The effects of interstimulus interval on event-related indices of attention: an auditory selective attention test of perceptual load theory.

PubMed

Gomes, Hilary; Barrett, Sophia; Duff, Martin; Barnhardt, Jack; Ritter, Walter

2008-03-01

We examined the impact of perceptual load by manipulating interstimulus interval (ISI) in two auditory selective attention studies that varied in the difficulty of the target discrimination. In the paradigm, channels were separated by frequency and target/deviant tones were softer in intensity. Three ISI conditions were presented: fast (300ms), medium (600ms) and slow (900ms). Behavioral (accuracy and RT) and electrophysiological measures (Nd, P3b) were observed. In both studies, participants evidenced poorer accuracy during the fast ISI condition than the slow suggesting that ISI impacted task difficulty. However, none of the three measures of processing examined, Nd amplitude, P3b amplitude elicited by unattended deviant stimuli, or false alarms to unattended deviants, were impacted by ISI in the manner predicted by perceptual load theory. The prediction based on perceptual load theory, that there would be more processing of irrelevant stimuli under conditions of low as compared to high perceptual load, was not supported in these auditory studies. Task difficulty/perceptual load impacts the processing of irrelevant stimuli in the auditory modality differently than predicted by perceptual load theory, and perhaps differently than in the visual modality.

Pavement remaining service interval implementation guidelines.

DOT National Transportation Integrated Search

2013-11-01

"Many important decisions are necessary in order to effectively provide and manage a pavement network. At the heart of this : process is the prediction of needed future construction events. One approach to providing a single numeric on the condition ...

Updating of aversive memories after temporal error detection is differentially modulated by mTOR across development

PubMed Central

Tallot, Lucille; Diaz-Mataix, Lorenzo; Perry, Rosemarie E.; Wood, Kira; LeDoux, Joseph E.; Mouly, Anne-Marie; Sullivan, Regina M.; Doyère, Valérie

2017-01-01

The updating of a memory is triggered whenever it is reactivated and a mismatch from what is expected (i.e., prediction error) is detected, a process that can be unraveled through the memory's sensitivity to protein synthesis inhibitors (i.e., reconsolidation). As noted in previous studies, in Pavlovian threat/aversive conditioning in adult rats, prediction error detection and its associated protein synthesis-dependent reconsolidation can be triggered by reactivating the memory with the conditioned stimulus (CS), but without the unconditioned stimulus (US), or by presenting a CS–US pairing with a different CS–US interval than during the initial learning. Whether similar mechanisms underlie memory updating in the young is not known. Using similar paradigms with rapamycin (an mTORC1 inhibitor), we show that preweaning rats (PN18–20) do form a long-term memory of the CS–US interval, and detect a 10-sec versus 30-sec temporal prediction error. However, the resulting updating/reconsolidation processes become adult-like after adolescence (PN30–40). Our results thus show that while temporal prediction error detection exists in preweaning rats, specific infant-type mechanisms are at play for associative learning and memory. PMID:28202715

On the accuracy and reliability of predictions by control-system theory.

PubMed

Bourbon, W T; Copeland, K E; Dyer, V R; Harman, W K; Mosley, B L

1990-12-01

In three experiments we used control-system theory (CST) to predict the results of tracking tasks on which people held a handle to keep a cursor even with a target on a computer screen. 10 people completed a total of 104 replications of the task. In each experiment, there were two conditions: in one, only the handle affected the position of the cursor; in the other, a random disturbance also affected the cursor. From a person's performance during Condition 1, we derived constants used in the CST model to predict the results of Condition 2. In two experiments, predictions occurred a few minutes before Condition 2; in one experiment, the delay was 1 yr. During a 1-min. experimental run, the positions of handle and cursor, produced by the person, were each sampled 1800 times, once every 1/30 sec. During a modeling run, the model predicted the positions of the handle and target for each of the 1800 intervals sampled in the experimental run. In 104 replications, the mean correlation between predicted and actual positions of the handle was .996; SD = .002.

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

PubMed

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

2017-01-01

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

Method and apparatus to predict the remaining service life of an operating system

DOEpatents

Greitzer, Frank L.; Kangas, Lars J.; Terrones, Kristine M.; Maynard, Melody A.; Pawlowski, Ronald A. , Ferryman; Thomas A.; Skorpik, James R.; Wilson, Bary W.

2008-11-25

A method and computer-based apparatus for monitoring the degradation of, predicting the remaining service life of, and/or planning maintenance for, an operating system are disclosed. Diagnostic information on degradation of the operating system is obtained through measurement of one or more performance characteristics by one or more sensors onboard and/or proximate the operating system. Though not required, it is preferred that the sensor data are validated to improve the accuracy and reliability of the service life predictions. The condition or degree of degradation of the operating system is presented to a user by way of one or more calculated, numeric degradation figures of merit that are trended against one or more independent variables using one or more mathematical techniques. Furthermore, more than one trendline and uncertainty interval may be generated for a given degradation figure of merit/independent variable data set. The trendline(s) and uncertainty interval(s) are subsequently compared to one or more degradation figure of merit thresholds to predict the remaining service life of the operating system. The present invention enables multiple mathematical approaches in determining which trendline(s) to use to provide the best estimate of the remaining service life.

Odor-context effects in free recall after a short retention interval: a new methodology for controlling adaptation.

PubMed

Isarida, Takeo; Sakai, Tetsuya; Kubota, Takayuki; Koga, Miho; Katayama, Yu; Isarida, Toshiko K

2014-04-01

The present study investigated context effects of incidental odors in free recall after a short retention interval (5 min). With a short retention interval, the results are not confounded by extraneous odors or encounters with the experimental odor and possible rehearsal during a long retention interval. A short study time condition (4 s per item), predicted not to be affected by adaptation to the odor, and a long study time condition (8 s per item) were used. Additionally, we introduced a new method for recovery from adaptation, where a dissimilar odor was briefly presented at the beginning of the retention interval, and we demonstrated the effectiveness of this technique. An incidental learning paradigm was used to prevent overshadowing from confounding the results. In three experiments, undergraduates (N = 200) incidentally studied words presented one-by-one and received a free recall test. Two pairs of odors and a third odor having different semantic-differential characteristics were selected from 14 familiar odors. One of the odors was presented during encoding, and during the test, the same odor (same-context condition) or the other odor within the pair (different-context condition) was presented. Without using a recovery-from-adaptation method, a significant odor-context effect appeared in the 4-s/item condition, but not in the 8-s/item condition. Using the recovery-from-adaptation method, context effects were found for both the 8- and the 4-s/item conditions. The size of the recovered odor-context effect did not change with study time. There were no serial position effects. Implications of the present findings are discussed.

Extreme precipitation patterns reduced terrestrial ecosystem production across biomass

USDA-ARS?s Scientific Manuscript database

Precipitation regimes are predicted to shift to more extreme patterns that are characterized by more intense rainfall events and longer dry intervals, yet their ecological impacts on vegetation production remain uncertain across biomes in natural climatic conditions. This in situ study investigated ...

Prediction future asset price which is non-concordant with the historical distribution

NASA Astrophysics Data System (ADS)

Seong, Ng Yew; Hin, Pooi Ah

2015-12-01

This paper attempts to predict the major characteristics of the future asset price which is non-concordant with the distribution estimated from the price today and the prices on a large number of previous days. The three major characteristics of the i-th non-concordant asset price are the length of the interval between the occurrence time of the previous non-concordant asset price and that of the present non-concordant asset price, the indicator which denotes that the non-concordant price is extremely small or large by its values -1 and 1 respectively, and the degree of non-concordance given by the negative logarithm of the probability of the left tail or right tail of which one of the end points is given by the observed future price. The vector of three major characteristics of the next non-concordant price is modelled to be dependent on the vectors corresponding to the present and l - 1 previous non-concordant prices via a 3-dimensional conditional distribution which is derived from a 3(l + 1)-dimensional power-normal mixture distribution. The marginal distribution for each of the three major characteristics can then be derived from the conditional distribution. The mean of the j-th marginal distribution is an estimate of the value of the j-th characteristics of the next non-concordant price. Meanwhile, the 100(α/2) % and 100(1 - α/2) % points of the j-th marginal distribution can be used to form a prediction interval for the j-th characteristic of the next non-concordant price. The performance measures of the above estimates and prediction intervals indicate that the fitted conditional distribution is satisfactory. Thus the incorporation of the distribution of the characteristics of the next non-concordant price in the model for asset price has a good potential of yielding a more realistic model.

Coincidence-enhanced stochastic resonance: experimental evidence challenges the psychophysical theory behind stochastic resonance.

PubMed

Perez, Claudio A; Cohn, Theodore E; Medina, Leonel E; Donoso, José R

2007-08-31

Stochastic resonance (SR) is the counterintuitive phenomenon in which noise enhances detection of sub-threshold stimuli. The SR psychophysical threshold theory establishes that the required amplitude to exceed the sensory threshold barrier can be reached by adding noise to a sub-threshold stimulus. The aim of this study was to test the SR theory by comparing detection results from two different randomly-presented stimulus conditions. In the first condition, optimal noise was present during the whole attention interval; in the second, the optimal noise was restricted to the same time interval as the stimulus. SR threshold theory predicts no difference between the two conditions because noise helps the sub-threshold stimulus to reach threshold in both cases. The psychophysical experimental method used a 300 ms rectangular force pulse as a stimulus within an attention interval of 1.5 s, applied to the index finger of six human subjects in the two distinct conditions. For all subjects we show that in the condition in which the noise was present only when synchronized with the stimulus, detection was better (p<0.05) than in the condition in which the noise was delivered throughout the attention interval. These results provide the first direct evidence that SR threshold theory is incomplete and that a new phenomenon has been identified, which we call Coincidence-Enhanced Stochastic Resonance (CESR). We propose that CESR might occur because subject uncertainty is reduced when noise points at the same temporal window as the stimulus.

Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

PubMed

Darrington, Richard T; Jiao, Jim

2004-04-01

Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

Assessing predictability of a hydrological stochastic-dynamical system

NASA Astrophysics Data System (ADS)

Gelfan, Alexander

2014-05-01

The water cycle includes the processes with different memory that creates potential for predictability of hydrological system based on separating its long and short memory components and conditioning long-term prediction on slower evolving components (similar to approaches in climate prediction). In the face of the Panta Rhei IAHS Decade questions, it is important to find a conceptual approach to classify hydrological system components with respect to their predictability, define predictable/unpredictable patterns, extend lead-time and improve reliability of hydrological predictions based on the predictable patterns. Representation of hydrological systems as the dynamical systems subjected to the effect of noise (stochastic-dynamical systems) provides possible tool for such conceptualization. A method has been proposed for assessing predictability of hydrological system caused by its sensitivity to both initial and boundary conditions. The predictability is defined through a procedure of convergence of pre-assigned probabilistic measure (e.g. variance) of the system state to stable value. The time interval of the convergence, that is the time interval during which the system losses memory about its initial state, defines limit of the system predictability. The proposed method was applied to assess predictability of soil moisture dynamics in the Nizhnedevitskaya experimental station (51.516N; 38.383E) located in the agricultural zone of the central European Russia. A stochastic-dynamical model combining a deterministic one-dimensional model of hydrothermal regime of soil with a stochastic model of meteorological inputs was developed. The deterministic model describes processes of coupled heat and moisture transfer through unfrozen/frozen soil and accounts for the influence of phase changes on water flow. The stochastic model produces time series of daily meteorological variables (precipitation, air temperature and humidity), whose statistical properties are similar to those of the corresponding series of the actual data measured at the station. Beginning from the initial conditions and being forced by Monte-Carlo generated synthetic meteorological series, the model simulated diverging trajectories of soil moisture characteristics (water content of soil column, moisture of different soil layers, etc.). Limit of predictability of the specific characteristic was determined through time of stabilization of variance of the characteristic between the trajectories, as they move away from the initial state. Numerical experiments were carried out with the stochastic-dynamical model to analyze sensitivity of the soil moisture predictability assessments to uncertainty in the initial conditions, to determine effects of the soil hydraulic properties and processes of soil freezing on the predictability. It was found, particularly, that soil water content predictability is sensitive to errors in the initial conditions and strongly depends on the hydraulic properties of soil under both unfrozen and frozen conditions. Even if the initial conditions are "well-established", the assessed predictability of water content of unfrozen soil does not exceed 30-40 days, while for frozen conditions it may be as long as 3-4 months. The latter creates opportunity for utilizing the autumn water content of soil as the predictor for spring snowmelt runoff in the region under consideration.

Image discrimination models predict detection in fixed but not random noise

NASA Technical Reports Server (NTRS)

Ahumada, A. J. Jr; Beard, B. L.; Watson, A. B. (Principal Investigator)

1997-01-01

By means of a two-interval forced-choice procedure, contrast detection thresholds for an aircraft positioned on a simulated airport runway scene were measured with fixed and random white-noise masks. The term fixed noise refers to a constant, or unchanging, noise pattern for each stimulus presentation. The random noise was either the same or different in the two intervals. Contrary to simple image discrimination model predictions, the same random noise condition produced greater masking than the fixed noise. This suggests that observers seem unable to hold a new noisy image for comparison. Also, performance appeared limited by internal process variability rather than by external noise variability, since similar masking was obtained for both random noise types.

Fractal analyses reveal independent complexity and predictability of gait

PubMed Central

Dierick, Frédéric; Nivard, Anne-Laure

2017-01-01

Locomotion is a natural task that has been assessed for decades and used as a proxy to highlight impairments of various origins. So far, most studies adopted classical linear analyses of spatio-temporal gait parameters. Here, we use more advanced, yet not less practical, non-linear techniques to analyse gait time series of healthy subjects. We aimed at finding more sensitive indexes related to spatio-temporal gait parameters than those previously used, with the hope to better identify abnormal locomotion. We analysed large-scale stride interval time series and mean step width in 34 participants while altering walking direction (forward vs. backward walking) and with or without galvanic vestibular stimulation. The Hurst exponent α and the Minkowski fractal dimension D were computed and interpreted as indexes expressing predictability and complexity of stride interval time series, respectively. These holistic indexes can easily be interpreted in the framework of optimal movement complexity. We show that α and D accurately capture stride interval changes in function of the experimental condition. Walking forward exhibited maximal complexity (D) and hence, adaptability. In contrast, walking backward and/or stimulation of the vestibular system decreased D. Furthermore, walking backward increased predictability (α) through a more stereotyped pattern of the stride interval and galvanic vestibular stimulation reduced predictability. The present study demonstrates the complementary power of the Hurst exponent and the fractal dimension to improve walking classification. Our developments may have immediate applications in rehabilitation, diagnosis, and classification procedures. PMID:29182659

Acquisition with partial and continuous reinforcement in pigeon autoshaping.

PubMed

Gottlieb, Daniel A

2004-08-01

Contemporary time accumulation models make the unique prediction that acquisition of a conditioned response will be equally rapid with partial and continuous reinforcement, if the time between conditioned stimuli is held constant. To investigate this, acquisition of conditioned responding was examined in pigeon autoshaping under conditions of 100% and 25% reinforcement, holding intertrial interval constant. Contrary to what was predicted, evidence for slowed acquisition in partially reinforced animals was observed with several response measures. However, asymptotic performance was superior with 25% reinforcement. A switching of reinforcement contingencies after initial acquisition did not immediately affect responding. After further sessions, partial reinforcement augmented responding, whereas continuous reinforcement did not, irrespective of an animal's reinforcement history. Subsequent training with a novel stimulus maintained the response patterns. These acquisition results generally support associative, rather than time accumulation, accounts of conditioning.

It's time to fear! Interval timing in odor fear conditioning in rats

PubMed Central

Shionoya, Kiseko; Hegoburu, Chloé; Brown, Bruce L.; Sullivan, Regina M.; Doyère, Valérie; Mouly, Anne-Marie

2013-01-01

Time perception is crucial to goal attainment in humans and other animals, and interval timing also guides fundamental animal behaviors. Accumulating evidence has made it clear that in associative learning, temporal relations between events are encoded, and a few studies suggest this temporal learning occurs very rapidly. Most of these studies, however, have used methodologies that do not permit investigating the emergence of this temporal learning. In the present study we monitored respiration, ultrasonic vocalization (USV) and freezing behavior in rats in order to perform fine-grain analysis of fear responses during odor fear conditioning. In this paradigm an initially neutral odor (the conditioned stimulus, CS) predicted the arrival of an aversive unconditioned stimulus (US, footshock) at a fixed 20-s time interval. We first investigated the development of a temporal pattern of responding related to CS-US interval duration. The data showed that during acquisition with odor-shock pairings, a temporal response pattern of respiration rate was observed. Changing the CS-US interval duration from 20-s to 30-s resulted in a shift of the temporal response pattern appropriate to the new duration thus demonstrating that the pattern reflected the learning of the CS-US interval. A temporal pattern was also observed during a retention test 24 h later for both respiration and freezing measures, suggesting that the animals had stored the interval duration in long-term memory. We then investigated the role of intra-amygdalar dopaminergic transmission in interval timing. For this purpose, the D1 dopaminergic receptors antagonist SCH23390 was infused in the basolateral amygdala before conditioning. This resulted in an alteration of timing behavior, as reflected in differential temporal patterns between groups observed in a 24 h retention test off drug. The present data suggest that D1 receptor dopaminergic transmission within the amygdala is involved in temporal processing. PMID:24098277

Intervals for posttest probabilities: a comparison of 5 methods.

PubMed

Mossman, D; Berger, J O

2001-01-01

Several medical articles discuss methods of constructing confidence intervals for single proportions and the likelihood ratio, but scant attention has been given to the systematic study of intervals for the posterior odds, or the positive predictive value, of a test. The authors describe 5 methods of constructing confidence intervals for posttest probabilities when estimates of sensitivity, specificity, and the pretest probability of a disorder are derived from empirical data. They then evaluate each method to determine how well the intervals' coverage properties correspond to their nominal value. When the estimates of pretest probabilities, sensitivity, and specificity are derived from more than 80 subjects and are not close to 0 or 1, all methods generate intervals with appropriate coverage properties. When these conditions are not met, however, the best-performing method is an objective Bayesian approach implemented by a simple simulation using a spreadsheet. Physicians and investigators can generate accurate confidence intervals for posttest probabilities in small-sample situations using the objective Bayesian approach.

A high-resolution model of the planetary boundary layer - Sensitivity tests and comparisons with SESAME-79 data

NASA Technical Reports Server (NTRS)

Zhang, D.; Anthes, R. A.

1982-01-01

A one-dimensional, planetary boundary layer (PBL) model is presented and verified using April 10, 1979 SESAME data. The model contains two modules to account for two different regimes of turbulent mixing. Separate parameterizations are made for stable and unstable conditions, with a predictive slab model for surface temperature. Atmospheric variables in the surface layer are calculated with a prognostic model, with moisture included in the coupled surface/PBL modeling. Sensitivity tests are performed for factors such as moisture availability, albedo, surface roughness, and thermal capacity, and a 24 hr simulation is summarized for day and night conditions. The comparison with the SESAME data comprises three hour intervals, using a time-dependent geostrophic wind. Close correlations were found with daytime conditions, but not in nighttime thermal structure, while the turbulence was faithfully predicted. Both geostrophic flow and surface characteristics were shown to have significant effects on the model predictions

Time and Order Effects on Causal Learning

ERIC Educational Resources Information Center

Alvarado, Angelica; Jara, Elvia; Vila, Javier; Rosas, Juan M.

2006-01-01

Five experiments were conducted to explore trial order and retention interval effects upon causal predictive judgments. Experiment 1 found that participants show a strong effect of trial order when a stimulus was sequentially paired with two different outcomes compared to a condition where both outcomes were presented intermixed. Experiment 2…

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS.

USGS Publications Warehouse

Cooley, Richard L.; Vecchia, Aldo V.

1987-01-01

A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that inclusion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.

Modelling volatility recurrence intervals in the Chinese commodity futures market

NASA Astrophysics Data System (ADS)

Zhou, Weijie; Wang, Zhengxin; Guo, Haiming

2016-09-01

The law of extreme event occurrence attracts much research. The volatility recurrence intervals of Chinese commodity futures market prices are studied: the results show that the probability distributions of the scaled volatility recurrence intervals have a uniform scaling curve for different thresholds q. So we can deduce the probability distribution of extreme events from normal events. The tail of a scaling curve can be well fitted by a Weibull form, which is significance-tested by KS measures. Both short-term and long-term memories are present in the recurrence intervals with different thresholds q, which denotes that the recurrence intervals can be predicted. In addition, similar to volatility, volatility recurrence intervals also have clustering features. Through Monte Carlo simulation, we artificially synthesise ARMA, GARCH-class sequences similar to the original data, and find out the reason behind the clustering. The larger the parameter d of the FIGARCH model, the stronger the clustering effect is. Finally, we use the Fractionally Integrated Autoregressive Conditional Duration model (FIACD) to analyse the recurrence interval characteristics. The results indicated that the FIACD model may provide a method to analyse volatility recurrence intervals.

Early identification of patients at risk of acute lung injury: evaluation of lung injury prediction score in a multicenter cohort study.

PubMed

Gajic, Ognjen; Dabbagh, Ousama; Park, Pauline K; Adesanya, Adebola; Chang, Steven Y; Hou, Peter; Anderson, Harry; Hoth, J Jason; Mikkelsen, Mark E; Gentile, Nina T; Gong, Michelle N; Talmor, Daniel; Bajwa, Ednan; Watkins, Timothy R; Festic, Emir; Yilmaz, Murat; Iscimen, Remzi; Kaufman, David A; Esper, Annette M; Sadikot, Ruxana; Douglas, Ivor; Sevransky, Jonathan; Malinchoc, Michael

2011-02-15

Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS). In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1-4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78-0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9-5.7). ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772).

Machine learning approaches for estimation of prediction interval for the model output.

PubMed

Shrestha, Durga L; Solomatine, Dimitri P

2006-03-01

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

Shape of the equatorial magnetopause affected by the radial interplanetary magnetic field

NASA Astrophysics Data System (ADS)

Grygorov, K.; Šafránková, J.; Němeček, Z.; Pi, G.; Přech, L.; Urbář, J.

2017-11-01

The ability of a prediction of the magnetopause location under various upstream conditions can be considered as a test of our understanding of the solar wind-magnetosphere interaction. The present magnetopause models are parametrized with the solar wind dynamic pressure and usually with the north-south interplanetary magnetic field (IMF) component. However, several studies pointed out an importance of the radial IMF component, but results of these studies are controversial up to now. The present study compares magnetopause observations by five THEMIS spacecraft during long lasting intervals of the radial IMF with two empirical magnetopause models. A comparison reveals that the magnetopause location is highly variable and that the average difference between the observed and predicted positions is ≈ + 0.7 RE under this condition. The difference does not depend on the local times and other parameters, like the upstream pressure, IMF north-south component, or tilt angle of the Earth dipole. We conclude that our results strongly support the suggestion on a global expansion of the equatorial magnetopause during intervals of the radial IMF.

Psychophysics of remembering.

PubMed Central

White, K G; Wixted, J T

1999-01-01

We present a new model of remembering in the context of conditional discrimination. For procedures such as delayed matching to sample, the effect of the sample stimuli at the time of remembering is represented by a pair of Thurstonian (normal) distributions of effective stimulus values. The critical assumption of the model is that, based on prior experience, each effective stimulus value is associated with a ratio of reinforcers obtained for previous correct choices of the comparison stimuli. That ratio determines the choice that is made on the basis of the matching law. The standard deviations of the distributions are assumed to increase with increasing retention-interval duration, and the distance between their means is assumed to be a function of other factors that influence overall difficulty of the discrimination. It is a behavioral model in that choice is determined by its reinforcement history. The model predicts that the biasing effects of the reinforcer differential increase with decreasing discriminability and with increasing retention-interval duration. Data from several conditions using a delayed matching-to-sample procedure with pigeons support the predictions. PMID:10028693

Commentary on Holmes et al. (2007): resolving the debate on when extinction risk is predictable.

PubMed

Ellner, Stephen P; Holmes, Elizabeth E

2008-08-01

We reconcile the findings of Holmes et al. (Ecology Letters, 10, 2007, 1182) that 95% confidence intervals for quasi-extinction risk were narrow for many vertebrates of conservation concern, with previous theory predicting wide confidence intervals. We extend previous theory, concerning the precision of quasi-extinction estimates as a function of population dynamic parameters, prediction intervals and quasi-extinction thresholds, and provide an approximation that specifies the prediction interval and threshold combinations where quasi-extinction estimates are precise (vs. imprecise). This allows PVA practitioners to define the prediction interval and threshold regions of safety (low risk with high confidence), danger (high risk with high confidence), and uncertainty.

Predicted Weather Display and Decision Support Interface for Flight Deck

NASA Technical Reports Server (NTRS)

Johnson, Walter W. (Inventor); Wong, Dominic G. (Inventor); Koteskey, Robert W. (Inventor); Wu, Shu-Chieh (Inventor)

2017-01-01

A system and method for providing visual depictions of a predictive weather forecast for in-route vehicle trajectory planning. The method includes displaying weather information on a graphical display, displaying vehicle position information on the graphical display, selecting a predictive interval, displaying predictive weather information for the predictive interval on the graphical display, and displaying predictive vehicle position information for the predictive interval on the graphical display, such that the predictive vehicle position information is displayed relative to the predictive weather information, for in-route trajectory planning.

Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation - A Systematic Review.

PubMed

Tomaselli Muensterman, Elena; Tisdale, James E

2018-06-08

Prolongation of the heart rate-corrected QT (QTc) interval increases the risk for torsades de pointes (TdP), a potentially fatal arrhythmia. The likelihood of TdP is higher in patients with risk factors, which include female sex, older age, heart failure with reduced ejection fraction, hypokalemia, hypomagnesemia, concomitant administration of ≥ 2 QTc interval-prolonging medications, among others. Assessment and quantification of risk factors may facilitate prediction of patients at highest risk for developing QTc interval prolongation and TdP. Investigators have utilized the field of predictive analytics, which generates predictions using techniques including data mining, modeling, machine learning, and others, to develop methods of risk quantification and prediction of QTc interval prolongation. Predictive analytics have also been incorporated into clinical decision support (CDS) tools to alert clinicians regarding patients at increased risk of developing QTc interval prolongation. The objectives of this paper are to assess the effectiveness of predictive analytics for identification of patients at risk of drug-induced QTc interval prolongation, and to discuss the efficacy of incorporation of predictive analytics into CDS tools in clinical practice. A systematic review of English language articles (human subjects only) was performed, yielding 57 articles, with an additional 4 articles identified from other sources; a total of 10 articles were included in this review. Risk scores for QTc interval prolongation have been developed in various patient populations including those in cardiac intensive care units (ICUs) and in broader populations of hospitalized or health system patients. One group developed a risk score that includes information regarding genetic polymorphisms; this score significantly predicted TdP. Development of QTc interval prolongation risk prediction models and incorporation of these models into CDS tools reduces the risk of QTc interval prolongation in cardiac ICUs and identifies health-system patients at increased risk for mortality. The impact of these QTc interval prolongation predictive analytics on overall patient safety outcomes, such as TdP and sudden cardiac death relative to the cost of development and implementation, requires further study. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

On the Development and Mechanics of Delayed Matching-to-Sample Performance

PubMed Central

Kangas, Brian D; Berry, Meredith S; Branch, Marc N

2011-01-01

Despite its frequent use to assess effects of environmental and pharmacological variables on short-term memory, little is known about the development of delayed matching-to-sample (DMTS) performance. This study was designed to examine the dimensions and dynamics of DMTS performance development over a long period of exposure to provide a more secure foundation for assessing stability in future research. Six pigeons were exposed to a DMTS task with variable delays for 300 sessions (i.e., 18,000 total trials; 3,600 trials per retention interval). Percent-correct and log-d measures used to quantify the development of conditional stimulus control under the procedure generally and at each of five retention intervals (0, 2, 4, 8 and 16-s) individually revealed that high levels of accuracy developed relatively quickly under the shorter retention intervals, but increases in accuracy under the longer retention intervals sometimes were not observed until 100–150 sessions had passed, with some still increasing at Session 300. Analyses of errors suggested that retention intervals induced biases by shifting control from the sample stimulus to control by position, something that was predicted by observed response biases during initial training. These results suggest that although it may require a great deal of exposure to DMTS prior to obtaining asymptotic steady state, quantification of model parameters may help predict trends when extended exposure is not feasible. PMID:21541127

Can 3-dimensional power Doppler indices improve the prenatal diagnosis of a potentially morbidly adherent placenta in patients with placenta previa?

PubMed

Haidar, Ziad A; Papanna, Ramesha; Sibai, Baha M; Tatevian, Nina; Viteri, Oscar A; Vowels, Patricia C; Blackwell, Sean C; Moise, Kenneth J

2017-08-01

Traditionally, 2-dimensional ultrasound parameters have been used for the diagnosis of a suspected morbidly adherent placenta previa. More objective techniques have not been well studied yet. The objective of the study was to determine the ability of prenatal 3-dimensional power Doppler analysis of flow and vascular indices to predict the morbidly adherent placenta objectively. A prospective cohort study was performed in women between 28 and 32 gestational weeks with known placenta previa. Patients underwent a two-dimensional gray-scale ultrasound that determined management decisions. 3-Dimensional power Doppler volumes were obtained during the same examination and vascular, flow, and vascular flow indices were calculated after manual tracing of the viewed placenta in the sweep; data were blinded to obstetricians. Morbidly adherent placenta was confirmed by histology. Severe morbidly adherent placenta was defined as increta/percreta on histology, blood loss >2000 mL, and >2 units of PRBC transfused. Sensitivities, specificities, predictive values, and likelihood ratios were calculated. Student t and χ 2 tests, logistic regression, receiver-operating characteristic curves, and intra- and interrater agreements using Kappa statistics were performed. The following results were found: (1) 50 women were studied: 23 had morbidly adherent placenta, of which 12 (52.2%) were severe morbidly adherent placenta; (2) 2-dimensional parameters diagnosed morbidly adherent placenta with a sensitivity of 82.6% (95% confidence interval, 60.4-94.2), a specificity of 88.9% (95% confidence interval, 69.7-97.1), a positive predictive value of 86.3% (95% confidence interval, 64.0-96.4), a negative predictive value of 85.7% (95% confidence interval, 66.4-95.3), a positive likelihood ratio of 7.4 (95% confidence interval, 2.5-21.9), and a negative likelihood ratio of 0.2 (95% confidence interval, 0.08-0.48); (3) mean values of the vascular index (32.8 ± 7.4) and the vascular flow index (14.2 ± 3.8) were higher in morbidly adherent placenta (P < .001); (4) area under the receiver-operating characteristic curve for the vascular and vascular flow indices were 0.99 and 0.97, respectively; (5) the vascular index ≥21 predicted morbidly adherent placenta with a sensitivity and a specificity of 95% (95% confidence interval, 88.2-96.9) and 91%, respectively (95% confidence interval, 87.5-92.4), 92% positive predictive value (95% confidence interval, 85.5-94.3), 90% negative predictive value (95% confidence interval, 79.9-95.3), positive likelihood ratio of 10.55 (95% confidence interval, 7.06-12.75), and negative likelihood ratio of 0.05 (95% confidence interval, 0.03-0.13); and (6) for the severe morbidly adherent placenta, 2-dimensional ultrasound had a sensitivity of 33.3% (95% confidence interval, 11.3-64.6), a specificity of 81.8% (95% confidence interval, 47.8-96.8), a positive predictive value of 66.7% (95% confidence interval, 24.1-94.1), a negative predictive value of 52.9% (95% confidence interval, 28.5-76.1), a positive likelihood ratio of 1.83 (95% confidence interval, 0.41-8.11), and a negative likelihood ratio of 0.81 (95% confidence interval, 0.52-1.26). A vascular index ≥31 predicted the diagnosis of a severe morbidly adherent placenta with a 100% sensitivity (95% confidence interval, 72-100), a 90% specificity (95% confidence interval, 81.7-93.8), an 88% positive predictive value (95% confidence interval, 55.0-91.3), a 100% negative predictive value (95% confidence interval, 90.9-100), a positive likelihood ratio of 10.0 (95% confidence interval, 3.93-16.13), and a negative likelihood ratio of 0 (95% confidence interval, 0-0.34). Intrarater and interrater agreements were 94% (P < .001) and 93% (P < .001), respectively. The vascular index accurately predicts the morbidly adherent placenta in patients with placenta previa. In addition, 3-dimensional power Doppler vascular and vascular flow indices were more predictive of severe cases of morbidly adherent placenta compared with 2-dimensional ultrasound. This objective technique may limit the variations in diagnosing morbidly adherent placenta because of the subjectivity of 2-dimensional ultrasound interpretations. Copyright © 2017 Elsevier Inc. All rights reserved.

Inverse analysis and regularisation in conditional source-term estimation modelling

NASA Astrophysics Data System (ADS)

Labahn, Jeffrey W.; Devaud, Cecile B.; Sipkens, Timothy A.; Daun, Kyle J.

2014-05-01

Conditional Source-term Estimation (CSE) obtains the conditional species mass fractions by inverting a Fredholm integral equation of the first kind. In the present work, a Bayesian framework is used to compare two different regularisation methods: zeroth-order temporal Tikhonov regulatisation and first-order spatial Tikhonov regularisation. The objectives of the current study are: (i) to elucidate the ill-posedness of the inverse problem; (ii) to understand the origin of the perturbations in the data and quantify their magnitude; (iii) to quantify the uncertainty in the solution using different priors; and (iv) to determine the regularisation method best suited to this problem. A singular value decomposition shows that the current inverse problem is ill-posed. Perturbations to the data may be caused by the use of a discrete mixture fraction grid for calculating the mixture fraction PDF. The magnitude of the perturbations is estimated using a box filter and the uncertainty in the solution is determined based on the width of the credible intervals. The width of the credible intervals is significantly reduced with the inclusion of a smoothing prior and the recovered solution is in better agreement with the exact solution. The credible intervals for temporal and spatial smoothing are shown to be similar. Credible intervals for temporal smoothing depend on the solution from the previous time step and a smooth solution is not guaranteed. For spatial smoothing, the credible intervals are not dependent upon a previous solution and better predict characteristics for higher mixture fraction values. These characteristics make spatial smoothing a promising alternative method for recovering a solution from the CSE inversion process.

Predicting Secchi disk depth from average beam attenuation in a deep, ultra-clear lake

USGS Publications Warehouse

Larson, G.L.; Hoffman, R.L.; Hargreaves, B.R.; Collier, R.W.

2007-01-01

We addressed potential sources of error in estimating the water clarity of mountain lakes by investigating the use of beam transmissometer measurements to estimate Secchi disk depth. The optical properties Secchi disk depth (SD) and beam transmissometer attenuation (BA) were measured in Crater Lake (Crater Lake National Park, Oregon, USA) at a designated sampling station near the maximum depth of the lake. A standard 20 cm black and white disk was used to measure SD. The transmissometer light source had a nearly monochromatic wavelength of 660 nm and a path length of 25 cm. We created a SD prediction model by regression of the inverse SD of 13 measurements recorded on days when environmental conditions were acceptable for disk deployment with BA averaged over the same depth range as the measured SD. The relationship between inverse SD and averaged BA was significant and the average 95% confidence interval for predicted SD relative to the measured SD was ??1.6 m (range = -4.6 to 5.5 m) or ??5.0%. Eleven additional sample dates tested the accuracy of the predictive model. The average 95% confidence interval for these sample dates was ??0.7 m (range = -3.5 to 3.8 m) or ??2.2%. The 1996-2000 time-series means for measured and predicted SD varied by 0.1 m, and the medians varied by 0.5 m. The time-series mean annual measured and predicted SD's also varied little, with intra-annual differences between measured and predicted mean annual SD ranging from -2.1 to 0.1 m. The results demonstrated that this prediction model reliably estimated Secchi disk depths and can be used to significantly expand optical observations in an environment where the conditions for standardized SD deployments are limited. ?? 2007 Springer Science+Business Media B.V.

Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

NASA Astrophysics Data System (ADS)

Kasiviswanathan, K.; Sudheer, K.

2013-05-01

Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived prediction interval for a selected hydrograph in the validation data set is presented in Fig 1. It is noted that most of the observed flows lie within the constructed prediction interval, and therefore provides information about the uncertainty of the prediction. One specific advantage of the method is that when ensemble mean value is considered as a forecast, the peak flows are predicted with improved accuracy by this method compared to traditional single point forecasted ANNs. Fig. 1 Prediction Interval for selected hydrograph

Modelling Short-Term Maximum Individual Exposure from Airborne Hazardous Releases in Urban Environments. Part ΙI: Validation of a Deterministic Model with Wind Tunnel Experimental Data.

PubMed

Efthimiou, George C; Bartzis, John G; Berbekar, Eva; Hertwig, Denise; Harms, Frank; Leitl, Bernd

2015-06-26

The capability to predict short-term maximum individual exposure is very important for several applications including, for example, deliberate/accidental release of hazardous substances, odour fluctuations or material flammability level exceedance. Recently, authors have proposed a simple approach relating maximum individual exposure to parameters such as the fluctuation intensity and the concentration integral time scale. In the first part of this study (Part I), the methodology was validated against field measurements, which are governed by the natural variability of atmospheric boundary conditions. In Part II of this study, an in-depth validation of the approach is performed using reference data recorded under truly stationary and well documented flow conditions. For this reason, a boundary-layer wind-tunnel experiment was used. The experimental dataset includes 196 time-resolved concentration measurements which detect the dispersion from a continuous point source within an urban model of semi-idealized complexity. The data analysis allowed the improvement of an important model parameter. The model performed very well in predicting the maximum individual exposure, presenting a factor of two of observations equal to 95%. For large time intervals, an exponential correction term has been introduced in the model based on the experimental observations. The new model is capable of predicting all time intervals giving an overall factor of two of observations equal to 100%.

Humidity-corrected Arrhenius equation: The reference condition approach.

PubMed

Naveršnik, Klemen; Jurečič, Rok

2016-03-16

Accelerated and stress stability data is often used to predict shelf life of pharmaceuticals. Temperature, combined with humidity accelerates chemical decomposition and the Arrhenius equation is used to extrapolate accelerated stability results to long-term stability. Statistical estimation of the humidity-corrected Arrhenius equation is not straightforward due to its non-linearity. A two stage nonlinear fitting approach is used in practice, followed by a prediction stage. We developed a single-stage statistical procedure, called the reference condition approach, which has better statistical properties (less collinearity, direct estimation of uncertainty, narrower prediction interval) and is significantly easier to use, compared to the existing approaches. Our statistical model was populated with data from a 35-day stress stability study on a laboratory batch of vitamin tablets and required mere 30 laboratory assay determinations. The stability prediction agreed well with the actual 24-month long term stability of the product. The approach has high potential to assist product formulation, specification setting and stability statements. Copyright © 2016 Elsevier B.V. All rights reserved.

Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy.

PubMed

Akl, A I; Sobh, M A; Enab, Y M; Tattersall, J

2001-12-01

The effect of dialysis on patients is conventionally predicted using a formal mathematical model. This approach requires many assumptions of the processes involved, and validation of these may be difficult. The validity of dialysis urea modeling using a formal mathematical model has been challenged. Artificial intelligence using neural networks (NNs) has been used to solve complex problems without needing a mathematical model or an understanding of the mechanisms involved. In this study, we applied an NN model to study and predict concentrations of urea during a hemodialysis session. We measured blood concentrations of urea, patient weight, and total urea removal by direct dialysate quantification (DDQ) at 30-minute intervals during the session (in 15 chronic hemodialysis patients). The NN model was trained to recognize the evolution of measured urea concentrations and was subsequently able to predict hemodialysis session time needed to reach a target solute removal index (SRI) in patients not previously studied by the NN model (in another 15 chronic hemodialysis patients). Comparing results of the NN model with the DDQ model, the prediction error was 10.9%, with a not significant difference between predicted total urea nitrogen (UN) removal and measured UN removal by DDQ. NN model predictions of time showed a not significant difference with actual intervals needed to reach the same SRI level at the same patient conditions, except for the prediction of SRI at the first 30-minute interval, which showed a significant difference (P = 0.001). This indicates the sensitivity of the NN model to what is called patient clearance time; the prediction error was 8.3%. From our results, we conclude that artificial intelligence applications in urea kinetics can give an idea of intradialysis profiling according to individual clinical needs. In theory, this approach can be extended easily to other solutes, making the NN model a step forward to achieving artificial-intelligent dialysis control.

A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling.

PubMed

Sato, Takako; Zaitsu, Kei; Tsuboi, Kento; Nomura, Masakatsu; Kusano, Maiko; Shima, Noriaki; Abe, Shuntaro; Ishii, Akira; Tsuchihashi, Hitoshi; Suzuki, Koichi

2015-05-01

Estimation of postmortem interval (PMI) is an important goal in judicial autopsy. Although many approaches can estimate PMI through physical findings and biochemical tests, accurate PMI calculation by these conventional methods remains difficult because PMI is readily affected by surrounding conditions, such as ambient temperature and humidity. In this study, Sprague-Dawley (SD) rats (10 weeks) were sacrificed by suffocation, and blood was collected by dissection at various time intervals (0, 3, 6, 12, 24, and 48 h; n = 6) after death. A total of 70 endogenous metabolites were detected in plasma by gas chromatography-tandem mass spectrometry (GC-MS/MS). Each time group was separated from each other on the principal component analysis (PCA) score plot, suggesting that the various endogenous metabolites changed with time after death. To prepare a prediction model of a PMI, a partial least squares (or projection to latent structure, PLS) regression model was constructed using the levels of significantly different metabolites determined by variable importance in the projection (VIP) score and the Kruskal-Wallis test (P < 0.05). Because the constructed PLS regression model could successfully predict each PMI, this model was validated with another validation set (n = 3). In conclusion, plasma metabolic profiling demonstrated its ability to successfully estimate PMI under a certain condition. This result can be considered to be the first step for using the metabolomics method in future forensic casework.

A simple, physiologically-based model of sea turtle remigration intervals and nesting population dynamics: Effects of temperature.

PubMed

Neeman, Noga; Spotila, James R; O'Connor, Michael P

2015-09-07

Variation in the yearly number of sea turtles nesting at rookeries can interfere with population estimates and obscure real population dynamics. Previous theoretical models suggested that this variation in nesting numbers may be driven by changes in resources at the foraging grounds. We developed a physiologically-based model that uses temperatures at foraging sites to predict foraging conditions, resource accumulation, remigration probabilities, and, ultimately, nesting numbers for a stable population of sea turtles. We used this model to explore several scenarios of temperature variation at the foraging grounds, including one-year perturbations and cyclical temperature oscillations. We found that thermally driven resource variation can indeed synchronize nesting in groups of turtles, creating cohorts, but that these cohorts tend to break down over 5-10 years unless regenerated by environmental conditions. Cohorts were broken down faster at lower temperatures. One-year perturbations of low temperature had a synchronizing effect on nesting the following year, while high temperature perturbations tended to delay nesting in a less synchronized way. Cyclical temperatures lead to cyclical responses both in nesting numbers and remigration intervals, with the amplitude and lag of the response depending on the duration of the cycle. Overall, model behavior is consistent with observations at nesting beaches. Future work should focus on refining the model to fit particular nesting populations and testing further whether or not it may be used to predict observed nesting numbers and remigration intervals. Copyright © 2015 Elsevier Ltd. All rights reserved.

Benefit and predictive factors for speech perception outcomes in pediatric bilateral cochlear implant recipients.

PubMed

Chang, Young-Soo; Hong, Sung Hwa; Kim, Eun Yeon; Choi, Ji Eun; Chung, Won-Ho; Cho, Yang-Sun; Moon, Il Joon

2018-05-18

Despite recent advancement in the prediction of cochlear implant outcome, the benefit of bilateral procedures compared to bimodal stimulation and how we predict speech perception outcomes of sequential bilateral cochlear implant based on bimodal auditory performance in children remain unclear. This investigation was performed: (1) to determine the benefit of sequential bilateral cochlear implant and (2) to identify the associated factors for the outcome of sequential bilateral cochlear implant. Observational and retrospective study. We retrospectively analyzed 29 patients with sequential cochlear implant following bimodal-fitting condition. Audiological evaluations were performed; the categories of auditory performance scores, speech perception with monosyllable and disyllables words, and the Korean version of Ling. Audiological evaluations were performed before sequential cochlear implant with the bimodal fitting condition (CI1+HA) and one year after the sequential cochlear implant with bilateral cochlear implant condition (CI1+CI2). The good Performance Group (GP) was defined as follows; 90% or higher in monosyllable and bisyllable tests with auditory-only condition or 20% or higher improvement of the scores with CI1+CI2. Age at first implantation, inter-implant interval, categories of auditory performance score, and various comorbidities were analyzed by logistic regression analysis. Compared to the CI1+HA, CI1+CI2 provided significant benefit in categories of auditory performance, speech perception, and Korean version of Ling results. Preoperative categories of auditory performance scores were the only associated factor for being GP (odds ratio=4.38, 95% confidence interval - 95%=1.07-17.93, p=0.04). The children with limited language development in bimodal condition should be considered as the sequential bilateral cochlear implant and preoperative categories of auditory performance score could be used as the predictor in speech perception after sequential cochlear implant. Copyright © 2018 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier Editora Ltda. All rights reserved.

Lifecourse social conditions and racial disparities in incidence of first stroke.

PubMed

Glymour, M Maria; Avendaño, Mauricio; Haas, Steven; Berkman, Lisa F

2008-12-01

Some previous studies found excess stroke rates among black subjects persisted after adjustment for socioeconomic status (SES), fueling speculation regarding racially patterned genetic predispositions to stroke. Previous research was hampered by incomplete SES assessments, without measures of childhood conditions or adult wealth. We assess the role of lifecourse SES in explaining stroke risk and stroke disparities. Health and Retirement Study participants age 50+ (n = 20,661) were followed on average 9.9 years for self- or proxy-reported first stroke (2175 events). Childhood social conditions (southern state of birth, parental SES, self-reported fair/poor childhood health, and attained height), adult SES (education, income, wealth, and occupational status) and traditional cardiovascular risk factors were used to predict first stroke onset using Cox proportional hazards models. Black subjects had a 48% greater risk of first stroke incidence than whites (95% confidence interval, 1.33-1.65). Childhood conditions predicted stroke risk in both blacks and whites, independently of adult SES. Adjustment for both childhood social conditions and adult SES measures attenuated racial differences to marginal significance (hazard ratio, 1.13; 95% CI, 1.00-1.28). Childhood social conditions predict stroke risk in black and White American adults. Additional adjustment for adult SES, in particular wealth, nearly eliminated the disparity in stroke risk between black and white subjects.

Crossover transition in the fluctuation of Internet

NASA Astrophysics Data System (ADS)

Qian, Jiang-Hai

2018-06-01

The inconsistent fluctuation behavior of Internet predicted by preferential attachment(PA) and Gibrat's law requires empirical investigations on the actual system. By using the interval-tunable Gibrat's law statistics, we find the actual fluctuation, characterized by the conditional standard deviation of the degree growth rate, changes with the interval length and displays a crossover transition from PA type to Gibrat's law type, which has not yet been captured by any previous models. We characterize the transition dynamics quantitatively and determine the applicative range of PA and Gibrat's law. The correlation analysis indicates the crossover transition may be attributed to the accumulative correlation between the internal links.

Illness perception and adherence to healthy behaviour in Jordanian coronary heart disease patients.

PubMed

Mosleh, Sultan M; Almalik, Mona Ma

2016-06-01

Patients diagnosed with coronary heart disease are strongly recommended to adopt healthier behaviours and adhere to prescribed medication. Previous research on patients with a wide range of health conditions has explored the role of patients' illness perceptions in explaining coping and health outcomes. However, among coronary heart disease patients, this has not been well examined. The purpose of this study was to explore coronary heart disease patients' illness perception beliefs and investigate whether these beliefs could predict adherence to healthy behaviours. A multi-centre cross-sectional study was conducted at four tertiary hospitals in Jordan. A convenience sample of 254 patients (73% response rate), who visited the cardiac clinic for routine review, participated in the study. Participants completed a self-reported questionnaire, which included the Brief Illness Perception Questionnaire, the Godin Leisure Time Activity questionnaire and the Morisky Medication Adherence Scale. Patients reported high levels of disease understanding (coherence) and they were convinced that they were able to control their condition by themselves and/or with appropriate treatment. Male patients perceived lower consequences (p<0.05) and had a better understanding of their illness than female patients (p<0.001). There were significant associations between increasing age and each of timeline (r=0.326, p<0.001), (r=0.146, p<0.024) and coherence (r=-0.166, p<0.010). Adjusted regression analysis showed that exercise adherence was predicted by both a strong perception in personal control (β 2.66, 95% confidence interval 1.28-4.04), timeline (β -1.85, 95% confidence interval 0. 8-2.88) and illness coherence (β 2.12, 95% confidence interval 0.35-3.90). Medication adherence was predicted by perception of personal control and treatment control. Adherence to a low-fat diet regimen was predicted by perception of illness coherence only (odds ratio 12, 95% confidence interval 1.04-1.33). Finally, the majority of patients thought that the cause of their heart problem was related to coronary heart disease risk factors such as obesity and high-fat meals. Patients' illness beliefs are candidates for a psycho-educational intervention that should be targeted at improved disease management practices and better adherence to recommended healthy behaviours. © The European Society of Cardiology 2014.

Stimulus-level interference disrupts repetition benefit during task switching in middle childhood

PubMed Central

Karayanidis, Frini; Jamadar, Sharna; Sanday, Dearne

2013-01-01

The task-switching paradigm provides a powerful tool to measure the development of core cognitive control processes. In this study, we use the alternating runs task-switching paradigm to assess preparatory control processes involved in flexibly preparing for a predictable change in task and stimulus-driven control processes involved in controlling stimulus-level interference. We present three experiments that examine behavioral and event-related potential (ERP) measures of task-switching performance in middle childhood and young adulthood under low and high stimulus interference conditions. Experiment 1 confirms that our new child-friendly tasks produce similar behavioral and electrophysiological findings in young adults as those previously reported. Experiment 2 examines task switching with univalent stimuli across a range of preparation intervals in middle childhood. Experiment 3 compares task switching with bivalent stimuli across the same preparation intervals in children and young adults. Children produced a larger RT switch cost than adults with univalent stimuli and a short preparation interval. Both children and adults showed significant reduction in switch cost with increasing preparation interval, but in children this was caused by greater increase in RT for repeat than switch trials. Response-locked ERPs showed intact preparation for univalent, but less efficient preparation for bivalent stimulus conditions. Stimulus-locked ERPs confirmed that children showed greater stimulus-level interference for repeat trials, especially with bivalent stimuli. We conclude that children show greater stimulus-level interference especially for repeat trials under high interference conditions, suggesting weaker mental representation of the current task set. PMID:24367317

Comparison Between Vortices Created and Evolving During Fixed and Dynamic Solar Wind Conditions

NASA Technical Reports Server (NTRS)

Collado-Vega, Yaireska M.; Kessel, R. L.; Sibeck, David Gary; Kalb, V. L.; Boller, R. A.; Rastaetter, L.

2013-01-01

We employ Magnetohydrodynamic (MHD) simulations to examine the creation and evolution of plasma vortices within the Earth's magnetosphere for steady solar wind plasma conditions. Very few vortices form during intervals of such solar wind conditions. Those that do remain in fixed positions for long periods (often hours) and exhibit rotation axes that point primarily in the x or y direction, parallel (or antiparallel) to the local magnetospheric magnetic field direction. Occasionally, the orientation of the axes rotates from the x direction to another direction. We compare our results with simulations previously done for unsteady solar wind conditions. By contrast, these vortices that form during intervals of varying solar wind conditions exhibit durations ranging from seconds (in the case of those with axes in the x or y direction) to minutes (in the case of those with axes in the z direction) and convect antisunward. The local-time dependent sense of rotation seen in these previously reported vortices suggests an interpretation in terms of the Kelvin-Helmholtz instability. For steady conditions, the biggest vortices developed on the dayside (about 6R(E) in diameter), had their rotation axes aligned with the y direction and had the longest periods of duration. We attribute these vortices to the flows set up by reconnection on the high latitude magnetopause during intervals of northward Interplanetary Magnetic Field (IMF) orientation. This is the first time that vortices due to high-latitude reconnection have been visualized. The model also successfully predicts the principal characteristics of previously reported plasma vortices within the magnetosphere, namely their dimension, flow velocities, and durations.

Construction of prediction intervals for Palmer Drought Severity Index using bootstrap

NASA Astrophysics Data System (ADS)

Beyaztas, Ufuk; Bickici Arikan, Bugrayhan; Beyaztas, Beste Hamiye; Kahya, Ercan

2018-04-01

In this study, we propose an approach based on the residual-based bootstrap method to obtain valid prediction intervals using monthly, short-term (three-months) and mid-term (six-months) drought observations. The effects of North Atlantic and Arctic Oscillation indexes on the constructed prediction intervals are also examined. Performance of the proposed approach is evaluated for the Palmer Drought Severity Index (PDSI) obtained from Konya closed basin located in Central Anatolia, Turkey. The finite sample properties of the proposed method are further illustrated by an extensive simulation study. Our results revealed that the proposed approach is capable of producing valid prediction intervals for future PDSI values.

The role of latex agglutination test for the etiological diagnosis of pleural effusion in children and adolescents.

PubMed

Camargos, Paulo; Fonseca, Ana Cristina; Amantéa, Sérgio; Oliveira, Elizabeth; Benfica, Maria das Graças; Chamone, Chequer

2017-05-01

The etiological diagnosis of pleural effusion is a difficult task because the diagnostic tools can only establish a definitive etiological diagnosis in at most 76% of cases. To verify the diagnostic accuracy of the latex agglutination test (LAT) for the etiological diagnosis of pleural effusions caused by Streptococcus pneumoniae and Haemophilus influenzae type b. After thoracocentesis, paired fresh samples of pleural fluid from 418 children and adolescents were included in this investigation. They were tested blindly and simultaneously through counterimmunoelectrophoresis (CIE) and LAT for both bacteria. Sensitivity, specificity, predictive values and likelihood ratios (LR) were calculated taking CIE as a reference standard. The sensitivity and specificity of LAT was 100% (95% confidence interval, 94.4%-100%) and 83.3% (95% confidence interval, 79.0%-87.0%), respectively, whereas the positive (calculated from Bayes' theorem) and negative predictive values were, respectively, lower than 1% and 100% (95% confidence interval, 98.8%-100%). Positive and negative LR were 6.0 (95% confidence interval, 4.7-7.6) and zero, respectively. Our results suggest that LAT is a useful tool for the etiological diagnosis of pleural effusion. It is a reliable, rapid, simple to perform and shows an excellent yield in our studied population, helping to prescribe appropriate antibiotics for this clinical condition. © 2015 John Wiley & Sons Ltd.

A field technique for estimating aquifer parameters using flow log data

USGS Publications Warehouse

Paillet, Frederick L.

2000-01-01

A numerical model is used to predict flow along intervals between producing zones in open boreholes for comparison with measurements of borehole flow. The model gives flow under quasi-steady conditions as a function of the transmissivity and hydraulic head in an arbitrary number of zones communicating with each other along open boreholes. The theory shows that the amount of inflow to or outflow from the borehole under any one flow condition may not indicate relative zone transmissivity. A unique inversion for both hydraulic-head and transmissivity values is possible if flow is measured under two different conditions such as ambient and quasi-steady pumping, and if the difference in open-borehole water level between the two flow conditions is measured. The technique is shown to give useful estimates of water levels and transmissivities of two or more water-producing zones intersecting a single interval of open borehole under typical field conditions. Although the modeling technique involves some approximation, the principle limit on the accuracy of the method under field conditions is the measurement error in the flow log data. Flow measurements and pumping conditions are usually adjusted so that transmissivity estimates are most accurate for the most transmissive zones, and relative measurement error is proportionately larger for less transmissive zones. The most effective general application of the borehole-flow model results when the data are fit to models that systematically include more production zones of progressively smaller transmissivity values until model results show that all accuracy in the data set is exhausted.A numerical model is used to predict flow along intervals between producing zones in open boreholes for comparison with measurements of borehole flow. The model gives flow under quasi-steady conditions as a function of the transmissivity and hydraulic head in an arbitrary number of zones communicating with each other along open boreholes. The theory shows that the amount of inflow to or outflow from the borehole under any one flow condition may not indicate relative zone transmissivity. A unique inversion for both hydraulic-head and transmissivity values is possible if flow is measured under two different conditions such as ambient and quasi-steady pumping, and if the difference in open-borehole water level between the two flow conditions is measured. The technique is shown to give useful estimates of water levels and transmissivities of two or more water-producing zones intersecting a single interval of open borehole under typical field conditions. Although the modeling technique involves some approximation, the principle limit on the accuracy of the method under field conditions is the measurement error in the flow log data. Flow measurements and pumping conditions are usually adjusted so that transmissivity estimates are most accurate for the most transmissive zones, and relative measurement error is proportionately larger for less transmissive zones. The most effective general application of the borehole-flow model results when the data are fit to models that symmetrically include more production zones of progressively smaller transmissivity values until model results show that all accuracy in the data set is exhausted.

Prediction Interval Development for Wind-Tunnel Balance Check-Loading

NASA Technical Reports Server (NTRS)

Landman, Drew; Toro, Kenneth G.; Commo, Sean A.; Lynn, Keith C.

2014-01-01

Results from the Facility Analysis Verification and Operational Reliability project revealed a critical gap in capability in ground-based aeronautics research applications. Without a standardized process for check-loading the wind-tunnel balance or the model system, the quality of the aerodynamic force data collected varied significantly between facilities. A prediction interval is required in order to confirm a check-loading. The prediction interval provides an expected upper and lower bound on balance load prediction at a given confidence level. A method has been developed which accounts for sources of variability due to calibration and check-load application. The prediction interval method of calculation and a case study demonstrating its use is provided. Validation of the methods is demonstrated for the case study based on the probability of capture of confirmation points.

Estimated preejection period (PEP) based on the detection of the R-wave and dZ/dt-min peaks in ECG and ICG

NASA Astrophysics Data System (ADS)

van Lien, René; Schutte, Nienke M.; Meijer, Jan H.; de Geus, Eco J. C.

2013-04-01

The validity of estimating the PEP from a fixed value for the Q-wave onset to the R-wave peak (QR) interval and from the R-wave peak to the dZ/dt-min peak (ISTI) interval is evaluated. Ninety-one subjects participated in a laboratory experiment in which a variety of physical and mental stressors were presented and 31 further subjects participated in a sequence of structured ambulatory activities in which large variation in posture and physical activity was induced. PEP, QR interval, and ISTI were scored. Across the diverse laboratory and ambulatory conditions the QR interval could be approximated by a fixed interval of 40 ms but 95% confidence intervals were large (25 to 54 ms). Multilevel analysis showed that 79% to 81% of the within and between-subject variation in the RB interval could be predicted by the ISTI. However, the optimal intercept and slope values varied significantly across subjects and study setting. Bland-Altman plots revealed a large discrepancy between the estimated PEP and the actual PEP based on the Q-wave onset and B-point. It is concluded that the estimated PEP can be a useful tool but cannot replace the actual PEP to index cardiac sympathetic control.

Early Identification of Patients at Risk of Acute Lung Injury

PubMed Central

Gajic, Ognjen; Dabbagh, Ousama; Park, Pauline K.; Adesanya, Adebola; Chang, Steven Y.; Hou, Peter; Anderson, Harry; Hoth, J. Jason; Mikkelsen, Mark E.; Gentile, Nina T.; Gong, Michelle N.; Talmor, Daniel; Bajwa, Ednan; Watkins, Timothy R.; Festic, Emir; Yilmaz, Murat; Iscimen, Remzi; Kaufman, David A.; Esper, Annette M.; Sadikot, Ruxana; Douglas, Ivor; Sevransky, Jonathan

2011-01-01

Rationale: Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. Objectives: To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS). Methods: In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Measurements and Main Results: Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1–4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78–0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9–5.7). Conclusions: ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772). PMID:20802164

A novel apparatus for non-contact measurement of heart rate variability: a system to prevent secondary exposure of medical personnel to toxic materials under biochemical hazard conditions, in monitoring sepsis or in predicting multiple organ dysfunction syndrome.

PubMed

Matsui, T; Arai, I; Gotoh, S; Hattori, H; Takase, B; Kikuchi, M; Ishihara, M

2005-10-01

The impaired balance of the low-frequency/high-frequency ratio obtained from spectral components of RR intervals can be a diagnostic test for sepsis. In addition, it is known that a reduction of heart rate variability (HRV) is useful in identifying septic patients at risk of the development of multiple organ dysfunction syndrome (MODS). We have reported a non-contact method using a microwave radar to monitor the heart and respiratory rates of a healthy person placed inside an isolator or of experimental animals exposed to toxic materials. With the purpose of preventing secondary exposure of medical personnel to toxic materials under biochemical hazard conditions, we designed a novel apparatus for non-contact measurement of HRV using a 1215 MHz microwave radar, a high-pass filter, and a personal computer. The microwave radar monitors only the small reflected waves from the subject's chest wall, which are modulated by the cardiac and respiratory motion. The high-pass filter enhances the cardiac signal and attenuates the respiratory signal. In a human trial, RR intervals derived from the non-contact apparatus significantly correlated with those derived from ECG (r=0.98, P<0.0001). The non-contact apparatus showed a similar power spectrum of RR intervals to that of ECG. Our non-contact HRV measurement apparatus appears promising for future pre-hospital monitoring of septic patients or for predicting MODS patients, inside isolators or in the field for mass casualties under biochemical hazard circumstances.

Task-set switching under cue-based versus memory-based switching conditions in younger and older adults.

PubMed

Kray, Jutta

2006-08-11

Adult age differences in task switching and advance preparation were examined by comparing cue-based and memory-based switching conditions. Task switching was assessed by determining two types of costs that occur at the general (mixing costs) and specific (switching costs) level of switching. Advance preparation was investigated by varying the time interval until the next task (short, middle, very long). Results indicated that the implementation of task sets was different for cue-based switching with random task sequences and memory-based switching with predictable task sequences. Switching costs were strongly reduced under cue-based switching conditions, indicating that task-set cues facilitate the retrieval of the next task. Age differences were found for mixing costs and for switching costs only under cue-based conditions in which older adults showed smaller switching costs than younger adults. It is suggested that older adults adopt a less extreme bias between two tasks than younger adults in situations associated with uncertainty. For cue-based switching with random task sequences, older adults are less engaged in a complete reconfiguration of task sets because of the probability of a further task change. Furthermore, the reduction of switching costs was more pronounced for cue- than memory-based switching for short preparation intervals, whereas the reduction of switch costs was more pronounced for memory- than cue-based switching for longer preparation intervals at least for older adults. Together these findings suggest that the implementation of task sets is functionally different for the two types of task-switching conditions.

Using conditional probability to identify trends in intra-day high-frequency equity pricing

NASA Astrophysics Data System (ADS)

Rechenthin, Michael; Street, W. Nick

2013-12-01

By examining the conditional probabilities of price movements in a popular US stock over different high-frequency intra-day timespans, varying levels of trend predictability are identified. This study demonstrates the existence of predictable short-term trends in the market; understanding the probability of price movement can be useful to high-frequency traders. Price movement was examined in trade-by-trade (tick) data along with temporal timespans between 1 s to 30 min for 52 one-week periods for one highly-traded stock. We hypothesize that much of the initial predictability of trade-by-trade (tick) data is due to traditional market dynamics, or the bouncing of the price between the stock’s bid and ask. Only after timespans of between 5 to 10 s does this cease to explain the predictability; after this timespan, two consecutive movements in the same direction occur with higher probability than that of movements in the opposite direction. This pattern holds up to a one-minute interval, after which the strength of the pattern weakens.

Statistics of return intervals between long heartbeat intervals and their usability for online prediction of disorders

NASA Astrophysics Data System (ADS)

Bogachev, Mikhail I.; Kireenkov, Igor S.; Nifontov, Eugene M.; Bunde, Armin

2009-06-01

We study the statistics of return intervals between large heartbeat intervals (above a certain threshold Q) in 24 h records obtained from healthy subjects. We find that both the linear and the nonlinear long-term memory inherent in the heartbeat intervals lead to power-laws in the probability density function PQ(r) of the return intervals. As a consequence, the probability WQ(t; Δt) that at least one large heartbeat interval will occur within the next Δt heartbeat intervals, with an increasing elapsed number of intervals t after the last large heartbeat interval, follows a power-law. Based on these results, we suggest a method of obtaining a priori information about the occurrence of the next large heartbeat interval, and thus to predict it. We show explicitly that the proposed method, which exploits long-term memory, is superior to the conventional precursory pattern recognition technique, which focuses solely on short-term memory. We believe that our results can be straightforwardly extended to obtain more reliable predictions in other physiological signals like blood pressure, as well as in other complex records exhibiting multifractal behaviour, e.g. turbulent flow, precipitation, river flows and network traffic.

Timescale dependent deformation of orogenic belts?

NASA Astrophysics Data System (ADS)

Hoth, S.; Friedrich, A. M.; Vietor, T.; Hoffmann-Rothe, A.; Kukowski, N.; Oncken, O.

2004-12-01

The principle aim to link geodetic, paleoseismologic and geologic estimates of fault slip is to extrapolate the respective rates from one timescale to the other to finally predict the recurrence interval of large earthquakes, which threat human habitats. This approach however, is based on two often implicitly made assumptions: a uniform slip distribution through time and space and no changes of the boundary conditions during the time interval of interest. Both assumptions are often hard to verify. A recent study, which analysed an exceptionally complete record of seismic slip for the Wasatch and related faults (Basin and Range province), ranging from 10 yr to 10 Myr suggests that such a link between geodetic and geologic rates might not exist, i.e., that our records of fault displacement may depend on the timescale over which they were measured. This view derives support from results of scaled 2D sandbox experiments, as well as numerical simulations with distinct elements, both of which investigated the effect of boundary conditions such as flexure, mechanic stratigraphy and erosion on the spatio-temporal distribution of deformation within bivergent wedges. We identified three types of processes based on their distinct spatio-temporal distribution of deformation. First, incremental strain and local strain rates are very short-lived are broadly distributed within the bivergent wedge and no temporal pattern could be established. Second, footwall shortcuts and the re-activation of either internal thrusts or of the retro shear-zone are irregularly distributed in time and are thus not predictable either, but last for a longer time interval. Third, the stepwise initiation and propagation of the deformation front is very regular in time, since it depends on the thickness of the incoming layer and on its internal and basal material properties. We consider the propagation of the deformation front as an internal clock of a thrust belt, which is therefore predictable. A deformation front advance cycle requires the longest timescale. Thus, despite known and constant boundary conditions during the simulations, we found only one regular temporal pattern of deformation in a steady active bivergent-wedge. We therefore propose that the structural inventory of an orogenic belt is hierarchically ordered with respect to accumulated slip, in analogy to the discharge pattern in a drainage network. The deformation front would have the highest, a branching splay the lowest order. Since kinematic boundary conditions control deformation front advance, its timing and the related maximum magnitude of finite strain, i.e. throw on the frontal thrust are predictable. However, the number of controlling factors, such as the degree of strain softening, the orientation of faults or fluid flow and resulting cementation of faults, responsible for the reactivation of faults increases with increasing distance from the deformation front. Since it is rarely possible to determine the complete network of forces within a wedge, the reactivation of lower order structures is not predictable in time and space. Two implications for field studies may emerge: A change of the propagation of deformation can only be determined, if at least two accretion cycles are sampled. The link between geodetic, paleoseismologic and geologic fault slip estimates can only be successfully derived if the position of the investigated fault within the hierarchical order has not changed over the time interval of interest.

Lifetime Estimation of the Upper Stage of GSAT-14 in Geostationary Transfer Orbit.

PubMed

Jeyakodi David, Jim Fletcher; Sharma, Ram Krishan

2014-01-01

The combination of atmospheric drag and lunar and solar perturbations in addition to Earth's oblateness influences the orbital lifetime of an upper stage in geostationary transfer orbit (GTO). These high eccentric orbits undergo fluctuations in both perturbations and velocity and are very sensitive to the initial conditions. The main objective of this paper is to predict the reentry time of the upper stage of the Indian geosynchronous satellite launch vehicle, GSLV-D5, which inserted the satellite GSAT-14 into a GTO on January 05, 2014, with mean perigee and apogee altitudes of 170 km and 35975 km. Four intervals of near linear variation of the mean apogee altitude observed were used in predicting the orbital lifetime. For these four intervals, optimal values of the initial osculating eccentricity and ballistic coefficient for matching the mean apogee altitudes were estimated with the response surface methodology using a genetic algorithm. It was found that the orbital lifetime from these four time spans was between 144 and 148 days.

Lifetime Estimation of the Upper Stage of GSAT-14 in Geostationary Transfer Orbit

PubMed Central

Jeyakodi David, Jim Fletcher; Sharma, Ram Krishan

2014-01-01

The combination of atmospheric drag and lunar and solar perturbations in addition to Earth's oblateness influences the orbital lifetime of an upper stage in geostationary transfer orbit (GTO). These high eccentric orbits undergo fluctuations in both perturbations and velocity and are very sensitive to the initial conditions. The main objective of this paper is to predict the reentry time of the upper stage of the Indian geosynchronous satellite launch vehicle, GSLV-D5, which inserted the satellite GSAT-14 into a GTO on January 05, 2014, with mean perigee and apogee altitudes of 170 km and 35975 km. Four intervals of near linear variation of the mean apogee altitude observed were used in predicting the orbital lifetime. For these four intervals, optimal values of the initial osculating eccentricity and ballistic coefficient for matching the mean apogee altitudes were estimated with the response surface methodology using a genetic algorithm. It was found that the orbital lifetime from these four time spans was between 144 and 148 days. PMID:27437491

A test of the reward-contrast hypothesis.

PubMed

Dalecki, Stefan J; Panoz-Brown, Danielle E; Crystal, Jonathon D

2017-12-01

Source memory, a facet of episodic memory, is the memory of the origin of information. Whereas source memory in rats is sustained for at least a week, spatial memory degraded after approximately a day. Different forgetting functions may suggest that two memory systems (source memory and spatial memory) are dissociated. However, in previous work, the two tasks used baiting conditions consisting of chocolate and chow flavors; notably, the source memory task used the relatively better flavor. Thus, according to the reward-contrast hypothesis, when chocolate and chow were presented within the same context (i.e., within a single radial maze trial), the chocolate location was more memorable than the chow location because of contrast. We tested the reward-contrast hypothesis using baiting configurations designed to produce reward-contrast. The reward-contrast hypothesis predicts that under these conditions, spatial memory will survive a 24-h retention interval. We documented elimination of spatial memory performance after a 24-h retention interval using a reward-contrast baiting pattern. These data suggest that reward contrast does not explain our earlier findings that source memory survives unusually long retention intervals. Copyright © 2017 Elsevier B.V. All rights reserved.

Different dimensions of the prediction error as a decisive factor for the triggering of the reconsolidation process.

PubMed

Agustina López, M; Jimena Santos, M; Cortasa, Santiago; Fernández, Rodrigo S; Carbó Tano, Martin; Pedreira, María E

2016-12-01

The reconsolidation process is the mechanism by which strength and/or content of consolidated memories are updated. Prediction error (PE) is the difference between the prediction made and current events. It is proposed as a necessary condition to trigger the reconsolidation process. Here we analyzed deeply the role of the PE in the associative memory reconsolidation in the crab Neohelice granulata. An incongruence between the learned temporal relationship between conditioned and unconditioned stimuli (CS-US) was enough to trigger the reconsolidation process. Moreover, after a partial reinforced training, a PE of 50% opened the possibility to labilize the consolidated memory with a reminder which included or not the US. Further, during an extinction training a small PE in the first interval between CSs was enough to trigger reconsolidation. Overall, we highlighted the relation between training history and different reactivation possibilities to recruit the process responsible of memory updating. Copyright © 2016 Elsevier Inc. All rights reserved.

Ventricular Cycle Length Characteristics Estimative of Prolonged RR Interval during Atrial Fibrillation

PubMed Central

CIACCIO, EDWARD J.; BIVIANO, ANGELO B.; GAMBHIR, ALOK; EINSTEIN, ANDREW J.; GARAN, HASAN

2014-01-01

Background When atrial fibrillation (AF) is incessant, imaging during a prolonged ventricular RR interval may improve image quality. It was hypothesized that long RR intervals could be predicted from preceding RR values. Methods From the PhysioNet database, electrocardiogram RR intervals were obtained from 74 persistent AF patients. An RR interval lengthened by at least 250 ms beyond the immediately preceding RR interval (termed T0 and T1, respectively) was considered prolonged. A two-parameter scatterplot was used to predict the occurrence of a prolonged interval T0. The scatterplot parameters were: (1) RR variability (RRv) estimated as the average second derivative from 10 previous pairs of RR differences, T13–T2, and (2) Tm–T1, the difference between Tm, the mean from T13 to T2, and T1. For each patient, scatterplots were constructed using preliminary data from the first hour. The ranges of parameters 1 and 2 were adjusted to maximize the proportion of prolonged RR intervals within range. These constraints were used for prediction of prolonged RR in test data collected during the second hour. Results The mean prolonged event was 1.0 seconds in duration. Actual prolonged events were identified with a mean positive predictive value (PPV) of 80% in the test set. PPV was >80% in 36 of 74 patients. An average of 10.8 prolonged RR intervals per 60 minutes was correctly identified. Conclusions A method was developed to predict prolonged RR intervals using two parameters and prior statistical sampling for each patient. This or similar methodology may help improve cardiac imaging in many longstanding persistent AF patients. PMID:23998759

Modeling stream fish distributions using interval-censored detection times.

PubMed

Ferreira, Mário; Filipe, Ana Filipa; Bardos, David C; Magalhães, Maria Filomena; Beja, Pedro

2016-08-01

Controlling for imperfect detection is important for developing species distribution models (SDMs). Occupancy-detection models based on the time needed to detect a species can be used to address this problem, but this is hindered when times to detection are not known precisely. Here, we extend the time-to-detection model to deal with detections recorded in time intervals and illustrate the method using a case study on stream fish distribution modeling. We collected electrofishing samples of six fish species across a Mediterranean watershed in Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled the probability of water presence in stream channels, and the probability of species occupancy conditional on water presence, in relation to environmental and spatial variables. We also modeled time-to-first detection conditional on occupancy in relation to local factors, using modified interval-censored exponential survival models. Posterior distributions of occupancy probabilities derived from the models were used to produce species distribution maps. Simulations indicated that the modified time-to-detection model provided unbiased parameter estimates despite interval-censoring. There was a tendency for spatial variation in detection rates to be primarily influenced by depth and, to a lesser extent, stream width. Species occupancies were consistently affected by stream order, elevation, and annual precipitation. Bayesian P-values and AUCs indicated that all models had adequate fit and high discrimination ability, respectively. Mapping of predicted occupancy probabilities showed widespread distribution by most species, but uncertainty was generally higher in tributaries and upper reaches. The interval-censored time-to-detection model provides a practical solution to model occupancy-detection when detections are recorded in time intervals. This modeling framework is useful for developing SDMs while controlling for variation in detection rates, as it uses simple data that can be readily collected by field ecologists.

The "where is it?" reflex: autoshaping the orienting response.

PubMed

Buzsáki, G

1982-05-01

The goal of this review is to compare two divergent lines of research on signal-centered behavior: the orienting reflex (OR) and autoshaping. A review of conditioning experiments in animals and humans suggests that the novelty hypothesis of the OR is no longer tenable. Only stimuli that represent biological "relevance" elicit ORs. A stimulus may be relevant a priori (i.e., unconditioned) or as a result of conditioning. Exposure to a conditioned stimulus (CS) that predicts a positive reinforcer causes the animal to orient to it throughout conditioning. Within the CS-US interval, the initial CS-directed orienting response is followed by US-directed tendencies. Experimental evidence is shown that the development and maintenance of the conditioned OR occur in a similar fashion both in response-independent (classical) and response-dependent (instrumental) paradigms. It is proposed that the conditioned OR and the signal-directed autoshaped response are identical. Signals predicting aversive events repel the subject from the source of the CS. It is suggested that the function of the CS is not only to signal the probability of US occurrence, but also to serve as a spatial cue to guide the animal in the environment.

The "where is it?" reflex: autoshaping the orienting response.

PubMed Central

Buzsáki, G

1982-01-01

The goal of this review is to compare two divergent lines of research on signal-centered behavior: the orienting reflex (OR) and autoshaping. A review of conditioning experiments in animals and humans suggests that the novelty hypothesis of the OR is no longer tenable. Only stimuli that represent biological "relevance" elicit ORs. A stimulus may be relevant a priori (i.e., unconditioned) or as a result of conditioning. Exposure to a conditioned stimulus (CS) that predicts a positive reinforcer causes the animal to orient to it throughout conditioning. Within the CS-US interval, the initial CS-directed orienting response is followed by US-directed tendencies. Experimental evidence is shown that the development and maintenance of the conditioned OR occur in a similar fashion both in response-independent (classical) and response-dependent (instrumental) paradigms. It is proposed that the conditioned OR and the signal-directed autoshaped response are identical. Signals predicting aversive events repel the subject from the source of the CS. It is suggested that the function of the CS is not only to signal the probability of US occurrence, but also to serve as a spatial cue to guide the animal in the environment. PMID:7097153

An actual load forecasting methodology by interval grey modeling based on the fractional calculus.

PubMed

Yang, Yang; Xue, Dingyü

2017-07-17

The operation processes for thermal power plant are measured by the real-time data, and a large number of historical interval data can be obtained from the dataset. Within defined periods of time, the interval information could provide important information for decision making and equipment maintenance. Actual load is one of the most important parameters, and the trends hidden in the historical data will show the overall operation status of the equipments. However, based on the interval grey parameter numbers, the modeling and prediction process is more complicated than the one with real numbers. In order not lose any information, the geometric coordinate features are used by the coordinates of area and middle point lines in this paper, which are proved with the same information as the original interval data. The grey prediction model for interval grey number by the fractional-order accumulation calculus is proposed. Compared with integer-order model, the proposed method could have more freedom with better performance for modeling and prediction, which can be widely used in the modeling process and prediction for the small amount interval historical industry sequence samples. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Changes in Pilot Behavior with Predictive System Status Information

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.

1998-01-01

Research has shown a strong pilot preference for predictive information of aircraft system status in the flight deck. However, changes in pilot behavior associated with using this predictive information have not been ascertained. The study described here quantified these changes using three types of predictive information (none, whether a parameter was changing abnormally, and the time for a parameter to reach an alert range) and three initial time intervals until a parameter alert range was reached (ITIs) (1 minute, 5 minutes, and 15 minutes). With predictive information, subjects accomplished most of their tasks before an alert occurred. Subjects organized the time they did their tasks by locus-of-control with no predictive information and for the 1-minute ITI, and by aviatenavigate-communicate for the time for a parameter to reach an alert range and the 15-minute conditions. Overall, predictive information and the longer ITIs moved subjects to performing tasks before the alert actually occurred and had them more mission oriented as indicated by their tasks grouping of aviate-navigate-communicate.

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Time-Based Loss in Visual Short-Term Memory is from Trace Decay, not Temporal Distinctiveness

PubMed Central

Ricker, Timothy J.; Spiegel, Lauren R.; Cowan, Nelson

2014-01-01

There is no consensus as to why forgetting occurs in short-term memory tasks. In past work, we have shown that forgetting occurs with the passage of time, but there are two classes of theories that can explain this effect. In the present work, we investigate the reason for time-based forgetting by contrasting the predictions of temporal distinctiveness and trace decay in the procedure in which we have observed such loss, involving memory for arrays of characters or letters across several seconds. The first theory, temporal distinctiveness, predicts that increasing the amount of time between trials will lead to less proactive interference, resulting in less forgetting across a retention interval. In the second theory, trace decay, temporal distinctiveness between trials is irrelevant to the loss over a retention interval. Using visual array change detection tasks in four experiments, we find small proactive interference effects on performance under some specific conditions, but no concomitant change in the effect of a retention interval. We conclude that trace decay is the more suitable class of explanations of the time-based forgetting in short-term memory that we have observed, and we suggest the need for further clarity in what the exact basis of that decay may be. PMID:24884646

Predicting Maps of Green Growth in Košice

NASA Astrophysics Data System (ADS)

Poorova, Zuzana; Vranayova, Zuzana

2017-10-01

The paper deals with the changing of the traditional roofs in the city of Košice into green roofs. Possible areas of city housing estates, after taking into account the conditions of each of them (types of buildings, statics of buildings), are listed in the paper. The research is picturing the prediction maps of Košice city from 2017 to 2042 in 5-years interval. The paper is a segment of a dissertation work focusing on changing traditional roofs into green roofs with the aim to retain water, calculate the amount of retained water and show possibilities how to use this water.

Structural reliability analysis under evidence theory using the active learning kriging model

NASA Astrophysics Data System (ADS)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.

A Comparison of Metamodeling Techniques via Numerical Experiments

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2016-01-01

This paper presents a comparative analysis of a few metamodeling techniques using numerical experiments for the single input-single output case. These experiments enable comparing the models' predictions with the phenomenon they are aiming to describe as more data is made available. These techniques include (i) prediction intervals associated with a least squares parameter estimate, (ii) Bayesian credible intervals, (iii) Gaussian process models, and (iv) interval predictor models. Aspects being compared are computational complexity, accuracy (i.e., the degree to which the resulting prediction conforms to the actual Data Generating Mechanism), reliability (i.e., the probability that new observations will fall inside the predicted interval), sensitivity to outliers, extrapolation properties, ease of use, and asymptotic behavior. The numerical experiments describe typical application scenarios that challenge the underlying assumptions supporting most metamodeling techniques.

The Processing of Attended and Predicted Sounds in Time.

PubMed

Paris, Tim; Kim, Jeesun; Davis, Chris

2016-01-01

Neural responses to an attended event are typically enhanced relative to those from an unattended one (attention enhancement). Conversely, neural responses to a predicted event are typically reduced relative to those from an unpredicted one (prediction suppression). What remains to be established is what happens with attended and predicted events. To examine the interaction between attention and prediction, we combined two robust paradigms developed for studying attention and prediction effects on ERPs into an orthogonal design. Participants were presented with sounds in attended or unattended intervals with onsets that were either predicted by a moving visual cue or unpredicted (no cue was provided). We demonstrated an N1 enhancement effect for attended sounds and an N1 suppression effect for predicted sounds; furthermore, an interaction between these effects was found that emerged early in the N1 (50-95 msec), indicating that attention enhancement only occurred when the sound was unpredicted. This pattern of results can be explained by the precision of the predictive cue that reduces the need for attention selection in the attended and predicted condition.

Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments

NASA Astrophysics Data System (ADS)

Rings, Joerg; Vrugt, Jasper A.; Schoups, Gerrit; Huisman, Johan A.; Vereecken, Harry

2012-05-01

Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of interest is a weighted average of pdfs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts, and reflect the individual models skill over a training (calibration) period. The original BMA approach presented by Raftery et al. (2005) assumes that the conditional pdf of each individual model is adequately described with a rather standard Gaussian or Gamma statistical distribution, possibly with a heteroscedastic variance. Here we analyze the advantages of using BMA with a flexible representation of the conditional pdf. A joint particle filtering and Gaussian mixture modeling framework is presented to derive analytically, as closely and consistently as possible, the evolving forecast density (conditional pdf) of each constituent ensemble member. The median forecasts and evolving conditional pdfs of the constituent models are subsequently combined using BMA to derive one overall predictive distribution. This paper introduces the theory and concepts of this new ensemble postprocessing method, and demonstrates its usefulness and applicability by numerical simulation of the rainfall-runoff transformation using discharge data from three different catchments in the contiguous United States. The revised BMA method receives significantly lower-prediction errors than the original default BMA method (due to filtering) with predictive uncertainty intervals that are substantially smaller but still statistically coherent (due to the use of a time-variant conditional pdf).

40 CFR 264.97 - General ground-water monitoring requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

... background median levels for each constituent. (3) A tolerance or prediction interval procedure in which an... each constituent in each compliance well is compared to the upper tolerance or prediction limit. (4) A... must be maintained. This performance standard does not apply to tolerance intervals, prediction...

Exclusion of RAI2 as the causative gene for Nance-Horan syndrome.

PubMed

Walpole, S M; Ronce, N; Grayson, C; Dessay, B; Yates, J R; Trump, D; Toutain, A

1999-05-01

Nance-Horan syndrome (NHS) is an X-linked condition characterised by congenital cataracts, microphthalmia and/or microcornea, unusual dental morphology, dysmorphic facial features, and developmental delay in some cases. Recent linkage studies have mapped the NHS disease gene to a 3.5-cM interval on Xp22.2 between DXS1053 and DXS443. We previously identified a human homologue of a mouse retinoic-acid-induced gene (RAI2) within the NHS critical flanking interval and have tested the gene as a candidate for Nance-Horan syndrome in nine NHS-affected families. Direct sequencing of the RAI2 gene and predicted promoter region has revealed no mutations in the families screened; RAI2 is therefore unlikely to be associated with NHS.

Optimal go/no-go ratios to maximize false alarms.

PubMed

Young, Michael E; Sutherland, Steven C; McCoy, Anthony W

2018-06-01

Despite the ubiquity of go/no-go tasks in the study of behavioral inhibition, there is a lack of evidence regarding the impact of key design characteristics, including the go/no-go ratio, intertrial interval, and number of types of go stimuli, on the production of different response classes of central interest. In the present study we sought to empirically determine the optimal conditions to maximize the production of a rare outcome of considerable interest to researchers: false alarms. As predicted, the shortest intertrial intervals (450 ms), intermediate go/no-go ratios (2:1 to 4:1), and the use of multiple types of go stimuli produced the greatest numbers of false alarms. These results are placed within the context of behavioral changes during learning.

Pliocene Model Intercomparison (PlioMIP) Phase 2: Scientific Objectives and Experimental Design

NASA Technical Reports Server (NTRS)

Haywood, A. M.; Dowsett, H. J.; Dolan, A. M.; Rowley, D.; Abe-Ouchi, A.; Otto-Bliesner, B.; Chandler, M. A.; Hunter, S. J.; Lunt, D. J.; Pound, M.;

On the estimation of risk associated with an attenuation prediction

NASA Technical Reports Server (NTRS)

Crane, R. K.

1992-01-01

Viewgraphs from a presentation on the estimation of risk associated with an attenuation prediction is presented. Topics covered include: link failure - attenuation exceeding a specified threshold for a specified time interval or intervals; risk - the probability of one or more failures during the lifetime of the link or during a specified accounting interval; the problem - modeling the probability of attenuation by rainfall to provide a prediction of the attenuation threshold for a specified risk; and an accounting for the inadequacy of a model or models.

Reliable prediction intervals with regression neural networks.

PubMed

Papadopoulos, Harris; Haralambous, Haris

2011-10-01

This paper proposes an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without assuming anything more than that the data are independent and identically distributed (i.i.d.). We evaluate the proposed method on four benchmark datasets and on the problem of predicting Total Electron Content (TEC), which is an important parameter in trans-ionospheric links; for the latter we use a dataset of more than 60000 TEC measurements collected over a period of 11 years. Our experimental results show that the prediction intervals produced by our method are both well calibrated and tight enough to be useful in practice. Copyright © 2011 Elsevier Ltd. All rights reserved.

[Primary Study on Predicting the Termination of Paroxysmal Atrial Fibrillation Based on a Novel RdR RR Intervals Scatter Plot].

PubMed

Lu, Hongwei; Zhang, Chenxi; Sun, Ying; Hao, Zhidong; Wang, Chunfang; Tian, Jiajia

2015-08-01

Predicting the termination of paroxysmal atrial fibrillation (AF) may provide a signal to decide whether there is a need to intervene the AF timely. We proposed a novel RdR RR intervals scatter plot in our study. The abscissa of the RdR scatter plot was set to RR intervals and the ordinate was set as the difference between successive RR intervals. The RdR scatter plot includes information of RR intervals and difference between successive RR intervals, which captures more heart rate variability (HRV) information. By RdR scatter plot analysis of one minute RR intervals for 50 segments with non-terminating AF and immediately terminating AF, it was found that the points in RdR scatter plot of non-terminating AF were more decentralized than the ones of immediately terminating AF. By dividing the RdR scatter plot into uniform grids and counting the number of non-empty grids, non-terminating AF and immediately terminating AF segments were differentiated. By utilizing 49 RR intervals, for 20 segments of learning set, 17 segments were correctly detected, and for 30 segments of test set, 20 segments were detected. While utilizing 66 RR intervals, for 18 segments of learning set, 16 segments were correctly detected, and for 28 segments of test set, 20 segments were detected. The results demonstrated that during the last one minute before the termination of paroxysmal AF, the variance of the RR intervals and the difference of the neighboring two RR intervals became smaller. The termination of paroxysmal AF could be successfully predicted by utilizing the RdR scatter plot, while the predicting accuracy should be further improved.

Forensic use of the Greulich and Pyle atlas: prediction intervals and relevance.

PubMed

Chaumoitre, K; Saliba-Serre, B; Adalian, P; Signoli, M; Leonetti, G; Panuel, M

2017-03-01

The Greulich and Pyle (GP) atlas is one of the most frequently used methods of bone age (BA) estimation. Our aim is to assess its accuracy and to calculate the prediction intervals at 95% for forensic use. The study was conducted on a multi-ethnic sample of 2614 individuals (1423 boys and 1191 girls) referred to the university hospital of Marseille (France) for simple injuries. Hand radiographs were analysed using the GP atlas. Reliability of GP atlas and agreement between BA and chronological age (CA) were assessed and prediction intervals at 95% were calculated. The repeatability was excellent and the reproducibility was good. Pearson's linear correlation coefficient between CA and BA was 0.983. The mean difference between BA and CA was -0.18 years (boys) and 0.06 years (girls). The prediction interval at 95% for CA was given for each GP category and ranged between 1.2 and more than 4.5 years. The GP atlas is a reproducible and repeatable method that is still accurate for the present population, with a high correlation between BA and CA. The prediction intervals at 95% are wide, reflecting individual variability, and should be known when the method is used in forensic cases. • The GP atlas is still accurate at the present time. • There is a high correlation between bone age and chronological age. • Individual variability must be known when GP is used in forensic cases. • Prediction intervals (95%) are large; around 4 years after 10 year olds.

Prediction Models for Dynamic Demand Response

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

Aman, Saima; Frincu, Marc; Chelmis, Charalampos

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2R, which we address inmore » this paper. Our first contribution is the formal definition of D 2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D 2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions, simplifying the complexity of big data challenge associated with D 2R.« less

Preliminary dynamic tests of a flight-type ejector

NASA Technical Reports Server (NTRS)

Drummond, Colin K.

1992-01-01

A thrust augmenting ejector was tested to provide experimental data to assist in the assessment of theoretical models to predict duct and ejector fluid-dynamic characteristics. Eleven full-scale thrust augmenting ejector tests were conducted in which a rapid increase in the ejector nozzle pressure ratio was effected through a unique bypass/burst-disk subsystem. The present work examines two cases representative of the test performance window. In the first case, the primary nozzle pressure ration (NPR) increased 36 percent from one unchoked (NPR = 1.29) primary flow condition to another (NPR = 1.75) over a 0.15 second interval. The second case involves choked primary flow conditions, where a 17 percent increase in primary nozzle flowrate (from NPR = 2.35 to NPR = 2.77) occurred over approximately 0.1 seconds. Transient signal treatment of the present dataset is discussed and initial interpretations of the results are compared with theoretical predictions for a similar STOVL ejector model.

Research on regional numerical weather prediction

NASA Technical Reports Server (NTRS)

Kreitzberg, C. W.

1976-01-01

Extension of the predictive power of dynamic weather forecasting to scales below the conventional synoptic or cyclonic scales in the near future is assessed. Lower costs per computation, more powerful computers, and a 100 km mesh over the North American area (with coarser mesh extending beyond it) are noted at present. Doubling the resolution even locally (to 50 km mesh) would entail a 16-fold increase in costs (including vertical resolution and halving the time interval), and constraints on domain size and length of forecast. Boundary conditions would be provided by the surrounding 100 km mesh, and time-varying lateral boundary conditions can be considered to handle moving phenomena. More physical processes to treat, more efficient numerical techniques, and faster computers (improved software and hardware) backing up satellite and radar data could produce further improvements in forecasting in the 1980s. Boundary layer modeling, initialization techniques, and quantitative precipitation forecasting are singled out among key tasks.

Karstification of an aquifer along the Birs River, Switzerland - from natural to anthropogenic dominated boundary conditions

NASA Astrophysics Data System (ADS)

Romanov, D.; Epting, J.; Huggenberger, P.; Kaufmann, G.

2009-04-01

Karst aquifers are very sensitive to environmental changes. Small variations of boundary conditions can trigger significant and fast changes of the basic properties of these geological formations. Furthermore, a large number of hydraulic structures have been built in Karst terrains and close to urban areas. Within such settings it is of primary importance to understand the basic processes governing the system and to predict the evolution of Karst aquifers in order to mitigate hazards. There has been great progress in numerical modeling of the evolution of Karst during the last decades. We are now able to model early karstification of locations with complicated geological and geochemical settings and our knowledge about basic processes governing Karst evolution has increased significantly. However, there are still not many modeling attempts with data from real Karst aquifers. A model describing the evolution of a gypsum Karst aquifer along the Birs River in Switzerland is presented in this study. The initial and boundary conditions for the simulations are taken from results of geophysical and geological field studies and a detailed 3D hydrogeological model of the area. Three time intervals of the aquifer's development are discussed in details. The first covers the natural karstification for a period between several hundreds up to a few thousands years. The results from this evolution period are used as initial conditions for the second interval, which covers the time between 1890 and 2007 AD. This period is characterized by anthropogenic alterations of the system through a man-made river dam, which considerably changes the evolution of the aquifer. In 2006 and 2007 AD - after serious subsidence of a nearby highway has been observed - technical measures have been conducted and thus the boundary conditions have changed once again. This is the beginning for the third modeled interval. A forecast for the following 100 years is developed. Our results correlate very well with the findings of the field studies of the area. Furthermore, predicted evolution timescales are reasonable from what is known about the past of the aquifer. The Karst evolution models allowed simulating the development of aquifer properties, which subsequently could be transferred to the 3D hydrogeological model, allowing a more realistic representation of subsurface heterogeneities. It could be demonstrated that the various investigative methods for Karst aquifer characterization are complementing each other and allow the interpretation of short-term impacts and long-term development on system-dynamics. The obtained results show that our models can be applied not only for theoretical research of simplified and idealized Karst aquifers, but also to places with complex geological and hydrological properties. Investigative methods for similar subsidence problems can be optimized, leading from general measurements and monitoring technologies to tools with predictive character.

Sensory cortical response to uncertainty and low salience during recognition of affective cues in musical intervals.

PubMed

Bravo, Fernando; Cross, Ian; Stamatakis, Emmanuel Andreas; Rohrmeier, Martin

2017-01-01

Previous neuroimaging studies have shown an increased sensory cortical response (i.e., heightened weight on sensory evidence) under higher levels of predictive uncertainty. The signal enhancement theory proposes that attention improves the quality of the stimulus representation, and therefore reduces uncertainty by increasing the gain of the sensory signal. The present study employed functional magnetic resonance imaging (fMRI) to investigate the neural correlates for ambiguous valence inferences signaled by auditory information within an emotion recognition paradigm. Participants categorized sound stimuli of three distinct levels of consonance/dissonance controlled by interval content. Separate behavioural and neuroscientific experiments were conducted. Behavioural results revealed that, compared with the consonance condition (perfect fourths, fifths and octaves) and the strong dissonance condition (minor/major seconds and tritones), the intermediate dissonance condition (minor thirds) was the most ambiguous, least salient and more cognitively demanding category (slowest reaction times). The neuroscientific findings were consistent with a heightened weight on sensory evidence whilst participants were evaluating intermediate dissonances, which was reflected in an increased neural response of the right Heschl's gyrus. The results support previous studies that have observed enhanced precision of sensory evidence whilst participants attempted to represent and respond to higher degrees of uncertainty, and converge with evidence showing preferential processing of complex spectral information in the right primary auditory cortex. These findings are discussed with respect to music-theoretical concepts and recent Bayesian models of perception, which have proposed that attention may heighten the weight of information coming from sensory channels to stimulate learning about unknown predictive relationships.

Sensory cortical response to uncertainty and low salience during recognition of affective cues in musical intervals

PubMed Central

Cross, Ian; Stamatakis, Emmanuel Andreas; Rohrmeier, Martin

2017-01-01

Previous neuroimaging studies have shown an increased sensory cortical response (i.e., heightened weight on sensory evidence) under higher levels of predictive uncertainty. The signal enhancement theory proposes that attention improves the quality of the stimulus representation, and therefore reduces uncertainty by increasing the gain of the sensory signal. The present study employed functional magnetic resonance imaging (fMRI) to investigate the neural correlates for ambiguous valence inferences signaled by auditory information within an emotion recognition paradigm. Participants categorized sound stimuli of three distinct levels of consonance/dissonance controlled by interval content. Separate behavioural and neuroscientific experiments were conducted. Behavioural results revealed that, compared with the consonance condition (perfect fourths, fifths and octaves) and the strong dissonance condition (minor/major seconds and tritones), the intermediate dissonance condition (minor thirds) was the most ambiguous, least salient and more cognitively demanding category (slowest reaction times). The neuroscientific findings were consistent with a heightened weight on sensory evidence whilst participants were evaluating intermediate dissonances, which was reflected in an increased neural response of the right Heschl’s gyrus. The results support previous studies that have observed enhanced precision of sensory evidence whilst participants attempted to represent and respond to higher degrees of uncertainty, and converge with evidence showing preferential processing of complex spectral information in the right primary auditory cortex. These findings are discussed with respect to music-theoretical concepts and recent Bayesian models of perception, which have proposed that attention may heighten the weight of information coming from sensory channels to stimulate learning about unknown predictive relationships. PMID:28422990

Adolescent mothers' anthropometrics and grandmothers' schooling predict infant anthropometrics in Ethiopia, India, Peru, and Vietnam.

PubMed

Schott, Whitney; Aurino, Elisabetta; Penny, Mary E; Behrman, Jere R

2017-10-24

We investigated intergenerational associations of adolescent mothers' and grandmothers' anthropometrics and schooling with adolescent mothers' offspring's anthropometrics in Ethiopia, India, Peru, and Vietnam. We examined birthweight (n = 283), birthweight Z-score (BWZ), conditional growth in weight-for-age Z-score (cWAZ, residuals from a regression of WAZ at last survey round on BWZ, sex, and age), and height-for-age Z-score (HAZ) of children born to older cohort adolescent girls in the Young Lives study. Our key independent variables were adolescent mothers' body size: HAZ and body-mass-index-for-age Z-score (BMIZ) at age 8, conditional HAZ (cHAZ, residuals from a regression of HAZ at the end of a growth period on prior HAZ, age, and sex), conditional BMIZ growth (cBMIZ, calculated analogously), and grandmaternal BMIZ, HAZ, and schooling. We adjusted for child, maternal, and household characteristics. Adolescent mothers' cHAZ (ages 8-15) predicted birthweight (β = 130 g, 95% confidence interval (CI) 31-228), BWZ (β = 0.31, CI 0.09-0.53), and cWAZ (β = 0.28, CI 0.04-0.51). Adolescent mothers' BMIZ at age 8 predicted birthweight (β = 79 g, CI 16-43) and BWZ (β = 0.22, CI 0.08-0.36). Adolescent mothers' cBMIZ (ages 12-15) predicted child cWAZ and HAZ. Grandmothers' schooling predicted grandchild birthweight (β = 22 g, CI 1-44) and BWZ (β = 0.05, CI 0.01-0.10). © 2017 New York Academy of Sciences.

Selected Streamflow Statistics and Regression Equations for Predicting Statistics at Stream Locations in Monroe County, Pennsylvania

USGS Publications Warehouse

Thompson, Ronald E.; Hoffman, Scott A.

2006-01-01

A suite of 28 streamflow statistics, ranging from extreme low to high flows, was computed for 17 continuous-record streamflow-gaging stations and predicted for 20 partial-record stations in Monroe County and contiguous counties in north-eastern Pennsylvania. The predicted statistics for the partial-record stations were based on regression analyses relating inter-mittent flow measurements made at the partial-record stations indexed to concurrent daily mean flows at continuous-record stations during base-flow conditions. The same statistics also were predicted for 134 ungaged stream locations in Monroe County on the basis of regression analyses relating the statistics to GIS-determined basin characteristics for the continuous-record station drainage areas. The prediction methodology for developing the regression equations used to estimate statistics was developed for estimating low-flow frequencies. This study and a companion study found that the methodology also has application potential for predicting intermediate- and high-flow statistics. The statistics included mean monthly flows, mean annual flow, 7-day low flows for three recurrence intervals, nine flow durations, mean annual base flow, and annual mean base flows for two recurrence intervals. Low standard errors of prediction and high coefficients of determination (R2) indicated good results in using the regression equations to predict the statistics. Regression equations for the larger flow statistics tended to have lower standard errors of prediction and higher coefficients of determination (R2) than equations for the smaller flow statistics. The report discusses the methodologies used in determining the statistics and the limitations of the statistics and the equations used to predict the statistics. Caution is indicated in using the predicted statistics for small drainage area situations. Study results constitute input needed by water-resource managers in Monroe County for planning purposes and evaluation of water-resources availability.

General intelligence predicts memory change across sleep.

PubMed

Fenn, Kimberly M; Hambrick, David Z

2015-06-01

Psychometric intelligence (g) is often conceptualized as the capability for online information processing but it is also possible that intelligence may be related to offline processing of information. Here, we investigated the relationship between psychometric g and sleep-dependent memory consolidation. Participants studied paired-associates and were tested after a 12-hour retention interval that consisted entirely of wake or included a regular sleep phase. We calculated the number of word-pairs that were gained and lost across the retention interval. In a separate session, participants completed a battery of cognitive ability tests to assess g. In the wake group, g was not correlated with either memory gain or memory loss. In the sleep group, we found that g correlated positively with memory gain and negatively with memory loss. Participants with a higher level of general intelligence showed more memory gain and less memory loss across sleep. Importantly, the correlation between g and memory loss was significantly stronger in the sleep condition than in the wake condition, suggesting that the relationship between g and memory loss across time is specific to time intervals that include sleep. The present research suggests that g not only reflects the capability for online cognitive processing, but also reflects capability for offline processes that operate during sleep.

Coherent nature of the radiation emitted in delayed luminescence of leaves

PubMed

Bajpai

1999-06-07

After exposure to light, a living system emits a photon signal of characteristic shape. The signal has a small decay region and a long tail region. The flux of photons in the decay region changes by 2 to 3 orders of magnitude, but remains almost constant in the tail region. The decaying part is attributed to delayed luminescence and the constant part to ultra-weak luminescence. Biophoton emission is the common name given to both kinds of luminescence, and photons emitted are called biophotons. The decay character of the biophoton signal is not exponential, which is suggestive of a coherent signal. We sought to establish the coherent nature by measuring the conditional probability of zero photon detection in a small interval Delta. Our measurements establish the coherent nature of biophotons emitted by different leaves at various temperatures in the range 15-50 degrees C. Our set up could measure the conditional probability for Delta

Prediction of Low Community Sanitation Coverage Using Environmental and Sociodemographic Factors in Amhara Region, Ethiopia

PubMed Central

Oswald, William E.; Stewart, Aisha E. P.; Flanders, W. Dana; Kramer, Michael R.; Endeshaw, Tekola; Zerihun, Mulat; Melaku, Birhanu; Sata, Eshetu; Gessesse, Demelash; Teferi, Tesfaye; Tadesse, Zerihun; Guadie, Birhan; King, Jonathan D.; Emerson, Paul M.; Callahan, Elizabeth K.; Moe, Christine L.; Clasen, Thomas F.

2016-01-01

This study developed and validated a model for predicting the probability that communities in Amhara Region, Ethiopia, have low sanitation coverage, based on environmental and sociodemographic conditions. Community sanitation coverage was measured between 2011 and 2014 through trachoma control program evaluation surveys. Information on environmental and sociodemographic conditions was obtained from available data sources and linked with community data using a geographic information system. Logistic regression was used to identify predictors of low community sanitation coverage (< 20% versus ≥ 20%). The selected model was geographically and temporally validated. Model-predicted probabilities of low community sanitation coverage were mapped. Among 1,502 communities, 344 (22.90%) had coverage below 20%. The selected model included measures for high topsoil gravel content, an indicator for low-lying land, population density, altitude, and rainfall and had reasonable predictive discrimination (area under the curve = 0.75, 95% confidence interval = 0.72, 0.78). Measures of soil stability were strongly associated with low community sanitation coverage, controlling for community wealth, and other factors. A model using available environmental and sociodemographic data predicted low community sanitation coverage for areas across Amhara Region with fair discrimination. This approach could assist sanitation programs and trachoma control programs, scaling up or in hyperendemic areas, to target vulnerable areas with additional activities or alternate technologies. PMID:27430547

Reference intervals and allometric scaling of two-dimensional echocardiographic measurements in 150 healthy cats.

PubMed

Karsten, Schober; Stephanie, Savino; Vedat, Yildiz

2017-11-10

The objective of the study was to evaluate the effects of body weight (BW), breed, and sex on two-dimensional (2D) echocardiographic measures, reference ranges, and prediction intervals using allometrically-scaled data of left atrial (LA) and left ventricular (LV) size and LV wall thickness in healthy cats. Study type was retrospective, observational, and clinical cohort. 150 healthy cats were enrolled and 2D echocardiograms analyzed. LA diameter, LV wall thickness, and LV dimension were quantified using three different imaging views. The effect of BW, breed, sex, age, and interaction (BW*sex) on echocardiographic variables was assessed using univariate and multivariate regression and linear mixed model analysis. Standard (using raw data) and allometrically scaled (Y=a × M b ) reference intervals and prediction intervals were determined. BW had a significant (P<0.05) independent effect on 2D variables whereas breed, sex, and age did not. There were clinically relevant differences between reference intervals using mean ± 2SD of raw data and mean and 95% prediction interval of allometrically-scaled variables, most prominent in larger (>6 kg) and smaller (<3 kg) cats. A clinically relevant difference between thickness of the interventricular septum (IVS) and dimension of the LV posterior wall (LVPW) was identified. In conclusion, allometric scaling and BW-based 95% prediction intervals should be preferred over conventional 2D echocardiographic reference intervals in cats, in particular in small and large cats. These results are particularly relevant to screening examinations for feline hypertrophic cardiomyopathy.

Evidence of functional declining and global comorbidity measured at baseline proved to be the strongest predictors for long-term death in elderly community residents aged 85 years: a 5-year follow-up evaluation, the OCTABAIX study

PubMed Central

Formiga, Francesc; Ferrer, Assumpta; Padros, Gloria; Montero, Abelardo; Gimenez-Argente, Carme; Corbella, Xavier

2016-01-01

Objective To investigate the predictive value of functional impairment, chronic conditions, and laboratory biomarkers of aging for predicting 5-year mortality in the elderly aged 85 years. Methods Predictive value for mortality of different geriatric assessments carried out during the OCTABAIX study was evaluated after 5 years of follow-up in 328 subjects aged 85 years. Measurements included assessment of functional status comorbidity, along with laboratory tests on vitamin D, cholesterol, CD4/CD8 ratio, hemoglobin, and serum thyrotropin. Results Overall, the mortality rate after 5 years of follow-up was 42.07%. Bivariate analysis showed that patients who survived were predominantly female (P=0.02), and they showed a significantly better baseline functional status for both basic (P<0.001) and instrumental (P<0.001) activities of daily living (Barthel and Lawton index), better cognitive performance (Spanish version of the Mini-Mental State Examination) (P<0.001), lower comorbidity conditions (Charlson) (P<0.001), lower nutritional risk (Mini Nutritional Assessment) (P<0.001), lower risk of falls (Tinetti gait scale) (P<0.001), less percentage of heart failure (P=0.03) and chronic obstructive pulmonary disease (P=0.03), and took less chronic prescription drugs (P=0.002) than nonsurvivors. Multivariate Cox regression analysis identified a decreased score in the Lawton index (hazard ratio 0.86, 95% confidence interval: 0.78–0.91) and higher comorbidity conditions (hazard ratio 1.20, 95% confidence interval: 1.08–1.33) as independent predictors of mortality at 5 years in the studied population. Conclusion The ability to perform instrumental activities of daily living and the global comorbidity assessed at baseline were the predictors of death, identified in our 85-year-old community-dwelling subjects after 5 years of follow-up. PMID:27143867

Explaining negative refraction without negative refractive indices.

PubMed

Talalai, Gregory A; Garner, Timothy J; Weiss, Steven J

2018-03-01

Negative refraction through a triangular prism may be explained without assigning a negative refractive index to the prism by using array theory. For the case of a beam incident upon the wedge, the array theory accurately predicts the beam transmission angle through the prism and provides an estimate of the frequency interval at which negative refraction occurs. The hypotenuse of the prism has a staircase shape because it is built of cubic unit cells. The large phase delay imparted by each unit cell, combined with the staircase shape of the hypotenuse, creates the necessary conditions for negative refraction. Full-wave simulations using the finite-difference time-domain method show that array theory accurately predicts the beam transmission angle.

Predicting Redox Conditions in Groundwater Using Statistical Techniques: Implications for Nitrate Transport in Groundwater and Streams

NASA Astrophysics Data System (ADS)

Tesoriero, A. J.; Terziotti, S.

2014-12-01

Nitrate trends in streams often do not match expectations based on recent nitrogen source loadings to the land surface. Groundwater discharge with long travel times has been suggested as the likely cause for these observations. The fate of nitrate in groundwater depends to a large extent on the occurrence of denitrification along flow paths. Because denitrification in groundwater is inhibited when dissolved oxygen (DO) concentrations are high, defining the oxic-suboxic interface has been critical in determining pathways for nitrate transport in groundwater and to streams at the local scale. Predicting redox conditions on a regional scale is complicated by the spatial variability of reaction rates. In this study, logistic regression and boosted classification tree analysis were used to predict the probability of oxic water in groundwater in the Chesapeake Bay watershed. The probability of oxic water (DO > 2 mg/L) was predicted by relating DO concentrations in over 3,000 groundwater samples to indicators of residence time and/or electron donor availability. Variables that describe position in the flow system (e.g., depth to top of the open interval), soil drainage and surficial geology were the most important predictors of oxic water. Logistic regression and boosted classification tree analysis correctly predicted the presence or absence of oxic conditions in over 75 % of the samples in both training and validation data sets. Predictions of the percentages of oxic wells in deciles of risk were very accurate (r2>0.9) in both the training and validation data sets. Depth to the bottom of the oxic layer was predicted and is being used to estimate the effect that groundwater denitrification has on stream nitrate concentrations and the time lag between the application of nitrogen at the land surface and its effect on streams.

Bayesian averaging over Decision Tree models for trauma severity scoring.

PubMed

Schetinin, V; Jakaite, L; Krzanowski, W

2018-01-01

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

Time-based loss in visual short-term memory is from trace decay, not temporal distinctiveness.

PubMed

Ricker, Timothy J; Spiegel, Lauren R; Cowan, Nelson

2014-11-01

There is no consensus as to why forgetting occurs in short-term memory tasks. In past work, we have shown that forgetting occurs with the passage of time, but there are 2 classes of theories that can explain this effect. In the present work, we investigate the reason for time-based forgetting by contrasting the predictions of temporal distinctiveness and trace decay in the procedure in which we have observed such loss, involving memory for arrays of characters or letters across several seconds. The 1st theory, temporal distinctiveness, predicts that increasing the amount of time between trials will lead to less proactive interference, resulting in less forgetting across a retention interval. In the 2nd theory, trace decay, temporal distinctiveness between trials is irrelevant to the loss over a retention interval. Using visual array change detection tasks in 4 experiments, we find small proactive interference effects on performance under some specific conditions, but no concomitant change in the effect of a retention interval. We conclude that trace decay is the more suitable class of explanations of the time-based forgetting in short-term memory that we have observed, and we suggest the need for further clarity in what the exact basis of that decay may be. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Environmental trends in extinction during the Paleozoic

NASA Technical Reports Server (NTRS)

Sepkoski, J. John, Jr.

1987-01-01

Extinction intensities calculated from 505 Paleozoic marine assemblages divided among six environmental zones and 40 stratigraphic intervals indicate that whole communities exhibit increasing extinction offshore but that genera within individual taxonomic classes tend to have their highest extinction onshore. The offshore trend at the community level results from a concentration of genera in classes with low characteristic extinction rates in nearshore environments. This finding is consistent with the ecologic expectation that organisms inhabiting unpredictably fluctuating environments should suffer more extinction than counterparts living under more predictably equitable conditions.

Optical modeling of stratopheric aerosols - Present status

NASA Technical Reports Server (NTRS)

Rosen, J. M.; Hofmann, D. J.

1986-01-01

A stratospheric aerosol optical model is developed which is based on a size distribution conforming to direct measurements. Additional constraints are consistent with large data sets of independently measured macroscopic aerosol properties such as mass and backscatter. The period under study covers background as well as highly disturbed volcanic conditions and an altitude interval ranging from the tropopause to about 30 km. The predictions of the model are used to form a basis for interpreting and intercomparing several diverse types of stratospheric aerosol measurement.

Evaluation of SAMe-TT2R2 Score on Predicting Success With Extended-Interval Warfarin Monitoring.

PubMed

Hwang, Andrew Y; Carris, Nicholas W; Dietrich, Eric A; Gums, John G; Smith, Steven M

2018-06-01

In patients with stable international normalized ratios, 12-week extended-interval warfarin monitoring can be considered; however, predictors of success with this strategy are unknown. The previously validated SAMe-TT 2 R 2 score (considering sex, age, medical history, treatment, tobacco, and race) predicts anticoagulation control during standard follow-up (every 4 weeks), with lower scores associated with greater time in therapeutic range. To evaluate the ability of the SAMe-TT 2 R 2 score in predicting success with extended-interval warfarin follow-up in patients with previously stable warfarin doses. In this post hoc analysis of a single-arm feasibility study, baseline SAMe-TT 2 R 2 scores were calculated for patients with ≥1 extended-interval follow-up visit. The primary analysis assessed achieved weeks of extended-interval follow-up according to baseline SAMe-TT 2 R 2 scores. A total of 47 patients receiving chronic anticoagulation completed a median of 36 weeks of extended-interval follow-up. The median baseline SAMe-TT 2 R 2 score was 1 (range 0-5). Lower SAMe-TT 2 R 2 scores appeared to be associated with greater duration of extended-interval follow-up achieved, though the differences between scores were not statistically significant. No individual variable of the SAMe-TT 2 R 2 score was associated with achieved weeks of extended-interval follow-up. Analysis of additional patient factors found that longer duration (≥24 weeks) of prior stable treatment was significantly associated with greater weeks of extended-interval follow-up completed ( P = 0.04). Conclusion and Relevance: This pilot study provides limited evidence that the SAMe-TT 2 R 2 score predicts success with extended-interval warfarin follow-up but requires confirmation in a larger study. Further research is also necessary to establish additional predictors of successful extended-interval warfarin follow-up.

A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

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

Hodge, Brian S; Krishnan, Venkat K; Zhang, Jie

Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertaintymore » and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.« less

Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

NASA Astrophysics Data System (ADS)

Abhilash, S.; Sahai, A. K.; Borah, N.; Chattopadhyay, R.; Joseph, S.; Sharmila, S.; De, S.; Goswami, B. N.; Kumar, Arun

2014-05-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

How to regress and predict in a Bland-Altman plot? Review and contribution based on tolerance intervals and correlated-errors-in-variables models.

PubMed

Francq, Bernard G; Govaerts, Bernadette

2016-06-30

Two main methodologies for assessing equivalence in method-comparison studies are presented separately in the literature. The first one is the well-known and widely applied Bland-Altman approach with its agreement intervals, where two methods are considered interchangeable if their differences are not clinically significant. The second approach is based on errors-in-variables regression in a classical (X,Y) plot and focuses on confidence intervals, whereby two methods are considered equivalent when providing similar measures notwithstanding the random measurement errors. This paper reconciles these two methodologies and shows their similarities and differences using both real data and simulations. A new consistent correlated-errors-in-variables regression is introduced as the errors are shown to be correlated in the Bland-Altman plot. Indeed, the coverage probabilities collapse and the biases soar when this correlation is ignored. Novel tolerance intervals are compared with agreement intervals with or without replicated data, and novel predictive intervals are introduced to predict a single measure in an (X,Y) plot or in a Bland-Atman plot with excellent coverage probabilities. We conclude that the (correlated)-errors-in-variables regressions should not be avoided in method comparison studies, although the Bland-Altman approach is usually applied to avert their complexity. We argue that tolerance or predictive intervals are better alternatives than agreement intervals, and we provide guidelines for practitioners regarding method comparison studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Running Speed Can Be Predicted from Foot Contact Time during Outdoor over Ground Running.

PubMed

de Ruiter, Cornelis J; van Oeveren, Ben; Francke, Agnieta; Zijlstra, Patrick; van Dieen, Jaap H

2016-01-01

The number of validation studies of commercially available foot pods that provide estimates of running speed is limited and these studies have been conducted under laboratory conditions. Moreover, internal data handling and algorithms used to derive speed from these pods are proprietary and thereby unclear. The present study investigates the use of foot contact time (CT) for running speed estimations, which potentially can be used in addition to the global positioning system (GPS) in situations where GPS performance is limited. CT was measured with tri axial inertial sensors attached to the feet of 14 runners, during natural over ground outdoor running, under optimized conditions for GPS. The individual relationships between running speed and CT were established during short runs at different speeds on two days. These relations were subsequently used to predict instantaneous speed during a straight line 4 km run with a single turning point halfway. Stopwatch derived speed, measured for each of 32 consecutive 125m intervals during the 4 km runs, was used as reference. Individual speed-CT relations were strong (r2 >0.96 for all trials) and consistent between days. During the 4km runs, median error (ranges) in predicted speed from CT 2.5% (5.2) was higher (P<0.05) than for GPS 1.6% (0.8). However, around the turning point and during the first and last 125m interval, error for GPS-speed increased to 5.0% (4.5) and became greater (P<0.05) than the error predicted from CT: 2.7% (4.4). Small speed fluctuations during 4km runs were adequately monitored with both methods: CT and GPS respectively explained 85% and 73% of the total speed variance during 4km runs. In conclusion, running speed estimates bases on speed-CT relations, have acceptable accuracy and could serve to backup or substitute for GPS during tarmac running on flat terrain whenever GPS performance is limited.

A Refined Prediction Model for Core and Lower Extremity Sprains and Strains Among Collegiate Football Players.

PubMed

Wilkerson, Gary B; Colston, Marisa A

2015-06-01

Researchers have identified high exposure to game conditions, low back dysfunction, and poor endurance of the core musculature as strong predictors for the occurrence of sprains and strains among collegiate football players. To refine a previously developed injury-prediction model through analysis of 3 consecutive seasons of data. Cohort study. National Collegiate Athletic Association Division I Football Championship Subdivision football program. For 3 consecutive years, all 152 team members (age = 19.7 ± 1.5 years, height = 1.84 ± 0.08 m, mass = 101.08 ± 19.28 kg) presented for a mandatory physical examination on the day before initiation of preseason practice sessions. Associations between preseason measurements and the subsequent occurrence of a core or lower extremity sprain or strain were established for 256 player-seasons of data. We used receiver operating characteristic analysis to identify optimal cut points for dichotomous categorizations of cases as high risk or low risk. Both logistic regression and Cox regression analyses were used to identify a multivariable injury-prediction model with optimal discriminatory power. Exceptionally good discrimination between injured and uninjured cases was found for a 3-factor prediction model that included equal to or greater than 1 game as a starter, Oswestry Disability Index score equal to or greater than 4, and poor wall-sit-hold performance. The existence of at least 2 of the 3 risk factors demonstrated 56% sensitivity, 80% specificity, an odds ratio of 5.28 (90% confidence interval = 3.31, 8.44), and a hazard ratio of 2.97 (90% confidence interval = 2.14, 4.12). High exposure to game conditions was the dominant injury risk factor for collegiate football players, but a surprisingly mild degree of low back dysfunction and poor core-muscle endurance appeared to be important modifiable risk factors that should be identified and addressed before participation.

A Refined Prediction Model for Core and Lower Extremity Sprains and Strains Among Collegiate Football Players

PubMed Central

Wilkerson, Gary B.; Colston, Marisa A.

2015-01-01

Context Researchers have identified high exposure to game conditions, low back dysfunction, and poor endurance of the core musculature as strong predictors for the occurrence of sprains and strains among collegiate football players. Objective To refine a previously developed injury-prediction model through analysis of 3 consecutive seasons of data. Design Cohort study. Setting National Collegiate Athletic Association Division I Football Championship Subdivision football program. Patients or Other Participants For 3 consecutive years, all 152 team members (age = 19.7 ± 1.5 years, height = 1.84 ± 0.08 m, mass = 101.08 ± 19.28 kg) presented for a mandatory physical examination on the day before initiation of preseason practice sessions. Main Outcome Measure(s) Associations between preseason measurements and the subsequent occurrence of a core or lower extremity sprain or strain were established for 256 player-seasons of data. We used receiver operating characteristic analysis to identify optimal cut points for dichotomous categorizations of cases as high risk or low risk. Both logistic regression and Cox regression analyses were used to identify a multivariable injury-prediction model with optimal discriminatory power. Results Exceptionally good discrimination between injured and uninjured cases was found for a 3-factor prediction model that included equal to or greater than 1 game as a starter, Oswestry Disability Index score equal to or greater than 4, and poor wall-sit–hold performance. The existence of at least 2 of the 3 risk factors demonstrated 56% sensitivity, 80% specificity, an odds ratio of 5.28 (90% confidence interval = 3.31, 8.44), and a hazard ratio of 2.97 (90% confidence interval = 2.14, 4.12). Conclusions High exposure to game conditions was the dominant injury risk factor for collegiate football players, but a surprisingly mild degree of low back dysfunction and poor core-muscle endurance appeared to be important modifiable risk factors that should be identified and addressed before participation. PMID:25844856

[The evaluation and prognosis of the psychophysiological status of a human operator].

PubMed

Sukhov, A E; Chaĭchenko, G M

1989-01-01

In experiments on 56 healthy subjects (18-20 years old) the quality of their activity was determined during compensatory watching the mark at complicating regimes of work. Depending on the difficulty of the task five groups of subjects were singled out with optimum working capacity in one of four working conditions: normal, ordinary and strenuous work, model of stress situation. It is established that the change of the number of significant correlative connections between main parameters of psychophysiological state of man-operator reflects the condition of his functional systems. On the basis of computation of total range of organization values of both R-R intervals of the ECG and duration of expiration, the success of the man-operator work in complex conditions of activity is predicted.

Gaussian process regression for tool wear prediction

NASA Astrophysics Data System (ADS)

Kong, Dongdong; Chen, Yongjie; Li, Ning

2018-05-01

To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

NASA Astrophysics Data System (ADS)

Borah, Nabanita; Sukumarpillai, Abhilash; Sahai, Atul Kumar; Chattopadhyay, Rajib; Joseph, Susmitha; De, Soumyendu; Nath Goswami, Bhupendra; Kumar, Arun

2014-05-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

NASA Astrophysics Data System (ADS)

Borah, N.; Abhilash, S.; Sahai, A. K.; Chattopadhyay, R.; Joseph, S.; Sharmila, S.; de, S.; Goswami, B.; Kumar, A.

2013-12-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISOs) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

Time and resource limits on working memory: cross-age consistency in counting span performance.

PubMed

Ransdell, Sarah; Hecht, Steven

2003-12-01

This longitudinal study separated resource demand effects from those of retention interval in a counting span task among 100 children tested in grade 2 and again in grades 3 and 4. A last card large counting span condition had an equivalent memory load to a last card small, but the last card large required holding the count over a longer retention interval. In all three waves of assessment, the last card large condition was found to be less accurate than the last card small. A model predicting reading comprehension showed that age was a significant predictor when entered first accounting for 26% of the variance, but counting span accounted for a further 22% of the variance. Span at Wave 1 accounted for significant unique variance at Wave 2 and at Wave 3. Results were similar for math calculation with age accounting for 31% of the variance and counting span accounting for a further 34% of the variance. Span at Wave 1 explained unique variance in math at Wave 2 and at Wave 3.

Cigarette smoke chemistry market maps under Massachusetts Department of Public Health smoking conditions.

PubMed

Morton, Michael J; Laffoon, Susan W

2008-06-01

This study extends the market mapping concept introduced by Counts et al. (Counts, M.E., Hsu, F.S., Tewes, F.J., 2006. Development of a commercial cigarette "market map" comparison methodology for evaluating new or non-conventional cigarettes. Regul. Toxicol. Pharmacol. 46, 225-242) to include both temporal cigarette and testing variation and also machine smoking with more intense puffing parameters, as defined by the Massachusetts Department of Public Health (MDPH). The study was conducted over a two year period and involved a total of 23 different commercial cigarette brands from the U.S. marketplace. Market mapping prediction intervals were developed for 40 mainstream cigarette smoke constituents and the potential utility of the market map as a comparison tool for new brands was demonstrated. The over-time character of the data allowed for the variance structure of the smoke constituents to be more completely characterized than is possible with one-time sample data. The variance was partitioned among brand-to-brand differences, temporal differences, and the remaining residual variation using a mixed random and fixed effects model. It was shown that a conventional weighted least squares model typically gave similar prediction intervals to those of the more complicated mixed model. For most constituents there was less difference in the prediction intervals calculated from over-time samples and those calculated from one-time samples than had been anticipated. One-time sample maps may be adequate for many purposes if the user is aware of their limitations. Cigarette tobacco fillers were analyzed for nitrate, nicotine, tobacco-specific nitrosamines, ammonia, chlorogenic acid, and reducing sugars. The filler information was used to improve predicting relationships for several of the smoke constituents, and it was concluded that the effects of filler chemistry on smoke chemistry were partial explanations of the observed brand-to-brand variation.

Experimental observation of the thermocapillary driven motion of bubbles in a molten glass under low gravity conditions

NASA Technical Reports Server (NTRS)

Smith, H. D.; Mattox, D. M.; Wilcox, W. R.; Subramanian, R. S.; Meyyappan, M.

1982-01-01

An experiment was carried out on board a Space Processing Applications Rocket with the aim of demonstrating bubble migration in molten glass due to a temperature gradient under low gravity conditions. During the flight, a sample of a sodium borate melt with a specific bubble array, contained in a platinum/fused silica cell, was subjected to a well defined temperature gradient for more than 4 minutes. Photographs taken at one second intervals during the experiment clearly show that the bubbles move toward the hot spot on the platinum heater strip. This result is consistent with the predictions of the theory of thermocapillary driven bubble motion.

A Valuable Tool in Predicting Poor Outcome due to Sepsis in Pediatric Intensive Care Unit: Tp-e/QT Ratio.

PubMed

Ozdemir, Rahmi; Isguder, Rana; Kucuk, Mehmet; Karadeniz, Cem; Ceylan, Gokhan; Katipoglu, Nagehan; Yilmazer, Murat Muhtar; Yozgat, Yilmaz; Mese, Timur; Agin, Hasan

2016-10-01

To assess the feasibility of 12-lead electrocardiographic (ECG) measures such as P wave dispersion (PWd), QT interval, QT dispersion (QTd), Tp-e interval, Tp-e/QT and Tp-e/QTc ratio in predicting poor outcome in patients diagnosed with sepsis in pediatric intensive care unit (PICU). Ninety-three patients diagnosed with sepsis, severe sepsis or septic shock and 103 age- and sex-matched healthy children were enrolled into the study. PWd, QT interval, QTd, Tp-e interval and Tp-e/QT, Tp-e/QTc ratios were obtained from a 12-lead electrocardiogram. PWd, QTd, Tp-e interval and Tp-e/QT, Tp-e/QTc ratios were significantly higher in septic patients compared with the controls. During the study period, 41 patients had died. In multivariate logistic regression analyses, only Tp-e/QT ratio was found to be an independent predictor of mortality. The ECG measurements can predict the poor outcome in patients with sepsis. The Tp-e/QT ratio may be a valuable tool in predicting mortality for patients with sepsis in the PICU. © The Author [2016]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Quantitative fetal fibronectin and cervical length to predict preterm birth in asymptomatic women with previous cervical surgery.

PubMed

Vandermolen, Brooke I; Hezelgrave, Natasha L; Smout, Elizabeth M; Abbott, Danielle S; Seed, Paul T; Shennan, Andrew H

2016-10-01

Quantitative fetal fibronectin testing has demonstrated accuracy for prediction of spontaneous preterm birth in asymptomatic women with a history of preterm birth. Predictive accuracy in women with previous cervical surgery (a potentially different risk mechanism) is not known. We sought to compare the predictive accuracy of cervicovaginal fluid quantitative fetal fibronectin and cervical length testing in asymptomatic women with previous cervical surgery to that in women with 1 previous preterm birth. We conducted a prospective blinded secondary analysis of a larger observational study of cervicovaginal fluid quantitative fetal fibronectin concentration in asymptomatic women measured with a Hologic 10Q system (Hologic, Marlborough, MA). Prediction of spontaneous preterm birth (<30, <34, and <37 weeks) with cervicovaginal fluid quantitative fetal fibronectin concentration in primiparous women who had undergone at least 1 invasive cervical procedure (n = 473) was compared with prediction in women who had previous spontaneous preterm birth, preterm prelabor rupture of membranes, or late miscarriage (n = 821). Relationship with cervical length was explored. The rate of spontaneous preterm birth <34 weeks in the cervical surgery group was 3% compared with 9% in previous spontaneous preterm birth group. Receiver operating characteristic curves comparing quantitative fetal fibronectin for prediction at all 3 gestational end points were comparable between the cervical surgery and previous spontaneous preterm birth groups (34 weeks: area under the curve, 0.78 [95% confidence interval 0.64-0.93] vs 0.71 [95% confidence interval 0.64-0.78]; P = .39). Prediction of spontaneous preterm birth using cervical length compared with quantitative fetal fibronectin for prediction of preterm birth <34 weeks of gestation offered similar prediction (area under the curve, 0.88 [95% confidence interval 0.79-0.96] vs 0.77 [95% confidence interval 0.62-0.92], P = .12 in the cervical surgery group; and 0.77 [95% confidence interval 0.70-0.84] vs 0.74 [95% confidence interval 0.67-0.81], P = .32 in the previous spontaneous preterm birth group). Prediction of spontaneous preterm birth using cervicovaginal fluid quantitative fetal fibronectin in asymptomatic women with cervical surgery is valid, and has comparative accuracy to that in women with a history of spontaneous preterm birth. Copyright © 2016 Elsevier Inc. All rights reserved.

Hypothesis testing and earthquake prediction.

PubMed

Jackson, D D

1996-04-30

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions.

Hypothesis testing and earthquake prediction.

PubMed Central

Jackson, D D

1996-01-01

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions. PMID:11607663

Frequency-following and connectivity of different visual areas in response to contrast-reversal stimulation.

PubMed

Stephen, Julia M; Ranken, Doug F; Aine, Cheryl J

2006-01-01

The sensitivity of visual areas to different temporal frequencies, as well as the functional connections between these areas, was examined using magnetoencephalography (MEG). Alternating circular sinusoids (0, 3.1, 8.7 and 14 Hz) were presented to foveal and peripheral locations in the visual field to target ventral and dorsal stream structures, respectively. It was hypothesized that higher temporal frequencies would preferentially activate dorsal stream structures. To determine the effect of frequency on the cortical response we analyzed the late time interval (220-770 ms) using a multi-dipole spatio-temporal analysis approach to provide source locations and timecourses for each condition. As an exploratory aspect, we performed cross-correlation analysis on the source timecourses to determine which sources responded similarly within conditions. Contrary to predictions, dorsal stream areas were not activated more frequently during high temporal frequency stimulation. However, across cortical sources the frequency-following response showed a difference, with significantly higher power at the second harmonic for the 3.1 and 8.7 Hz stimulation and at the first and second harmonics for the 14 Hz stimulation with this pattern seen robustly in area V1. Cross-correlations of the source timecourses showed that both low- and high-order visual areas, including dorsal and ventral stream areas, were significantly correlated in the late time interval. The results imply that frequency information is transferred to higher-order visual areas without translation. Despite the less complex waveforms seen in the late interval of time, the cross-correlation results show that visual, temporal and parietal cortical areas are intricately involved in late-interval visual processing.

Rapid induction of single donor chimerism after double umbilical cord blood transplantation preceded by reduced intensity conditioning: results of the HOVON 106 phase II study

PubMed Central

Somers, Judith A.E.; Braakman, Eric; van der Holt, Bronno; Petersen, Eefke J.; Marijt, Erik W.A.; Huisman, Cynthia; Sintnicolaas, Kees; Oudshoorn, Machteld; Groenendijk-Sijnke, Marlies E.; Brand, Anneke; Cornelissen, Jan J.

2014-01-01

Double umbilical cord blood transplantation is increasingly applied in the treatment of adult patients with high-risk hematological malignancies and has been associated with improved engraftment as compared to that provided by single unit cord blood transplantation. The mechanism of improved engraftment is, however, still incompletely understood as only one unit survives. In this multicenter phase II study we evaluated engraftment, early chimerism, recovery of different cell lineages and transplant outcome in 53 patients who underwent double cord blood transplantation preceded by a reduced intensity conditioning regimen. Primary graft failure occurred in one patient. Engraftment was observed in 92% of patients with a median time to neutrophil recovery of 36 days (range, 15–102). Ultimate single donor chimerism was established in 94% of patients. Unit predominance occurred by day 11 after transplantation and early CD4+ T-cell chimerism predicted for unit survival. Total nucleated cell viability was also associated with unit survival. With a median follow up of 35 months (range, 10–51), the cumulative incidence of relapse and non-relapse mortality rate at 2 years were 39% and 19%, respectively. Progressionfree survival and overall survival rates at 2 years were 42% (95% confidence interval, 28–56) and 57% (95% confidence interval, 43–70), respectively. Double umbilical cord blood transplantation preceded by a reduced intensity conditioning regimen using cyclophosphamide/fludarabine/4 Gy total body irradiation results in a high engraftment rate with low non-relapse mortality. Moreover, prediction of unit survival by early CD4+ lymphocyte chimerism might suggest a role for CD4+ lymphocyte mediated unit-versus-unit alloreactivity. www.trialregister.nl NTR1573. PMID:25107890

Multidimensional assessment of patient condition and mutational analysis in peripheral blood, as tools to improve outcome prediction in myelodysplastic syndromes: A prospective study of the Spanish MDS group.

PubMed

Ramos, Fernando; Robledo, Cristina; Pereira, Arturo; Pedro, Carmen; Benito, Rocío; de Paz, Raquel; Del Rey, Mónica; Insunza, Andrés; Tormo, Mar; Díez-Campelo, María; Xicoy, Blanca; Salido, Eduardo; Sánchez-Del-Real, Javier; Arenillas, Leonor; Florensa, Lourdes; Luño, Elisa; Del Cañizo, Consuelo; Sanz, Guillermo F; María Hernández-Rivas, Jesús

2017-09-01

The International Prognostic Scoring System and its revised form (IPSS-R) are the most widely used indices for prognostic assessment of patients with myelodysplastic syndromes (MDS), but can only partially account for the observed variation in patient outcomes. This study aimed to evaluate the relative contribution of patient condition and mutational status in peripheral blood when added to the IPSS-R, for estimating overall survival and the risk of leukemic transformation in patients with MDS. A prospective cohort (2006-2015) of 200 consecutive patients with MDS were included in the study series and categorized according to the IPSS-R. Patients were further stratified according to patient condition (assessed using the multidimensional Lee index for older adults) and genetic mutations (peripheral blood samples screened using next-generation sequencing). The change in likelihood-ratio was tested in Cox models after adding individual covariates. The addition of the Lee index to the IPSS-R significantly improved prediction of overall survival [hazard ratio (HR) 3.02, 95% confidence interval (CI) 1.96-4.66, P < 0.001), and mutational analysis significantly improved prediction of leukemic evolution (HR 2.64, 1.56-4.46, P < 0.001). Non-leukemic death was strongly linked to patient condition (HR 2.71, 1.72-4.25, P < 0.001), but not to IPSS-R score (P = 0.35) or mutational status (P = 0.75). Adjustment for exposure to disease-modifying therapy, evaluated as a time-dependent covariate, had no effect on the proposed model's predictive ability. In conclusion, patient condition, assessed by the multidimensional Lee index and patient mutational status can improve the prediction of clinical outcomes of patients with MDS already stratified by IPSS-R. © 2017 Wiley Periodicals, Inc.

Differential Effects of the Cannabinoid Agonist WIN55,212-2 on Delay and Trace Eyeblink Conditioning

PubMed Central

Steinmetz, Adam B.; Freeman, John H.

2014-01-01

Central cannabinoid-1 receptors (CB1R) play a role in the acquisition of delay eyeblink conditioning but not trace eyeblink conditioning in humans and animals. However, it is not clear why trace conditioning is immune to the effects of cannabinoid receptor compounds. The current study examined the effects of variants of delay and trace conditioning procedures to elucidate the factors that determine the effects of CB1R agonists on eyeblink conditioning. In Experiment 1 rats were administered the cannabinoid agonist WIN55,212-2 during delay, long delay, or trace conditioning. Rats were impaired during delay and long delay but not trace conditioning; the impairment was greater for long delay than delay conditioning. Trace conditioning was further examined in Experiment 2 by manipulating the trace interval and keeping constant the conditioned stimulus (CS) duration. It was found that when the trace interval was 300 ms or less WIN55,212-2 administration impaired the rate of learning. Experiment 3 tested whether the trace interval duration or the relative durations of the CS and trace interval were critical parameters influencing the effects of WIN55,212-2 on eyeblink conditioning. Rats were not impaired with a 100 ms CS, 200 ms trace paradigm but were impaired with a 1000 ms CS, 500 ms trace paradigm, indicating that the duration of the trace interval does not matter but the proportion of the interstimulus interval occupied by the CS relative to the trace period is critical. Taken together the results indicate that cannabinoid agonists affect cerebellar learning the CS is longer than the trace interval. PMID:24128358

Relationships between lake-level changes and water and salt budgets in the Dead Sea during extreme aridities in the Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Kiro, Yael; Goldstein, Steven L.; Garcia-Veigas, Javier; Levy, Elan; Kushnir, Yochanan; Stein, Mordechai; Lazar, Boaz

2017-04-01

Thick halite intervals recovered by the Dead Sea Deep Drilling Project cores show evidence for severely arid climatic conditions in the eastern Mediterranean during the last three interglacials. In particular, the core interval corresponding to the peak of the last interglacial (Marine Isotope Stage 5e or MIS 5e) contains ∼30 m of salt over 85 m of core length, making this the driest known period in that region during the late Quaternary. This study reconstructs Dead Sea lake levels during the salt deposition intervals, based on water and salt budgets derived from the Dead Sea brine composition and the amount of salt in the core. Modern water and salt budgets indicate that halite precipitates only during declining lake levels, while the amount of dissolved Na+ and Cl- accumulates during wetter intervals. Based on the compositions of Dead Sea brines from pore waters and halite fluid inclusions, we estimate that ∼12-16 cm of halite precipitated per meter of lake-level drop. During periods of halite precipitation, the Mg2+ concentration increases and the Na+/Cl- ratio decreases in the lake. Our calculations indicate major lake-level drops of ∼170 m from lake levels of 320 and 310 m below sea level (mbsl) down to lake levels of ∼490 and ∼480 mbsl, during MIS 5e and the Holocene, respectively. These lake levels are much lower than typical interglacial lake levels of around 400 mbsl. These lake-level drops occurred as a result of major decreases in average fresh water runoff, to ∼40% of the modern value (pre-1964, before major fresh water diversions), reflecting severe droughts during which annual precipitation in Jerusalem was lower than 350 mm/y, compared to ∼600 mm/y today. Nevertheless, even during salt intervals, the changes in halite facies and the occurrence of alternating periods of halite and detritus in the Dead Sea core stratigraphy reflect fluctuations between drier and wetter conditions around our estimated average. The halite intervals include periods that are richer and poorer in halite, indicating (based on the sedimentation rate) that severe dry conditions with water availability as low as ∼20% of the present day, continued for periods of decades to centuries, and fluctuated with wetter conditions that spanned centuries to millennia when water availability was ∼50-100% of the present day. These conclusions have potential implications for the coming decades, as climate models predict greater aridity in the region.

Weighted regression analysis and interval estimators

Treesearch

Donald W. Seegrist

1974-01-01

A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

NASA Astrophysics Data System (ADS)

Simonetto, Andrea; Mokhtari, Aryan; Koppel, Alec; Leus, Geert; Ribeiro, Alejandro

2016-09-01

This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of $1/h$, where $h$ is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists either of one or multiple gradient steps or Newton steps, which respectively correspond to the gradient trajectory tracking (GTT) or Newton trajectory tracking (NTT) algorithms. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as $O(h^2)$, and in some cases as $O(h^4)$, which outperforms the state-of-the-art error bound of $O(h)$ for correction-only methods in the gradient-correction step. Moreover, when the characteristics of the objective function variation are not available, we propose approximate gradient and Newton tracking algorithms (AGT and ANT, respectively) that still attain these asymptotical error bounds. Numerical simulations demonstrate the practical utility of the proposed methods and that they improve upon existing techniques by several orders of magnitude.

Prediction and uncertainty in human Pavlovian to instrumental transfer.

PubMed

Trick, Leanne; Hogarth, Lee; Duka, Theodora

2011-05-01

Attentional capture and behavioral control by conditioned stimuli have been dissociated in animals. The current study assessed this dissociation in humans. Participants were trained on a Pavlovian schedule in which 3 visual stimuli, A, B, and C, predicted the occurrence of an aversive noise with 90%, 50%, or 10% probability, respectively. Participants then went on to separate instrumental training in which a key-press response canceled the aversive noise with a .5 probability on a variable interval schedule. Finally, in the transfer phase, the 3 Pavlovian stimuli were presented in this instrumental schedule and were no longer differentially predictive of the outcome. Observing times and gaze dwell time indexed attention to these stimuli in both training and transfer. Aware participants acquired veridical outcome expectancies in training--that is, A > B > C, and these expectancies persisted into transfer. Most important, the transfer effect accorded with these expectancies, A > B > C. By contrast, observing times accorded with uncertainty--that is, they showed B > A = C during training, and B < A = C in the transfer phase. Dwell time bias supported this association between attention and uncertainty, although these data showed a slightly more complicated pattern. Overall, the study suggests that transfer is linked to outcome prediction and is dissociated from attention to conditioned stimuli, which is linked to outcome uncertainty.

The influence of weather on migraine – are migraine attacks predictable?

PubMed Central

Hoffmann, Jan; Schirra, Tonio; Lo, Hendra; Neeb, Lars; Reuter, Uwe; Martus, Peter

2015-01-01

Objective The study aimed at elucidating a potential correlation between specific meteorological variables and the prevalence and intensity of migraine attacks as well as exploring a potential individual predictability of a migraine attack based on meteorological variables and their changes. Methods Attack prevalence and intensity of 100 migraineurs were correlated with atmospheric pressure, relative air humidity, and ambient temperature in 4-h intervals over 12 consecutive months. For each correlation, meteorological parameters at the time of the migraine attack as well as their variation within the preceding 24 h were analyzed. For migraineurs showing a positive correlation, logistic regression analysis was used to assess the predictability of a migraine attack based on meteorological information. Results In a subgroup of migraineurs, a significant weather sensitivity could be observed. In contrast, pooled analysis of all patients did not reveal a significant association. An individual prediction of a migraine attack based on meteorological data was not possible, mainly as a result of the small prevalence of attacks. Interpretation The results suggest that only a subgroup of migraineurs is sensitive to specific weather conditions. Our findings may provide an explanation as to why previous studies, which commonly rely on a pooled analysis, show inconclusive results. The lack of individual attack predictability indicates that the use of preventive measures based on meteorological conditions is not feasible. PMID:25642431

Effects of Temporal Features and Order on the Apparent duration of a Visual Stimulus

PubMed Central

Bruno, Aurelio; Ayhan, Inci; Johnston, Alan

2012-01-01

The apparent duration of a visual stimulus has been shown to be influenced by its speed. For low speeds, apparent duration increases linearly with stimulus speed. This effect has been ascribed to the number of changes that occur within a visual interval. Accordingly, a higher number of changes should produce an increase in apparent duration. In order to test this prediction, we asked subjects to compare the relative duration of a 10-Hz drifting comparison stimulus with a standard stimulus that contained a different number of changes in different conditions. The standard could be static, drifting at 10 Hz, or mixed (a combination of variable duration static and drifting intervals). In this last condition the number of changes was intermediate between the static and the continuously drifting stimulus. For all standard durations, the mixed stimulus looked significantly compressed (∼20% reduction) relative to the drifting stimulus. However, no difference emerged between the static (that contained no changes) and the mixed stimuli (which contained an intermediate number of changes). We also observed that when the standard was displayed first, it appeared compressed relative to when it was displayed second with a magnitude that depended on standard duration. These results are at odds with a model of time perception that simply reflects the number of temporal features within an interval in determining the perceived passing of time. PMID:22461778

Drug-physiology interaction and its influence on the QT prolongation-mechanistic modeling study.

PubMed

Wiśniowska, Barbara; Polak, Sebastian

2018-06-01

The current study is an example of drug-disease interaction modeling where a drug induces a condition which can affect the pharmacodynamics of other concomitantly taken drugs. The electrophysiological effects of hypokaliemia and heart rate changes induced by the antiasthmatic drugs were simulated with the use of the cardiac safety simulator. Biophysically detailed model of the human cardiac physiology-ten Tusscher ventricular cardiomyocyte cell model-was employed to generate pseudo-ECG signals and QTc intervals for 44 patients from four clinical studies. Simulated and observed mean QTc values with standard deviation (SD) for each reported study point were compared and differences were analyzed with Student's t test (α = 0.05). The simulated results reflected the QTc interval changes measured in patients, as well as their clinically observed interindividual variability. The QTc interval changes were highly correlated with the change in plasma potassium both in clinical studies and in the simulations (Pearson's correlation coefficient > 0.55). The results suggest that the modeling and simulation approach could provide valuable quantitative insight into the cardiological effect of the potassium and heart rate changes caused by electrophysiologically inactive, non-cardiological drugs. This allows to simulate and predict the joint effect of several risk factors for QT prolongation, e.g., drug-dependent QT prolongation due to the ion channels inhibition and the current patient physiological conditions.

Fast and nondestructive determination of protein content in rapeseeds (Brassica napus L.) using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS).

PubMed

Lu, Yuzhen; Du, Changwen; Yu, Changbing; Zhou, Jianmin

2014-08-01

Fast and non-destructive determination of rapeseed protein content carries significant implications in rapeseed production. This study presented the first attempt of using Fourier transform mid-infrared photoacoustic spectroscopy (FTIR-PAS) to quantify protein content of rapeseed. The full-spectrum model was first built using partial least squares (PLS). Interval selection methods including interval partial least squares (iPLS), synergy interval partial least squares (siPLS), backward elimination interval partial least squares (biPLS) and dynamic backward elimination interval partial least squares (dyn-biPLS) were then employed to select the relevant band or band combination for PLS modeling. The full-spectrum PLS model achieved an ratio of prediction to deviation (RPD) of 2.047. In comparison, all interval selection methods produced better results than full-spectrum modeling. siPLS achieved the best predictive accuracy with an RPD of 3.215 when the spectrum was sectioned into 25 intervals, and two intervals (1198-1335 and 1614-1753 cm(-1) ) were selected. iPLS excelled biPLS and dyn-biPLS, and dyn-biPLS performed slightly better than biPLS. FTIR-PAS was verified as a promising analytical tool to quantify rapeseed protein content. Interval selection could extract the relevant individual band or synergy band associated with the sample constituent of interest, and then improve the prediction accuracy of the full-spectrum model. © 2013 Society of Chemical Industry.

Cardiorespiratory Information Dynamics during Mental Arithmetic and Sustained Attention

PubMed Central

Widjaja, Devy; Montalto, Alessandro; Vlemincx, Elke; Marinazzo, Daniele; Van Huffel, Sabine; Faes, Luca

2015-01-01

An analysis of cardiorespiratory dynamics during mental arithmetic, which induces stress, and sustained attention was conducted using information theory. The information storage and internal information of heart rate variability (HRV) were determined respectively as the self-entropy of the tachogram, and the self-entropy of the tachogram conditioned to the knowledge of respiration. The information transfer and cross information from respiration to HRV were assessed as the transfer and cross-entropy, both measures of cardiorespiratory coupling. These information-theoretic measures identified significant nonlinearities in the cardiorespiratory time series. Additionally, it was shown that, although mental stress is related to a reduction in vagal activity, no difference in cardiorespiratory coupling was found when several mental states (rest, mental stress, sustained attention) are compared. However, the self-entropy of HRV conditioned to respiration was very informative to study the predictability of RR interval series during mental tasks, and showed higher predictability during mental arithmetic compared to sustained attention or rest. PMID:26042824

Funnel plot control limits to identify poorly performing healthcare providers when there is uncertainty in the value of the benchmark.

PubMed

Manktelow, Bradley N; Seaton, Sarah E; Evans, T Alun

2016-12-01

There is an increasing use of statistical methods, such as funnel plots, to identify poorly performing healthcare providers. Funnel plots comprise the construction of control limits around a benchmark and providers with outcomes falling outside the limits are investigated as potential outliers. The benchmark is usually estimated from observed data but uncertainty in this estimate is usually ignored when constructing control limits. In this paper, the use of funnel plots in the presence of uncertainty in the value of the benchmark is reviewed for outcomes from a Binomial distribution. Two methods to derive the control limits are shown: (i) prediction intervals; (ii) tolerance intervals Tolerance intervals formally include the uncertainty in the value of the benchmark while prediction intervals do not. The probability properties of 95% control limits derived using each method were investigated through hypothesised scenarios. Neither prediction intervals nor tolerance intervals produce funnel plot control limits that satisfy the nominal probability characteristics when there is uncertainty in the value of the benchmark. This is not necessarily to say that funnel plots have no role to play in healthcare, but that without the development of intervals satisfying the nominal probability characteristics they must be interpreted with care. © The Author(s) 2014.

Predicting Operator Execution Times Using CogTool

NASA Technical Reports Server (NTRS)

Santiago-Espada, Yamira; Latorella, Kara A.

2013-01-01

Researchers and developers of NextGen systems can use predictive human performance modeling tools as an initial approach to obtain skilled user performance times analytically, before system testing with users. This paper describes the CogTool models for a two pilot crew executing two different types of a datalink clearance acceptance tasks, and on two different simulation platforms. The CogTool time estimates for accepting and executing Required Time of Arrival and Interval Management clearances were compared to empirical data observed in video tapes and registered in simulation files. Results indicate no statistically significant difference between empirical data and the CogTool predictions. A population comparison test found no significant differences between the CogTool estimates and the empirical execution times for any of the four test conditions. We discuss modeling caveats and considerations for applying CogTool to crew performance modeling in advanced cockpit environments.

A dual memory theory of the testing effect.

PubMed

Rickard, Timothy C; Pan, Steven C

2017-06-05

A new theoretical framework for the testing effect-the finding that retrieval practice is usually more effective for learning than are other strategies-is proposed, the empirically supported tenet of which is that separate memories form as a consequence of study and test events. A simplest case quantitative model is derived from that framework for the case of cued recall. With no free parameters, that model predicts both proportion correct in the test condition and the magnitude of the testing effect across 10 experiments conducted in our laboratory, experiments that varied with respect to material type, retention interval, and performance in the restudy condition. The model also provides the first quantitative accounts of (a) the testing effect as a function of performance in the restudy condition, (b) the upper bound magnitude of the testing effect, (c) the effect of correct answer feedback, (d) the testing effect as a function of retention interval for the cases of feedback and no feedback, and (e) the effect of prior learning method on subsequent learning through testing. Candidate accounts of several other core phenomena in the literature, including test-potentiated learning, recognition versus cued recall training effects, cued versus free recall final test effects, and other select transfer effects, are also proposed. Future prospects and relations to other theories are discussed.

Method of and apparatus for modeling interactions

DOEpatents

Budge, Kent G.

2004-01-13

A method and apparatus for modeling interactions can accurately model tribological and other properties and accommodate topological disruptions. Two portions of a problem space are represented, a first with a Lagrangian mesh and a second with an ALE mesh. The ALE and Lagrangian meshes are constructed so that each node on the surface of the Lagrangian mesh is in a known correspondence with adjacent nodes in the ALE mesh. The interaction can be predicted for a time interval. Material flow within the ALE mesh can accurately model complex interactions such as bifurcation. After prediction, nodes in the ALE mesh in correspondence with nodes on the surface of the Lagrangian mesh can be mapped so that they are once again adjacent to their corresponding Lagrangian mesh nodes. The ALE mesh can then be smoothed to reduce mesh distortion that might reduce the accuracy or efficiency of subsequent prediction steps. The process, from prediction through mapping and smoothing, can be repeated until a terminal condition is reached.

Impact of direct-to-consumer predictive genomic testing on risk perception and worry among patients receiving routine care in a preventive health clinic.

PubMed

James, Katherine M; Cowl, Clayton T; Tilburt, Jon C; Sinicrope, Pamela S; Robinson, Marguerite E; Frimannsdottir, Katrin R; Tiedje, Kristina; Koenig, Barbara A

2011-10-01

To assess the impact of direct-to-consumer (DTC) predictive genomic risk information on perceived risk and worry in the context of routine clinical care. Patients attending a preventive medicine clinic between June 1 and December 18, 2009, were randomly assigned to receive either genomic risk information from a DTC product plus usual care (n=74) or usual care alone (n=76). At intervals of 1 week and 1 year after their clinic visit, participants completed surveys containing validated measures of risk perception and levels of worry associated with the 12 conditions assessed by the DTC product. Of 345 patients approached, 150 (43%) agreed to participate, 64 (19%) refused, and 131 (38%) did not respond. Compared with those receiving usual care, participants who received genomic risk information initially rated their risk as higher for 4 conditions (abdominal aneurysm [P=.001], Graves disease [P=.04], obesity [P=.01], and osteoarthritis [P=.04]) and lower for one (prostate cancer [P=.02]). Although differences were not significant, they also reported higher levels of worry for 7 conditions and lower levels for 5 others. At 1 year, there were no significant differences between groups. Predictive genomic risk information modestly influences risk perception and worry. The extent and direction of this influence may depend on the condition being tested and its baseline prominence in preventive health care and may attenuate with time.

Emotional arousal predicts intertemporal choice

PubMed Central

Lempert, Karolina M.; Johnson, Eli; Phelps, Elizabeth A.

2016-01-01

People generally prefer immediate rewards to rewards received after a delay, often even when the delayed reward is larger. This phenomenon is known as temporal discounting. It has been suggested that preferences for immediate rewards may be due to their being more concrete than delayed rewards. This concreteness may evoke an enhanced emotional response. Indeed, manipulating the representation of a future reward to make it more concrete has been shown to heighten the reward’s subjective emotional intensity, making people more likely to choose it. Here we use an objective measure of arousal – pupil dilation – to investigate if emotional arousal mediates the influence of delayed reward concreteness on choice. We recorded pupil dilation responses while participants made choices between immediate and delayed rewards. We manipulated concreteness through time interval framing: delayed rewards were presented either with the date on which they would be received (e.g., “$30, May 3”; DATE condition, more concrete) or in terms of delay to receipt (e.g., “$30, 7 days; DAYS condition, less concrete). Contrary to prior work, participants were not overall more patient in the DATE condition. However, there was individual variability in response to time framing, and this variability was predicted by differences in pupil dilation between conditions. Emotional arousal increased as the subjective value of delayed rewards increased, and predicted choice of the delayed reward on each trial. This study advances our understanding of the role of emotion in temporal discounting. PMID:26882337

Performance factors in associative learning: assessment of the sometimes competing retrieval model.

PubMed

Witnauer, James E; Wojick, Brittany M; Polack, Cody W; Miller, Ralph R

2012-09-01

Previous simulations revealed that the sometimes competing retrieval model (SOCR; Stout & Miller, Psychological Review, 114, 759-783, 2007), which assumes local error reduction, can explain many cue interaction phenomena that elude traditional associative theories based on total error reduction. Here, we applied SOCR to a new set of Pavlovian phenomena. Simulations used a single set of fixed parameters to simulate each basic effect (e.g., blocking) and, for specific experiments using different procedures, used fitted parameters discovered through hill climbing. In simulation 1, SOCR was successfully applied to basic acquisition, including the overtraining effect, which is context dependent. In simulation 2, we applied SOCR to basic extinction and renewal. SOCR anticipated these effects with both fixed parameters and best-fitting parameters, although the renewal effects were weaker than those observed in some experiments. In simulation 3a, feature-negative training was simulated, including the often observed transition from second-order conditioning to conditioned inhibition. In simulation 3b, SOCR predicted the observation that conditioned inhibition after feature-negative and differential conditioning depends on intertrial interval. In simulation 3c, SOCR successfully predicted failure of conditioned inhibition to extinguish with presentations of the inhibitor alone under most circumstances. In simulation 4, cue competition, including blocking (4a), recovery from relative validity (4b), and unblocking (4c), was simulated. In simulation 5, SOCR correctly predicted that inhibitors gain more behavioral control than do excitors when they are trained in compound. Simulation 6 demonstrated that SOCR explains the slower acquisition observed following CS-weak shock pairings.

Temporal prediction modulates the evaluative processing of "good" action feedback: An electrophysiological study.

PubMed

Kimura, Kenta; Kimura, Motohiro; Iwaki, Sunao

2016-10-01

The present study aimed to investigate whether or not the evaluative processing of action feedback can be modulated by temporal prediction. For this purpose, we examined the effects of the predictability of the timing of action feedback on an ERP effect that indexed the evaluative processing of action feedback, that is, an ERP effect that has been interpreted as a feedback-related negativity (FRN) elicited by "bad" action feedback or a reward positivity (RewP) elicited by "good" action feedback. In two types of experimental blocks, the participants performed a gambling task in which they chose one of two cards and received an action feedback that indicated monetary gain or loss. In fixed blocks, the time interval between the participant's choice and the onset of the action feedback was fixed at 0, 500, or 1,000 ms in separate blocks; thus, the timing of action feedback was predictable. In mixed blocks, the time interval was randomly chosen from the same three intervals with equal probability; thus, the timing was less predictable. The results showed that the FRN/RewP was smaller in mixed than fixed blocks for the 0-ms interval trial, whereas there was no difference between the two block types for the 500-ms and 1,000-ms interval trials. Interestingly, the smaller FRN/RewP was due to the modulation of gain ERPs rather than loss ERPs. These results suggest that temporal prediction can modulate the evaluative processing of action feedback, and particularly good feedback, such as that which indicates monetary gain. © 2016 Society for Psychophysiological Research.

Psychosocial predictors of somatic symptoms in adolescents of parents with HIV: a six-year longitudinal study.

PubMed

Bursch, B; Lester, P; Jiang, L; Rotheram-Borus, M J; Weiss, R

2008-07-01

The objective of this study was to identify salient parent and adolescent psychosocial factors related to somatic symptoms in adolescents. As part of a larger intervention study conducted in New York, 409 adolescents were recruited from 269 parents with HIV. A longitudinal model predicted adolescent somatization scores six years after baseline assessment. Adolescent somatic symptoms were assessed at baseline and at 3-month intervals for the first two years and then at 6-month intervals using the Brief Symptom Inventory. Baseline data from adolescents and parents were used to predict adolescent somatic symptoms. Variables related to increased adolescent somatic symptoms over six years included being younger and female; an increased number of adolescent medical hospitalizations; more stressful life events; adolescent perception of a highly rejecting parenting style; more parent-youth conflict; no experience of parental death; and parental distress over their own pain symptoms. Our findings extend the literature by virtue of the longitudinal design; inclusion of both parent and child variables in one statistical model; identification of study participants by their potentially stressful living condition rather than by disease or somatic symptom status; and inclusion of serious parental illness and death in the study.

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization.

PubMed

Lee, Ching-Hung; Chang, Feng-Yu; Lin, Chih-Min

2014-03-01

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

Effects of metric hierarchy and rhyme predictability on word duration in The Cat in the Hat.

PubMed

Breen, Mara

2018-05-01

Word durations convey many types of linguistic information, including intrinsic lexical features like length and frequency and contextual features like syntactic and semantic structure. The current study was designed to investigate whether hierarchical metric structure and rhyme predictability account for durational variation over and above other features in productions of a rhyming, metrically-regular children's book: The Cat in the Hat (Dr. Seuss, 1957). One-syllable word durations and inter-onset intervals were modeled as functions of segment number, lexical frequency, word class, syntactic structure, repetition, and font emphasis. Consistent with prior work, factors predicting longer word durations and inter-onset intervals included more phonemes, lower frequency, first mention, alignment with a syntactic boundary, and capitalization. A model parameter corresponding to metric grid height improved model fit of word durations and inter-onset intervals. Specifically, speakers realized five levels of metric hierarchy with inter-onset intervals such that interval duration increased linearly with increased height in the metric hierarchy. Conversely, speakers realized only three levels of metric hierarchy with word duration, demonstrating that they shortened the highly predictable rhyme resolutions. These results further understanding of the factors that affect spoken word duration, and demonstrate the myriad cues that children receive about linguistic structure from nursery rhymes. Copyright © 2018 Elsevier B.V. All rights reserved.

On the use and the performance of software reliability growth models

NASA Technical Reports Server (NTRS)

Keiller, Peter A.; Miller, Douglas R.

1991-01-01

We address the problem of predicting future failures for a piece of software. The number of failures occurring during a finite future time interval is predicted from the number failures observed during an initial period of usage by using software reliability growth models. Two different methods for using the models are considered: straightforward use of individual models, and dynamic selection among models based on goodness-of-fit and quality-of-prediction criteria. Performance is judged by the relative error of the predicted number of failures over future finite time intervals relative to the number of failures eventually observed during the intervals. Six of the former models and eight of the latter are evaluated, based on their performance on twenty data sets. Many open questions remain regarding the use and the performance of software reliability growth models.

Cure modeling in real-time prediction: How much does it help?

PubMed

Ying, Gui-Shuang; Zhang, Qiang; Lan, Yu; Li, Yimei; Heitjan, Daniel F

2017-08-01

Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals. Copyright © 2017 Elsevier Inc. All rights reserved.

Effect of Body Weight on Echocardiographic Measurements in 19,866 Pure-Bred Cats with or without Heart Disease.

PubMed

Häggström, J; Andersson, Å O; Falk, T; Nilsfors, L; OIsson, U; Kresken, J G; Höglund, K; Rishniw, M; Tidholm, A; Ljungvall, I

2016-09-01

Echocardiography is a cost-efficient method to screen cats for presence of heart disease. Current reference intervals for feline cardiac dimensions do not account for body weight (BW). To study the effect of BW on heart rate (HR), aortic (Ao), left atrial (LA) and ventricular (LV) linear dimensions in cats, and to calculate 95% prediction intervals for these variables in normal adult pure-bred cats. 19 866 pure-bred cats. Clinical data from heart screens conducted between 1999 and 2014 were included. Associations between BW, HR, and cardiac dimensions were assessed using univariate linear models and allometric scaling, including all cats, and only those considered normal, respectively. Prediction intervals were created using 95% confidence intervals obtained from regression curves. Associations between BW and echocardiographic dimensions were best described by allometric scaling, and all dimensions increased with increasing BW (all P<0.001). Strongest associations were found between BW and Ao, LV end diastolic, LA dimensions, and thickness of LV free wall. Weak linear associations were found between BW and HR and left atrial to aortic ratio (LA:Ao), for which HR decreased with increasing BW (P<0.001), and LA:Ao increased with increasing BW (P<0.001). Marginal differences were found for prediction formulas and prediction intervals when the dataset included all cats versus only those considered normal. BW had a clinically relevant effect on echocardiographic dimensions in cats, and BW based 95% prediction intervals may help in screening cats for heart disease. Copyright © 2016 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Differentiating the origin of outflow tract ventricular arrhythmia using a simple, novel approach.

PubMed

Efimova, Elena; Dinov, Borislav; Acou, Willem-Jan; Schirripa, Valentina; Kornej, Jelena; Kosiuk, Jedrzej; Rolf, Sascha; Sommer, Philipp; Richter, Sergio; Bollmann, Andreas; Hindricks, Gerhard; Arya, Arash

2015-07-01

Numerous electrocardiographic (ECG) criteria have been proposed to identify localization of outflow tract ventricular arrhythmias (OT-VAs); however, in some cases, it is difficult to accurately localize the origin of OT-VA using the surface ECG. The purpose of this study was to assess a simple criterion for localization of OT-VAs during electrophysiology study. We measured the interval from the onset of the earliest QRS complex of premature ventricular contractions (PVCs) to the distal right ventricular apical signal (the QRS-RVA interval) in 66 patients (31 men aged 53.3 ± 14.0 years; right ventricular outflow tract [RVOT] origin in 37) referred for ablation of symptomatic outflow tract PVCs. We prospectively validated this criterion in 39 patients (22 men aged 52 ± 15 years; RVOT origin in 19). Compared with patients with RVOT PVCs, the QRS-RVA interval was significantly longer in patients with left ventricular outflow tract (LVOT) PVCs (70 ± 14 vs 33.4±10 ms, P < .001). Receiver operating characteristic analysis showed that a QRS-RVA interval ≥49 ms had sensitivity, specificity, and positive and negative predictive values of 100%, 94.6%, 93.5%, and 100%, respectively, for prediction of an LVOT origin. The same analysis in the validation cohort showed sensitivity, specificity, and positive and negative predictive values of 94.7%, 95%, 95%, and 94.7%, respectively. When these data were combined, a QRS-RVA interval ≥49 ms had sensitivity, specificity, and positive and negative predictive values of 98%, 94.6%, 94.1%, and 98.1%, respectively, for prediction of an LVOT origin. A QRS-RVA interval ≥49 ms suggests an LVOT origin. The QRS-RVA interval is a simple and accurate criterion for differentiating the origin of outflow tract arrhythmia during electrophysiology study; however, the accuracy of this criterion in identifying OT-VA from the right coronary cusp is limited. Copyright © 2015 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

A predictive model of geosynchronous magnetopause crossings

NASA Astrophysics Data System (ADS)

Dmitriev, A.; Suvorova, A.; Chao, J.-K.

2011-05-01

We have developed a model predicting whether or not the magnetopause crosses geosynchronous orbit at a given location for given solar wind pressure Psw, Bz component of the interplanetary magnetic field (IMF), and geomagnetic conditions characterized by 1 min SYM-H index. The model is based on more than 300 geosynchronous magnetopause crossings (GMCs) and about 6000 min when geosynchronous satellites of GOES and Los Alamos National Laboratory (LANL) series are located in the magnetosheath (so-called MSh intervals) in 1994-2001. Minimizing of the Psw required for GMCs and MSh intervals at various locations, Bz, and SYM-H allows describing both an effect of magnetopause dawn-dusk asymmetry and saturation of Bz influence for very large southward IMF. The asymmetry is strong for large negative Bz and almost disappears when Bz is positive. We found that the larger the amplitude of negative SYM-H, the lower the solar wind pressure required for GMCs. We attribute this effect to a depletion of the dayside magnetic field by a storm time intensification of the cross-tail current. It is also found that the magnitude of threshold for Bz saturation increases with SYM-H index such that for small negative and positive SYM-H the effect of saturation diminishes. This supports an idea that enhanced thermal pressure of the magnetospheric plasma and ring current particles during magnetic storms results in the saturation of magnetic effect of the IMF Bz at the dayside magnetopause. A noticeable advantage of the model's prediction capabilities in comparison with other magnetopause models makes the model useful for space weather predictions.

Groove Pancreatitis, a Masquerading Yet Distinct Clinicopathological Entity: Analysis of Risk Factors and Differentiation.

PubMed

Oza, Veeral M; Skeans, Jacob M; Muscarella, Peter; Walker, Jon P; Sklaw, Brett C; Cronley, Kevin M; El-Dika, Samer; Swanson, Benjamin; Hinton, Alice; Conwell, Darwin L; Krishna, Somashekar G

2015-08-01

Our objective was to delineate predictive factors differentiating groove pancreatitis (GP) from other lesions involving the head of the pancreas (HOP). A case-control study of patients older than 10 years was performed comparing patients with GP to those with other surgically resected HOP lesions. Thirteen patients with GP (mean ± SD age, 51.9 ± 10.5 years; 11 males [84.6%]), all with a history of smoking (mean, 37.54 ± 17.8 pack-years), were identified. Twelve patients (92.3%) had a history of heavy alcohol drinking (heavy alcohol [EtOH]). The mean lesion size was 2.6 ± 1.1 cm, and the CA 19-9 was elevated (>37 IU/mL) in 5 patients (45.5%). The most common histopathologic condition was duodenal wall cyst with myofibroblastic proliferation and changes of chronic pancreatitis in the HOP.Univariate analysis revealed decreasing age, male sex, weight loss, nausea/vomiting, heavy EtOH, smoking, and a history of chronic pancreatitis were predictive of GP. A multivariate analysis among smokers demonstrated that weight loss (P = 0.006; odds ratio, 11.96; 95% confidence interval, 2.1-70.2), and heavy EtOH (P < 0.001; odds ratio, 82.2; 95% confidence interval, 9.16-738.1) were most predictive of GP. Compared to pancreatic adenocarcinoma (n = 183), weight loss and heavy EtOH remained predictive of GP. Groove pancreatitis in the HOP is associated with a history of heavy EtOH and weight loss. In the absence of these symptoms, it is essential to rule out a malignant lesion.

Transient stress-coupling between the 1992 Landers and 1999 Hector Mine, California, earthquakes

USGS Publications Warehouse

Masterlark, Timothy; Wang, H.F.

2002-01-01

A three-dimensional finite-element model (FEM) of the Mojave block region in southern California is constructed to investigate transient stress-coupling between the 1992 Landers and 1999 Hector Mine earthquakes. The FEM simulates a poroelastic upper-crust layer coupled to a viscoelastic lower-crust layer, which is decoupled from the upper mantle. FEM predictions of the transient mechanical behavior of the crust are constrained by global positioning system (GPS) data, interferometric synthetic aperture radar (InSAR) images, fluid-pressure data from water wells, and the dislocation source of the 1999 Hector Mine earthquake. Two time-dependent parameters, hydraulic diffusivity of the upper crust and viscosity of the lower crust, are calibrated to 10–2 m2·sec–1 and 5 × 1018 Pa·sec respectively. The hydraulic diffusivity is relatively insensitive to heterogeneous fault-zone permeability specifications and fluid-flow boundary conditions along the elastic free-surface at the top of the problem domain. The calibrated FEM is used to predict the evolution of Coulomb stress during the interval separating the 1992 Landers and 1999 Hector Mine earthquakes. The predicted change in Coulomb stress near the hypocenter of the Hector Mine earthquake increases from 0.02 to 0.05 MPa during the 7-yr interval separating the two events. This increase is primarily attributed to the recovery of decreased excess fluid pressure from the 1992 Landers coseismic (undrained) strain field. Coulomb stress predictions are insensitive to small variations of fault-plane dip and hypocentral depth estimations of the Hector Mine rupture.

The Influence of Prior Handling on the Effective CS-US Interval in Long-Trace Taste-Aversion Conditioning in Rats

ERIC Educational Resources Information Center

Hinderliter, Charles F.; Andrews, Amy; Misanin, James R.

2012-01-01

In conditioned taste aversion (CTA), a taste, the conditioned stimulus (CS), is paired with an illness-inducing stimulus, the unconditioned stimulus (US), to produce CS-US associations at very long (hours) intervals, a result that appears to violate the law of contiguity. The specific length of the maximum effective trace interval that has been…

Extinction of Pavlovian conditioning: The influence of trial number and reinforcement history.

PubMed

Chan, C K J; Harris, Justin A

2017-08-01

Pavlovian conditioning is sensitive to the temporal relationship between the conditioned stimulus (CS) and the unconditioned stimulus (US). This has motivated models that describe learning as a process that continuously updates associative strength during the trial or specifically encodes the CS-US interval. These models predict that extinction of responding is also continuous, such that response loss is proportional to the cumulative duration of exposure to the CS without the US. We review evidence showing that this prediction is incorrect, and that extinction is trial-based rather than time-based. We also present two experiments that test the importance of trials versus time on the Partial Reinforcement Extinction Effect (PREE), in which responding extinguishes more slowly for a CS that was inconsistently reinforced with the US than for a consistently reinforced one. We show that increasing the number of extinction trials of the partially reinforced CS, relative to the consistently reinforced CS, overcomes the PREE. However, increasing the duration of extinction trials by the same amount does not overcome the PREE. We conclude that animals learn about the likelihood of the US per trial during conditioning, and learn trial-by-trial about the absence of the US during extinction. Moreover, what they learn about the likelihood of the US during conditioning affects how sensitive they are to the absence of the US during extinction. Copyright © 2017 Elsevier B.V. All rights reserved.

ECG parameters predict left ventricular conduction delay in patients with left ventricular dysfunction.

PubMed

Pastore, Gianni; Maines, Massimiliano; Marcantoni, Lina; Zanon, Francesco; Noventa, Franco; Corbucci, Giorgio; Baracca, Enrico; Aggio, Silvio; Picariello, Claudio; Lanza, Daniela; Rigatelli, Gianluca; Carraro, Mauro; Roncon, Loris; Barold, S Serge

2016-12-01

Estimating left ventricular electrical delay (Q-LV) from a 12-lead ECG may be important in evaluating cardiac resynchronization therapy (CRT). The purpose of this study was to assess the impact of Q-LV interval on ECG configuration. One hundred ninety-two consecutive patients undergoing CRT implantation were divided electrocardiographically into 3 groups: left bundle branch block (LBBB), right bundle branch block (RBBB), and nonspecific intraventricular conduction delay (IVCD). The IVCD group was further subdivided into 81 patients with left (L)-IVCD and 15 patients with right (R)-IVCD (resembling RBBB, but without S wave in leads I and aVL). The Q-LV interval in the different groups and the relationship between ECG parameters and the maximum Q-LV interval were analyzed. Patients with LBBB presented a long Q-LV interval (147.7 ± 14.6 ms, all exceeding cutoff value of 110 ms), whereas RBBB patients presented a very short Q-LV interval (75.2 ± 16.3 ms, all <110 ms). Patients with an IVCD displayed a wide range of Q-LV intervals. In L-IVCD, mid-QRS notching/slurring showed the strongest correlation with a longer Q-LV interval, followed, in decreasing order, by QRS duration >150 ms and intrinsicoid deflection >60 ms. Isolated mid-QRS notching/slurring predicted Q-LV interval >110 ms in 68% of patients. The R-IVCD group presented an unexpectedly longer Q-LV interval (127.0 ± 12.5 ms; 13/15 patients had Q-LV >110 ms). Patients with LBBB have a very prolonged Q-LV interval. Mid-QRS notching in lateral leads strongly predicts a longer Q-LV interval in L-IVCD patients. Patients with R-IVCD constitute a subgroup of patients with a long Q-LV interval. Copyright © 2016 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

The association between the metabolic syndrome and metabolic syndrome score and pulmonary function in non-smoking adults.

PubMed

Yoon, Hyun; Gi, Mi Young; Cha, Ju Ae; Yoo, Chan Uk; Park, Sang Muk

2018-03-01

This study assessed the association of metabolic syndrome and metabolic syndrome score with the predicted forced vital capacity and predicted forced expiratory volume in 1 s (predicted forced expiratory volume in 1 s) values in Korean non-smoking adults. We analysed data obtained from 6684 adults during the 2013-2015 Korean National Health and Nutrition Examination Survey. After adjustment for related variables, metabolic syndrome ( p < 0.001) and metabolic syndrome score ( p < 0.001) were found to be inversely associated with the predicted forced vital capacity and forced expiratory volume in 1 s values. The odds ratios of restrictive pulmonary disease (the predicted forced vital capacity < 80.0% with forced expiratory volume in 1 s/FVC ⩾ 70.0%) by metabolic syndrome score with metabolic syndrome score 0 as a reference group showed no significance for metabolic syndrome score 1 [1.061 (95% confidence interval, 0.755-1.490)] and metabolic syndrome score 2 [1.247 (95% confidence interval, 0.890-1.747)], but showed significant for metabolic syndrome score 3 [1.433 (95% confidence interval, 1.010-2.033)] and metabolic syndrome score ⩾ 4 [1.760 (95% confidence interval, 1.216-2.550)]. In addition, the odds ratio of restrictive pulmonary disease of the metabolic syndrome [1.360 (95% confidence interval, 1.118-1.655)] was significantly higher than those of non-metabolic syndrome. Metabolic syndrome and metabolic syndrome score were inversely associated with the predicted forced vital capacity and forced expiratory volume in 1 s values in Korean non-smoking adults. In addition, metabolic syndrome and metabolic syndrome score were positively associated with the restrictive pulmonary disease.

Effect of antacids on predicted steady-state cimetidine concentrations.

PubMed

Russell, W L; Lopez, L M; Normann, S A; Doering, P L; Guild, R T

1984-05-01

The purpose of this study was to evaluate effects of antacids on predicted steady-state concentrations of cimetidine. Ten healthy volunteers received in random order one week apart, cimetidine and cimetidine and antacid suspension. Blood was obtained at specified times and analyzed for cimetidine. Bioavailability was assessed by comparison of peak concentration, time to peak concentration, area under the curve, and time spent over 0.5 micrograms/ml. Single-dose data were extrapolated to steady-state using computer simulation. Concurrent administration of antacid suspension reduced parameters of bioavailability approximately 30%. When steady-state conditions were simulated, concentrations of cimetidine greater than or equal to 0.5 micrograms/ml were maintained for the entire dosing interval in seven of 10 subjects. These data suggest that temporal separation of cimetidine and antacid suspension may be unnecessary.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report.

PubMed

Kuppuraj, Sengottuvel; Duta, Mihaela; Thompson, Paul; Bishop, Dorothy

2018-02-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory-picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test-retest reliability ( r  = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report

PubMed Central

Duta, Mihaela; Thompson, Paul

2018-01-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory–picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test–retest reliability (r = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process. PMID:29515876

Application of Interval Predictor Models to Space Radiation Shielding

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy,Daniel P.; Norman, Ryan B.; Blattnig, Steve R.

2016-01-01

This paper develops techniques for predicting the uncertainty range of an output variable given input-output data. These models are called Interval Predictor Models (IPM) because they yield an interval valued function of the input. This paper develops IPMs having a radial basis structure. This structure enables the formal description of (i) the uncertainty in the models parameters, (ii) the predicted output interval, and (iii) the probability that a future observation would fall in such an interval. In contrast to other metamodeling techniques, this probabilistic certi cate of correctness does not require making any assumptions on the structure of the mechanism from which data are drawn. Optimization-based strategies for calculating IPMs having minimal spread while containing all the data are developed. Constraints for bounding the minimum interval spread over the continuum of inputs, regulating the IPMs variation/oscillation, and centering its spread about a target point, are used to prevent data over tting. Furthermore, we develop an approach for using expert opinion during extrapolation. This metamodeling technique is illustrated using a radiation shielding application for space exploration. In this application, we use IPMs to describe the error incurred in predicting the ux of particles resulting from the interaction between a high-energy incident beam and a target.

Change of pH during excess sludge fermentation under alkaline, acidic and neutral conditions.

PubMed

Yuan, Yue; Peng, Yongzhen; Liu, Ye; Jin, Baodan; Wang, Bo; Wang, Shuying

2014-12-01

The change in pH during excess sludge (ES) fermentation of varying sludge concentrations was investigated in a series of reactors at alkaline, acidic, and neutral pHs. The results showed that the changes were significantly affected by fermentative conditions. Under different conditions, pH exhibited changing profiles. When ES was fermented under alkaline conditions, pH decreased in a range of (10±1). At the beginning of alkaline fermentation, pH dropped significantly, at intervals of 4h, 4h, and 5h with sludge concentrations of 8665.6mg/L, 6498.8mg/L, and 4332.5mg/L, then it would become moderate. However, under acidic conditions, pH increased from 4 to 5. Finally, under neutral conditions pH exhibited a decrease then an increase throughout entire fermentation process. Further study showed short-chain fatty acids (SCFAs), ammonia nitrogen and cations contributed to pH change under various fermentation conditions. This study presents a novel strategy based on pH change to predict whether SCFAs reach their stable stage. Copyright © 2014 Elsevier Ltd. All rights reserved.

Decadal climate predictions improved by ocean ensemble dispersion filtering

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.