expert system and the master system gather information from the dedicated servers to support the launch decision-making process. Since the test bed is based on the web system, reducing network traffic and optimizing the knowledge base is critical to its success of real-time or near real-time operations. Jess, a fast rule engine and powerful scripting environment developed at Sandia National Laboratory has been adopted to build the expert systems providing robustness and scalability. Jess also supports XML representation of knowledge base with forward and backward chaining inference mechanism. Facts added - to working memory during run-time operations facilitates analyses of multiple scenarios. Knowledge base can be distributed with one inference engine performing the inference process. This paper discusses details of the knowledge base and inference engine using Jess for a launch and range virtual test bed.

Spatiotemporal evolution of dehydration reactions in subduction zones (Invited)

NASA Astrophysics Data System (ADS)

Padron-Navarta, J.

2013-12-01

Large-scale deep water cycling takes place through subduction zones in the Earth, making our planet unique in the solar system. This idiosyncrasy is the result of a precise but unknown balance between in-gassing and out-gassing fluxes of volatiles. Water is incorporated into hydrous minerals during seafloor alteration of the oceanic lithosphere. The cycling of volatiles is triggered by dehydration of these minerals that release fluids from the subducting slab to the mantle wedge and eventually to the crust or to the deep mantle. Whereas the loci of such reactions are reasonably well established, the mechanisms of fluid migration during dehydration reactions are still barely known. One of the challenges is that dehydration reactions are dynamic features evolving in time and space. Experimental data on low-temperature dehydration reactions (i.e. gypsum) and numerical models applied to middle-crust conditions point to a complex spatiotemporal evolution of the dehydration process. The extrapolation of these inferences to subduction settings has not yet been explored but it is essential to understand the dynamism of these settings. Here I propose an alternative approach to tackle this problem through the textural study of high-pressure terrains that experienced dehydration reactions. Spatiotemporal evolution of dehydration reactions should be recorded during mineral nucleation and growth through variations in time and space of the reaction rate. Insights on the fluid migration mechanism could be inferred therefore by noting changes in the texture of prograde assemblages. The dehydration of antigorite in serpentinite is a perfect candidate to test this approach as it releases a significant amount of fluid and produces a concomitant porosity. Unusual alternation of equilibrium and disequilibrium textures observed in Cerro del Almirez (Betic Cordillera, S Spain)[1, 2] attest for a complex fluid migration pattern for one of the most relevant reactions in subduction zones. This opens the possibility to correlate textural features recorded in high-pressure terrains with the physical fingerprint of dehydration reactions such as fluid flow rates and eventually seismicity or tremor. References [1] Padrón-Navarta, J. A., Tommasi, A., Garrido, C. J., López Sánchez-Vizcaíno, V., Gómez-Pugnaire, M. T., Jabaloy, A. & Vauchez, A. (2010). Fluid transfer into the wedge controlled by high-pressure hydrofracturing in the cold top-slab mantle. Earth and Planetary Science Letters 297, 271-286. [2] Padrón-Navarta, J. A., López Sánchez-Vizcaíno, V., Garrido, C. J. & Gómez-Pugnaire, M. T. (2011). Metamorphic Record of High-pressure Dehydration of Antigorite Serpentinite to Chlorite Harzburgite in a Subduction Setting (Cerro del Almirez, Nevado-Filábride Complex, Southern Spain). Journal of Petrology 52, 2047-2078.

SWPhylo - A Novel Tool for Phylogenomic Inferences by Comparison of Oligonucleotide Patterns and Integration of Genome-Based and Gene-Based Phylogenetic Trees.

PubMed

Yu, Xiaoyu; Reva, Oleg N

2018-01-01

Modern phylogenetic studies may benefit from the analysis of complete genome sequences of various microorganisms. Evolutionary inferences based on genome-scale analysis are believed to be more accurate than the gene-based alternative. However, the computational complexity of current phylogenomic procedures, inappropriateness of standard phylogenetic tools to process genome-wide data, and lack of reliable substitution models which correlates with alignment-free phylogenomic approaches deter microbiologists from using these opportunities. For example, the super-matrix and super-tree approaches of phylogenomics use multiple integrated genomic loci or individual gene-based trees to infer an overall consensus tree. However, these approaches potentially multiply errors of gene annotation and sequence alignment not mentioning the computational complexity and laboriousness of the methods. In this article, we demonstrate that the annotation- and alignment-free comparison of genome-wide tetranucleotide frequencies, termed oligonucleotide usage patterns (OUPs), allowed a fast and reliable inference of phylogenetic trees. These were congruent to the corresponding whole genome super-matrix trees in terms of tree topology when compared with other known approaches including 16S ribosomal RNA and GyrA protein sequence comparison, complete genome-based MAUVE, and CVTree methods. A Web-based program to perform the alignment-free OUP-based phylogenomic inferences was implemented at http://swphylo.bi.up.ac.za/. Applicability of the tool was tested on different taxa from subspecies to intergeneric levels. Distinguishing between closely related taxonomic units may be enforced by providing the program with alignments of marker protein sequences, eg, GyrA.

SWPhylo – A Novel Tool for Phylogenomic Inferences by Comparison of Oligonucleotide Patterns and Integration of Genome-Based and Gene-Based Phylogenetic Trees

PubMed Central

Yu, Xiaoyu; Reva, Oleg N

2018-01-01

Modern phylogenetic studies may benefit from the analysis of complete genome sequences of various microorganisms. Evolutionary inferences based on genome-scale analysis are believed to be more accurate than the gene-based alternative. However, the computational complexity of current phylogenomic procedures, inappropriateness of standard phylogenetic tools to process genome-wide data, and lack of reliable substitution models which correlates with alignment-free phylogenomic approaches deter microbiologists from using these opportunities. For example, the super-matrix and super-tree approaches of phylogenomics use multiple integrated genomic loci or individual gene-based trees to infer an overall consensus tree. However, these approaches potentially multiply errors of gene annotation and sequence alignment not mentioning the computational complexity and laboriousness of the methods. In this article, we demonstrate that the annotation- and alignment-free comparison of genome-wide tetranucleotide frequencies, termed oligonucleotide usage patterns (OUPs), allowed a fast and reliable inference of phylogenetic trees. These were congruent to the corresponding whole genome super-matrix trees in terms of tree topology when compared with other known approaches including 16S ribosomal RNA and GyrA protein sequence comparison, complete genome-based MAUVE, and CVTree methods. A Web-based program to perform the alignment-free OUP-based phylogenomic inferences was implemented at http://swphylo.bi.up.ac.za/. Applicability of the tool was tested on different taxa from subspecies to intergeneric levels. Distinguishing between closely related taxonomic units may be enforced by providing the program with alignments of marker protein sequences, eg, GyrA. PMID:29511354

Cultural effects on the association between election outcomes and face-based trait inferences

PubMed Central

Adolphs, Ralph; Alvarez, R. Michael

2017-01-01

How competent a politician looks, as assessed in the laboratory, is correlated with whether the politician wins in real elections. This finding has led many to investigate whether the association between candidate appearances and election outcomes transcends cultures. However, these studies have largely focused on European countries and Caucasian candidates. To the best of our knowledge, there are only four cross-cultural studies that have directly investigated how face-based trait inferences correlate with election outcomes across Caucasian and Asian cultures. These prior studies have provided some initial evidence regarding cultural differences, but methodological problems and inconsistent findings have complicated our understanding of how culture mediates the effects of candidate appearances on election outcomes. Additionally, these four past studies have focused on positive traits, with a relative neglect of negative traits, resulting in an incomplete picture of how culture may impact a broader range of trait inferences. To study Caucasian-Asian cultural effects with a more balanced experimental design, and to explore a more complete profile of traits, here we compared how Caucasian and Korean participants’ inferences of positive and negative traits correlated with U.S. and Korean election outcomes. Contrary to previous reports, we found that inferences of competence (made by participants from both cultures) correlated with both U.S. and Korean election outcomes. Inferences of open-mindedness and threat, two traits neglected in previous cross-cultural studies, were correlated with Korean but not U.S. election outcomes. This differential effect was found in trait judgments made by both Caucasian and Korean participants. Interestingly, the faster the participants made face-based trait inferences, the more strongly those inferences were correlated with real election outcomes. These findings provide new insights into cultural effects and the difficult question of causality underlying the association between facial inferences and election outcomes. We also discuss the implications for political science and cognitive psychology. PMID:28700647

Cultural effects on the association between election outcomes and face-based trait inferences.

PubMed

Lin, Chujun; Adolphs, Ralph; Alvarez, R Michael

2017-01-01

How competent a politician looks, as assessed in the laboratory, is correlated with whether the politician wins in real elections. This finding has led many to investigate whether the association between candidate appearances and election outcomes transcends cultures. However, these studies have largely focused on European countries and Caucasian candidates. To the best of our knowledge, there are only four cross-cultural studies that have directly investigated how face-based trait inferences correlate with election outcomes across Caucasian and Asian cultures. These prior studies have provided some initial evidence regarding cultural differences, but methodological problems and inconsistent findings have complicated our understanding of how culture mediates the effects of candidate appearances on election outcomes. Additionally, these four past studies have focused on positive traits, with a relative neglect of negative traits, resulting in an incomplete picture of how culture may impact a broader range of trait inferences. To study Caucasian-Asian cultural effects with a more balanced experimental design, and to explore a more complete profile of traits, here we compared how Caucasian and Korean participants' inferences of positive and negative traits correlated with U.S. and Korean election outcomes. Contrary to previous reports, we found that inferences of competence (made by participants from both cultures) correlated with both U.S. and Korean election outcomes. Inferences of open-mindedness and threat, two traits neglected in previous cross-cultural studies, were correlated with Korean but not U.S. election outcomes. This differential effect was found in trait judgments made by both Caucasian and Korean participants. Interestingly, the faster the participants made face-based trait inferences, the more strongly those inferences were correlated with real election outcomes. These findings provide new insights into cultural effects and the difficult question of causality underlying the association between facial inferences and election outcomes. We also discuss the implications for political science and cognitive psychology.

Genie Inference Engine Rule Writer’s Guide.

DTIC Science & Technology

1987-08-01

33 APPENDIX D. Animal Bootstrap File.............................................................. 39...APPENDIX E. Sample Run of Animal Identification Expert System.......................... 43 APPENDIX F. Numeric Test Knowledge Base...and other data s.tructures stored in the knowledge base (KB), queries the user for input, and draws conclusions. Genie (GENeric Inference Engine) is

Raman and surface enhanced Raman scattering study of the orientation of cruciform 9,10-anthracene thiophene and furan derivatives deposited on a gold colloidal surface

NASA Astrophysics Data System (ADS)

Muñoz-Pérez, J.; Leyton, P.; Paipa, C.; Soto, J. P.; Brunet, J.; Gómez-Jeria, J. S.; Campos-Vallette, M. M.

2016-10-01

The 9,10-di(thiophen-2-yl)anthracene (TAT), 9,10-di(furan-2-yl)anthracene (FAF) and 2-[(10-(thiophen-2-yl)anthracen-9-yl)]furan (TAF) cruciform molecular systems were synthesized using one-step coupling reactions and structurally characterized via Raman, infrared, 1H NMR, 13C NMR and mass spectroscopies. The orientation of the analytes on a gold colloidal surface was inferred from a surface-enhanced Raman scattering (SERS) study. The metal surface interaction was driven by the S and O atoms of the thiophene and furan α-substituents, and the plane of the anthracene fragment remained parallel to the surface. Theoretical calculations based on a simplified molecular model for the analyte-surface interaction provide a good representation of the experimental data.

Reaction rates for mesoscopic reaction-diffusion kinetics

DOE PAGES

Hellander, Stefan; Hellander, Andreas; Petzold, Linda

2015-02-23

The mesoscopic reaction-diffusion master equation (RDME) is a popular modeling framework frequently applied to stochastic reaction-diffusion kinetics in systems biology. The RDME is derived from assumptions about the underlying physical properties of the system, and it may produce unphysical results for models where those assumptions fail. In that case, other more comprehensive models are better suited, such as hard-sphere Brownian dynamics (BD). Although the RDME is a model in its own right, and not inferred from any specific microscale model, it proves useful to attempt to approximate a microscale model by a specific choice of mesoscopic reaction rates. In thismore » paper we derive mesoscopic scale-dependent reaction rates by matching certain statistics of the RDME solution to statistics of the solution of a widely used microscopic BD model: the Smoluchowski model with a Robin boundary condition at the reaction radius of two molecules. We also establish fundamental limits on the range of mesh resolutions for which this approach yields accurate results and show both theoretically and in numerical examples that as we approach the lower fundamental limit, the mesoscopic dynamics approach the microscopic dynamics. Finally, we show that for mesh sizes below the fundamental lower limit, results are less accurate. Thus, the lower limit determines the mesh size for which we obtain the most accurate results.« less

Reaction rates for mesoscopic reaction-diffusion kinetics

PubMed Central

Hellander, Stefan; Hellander, Andreas; Petzold, Linda

2016-01-01

The mesoscopic reaction-diffusion master equation (RDME) is a popular modeling framework frequently applied to stochastic reaction-diffusion kinetics in systems biology. The RDME is derived from assumptions about the underlying physical properties of the system, and it may produce unphysical results for models where those assumptions fail. In that case, other more comprehensive models are better suited, such as hard-sphere Brownian dynamics (BD). Although the RDME is a model in its own right, and not inferred from any specific microscale model, it proves useful to attempt to approximate a microscale model by a specific choice of mesoscopic reaction rates. In this paper we derive mesoscopic scale-dependent reaction rates by matching certain statistics of the RDME solution to statistics of the solution of a widely used microscopic BD model: the Smoluchowski model with a Robin boundary condition at the reaction radius of two molecules. We also establish fundamental limits on the range of mesh resolutions for which this approach yields accurate results and show both theoretically and in numerical examples that as we approach the lower fundamental limit, the mesoscopic dynamics approach the microscopic dynamics. We show that for mesh sizes below the fundamental lower limit, results are less accurate. Thus, the lower limit determines the mesh size for which we obtain the most accurate results. PMID:25768640

Study on the interaction mechanism between aromatic amino acids and quercetin

NASA Astrophysics Data System (ADS)

Gou, Xingxing; Pu, Xiaohua; Li, Zongxiao

2017-11-01

In this paper, we selected quercetin and aromatic amino acids (tryptophan, tyrosine, phenylalanine) as the research objects to investigate the change rules in the reaction process. The thermodynamic functions (Ka, Δ G, and Δ S) of the interactions between quercetin and aromatic amino acids (tryptophan, tyrosine, phenylalanine) were measured by isothermal titration calorimetry. The values of binding constant (Ka) reached maximum at 25°C; the entropies and Gibbs free energies were both negative at different temperatures. The kinetic parameters of quercetin and amino acids in the interaction process was determined by microcalorimetry. The results inferred that the driving force of the reaction was hydrogen bond or van der Waals force.

Time-Resolved Molecular Characterization of Limonene/Ozone Aerosol using High-Resolution Electrospray Ionization Mass Spectrometry

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

Bateman, Adam P.; Nizkorodov, Serguei; Laskin, Julia

2009-09-09

Molecular composition of limonene/O3 secondary organic aerosol (SOA) was investigated using high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) as a function of reaction time. SOA was generated by ozonation of D-limonene in a reaction chamber and sampled at different time intervals using a cascade impactor. The SOA samples were extracted into acetonitrile and analyzed using a HR-ESI-MS instrument with a resolving power of 100,000 (m/Δm). The resulting mass spectra provided detailed information about the extent of oxidation inferred from the O:C ratios, double bond equivalency (DBE) factors, and aromaticity indexes (AI) in hundreds of identified individual SOA species.

Plasticity in probabilistic reaction norms for maturation in a salmonid fish

PubMed Central

Morita, Kentaro; Tsuboi, Jun-ichi; Nagasawa, Toru

2009-01-01

The relationship between body size and the probability of maturing, often referred to as the probabilistic maturation reaction norm (PMRN), has been increasingly used to infer genetic variation in maturation schedule. Despite this trend, few studies have directly evaluated plasticity in the PMRN. A transplant experiment using white-spotted charr demonstrated that the PMRN for precocious males exhibited plasticity. A smaller threshold size at maturity occurred in charr inhabiting narrow streams where more refuges are probably available for small charr, which in turn might enhance the reproductive success of sneaker precocious males. Our findings suggested that plastic effects should clearly be included in investigations of variation in PMRNs. PMID:19493875

Probing the carbonyl functionality of a petroleum resin and asphaltene through oximation and schiff base formation in conjunction with N-15 NMR

USGS Publications Warehouse

Thorn, Kevin A.; Cox, Larry G.

2015-01-01

Despite recent advances in spectroscopic techniques, there is uncertainty regarding the nature of the carbonyl groups in the asphaltene and resin fractions of crude oil, information necessary for an understanding of the physical properties and environmental fate of these materials. Carbonyl and hydroxyl group functionalities are not observed in natural abundance 13C nuclear magnetic resonance (NMR) spectra of asphaltenes and resins and therefore require spin labeling techniques for detection. In this study, the carbonyl functionalities of the resin and asphaltene fractions from a light aliphatic crude oil that is the source of groundwater contamination at the long term USGS study site near Bemidji, Minnesota, have been examined through reaction with 15N-labeled hydroxylamine and aniline in conjunction with analysis by solid and liquid state 15N NMR. Ketone groups were revealed through 15N NMR detection of their oxime and Schiff base derivatives, and esters through their hydroxamic acid derivatives. Anilinohydroquinone adducts provided evidence for quinones. Some possible configurations of the ketone groups in the resin and asphaltene fractions can be inferred from a consideration of the likely reactions that lead to heterocyclic condensation products with aniline and to the Beckmann reaction products from the initially formed oximes. These include aromatic ketones and ketones adjacent to quaternary carbon centers, β-hydroxyketones, β-diketones, and β-ketoesters. In a solid state cross polarization/magic angle spinning (CP/MAS) 15N NMR spectrum recorded on the underivatized asphaltene as a control, carbazole and pyrrole-like nitrogens were the major naturally abundant nitrogens detected.

Gyroscope-reduced inertial navigation system for flight vehicle motion estimation

NASA Astrophysics Data System (ADS)

Wang, Xin; Xiao, Lu

2017-01-01

In this paper, a novel configuration of strategically distributed accelerometer sensors with the aid of one gyro to infer a flight vehicle's angular motion is presented. The MEMS accelerometer and gyro sensors are integrated to form a gyroscope-reduced inertial measurement unit (GR-IMU). The motivation for gyro aided accelerometers array is to have direct measurements of angular rates, which is an improvement to the traditional gyroscope-free inertial system that employs only direct measurements of specific force. Some technical issues regarding error calibration in accelerometers and gyro in GR-IMU are put forward. The GR-IMU based inertial navigation system can be used to find a complete attitude solution for flight vehicle motion estimation. Results of numerical simulation are given to illustrate the effectiveness of the proposed configuration. The gyroscope-reduced inertial navigation system based on distributed accelerometer sensors can be developed into a cost effective solution for a fast reaction, MEMS based motion capture system. Future work will include the aid from external navigation references (e.g. GPS) to improve long time mission performance.

Pseudocontingencies and Choice Behavior in Probabilistic Environments with Context-Dependent Outcomes

ERIC Educational Resources Information Center

Meiser, Thorsten; Rummel, Jan; Fleig, Hanna

2018-01-01

Pseudocontingencies are inferences about correlations in the environment that are formed on the basis of statistical regularities like skewed base rates or varying base rates across environmental contexts. Previous research has demonstrated that pseudocontingencies provide a pervasive mechanism of inductive inference in numerous social judgment…

Preverbal Infants Infer Third-Party Social Relationships Based on Language

ERIC Educational Resources Information Center

Liberman, Zoe; Woodward, Amanda L.; Kinzler, Katherine D.

2017-01-01

Language provides rich social information about its speakers. For instance, adults and children make inferences about a speaker's social identity, geographic origins, and group membership based on her language and accent. Although infants prefer speakers of familiar languages (Kinzler, Dupoux, & Spelke, 2007), little is known about the…

Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training

ERIC Educational Resources Information Center

Baschera, Gian-Marco; Gross, Markus

2010-01-01

We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…

Cognitive Integrity Predicts Transitive Inference Performance Bias and Success

ERIC Educational Resources Information Center

Moses, Sandra N.; Villate, Christina; Binns, Malcolm A.; Davidson, Patrick S. R.; Ryan, Jennifer D.

2008-01-01

Transitive inference has traditionally been regarded as a relational proposition-based reasoning task, however, recent investigations question the validity of this assumption. Although some results support the use of a relational proposition-based approach, other studies find evidence for the use of associative learning. We examined whether…

Inference and the Introductory Statistics Course

ERIC Educational Resources Information Center

Pfannkuch, Maxine; Regan, Matt; Wild, Chris; Budgett, Stephanie; Forbes, Sharleen; Harraway, John; Parsonage, Ross

2011-01-01

This article sets out some of the rationale and arguments for making major changes to the teaching and learning of statistical inference in introductory courses at our universities by changing from a norm-based, mathematical approach to more conceptually accessible computer-based approaches. The core problem of the inferential argument with its…

Inference Generation during Text Comprehension by Adults with Right Hemisphere Brain Damage: Activation Failure Versus Multiple Activation.

ERIC Educational Resources Information Center

Tompkins, Connie A.; Fassbinder, Wiltrud; Blake, Margaret Lehman; Baumgaertner, Annette; Jayaram, Nandini

2004-01-01

ourse comprehensionEvidence conflicts as to whether adults with right hemisphere brain damage (RHD) generate inferences during text comprehension. M. Beeman (1993) reported that adults with RHD fail to activate the lexical-semantic bases of routine bridging inferences, which are necessary for comprehension. But other evidence indicates that adults…

Theory of Mind: An Overview and Behavioral Perspective

ERIC Educational Resources Information Center

Schlinger, Henry D., Jr.

2009-01-01

Theory of mind (ToM) refers to the ability of an individual to make inferences about what others may be thinking or feeling and to predict what they may do in a given situation based on those inferences. Discussions of ToM focus almost exclusively on inferred cognitive structures and processes and shed little light on the actual behaviors…

Research Methods in Child Disaster Studies: A Review of Studies Generated by the September 11, 2001, Terrorist Attacks; the 2004 Indian Ocean Tsunami; and Hurricane Katrina

ERIC Educational Resources Information Center

Pfefferbaum, Betty; Weems, Carl F.; Scott, Brandon G.; Nitiéma, Pascal; Noffsinger, Mary A.; Pfefferbaum, Rose L.; Varma, Vandana; Chakraburtty, Amarsha

2013-01-01

Background: A comprehensive review of the design principles and methodological approaches that have been used to make inferences from the research on disasters in children is needed. Objective: To identify the methodological approaches used to study children's reactions to three recent major disasters--the September 11, 2001, attacks; the…

How Pragmatic Interpretations Arise from Conditionals: Profiling the Affirmation of the Consequent Argument with Reaction Time and EEG Measures

ERIC Educational Resources Information Center

Bonnefond, Mathilde; Van der Henst, Jean-Baptiste; Gougain, Marion; Robic, Suzanne; Olsen, Matthew D.; Weiss, Oshri; Noveck, Ira

2012-01-01

Conditional reasoning consists in combining a conditional premise with a categorical premise and inferring a conclusion from them. Two well-known conditional arguments are Modus Ponens (MP: "If P then Q; P//"therefore Q), which is logically valid and Affirmation of the Consequent (AC: "If P then Q; Q//"therefore "P"), which is not. The latter is…

Evaluation of Neutron-induced Cross Sections and their Related Covariances with Physical Constraints

NASA Astrophysics Data System (ADS)

De Saint Jean, C.; Archier, P.; Privas, E.; Noguère, G.; Habert, B.; Tamagno, P.

2018-02-01

Nuclear data, along with numerical methods and the associated calculation schemes, continue to play a key role in reactor design, reactor core operating parameters calculations, fuel cycle management and criticality safety calculations. Due to the intensive use of Monte-Carlo calculations reducing numerical biases, the final accuracy of neutronic calculations increasingly depends on the quality of nuclear data used. This paper gives a broad picture of all ingredients treated by nuclear data evaluators during their analyses. After giving an introduction to nuclear data evaluation, we present implications of using the Bayesian inference to obtain evaluated cross sections and related uncertainties. In particular, a focus is made on systematic uncertainties appearing in the analysis of differential measurements as well as advantages and drawbacks one may encounter by analyzing integral experiments. The evaluation work is in general done independently in the resonance and in the continuum energy ranges giving rise to inconsistencies in evaluated files. For future evaluations on the whole energy range, we call attention to two innovative methods used to analyze several nuclear reaction models and impose constraints. Finally, we discuss suggestions for possible improvements in the evaluation process to master the quantification of uncertainties. These are associated with experiments (microscopic and integral), nuclear reaction theories and the Bayesian inference.

Can People Guess What Happened to Others from Their Reactions?

PubMed Central

Pillai, Dhanya; Sheppard, Elizabeth; Mitchell, Peter

2012-01-01

Are we able to infer what happened to a person from a brief sample of his/her behaviour? It has been proposed that mentalising skills can be used to retrodict as well as predict behaviour, that is, to determine what mental states of a target have already occurred. The current study aimed to develop a paradigm to explore these processes, which takes into account the intricacies of real-life situations in which reasoning about mental states, as embodied in behaviour, may be utilised. A novel task was devised which involved observing subtle and naturalistic reactions of others in order to determine the event that had previously taken place. Thirty-five participants viewed videos of real individuals reacting to the researcher behaving in one of four possible ways, and were asked to judge which of the four ‘scenarios’ they thought the individual was responding to. Their eye movements were recorded to establish the visual strategies used. Participants were able to deduce successfully from a small sample of behaviour which scenario had previously occurred. Surprisingly, looking at the eye region was associated with poorer identification of the scenarios, and eye movement strategy varied depending on the event experienced by the person in the video. This suggests people flexibly deploy their attention using a retrodictive mindreading process to infer events. PMID:23226227

Clinopyroxene precursors to amphibole sponge in arc crust

PubMed Central

Smith, Daniel J.

2014-01-01

The formation of amphibole cumulates beneath arc volcanoes is a key control on magma geochemistry, and generates a hydrous lower crust. Despite being widely inferred from trace element geochemistry as a major lower crustal phase, amphibole is neither abundant nor common as a phenocryst phase in arc lavas and erupted pyroclasts, prompting some authors to refer to it as a ‘cryptic’ fractionating phase. This study provides evidence that amphibole develops by evolved melts overprinting earlier clinopyroxene—a near-ubiquitous mineral in arc magmas. Reaction-replacement of clinopyroxene ultimately forms granoblastic amphibole lithologies. Reaction-replacement amphiboles have more primitive trace element chemistry (for example, lower concentrations of incompatible Pb) than amphibole phenocrysts, but still have chemistries suitable for producing La/Yb and Dy/Yb ‘amphibole sponge’ signatures. Amphibole can fractionate cryptically as reactions between melt and mush in lower crustal ‘hot zones’ produce amphibole-rich assemblages, without significant nucleation and growth of amphibole phenocrysts. PMID:25002269

Sleep-deprivation effect on human performance: a meta-analysis approach

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

Candice D. Griffith; Candice D. Griffith; Sankaran Mahadevan

Human fatigue is hard to define since there is no direct measure of fatigue, much like stress. Instead fatigue must be inferred from measures that are affected by fatigue. One such measurable output affected by fatigue is reaction time. In this study the relationship of reaction time to sleep deprivation is studied. These variables were selected because reaction time and hours of sleep deprivation are straightforward characteristics of fatigue to begin the investigation of fatigue effects on performance. Meta-analysis, a widely used procedure in medical and psychological studies, is applied to the variety of fatigue literature collected from various fieldsmore » in this study. Meta-analysis establishes a procedure for coding and analyzing information from various studies to compute an effect size. In this research the effect size reported is the difference between standardized means, and is found to be -0.6341, implying a strong relationship between sleep deprivation and performance degradation.« less

Measurements of the O+ plus N2 and O+ plus O2 reaction rates from 300 to 900 K

NASA Technical Reports Server (NTRS)

Chen, A.; Johnsen, R.; Biondi, M. A.

1977-01-01

Rate coefficients for the O(+) + N2 atom transfer and O(+) + O2 charge transfer reactions are determined at thermal energies between 300 K and 900 K difference in a heated drift tube mass spectrometer apparatus. At 300 K the values K(O(+) + N2) = (1.2 plus or minus 0.1) x 10 to the negative 12 power cubic cm/sec and k(O(+) + O2) = (2.1 plus or minus 0.2) x 10 to the negative 11 power cubic cm/sec were obtained, with a 50% difference decrease in the reaction rates upon heating to 700 K. These results are in good agreement with heated flowing afterglow results, but the O(+) + O2 thermal rate coefficients are systematically lower than equivalent Maxwellian rates inferred by conversion of nonthermal drift tube and flow drift data.

Inference of Expanded Lrp-Like Feast/Famine Transcription Factor Targets in a Non-Model Organism Using Protein Structure-Based Prediction

PubMed Central

Ashworth, Justin; Plaisier, Christopher L.; Lo, Fang Yin; Reiss, David J.; Baliga, Nitin S.

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer. PMID:25255272

Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction.

PubMed

Ashworth, Justin; Plaisier, Christopher L; Lo, Fang Yin; Reiss, David J; Baliga, Nitin S

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.

Fast-ion distributions from third harmonic ICRF heating studied with neutron emission spectroscopy

NASA Astrophysics Data System (ADS)

Hellesen, C.; Gatu Johnson, M.; Andersson Sundén, E.; Conroy, S.; Ericsson, G.; Eriksson, J.; Sjöstrand, H.; Weiszflog, M.; Johnson, T.; Gorini, G.; Nocente, M.; Tardocchi, M.; Kiptily, V. G.; Pinches, S. D.; Sharapov, S. E.; EFDA Contributors, JET

2013-11-01

The fast-ion distribution from third harmonic ion cyclotron resonance frequency (ICRF) heating on the Joint European Torus is studied using neutron emission spectroscopy with the time-of-flight spectrometer TOFOR. The energy dependence of the fast deuteron distribution function is inferred from the measured spectrum of neutrons born in DD fusion reactions, and the inferred distribution is compared with theoretical models for ICRF heating. Good agreements between modelling and measurements are seen with clear features in the fast-ion distribution function, that are due to the finite Larmor radius of the resonating ions, replicated. Strong synergetic effects between ICRF and neutral beam injection heating were also seen. The total energy content of the fast-ion population derived from TOFOR data was in good agreement with magnetic measurements for values below 350 kJ.

Experimental signatures of suprathermal ion distribution in inertial confinement fusion implosions

NASA Astrophysics Data System (ADS)

Kagan, Grigory; Svyatskiy, Daniil; Rinderknecht, Hans; Rosenberg, Michael; Zylstra, Alex; Huang, Cheng-Kun; McDevitt, Christopher

2015-11-01

The distribution function of suprathermal ions is found to be self-similar under conditions relevant to inertial confinement fusion hot-spots. By utilizing this feature, interference between the hydro-instabilities and kinetic effects is for the first time assessed quantitatively to find that the instabilities substantially aggravate the fusion reactivity reduction. The ion tail depletion is also shown to lower the experimentally inferred ion temperature, a novel kinetic effect that may explain the discrepancy between the exploding pusher experiments and rad-hydro simulations and contribute to the observation that temperature inferred from DD reaction products is lower than from DT at National Ignition Facility. This work is performed under the auspices of the U.S. Dept. of Energy by the Los Alamos National Security, LLC, Los Alamos National Laboratory under Contract No. DE-AC52-06NA25396.

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.

PubMed

Wright, Adam; Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

Implementing and analyzing the multi-threaded LP-inference

NASA Astrophysics Data System (ADS)

Bolotova, S. Yu; Trofimenko, E. V.; Leschinskaya, M. V.

2018-03-01

The logical production equations provide new possibilities for the backward inference optimization in intelligent production-type systems. The strategy of a relevant backward inference is aimed at minimization of a number of queries to external information source (either to a database or an interactive user). The idea of the method is based on the computing of initial preimages set and searching for the true preimage. The execution of each stage can be organized independently and in parallel and the actual work at a given stage can also be distributed between parallel computers. This paper is devoted to the parallel algorithms of the relevant inference based on the advanced scheme of the parallel computations “pipeline” which allows to increase the degree of parallelism. The author also provides some details of the LP-structures implementation.

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.

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

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.

Terminal-Area Aircraft Intent Inference Approach Based on Online Trajectory Clustering.

PubMed

Yang, Yang; Zhang, Jun; Cai, Kai-quan

2015-01-01

Terminal-area aircraft intent inference (T-AII) is a prerequisite to detect and avoid potential aircraft conflict in the terminal airspace. T-AII challenges the state-of-the-art AII approaches due to the uncertainties of air traffic situation, in particular due to the undefined flight routes and frequent maneuvers. In this paper, a novel T-AII approach is introduced to address the limitations by solving the problem with two steps that are intent modeling and intent inference. In the modeling step, an online trajectory clustering procedure is designed for recognizing the real-time available routes in replacing of the missed plan routes. In the inference step, we then present a probabilistic T-AII approach based on the multiple flight attributes to improve the inference performance in maneuvering scenarios. The proposed approach is validated with real radar trajectory and flight attributes data of 34 days collected from Chengdu terminal area in China. Preliminary results show the efficacy of the presented approach.

Genealogical and evolutionary inference with the human Y chromosome.

PubMed

Stumpf, M P; Goldstein, D B

2001-03-02

Population genetics has emerged as a powerful tool for unraveling human history. In addition to the study of mitochondrial and autosomal DNA, attention has recently focused on Y-chromosome variation. Ambiguities and inaccuracies in data analysis, however, pose an important obstacle to further development of the field. Here we review the methods available for genealogical inference using Y-chromosome data. Approaches can be divided into those that do and those that do not use an explicit population model in genealogical inference. We describe the strengths and weaknesses of these model-based and model-free approaches, as well as difficulties associated with the mutation process that affect both methods. In the case of genealogical inference using microsatellite loci, we use coalescent simulations to show that relatively simple generalizations of the mutation process can greatly increase the accuracy of genealogical inference. Because model-free and model-based approaches have different biases and limitations, we conclude that there is considerable benefit in the continued use of both types of approaches.

Faster Mass Spectrometry-based Protein Inference: Junction Trees are More Efficient than Sampling and Marginalization by Enumeration

PubMed Central

Serang, Oliver; Noble, William Stafford

2012-01-01

The problem of identifying the proteins in a complex mixture using tandem mass spectrometry can be framed as an inference problem on a graph that connects peptides to proteins. Several existing protein identification methods make use of statistical inference methods for graphical models, including expectation maximization, Markov chain Monte Carlo, and full marginalization coupled with approximation heuristics. We show that, for this problem, the majority of the cost of inference usually comes from a few highly connected subgraphs. Furthermore, we evaluate three different statistical inference methods using a common graphical model, and we demonstrate that junction tree inference substantially improves rates of convergence compared to existing methods. The python code used for this paper is available at http://noble.gs.washington.edu/proj/fido. PMID:22331862

Olivine fabrics and tectonic evolution of fore-arc mantles: A natural perspective from the Songshugou dunite and harzburgite in the Qinling orogenic belt, central China

NASA Astrophysics Data System (ADS)

Cao, Yi; Jung, Haemyeong; Song, Shuguang

2017-03-01

To advance our understanding of the deformation characteristics, rheological behaviors, and tectonic evolution of the fore-arc lithospheric mantle, we analyzed mineral fabrics for a large spinel-bearing ultramafic massif in the Songshugou area in the Qinling orogenic belt, central China. In the spinel-poor coarse-grained dunite, stronger A/D-type and weaker C-type-like fabrics were found, whereas the spinel-rich coarse-grained dunite displayed a comparatively stronger B-type-like fabric. These olivine fabrics are high-T fabrics influenced by the presence of melt, in which B and C-type-like fabrics are inferred to be produced by melt-assisted grain boundary sliding during synkinematic high-T melt-rock reactions. In contrast, the spinel-poor porphyroclastic and fine-grained dunites present weak AG and B-type-like fabrics, respectively. Their olivine fabrics (low-T fabrics) are inferred to transform from A/D-type fabric in their coarse-grained counterparts possibly through mylonitization process assisted by low-T fluid-rock reactions, during which strain was accommodated by the fluid-enhanced dislocation slip and/or fluid-assisted grain boundary sliding processes. Combined with the tectonic results of our previous work, the high-T olivine fabrics are probably related to a young and warm fore-arc mantle where intense partial melting and high-T boninitic melt-rock reactions prevalently occurred, whereas the low-T olivine fabrics likely reflect the evolving tectonic settings through the cooling fore-arc mantle to a continental lower crust in a collisional orogeny where low-T fluid-rock reactions were pervasively activated. These low-T olivine fabrics imply that though cold, the fore-arc lithospheric mantle may be locally weak (˜20-30 MPa), allowing ductile deformation to occur at a geologically significant strain rate.

Language Acquisition in a Unification-Based Grammar Processing System Using a Real-World Knowledge Base.

ERIC Educational Resources Information Center

Russell, Dale W.

An obstacle in Natural Language understanding is the existence of lexical gaps, i.e. words or word senses that are not in the lexicon of the system. This thesis describes the implementation of MURRAY, a learning mechanism which infers the properties of a new lexical item from its syntactical environment and infers its meaning based on context and…

Using Corpus Linguistics to Examine the Extrapolation Inference in the Validity Argument for a High-Stakes Speaking Assessment

ERIC Educational Resources Information Center

LaFlair, Geoffrey T.; Staples, Shelley

2017-01-01

Investigations of the validity of a number of high-stakes language assessments are conducted using an argument-based approach, which requires evidence for inferences that are critical to score interpretation (Chapelle, Enright, & Jamieson, 2008b; Kane, 2013). The current study investigates the extrapolation inference for a high-stakes test of…

Inferring Phylogenetic Networks Using PhyloNet.

PubMed

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

ShinyKGode: an interactive application for ODE parameter inference using gradient matching.

PubMed

Wandy, Joe; Niu, Mu; Giurghita, Diana; Daly, Rónán; Rogers, Simon; Husmeier, Dirk

2018-07-01

Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here, we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching. ShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualized alongside diagnostic plots to assess convergence. The R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode. Supplementary data are available at Bioinformatics online.

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

PubMed

Martínez-Pérez, Francisco E; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C; Rodríguez, Marcela D

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user.

Community trait overdispersion due to trophic interactions: concerns for assembly process inference

PubMed Central

Petchey, Owen L.

2016-01-01

The expected link between competitive exclusion and community trait overdispersion has been used to infer competition in local communities, and trait clustering has been interpreted as habitat filtering. Such community assembly process inference has received criticism for ignoring trophic interactions, as competition and trophic interactions might create similar trait patterns. While other theoretical studies have generally demonstrated the importance of predation for coexistence, ours provides the first quantitative demonstration of such effects on assembly process inference, using a trait-based ecological model to simulate the assembly of a competitive primary consumer community with and without the influence of trophic interactions. We quantified and contrasted trait dispersion/clustering of the competitive communities with the absence and presence of secondary consumers. Trophic interactions most often decreased trait clustering (i.e. increased dispersion) in the competitive communities due to evenly distributed invasions of secondary consumers and subsequent competitor extinctions over trait space. Furthermore, effects of trophic interactions were somewhat dependent on model parameters and clustering metric. These effects create considerable problems for process inference from trait distributions; one potential solution is to use more process-based and inclusive models in inference. PMID:27733548

Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

PubMed Central

Martínez-Pérez, Francisco E.; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C.; Rodríguez, Marcela D.

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user. PMID:22368512

Estimation of parameter uncertainty for an activated sludge model using Bayesian inference: a comparison with the frequentist method.

PubMed

Zonta, Zivko J; Flotats, Xavier; Magrí, Albert

2014-08-01

The procedure commonly used for the assessment of the parameters included in activated sludge models (ASMs) relies on the estimation of their optimal value within a confidence region (i.e. frequentist inference). Once optimal values are estimated, parameter uncertainty is computed through the covariance matrix. However, alternative approaches based on the consideration of the model parameters as probability distributions (i.e. Bayesian inference), may be of interest. The aim of this work is to apply (and compare) both Bayesian and frequentist inference methods when assessing uncertainty for an ASM-type model, which considers intracellular storage and biomass growth, simultaneously. Practical identifiability was addressed exclusively considering respirometric profiles based on the oxygen uptake rate and with the aid of probabilistic global sensitivity analysis. Parameter uncertainty was thus estimated according to both the Bayesian and frequentist inferential procedures. Results were compared in order to evidence the strengths and weaknesses of both approaches. Since it was demonstrated that Bayesian inference could be reduced to a frequentist approach under particular hypotheses, the former can be considered as a more generalist methodology. Hence, the use of Bayesian inference is encouraged for tackling inferential issues in ASM environments.

Where the Wild Things Are: Informal Experience and Ecological Reasoning

ERIC Educational Resources Information Center

Coley, John D.

2012-01-01

Category-based induction requires selective use of different relations to guide inferences; this article examines the development of inferences based on ecological relations among living things. Three hundred and forty-six 6-, 8-, and 10-year-old children from rural, suburban, and urban communities projected novel "diseases" or "insides" from one…

Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho

Treesearch

Tzeidle N. Wasserman; Samuel A. Cushman; Michael K. Schwartz; David O. Wallin

2010-01-01

Individual-based analyses relating landscape structure to genetic distances across complex landscapes enable rigorous evaluation of multiple alternative hypotheses linking landscape structure to gene flow. We utilize two extensions to increase the rigor of the individual-based causal modeling approach to inferring relationships between landscape patterns and gene flow...

Mapping the binding site of aflatoxin B/sub 1/ in DNA: systematic analysis of the reactivity of aflatoxin B/sub 1/ with guanines in different DNA sequences

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

Benasutti, M.; Ejadi, S.; Whitlow, M.D.

The mutagenic and carcinogenic chemical aflatoxin B/sub 1/ (AFB/sub 1/) reacts almost exclusively at the N(7)-position of guanine following activation to its reactive form, the 8,9-epoxide (AFB/sub 1/ oxide). In general N(7)-guanine adducts yield DNA strand breaks when heated in base, a property that serves as the basis for the Maxam-Gilbert DNA sequencing reaction specific for guanine. Using DNA sequencing methods, other workers have shown that AFB/sub 1/ oxide gives strand breaks at positions of guanines; however, the guanine bands varied in intensity. This phenomenon has been used to infer that AFB/sub 1/ oxide prefers to react with guanines inmore » some sequence contexts more than in others and has been referred to as sequence specificity of binding. Herein, data on the reaction of AFB/sub 1/ oxide with several synthetic DNA polymers with different sequences are presented, and (following hydrolysis) adduct levels are determine by high-pressure liquid chromatography. These results reveal that for AFB/sub 1/ oxide (1) the N(7)-guanine adduct is the major adduct found in all of the DNA polymers, (2) adduct levels vary in different sequences, and, thus, sequence specificity is also observed by this more direct method, and (3) the intensity of bands in DNA sequencing gels is likely to reflect adduct levels formed at the N(7)-position of guanine. Knowing this, a reinvestigation of the reactivity of guanines in different DNA sequences using DNA sequencing methods was undertaken. Methods are developed to determine the X (5'-side) base and the Y (3'-side) base are most influential in determining guanine reactivity. These rules in conjunction with molecular modeling studies were used to assess the binding sites that might be utilized by AFB/sub 1/ oxide in its reaction with DNA.« less

Modeling and testing of reactive contaminant transport in drinking water pipes: chlorine response and implications for online contaminant detection.

PubMed

Jeffrey Yang, Y; Goodrich, James A; Clark, Robert M; Li, Sylvana Y

2008-03-01

A modified one-dimensional Danckwerts convection-dispersion-reaction (CDR) model is numerically simulated to explain the observed chlorine residual loss for a "slug" of reactive contaminants instantaneously introduced into a drinking water pipe of assumed no or negligible wall demand. In response to longitudinal dispersion, a contaminant propagates into the bulk phase where it reacts with disinfectants in the water. This process generates a U-shaped pattern of chlorine residual loss in a time-series concentration plot. Numerical modeling indicates that the residual loss curve geometry (i.e., slope, depth, and width) is a function of several variables such as axial Péclet number, reaction rate constants, molar fraction of the fast- and slow-reacting contaminants, and the quasi-steady-state chlorine decay inside the "slug" which serves as a boundary condition of the CDR model. Longitudinal dispersion becomes dominant for less reactive contaminants. Pilot-scale pipe flow experiments for a non-reactive sodium fluoride tracer and the fast-reacting aldicarb, a pesticide, were conducted under turbulent flow conditions (Re=9020 and 25,000). Both the experimental results and the CDR modeling are in agreement showing a close relationship among the aldicarb contaminant "slug", chlorine residual loss and its variations, and a concentration increase of chloride as the final reaction product. Based on these findings, the residual loss curve and its geometry are useful tools to identify the presence of a contaminant "slug" and infer its reactive properties in adaptive contaminant detections.

Big Bang 6Li nucleosynthesis studied deep underground (LUNA collaboration)

NASA Astrophysics Data System (ADS)

Trezzi, D.; Anders, M.; Aliotta, M.; Bellini, A.; Bemmerer, D.; Boeltzig, A.; Broggini, C.; Bruno, C. G.; Caciolli, A.; Cavanna, F.; Corvisiero, P.; Costantini, H.; Davinson, T.; Depalo, R.; Elekes, Z.; Erhard, M.; Ferraro, F.; Formicola, A.; Fülop, Zs.; Gervino, G.; Guglielmetti, A.; Gustavino, C.; Gyürky, Gy.; Junker, M.; Lemut, A.; Marta, M.; Mazzocchi, C.; Menegazzo, R.; Mossa, V.; Pantaleo, F.; Prati, P.; Rossi Alvarez, C.; Scott, D. A.; Somorjai, E.; Straniero, O.; Szücs, T.; Takacs, M.

2017-03-01

The correct prediction of the abundances of the light nuclides produced during the epoch of Big Bang Nucleosynthesis (BBN) is one of the main topics of modern cosmology. For many of the nuclear reactions that are relevant for this epoch, direct experimental cross section data are available, ushering the so-called "age of precision". The present work addresses an exception to this current status: the 2H(α,γ)6Li reaction that controls 6Li production in the Big Bang. Recent controversial observations of 6Li in metal-poor stars have heightened the interest in understanding primordial 6Li production. If confirmed, these observations would lead to a second cosmological lithium problem, in addition to the well-known 7Li problem. In the present work, the direct experimental cross section data on 2H(α,γ)6Li in the BBN energy range are reported. The measurement has been performed deep underground at the LUNA (Laboratory for Underground Nuclear Astrophysics) 400 kV accelerator in the Laboratori Nazionali del Gran Sasso, Italy. The cross section has been directly measured at the energies of interest for Big Bang Nucleosynthesis for the first time, at Ecm = 80, 93, 120, and 133 keV. Based on the new data, the 2H(α,γ)6Li thermonuclear reaction rate has been derived. Our rate is even lower than previously reported, thus increasing the discrepancy between predicted Big Bang 6Li abundance and the amount of primordial 6Li inferred from observations.

Probabilistic models and uncertainty quantification for the ionization reaction rate of atomic Nitrogen

NASA Astrophysics Data System (ADS)

Miki, K.; Panesi, M.; Prudencio, E. E.; Prudhomme, S.

2012-05-01

The objective in this paper is to analyze some stochastic models for estimating the ionization reaction rate constant of atomic Nitrogen (N + e- → N+ + 2e-). Parameters of the models are identified by means of Bayesian inference using spatially resolved absolute radiance data obtained from the Electric Arc Shock Tube (EAST) wind-tunnel. The proposed methodology accounts for uncertainties in the model parameters as well as physical model inadequacies, providing estimates of the rate constant that reflect both types of uncertainties. We present four different probabilistic models by varying the error structure (either additive or multiplicative) and by choosing different descriptions of the statistical correlation among data points. In order to assess the validity of our methodology, we first present some calibration results obtained with manufactured data and then proceed by using experimental data collected at EAST experimental facility. In order to simulate the radiative signature emitted in the shock-heated air plasma, we use a one-dimensional flow solver with Park's two-temperature model that simulates non-equilibrium effects. We also discuss the implications of the choice of the stochastic model on the estimation of the reaction rate and its uncertainties. Our analysis shows that the stochastic models based on correlated multiplicative errors are the most plausible models among the four models proposed in this study. The rate of the atomic Nitrogen ionization is found to be (6.2 ± 3.3) × 1011 cm3 mol-1 s-1 at 10,000 K.

Common-Sense Rule Inference

NASA Astrophysics Data System (ADS)

Lombardi, Ilaria; Console, Luca

In the paper we show how rule-based inference can be made more flexible by exploiting semantic information associated with the concepts involved in the rules. We introduce flexible forms of common sense reasoning in which whenever no rule applies to a given situation, the inference engine can fire rules that apply to more general or to similar situations. This can be obtained by defining new forms of match between rules and the facts in the working memory and new forms of conflict resolution. We claim that in this way we can overcome some of the brittleness problems that are common in rule-based systems.

Evolution of amino acid metabolism inferred through cladistic analysis.

PubMed

Cunchillos, Chomin; Lecointre, Guillaume

2003-11-28

Because free amino acids were most probably available in primitive abiotic environments, their metabolism is likely to have provided some of the very first metabolic pathways of life. What were the first enzymatic reactions to emerge? A cladistic analysis of metabolic pathways of the 16 aliphatic amino acids and 2 portions of the Krebs cycle was performed using four criteria of homology. The analysis is not based on sequence comparisons but, rather, on coding similarities in enzyme properties. The properties used are shared specific enzymatic activity, shared enzymatic function without substrate specificity, shared coenzymes, and shared functional family. The tree shows that the earliest pathways to emerge are not portions of the Krebs cycle but metabolisms of aspartate, asparagine, glutamate, and glutamine. The views of Horowitz (Horowitz, N. H. (1945) Proc. Natl. Acad. Sci. U. S. A. 31, 153-157) and Cordón (Cordón, F. (1990) Tratado Evolucionista de Biologia, Aguilar, Madrid, Spain), according to which the upstream reactions in the catabolic pathways and the downstream reactions in the anabolic pathways are the earliest in evolution, are globally corroborated; however, with some exceptions. These are due to later opportunistic connections of pathways (actually already suggested by these authors). Earliest enzymatic functions are mostly catabolic; they were deaminations, transaminations, and decarboxylations. From the consensus tree we extracted four time spans for amino acid metabolism development. For some amino acids catabolism and biosynthesis occurred at the same time (Asp, Glu, Lys, Leu, Ala, Val, Ile, Pro, Arg). For others ultimate reactions that use amino acids as a substrate or as a product are distinct in time, with catabolism preceding anabolism for Asn, Gln, and Cys and anabolism preceding catabolism for Ser, Met, and Thr. Cladistic analysis of the structure of biochemical pathways makes hypotheses in biochemical evolution explicit and parsimonious.

Illusory inferences from a disjunction of conditionals: a new mental models account.

PubMed

Barrouillet, P; Lecas, J F

2000-08-14

(Johnson-Laird, P.N., & Savary, F. (1999, Illusory inferences: a novel class of erroneous deductions. Cognition, 71, 191-229.) have recently presented a mental models account, based on the so-called principle of truth, for the occurrence of inferences that are compelling but invalid. This article presents an alternative account of the illusory inferences resulting from a disjunction of conditionals. In accordance with our modified theory of mental models of the conditional, we show that the way individuals represent conditionals leads them to misinterpret the locus of the disjunction and prevents them from drawing conclusions from a false conditional, thus accounting for the compelling character of the illusory inference.

Automatic physical inference with information maximizing neural networks

NASA Astrophysics Data System (ADS)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

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.

Photofragmentation of Gas-Phase Lanthanide Cyclopentadienyl Complexes: Experimental and Time-Dependent Excited-State Molecular Dynamics

PubMed Central

2015-01-01

Unimolecular gas-phase laser-photodissociation reaction mechanisms of open-shell lanthanide cyclopentadienyl complexes, Ln(Cp)3 and Ln(TMCp)3, are analyzed from experimental and computational perspectives. The most probable pathways for the photoreactions are inferred from photoionization time-of-flight mass spectrometry (PI-TOF-MS), which provides the sequence of reaction intermediates and the distribution of final products. Time-dependent excited-state molecular dynamics (TDESMD) calculations provide insight into the electronic mechanisms for the individual steps of the laser-driven photoreactions for Ln(Cp)3. Computational analysis correctly predicts several key reaction products as well as the observed branching between two reaction pathways: (1) ligand ejection and (2) ligand cracking. Simulations support our previous assertion that both reaction pathways are initiated via a ligand-to-metal charge-transfer (LMCT) process. For the more complex chemistry of the tetramethylcyclopentadienyl complexes Ln(TMCp)3, TMESMD is less tractable, but computational geometry optimization reveals the structures of intermediates deduced from PI-TOF-MS, including several classic “tuck-in” structures and products of Cp ring expansion. The results have important implications for metal–organic catalysis and laser-assisted metal–organic chemical vapor deposition (LCVD) of insulators with high dielectric constants. PMID:24910492

The Effect of Varying Biting Position on Relative Jaw Muscle EMG activity

DTIC Science & Technology

1988-09-01

with muscle force is the key to 13 this approach as it allows inference of muscle contraction activity from EMG data. This relationship has been the...5! 15 LITERATURE REVIEW Introduction: The study of the physiology of bite force, muscle contraction force, joint reaction force and the lever system...Currently, the best method of indirectly observing muscle contraction activity is through electromyography. Although there appears to be a time delay

Inferring Time-Varying Network Topologies from Gene Expression Data

PubMed Central

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence. PMID:18309363

Inferring time-varying network topologies from gene expression data.

PubMed

Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

The Effect of Simulation-Based Learning on Prospective Teachers' Inference Skills in Teaching Probability

ERIC Educational Resources Information Center

Koparan, Timur; Yilmaz, Gül Kaleli

2015-01-01

The effect of simulation-based probability teaching on the prospective teachers' inference skills has been examined with this research. In line with this purpose, it has been aimed to examine the design, implementation and efficiency of a learning environment for experimental probability. Activities were built on modeling, simulation and the…

Assessment of statistical education in Indonesia: Preliminary results and initiation to simulation-based inference

NASA Astrophysics Data System (ADS)

Saputra, K. V. I.; Cahyadi, L.; Sembiring, U. A.

2018-01-01

Start in this paper, we assess our traditional elementary statistics education and also we introduce elementary statistics with simulation-based inference. To assess our statistical class, we adapt the well-known CAOS (Comprehensive Assessment of Outcomes in Statistics) test that serves as an external measure to assess the student’s basic statistical literacy. This test generally represents as an accepted measure of statistical literacy. We also introduce a new teaching method on elementary statistics class. Different from the traditional elementary statistics course, we will introduce a simulation-based inference method to conduct hypothesis testing. From the literature, it has shown that this new teaching method works very well in increasing student’s understanding of statistics.

Uncertainty quantification of reaction mechanisms accounting for correlations introduced by rate rules and fitted Arrhenius parameters

DOE PAGES

Prager, Jens; Najm, Habib N.; Sargsyan, Khachik; ...

2013-02-23

We study correlations among uncertain Arrhenius rate parameters in a chemical model for hydrocarbon fuel-air combustion. We consider correlations induced by the use of rate rules for modeling reaction rate constants, as well as those resulting from fitting rate expressions to empirical measurements arriving at a joint probability density for all Arrhenius parameters. We focus on homogeneous ignition in a fuel-air mixture at constant-pressure. We also outline a general methodology for this analysis using polynomial chaos and Bayesian inference methods. Finally, we examine the uncertainties in both the Arrhenius parameters and in predicted ignition time, outlining the role of correlations,more » and considering both accuracy and computational efficiency.« less

"I know you self-handicapped last exam": gender differences in reactions to self-handicapping.

PubMed

Hirt, Edward R; McCrea, Sean M; Boris, Hillary I

2003-01-01

Past research has shown that self-handicapping involves the trade-off of ability-related attributional benefits for interpersonal costs. Study 1 examined whether perceiver or target sex moderates impressions of self-handicapping targets. Although target sex was not an important factor, female perceivers were consistently more critical of behavioral self-handicappers. Two additional studies replicated this gender difference with variations of the handicap. Study 3 examined the motives inferred by perceivers and found that women not only view self-handicappers as more unmotivated but also report greater suspicion of self-handicapping motives; furthermore, these differences in perceived motives mediated sex differences in reactions to self-handicappers. Implications for the effectiveness of self-handicapping as an impression management strategy are discussed.

Nuclear science experiments with a bright neutron source from fusion reactions on the OMEGA Laser System

NASA Astrophysics Data System (ADS)

Forrest, C. J.; Knauer, J. P.; Schroeder, W. U.; Glebov, V. Yu.; Radha, P. B.; Regan, S. P.; Sangster, T. C.; Sickles, M.; Stoeckl, C.; Szczepanski, J.

2018-04-01

Subnanosecond impulses of 1013 to 1014 neutrons, produced in direct-drive laser inertial confinement fusion implosions, have been used to irradiate deuterated targets at the OMEGA Laser System (Boehly et al., 1997). The target compounds include heavy water (D2O) and deuterated benzene (C6D6). Yields and energy spectra of neutrons from D(n,2n)p to study the breakup reaction have been measured at a forward angle of θlab = 3 .5∘ ± 3.5° with a sensitive, high-dynamic-range neutron time-of-flight spectrometer to infer the double-differential breakup cross section d2 σ/dE d Ω for 14-MeV D-T fusion neutrons.

Ion-ion charge exchange processes. Final technical report, June 1, 1977-May 31, 1978

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

Poe, R.T.; Choi, B.H.

Under the auspices of ERDA, we have undertaken a vigorous study of ion-ion charge exchange process pertinent to the storage-ring configurations in the heavy-ion fusion program. One particular reaction, singly charged helium charge exchange, was investigated in detail. General trend of the singly charged heavy-ion charge exchange reaction can be inferred from the present study. Some of our results were presented at Proceedings of the Heavy-Ion Fusion Workshop, Argonne National Laboratory (September 1978) as a paper entitled Charge Exchange Between Singly Ionized Helium Ions, by B.H. Choi, R.T. Poe and K.T. Tang. Here, we briefly describe our method and reportmore » the results.« less

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record

PubMed Central

Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Background Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. Objective To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. Study design and methods We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100 000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100 000 records to assess its accuracy. Results Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100 000 randomly selected patients showed high sensitivity (range: 62.8–100.0%) and positive predictive value (range: 79.8–99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. Conclusion We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts. PMID:21613643

Hydrogen-abstraction reactions of fully hydrogenated fullerene cages with the amino radical: a density functional study

NASA Astrophysics Data System (ADS)

Anafcheh, Maryam

2018-01-01

We have applied density functional theory calculations to study the reactions of NH2 + CnHn (n = 20, 40, 50, 60, 70 and 80). Due to the hard curvature in C20 cage, the NH2• + C20H20 → NH3 + C20H19• reaction is nearly thermoneutral with a high potential barrier height. For the CnHn fulleranes with n > 20 the transition states appear earlier on the reaction paths, as can be anticipated for exothermic reactions. Using the spherical excess parameter, we distinguished different curvatures on the surfaces of fullerane cages. The reaction enthalpies ΔH°298 and potential barrier heights ΔETS of the considered reactions indicate good correlation with the values of ϕi parameter, showing an upward trend with the curvature increasing at carbon sites. We have also investigated the H-abstraction of the chemical derivatives of the C20H20 cage (C20H19-CH3, C20H19-CH2CH3 and C20H19-CH2CH2CH3) in comparison to the corresponding isolated alkanes (CH4, C2H6 and C3H8). Overall, it could be inferred that the H-abstraction from the primary and secondary C-H bonds of isolated alkanes could occur more easily than fullarane derivatives.

Preverbal infants identify emotional reactions that are incongruent with goal outcomes

PubMed Central

Skerry, Amy E.; Spelke, Elizabeth S.

2014-01-01

Identifying the goal of another agent’s action allows an observer to make inferences not only about the outcomes the agent will pursue in the future and the means to be deployed in a given context, but also about the emotional consequences of goal-related outcomes. While numerous studies have characterized the former abilities in infancy, expectations about emotions have gone relatively unexplored. Using a violation of expectation paradigm, we present infants with an agent who attains or fails to attain a demonstrated goal, and reacts with positive or negative affect. Across several studies, we find that infants’ attention to a given emotional display differs depending on whether that reaction is congruent with the preceding goal outcome. Specifically, infants look longer at a negative emotional display when it follows a completed goal compared to when it follows a failed goal. The present results suggest that infants’ goal representations support expectations not only about future actions but also about emotional reactions, and that infants in the first year of life can relate different emotional reactions to conditions that elicit them. PMID:24321623

Exploring Nested Reaction Fronts to Understand How Oxygen Cracks Rocks, Carbonic and Sulfuric Acids Dissolve Rocks, and Water Transports Rocks during Weathering

NASA Astrophysics Data System (ADS)

Brantley, S. L.; Gu, X.; Sullivan, P. L.; Kim, H.; Stinchcomb, G. E.; Lebedeva, M.; Balashov, V. N.

2016-12-01

To first order, weathering is the reaction of rocks with oxidants (oxygen, nitrate, etc.), acids (carbonic, sulfuric, and organic acids), and water. To explore weathering we have been studying the depth intervals in soils, saprolite, and weathering rock where mineral reactions are localized - "reaction fronts". We limit the study to ridges or catchments in climates where precipitation is greater than potential evapotranspiration. For example, in the Susquehanna Shale Hills Critical Zone Observatory, we observe reaction fronts that generally define very rough surfaces in 3D that mimic the land surface topography, although with lower relief. Overall, the fronts form nested curved surfaces. In Shale Hills, the deepest reaction fronts are oxidation of pyrite, and dissolution of carbonate. The carbonate is inferred to dissolve at least partly due to the sulfuric acid produced by the pyrite. In addition to pyrite, chlorite also starts to oxidize at the water table. We hypothesize that these dissolution and oxidation reactions open pores and cause microfracturing that open the rock to infiltration of advecting meteoric waters. At much shallower depths, illite is observed to dissolve. In Shale Hills, these reaction fronts - pyrite, carbonate, illite - separate over meters beneath the ridges. Such separated reaction fronts have also been observed in other fractured lithologies where oxidation is the deepest reaction and is associated with weathering-induced fractures. In contrast, in some massive mafic rocks, reaction fronts are almost co-located. By studying the geometry of reaction fronts, it may be possible to elucidate the relative importance of how oxygen cracks rocks; carbonic, organic, and sulfuric acids dissolve rocks; and water mobilizes rock materials during weathering.

Statistical Inferences from Formaldehyde Dna-Protein Cross-Link Data

EPA Science Inventory

Physiologically-based pharmacokinetic (PBPK) modeling has reached considerable sophistication in its application in the pharmacological and environmental health areas. Yet, mature methodologies for making statistical inferences have not been routinely incorporated in these applic...

In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics.

PubMed

Audain, Enrique; Uszkoreit, Julian; Sachsenberg, Timo; Pfeuffer, Julianus; Liang, Xiao; Hermjakob, Henning; Sanchez, Aniel; Eisenacher, Martin; Reinert, Knut; Tabb, David L; Kohlbacher, Oliver; Perez-Riverol, Yasset

2017-01-06

In mass spectrometry-based shotgun proteomics, protein identifications are usually the desired result. However, most of the analytical methods are based on the identification of reliable peptides and not the direct identification of intact proteins. Thus, assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Currently, different protein inference algorithms and tools are available for the proteomics community. Here, we evaluated five software tools for protein inference (PIA, ProteinProphet, Fido, ProteinLP, MSBayesPro) using three popular database search engines: Mascot, X!Tandem, and MS-GF+. All the algorithms were evaluated using a highly customizable KNIME workflow using four different public datasets with varying complexities (different sample preparation, species and analytical instruments). We defined a set of quality control metrics to evaluate the performance of each combination of search engines, protein inference algorithm, and parameters on each dataset. We show that the results for complex samples vary not only regarding the actual numbers of reported protein groups but also concerning the actual composition of groups. Furthermore, the robustness of reported proteins when using databases of differing complexities is strongly dependant on the applied inference algorithm. Finally, merging the identifications of multiple search engines does not necessarily increase the number of reported proteins, but does increase the number of peptides per protein and thus can generally be recommended. Protein inference is one of the major challenges in MS-based proteomics nowadays. Currently, there are a vast number of protein inference algorithms and implementations available for the proteomics community. Protein assembly impacts in the final results of the research, the quantitation values and the final claims in the research manuscript. Even though protein inference is a crucial step in proteomics data analysis, a comprehensive evaluation of the many different inference methods has never been performed. Previously Journal of proteomics has published multiple studies about other benchmark of bioinformatics algorithms (PMID: 26585461; PMID: 22728601) in proteomics studies making clear the importance of those studies for the proteomics community and the journal audience. This manuscript presents a new bioinformatics solution based on the KNIME/OpenMS platform that aims at providing a fair comparison of protein inference algorithms (https://github.com/KNIME-OMICS). Six different algorithms - ProteinProphet, MSBayesPro, ProteinLP, Fido and PIA- were evaluated using the highly customizable workflow on four public datasets with varying complexities. Five popular database search engines Mascot, X!Tandem, MS-GF+ and combinations thereof were evaluated for every protein inference tool. In total >186 proteins lists were analyzed and carefully compare using three metrics for quality assessments of the protein inference results: 1) the numbers of reported proteins, 2) peptides per protein, and the 3) number of uniquely reported proteins per inference method, to address the quality of each inference method. We also examined how many proteins were reported by choosing each combination of search engines, protein inference algorithms and parameters on each dataset. The results show that using 1) PIA or Fido seems to be a good choice when studying the results of the analyzed workflow, regarding not only the reported proteins and the high-quality identifications, but also the required runtime. 2) Merging the identifications of multiple search engines gives almost always more confident results and increases the number of peptides per protein group. 3) The usage of databases containing not only the canonical, but also known isoforms of proteins has a small impact on the number of reported proteins. The detection of specific isoforms could, concerning the question behind the study, compensate for slightly shorter reports using the parsimonious reports. 4) The current workflow can be easily extended to support new algorithms and search engine combinations. Copyright © 2016. Published by Elsevier B.V.

The disappointing gift: dispositional and situational moderators of emotional expressions.

PubMed

Tobin, Renée M; Graziano, William G

2011-10-01

Inferences about emotions in children are limited by studies that rely on only one research method. Convergence across methods provides a stronger basis for inference by identifying method variance. This multimethod study of 116 children (mean age=8.21 years) examined emotional displays during social exchange. Each child received a desirable gift and later an undesirable gift after performing tasks, with or without mother present. Children's reactions were observed and coded. Children displayed more positive affect with mother present than with mother absent. Independent ratings of children by adults revealed that children lower in the personality dimension of Agreeableness displayed more negative emotion than their peers following the receipt of an undesirable gift. A curvilinear interaction between Agreeableness and mother condition predicted negative affect displays. Emotional assessment is discussed in terms of links to social exchange and the development of expressive behavior. Copyright © 2011 Elsevier Inc. All rights reserved.

The ecology and evolution of temperature-dependent reaction norms for sex determination in reptiles: a mechanistic conceptual model.

PubMed

Pezaro, Nadav; Doody, J Sean; Thompson, Michael B

2017-08-01

Sex-determining mechanisms are broadly categorised as being based on either genetic or environmental factors. Vertebrate sex determination exhibits remarkable diversity but displays distinct phylogenetic patterns. While all eutherian mammals possess XY male heterogamety and female heterogamety (ZW) is ubiquitous in birds, poikilothermic vertebrates (fish, amphibians and reptiles) exhibit multiple genetic sex-determination (GSD) systems as well as environmental sex determination (ESD). Temperature is the factor controlling ESD in reptiles and temperature-dependent sex determination (TSD) in reptiles has become a focal point in the study of this phenomenon. Current patterns of climate change may cause detrimental skews in the population sex ratios of reptiles exhibiting TSD. Understanding the patterns of variation, both within and among populations and linking such patterns with the selection processes they are associated with, is the central challenge of research aimed at predicting the capacity of populations to adapt to novel conditions. Here we present a conceptual model that innovates by defining an individual reaction norm for sex determination as a range of incubation temperatures. By deconstructing individual reaction norms for TSD and revealing their underlying interacting elements, we offer a conceptual solution that explains how variation among individual reaction norms can be inferred from the pattern of population reaction norms. The model also links environmental variation with the different patterns of TSD and describes the processes from which they may arise. Specific climate scenarios are singled out as eco-evolutionary traps that may lead to demographic extinction or a transition to either male or female heterogametic GSD. We describe how the conceptual principles can be applied to interpret TSD data and to explain the adaptive capacity of TSD to climate change as well as its limits and the potential applications for conservation and management programs. © 2016 Cambridge Philosophical Society.

Magma Mixing Chronometry: Quantitative 3D Tomographic Analysis of Biotite Breakdown in Heating Experiments

NASA Astrophysics Data System (ADS)

Grocke, S. B.; Andrews, B. J.; Manga, M.; Quinn, E. T.

2015-12-01

Dacite lavas from Chaos Crags, Lassen Volcanic Center, CA contain inclusions of more mafic magmas, suggesting that mixing or mingling of magmas occurred just prior to lava dome extrusion, and perhaps triggered the eruption. The timescales between the mixing event and eruption are unknown, but reaction rims on biotite grains hosted in the Chaos Crags dacite may provide a record of the timescale (i.e., chronometer) between mixing and eruption. To quantify the effect of pre-eruptive heating on the formation of reaction rims on biotite, we conducted isobaric (150 MPa), H2O-saturated, heating experiments on the dacite end-member. In heating experiments, we held the natural dacite at 800°C and 150MPa for 96 hours and then isobarically heated the experiments to 825 and 850°C (temperatures above the biotite liquidus, <815°C at 150MPa) for durations ≤96 hours. We analyzed run products using high-resolution SEM imaging and synchrotron-based X-ray tomography, which provides a 3-dimensional rendering of biotite breakdown reaction products and textures. X-ray tomography images of experimental run products reveal that in all heating experiments, biotite breakdown occurs and reaction products include orthopyroxenes, Fe-Ti oxides, and vapor (inferred from presence of bubbles). Experiments heated to 850°C for 96 h show extensive breakdown, consisting of large orthopyroxene crystals, Fe-Ti oxide laths (<100μm), and bubbles. When the process of biotite breakdown goes to completion, the resulting H2O bubble comprises roughly the equivalent volume of the original biotite crystal. This observation suggests that biotite breakdown can add significant water to the melt and lead to extensive bubble formation. Although bubble expansion and magma flow may disrupt the reaction products in some magmas, our experiments suggest that biotite breakdown textures in natural samples can be used as a chronometer for pre-eruptive magma mixing.

Finding user personal interests by tweet-mining using advanced machine learning algorithm in R

NASA Astrophysics Data System (ADS)

Krithika, L. B.; Roy, P.; Asha Jerlin, M.

2017-11-01

The social-media plays a key role in every individual’s life by anyone’s personal views about their liking-ness/disliking-ness. This methodology is a sharp departure from the traditional techniques of inferring interests of a user from the tweets that he/she posts or receives. It is showed that the topics of interest inferred by the proposed methodology are far superior than the topics extracted by state-of-the-art techniques such as using topic models (Labelled LDA) on tweets. Based upon the proposed methodology, a system has been built, “Who is interested in what”, which can infer the interests of millions of Twitter users. A novel mechanism is proposed to infer topics of interest of individual users in the twitter social network. It has been observed that in twitter, a user generally follows experts on various topics of his/her interest in order to acquire information on those topics. A methodology based on social annotations is used to first deduce the topical expertise of popular twitter users and then transitively infer the interests of the users who follow them.

Inductive reasoning about causally transmitted properties.

PubMed

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

2008-11-01

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

Inferring Demographic History Using Two-Locus Statistics.

PubMed

Ragsdale, Aaron P; Gutenkunst, Ryan N

2017-06-01

Population demographic history may be learned from contemporary genetic variation data. Methods based on aggregating the statistics of many single loci into an allele frequency spectrum (AFS) have proven powerful, but such methods ignore potentially informative patterns of linkage disequilibrium (LD) between neighboring loci. To leverage such patterns, we developed a composite-likelihood framework for inferring demographic history from aggregated statistics of pairs of loci. Using this framework, we show that two-locus statistics are more sensitive to demographic history than single-locus statistics such as the AFS. In particular, two-locus statistics escape the notorious confounding of depth and duration of a bottleneck, and they provide a means to estimate effective population size based on the recombination rather than mutation rate. We applied our approach to a Zambian population of Drosophila melanogaster Notably, using both single- and two-locus statistics, we inferred a substantially lower ancestral effective population size than previous works and did not infer a bottleneck history. Together, our results demonstrate the broad potential for two-locus statistics to enable powerful population genetic inference. Copyright © 2017 by the Genetics Society of America.

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

Development of MRM-based assays for the absolute quantitation of plasma proteins.

PubMed

Kuzyk, Michael A; Parker, Carol E; Domanski, Dominik; Borchers, Christoph H

2013-01-01

Multiple reaction monitoring (MRM), sometimes called selected reaction monitoring (SRM), is a directed tandem mass spectrometric technique performed on to triple quadrupole mass spectrometers. MRM assays can be used to sensitively and specifically quantify proteins based on peptides that are specific to the target protein. Stable-isotope-labeled standard peptide analogues (SIS peptides) of target peptides are added to enzymatic digests of samples, and quantified along with the native peptides during MRM analysis. Monitoring of the intact peptide and a collision-induced fragment of this peptide (an ion pair) can be used to provide information on the absolute peptide concentration of the peptide in the sample and, by inference, the concentration of the intact protein. This technique provides high specificity by selecting for biophysical parameters that are unique to the target peptides: (1) the molecular weight of the peptide, (2) the generation of a specific fragment from the peptide, and (3) the HPLC retention time during LC/MRM-MS analysis. MRM is a highly sensitive technique that has been shown to be capable of detecting attomole levels of target peptides in complex samples such as tryptic digests of human plasma. This chapter provides a detailed description of how to develop and use an MRM protein assay. It includes sections on the critical "first step" of selecting the target peptides, as well as optimization of MRM acquisition parameters for maximum sensitivity of the ion pairs that will be used in the final method, and characterization of the final MRM assay.

Image-Based Measurement of H2O2 Reaction-Diffusion in Wounded Zebrafish Larvae.

PubMed

Jelcic, Mark; Enyedi, Balázs; Xavier, João B; Niethammer, Philipp

2017-05-09

Epithelial injury induces rapid recruitment of antimicrobial leukocytes to the wound site. In zebrafish larvae, activation of the epithelial NADPH oxidase Duox at the wound margin is required early during this response. Before injury, leukocytes are near the vascular region, that is, ∼100-300 μm away from the injury site. How Duox establishes long-range signaling to leukocytes is unclear. We conceived that extracellular hydrogen peroxide (H 2 O 2 ) generated by Duox diffuses through the tissue to directly regulate chemotactic signaling in these cells. But before it can oxidize cellular proteins, H 2 O 2 must get past the antioxidant barriers that protect the cellular proteome. To test whether, or on which length scales this occurs during physiological wound signaling, we developed a computational method based on reaction-diffusion principles that infers H 2 O 2 degradation rates from intravital H 2 O 2 -biosensor imaging data. Our results indicate that at high tissue H 2 O 2 levels the peroxiredoxin-thioredoxin antioxidant chain becomes overwhelmed, and H 2 O 2 degradation stalls or ceases. Although the wound H 2 O 2 gradient reaches deep into the tissue, it likely overcomes antioxidant barriers only within ∼30 μm of the wound margin. Thus, Duox-mediated long-range signaling may require other spatial relay mechanisms besides extracellular H 2 O 2 diffusion. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

An Investigation and Interpretation of Selected Topics in Uncertainty Reasoning

DTIC Science & Technology

1989-12-01

Characterizing seconditry uncertainty as spurious evidence and including it in the inference process , It was shown that probability ratio graphs are a...in the inference process has great impact on the computational complexity of an Inference process . viii An Investigation and Interpretation of...Systems," he outlines a five step process that incorporates Blyeslan reasoning in the development of the expert system rule base: 1. A group of

The impact of category structure and training methodology on learning and generalizing within-category representations.

PubMed

Ell, Shawn W; Smith, David B; Peralta, Gabriela; Hélie, Sébastien

2017-08-01

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.

Brain Imaging, Forward Inference, and Theories of Reasoning

PubMed Central

Heit, Evan

2015-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities. PMID:25620926

Brain imaging, forward inference, and theories of reasoning.

PubMed

Heit, Evan

2014-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities.

Reaction kinetics of dolomite rim growth

NASA Astrophysics Data System (ADS)

Helpa, V.; Rybacki, E.; Abart, R.; Morales, L. F. G.; Rhede, D.; Jeřábek, P.; Dresen, G.

2014-04-01

Reaction rims of dolomite (CaMg[CO3]2) were produced by solid-state reactions at the contacts of oriented calcite (CaCO3) and magnesite (MgCO3) single crystals at 400 MPa pressure, 750-850 °C temperature, and 3-146 h annealing time to determine the reaction kinetics. The dolomite reaction rims show two different microstructural domains. Elongated palisades of dolomite grew perpendicular into the MgCO3 interface with length ranging from about 6 to 41 µm. At the same time, a 5-71 µm wide rim of equiaxed granular dolomite grew at the contact with CaCO3. Platinum markers showed that the original interface is located at the boundary between the granular and palisade-forming dolomite. In addition to dolomite, a 12-80 µm thick magnesio-calcite layer formed between the dolomite reaction rims and the calcite single crystals. All reaction products show at least an axiotactic crystallographic relationship with respect to calcite reactant, while full topotaxy to calcite prevails within the granular dolomite and magnesio-calcite. Dolomite grains frequently exhibit growth twins characterized by a rotation of 180° around one of the equivalent axis. From mass balance considerations, it is inferred that the reaction rim of dolomite grew by counter diffusion of MgO and CaO. Assuming an Arrhenius-type temperature dependence, activation energies for diffusion of CaO and MgO are E a (CaO) = 192 ± 54 kJ/mol and E a (MgO) = 198 ± 44 kJ/mol, respectively.

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.

Inferring Higher Functional Information for RIKEN Mouse Full-Length cDNA Clones With FACTS

PubMed Central

Nagashima, Takeshi; Silva, Diego G.; Petrovsky, Nikolai; Socha, Luis A.; Suzuki, Harukazu; Saito, Rintaro; Kasukawa, Takeya; Kurochkin, Igor V.; Konagaya, Akihiko; Schönbach, Christian

2003-01-01

FACTS (Functional Association/Annotation of cDNA Clones from Text/Sequence Sources) is a semiautomated knowledge discovery and annotation system that integrates molecular function information derived from sequence analysis results (sequence inferred) with functional information extracted from text. Text-inferred information was extracted from keyword-based retrievals of MEDLINE abstracts and by matching of gene or protein names to OMIM, BIND, and DIP database entries. Using FACTS, we found that 47.5% of the 60,770 RIKEN mouse cDNA FANTOM2 clone annotations were informative for text searches. MEDLINE queries yielded molecular interaction-containing sentences for 23.1% of the clones. When disease MeSH and GO terms were matched with retrieved abstracts, 22.7% of clones were associated with potential diseases, and 32.5% with GO identifiers. A significant number (23.5%) of disease MeSH-associated clones were also found to have a hereditary disease association (OMIM Morbidmap). Inferred neoplastic and nervous system disease represented 49.6% and 36.0% of disease MeSH-associated clones, respectively. A comparison of sequence-based GO assignments with informative text-based GO assignments revealed that for 78.2% of clones, identical GO assignments were provided for that clone by either method, whereas for 21.8% of clones, the assignments differed. In contrast, for OMIM assignments, only 28.5% of clones had identical sequence-based and text-based OMIM assignments. Sequence, sentence, and term-based functional associations are included in the FACTS database (http://facts.gsc.riken.go.jp/), which permits results to be annotated and explored through web-accessible keyword and sequence search interfaces. The FACTS database will be a critical tool for investigating the functional complexity of the mouse transcriptome, cDNA-inferred interactome (molecular interactions), and pathome (pathologies). PMID:12819151

Emotional ties that bind: the roles of valence and consistency of group emotion in inferences of cohesiveness and common fate.

PubMed

Magee, Joe C; Tiedens, Larissa Z

2006-12-01

In three studies, observers based inferences about the cohesiveness and common fate of groups on the emotions expressed by group members. The valence of expressions affected cohesiveness inferences, whereas the consistency of expressions affected inferences of whether members have common fate. These emotion composition effects were stronger than those due to the race or sex composition of the group. Furthermore, the authors show that emotion valence and consistency are differentially involved in judgments about the degree to which the group as a whole was responsible for group performance. Finally, it is demonstrated that valence-cohesiveness effects are mediated by inferences of interpersonal liking and that consistency-common fate effects are mediated by inferences of psychological similarity. These findings have implications for the literature on entitativity and regarding the function of emotions in social contexts.

Wisdom of crowds for robust gene network inference

PubMed Central

Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicci; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R.; Kellis, Manolis; Collins, James J.; Stolovitzky, Gustavo

2012-01-01

Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662

Theory-based Bayesian Models of Inductive Inference

DTIC Science & Technology

2010-07-19

Subjective randomness and natural scene statistics. Psychonomic Bulletin & Review . http://cocosci.berkeley.edu/tom/papers/randscenes.pdf Page 1...in press). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review . http://cocosci.berkeley.edu/tom

Intuitive statistics by 8-month-old infants

PubMed Central

Xu, Fei; Garcia, Vashti

2008-01-01

Human learners make inductive inferences based on small amounts of data: we generalize from samples to populations and vice versa. The academic discipline of statistics formalizes these intuitive statistical inferences. What is the origin of this ability? We report six experiments investigating whether 8-month-old infants are “intuitive statisticians.” Our results showed that, given a sample, the infants were able to make inferences about the population from which the sample had been drawn. Conversely, given information about the entire population of relatively small size, the infants were able to make predictions about the sample. Our findings provide evidence that infants possess a powerful mechanism for inductive learning, either using heuristics or basic principles of probability. This ability to make inferences based on samples or information about the population develops early and in the absence of schooling or explicit teaching. Human infants may be rational learners from very early in development. PMID:18378901

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.

Apes are intuitive statisticians.

PubMed

Rakoczy, Hannes; Clüver, Annette; Saucke, Liane; Stoffregen, Nicole; Gräbener, Alice; Migura, Judith; Call, Josep

2014-04-01

Inductive learning and reasoning, as we use it both in everyday life and in science, is characterized by flexible inferences based on statistical information: inferences from populations to samples and vice versa. Many forms of such statistical reasoning have been found to develop late in human ontogeny, depending on formal education and language, and to be fragile even in adults. New revolutionary research, however, suggests that even preverbal human infants make use of intuitive statistics. Here, we conducted the first investigation of such intuitive statistical reasoning with non-human primates. In a series of 7 experiments, Bonobos, Chimpanzees, Gorillas and Orangutans drew flexible statistical inferences from populations to samples. These inferences, furthermore, were truly based on statistical information regarding the relative frequency distributions in a population, and not on absolute frequencies. Intuitive statistics in its most basic form is thus an evolutionarily more ancient rather than a uniquely human capacity. Copyright © 2014 Elsevier B.V. All rights reserved.

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

PubMed Central

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583

Effect of age on variability in the production of text-based global inferences.

PubMed

Williams, Lynne J; Dunlop, Joseph P; Abdi, Hervé

2012-01-01

As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one's world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation--a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging.

The Development of Adaptive Decision Making: Recognition-Based Inference in Children and Adolescents

ERIC Educational Resources Information Center

Horn, Sebastian S.; Ruggeri, Azzurra; Pachur, Thorsten

2016-01-01

Judgments about objects in the world are often based on probabilistic information (or cues). A frugal judgment strategy that utilizes memory (i.e., the ability to discriminate between known and unknown objects) as a cue for inference is the recognition heuristic (RH). The usefulness of the RH depends on the structure of the environment,…

A Logical Framework for Service Migration Based Survivability

DTIC Science & Technology

2016-06-24

platforms; Service Migration Strategy Fuzzy Inference System Knowledge Base Fuzzy rules representing domain expert knowledge about implications of...service migration strategy. Our approach uses expert knowledge as linguistic reasoning rules and takes service programs damage assessment, service...programs complexity, and available network capability as input. The fuzzy inference system includes four components as shown in Figure 5: (1) a knowledge

Automatic approach to deriving fuzzy slope positions

NASA Astrophysics Data System (ADS)

Zhu, Liang-Jun; Zhu, A.-Xing; Qin, Cheng-Zhi; Liu, Jun-Zhi

2018-03-01

Fuzzy characterization of slope positions is important for geographic modeling. Most of the existing fuzzy classification-based methods for fuzzy characterization require extensive user intervention in data preparation and parameter setting, which is tedious and time-consuming. This paper presents an automatic approach to overcoming these limitations in the prototype-based inference method for deriving fuzzy membership value (or similarity) to slope positions. The key contribution is a procedure for finding the typical locations and setting the fuzzy inference parameters for each slope position type. Instead of being determined totally by users in the prototype-based inference method, in the proposed approach the typical locations and fuzzy inference parameters for each slope position type are automatically determined by a rule set based on prior domain knowledge and the frequency distributions of topographic attributes. Furthermore, the preparation of topographic attributes (e.g., slope gradient, curvature, and relative position index) is automated, so the proposed automatic approach has only one necessary input, i.e., the gridded digital elevation model of the study area. All compute-intensive algorithms in the proposed approach were speeded up by parallel computing. Two study cases were provided to demonstrate that this approach can properly, conveniently and quickly derive the fuzzy slope positions.

Photolysis Rate Coefficient Calculations in Support of SOLVE II

NASA Technical Reports Server (NTRS)

Swartz, William H.

2005-01-01

A quantitative understanding of photolysis rate coefficients (or "j-values") is essential to determining the photochemical reaction rates that define ozone loss and other crucial processes in the atmosphere. j-Values can be calculated with radiative transfer models, derived from actinic flux observations, or inferred from trace gas measurements. The primary objective of the present effort was the accurate calculation of j-values in the Arctic twilight along NASA DC-8 flight tracks during the second SAGE III Ozone Loss and Validation Experiment (SOLVE II), based in Kiruna, Sweden (68 degrees N, 20 degrees E) during January-February 2003. The JHU/APL radiative transfer model was utilized to produce a large suite of j-values for photolysis processes (over 70 reactions) relevant to the upper troposphere and lower stratosphere. The calculations take into account the actual changes in ozone abundance and apparent albedo of clouds and the Earth surface along the aircraft flight tracks as observed by in situ and remote sensing platforms (e.g., EP-TOMS). A secondary objective was to analyze solar irradiance data from NCAR s Direct beam Irradiance Atmospheric Spectrometer (DIAS) on-board the NASA DC-8 and to start the development of a flexible, multi-species spectral fitting technique for the independent retrieval of O3,O2.02, and aerosol optical properties.

The systematic annotation of the three main GPCR families in Reactome.

PubMed

Jassal, Bijay; Jupe, Steven; Caudy, Michael; Birney, Ewan; Stein, Lincoln; Hermjakob, Henning; D'Eustachio, Peter

2010-07-29

Reactome is an open-source, freely available database of human biological pathways and processes. A major goal of our work is to provide an integrated view of cellular signalling processes that spans from ligand-receptor interactions to molecular readouts at the level of metabolic and transcriptional events. To this end, we have built the first catalogue of all human G protein-coupled receptors (GPCRs) known to bind endogenous or natural ligands. The UniProt database has records for 797 proteins classified as GPCRs and sorted into families A/1, B/2 and C/3 on the basis of amino acid sequence. To these records we have added details from the IUPHAR database and our own manual curation of relevant literature to create reactions in which 563 GPCRs bind ligands and also interact with specific G-proteins to initiate signalling cascades. We believe the remaining 234 GPCRs are true orphans. The Reactome GPCR pathway can be viewed as a detailed interactive diagram and can be exported in many forms. It provides a template for the orthology-based inference of GPCR reactions for diverse model organism species, and can be overlaid with protein-protein interaction and gene expression datasets to facilitate overrepresentation studies and other forms of pathway analysis. Database URL: http://www.reactome.org.

Nuclemeter: A Reaction-Diffusion Column for Quantifying Nucleic Acids Undergoing Enzymatic Amplification

NASA Astrophysics Data System (ADS)

Bau, Haim; Liu, Changchun; Killawala, Chitvan; Sadik, Mohamed; Mauk, Michael

2014-11-01

Real-time amplification and quantification of specific nucleic acid sequences plays a major role in many medical and biotechnological applications. In the case of infectious diseases, quantification of the pathogen-load in patient specimens is critical to assessing disease progression, effectiveness of drug therapy, and emergence of drug-resistance. Typically, nucleic acid quantification requires sophisticated and expensive instruments, such as real-time PCR machines, which are not appropriate for on-site use and for low resource settings. We describe a simple, low-cost, reactiondiffusion based method for end-point quantification of target nucleic acids undergoing enzymatic amplification. The number of target molecules is inferred from the position of the reaction-diffusion front, analogous to reading temperature in a mercury thermometer. We model the process with the Fisher Kolmogoroff Petrovskii Piscounoff (FKPP) Equation and compare theoretical predictions with experimental observations. The proposed method is suitable for nucleic acid quantification at the point of care, compatible with multiplexing and high-throughput processing, and can function instrument-free. C.L. was supported by NIH/NIAID K25AI099160; M.S. was supported by the Pennsylvania Ben Franklin Technology Development Authority; C.K. and H.B. were funded, in part, by NIH/NIAID 1R41AI104418-01A1.

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

When is an image a health claim? A false-recollection method to detect implicit inferences about products' health benefits.

PubMed

Klepacz, Naomi A; Nash, Robert A; Egan, M Bernadette; Hodgkins, Charo E; Raats, Monique M

2016-08-01

Images on food and dietary supplement packaging might lead people to infer (appropriately or inappropriately) certain health benefits of those products. Research on this issue largely involves direct questions, which could (a) elicit inferences that would not be made unprompted, and (b) fail to capture inferences made implicitly. Using a novel memory-based method, in the present research, we explored whether packaging imagery elicits health inferences without prompting, and the extent to which these inferences are made implicitly. In 3 experiments, participants saw fictional product packages accompanied by written claims. Some packages contained an image that implied a health-related function (e.g., a brain), and some contained no image. Participants studied these packages and claims, and subsequently their memory for seen and unseen claims were tested. When a health image was featured on a package, participants often subsequently recognized health claims that-despite being implied by the image-were not truly presented. In Experiment 2, these recognition errors persisted despite an explicit warning against treating the images as informative. In Experiment 3, these findings were replicated in a large consumer sample from 5 European countries, and with a cued-recall test. These findings confirm that images can act as health claims, by leading people to infer health benefits without prompting. These inferences appear often to be implicit, and could therefore be highly pervasive. The data underscore the importance of regulating imagery on product packaging; memory-based methods represent innovative ways to measure how leading (or misleading) specific images can be. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining

PubMed Central

Truccolo, Wilson

2017-01-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics (“order parameters”) inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. PMID:28336305

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.

PubMed

Truccolo, Wilson

2016-11-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. Published by Elsevier Ltd.

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.

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.

Statistical numeracy as a moderator of (pseudo)contingency effects on decision behavior.

PubMed

Fleig, Hanna; Meiser, Thorsten; Ettlin, Florence; Rummel, Jan

2017-03-01

Pseudocontingencies denote contingency estimates inferred from base rates rather than from cell frequencies. We examined the role of statistical numeracy for effects of such fallible but adaptive inferences on choice behavior. In Experiment 1, we provided information on single observations as well as on base rates and tracked participants' eye movements. In Experiment 2, we manipulated the availability of information on cell frequencies and base rates between conditions. Our results demonstrate that a focus on base rates rather than cell frequencies benefits pseudocontingency effects. Learners who are more proficient in (conditional) probability calculation prefer to rely on cell frequencies in order to judge contingencies, though, as was evident from their gaze behavior. If cell frequencies are available in summarized format, they may infer the true contingency between options and outcomes. Otherwise, however, even highly numerate learners are susceptible to pseudocontingency effects. Copyright © 2017 Elsevier B.V. All rights reserved.

Difference to Inference: teaching logical and statistical reasoning through on-line interactivity.

PubMed

Malloy, T E

2001-05-01

Difference to Inference is an on-line JAVA program that simulates theory testing and falsification through research design and data collection in a game format. The program, based on cognitive and epistemological principles, is designed to support learning of the thinking skills underlying deductive and inductive logic and statistical reasoning. Difference to Inference has database connectivity so that game scores can be counted as part of course grades.

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

PubMed

Astola, Laura; Molenaar, Jaap

2014-07-01

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

Impaired Relational Organization of Propositions, but Intact Transitive Inference, in Aging: Implications for Understanding Underlying Neural Integrity

ERIC Educational Resources Information Center

Ryan, Jennifer D.; Moses, Sandra N.; Villate, Christina

2009-01-01

The ability to perform relational proposition-based reasoning was assessed in younger and older adults using the transitive inference task in which subjects learned a series of premise pairs (A greater than B, B greater than C, C greater than D, D greater than E, E greater than F) and were asked to make inference judgments (B?D, B?E, C?E).…

Nuclear Diagnostics at the National Ignition Facility, 2013-2015

NASA Astrophysics Data System (ADS)

Yeamans, C. B.; Cassata, W. S.; Church, J. A.; Fittinghoff, D. N.; Gatu Johnson, M.; Gharibyan, N.; Határik, R.; Sayre, D. B.; Sio, H. W.; Bionta, R. M.; Bleuel, D. L.; Caggiano, J. A.; Cerjan, C. J.; Cooper, G. W.; Eckart, M. J.; Edwards, E. R.; Faye, S. A.; Forrest, C. J.; Frenje, J. A.; Glebov, V. Yu; Grant, P. M.; Grim, G. P.; Hartouni, E. P.; Herrmann, H. W.; Kilkenny, J. D.; Knauer, J. P.; Mackinnon, A. J.; Merrill, F. E.; Moody, K. J.; Moran, M. J.; Petrasso, R. D.; Phillips, T. W.; Rinderknecht, H. G.; Schneider, D. H. G.; Sepke, S. M.; Shaughnessy, D. A.; Stoeffl, W.; Velsko, C. A.; Volegov, P.

2016-05-01

The National Ignition Facility (NIF) relies on a suite of nuclear diagnostics to measure the neutronic output of experiments. Neutron time-of-flight (NTOF) and neutron activation diagnostics (NAD) provide performance metrics of absolute neutron yield and neutron spectral content: spectral width and non-thermal content, from which implosion physical quantities of temperature and scattering mass are inferred. Spatially-distributed flange- mounted NADs (FNAD) measure, with nearly identical systematic uncertainties, primary DT neutron emission to infer a whole-sky neutron field. An automated FNAD system is being developed. A magnetic recoil spectrometer (MRS) shares few systematics with comparable NTOF and NAD devices, and as such is deployed for independent measurement of the primary neutronic quantities. The gas-Cherenkov Gamma Reaction History (GRH) instrument records four energy channels of time-resolved gamma emission to measure nuclear bang time and burn width, as well as to infer carbon areal density in experiments utilizing plastic or diamond capsules. A neutron imaging system (NIS) takes two images of the neutron source, typically gated to create coregistered 13-15 MeV primary and 6-12 MeV downscattered images. The radiochemical analysis of gaseous samples (RAGS) instrument pumps target chamber gas to a chemical reaction and fractionation system configured with gamma counters, allowing measurement of radionuclides with half-lives as short as 8 seconds. Solid radiochemistry collectors (SRC) with backing NAD foils collect target debris, where activated materials from the target assembly are used as indicators of neutron spectrum content, and also serve as the primary diagnostic for nuclear forensic science experiments. Particle time-of-flight (PTOF) measures compression-bang time using DT- or DD-neutrons, as well as shock bang-time using D3He-protons for implosions with lower x-ray background. In concert, these diagnostics serve to measure the basic and advanced quantities required to understand NIF experimental results.

Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization

PubMed Central

Hasegawa, Takanori; Yamaguchi, Rui; Nagasaki, Masao; Miyano, Satoru; Imoto, Seiya

2014-01-01

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information. PMID:25162401

Inferring Muscle-Tendon Unit Power from Ankle Joint Power during the Push-Off Phase of Human Walking: Insights from a Multiarticular EMG-Driven Model

PubMed Central

2016-01-01

Introduction Inverse dynamics joint kinetics are often used to infer contributions from underlying groups of muscle-tendon units (MTUs). However, such interpretations are confounded by multiarticular (multi-joint) musculature, which can cause inverse dynamics to over- or under-estimate net MTU power. Misestimation of MTU power could lead to incorrect scientific conclusions, or to empirical estimates that misguide musculoskeletal simulations, assistive device designs, or clinical interventions. The objective of this study was to investigate the degree to which ankle joint power overestimates net plantarflexor MTU power during the Push-off phase of walking, due to the behavior of the flexor digitorum and hallucis longus (FDHL)–multiarticular MTUs crossing the ankle and metatarsophalangeal (toe) joints. Methods We performed a gait analysis study on six healthy participants, recording ground reaction forces, kinematics, and electromyography (EMG). Empirical data were input into an EMG-driven musculoskeletal model to estimate ankle power. This model enabled us to parse contributions from mono- and multi-articular MTUs, and required only one scaling and one time delay factor for each subject and speed, which were solved for based on empirical data. Net plantarflexing MTU power was computed by the model and quantitatively compared to inverse dynamics ankle power. Results The EMG-driven model was able to reproduce inverse dynamics ankle power across a range of gait speeds (R2 ≥ 0.97), while also providing MTU-specific power estimates. We found that FDHL dynamics caused ankle power to slightly overestimate net plantarflexor MTU power, but only by ~2–7%. Conclusions During Push-off, FDHL MTU dynamics do not substantially confound the inference of net plantarflexor MTU power from inverse dynamics ankle power. However, other methodological limitations may cause inverse dynamics to overestimate net MTU power; for instance, due to rigid-body foot assumptions. Moving forward, the EMG-driven modeling approach presented could be applied to understand other tasks or larger multiarticular MTUs. PMID:27764110

Inferring Muscle-Tendon Unit Power from Ankle Joint Power during the Push-Off Phase of Human Walking: Insights from a Multiarticular EMG-Driven Model.

PubMed

Honert, Eric C; Zelik, Karl E

2016-01-01

Inverse dynamics joint kinetics are often used to infer contributions from underlying groups of muscle-tendon units (MTUs). However, such interpretations are confounded by multiarticular (multi-joint) musculature, which can cause inverse dynamics to over- or under-estimate net MTU power. Misestimation of MTU power could lead to incorrect scientific conclusions, or to empirical estimates that misguide musculoskeletal simulations, assistive device designs, or clinical interventions. The objective of this study was to investigate the degree to which ankle joint power overestimates net plantarflexor MTU power during the Push-off phase of walking, due to the behavior of the flexor digitorum and hallucis longus (FDHL)-multiarticular MTUs crossing the ankle and metatarsophalangeal (toe) joints. We performed a gait analysis study on six healthy participants, recording ground reaction forces, kinematics, and electromyography (EMG). Empirical data were input into an EMG-driven musculoskeletal model to estimate ankle power. This model enabled us to parse contributions from mono- and multi-articular MTUs, and required only one scaling and one time delay factor for each subject and speed, which were solved for based on empirical data. Net plantarflexing MTU power was computed by the model and quantitatively compared to inverse dynamics ankle power. The EMG-driven model was able to reproduce inverse dynamics ankle power across a range of gait speeds (R2 ≥ 0.97), while also providing MTU-specific power estimates. We found that FDHL dynamics caused ankle power to slightly overestimate net plantarflexor MTU power, but only by ~2-7%. During Push-off, FDHL MTU dynamics do not substantially confound the inference of net plantarflexor MTU power from inverse dynamics ankle power. However, other methodological limitations may cause inverse dynamics to overestimate net MTU power; for instance, due to rigid-body foot assumptions. Moving forward, the EMG-driven modeling approach presented could be applied to understand other tasks or larger multiarticular MTUs.

IMNN: Information Maximizing Neural Networks

NASA Astrophysics Data System (ADS)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.

A review of causal inference for biomedical informatics

PubMed Central

Kleinberg, Samantha; Hripcsak, George

2011-01-01

Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods. PMID:21782035

Thermochemistry of CaO-MgO-Al2O3-SiO2 (CMAS) and Advanced Thermal and Environmental Barrier Coating Systems

NASA Technical Reports Server (NTRS)

Costa, Gustavo C. C.; Zhu, Dongming

2016-01-01

CaO-MgO-Al2O3-SiO2 (CMAS) oxides are constituents in a broad number of materials and minerals which have recently inferred to discussions in materials science, planetary science, geochemistry and cosmochemistry communities. In materials science, there is increasing interest in the degradation studies of thermal (TBC) and environmental (EBC) barrier coatings of gas turbines by molten CMAS. These coatings have been explored to be applied on silicon-based ceramics and composites which are lighter and more temperature capable hot-section materials of gas turbines than the current Ni-based superalloys. The degradation of the coatings occurs when CMAS minerals carried by the intake air into gas turbines, e.g. in aircraft engines, reacts at high temperatures (1000C) with the coating materials. This causes premature failure of the static and rotating components of the turbine engines. We discuss some preliminary results of the reactions between CMAS and Rare-Earth (RE Y, Yb and Gd) oxide stabilized ZrO2 systems, and stability of the resulting oxides and silicates.

Exploratory tests of two strut fuel injectors for supersonic combustion

NASA Technical Reports Server (NTRS)

Anderson, G. Y.; Gooderum, P. B.

1974-01-01

Results of supersonic mixing and combustion tests performed with two simple strut injector configurations, one with parallel injectors and one with perpendicular injectors, are presented and analyzed. Good agreement is obtained between static pressure measured on the duct wall downstream of the strut injectors and distributions obtained from one-dimensional calculations. Measured duct heat load agrees with results of the one-dimensional calculations for moderate amounts of reaction, but is underestimated when large separated regions occur near the injection location. For the parallel injection strut, good agreement is obtained between the shape of the injected fuel distribution inferred from gas sample measurements at the duct exit and the distribution calculated with a multiple-jet mixing theory. The overall fraction of injected fuel reacted in the multiple-jet calculation closely matches the amount of fuel reaction necessary to match static pressure with the one-dimensional calculation. Gas sample measurements with the perpendicular injection strut also give results consistent with the amount of fuel reaction in the one-dimensional calculation.

Analysis of electron transfer processes across liquid/liquid interfaces: estimation of free energy of activation using diffuse boundary model.

PubMed

Harinipriya, S; Sangaranarayanan, M V

2006-01-31

The evaluation of the free energy of activation pertaining to the electron-transfer reactions occurring at liquid/liquid interfaces is carried out employing a diffuse boundary model. The interfacial solvation numbers are estimated using a lattice gas model under the quasichemical approximation. The standard reduction potentials of the redox couples, appropriate inner potential differences, dielectric permittivities, as well as the width of the interface are included in the analysis. The methodology is applied to the reaction between [Fe(CN)6](3-/4-) and [Lu(biphthalocyanine)](3+/4+) at water/1,2-dichloroethane interface. The rate-determining step is inferred from the estimated free energy of activation for the constituent processes. The results indicate that the solvent shielding effect and the desolvation of the reactants at the interface play a central role in dictating the free energy of activation. The heterogeneous electron-transfer rate constant is evaluated from the molar reaction volume and the frequency factor.

7Be(n,α) and 7Be(n,p) cross-section measurement for the cosmological lithium problem at the n_TOF facility at CERN

NASA Astrophysics Data System (ADS)

Barbagallo, M.; Colonna, N.; Aberle, O.; Andrzejewski, J.; Audouin, L.; Bécares, V.; Bacak, M.; Balibrea, J.; Barros, S.; Bečvář, F.; Beinrucker, C.; Berthoumieux, E.; Billowes, J.; Bosnar, D.; Brugger, M.; Caamaño, M.; Calviño, F.; Calviani, M.; Cano-Ott, D.; Cardella, R.; Casanovas, A.; Castelluccio, D. M.; Cerutti, F.; Chen, Y. H.; Chiaveri, E.; Cortés, G.; Cortés-Giraldo, M. A.; Cosentino, L.; Damone, L. A.; Diakaki, M.; Domingo-Pardo, C.; Dressler, R.; Dupont, E.; Durán, I.; Fernández-Domínguez, B.; Ferrari, A.; Ferreira, P.; Finocchiaro, P.; Furman, V.; Göbel, K.; García, A. R.; Gawlik, A.; Glodariu, T.; Gonçalves, I. F.; González, E.; Goverdovski, A.; Griesmayer, E.; Guerrero, C.; Gunsing, F.; Harada, H.; Heftrich, T.; Heinitz, S.; Heyse, J.; Jenkins, D. G.; Jericha, E.; Käppeler, F.; Kadi, Y.; Katabuchi, T.; Kavrigin, P.; Ketlerov, V.; Khryachkov, V.; Kimura, A.; Kivel, N.; Kokkoris, M.; Krtička, M.; Leal-Cidoncha, E.; Lederer, C.; Leeb, H.; Lerendegui-Marco, J.; Meo, S. Lo; Lonsdale, S. J.; Losito, R.; Macina, D.; Marganiec, J.; Martínez, T.; Massimi, C.; Mastinu, P.; Mastromarco, M.; Matteucci, F.; Maugeri, E. A.; Mendoza, E.; Mengoni, A.; Milazzo, P. M.; Mingrone, F.; Mirea, M.; Montesano, S.; Musumarra, A.; Nolte, R.; Oprea, A.; Patronis, N.; Pavlik, A.; Perkowski, J.; Porras, J. I.; Praena, J.; Quesada, J. M.; Rajeev, K.; Rauscher, T.; Reifarth, R.; Riego-Perez, A.; Rout, P. C.; Rubbia, C.; Ryan, J. A.; Sabaté-Gilarte, M.; Saxena, A.; Schillebeeckx, P.; Schmidt, S.; Schumann, D.; Sedyshev, P.; Smith, A. G.; Stamatopoulos, A.; Tagliente, G.; Tain, J. L.; Tarifeño-Saldivia, A.; Tassan-Got, L.; Tsinganis, A.; Valenta, S.; Vannini, G.; Variale, V.; Vaz, P.; Ventura, A.; Vlachoudis, V.; Vlastou, R.; Wallner, A.; Warren, S.; Weigand, M.; Weiss, C.; Wolf, C.; Woods, P. J.; Wright, T.; Žugec, P.

2017-09-01

The Cosmological Lithium Problem refers to the large discrepancy between the abundance of primordial 7Li predicted by the standard theory of Big Bang Nucleosynthesis and the value inferred from the so-called "Spite plateau" in halo stars. A possible explanation for this longstanding puzzle in Nuclear Astrophysics is related to the incorrect estimation of the destruction rate of 7Be, which is responsible for the production of 95% of primordial Lithium. While charged-particle induced reactions have mostly been ruled out, data on the 7Be(n,α) and 7Be(n,p) reactions are scarce or completely missing, so that a large uncertainty still affects the abundance of 7Li predicted by the standard theory of Big Bang Nucleosynthesis. Both reactions have been measured at the n_TOF facility at CERN, providing for the first time data in a wide neutron energy range.

A Trojan Horse Approach to the Production of {sup 18}F in Novae

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

Cognata, M. La; Pizzone, R. G.; Cherubini, S.

Crucial information on nova nucleosynthesis can be potentially inferred from γ -ray signals powered by {sup 18}F decay. Therefore, the reaction network producing and destroying this radioactive isotope has been extensively studied in the last years. Among those reactions, the {sup 18}F(p, α ){sup 15}O cross-section has been measured by means of several dedicated experiments, both using direct and indirect methods. The presence of interfering resonances in the energy region of astrophysical interest has been reported by many authors including the recent applications of the Trojan Horse Method. In this work, we evaluate what changes are introduced by the Trojanmore » Horse data in the {sup 18}F(p, α ){sup 15}O astrophysical factor recommended in a recent R-matrix analysis, accounting for existing direct and indirect measurements. Then the updated reaction rate is calculated and parameterized and implications of the new results on nova nucleosynthesis are thoroughly discussed.« less

Characterization of galactomannan derivatives in roasted coffee beverages.

PubMed

Nunes, Fernando M; Reis, Ana; Domingues, M Rosário M; Coimbra, Manuel A

2006-05-03

In this work, the galactomannans from roasted coffee infusions were purified by 50% ethanol precipitation, anion exchange chromatography, and phenylboronic acid-immobilized Sepharose chromatography. Specific enzymatic hydrolysis of the beta-(1-->4)-D-mannan backbone allowed us to conclude that the galactomannans of roasted coffee infusions are high molecular weight supports of low molecular weight brown compounds. Also, the molecular weight of the brown compounds linked to the galactomannan increases with the increase of the coffee degree of roast. The reaction pathways of galactomannans during the coffee roasting process were inferred from the detection of specific chemical markers by gas chromatography-electron impact mass spectrometry and/or electrospray ionization tandem mass spectrometry. Maillard reaction, caramelization, isomerization, oxidation, and decarboxylation pathways were identified by detection of Amadori compounds, 1,6-beta-anhydromannose, fructose, glucose, mannonic acid, 2-ketogluconic acid, and arabinonic acid in the reducing end of the obtained oligosaccharides. The implication of the several competitive reaction pathways is discussed and related to the structural changes of the galactomannans present in the roasted coffee infusions.

The Origins and Evolution of Molecules in Icy Solids

NASA Technical Reports Server (NTRS)

Hudson, Reggie L.; Moore, Marla H.

2010-01-01

Astronomical observations of the past few decades have revealed the existence of a variety of molecules in extraterrestrial ices. These molecules include H2O, CO, and CO2, and organics such as CH4, CH30H, and C2H6. Some ices are dominated by polar molecules, while non-polar species appear to dominate others. Observations, mainly in the radio and IR regions, have allowed the inference of other solid-phase molecules whose formation remains difficult to explain by gas-phase chemistry alone. Several laboratory research groups have reported on extensive experiments on the solid-phase reaction chemistry of icy materials, generally as initiated by either ionizing radiation or vacuum-UV photons. These experiments not only permit molecular identifications to be made from astronomical observations, but also allow predictions of yet unidentified molecules. This laboratory approach has evolved over more than 30 years with much of the earliest work focusing on complex mixtures thought to represent either cometary or interstellar ices. Although those early experiments documented a rich solid-state photo- and radiation chemistry, they revealed few details of reactions for particular molecules, partly due to the multi-component nature of the samples. Since then, model systems have been examined that allow the chemistry of individual species and specific reactions to be probed. Reactions involving most of the smaller astronomical molecules have now been studied and specific processes identified. Current laboratory work suggests that a variety of reactions occur in extraterrestrial ices, including acid-base processes, radical dimerizations, proton transfers, oxidations, reductions, and isomerizations. This workshop presentation will focus on chemical reactions relevant to solar system and interstellar ices. While most of the work will be drawn from that to which the speaker has contributed, results from other laboratories also will be included. Suggestions for future studies will be made, with an emphasis on some present deficiencies. The speaker's work has been generously supported by these NASA research programs: Cassini Data Analysis, Exobiology, Mars Fundamental Research, Outer Planets Research, Planetary Atmospheres, Planetary Geology and Geophysics, and the NASA Astrobiology Institute.

Mirrored continuum and molecular scale simulations of the ignition of gamma phase RDX

NASA Astrophysics Data System (ADS)

Stewart, D. Scott; Chaudhuri, Santanu; Joshi, Kaushik; Lee, Kiabek

2015-06-01

We consider the ignition of a high-pressure gamma-phase of an explosive crystal of RDX which forms during overdriven shock initiation. Molecular dynamics (MD), with first-principles based or reactive force field based molecular potentials, provides a description of the chemistry as an extremely complex reaction network. The results of the molecular simulation is analyzed by sorting molecular product fragments into high and low molecular groups, to represent identifiable components that can be interpreted by a continuum model. A continuum model based on a Gibbs formulation, that has a single temperature and stress state for the mixture is used to represent the same RDX material and its chemistry. Each component in the continuum model has a corresponding Gibbs continuum potential, that are in turn inferred from molecular MD informed equation of state libraries such as CHEETAH, or are directly simulated by Monte Carlo MD simulations. Information about transport, kinetic rates and diffusion are derived from the MD simulation and the growth of a reactive hot spot in the RDX is studied with both simulations that mirror the other results to provide an essential, continuum/atomistic link. Supported by N000014-12-1-0555, subaward-36561937 (ONR).

A Comparative Study of Spectral Auroral Intensity Predictions From Multiple Electron Transport Models

NASA Astrophysics Data System (ADS)

Grubbs, Guy; Michell, Robert; Samara, Marilia; Hampton, Donald; Hecht, James; Solomon, Stanley; Jahn, Jorg-Micha

2018-01-01

It is important to routinely examine and update models used to predict auroral emissions resulting from precipitating electrons in Earth's magnetotail. These models are commonly used to invert spectral auroral ground-based images to infer characteristics about incident electron populations when in situ measurements are unavailable. In this work, we examine and compare auroral emission intensities predicted by three commonly used electron transport models using varying electron population characteristics. We then compare model predictions to same-volume in situ electron measurements and ground-based imaging to qualitatively examine modeling prediction error. Initial comparisons showed differences in predictions by the GLobal airglOW (GLOW) model and the other transport models examined. Chemical reaction rates and radiative rates in GLOW were updated using recent publications, and predictions showed better agreement with the other models and the same-volume data, stressing that these rates are important to consider when modeling auroral processes. Predictions by each model exhibit similar behavior for varying atmospheric constants, energies, and energy fluxes. Same-volume electron data and images are highly correlated with predictions by each model, showing that these models can be used to accurately derive electron characteristics and ionospheric parameters based solely on multispectral optical imaging data.

Single-Cell-Based Platform for Copy Number Variation Profiling through Digital Counting of Amplified Genomic DNA Fragments.

PubMed

Li, Chunmei; Yu, Zhilong; Fu, Yusi; Pang, Yuhong; Huang, Yanyi

2017-04-26

We develop a novel single-cell-based platform through digital counting of amplified genomic DNA fragments, named multifraction amplification (mfA), to detect the copy number variations (CNVs) in a single cell. Amplification is required to acquire genomic information from a single cell, while introducing unavoidable bias. Unlike prevalent methods that directly infer CNV profiles from the pattern of sequencing depth, our mfA platform denatures and separates the DNA molecules from a single cell into multiple fractions of a reaction mix before amplification. By examining the sequencing result of each fraction for a specific fragment and applying a segment-merge maximum likelihood algorithm to the calculation of copy number, we digitize the sequencing-depth-based CNV identification and thus provide a method that is less sensitive to the amplification bias. In this paper, we demonstrate a mfA platform through multiple displacement amplification (MDA) chemistry. When performing the mfA platform, the noise of MDA is reduced; therefore, the resolution of single-cell CNV identification can be improved to 100 kb. We can also determine the genomic region free of allelic drop-out with mfA platform, which is impossible for conventional single-cell amplification methods.

Learning Quantitative Sequence-Function Relationships from Massively Parallel Experiments

NASA Astrophysics Data System (ADS)

Atwal, Gurinder S.; Kinney, Justin B.

2016-03-01

A fundamental aspect of biological information processing is the ubiquity of sequence-function relationships—functions that map the sequence of DNA, RNA, or protein to a biochemically relevant activity. Most sequence-function relationships in biology are quantitative, but only recently have experimental techniques for effectively measuring these relationships been developed. The advent of such "massively parallel" experiments presents an exciting opportunity for the concepts and methods of statistical physics to inform the study of biological systems. After reviewing these recent experimental advances, we focus on the problem of how to infer parametric models of sequence-function relationships from the data produced by these experiments. Specifically, we retrace and extend recent theoretical work showing that inference based on mutual information, not the standard likelihood-based approach, is often necessary for accurately learning the parameters of these models. Closely connected with this result is the emergence of "diffeomorphic modes"—directions in parameter space that are far less constrained by data than likelihood-based inference would suggest. Analogous to Goldstone modes in physics, diffeomorphic modes arise from an arbitrarily broken symmetry of the inference problem. An analytically tractable model of a massively parallel experiment is then described, providing an explicit demonstration of these fundamental aspects of statistical inference. This paper concludes with an outlook on the theoretical and computational challenges currently facing studies of quantitative sequence-function relationships.

Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood.

PubMed

Wu, Yufeng

2012-03-01

Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets. © 2011 The Author. Evolution© 2011 The Society for the Study of Evolution.

Use of the Richtmyer-Meshkov Instability to Infer Yield Stress at High-Energy Densities

NASA Astrophysics Data System (ADS)

Dimonte, Guy; Terrones, G.; Cherne, F. J.; Germann, T. C.; Dupont, V.; Kadau, K.; Buttler, W. T.; Oro, D. M.; Morris, C.; Preston, D. L.

2011-12-01

We use the Richtmyer-Meshkov instability (RMI) at a metal-gas interface to infer the metal’s yield stress (Y) under shock loading and release. We first model how Y stabilizes the RMI using hydrodynamics simulations with a perfectly plastic constitutive relation for copper (Cu). The model is then tested with molecular dynamics (MD) of crystalline Cu by comparing the inferred Y from RMI simulations with direct stress-strain calculations, both with MD at the same conditions. Finally, new RMI experiments with solid Cu validate our simulation-based model and infer Y˜0.47GPa for a 36 GPa shock.

Uncertainties in cylindrical anode current inferences on pulsed power drivers

NASA Astrophysics Data System (ADS)

Porwitzky, Andrew; Brown, Justin

2018-06-01

For over a decade, velocimetry based techniques have been used to infer the electrical current delivered to dynamic materials properties experiments on pulsed power drivers such as the Z Machine. Though originally developed for planar load geometries, in recent years, inferring the current delivered to cylindrical coaxial loads has become a valuable diagnostic tool for numerous platforms. Presented is a summary of uncertainties that can propagate through the current inference technique when applied to expanding cylindrical anodes. An equation representing quantitative uncertainty is developed which shows the unfold method to be accurate to a few percent above 10 MA of load current.

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.

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.

The abundance of boron in three halo stars

NASA Technical Reports Server (NTRS)

Duncan, Douglas K.; Lambert, David L.; Lemke, Michael

1992-01-01

B abundances for three halo stars: HD 140283, HD 19445, and HD 201891 are presented. Using recent determinations of the Be abundance in HD 140283, B/Be of 10 +5/-4 is found for this star, and similar ratios are inferred for HD 19445 and HD 201891. This ratio is equal to the minimum value of 10 expected from a synthesis of B and Be by high-energy cosmic-ray spallation reactions in the interstellar medium. It is shown that the accompanying synthesis of Li by alpha on alpha fusion reactions is probably a minor contributor to the observed 'primordial' Li of halo stars. The observed constant ratios of B/O and Be/O are expected if the principal channel of synthesis involves cosmic-ray CNO nuclei from the supernovae colliding with interstellar protons.

Development of a Spacecraft Materials Selector Expert System

NASA Technical Reports Server (NTRS)

Pippin, G.; Kauffman, W. (Technical Monitor)

2002-01-01

This report contains a description of the knowledge base tool and examples of its use. A downloadable version of the Spacecraft Materials Selector (SMS) knowledge base is available through the NASA Space Environments and Effects Program. The "Spacecraft Materials Selector" knowledge base is part of an electronic expert system. The expert system consists of an inference engine that contains the "decision-making" code and the knowledge base that contains the selected body of information. The inference engine is a software package previously developed at Boeing, called the Boeing Expert System Tool (BEST) kit.

Signatures of a quantum diffusion limited hydrogen atom tunneling reaction.

PubMed

Balabanoff, Morgan E; Ruzi, Mahmut; Anderson, David T

2017-12-20

We are studying the details of hydrogen atom (H atom) quantum diffusion in highly enriched parahydrogen (pH 2 ) quantum solids doped with chemical species in an effort to better understand H atom transport and reactivity under these conditions. In this work we present kinetic studies of the 193 nm photo-induced chemistry of methanol (CH 3 OH) isolated in solid pH 2 . Short-term irradiation of CH 3 OH at 1.8 K readily produces CH 2 O and CO which we detect using FTIR spectroscopy. The in situ photochemistry also produces CH 3 O and H atoms which we can infer from the post-photolysis reaction kinetics that display significant CH 2 OH growth. The CH 2 OH growth kinetics indicate at least three separate tunneling reactions contribute; (i) reactions of photoproduced CH 3 O with the pH 2 host, (ii) H atom reactions with the CH 2 O photofragment, and (iii) long-range migration of H atoms and reaction with CH 3 OH. We assign the rapid CH 2 OH growth to the following CH 3 O + H 2 → CH 3 OH + H → CH 2 OH + H 2 two-step sequential tunneling mechanism by conducting analogous kinetic measurements using deuterated methanol (CD 3 OD). By performing photolysis experiments at 1.8 and 4.3 K, we show the post-photolysis reaction kinetics change qualitatively over this small temperature range. We use this qualitative change in the reaction kinetics with temperature to identify reactions that are quantum diffusion limited. While these results are specific to the conditions that exist in pH 2 quantum solids, they have direct implications on the analogous low temperature H atom tunneling reactions that occur on metal surfaces and on interstellar grains.

Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range

NASA Astrophysics Data System (ADS)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Newman, Andrew J.; Hughes, Mimi; McGurk, Bruce; Lundquist, Jessica D.

2018-01-01

Given uncertainty in precipitation gauge-based gridded datasets over complex terrain, we use multiple streamflow observations as an additional source of information about precipitation, in order to identify spatial and temporal differences between a gridded precipitation dataset and precipitation inferred from streamflow. We test whether gridded datasets capture across-crest and regional spatial patterns of variability, as well as year-to-year variability and trends in precipitation, in comparison to precipitation inferred from streamflow. We use a Bayesian model calibration routine with multiple lumped hydrologic model structures to infer the most likely basin-mean, water-year total precipitation for 56 basins with long-term (>30 year) streamflow records in the Sierra Nevada mountain range of California. We compare basin-mean precipitation derived from this approach with basin-mean precipitation from a precipitation gauge-based, 1/16° gridded dataset that has been used to simulate and evaluate trends in Western United States streamflow and snowpack over the 20th century. We find that the long-term average spatial patterns differ: in particular, there is less precipitation in the gridded dataset in higher-elevation basins whose aspect faces prevailing cool-season winds, as compared to precipitation inferred from streamflow. In a few years and basins, there is less gridded precipitation than there is observed streamflow. Lower-elevation, southern, and east-of-crest basins show better agreement between gridded and inferred precipitation. Implied actual evapotranspiration (calculated as precipitation minus streamflow) then also varies between the streamflow-based estimates and the gridded dataset. Absolute uncertainty in precipitation inferred from streamflow is substantial, but the signal of basin-to-basin and year-to-year differences are likely more robust. The findings suggest that considering streamflow when spatially distributing precipitation in complex terrain may improve its representation, particularly for basins whose orientations (e.g., windward-facing) are favored for orographic precipitation enhancement.

Forensic genetic informativeness of an SNP panel consisting of 19 multi-allelic SNPs.

PubMed

Gao, Zehua; Chen, Xiaogang; Zhao, Yuancun; Zhao, Xiaohong; Zhang, Shu; Yang, Yiwen; Wang, Yufang; Zhang, Ji

2018-05-01

Current research focusing on forensic personal identification, phenotype inference and ancestry information on single-nucleotide polymorphisms (SNPs) has been widely reported. In the present study, we focused on tetra-allelic SNPs in the Chinese Han population. A total of 48 tetra-allelic SNPs were screened out from the Chinese Han population of the 1000 Genomes Database, including Chinese Han in Beijing (CHB) and Chinese Han South (CHS). Considering the forensic genetic requirement for the polymorphisms, only 11 tetra-allelic SNPs with a heterozygosity >0.06 were selected for further multiplex panel construction. In order to meet the demands of personal identification and parentage identification, an additional 8 tri-allelic SNPs were combined into the final multiplex panel. To ensure application in the degraded DNA analysis, all the PCR products were designed to be 87-188 bp. Employing multiple PCR reactions and SNaPshot minisequencing, 511 unrelated Chinese Han individuals from Sichuan were genotyped. The combined match probability (CMP), combined discrimination power (CDP), and cumulative probability of exclusion (CPE) of the panel were 6.07 × 10 -11 , 0.9999999999393 and 0.996764, respectively. Based on the population data retrieved from the 1000 Genomes Project, Fst values between Chinese Han in Sichuan (SCH) and all the populations included in the 1000 Genomes Project were calculated. The results indicated that two SNPs in this panel may contain ancestry information and may be used as markers of forensic biogeographical ancestry inference. Copyright © 2018 Elsevier B.V. All rights reserved.

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis

PubMed Central

Astola, Laura; Molenaar, Jaap

2014-01-01

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data. PMID:27600344

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

On the Inference of Functional Circadian Networks Using Granger Causality

PubMed Central

Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.

2015-01-01

Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748

Tracing iron-carbon redox from surface to core

NASA Astrophysics Data System (ADS)

McCammon, C. A.; Cerantola, V.; Bykova, E.; Kupenko, I.; Bykov, M.; Chumakov, A. I.; Rüffer, R.; Dubrovinsky, L. S.

2017-12-01

Numerous redox reactions separate the Earth's oxidised surface from its reduced core. Many involve iron, the Earth's most abundant element and the mantle's most abundant transition element. Most iron redox reactions (although not all) also involve other elements, including carbon, where iron-carbon interactions drive a number of important processes within the Earth, for example diamond formation. Many of the Earth's redox boundaries are sharp, much like the seismic properties that define them, for example between the lower mantle and the core. Other regions that appear seismically homogeneous, for example the lower mantle, harbour a wealth of reactions between oxidised and reduced phases of iron and carbon. We have undertaken many experiments at high pressure and high temperature on phases containing iron and carbon using synchrotron-based X-rays to probe structures and iron oxidation states. Results demonstrate the dominant role that crystal structures play in determining the stable oxidation states of iron and carbon, even when oxygen fugacity (and common sense) would suggest otherwise. Iron in bridgmanite, for example, occurs predominantly in its oxidised form (ferric iron) throughout the lower mantle, despite the inferred reducing conditions. Newly discovered structures of iron carbonate also stabilise ferric iron, while simultaneously reducing some carbon to diamond to balance charge. Other high-pressure iron carbonates appear to be associated with the emerging zoo of iron oxide phases, involving transitions between ferrous and ferric iron through the exchange of oxygen. The presentation will trace redox relations between iron and carbon from the Earth's surface to its core, with an emphasis on recent experimental results.

High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics

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

Pan, Jian-Bo; Ji, Nan; Pan, Wen

2014-01-01

Drugs may induce adverse drug reactions (ADRs) when they unexpectedly bind to proteins other than their therapeutic targets. Identification of these undesired protein binding partners, called off-targets, can facilitate toxicity assessment in the early stages of drug development. In this study, a computational framework was introduced for the exploration of idiosyncratic mechanisms underlying analgesic-induced severe adverse drug reactions (SADRs). The putative analgesic-target interactions were predicted by performing reverse docking of analgesics or their active metabolites against human/mammal protein structures in a high-throughput manner. Subsequently, bioinformatics analyses were undertaken to identify ADR-associated proteins (ADRAPs) and pathways. Using the pathways and ADRAPsmore » that this analysis identified, the mechanisms of SADRs such as cardiac disorders were explored. For instance, 53 putative ADRAPs and 24 pathways were linked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments. Moreover, it was inferred that pathways such as base excision repair, glycolysis/glyconeogenesis, ErbB signaling, calcium signaling, and phosphatidyl inositol signaling likely play pivotal roles in drug-induced cardiac disorders. In conclusion, our framework offers an opportunity to globally understand SADRs at the molecular level, which has been difficult to realize through experiments. It also provides some valuable clues for drug repurposing. - Highlights: • A novel computational framework was developed for mechanistic study of SADRs. • Off-targets of drugs were identified in large scale and in a high-throughput manner. • SADRs like cardiac disorders were systematically explored in molecular networks. • A number of ADR-associated proteins were identified.« less

Consistent Modeling of GS 1826-24 X-Ray Bursts for Multiple Accretion Rates Demonstrates the Possibility of Constraining rp-process Reaction Rates

DOE PAGES

Meisel, Zach

2018-06-21

Type-I X-ray burst light curves encode unique information about the structure of accreting neutron stars and the nuclear reaction rates of the rp-process that powers bursts. Using the first model calculations of hydrogen/helium-burning bursts for a large range of astrophysical conditions performed with the code MESA, this work shows that simultaneous model–observation comparisons for bursts from several accretion ratesmore » $$\\dot{M}$$ are required to remove degeneracies in astrophysical conditions that otherwise reproduce bursts for a single $$\\dot{M}$$ and that such consistent multi-epoch modeling could possibly limit the 15O(α, γ) 19Ne reaction rate. Comparisons to the 1998, 2000, and 2007 bursting epochs of the neutron star GS 1826-24 show that $$\\dot{M}$$ must be larger than previously inferred and that the shallow heating in this source must be below 0.5 MeV/u, providing a new method to constrain the shallow heating mechanism in the outer layers of accreting neutron stars. Lastly, features of the light curve rise are used to demonstrate that a lower limit could likely be placed on the 15O(α, γ) reaction rate, demonstrating the possibility of constraining nuclear reaction rates with X-ray burst light curves.« less

Consistent Modeling of GS 1826-24 X-Ray Bursts for Multiple Accretion Rates Demonstrates the Possibility of Constraining rp-process Reaction Rates

NASA Astrophysics Data System (ADS)

Meisel, Zach

2018-06-01

Type-I X-ray burst light curves encode unique information about the structure of accreting neutron stars and the nuclear reaction rates of the rp-process that powers bursts. Using the first model calculations of hydrogen/helium-burning bursts for a large range of astrophysical conditions performed with the code MESA, this work shows that simultaneous model–observation comparisons for bursts from several accretion rates \\dot{M} are required to remove degeneracies in astrophysical conditions that otherwise reproduce bursts for a single \\dot{M} and that such consistent multi-epoch modeling could possibly limit the 15O(α, γ)19Ne reaction rate. Comparisons to the 1998, 2000, and 2007 bursting epochs of the neutron star GS 1826-24 show that \\dot{M} must be larger than previously inferred and that the shallow heating in this source must be below 0.5 MeV/u, providing a new method to constrain the shallow heating mechanism in the outer layers of accreting neutron stars. Features of the light curve rise are used to demonstrate that a lower limit could likely be placed on the 15O(α, γ) reaction rate, demonstrating the possibility of constraining nuclear reaction rates with X-ray burst light curves.

Consistent Modeling of GS 1826-24 X-Ray Bursts for Multiple Accretion Rates Demonstrates the Possibility of Constraining rp-process Reaction Rates

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

Meisel, Zach

Type-I X-ray burst light curves encode unique information about the structure of accreting neutron stars and the nuclear reaction rates of the rp-process that powers bursts. Using the first model calculations of hydrogen/helium-burning bursts for a large range of astrophysical conditions performed with the code MESA, this work shows that simultaneous model–observation comparisons for bursts from several accretion ratesmore » $$\\dot{M}$$ are required to remove degeneracies in astrophysical conditions that otherwise reproduce bursts for a single $$\\dot{M}$$ and that such consistent multi-epoch modeling could possibly limit the 15O(α, γ) 19Ne reaction rate. Comparisons to the 1998, 2000, and 2007 bursting epochs of the neutron star GS 1826-24 show that $$\\dot{M}$$ must be larger than previously inferred and that the shallow heating in this source must be below 0.5 MeV/u, providing a new method to constrain the shallow heating mechanism in the outer layers of accreting neutron stars. Lastly, features of the light curve rise are used to demonstrate that a lower limit could likely be placed on the 15O(α, γ) reaction rate, demonstrating the possibility of constraining nuclear reaction rates with X-ray burst light curves.« less

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

PubMed

Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming

2014-11-30

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

The next chapter in experimental petrology: Metamorphic dehydration of polycrystalline gypsum captured in 3D microtomographic time series datasets

NASA Astrophysics Data System (ADS)

Bedford, John; Fusseis, Florian; Leclere, Henry; Wheeler, John; Faulkner, Dan

2016-04-01

Nucleation and growth of new minerals in response to disequilibrium is the most fundamental metamorphic process. However, our current kinetic models of metamorphic reactions are largely based on inference from fossil mineral assemblages, rather than from direct observation. The experimental investigation of metamorphism has also been limited, typically to concealed vessels that restrict the possibility of direct microstructural monitoring. Here we present one of the first time series datasets that captures a metamorphic reaction, dehydration of polycrystalline gypsum to form hemihydrate, in a series of three dimensional x-ray microtomographic datasets. We achieved this by installing an x-ray transparent hydrothermal cell (Fusseis et al., 2014, J. Synchrotron Rad. 21, 251-253) in the microtomography beamline 2BM at the Advanced Photon Source (USA). In the cell, we heated a millimetre-sized sample of Volterra Alabaster to 388 K while applying an effective pressure of 5 MPa. Using hard x-rays that penetrate the pressure vessel, we imaged the specimen 40 times while it reacted for approximately 10 hours. Each microtomographic dataset was acquired in 300 seconds without interrupting the reaction. Our absorption microtomographic data have a voxel size of 1.3 μm, which suffices to analyse the reaction progress in 4D. Gypsum can clearly be distinguished from hemihydrate and pores, which form due to the large negative solid volume change. On the resolved scale, the first hemihydrate needles appear after about 2 hours. Our data allow tracking of individual needles throughout the entire experiment. We quantified their growth rates by measuring their circumference. While individual grains grow at different rates, they all start slowly during the initial nucleation stage, then accelerate and grow steadily between about 200 and 400 minutes before reaction rate decelerates again. Hemihydrate needles are surrounded by porous haloes, which grow with the needles, link up and eventually encapsulate the remaining gypsum crystals. The reaction appears to be homogenously distributed throughout the sample and we find no evidence for metamorphic overpressure. We used an advanced machine learning algorithm (http://fiji.sc/Trainable_Weka_Segmentation) to segment the porosity from the microtomographic data and quantify it in Fiji (Schindelin et al., 2012, Nature Methods 9, 676-682). The porosity evolution follows the grain growth curves and reaches 23%, which indicates that the dehydration reaction is 80% complete. Our 4D data provide a unique opportunity not only to explore evolving reaction microtextures in spectacular visualisations but also to test general metamorphic theory. Our data, which track the entire reaction history from nucleation through to interaction with surrounding grains raise several questions. While individual grains grow quicker than others, why do, when grain growth is normalised against final grain size, all grains have a very similar growth history? This is not currently explained by an Avrami type model, and an alternative model for metamorphic reaction kinetics may be based on our data. Do metamorphic transport distances change during the reaction as the pore structure evolves? What controls the orientation of hemihydrate needles? In this presentation we present not only the images and data highlighting these questions, but also explore possible answers.

Unraveling multiple changes in complex climate time series using Bayesian inference

NASA Astrophysics Data System (ADS)

Berner, Nadine; Trauth, Martin H.; Holschneider, Matthias

2016-04-01

Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of observations. Unraveling such transitions yields essential information for the understanding of the observed system. The precise detection and basic characterization of underlying changes is therefore of particular importance in environmental sciences. We present a kernel-based Bayesian inference approach to investigate direct as well as indirect climate observations for multiple generic transition events. In order to develop a diagnostic approach designed to capture a variety of natural processes, the basic statistical features of central tendency and dispersion are used to locally approximate a complex time series by a generic transition model. A Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of such a transition. To systematically investigate time series for multiple changes occurring at different temporal scales, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. Thus, based on a generic transition model a probability expression is derived that is capable to indicate multiple changes within a complex time series. We discuss the method's performance by investigating direct and indirect climate observations. The approach is applied to environmental time series (about 100 a), from the weather station in Tuscaloosa, Alabama, and confirms documented instrumentation changes. Moreover, the approach is used to investigate a set of complex terrigenous dust records from the ODP sites 659, 721/722 and 967 interpreted as climate indicators of the African region of the Plio-Pleistocene period (about 5 Ma). The detailed inference unravels multiple transitions underlying the indirect climate observations coinciding with established global climate events.

On the Accuracy of Language Trees

PubMed Central

Pompei, Simone; Loreto, Vittorio; Tria, Francesca

2011-01-01

Historical linguistics aims at inferring the most likely language phylogenetic tree starting from information concerning the evolutionary relatedness of languages. The available information are typically lists of homologous (lexical, phonological, syntactic) features or characters for many different languages: a set of parallel corpora whose compilation represents a paramount achievement in linguistics. From this perspective the reconstruction of language trees is an example of inverse problems: starting from present, incomplete and often noisy, information, one aims at inferring the most likely past evolutionary history. A fundamental issue in inverse problems is the evaluation of the inference made. A standard way of dealing with this question is to generate data with artificial models in order to have full access to the evolutionary process one is going to infer. This procedure presents an intrinsic limitation: when dealing with real data sets, one typically does not know which model of evolution is the most suitable for them. A possible way out is to compare algorithmic inference with expert classifications. This is the point of view we take here by conducting a thorough survey of the accuracy of reconstruction methods as compared with the Ethnologue expert classifications. We focus in particular on state-of-the-art distance-based methods for phylogeny reconstruction using worldwide linguistic databases. In order to assess the accuracy of the inferred trees we introduce and characterize two generalizations of standard definitions of distances between trees. Based on these scores we quantify the relative performances of the distance-based algorithms considered. Further we quantify how the completeness and the coverage of the available databases affect the accuracy of the reconstruction. Finally we draw some conclusions about where the accuracy of the reconstructions in historical linguistics stands and about the leading directions to improve it. PMID:21674034

Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality.

PubMed

Drugowitsch, Jan; Wyart, Valentin; Devauchelle, Anne-Dominique; Koechlin, Etienne

2016-12-21

Making decisions in uncertain environments often requires combining multiple pieces of ambiguous information from external cues. In such conditions, human choices resemble optimal Bayesian inference, but typically show a large suboptimal variability whose origin remains poorly understood. In particular, this choice suboptimality might arise from imperfections in mental inference rather than in peripheral stages, such as sensory processing and response selection. Here, we dissociate these three sources of suboptimality in human choices based on combining multiple ambiguous cues. Using a novel quantitative approach for identifying the origin and structure of choice variability, we show that imperfections in inference alone cause a dominant fraction of suboptimal choices. Furthermore, two-thirds of this suboptimality appear to derive from the limited precision of neural computations implementing inference rather than from systematic deviations from Bayes-optimal inference. These findings set an upper bound on the accuracy and ultimate predictability of human choices in uncertain environments. Copyright © 2016 Elsevier Inc. All rights reserved.

Computational State Space Models for Activity and Intention Recognition. A Feasibility Study

PubMed Central

Krüger, Frank; Nyolt, Martin; Yordanova, Kristina; Hein, Albert; Kirste, Thomas

2014-01-01

Background Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. Methods A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. Results The symbolic domain model was found to have more than states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure. Conclusions Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance penalty. However, our results also show that research on CSSMs needs to consider sufficiently complex domains in order to understand the effects of design decisions such as choice of heuristics or inference procedure on performance. PMID:25372138

Reasoning about Causal Relationships: Inferences on Causal Networks

PubMed Central

Rottman, Benjamin Margolin; Hastie, Reid

2013-01-01

Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks—[X→Y→Z, X←Y→Z, X→Y←Z]—supply answers for questions like, “Suppose both X and Y occur, what is the probability Z occurs?” or “Suppose you intervene and make Y occur, what is the probability Z occurs?” In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks. Overall, when the normative calculations imply that an inference should increase, judgments usually go up; when calculations imply a decrease, judgments usually go down. However, two systematic deviations appear. First, people’s inferences violate the Markov assumption. For example, when inferring Z from the structure X→Y→Z, people think that X is relevant even when Y completely mediates the relationship between X and Z. Second, even when people’s inferences are directionally consistent with the normative calculations, they are often not as sensitive to the parameters and the structure of the network as they should be. We conclude with a discussion of productive directions for future research. PMID:23544658

Application of AI techniques to infer vegetation characteristics from directional reflectance(s)

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Smith, J. A.; Harrison, P. A.; Harrison, P. R.

1994-01-01

Traditionally, the remote sensing community has relied totally on spectral knowledge to extract vegetation characteristics. However, there are other knowledge bases (KB's) that can be used to significantly improve the accuracy and robustness of inference techniques. Using AI (artificial intelligence) techniques a KB system (VEG) was developed that integrates input spectral measurements with diverse KB's. These KB's consist of data sets of directional reflectance measurements, knowledge from literature, and knowledge from experts which are combined into an intelligent and efficient system for making vegetation inferences. VEG accepts spectral data of an unknown target as input, determines the best techniques for inferring the desired vegetation characteristic(s), applies the techniques to the target data, and provides a rigorous estimate of the accuracy of the inference. VEG was developed to: infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; infer percent ground cover from any combination of nadir and/or off-nadir view angles; infer unknown view angle(s) from known view angle(s) (known as view angle extension); and discriminate between user defined vegetation classes using spectral and directional reflectance relationships developed from an automated learning algorithm. The errors for these techniques were generally very good ranging between 2 to 15% (proportional root mean square). The system is designed to aid scientists in developing, testing, and applying new inference techniques using directional reflectance data.

It's About Time: A Critique of Macroecological Inferences Concerning Plant Competition.

PubMed

Damgaard, Christian; Weiner, Jacob

2017-02-01

Several macroecological studies have used static spatial data to evaluate plant competition in natural ecosystems and to investigate its role in plant community dynamics and species assembly. The assumptions on which the inferences are based have not been consistent with ecological knowledge. Inferences about processes, such as competition, from static data are weak. Macroecology will benefit more from dynamic data, even if limited, than from increasingly sophisticated analyses of static spatial patterns. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Causal Inference and Explaining Away in a Spiking Network

PubMed Central

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Causal Inference and Explaining Away in a Spiking Network.

PubMed

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-12-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.

Bayesian inference of interaction properties of noisy dynamical systems with time-varying coupling: capabilities and limitations

NASA Astrophysics Data System (ADS)

Wilting, Jens; Lehnertz, Klaus

2015-08-01

We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.

Effect of Age on Variability in the Production of Text-Based Global Inferences

PubMed Central

Williams, Lynne J.; Dunlop, Joseph P.; Abdi, Hervé

2012-01-01

As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one’s world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation–a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging. PMID:22590523

Data-driven sensitivity inference for Thomson scattering electron density measurement systems.

PubMed

Fujii, Keisuke; Yamada, Ichihiro; Hasuo, Masahiro

2017-01-01

We developed a method to infer the calibration parameters of multichannel measurement systems, such as channel variations of sensitivity and noise amplitude, from experimental data. We regard such uncertainties of the calibration parameters as dependent noise. The statistical properties of the dependent noise and that of the latent functions were modeled and implemented in the Gaussian process kernel. Based on their statistical difference, both parameters were inferred from the data. We applied this method to the electron density measurement system by Thomson scattering for the Large Helical Device plasma, which is equipped with 141 spatial channels. Based on the 210 sets of experimental data, we evaluated the correction factor of the sensitivity and noise amplitude for each channel. The correction factor varies by ≈10%, and the random noise amplitude is ≈2%, i.e., the measurement accuracy increases by a factor of 5 after this sensitivity correction. The certainty improvement in the spatial derivative inference was demonstrated.

Active inference and learning.

PubMed

Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni

2016-09-01

This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

PubMed

Pajak, Bozena; Fine, Alex B; Kleinschmidt, Dave F; Jaeger, T Florian

2016-12-01

We present a framework of second and additional language (L2/L n ) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/L n learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/L n acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/L n learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing

PubMed Central

Pajak, Bozena; Fine, Alex B.; Kleinschmidt, Dave F.; Jaeger, T. Florian

2015-01-01

We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa. PMID:28348442

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

Widespread bacterial populations at glacier beds and their relationship to rock weathering and carbon cycling

NASA Astrophysics Data System (ADS)

Sharp, Martin; Parkes, John; Cragg, Barry; Fairchild, Ian J.; Lamb, Helen; Tranter, Martyn

1999-02-01

Bacterial populations found in subglacial meltwaters and basal ice are comparable to those in the active layer of permafrost and orders of magnitude larger than those found in ice cores from large ice sheets. Populations increase with sediment concentration, and 5% 24% of the bacteria are dividing or have just divided, suggesting that the populations are active. These findings (1) support inferences from recent studies of basal ice and meltwater chemistry that microbially mediated redox reactions may be important at glacier beds, (2) challenge the view that chemical weathering in glacial environments arises from purely inorganic reactions, and (3) raise the possibilities that redox reactions are a major source of protons consumed in subglacial weathering and that these reactions may be the dominant proton source beneath ice sheets where meltwaters are isolated from an atmospheric source of CO2. Microbial mediation may increase the rate of sulfide oxidation under subglacial conditions, a suggestion supported by the results of simple weathering experiments. If subglacial bacterial populations can oxidize and ferment organic carbon, it is important to reconsider the fate of soil organic carbon accumulated under interglacial conditions in areas subsequently overridden by Pleistocene ice sheets.

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.

Ultra-diffuse hydrothermal venting supports Fe-oxidizing bacteria and massive umber deposition at 5000 m off Hawaii

PubMed Central

Edwards, Katrina J; Glazer, B T; Rouxel, O J; Bach, W; Emerson, D; Davis, R E; Toner, B M; Chan, C S; Tebo, B M; Staudigel, H; Moyer, C L

2011-01-01

A novel hydrothermal field has been discovered at the base of Lōihi Seamount, Hawaii, at 5000 mbsl. Geochemical analyses demonstrate that ‘FeMO Deep', while only 0.2 °C above ambient seawater temperature, derives from a distal, ultra-diffuse hydrothermal source. FeMO Deep is expressed as regional seafloor seepage of gelatinous iron- and silica-rich deposits, pooling between and over basalt pillows, in places over a meter thick. The system is capped by mm to cm thick hydrothermally derived iron-oxyhydroxide- and manganese-oxide-layered crusts. We use molecular analyses (16S rDNA-based) of extant communities combined with fluorescent in situ hybridizations to demonstrate that FeMO Deep deposits contain living iron-oxidizing Zetaproteobacteria related to the recently isolated strain Mariprofundus ferroxydans. Bioenergetic calculations, based on in-situ electrochemical measurements and cell counts, indicate that reactions between iron and oxygen are important in supporting chemosynthesis in the mats, which we infer forms a trophic base of the mat ecosystem. We suggest that the biogenic FeMO Deep hydrothermal deposit represents a modern analog for one class of geological iron deposits known as ‘umbers' (for example, Troodos ophilolites, Cyprus) because of striking similarities in size, setting and internal structures. PMID:21544100

A comparison of gantry-mounted x-ray-based real-time target tracking methods.

PubMed

Montanaro, Tim; Nguyen, Doan Trang; Keall, Paul J; Booth, Jeremy; Caillet, Vincent; Eade, Thomas; Haddad, Carol; Shieh, Chun-Chien

2018-03-01

Most modern radiotherapy machines are built with a 2D kV imaging system. Combining this imaging system with a 2D-3D inference method would allow for a ready-made option for real-time 3D tumor tracking. This work investigates and compares the accuracy of four existing 2D-3D inference methods using both motion traces inferred from external surrogates and measured internally from implanted beacons. Tumor motion data from 160 fractions (46 thoracic/abdominal patients) of Synchrony traces (inferred traces), and 28 fractions (7 lung patients) of Calypso traces (internal traces) from the LIGHT SABR trial (NCT02514512) were used in this study. The motion traces were used as the ground truth. The ground truth trajectories were used in silico to generate 2D positions projected on the kV detector. These 2D traces were then passed to the 2D-3D inference methods: interdimensional correlation, Gaussian probability density function (PDF), arbitrary-shape PDF, and the Kalman filter. The inferred 3D positions were compared with the ground truth to determine tracking errors. The relationships between tracking error and motion magnitude, interdimensional correlation, and breathing periodicity index (BPI) were also investigated. Larger tracking errors were observed from the Calypso traces, with RMS and 95th percentile 3D errors of 0.84-1.25 mm and 1.72-2.64 mm, compared to 0.45-0.68 mm and 0.74-1.13 mm from the Synchrony traces. The Gaussian PDF method was found to be the most accurate, followed by the Kalman filter, the interdimensional correlation method, and the arbitrary-shape PDF method. Tracking error was found to strongly and positively correlate with motion magnitude for both the Synchrony and Calypso traces and for all four methods. Interdimensional correlation and BPI were found to negatively correlate with tracking error only for the Synchrony traces. The Synchrony traces exhibited higher interdimensional correlation than the Calypso traces especially in the anterior-posterior direction. Inferred traces often exhibit higher interdimensional correlation, which are not true representation of thoracic/abdominal motion and may underestimate kV-based tracking errors. The use of internal traces acquired from systems such as Calypso is advised for future kV-based tracking studies. The Gaussian PDF method is the most accurate 2D-3D inference method for tracking thoracic/abdominal targets. Motion magnitude has significant impact on 2D-3D inference error, and should be considered when estimating kV-based tracking error. © 2018 American Association of Physicists in Medicine.

System and method for responding to ground and flight system malfunctions

NASA Technical Reports Server (NTRS)

Anderson, Julie J. (Inventor); Fussell, Ronald M. (Inventor)

2010-01-01

A system for on-board anomaly resolution for a vehicle has a data repository. The data repository stores data related to different systems, subsystems, and components of the vehicle. The data stored is encoded in a tree-based structure. A query engine is coupled to the data repository. The query engine provides a user and automated interface and provides contextual query to the data repository. An inference engine is coupled to the query engine. The inference engine compares current anomaly data to contextual data stored in the data repository using inference rules. The inference engine generates a potential solution to the current anomaly by referencing the data stored in the data repository.

Bayesian multimodel inference for dose-response studies

USGS Publications Warehouse

Link, W.A.; Albers, P.H.

2007-01-01

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Key issues, observations and goals for coupled, thermodynamic/geodynamic models

NASA Astrophysics Data System (ADS)

Kelemen, P. B.

2017-12-01

In coupled, thermodynamic/geodynamic models, focus should be on processes involving major rock forming minerals and simple fluid compositions, and parameters with first-order effects on likely dynamic processes: In a given setting, will fluid mass increase or decrease? How about solid density? Will flow become localized or diffuse? Will rocks flow or break? How do reactions affect global processes such as formation and evolution of the plates, plate boundary deformation, metamorphism, weathering, climate and geochemical cycles. Important reaction feedbacks in geodynamics include formation of dissolution channels and armored channels; divergence of flow and formation of permeability barriers due to crystallization in pore space; localization of fluid transport and ductile deformation in shear zones; reaction-driven cracking; mechanical channels granular media; shear heating; density instabilities; viscous fluid-weakening; fluid-induced frictional failure; and hydraulic fracture. Density instabilities often lead to melting, and there is an interesting dialectic between porous flow and diapirs. The best models provide a simple but comprehensive framework that can account for the general features in many or most of these phenomena. Ideally, calculations based on thermodynamic data and rheological observations alone should delineate the regimes in which each of these processes will occur and the boundaries between them. These often start with "toy models" and lab experiments on analog systems, with highly approximate scaling to simplified geological conditions and materials. Geologic observations provide the best constraints where `frozen' fluid transport pathways or deformation processes are preserved. Inferences about completed processes based on fluid or solid products alone is more challenging and less unique. Not all important processes have good examples in outcrop, so directed searches for specific phenomena may fail. A highly generalized approach provides a way forward, allowing serendipitous discoveries of iconic examples wherever they are best developed. These then constrain and inspire the overall "phase diagram" of geodynamic processes.

EAPhy: A Flexible Tool for High-throughput Quality Filtering of Exon-alignments and Data Processing for Phylogenetic Methods.

PubMed

Blom, Mozes P K

2015-08-05

Recently developed molecular methods enable geneticists to target and sequence thousands of orthologous loci and infer evolutionary relationships across the tree of life. Large numbers of genetic markers benefit species tree inference but visual inspection of alignment quality, as traditionally conducted, is challenging with thousands of loci. Furthermore, due to the impracticality of repeated visual inspection with alternative filtering criteria, the potential consequences of using datasets with different degrees of missing data remain nominally explored in most empirical phylogenomic studies. In this short communication, I describe a flexible high-throughput pipeline designed to assess alignment quality and filter exonic sequence data for subsequent inference. The stringency criteria for alignment quality and missing data can be adapted based on the expected level of sequence divergence. Each alignment is automatically evaluated based on the stringency criteria specified, significantly reducing the number of alignments that require visual inspection. By developing a rapid method for alignment filtering and quality assessment, the consistency of phylogenetic estimation based on exonic sequence alignments can be further explored across distinct inference methods, while accounting for different degrees of missing data.

Studies on the nature of the primary reactions of photosystem II in photosynthesis. I. The electrochromic 515 nm absorption change as an appropriate indicator for the functional state of the photochemical active centers of system II in DCMY poisoned chloroplasts.

PubMed

Renger, G; Wolff, C

1975-01-01

The field indicating electrochromic 515 nm absorption change has been measured under different excitation conditions in DCMU poisoned chloroplasts in the presence of benzylviologen as electron acceptor. It has been found: 1. The amplitude of the 515 nm absorption change is nearly completely suppressed under repetitive single turnover flash excitation conditions which kinetically block the back reaction around system II (P. Bennoun, Biochim. Biophys. Acta 216, 357 [1970]). 2. The amplitude of the 515 nm absorption change measured under repetitive single turnover flash excitation conditions which allow the completion of the back reaction during the dark time between the flashes (measuring light beam switched off) amounts in the presence of 2 mum DCMU nearly 50% of the electrochromic 515 nm amplitude obtained in the absence of DCMU. In DCMU poisoned chloroplasts this amplitude is significantly decreased by hydroxylaminhydrochloride, but nearly doubled in the presence of CDIP+ascorbate. 3. The dependence of the 515 nm amplitude on the time td between the flashes kinetically resembles the back reaction around system ?II. The time course of the back reaction can be fairly described either by a second order reaction or by a two phase exponential kinetics. 4. 1,3-dinitrobenzene (DNE) or alpha-bromo-alpha-benzylmalodinitril (BBMD) reduce the 515 nm amplitude in DCMU poisoned chloroplasts, but seem to influecne only slightly the kinetics of the back reaction. 5. The dependence of the 515 nm amplitude on the flash light intensity (the amplitude normalized to 1 at 100% flash light intensity) is not changed by DNB. Based on these experimental data it has been concluded that in DCMU poisoned chloroplasts the amplitude of the 515 nm absorption change reflects the functional state of photosystem II centers (designated as photoelectric dipole generators II) under suitable excitation conditions. Furthermore, it is inferred that in DCMU poisoned chlorplasts the photoelectric dipole generators II either cooperate (probably as twin-pairs) or exist in two functionally different forms. With respect to BBMD and DNB it is assumed that these agents transform the phtooelectric dipole generators II into powerful nonphotochemical quenchers, which significantly reduce the variable fluorescence in DCMU-poisoned chloroplasts.

Diffusion modelling of metamorphic layered coronas with stability criterion and consideration of affinity

NASA Astrophysics Data System (ADS)

Ashworth, J. R.; Sheplev, V. S.

1997-09-01

Layered coronas between two reactant minerals can, in many cases, be attributed to diffusion-controlled growth with local equilibrium. This paper clarifies and unifies the previous approaches of various authors to the simplest form of modelling, which uses no assumed values for thermochemical quantities. A realistic overall reaction must be estimated from measured overall proportions of minerals and their major element compositions. Modelling is not restricted to a particular number of components S, relative to the number of phases Φ. IfΦ > S + 1, the overall reaction is a combination of simultaneous reactions. The stepwise method, solving for the local reaction at each boundary in turn, is extended to allow for recurrence of a mineral (its presence in two parts of the layer structure separated by a gap). The equations are also given in matrix form. A thermodynamic stability criterion is derived, determining which layer sequence is truly stable if several are computable from the same inputs. A layer structure satisfying the stability criterion has greater growth rate (and greater rate of entropy production) than the other computable layer sequences. This criterion of greatest entropy production is distinct from Prigogine's theorem of minimum entropy production, which distinguishes the stationary or quasi-stationary state from other states of the same layer sequence. The criterion leads to modification of previous results for coronas comprising hornblende, spinel, and orthopyroxene between olivine (Ol) and plagioclase (Pl). The outcome supports the previous inference that Si, and particularly Al, commonly behave as immobile relative to other cation-forming major elements. The affinity (-ΔG) of a corona-forming reaction is estimated, using previous estimates of diffusion coefficient and the duration t of reaction, together with a new model quantity (-ΔG) *. For an example of the Ol + Pl reaction, a rough calculation gives (-ΔG) > 1.7RT (per mole of P1 consumed, based on a 24-oxygen formula for Pl). At 600-700°C, this represents (-ΔG) > 10kJ mol -1 and departure from equilibrium temperature by at least ˜ 100°C. The lower end of this range is petrologically reasonable and, for t < 100Ma, corresponds to a Fick's-law diffusion coefficient for Al, DAl > 10 -25m 2s -1, larger than expected for lattice diffusion but consistent with fluid-absent grain-boundary diffusion and small concentration gradients.

Weathering of the Rio Blanco quartz diorite, Luquillo Mountains, Puerto Rico: Coupling oxidation, dissolution, and fracturing

USGS Publications Warehouse

Buss, H.L.; Sak, P.B.; Webb, S.M.; Brantley, S.L.

2008-01-01

In the mountainous Rio Icacos watershed in northeastern Puerto Rico, quartz diorite bedrock weathers spheroidally, producing a 0.2-2 m thick zone of partially weathered rock layers (???2.5 cm thickness each) called rindlets, which form concentric layers around corestones. Spheroidal fracturing has been modeled to occur when a weathering reaction with a positive ??V of reaction builds up elastic strain energy. The rates of spheroidal fracturing and saprolite formation are therefore controlled by the rate of the weathering reaction. Chemical, petrographic, and spectroscopic evidence demonstrates that biotite oxidation is the most likely fracture-inducing reaction. This reaction occurs with an expansion in d (0 0 1) from 10.0 to 10.5 A??, forming 'altered biotite'. Progressive biotite oxidation across the rindlet zone was inferred from thin sections and gradients in K and Fe(II). Using the gradient in Fe(II) and constraints based on cosmogenic age dates, we calculated a biotite oxidation reaction rate of 8.2 ?? 10-14 mol biotite m-2 s-1. Biotite oxidation was documented within the bedrock corestone by synchrotron X-ray microprobe fluorescence imaging and XANES. X-ray microprobe images of Fe(II) and Fe(III) at 2 ??m resolution revealed that oxidized zones within individual biotite crystals are the first evidence of alteration of the otherwise unaltered corestone. Fluids entering along fractures lead to the dissolution of plagioclase within the rindlet zone. Within 7 cm surrounding the rindlet-saprolite interface, hornblende dissolves to completion at a rate of 6.3 ?? 10-13 mol hornblende m-2 s-1: the fastest reported rate of hornblende weathering in the field. This rate is consistent with laboratory-derived hornblende dissolution rates. By revealing the coupling of these mineral weathering reactions to fracturing and porosity formation we are able to describe the process by which the quartz diorite bedrock disaggregates and forms saprolite. In the corestone, biotite oxidation induces spheroidal fracturing, facilitating the influx of fluids that react with other minerals, dissolving plagioclase and chlorite, creating additional porosity, and eventually dissolving hornblende and precipitating secondary minerals. The thickness of the resultant saprolite is maintained at steady state by a positive feedback between the denudation rate and the weathering advance rate driven by the concentration of pore water O2 at the bedrock-saprolite interface. ?? 2008 Elsevier Ltd. All rights reserved.

On Some Assumptions of the Null Hypothesis Statistical Testing

ERIC Educational Resources Information Center

Patriota, Alexandre Galvão

2017-01-01

Bayesian and classical statistical approaches are based on different types of logical principles. In order to avoid mistaken inferences and misguided interpretations, the practitioner must respect the inference rules embedded into each statistical method. Ignoring these principles leads to the paradoxical conclusions that the hypothesis…

Building Intuitions about Statistical Inference Based on Resampling

ERIC Educational Resources Information Center

Watson, Jane; Chance, Beth

2012-01-01

Formal inference, which makes theoretical assumptions about distributions and applies hypothesis testing procedures with null and alternative hypotheses, is notoriously difficult for tertiary students to master. The debate about whether this content should appear in Years 11 and 12 of the "Australian Curriculum: Mathematics" has gone on…

Conceptual Influences on Category-Based Induction

ERIC Educational Resources Information Center

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 vs. perceptual similarity. Seven experiments with 4- to…

Learning and Retention through Predictive Inference and Classification

ERIC Educational Resources Information Center

Sakamoto, Yasuaki; Love, Bradley C.

2010-01-01

Work in category learning addresses how humans acquire knowledge and, thus, should inform classroom practices. In two experiments, we apply and evaluate intuitions garnered from laboratory-based research in category learning to learning tasks situated in an educational context. In Experiment 1, learning through predictive inference and…

Toward Webscale, Rule-Based Inference on the Semantic Web Via Data Parallelism

DTIC Science & Technology

2013-02-01

Another work distinct from its peers is the work on approximate reasoning by Rudolph et al. [34] in which multiple inference sys- tems were combined not...Workshop Scalable Semantic Web Knowledge Base Systems, 2010, pp. 17–31. [34] S. Rudolph , T. Tserendorj, and P. Hitzler, “What is approximate reasoning...2013] [55] M. Duerst and M. Suignard. (2005, Jan .). RFC 3987 – internationalized resource identifiers (IRIs). IETF. [Online]. Available: http

Stochastic inference with spiking neurons in the high-conductance state

NASA Astrophysics Data System (ADS)

Petrovici, Mihai A.; Bill, Johannes; Bytschok, Ilja; Schemmel, Johannes; Meier, Karlheinz

2016-10-01

The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.

An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

PubMed

Liang, Ying; Liao, Bo; Zhu, Wen

2017-01-01

Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

Learning quadratic receptive fields from neural responses to natural stimuli.

PubMed

Rajan, Kanaka; Marre, Olivier; Tkačik, Gašper

2013-07-01

Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are selective for only a small number of linear projections of a potentially high-dimensional input. In this review, we explore recent modeling approaches where the neural response depends on the quadratic form of the input rather than on its linear projection, that is, the neuron is sensitive to the local covariance structure of the signal preceding the spike. To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic) stimulus distribution, we review several inference methods, focusing in particular on two information theory-based approaches (maximization of stimulus energy and of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered covariance and extensions of generalized linear models). We analyze the formal relationship between the likelihood-based and information-based approaches to demonstrate how they lead to consistent inference. We demonstrate the practical feasibility of these procedures by using model neurons responding to a flickering variance stimulus.

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.

High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections.

PubMed

Cocco, S; Monasson, R; Sessak, V

2011-05-01

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that maximum likelihood inference is deeply related to principal component analysis when the amplitude of the pattern components ξ is negligible compared to √N. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in ξ/√N. We stress the need to generalize the Hopfield model and include both attractive and repulsive patterns in order to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size N and of the amplitude ξ. The inference approach is illustrated on synthetic and biological data.

Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

PubMed Central

Karatsidis, Angelos; Bellusci, Giovanni; Schepers, H. Martin; de Zee, Mark; Andersen, Michael S.; Veltink, Peter H.

2016-01-01

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory. PMID:28042857

Detection of airborne carbon nanotubes based on the reactivity of the embedded catalyst.

PubMed

Neubauer, N; Kasper, G

2015-01-01

A previously described method for detecting catalyst particles in workplace air((1,2)) was applied to airborne carbon nanotubes (CNT). It infers the CNT concentration indirectly from the catalytic activity of metallic nanoparticles embedded as part of the CNT production process. Essentially, one samples airborne CNT onto a filter enclosed in a tiny chemical reactor and then initiates a gas-phase catalytic reaction on the sample. The change in concentration of one of the reactants is then determined by an IR sensor as measure of activity. The method requires a one-point calibration with a CNT sample of known mass. The suitability of the method was tested with nickel containing (25 or 38% by weight), well-characterized multi-walled CNT aerosols generated freshly in the lab for each experiment. Two chemical reactions were investigated, of which the oxidation of CO to CO2 at 470°C was found to be more effective, because nearly 100% of the nickel was exposed at that temperature by burning off the carbon, giving a linear relationship between CO conversion and nickel mass. Based on the investigated aerosols, a lower detection limit of 1 μg of sampled nickel was estimated. This translates into sampling times ranging from minutes to about one working day, depending on airborne CNT concentration and catalyst content, as well as sampling flow rate. The time for the subsequent chemical analysis is on the order of minutes, regardless of the time required to accumulate the sample and can be done on site.

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.

A comparison of high-resolution pollen-inferred climate data from central Minnesota, USA, to 19th century US military fort climate data and tree-ring inferred climate reconstructions

NASA Astrophysics Data System (ADS)

St Jacques, J.; Cumming, B. F.; Sauchyn, D.; Vanstone, J. R.; Dickenson, J.; Smol, J. P.

2013-12-01

A vital component of paleoclimatology is the validation of paleoclimatological reconstructions. Unfortunately, there is scant instrumental data prior to the 20th century available for this. Hence, typically, we can only do long-term validation using other proxy-inferred climate reconstructions. Minnesota, USA, with its long military fort climate records beginning in 1820 and early dense network of climate stations, offers a rare opportunity for proxy validation. We compare a high-resolution (4-year), millennium-scale, pollen-inferred paleoclimate record derived from varved Lake Mina in central Minnesota to early military fort records and dendroclimatological records. When inferring a paleoclimate record from a pollen record, we rely upon the pollen-climate relationship being constant in time. However, massive human impacts have significantly altered vegetation; and the relationship between modern instrumental climate data and the modern pollen rain becomes altered from what it was in the past. In the Midwest, selective logging, fire suppression, deforestation and agriculture have strongly influenced the modern pollen rain since Euro-American settlement in the mid-1800s. We assess the signal distortion introduced by using the conventional method of modern post-settlement pollen and climate calibration sets to infer climate at Lake Mina from pre-settlement pollen data. Our first February and May temperature reconstructions are based on a pollen dataset contemporaneous with early settlement to which corresponding climate data from the earliest instrumental records has been added to produce a 'pre-settlement' calibration set. The second February and May temperature reconstructions are based on a conventional 'modern' pollen-climate dataset from core-top pollen samples and modern climate normals. The temperature reconstructions are then compared to the earliest instrumental records from Fort Snelling, Minnesota, and it is shown that the reconstructions based on the pre-settlement calibration set give much more credible reconstructions. We then compare the temperature reconstructions based upon the two calibration sets for AD 1116-2002. Significant signal flattening and bias exist when using the conventional modern pollen-climate calibration set rather than the pre-settlement pollen-climate calibration set, resulting in an overestimation of Little Ice Age monthly mean temperatures of 0.5-1.5 oC. Therefore, regional warming from anthropogenic global warming is significantly underestimated when using the conventional method of building pollen-climate calibration sets. We also compare the Lake Mina pollen-inferred effective moisture record to early 19th century climate data and to a four-century tree-ring inferred moisture reconstruction based upon sites in Minnesota and the Dakotas. This comparison shows that regional tree-ring reconstructions are biased towards dry conditions and record wet periods poorly relative to high-resolution pollen reconstructions, giving a false impression of regional aridity. It also suggests that varve chronologies should be based upon cross-dating to ensure a more accurate chronology.

Processing inferences at the semantics/pragmatics frontier: disjunctions and free choice.

PubMed

Chemla, Emmanuel; Bott, Lewis

2014-03-01

Linguistic inferences have traditionally been studied and categorized in several categories, such as entailments, implicatures or presuppositions. This typology is mostly based on traditional linguistic means, such as introspective judgments about phrases occurring in different constructions, in different conversational contexts. More recently, the processing properties of these inferences have also been studied (see, e.g., recent work showing that scalar implicatures is a costly phenomenon). Our focus is on free choice permission, a phenomenon by which conjunctive inferences are unexpectedly added to disjunctive sentences. For instance, a sentence such as "Mary is allowed to eat an ice-cream or a cake" is normally understood as granting permission both for eating an ice-cream and for eating a cake. We provide data from four processing studies, which show that, contrary to arguments coming from the theoretical literature, free choice inferences are different from scalar implicatures. Copyright © 2013 Elsevier B.V. All rights reserved.

[Causal inference in medicine--a historical view in epidemiology].

PubMed

Tsuda, T; Babazono, A; Mino, Y; Matsuoka, H; Yamamoto, E

1996-07-01

Changes of causal inference concepts in medicine, especially those having to do with chronic diseases, were reviewed. The review is divided into five sections. First, several articles on the increased academic acceptance of observational research are cited. Second, the definitions of confounder and effect modifier concepts are explained. Third, the debate over the so-called "criteria for causal inference" was discussed. Many articles have pointed out various problems related to the lack of logical bases for standard criteria, however, such criteria continue to be misapplied in Japan. Fourth, the Popperian and verificationist concepts of causal inference are summarized. Lastly, a recent controversy on meta-analysis is explained. Causal inference plays an important role in epidemiologic theory and medicine. However, because this concept has not been well-introduced in Japan, there has been much misuse of the concept, especially when used for conventional criteria.

BagReg: Protein inference through machine learning.

PubMed

Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou

2015-08-01

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Evaluating Fast Maximum Likelihood-Based Phylogenetic Programs Using Empirical Phylogenomic Data Sets

PubMed Central

Zhou, Xiaofan; Shen, Xing-Xing; Hittinger, Chris Todd

2018-01-01

Abstract The sizes of the data matrices assembled to resolve branches of the tree of life have increased dramatically, motivating the development of programs for fast, yet accurate, inference. For example, several different fast programs have been developed in the very popular maximum likelihood framework, including RAxML/ExaML, PhyML, IQ-TREE, and FastTree. Although these programs are widely used, a systematic evaluation and comparison of their performance using empirical genome-scale data matrices has so far been lacking. To address this question, we evaluated these four programs on 19 empirical phylogenomic data sets with hundreds to thousands of genes and up to 200 taxa with respect to likelihood maximization, tree topology, and computational speed. For single-gene tree inference, we found that the more exhaustive and slower strategies (ten searches per alignment) outperformed faster strategies (one tree search per alignment) using RAxML, PhyML, or IQ-TREE. Interestingly, single-gene trees inferred by the three programs yielded comparable coalescent-based species tree estimations. For concatenation-based species tree inference, IQ-TREE consistently achieved the best-observed likelihoods for all data sets, and RAxML/ExaML was a close second. In contrast, PhyML often failed to complete concatenation-based analyses, whereas FastTree was the fastest but generated lower likelihood values and more dissimilar tree topologies in both types of analyses. Finally, data matrix properties, such as the number of taxa and the strength of phylogenetic signal, sometimes substantially influenced the programs’ relative performance. Our results provide real-world gene and species tree phylogenetic inference benchmarks to inform the design and execution of large-scale phylogenomic data analyses. PMID:29177474

A Bayesian Framework for Analysis of Pseudo-Spatial Models of Comparable Engineered Systems with Application to Spacecraft Anomaly Prediction Based on Precedent Data

NASA Astrophysics Data System (ADS)

Ndu, Obibobi Kamtochukwu

To ensure that estimates of risk and reliability inform design and resource allocation decisions in the development of complex engineering systems, early engagement in the design life cycle is necessary. An unfortunate constraint on the accuracy of such estimates at this stage of concept development is the limited amount of high fidelity design and failure information available on the actual system under development. Applying the human ability to learn from experience and augment our state of knowledge to evolve better solutions mitigates this limitation. However, the challenge lies in formalizing a methodology that takes this highly abstract, but fundamentally human cognitive, ability and extending it to the field of risk analysis while maintaining the tenets of generalization, Bayesian inference, and probabilistic risk analysis. We introduce an integrated framework for inferring the reliability, or other probabilistic measures of interest, of a new system or a conceptual variant of an existing system. Abstractly, our framework is based on learning from the performance of precedent designs and then applying the acquired knowledge, appropriately adjusted based on degree of relevance, to the inference process. This dissertation presents a method for inferring properties of the conceptual variant using a pseudo-spatial model that describes the spatial configuration of the family of systems to which the concept belongs. Through non-metric multidimensional scaling, we formulate the pseudo-spatial model based on rank-ordered subjective expert perception of design similarity between systems that elucidate the psychological space of the family. By a novel extension of Kriging methods for analysis of geospatial data to our "pseudo-space of comparable engineered systems", we develop a Bayesian inference model that allows prediction of the probabilistic measure of interest.

First measurements of deuterium-tritium and deuterium-deuterium fusion reaction yields in ignition-scalable direct-drive implosions

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

Forrest, C. J.; Radha, P. B.; Knauer, J. P.

In this study, the deuterium-tritium (D-T) and deuterium-deuterium neutron yield ratio in cryogenic inertial confinement fusion (ICF) experiments is used to examine multifluid effects, traditionally not included in ICF modeling. This ratio has been measured for ignition-scalable direct-drive cryogenic DT implosions at the Omega Laser Facility using a high-dynamic-range neutron time-of-flight spectrometer. The experimentally inferred yield ratio is consistent with both the calculated values of the nuclear reaction rates and the measured preshot target-fuel composition. These observations indicate that the physical mechanisms that have been proposed to alter the fuel composition, such as species separation of the hydrogen isotopes, aremore » not significant during the period of peak neutron production in ignition-scalable cryogenic direct-drive DT implosions.« less

First measurements of deuterium-tritium and deuterium-deuterium fusion reaction yields in ignition-scalable direct-drive implosions

DOE PAGES

Forrest, C. J.; Radha, P. B.; Knauer, J. P.; ...

2017-03-03

In this study, the deuterium-tritium (D-T) and deuterium-deuterium neutron yield ratio in cryogenic inertial confinement fusion (ICF) experiments is used to examine multifluid effects, traditionally not included in ICF modeling. This ratio has been measured for ignition-scalable direct-drive cryogenic DT implosions at the Omega Laser Facility using a high-dynamic-range neutron time-of-flight spectrometer. The experimentally inferred yield ratio is consistent with both the calculated values of the nuclear reaction rates and the measured preshot target-fuel composition. These observations indicate that the physical mechanisms that have been proposed to alter the fuel composition, such as species separation of the hydrogen isotopes, aremore » not significant during the period of peak neutron production in ignition-scalable cryogenic direct-drive DT implosions.« less

Moving attention - Evidence for time-invariant shifts of visual selective attention

NASA Technical Reports Server (NTRS)

Remington, R.; Pierce, L.

1984-01-01

Two experiments measured the time to shift spatial selective attention across the visual field to targets 2 or 10 deg from central fixation. A central arrow cued the most likely target location. The direction of attention was inferred from reaction times to expected, unexpected, and neutral locations. The development of a spatial attentional set with time was examined by presenting target probes at varying times after the cue. There were no effects of distance on the time course of the attentional set. Reaction times for far locations were slower than for near, but the effects of attention were evident by 150 msec in both cases. Spatial attention does not shift with a characteristic, fixed velocity. Rather, velocity is proportional to distance, resulting in a movement time that is invariant over the distances tested.

Nuclear science experiments with a bright neutron source from fusion reactions on the OMEGA Laser System

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

Forrest, C. J.; Knauer, J. P.; Schroeder, W. U.

Subnanosecond impulses of 10 13 to 10 14 neutrons, produced in direct-drive laser inertial confinement fusion implosions, have been used to irradiate deuterated targets at the OMEGA Laser System [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)]. The target compounds include heavy water (D 2O) and deuterated benzene (C 6D 6). Yields and energy spectra of neutrons from D(n,2n)p to study the breakup reaction have been measured at a forward angle of θlab = 3.5 degrees plus/minus 3.5 degrees with a sensitive, high-dynamic-range neutron time-of-flight spectrometer to infer the double-differential breakup cross section d2sigma/dEdomega for 14-MeV D–T fusionmore » neutrons.« less

Nuclear science experiments with a bright neutron source from fusion reactions on the OMEGA Laser System

DOE PAGES

Forrest, C. J.; Knauer, J. P.; Schroeder, W. U.; ...

2018-01-31

Subnanosecond impulses of 10 13 to 10 14 neutrons, produced in direct-drive laser inertial confinement fusion implosions, have been used to irradiate deuterated targets at the OMEGA Laser System [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)]. The target compounds include heavy water (D 2O) and deuterated benzene (C 6D 6). Yields and energy spectra of neutrons from D(n,2n)p to study the breakup reaction have been measured at a forward angle of θlab = 3.5 degrees plus/minus 3.5 degrees with a sensitive, high-dynamic-range neutron time-of-flight spectrometer to infer the double-differential breakup cross section d2sigma/dEdomega for 14-MeV D–T fusionmore » neutrons.« less

Teaching machines to find mantle composition

NASA Astrophysics Data System (ADS)

Atkins, Suzanne; Tackley, Paul; Trampert, Jeannot; Valentine, Andrew

2017-04-01

The composition of the mantle affects many geodynamical processes by altering factors such as the density, the location of phase changes, and melting temperature. The inferences we make about mantle composition also determine how we interpret the changes in velocity, reflections, attenuation and scattering seen by seismologists. However, the bulk composition of the mantle is very poorly constrained. Inferences are made from meteorite samples, rock samples from the Earth and inferences made from geophysical data. All of these approaches require significant assumptions and the inferences made are subject to large uncertainties. Here we present a new method for inferring mantle composition, based on pattern recognition machine learning, which uses large scale in situ observations of the mantle to make fully probabilistic inferences of composition for convection simulations. Our method has an advantage over other petrological approaches because we use large scale geophysical observations. This means that we average over much greater length scales and do not need to rely on extrapolating from localised samples of the mantle or planetary disk. Another major advantage of our method is that it is fully probabilistic. This allows us to include all of the uncertainties inherent in the inference process, giving us far more information about the reliability of the result than other methods. Finally our method includes the impact of composition on mantle convection. This allows us to make much more precise inferences from geophysical data than other geophysical approaches, which attempt to invert one observation with no consideration of the relationship between convection and composition. We use a sampling based inversion method, using hundreds of convection simulations run using StagYY with self consistent mineral physics properties calculated using the PerpleX package. The observations from these simulations are used to train a neural network to make a probabilistic inference for major element oxide composition of the mantle. We find we can constrain bulk mantle FeO molar percent, FeO/MgO and FeO/SiO2 using observations of the temperature and density structure of the mantle in convection simulations.

Spurious correlations and inference in landscape genetics

Treesearch

Samuel A. Cushman; Erin L. Landguth

2010-01-01

Reliable interpretation of landscape genetic analyses depends on statistical methods that have high power to identify the correct process driving gene flow while rejecting incorrect alternative hypotheses. Little is known about statistical power and inference in individual-based landscape genetics. Our objective was to evaluate the power of causalmodelling with partial...

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.

Evaluating Great Lakes bald eagle nesting habitat with Bayesian inference

Treesearch

Teryl G. Grubb; William W. Bowerman; Allen J. Bath; John P. Giesy; D. V. Chip Weseloh

2003-01-01

Bayesian inference facilitated structured interpretation of a nonreplicated, experience-based survey of potential nesting habitat for bald eagles (Haliaeetus leucocephalus) along the five Great Lakes shorelines. We developed a pattern recognition (PATREC) model of our aerial search image with six habitat attributes: (a) tree cover, (b) proximity and...

Hybrid Optical Inference Machines

DTIC Science & Technology

1991-09-27

with labels. Now, events. a set of facts cal be generated in the dyadic form "u, R 1,2" Eichmann and Caulfield (19] consider the same type of and can...these enceding-schemes. These architectures are-based pri- 19. G. Eichmann and H. J. Caulfield, "Optical Learning (Inference)marily on optical inner

Ecosystem Food Web Lift-The-Flap Pages

ERIC Educational Resources Information Center

Atwood-Blaine, Dana; Rule, Audrey C.; Morgan, Hannah

2016-01-01

In the lesson on which this practical article is based, third grade students constructed a "lift-the-flap" page to explore food webs on the prairie. The moveable papercraft focused student attention on prairie animals' external structures and how the inferred functions of those structures could support further inferences about the…

A Fiducial Approach to Extremes and Multiple Comparisons

ERIC Educational Resources Information Center

Wandler, Damian V.

2010-01-01

Generalized fiducial inference is a powerful tool for many difficult problems. Based on an extension of R. A. Fisher's work, we used generalized fiducial inference for two extreme value problems and a multiple comparison procedure. The first extreme value problem is dealing with the generalized Pareto distribution. The generalized Pareto…

Perceived Arousal, Focus of Attention, and Avoidance Behavior

ERIC Educational Resources Information Center

Carver, Charles S.; Blaney, Paul H.

1977-01-01

Attribution theory holds that perceived arousal may cause a person to draw an inference about his emotions and base his subsequent behavior on that inference. Recent research suggests, however, that this account does not entirely explain the influence of false arousal feedback on simultaneously occurring avoidance behavior. Proposes a behavior…

Hemispheric Inference Priming during Comprehension of Conversations and Narratives

ERIC Educational Resources Information Center

Powers, Chivon; Bencic, Rachel; Horton, William S.; Beeman, Mark

2012-01-01

In this study we examined asymmetric semantic activation patterns as people listened to conversations and narratives that promoted causal inferences. Based on the hypothesis that understanding the unique features of conversational input may benefit from or require a modified pattern of conceptual activation during conversation, we compared…

On Inference Rules of Logic-Based Information Retrieval Systems.

ERIC Educational Resources Information Center

Chen, Patrick Shicheng

1994-01-01

Discussion of relevance and the needs of the users in information retrieval focuses on a deductive object-oriented approach and suggests eight inference rules for the deduction. Highlights include characteristics of a deductive object-oriented system, database and data modeling language, implementation, and user interface. (Contains 24…

Naive Probability: A Mental Model Theory of Extensional Reasoning.

ERIC Educational Resources Information Center

Johnson-Laird, P. N.; Legrenzi, Paolo; Girotto, Vittorio; Legrenzi, Maria Sonino; Caverni, Jean-Paul

1999-01-01

Outlines a theory of naive probability in which individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an "extensional" way. The theory accommodates reasoning based on numerical premises, and explains how naive reasoners can infer posterior probabilities without relying on Bayes's theorem.…

Pig Data and Bayesian Inference on Multinomial Probabilities

ERIC Educational Resources Information Center

Kern, John C.

2006-01-01

Bayesian inference on multinomial probabilities is conducted based on data collected from the game Pass the Pigs[R]. Prior information on these probabilities is readily available from the instruction manual, and is easily incorporated in a Dirichlet prior. Posterior analysis of the scoring probabilities quantifies the discrepancy between empirical…

Perspective: Maximum caliber is a general variational principle for dynamical systems

NASA Astrophysics Data System (ADS)

Dixit, Purushottam D.; Wagoner, Jason; Weistuch, Corey; Pressé, Steve; Ghosh, Kingshuk; Dill, Ken A.

2018-01-01

We review here Maximum Caliber (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical trajectories what the principle of maximum entropy is to equilibrium states or stationary populations. In Max Cal, you maximize a path entropy over all possible pathways, subject to dynamical constraints, in order to predict relative path weights. Many well-known relationships of non-equilibrium statistical physics—such as the Green-Kubo fluctuation-dissipation relations, Onsager's reciprocal relations, and Prigogine's minimum entropy production—are limited to near-equilibrium processes. Max Cal is more general. While it can readily derive these results under those limits, Max Cal is also applicable far from equilibrium. We give examples of Max Cal as a method of inference about trajectory distributions from limited data, finding reaction coordinates in bio-molecular simulations, and modeling the complex dynamics of non-thermal systems such as gene regulatory networks or the collective firing of neurons. We also survey its basis in principle and some limitations.

Perspective: Maximum caliber is a general variational principle for dynamical systems.

PubMed

Dixit, Purushottam D; Wagoner, Jason; Weistuch, Corey; Pressé, Steve; Ghosh, Kingshuk; Dill, Ken A

2018-01-07

We review here Maximum Caliber (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical trajectories what the principle of maximum entropy is to equilibrium states or stationary populations. In Max Cal, you maximize a path entropy over all possible pathways, subject to dynamical constraints, in order to predict relative path weights. Many well-known relationships of non-equilibrium statistical physics-such as the Green-Kubo fluctuation-dissipation relations, Onsager's reciprocal relations, and Prigogine's minimum entropy production-are limited to near-equilibrium processes. Max Cal is more general. While it can readily derive these results under those limits, Max Cal is also applicable far from equilibrium. We give examples of Max Cal as a method of inference about trajectory distributions from limited data, finding reaction coordinates in bio-molecular simulations, and modeling the complex dynamics of non-thermal systems such as gene regulatory networks or the collective firing of neurons. We also survey its basis in principle and some limitations.

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.

Inference of the sparse kinetic Ising model using the decimation method

NASA Astrophysics Data System (ADS)

Decelle, Aurélien; Zhang, Pan

2015-05-01

In this paper we study the inference of the kinetic Ising model on sparse graphs by the decimation method. The decimation method, which was first proposed in Decelle and Ricci-Tersenghi [Phys. Rev. Lett. 112, 070603 (2014), 10.1103/PhysRevLett.112.070603] for the static inverse Ising problem, tries to recover the topology of the inferred system by setting the weakest couplings to zero iteratively. During the decimation process the likelihood function is maximized over the remaining couplings. Unlike the ℓ1-optimization-based methods, the decimation method does not use the Laplace distribution as a heuristic choice of prior to select a sparse solution. In our case, the whole process can be done auto-matically without fixing any parameters by hand. We show that in the dynamical inference problem, where the task is to reconstruct the couplings of an Ising model given the data, the decimation process can be applied naturally into a maximum-likelihood optimization algorithm, as opposed to the static case where pseudolikelihood method needs to be adopted. We also use extensive numerical studies to validate the accuracy of our methods in dynamical inference problems. Our results illustrate that, on various topologies and with different distribution of couplings, the decimation method outperforms the widely used ℓ1-optimization-based methods.

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.

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.

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.

Improved orthologous databases to ease protozoan targets inference.

PubMed

Kotowski, Nelson; Jardim, Rodrigo; Dávila, Alberto M R

2015-09-29

Homology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another. In addition, comparative genomics pipelines are widely adopted tools designed using different bioinformatics applications and algorithms. In this article, we propose a methodology to build improved orthologous databases with the potential to aid on protozoan target identification, one of the many tasks which benefit from comparative genomics tools. Our analyses are based on OrthoSearch, a comparative genomics pipeline originally designed to infer orthologs through protein-profile comparison, supported by an HMM, reciprocal best hits based approach. Our methodology allows OrthoSearch to confront two orthologous databases and to generate an improved new one. Such can be later used to infer potential protozoan targets through a similarity analysis against the human genome. The protein sequences of Cryptosporidium hominis, Entamoeba histolytica and Leishmania infantum genomes were comparatively analyzed against three orthologous databases: (i) EggNOG KOG, (ii) ProtozoaDB and (iii) Kegg Orthology (KO). That allowed us to create two new orthologous databases, "KO + EggNOG KOG" and "KO + EggNOG KOG + ProtozoaDB", with 16,938 and 27,701 orthologous groups, respectively. Such new orthologous databases were used for a regular OrthoSearch run. By confronting "KO + EggNOG KOG" and "KO + EggNOG KOG + ProtozoaDB" databases and protozoan species we were able to detect the following total of orthologous groups and coverage (relation between the inferred orthologous groups and the species total number of proteins): Cryptosporidium hominis: 1,821 (11 %) and 3,254 (12 %); Entamoeba histolytica: 2,245 (13 %) and 5,305 (19 %); Leishmania infantum: 2,702 (16 %) and 4,760 (17 %). Using our HMM-based methodology and the largest created orthologous database, it was possible to infer 13 orthologous groups which represent potential protozoan targets; these were found because of our distant homology approach. We also provide the number of species-specific, pair-to-pair and core groups from such analyses, depicted in Venn diagrams. The orthologous databases generated by our HMM-based methodology provide a broader dataset, with larger amounts of orthologous groups when compared to the original databases used as input. Those may be used for several homology inference analyses, annotation tasks and protozoan targets identification.

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.

PubMed

Zhao, Chao; Jiang, Jingchi; Guan, Yi; Guo, Xitong; He, Bin

2018-05-01

Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. Copyright © 2018 Elsevier B.V. All rights reserved.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

PubMed

Chung, Michael Jae-Yoon; Friesen, Abram L; Fox, Dieter; Meltzoff, Andrew N; Rao, Rajesh P N

2015-01-01

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

PubMed Central

Chung, Michael Jae-Yoon; Friesen, Abram L.; Fox, Dieter; Meltzoff, Andrew N.; Rao, Rajesh P. N.

2015-01-01

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration. PMID:26536366

Inductive Selectivity in Children’s Cross-classified Concepts

PubMed Central

Nguyen, Simone P.

2012-01-01

Cross-classified items pose an interesting challenge to children’s induction since these items belong to many different categories, each of which may serve as a basis for a different type of inference. Inductive selectivity is the ability to appropriately make different types of inferences about a single cross-classifiable item based on its different category memberships. This research includes five experiments that examine the development of inductive selectivity in 3-, 4-, and 5-year-olds (N = 272). Overall, the results show that by age 4 years, children have inductive selectivity with taxonomic and script categories. That is, children use taxonomic categories to make biochemical inferences about an item whereas children use script categories to make situational inferences about an item. PMID:22803510

Explanatory Preferences Shape Learning and Inference.

PubMed

Lombrozo, Tania

2016-10-01

Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

PubMed Central

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

2016-01-01

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

I know why you voted for Trump: (Over)inferring motives based on choice.

PubMed

Barasz, Kate; Kim, Tami; Evangelidis, Ioannis

2018-05-10

People often speculate about why others make the choices they do. This paper investigates how such inferences are formed as a function of what is chosen. Specifically, when observers encounter someone else's choice (e.g., of political candidate), they use the chosen option's attribute values (e.g., a candidate's specific stance on a policy issue) to infer the importance of that attribute (e.g., the policy issue) to the decision-maker. Consequently, when a chosen option has an attribute whose value is extreme (e.g., an extreme policy stance), observers infer-sometimes incorrectly-that this attribute disproportionately motivated the decision-maker's choice. Seven studies demonstrate how observers use an attribute's value to infer its weight-the value-weight heuristic-and identify the role of perceived diagnosticity: more extreme attribute values give observers the subjective sense that they know more about a decision-maker's preferences, and in turn, increase the attribute's perceived importance. The paper explores how this heuristic can produce erroneous inferences and influence broader beliefs about decision-makers. Copyright © 2018 Elsevier B.V. All rights reserved.

A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data

PubMed Central

Gallopin, Mélina; Rau, Andrea; Jaffrézic, Florence

2013-01-01

Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data. PMID:24147011

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.

Cluster mass inference via random field theory.

PubMed

Zhang, Hui; Nichols, Thomas E; Johnson, Timothy D

2009-01-01

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.

Tree-Structured Infinite Sparse Factor Model

PubMed Central

Zhang, XianXing; Dunson, David B.; Carin, Lawrence

2013-01-01

A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images. PMID:25279389

Missing data imputation and haplotype phase inference for genome-wide association studies

PubMed Central

Browning, Sharon R.

2009-01-01

Imputation of missing data and the use of haplotype-based association tests can improve the power of genome-wide association studies (GWAS). In this article, I review methods for haplotype inference and missing data imputation, and discuss their application to GWAS. I discuss common features of the best algorithms for haplotype phase inference and missing data imputation in large-scale data sets, as well as some important differences between classes of methods, and highlight the methods that provide the highest accuracy and fastest computational performance. PMID:18850115

Method of fuzzy inference for one class of MISO-structure systems with non-singleton inputs

NASA Astrophysics Data System (ADS)

Sinuk, V. G.; Panchenko, M. V.

2018-03-01

In fuzzy modeling, the inputs of the simulated systems can receive both crisp values and non-Singleton. Computational complexity of fuzzy inference with fuzzy non-Singleton inputs corresponds to an exponential. This paper describes a new method of inference, based on the theorem of decomposition of a multidimensional fuzzy implication and a fuzzy truth value. This method is considered for fuzzy inputs and has a polynomial complexity, which makes it possible to use it for modeling large-dimensional MISO-structure systems.

Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library

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

Bates, Cameron Russell; Mckigney, Edward Allen

The use of Bayesian inference in data analysis has become the standard for large scienti c experiments [1, 2]. The Monte Carlo Codes Group(XCP-3) at Los Alamos has developed a simple set of algorithms currently implemented in C++ and Python to easily perform at-prior Markov Chain Monte Carlo Bayesian inference with pure Metropolis sampling. These implementations are designed to be user friendly and extensible for customization based on speci c application requirements. This document describes the algorithmic choices made and presents two use cases.

A facility for gas- and condensed-phase measurements behind shock waves

NASA Astrophysics Data System (ADS)

Petersen, Eric L.; Rickard, Matthew J. A.; Crofton, Mark W.; Abbey, Erin D.; Traum, Matthew J.; Kalitan, Danielle M.

2005-09-01

A shock-tube facility consisting of two, single-pulse shock tubes for the study of fundamental processes related to gas-phase chemical kinetics and the formation and reaction of solid and liquid aerosols at elevated temperatures is described. Recent upgrades and additions include a new high-vacuum system, a new gas-handling system, a new control system and electronics, an optimized velocity-detection scheme, a computer-based data acquisition system, several optical diagnostics, and new techniques and procedures for handling experiments involving gas/powder mixtures. Test times on the order of 3 ms are possible with reflected-shock pressures up to 100 atm and temperatures greater than 4000 K. Applications for the shock-tube facility include the study of ignition delay times of fuel/oxidizer mixtures, the measurement of chemical kinetic reaction rates, the study of fundamental particle formation from the gas phase, and solid-particle vaporization, among others. The diagnostic techniques include standard differential laser absorption, FM laser absorption spectroscopy, laser extinction for particle volume fraction and size, temporally and spectrally resolved emission from gas-phase species, and a scanning mobility particle sizer for particle size distributions. Details on the set-up and operation of the shock tube and diagnostics are given, the results of a detailed uncertainty analysis on the accuracy of the test temperature inferred from the incident-shock velocity are provided, and some recent results are presented.

An interactive activation and competition model of person knowledge, suggested by proactive interference by traits spontaneously inferred from behaviours.

PubMed

Wang, Yuanbo E; Higgins, Nancy C; Uleman, James S; Michaux, Aaron; Vipond, Douglas

2016-03-01

People unconsciously and unintentionally make inferences about others' personality traits based on their behaviours. In this study, a classic memory phenomenon--proactive interference (PI)--is for the first time used to detect spontaneous trait inferences. PI should occur when lists of behaviour descriptions, all implying the same trait, are to be remembered. Switching to a new trait should produce 'release' from proactive interference (or RPI). Results from two experiments supported these predictions. PI and RPI effects are consistent with an interactive activation and competition model of person perception (e.g., McNeill & Burton, 2002, J. Exp. Psychol., 55A, 1141), which predicts categorical organization of social behaviours based on personality traits. Advantages of this model are discussed. © 2015 The British Psychological Society.

Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

NASA Astrophysics Data System (ADS)

Khoury, Mehdi; Liu, Honghai

This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.

An expert system shell for inferring vegetation characteristics

NASA Technical Reports Server (NTRS)

Harrison, P. Ann; Harrison, Patrick R.

1992-01-01

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The report describes the extensions that have been made to the first generation version of VEG. An interface to a file of unkown cover type data has been constructed. An interface that allows the results of VEG to be written to a file has been implemented. A learning system that learns class descriptions from a data base of historical cover type data and then uses the learned class descriptions to classify an unknown sample has been built. This system has an interface that integrates it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented.

[Causal inference in medicine: a reaction to the report, "incidence of Minamata disease in communities along the Agano River, Niigata, Japan--patterns of the exposure and official diagnosis of patients"].

PubMed

Tsuda, T; Mino, Y; Yamamoto, E; Matsuoka, H; Babazono, A; Shigemi, J; Miyai, M

1997-07-01

Kondo's "Incidence of Minamata Disease in Communities along the Agano River, Niigata, Japan (Jap. J. Hyg. 51:599-611;1996)" is critically reviewed. The data of the article were obtained from most of the residents living in the Agano river villages where Minamata disease was discovered in June, 1965. However, sampling proportions were much different between in the population base and in the cases. The method of identification of cases from the data and the reason for the difference were not clearly demonstrated. The citations of reference articles are insufficient despite the fact that other epidemiologic studies on methyl-mercury poisoning have been reported not only in Japan, but also around the world. His "analysis of the recognized patients" is erroneous. Both the sampling scheme of information of hair mercury and the modeling of the analysis are based on Kondo's arbitrary interpretation, not on epidemiologic theory. His "analysis of the rejected applicants" is also erroneous. His calculations of the attributable proportion are incorrect and self-induced in both the assignments of data and analysis of data. Kondo has failed to study the epidemiologic theories in light of changes in the field. Therefore, his article is lacking in epidemiologic theory, a logical base and scientific inference. In Japan, epidemiologic methodology has rarely been used in studies on Minamata Disease in either Kumamoto and Niigata. The government has used neurologically specific diagnosis based on combinations of symptoms to judge the causality between each of symptoms and methyl-mercury poisoning. Epidemiologic data obtained in Minamata, Kumamoto in 1971 indicate that the criteria set by the government in 1977 have produced much more false-negative patients than false-positive patients. As a result, a huge number of symptomatic patients, including those with peripheral neuropathy or with constriction of the visual field, did not receive any help or compensation until 1995. The authors emphasize that the causal relationship between each symptom and methyl-mercury exposure should be reevaluated epidemiologically in Japan.

Testing the implicit processing hypothesis of precognitive dream experience.

PubMed

Valášek, Milan; Watt, Caroline; Hutton, Jenny; Neill, Rebecca; Nuttall, Rachel; Renwick, Grace

2014-08-01

Seemingly precognitive (prophetic) dreams may be a result of one's unconscious processing of environmental cues and having an implicit inference based on these cues manifest itself in one's dreams. We present two studies exploring this implicit processing hypothesis of precognitive dream experience. Study 1 investigated the relationship between implicit learning, transliminality, and precognitive dream belief and experience. Participants completed the Serial Reaction Time task and several questionnaires. We predicted a positive relationship between the variables. With the exception of relationships between transliminality and precognitive dream belief and experience, this prediction was not supported. Study 2 tested the hypothesis that differences in the ability to notice subtle cues explicitly might account for precognitive dream beliefs and experiences. Participants completed a modified version of the flicker paradigm. We predicted a negative relationship between the ability to explicitly detect changes and precognitive dream variables. This relationship was not found. There was also no relationship between precognitive dream belief and experience and implicit change detection. Copyright © 2014 Elsevier Inc. All rights reserved.

Inference and Prediction of Metabolic Network Fluxes

PubMed Central

Nikoloski, Zoran; Perez-Storey, Richard; Sweetlove, Lee J.

2015-01-01

In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping. PMID:26392262

Meeting the Discipline-Culture Framework of Physics Knowledge: A Teaching Experience in Italian Secondary School

NASA Astrophysics Data System (ADS)

Levrini, Olivia; Bertozzi, Eugenio; Gagliardi, Marta; Tomasini, Nella Grimellini; Pecori, Barbara; Tasquier, Giulia; Galili, Igal

2014-09-01

The paper deals with physics teaching/learning in high school. An investigation in three upper secondary school classes in Italy explored the reactions of students to a structuring lecture on optics within the discipline-culture (DC) framework that organises physics knowledge around four interrelated fundamental theories of light. The lecture presented optics as an unfolding conceptual discourse of physicists regarding the nature of light. Along with the knowledge constructed in a school course of a scientific lyceum, the students provided epistemological comments, displaying their perception of physics knowledge presented in the classroom. Students' views and knowledge were investigated by questionnaires prior to and after the lecture and in special discussions held in each class. They revealed a variety of attitudes and views which allowed inferences about the potential of the DC framework in an educational context. The findings and interpretation indicate the positive and stimulating impact of the lecture and the way in which DC-based approach to knowledge organization makes physics at school cultural and attractive.

Mechanism of Electro-Coagulation with Al/Fe Periodically Reversing Treating Berberine Pharmaceutical Wastewater

NASA Astrophysics Data System (ADS)

Sun, Zhaonan; Liu, Zheng; Hu, Xiaomin

2017-05-01

The method of treating pharmaceutical wastewater by electro-coagulation with Al/Fe periodically reversing (ECPR) was proposed based on traditional electrochemical method. The principle of ECPR was generalized. Mechanism of ECPR to treat berberine pharmaceutical wastewater was investigated. Treatability and mechanism studies were conducted under laboratory conditions. For berberine wastewater (800 mg/L), decolourization efficiency and COD removal efficiency were highest to 98% and 95% respectively when voltage was 8V, reaction time was 60 min, alternating period was 10 S electrolyte concentration was 0.015 mol/L, stirring speed was 750 rpm, pH value was 3-10 and distance between two plates was 0.6 cm. For removal berberine, flocculation, floatation and oxidation provided 73%, 8% and 18% removal efficiency, which can be inferred by analysing UV-visible absorption spectrum, acidification experiment, EDTA shielding experiment, structure-activity relationship, oxidation and floatation. Meanwhile decolourization and COD removal conformed to apparent pseudo-first order and zero-order kinetics for 200mg/L and 400-1000 mg/L berberine wastewater respectively.

Rate Coefficients for Reactions of Ethynyl Radical (C2H) With HCN and CH3CN: Implications for the Formation of Comples Nitriles on Titan

NASA Technical Reports Server (NTRS)

Hoobler, Ray J.; Leone, Stephen R.

1997-01-01

Rate coefficients for the reactions of C2H + HCN yields products and C2H + CH3CN yields products have been measured over the temperature range 262-360 K. These experiments represent an ongoing effort to accurately measure reaction rate coefficients of the ethynyl radical, C2H, relevant to planetary atmospheres such as those of Jupiter and Saturn and its satellite Titan. Laser photolysis of C2H2 is used to produce C2H, and transient infrared laser absorption is employed to measure the decay of C2H to obtain the subsequent reaction rates in a transverse flow cell. Rate constants for the reaction C2H + HCN yields products are found to increase significantly with increasing temperature and are measured to be (3.9-6.2) x 10(exp 13) cm(exp 3) molecules(exp -1) s(exp -1) over the temperature range of 297-360 K. The rate constants for the reaction C2H + CH3CN yields products are also found to increase substantially with increasing temperature and are measured to be (1.0-2.1) x 10(exp -12) cm(exp 3) molecules(exp -1) s(exp -1) over the temperature range of 262-360 K. For the reaction C2H + HCN yields products, ab initio calculations of transition state structures are used to infer that the major products form via an addition/elimination pathway. The measured rate constants for the reaction of C2H + HCN yields products are significantly smaller than values currently employed in photochemical models of Titan, which will affect the HC3N distribution.

Two sources of evidence on the non-automaticity of true and false belief ascription.

PubMed

Back, Elisa; Apperly, Ian A

2010-04-01

A recent study by Apperly et al. (2006) found evidence that adults do not automatically infer false beliefs while watching videos that afford such inferences. This method was extended to examine true beliefs, which are sometimes thought to be ascribed by "default" (e.g., Leslie & Thaiss, 1992). Sequences of pictures were presented in which the location of an object and a character's belief about the location of the object often changed. During the picture sequences participants responded to an unpredictable probe picture about where the character believed the object to be located or where the object was located in reality. In Experiment 1 participants were not directly instructed to track the character's beliefs about the object. There was a significant reaction time cost for belief probes compared with matched reality probes, whether the character's belief was true or false. In Experiment 2, participants were asked to track where the character thought the object was located, responses to belief probes were faster than responses to reality probes, suggesting that the difference observed in Experiment 1 was not due to intrinsic differences between the probes, but was more likely to be due to participants inferring beliefs ad hoc in response to the probe. In both Experiments 1 and 2, responses to belief and reality probes were faster in the true belief condition than in the false belief condition. In Experiment 3 this difference was largely eliminated when participants had fewer reasons to make belief inferences spontaneously. These two lines of evidence are neatly explained by the proposition that neither true nor false beliefs are ascribed automatically, but that belief ascription may occur spontaneously in response to task demands. Copyright 2009 Elsevier B.V. All rights reserved.