Sample records for accurately predict timing

  1. Mental models accurately predict emotion transitions.

    PubMed

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  2. Mental models accurately predict emotion transitions

    PubMed Central

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  3. Accurate Identification of Fear Facial Expressions Predicts Prosocial Behavior

    PubMed Central

    Marsh, Abigail A.; Kozak, Megan N.; Ambady, Nalini

    2009-01-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants’ ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale. PMID:17516803

  4. Accurate identification of fear facial expressions predicts prosocial behavior.

    PubMed

    Marsh, Abigail A; Kozak, Megan N; Ambady, Nalini

    2007-05-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants' ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale.

  5. Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

    NASA Astrophysics Data System (ADS)

    Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

    2018-02-01

    Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

  6. Can We Predict Patient Wait Time?

    PubMed

    Pianykh, Oleg S; Rosenthal, Daniel I

    2015-10-01

    The importance of patient wait-time management and predictability can hardly be overestimated: For most hospitals, it is the patient queues that drive and define every bit of clinical workflow. The objective of this work was to study the predictability of patient wait time and identify its most influential predictors. To solve this problem, we developed a comprehensive list of 25 wait-related parameters, suggested in earlier work and observed in our own experiments. All parameters were chosen as derivable from a typical Hospital Information System dataset. The parameters were fed into several time-predicting models, and the best parameter subsets, discovered through exhaustive model search, were applied to a large sample of actual patient wait data. We were able to discover the most efficient wait-time prediction factors and models, such as the line-size models introduced in this work. Moreover, these models proved to be equally accurate and computationally efficient. Finally, the selected models were implemented in our patient waiting areas, displaying predicted wait times on the monitors located at the front desks. The limitations of these models are also discussed. Optimal regression models based on wait-line sizes can provide accurate and efficient predictions for patient wait time. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  7. Parturition prediction and timing of canine pregnancy

    PubMed Central

    Kim, YeunHee; Travis, Alexander J.; Meyers-Wallen, Vicki N.

    2007-01-01

    An accurate method of predicting the date of parturition in the bitch is clinically useful to minimize or prevent reproductive losses by timely intervention. Similarly, an accurate method of timing canine ovulation and gestation is critical for development of assisted reproductive technologies, e.g. estrous synchronization and embryo transfer. This review discusses present methods for accurately timing canine gestational age and outlines their use in clinical management of high-risk pregnancies and embryo transfer research. PMID:17904630

  8. Are EMS call volume predictions based on demand pattern analysis accurate?

    PubMed

    Brown, Lawrence H; Lerner, E Brooke; Larmon, Baxter; LeGassick, Todd; Taigman, Michael

    2007-01-01

    Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis. To evaluate the accuracy of call volume predictions calculated using demand pattern analysis. Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics. There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems. Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over

  9. Time-Accurate Numerical Prediction of Free Flight Aerodynamics of a Finned Projectile

    DTIC Science & Technology

    2005-09-01

    develop (with fewer dollars) more lethal and effective munitions. The munitions must stay abreast of the latest technology available to our...consuming. Computer simulations can and have provided an effective means of determining the unsteady aerodynamics and flight mechanics of guided projectile...Recently, the time-accurate technique was used to obtain improved results for Magnus moment and roll damping moment of a spinning projectile at transonic

  10. Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

    NASA Technical Reports Server (NTRS)

    Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

    2016-01-01

    Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

  11. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    NASA Astrophysics Data System (ADS)

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  12. Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

    PubMed Central

    Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

    2013-01-01

    Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eospredicted 64% of sputum Neupredict both sputum Eos and Neu accurately assigned only 41% of samples. Conclusion Despite statistically significant associations FeNO, IgE, blood Eos and Neu, FEV1%predicted, and age are poor surrogates, separately and combined, for accurately predicting sputum eosinophils and neutrophils. PMID:23706399

  13. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

    PubMed Central

    Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D.; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

    2014-01-01

    Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. PMID:24453961

  14. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    NASA Astrophysics Data System (ADS)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2017-04-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  15. Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

    NASA Astrophysics Data System (ADS)

    Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

    2017-10-01

    To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

  16. Accurate Binding Free Energy Predictions in Fragment Optimization.

    PubMed

    Steinbrecher, Thomas B; Dahlgren, Markus; Cappel, Daniel; Lin, Teng; Wang, Lingle; Krilov, Goran; Abel, Robert; Friesner, Richard; Sherman, Woody

    2015-11-23

    Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

  17. An accurate model for predicting high frequency noise of nanoscale NMOS SOI transistors

    NASA Astrophysics Data System (ADS)

    Shen, Yanfei; Cui, Jie; Mohammadi, Saeed

    2017-05-01

    A nonlinear and scalable model suitable for predicting high frequency noise of N-type Metal Oxide Semiconductor (NMOS) transistors is presented. The model is developed for a commercial 45 nm CMOS SOI technology and its accuracy is validated through comparison with measured performance of a microwave low noise amplifier. The model employs the virtual source nonlinear core and adds parasitic elements to accurately simulate the RF behavior of multi-finger NMOS transistors up to 40 GHz. For the first time, the traditional long-channel thermal noise model is supplemented with an injection noise model to accurately represent the noise behavior of these short-channel transistors up to 26 GHz. The developed model is simple and easy to extract, yet very accurate.

  18. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    DOE PAGES

    An, Zhe; Rey, Daniel; Ye, Jingxin; ...

    2017-01-16

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of themore » full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. Here, we show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.« less

  19. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

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

    An, Zhe; Rey, Daniel; Ye, Jingxin

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of themore » full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. Here, we show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.« less

  20. Can phenological models predict tree phenology accurately under climate change conditions?

    NASA Astrophysics Data System (ADS)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  1. Calibrating GPS With TWSTFT For Accurate Time Transfer

    DTIC Science & Technology

    2008-12-01

    40th Annual Precise Time and Time Interval (PTTI) Meeting 577 CALIBRATING GPS WITH TWSTFT FOR ACCURATE TIME TRANSFER Z. Jiang1 and...primary time transfer techniques are GPS and TWSTFT (Two-Way Satellite Time and Frequency Transfer, TW for short). 83% of UTC time links are...Calibrating GPS With TWSTFT For Accurate Time Transfer 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT

  2. Technical Note: Using experimentally determined proton spot scanning timing parameters to accurately model beam delivery time.

    PubMed

    Shen, Jiajian; Tryggestad, Erik; Younkin, James E; Keole, Sameer R; Furutani, Keith M; Kang, Yixiu; Herman, Michael G; Bues, Martin

    2017-10-01

    To accurately model the beam delivery time (BDT) for a synchrotron-based proton spot scanning system using experimentally determined beam parameters. A model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x- and y-directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (T BDT ) were compared with the BDTs recorded in the treatment delivery log files (T Log ): ∆t = T Log -T BDT . The experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x-direction and 19.3 m/s in y-direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = -0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = -7.48 ± 6.97 s). An accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may

  3. A time accurate prediction of the viscous flow in a turbine stage including a rotor in motion

    NASA Astrophysics Data System (ADS)

    Shavalikul, Akamol

    accurate flow characteristics in the NGV domain and the rotor domain with less computational time and computer memory requirements. In contrast, the time accurate flow simulation can predict all unsteady flow characteristics occurring in the turbine stage, but with high computational resource requirements. (Abstract shortened by UMI.)

  4. 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.

  5. ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

    PubMed

    Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

    2016-11-01

    ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P < 0.0001). 394 (61.2%) of physicians' estimates about the percentage probability of post-thrombolysis symptomatic intracranial haemorrhage were accurate compared with 583 (90.5%) of SEDAN score estimates (P < 0.0001). 160 (24.8%) of physicians' estimates about post-thrombolysis 3-month percentage probability of mRS 0-2 were accurate compared with 240 (37.3%) DRAGON score estimates (P < 0.0001). 260 (40.4%) of physicians' estimates about the percentage probability of post-thrombolysis mRS 5-6 were accurate compared with 518 (80.4%) DRAGON score estimates (P < 0.0001). ASTRAL, DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

  6. Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

    PubMed

    Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

    2013-01-02

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

  7. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

    PubMed

    Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

    2017-04-01

    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

  8. Automatic intraaortic balloon pump timing using an intrabeat dicrotic notch prediction algorithm.

    PubMed

    Schreuder, Jan J; Castiglioni, Alessandro; Donelli, Andrea; Maisano, Francesco; Jansen, Jos R C; Hanania, Ramzi; Hanlon, Pat; Bovelander, Jan; Alfieri, Ottavio

    2005-03-01

    The efficacy of intraaortic balloon counterpulsation (IABP) during arrhythmic episodes is questionable. A novel algorithm for intrabeat prediction of the dicrotic notch was used for real time IABP inflation timing control. A windkessel model algorithm was used to calculate real-time aortic flow from aortic pressure. The dicrotic notch was predicted using a percentage of calculated peak flow. Automatic inflation timing was set at intrabeat predicted dicrotic notch and was combined with automatic IAB deflation. Prophylactic IABP was applied in 27 patients with low ejection fraction (< 35%) undergoing cardiac surgery. Analysis of IABP at a 1:4 ratio revealed that IAB inflation occurred at a mean of 0.6 +/- 5 ms from the dicrotic notch. In all patients accurate automatic timing at a 1:1 assist ratio was performed. Seventeen patients had episodes of severe arrhythmia, the novel IABP inflation algorithm accurately assisted 318 of 320 arrhythmic beats at a 1:1 ratio. The novel real-time intrabeat IABP inflation timing algorithm performed accurately in all patients during both regular rhythms and severe arrhythmia, allowing fully automatic intrabeat IABP timing.

  9. Prediction of retention times in comprehensive two-dimensional gas chromatography using thermodynamic models.

    PubMed

    McGinitie, Teague M; Harynuk, James J

    2012-09-14

    A method was developed to accurately predict both the primary and secondary retention times for a series of alkanes, ketones and alcohols in a flow-modulated GC×GC system. This was accomplished through the use of a three-parameter thermodynamic model where ΔH, ΔS, and ΔC(p) for an analyte's interaction with the stationary phases in both dimensions are known. Coupling this thermodynamic model with a time summation calculation it was possible to accurately predict both (1)t(r) and (2)t(r) for all analytes. The model was able to predict retention times regardless of the temperature ramp used, with an average error of only 0.64% for (1)t(r) and an average error of only 2.22% for (2)t(r). The model shows promise for the accurate prediction of retention times in GC×GC for a wide range of compounds and is able to utilize data collected from 1D experiments. Copyright © 2012 Elsevier B.V. All rights reserved.

  10. High Order Schemes in Bats-R-US for Faster and More Accurate Predictions

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Toth, G.; Gombosi, T. I.

    2014-12-01

    BATS-R-US is a widely used global magnetohydrodynamics model that originally employed second order accurate TVD schemes combined with block based Adaptive Mesh Refinement (AMR) to achieve high resolution in the regions of interest. In the last years we have implemented fifth order accurate finite difference schemes CWENO5 and MP5 for uniform Cartesian grids. Now the high order schemes have been extended to generalized coordinates, including spherical grids and also to the non-uniform AMR grids including dynamic regridding. We present numerical tests that verify the preservation of free-stream solution and high-order accuracy as well as robust oscillation-free behavior near discontinuities. We apply the new high order accurate schemes to both heliospheric and magnetospheric simulations and show that it is robust and can achieve the same accuracy as the second order scheme with much less computational resources. This is especially important for space weather prediction that requires faster than real time code execution.

  11. CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

    NASA Astrophysics Data System (ADS)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-04-01

    CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

  12. Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

    PubMed

    Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

    2016-08-01

    Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    NASA Technical Reports Server (NTRS)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  14. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals.

    PubMed

    Doré, Bruce P; Meksin, Robert; Mather, Mara; Hirst, William; Ochsner, Kevin N

    2016-06-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting (a) the overall intensity of their future negative emotion, and (b) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  15. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals

    PubMed Central

    Doré, B.P.; Meksin, R.; Mather, M.; Hirst, W.; Ochsner, K.N

    2016-01-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting 1) the overall intensity of their future negative emotion, and 2) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. PMID:27100309

  16. Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

    NASA Astrophysics Data System (ADS)

    Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

    2017-03-01

    Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

  17. Predicting Time to Hospital Discharge for Extremely Preterm Infants

    PubMed Central

    Hintz, Susan R.; Bann, Carla M.; Ambalavanan, Namasivayam; Cotten, C. Michael; Das, Abhik; Higgins, Rosemary D.

    2010-01-01

    As extremely preterm infant mortality rates have decreased, concerns regarding resource utilization have intensified. Accurate models to predict time to hospital discharge could aid in resource planning, family counseling, and perhaps stimulate quality improvement initiatives. Objectives For infants <27 weeks estimated gestational age (EGA), to develop, validate and compare several models to predict time to hospital discharge based on time-dependent covariates, and based on the presence of 5 key risk factors as predictors. Patients and Methods This was a retrospective analysis of infants <27 weeks EGA, born 7/2002-12/2005 and surviving to discharge from a NICHD Neonatal Research Network site. Time to discharge was modeled as continuous (postmenstrual age at discharge, PMAD), and categorical variables (“Early” and “Late” discharge). Three linear and logistic regression models with time-dependent covariate inclusion were developed (perinatal factors only, perinatal+early neonatal factors, perinatal+early+later factors). Models for Early and Late discharge using the cumulative presence of 5 key risk factors as predictors were also evaluated. Predictive capabilities were compared using coefficient of determination (R2) for linear models, and AUC of ROC curve for logistic models. Results Data from 2254 infants were included. Prediction of PMAD was poor, with only 38% of variation explained by linear models. However, models incorporating later clinical characteristics were more accurate in predicting “Early” or “Late” discharge (full models: AUC 0.76-0.83 vs. perinatal factor models: AUC 0.56-0.69). In simplified key risk factors models, predicted probabilities for Early and Late discharge compared favorably with observed rates. Furthermore, the AUC (0.75-0.77) were similar to those of models including the full factor set. Conclusions Prediction of Early or Late discharge is poor if only perinatal factors are considered, but improves substantially with

  18. A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

    PubMed

    Liu, Zitao; Hauskrecht, Milos

    2017-11-01

    Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

  19. Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction.

    PubMed

    Chen, Kun; Liang, Yu; Gao, Zengliang; Liu, Yi

    2017-08-08

    Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors.

  20. Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction

    PubMed Central

    Chen, Kun; Liang, Yu; Gao, Zengliang; Liu, Yi

    2017-01-01

    Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors. PMID:28786957

  1. SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

    PubMed

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-05-01

    Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are

  2. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

    PubMed Central

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-01-01

    Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of

  3. Strainrange partitioning life predictions of the long time metal properties council creep-fatigue tests

    NASA Technical Reports Server (NTRS)

    Saltsman, J. F.; Halford, G. R.

    1979-01-01

    The method of strainrange partitioning is used to predict the cyclic lives of the Metal Properties Council's long time creep-fatigue interspersion tests of several steel alloys. Comparisons are made with predictions based upon the time- and cycle-fraction approach. The method of strainrange partitioning is shown to give consistently more accurate predictions of cyclic life than is given by the time- and cycle-fraction approach.

  4. How accurate is our clinical prediction of "minimal prostate cancer"?

    PubMed

    Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

    2013-07-01

    Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

  5. A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

    NASA Astrophysics Data System (ADS)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-03-01

    Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

  6. Methods and on-farm devices to predict calving time in cattle.

    PubMed

    Saint-Dizier, Marie; Chastant-Maillard, Sylvie

    2015-09-01

    In livestock farming, accurate prediction of calving time is a key factor for profitability and animal welfare. The most accurate and sensitive methods to date for prediction of calving within 24 h are the measurement of pelvic ligament relaxation and assays for circulating progesterone and oestradiol-17β. Conversely, the absence of calving within the next 12-24 h can be accurately predicted by the measurement of incremental daily decrease in vaginal temperature and by the combination of pelvic ligament relaxation and teat filling estimates. Continuous monitoring systems can detect behavioural changes occurring on the actual day of calving, some of them being accentuated in the last few hours before delivery; standing/lying transitions, tail raising, feeding time, and dry matter and water intakes differ between cows with dystocia and those with eutocia. Use of these behavioural changes has the potential to improve the management of calving. Currently, four types of devices for calving detection are on the market: inclinometers and accelerometers detecting tail raising and overactivity, abdominal belts monitoring uterine contractions, vaginal probes detecting a decrease in vaginal temperature and expulsion of the allantochorion, and devices placed in the vagina or on the vulvar lips that detect calf expulsion. The performance of these devices under field conditions and their capacity to predict dystocia require further investigation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories

    NASA Astrophysics Data System (ADS)

    Xu, Tao; Li, Xiang; Claramunt, Christophe

    2018-06-01

    Accurate travel time prediction is undoubtedly of importance to both traffic managers and travelers. In highly-urbanized areas, trip-oriented travel time prediction (TOTTP) is valuable to travelers rather than traffic managers as the former usually expect to know the travel time of a trip which may cross over multiple road sections. There are two obstacles to the development of TOTTP, including traffic complexity and traffic data coverage.With large scale historical vehicle trajectory data and meteorology data, this research develops a BPNN-based approach through integrating multiple factors affecting trip travel time into a BPNN model to predict trip-oriented travel time for OD pairs in urban network. Results of experiments demonstrate that it helps discover the dominate trends of travel time changes daily and weekly, and the impact of weather conditions is non-trivial.

  8. Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

    PubMed

    Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

    2015-09-01

    There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

  9. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

    PubMed

    Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D; Clifford, Gari D; Buchman, Timothy G

    2018-04-01

    Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None. High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the

  10. High accurate time system of the Low Latitude Meridian Circle.

    NASA Astrophysics Data System (ADS)

    Yang, Jing; Wang, Feng; Li, Zhiming

    In order to obtain the high accurate time signal for the Low Latitude Meridian Circle (LLMC), a new GPS accurate time system is developed which include GPS, 1 MC frequency source and self-made clock system. The second signal of GPS is synchronously used in the clock system and information can be collected by a computer automatically. The difficulty of the cancellation of the time keeper can be overcomed by using this system.

  11. Multigrid time-accurate integration of Navier-Stokes equations

    NASA Technical Reports Server (NTRS)

    Arnone, Andrea; Liou, Meng-Sing; Povinelli, Louis A.

    1993-01-01

    Efficient acceleration techniques typical of explicit steady-state solvers are extended to time-accurate calculations. Stability restrictions are greatly reduced by means of a fully implicit time discretization. A four-stage Runge-Kutta scheme with local time stepping, residual smoothing, and multigridding is used instead of traditional time-expensive factorizations. Some applications to natural and forced unsteady viscous flows show the capability of the procedure.

  12. ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

    NASA Astrophysics Data System (ADS)

    Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2016-05-01

    At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

  13. Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

    PubMed Central

    Burger, Lukas; van Nimwegen, Erik

    2008-01-01

    Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

  14. Influence of accurate and inaccurate 'split-time' feedback upon 10-mile time trial cycling performance.

    PubMed

    Wilson, Mathew G; Lane, Andy M; Beedie, Chris J; Farooq, Abdulaziz

    2012-01-01

    The objective of the study is to examine the impact of accurate and inaccurate 'split-time' feedback upon a 10-mile time trial (TT) performance and to quantify power output into a practically meaningful unit of variation. Seven well-trained cyclists completed four randomised bouts of a 10-mile TT on a SRM™ cycle ergometer. TTs were performed with (1) accurate performance feedback, (2) without performance feedback, (3) and (4) false negative and false positive 'split-time' feedback showing performance 5% slower or 5% faster than actual performance. There were no significant differences in completion time, average power output, heart rate or blood lactate between the four feedback conditions. There were significantly lower (p < 0.001) average [Formula: see text] (ml min(-1)) and [Formula: see text] (l min(-1)) scores in the false positive (3,485 ± 596; 119 ± 33) and accurate (3,471 ± 513; 117 ± 22) feedback conditions compared to the false negative (3,753 ± 410; 127 ± 27) and blind (3,772 ± 378; 124 ± 21) feedback conditions. Cyclists spent a greater amount of time in a '20 watt zone' 10 W either side of average power in the negative feedback condition (fastest) than the accurate feedback (slowest) condition (39.3 vs. 32.2%, p < 0.05). There were no significant differences in the 10-mile TT performance time between accurate and inaccurate feedback conditions, despite significantly lower average [Formula: see text] and [Formula: see text] scores in the false positive and accurate feedback conditions. Additionally, cycling with a small variation in power output (10 W either side of average power) produced the fastest TT. Further psycho-physiological research should examine the mechanism(s) why lower [Formula: see text] and [Formula: see text] scores are observed when cycling in a false positive or accurate feedback condition compared to a false negative or blind feedback condition.

  15. An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

    PubMed

    De Vore, Karl W; Fatahi, Nadia M; Sass, John E

    2016-08-01

    Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

  16. A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

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

    Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.

    2004-12-01

    We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, andmore » its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.« less

  17. Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

    PubMed Central

    Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

    2015-01-01

    Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903

  18. Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

    PubMed

    Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

    2015-01-01

    Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

  19. Accurate prediction of vaccine stability under real storage conditions and during temperature excursions.

    PubMed

    Clénet, Didier

    2018-04-01

    Due to their thermosensitivity, most vaccines must be kept refrigerated from production to use. To successfully carry out global immunization programs, ensuring the stability of vaccines is crucial. In this context, two important issues are critical, namely: (i) predicting vaccine stability and (ii) preventing product damage due to excessive temperature excursions outside of the recommended storage conditions (cold chain break). We applied a combination of advanced kinetics and statistical analyses on vaccine forced degradation data to accurately describe the loss of antigenicity for a multivalent freeze-dried inactivated virus vaccine containing three variants. The screening of large amounts of kinetic models combined with a statistical model selection approach resulted in the identification of two-step kinetic models. Predictions based on kinetic analysis and experimental stability data were in agreement, with approximately five percentage points difference from real values for long-term stability storage conditions, after excursions of temperature and during experimental shipments of freeze-dried products. Results showed that modeling a few months of forced degradation can be used to predict various time and temperature profiles endured by vaccines, i.e. long-term stability, short time excursions outside the labeled storage conditions or shipments at ambient temperature, with high accuracy. Pharmaceutical applications of the presented kinetics-based approach are discussed. Copyright © 2018 The Author. Published by Elsevier B.V. All rights reserved.

  20. Using single leg standing time to predict the fall risk in elderly.

    PubMed

    Chang, Chun-Ju; Chang, Yu-Shin; Yang, Sai-Wei

    2013-01-01

    In clinical evaluation, we used to evaluate the fall risk according to elderly falling experience or the balance assessment tool. Because of the tool limitation, sometimes we could not predict accurately. In this study, we first analyzed 15 healthy elderly (without falling experience) and 15 falling elderly (1~3 time falling experience) balance performance in previous research. After 1 year follow up, there was only 1 elderly fall down during this period. It seemed like that falling experience had a ceiling effect on the falling prediction. But we also found out that using single leg standing time could be more accurately to help predicting the fall risk, especially for the falling elderly who could not stand over 10 seconds by single leg, and with a significant correlation between the falling experience and single leg standing time (r = -0.474, p = 0.026). The results also showed that there was significant body sway just before they falling down, and the COP may be an important characteristic in the falling elderly group.

  1. Accurate prediction of energy expenditure using a shoe-based activity monitor.

    PubMed

    Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

    2011-07-01

    The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

  2. Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

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

    Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng

    This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA)more » models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.« less

  3. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

    PubMed Central

    Noecker, Cecilia; Schaefer, Krista; Zaccheo, Kelly; Yang, Yiding; Day, Judy; Ganusov, Vitaly V.

    2015-01-01

    Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results

  4. Predicting germination in semi-arid wildland seedbeds II. Field validation of wet thermal-time models

    Treesearch

    Jennifer K. Rawlins; Bruce A. Roundy; Dennis Eggett; Nathan Cline

    2011-01-01

    Accurate prediction of germination for species used for semi-arid land revegetation would support selection of plant materials for specific climatic conditions and sites. Wet thermal-time models predict germination time by summing progress toward germination subpopulation percentages as a function of temperature across intermittent wet periods or within singular wet...

  5. Rapid and accurate prediction and scoring of water molecules in protein binding sites.

    PubMed

    Ross, Gregory A; Morris, Garrett M; Biggin, Philip C

    2012-01-01

    Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

  6. A time-accurate finite volume method valid at all flow velocities

    NASA Technical Reports Server (NTRS)

    Kim, S.-W.

    1993-01-01

    A finite volume method to solve the Navier-Stokes equations at all flow velocities (e.g., incompressible, subsonic, transonic, supersonic and hypersonic flows) is presented. The numerical method is based on a finite volume method that incorporates a pressure-staggered mesh and an incremental pressure equation for the conservation of mass. Comparison of three generally accepted time-advancing schemes, i.e., Simplified Marker-and-Cell (SMAC), Pressure-Implicit-Splitting of Operators (PISO), and Iterative-Time-Advancing (ITA) scheme, are made by solving a lid-driven polar cavity flow and self-sustained oscillatory flows over circular and square cylinders. Calculated results show that the ITA is the most stable numerically and yields the most accurate results. The SMAC is the most efficient computationally and is as stable as the ITA. It is shown that the PISO is the most weakly convergent and it exhibits an undesirable strong dependence on the time-step size. The degenerated numerical results obtained using the PISO are attributed to its second corrector step that cause the numerical results to deviate further from a divergence free velocity field. The accurate numerical results obtained using the ITA is attributed to its capability to resolve the nonlinearity of the Navier-Stokes equations. The present numerical method that incorporates the ITA is used to solve an unsteady transitional flow over an oscillating airfoil and a chemically reacting flow of hydrogen in a vitiated supersonic airstream. The turbulence fields in these flow cases are described using multiple-time-scale turbulence equations. For the unsteady transitional over an oscillating airfoil, the fluid flow is described using ensemble-averaged Navier-Stokes equations defined on the Lagrangian-Eulerian coordinates. It is shown that the numerical method successfully predicts the large dynamic stall vortex (DSV) and the trailing edge vortex (TEV) that are periodically generated by the oscillating airfoil

  7. Real-time stylistic prediction for whole-body human motions.

    PubMed

    Matsubara, Takamitsu; Hyon, Sang-Ho; Morimoto, Jun

    2012-01-01

    The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. Helicopter flight dynamics simulation with a time-accurate free-vortex wake model

    NASA Astrophysics Data System (ADS)

    Ribera, Maria

    This dissertation describes the implementation and validation of a coupled rotor-fuselage simulation model with a time-accurate free-vortex wake model capable of capturing the response to maneuvers of arbitrary amplitude. The resulting model has been used to analyze different flight conditions, including both steady and transient maneuvers. The flight dynamics model is based on a system of coupled nonlinear rotor-fuselage differential equations in first-order, state-space form. The rotor model includes flexible blades, with coupled flap-lag-torsion dynamics and swept tips; the rigid body dynamics are modeled with the non-linear Euler equations. The free wake models the rotor flow field by tracking the vortices released at the blade tips. Their behavior is described by the equations of vorticity transport, which is approximated using finite differences, and solved using a time-accurate numerical scheme. The flight dynamics model can be solved as a system of non-linear algebraic trim equations to determine the steady state solution, or integrated in time in response to pilot-applied controls. This study also implements new approaches to reduce the prohibitive computational costs associated with such complex models without losing accuracy. The mathematical model was validated for trim conditions in level flight, turns, climbs and descents. The results obtained correlate well with flight test data, both in level flight as well as turning and climbing and descending flight. The swept tip model was also found to improve the trim predictions, particularly at high speed. The behavior of the rigid body and the rotor blade dynamics were also studied and related to the aerodynamic load distributions obtained with the free wake induced velocities. The model was also validated in a lateral maneuver from hover. The results show improvements in the on-axis prediction, and indicate a possible relation between the off-axis prediction and the lack of rotor-body interaction

  9. Multi-fidelity machine learning models for accurate bandgap predictions of solids

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

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

  10. Multi-fidelity machine learning models for accurate bandgap predictions of solids

    DOE PAGES

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    2016-12-28

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

  11. Comparison of time series models for predicting campylobacteriosis risk in New Zealand.

    PubMed

    Al-Sakkaf, A; Jones, G

    2014-05-01

    Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.

  12. Measuring the value of accurate link prediction for network seeding.

    PubMed

    Wei, Yijin; Spencer, Gwen

    2017-01-01

    The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

  13. Extracting Time-Accurate Acceleration Vectors From Nontrivial Accelerometer Arrangements.

    PubMed

    Franck, Jennifer A; Blume, Janet; Crisco, Joseph J; Franck, Christian

    2015-09-01

    Sports-related concussions are of significant concern in many impact sports, and their detection relies on accurate measurements of the head kinematics during impact. Among the most prevalent recording technologies are videography, and more recently, the use of single-axis accelerometers mounted in a helmet, such as the HIT system. Successful extraction of the linear and angular impact accelerations depends on an accurate analysis methodology governed by the equations of motion. Current algorithms are able to estimate the magnitude of acceleration and hit location, but make assumptions about the hit orientation and are often limited in the position and/or orientation of the accelerometers. The newly formulated algorithm presented in this manuscript accurately extracts the full linear and rotational acceleration vectors from a broad arrangement of six single-axis accelerometers directly from the governing set of kinematic equations. The new formulation linearizes the nonlinear centripetal acceleration term with a finite-difference approximation and provides a fast and accurate solution for all six components of acceleration over long time periods (>250 ms). The approximation of the nonlinear centripetal acceleration term provides an accurate computation of the rotational velocity as a function of time and allows for reconstruction of a multiple-impact signal. Furthermore, the algorithm determines the impact location and orientation and can distinguish between glancing, high rotational velocity impacts, or direct impacts through the center of mass. Results are shown for ten simulated impact locations on a headform geometry computed with three different accelerometer configurations in varying degrees of signal noise. Since the algorithm does not require simplifications of the actual impacted geometry, the impact vector, or a specific arrangement of accelerometer orientations, it can be easily applied to many impact investigations in which accurate kinematics need

  14. Time-Accurate, Unstructured-Mesh Navier-Stokes Computations with the Space-Time CESE Method

    NASA Technical Reports Server (NTRS)

    Chang, Chau-Lyan

    2006-01-01

    Application of the newly emerged space-time conservation element solution element (CESE) method to compressible Navier-Stokes equations is studied. In contrast to Euler equations solvers, several issues such as boundary conditions, numerical dissipation, and grid stiffness warrant systematic investigations and validations. Non-reflecting boundary conditions applied at the truncated boundary are also investigated from the stand point of acoustic wave propagation. Validations of the numerical solutions are performed by comparing with exact solutions for steady-state as well as time-accurate viscous flow problems. The test cases cover a broad speed regime for problems ranging from acoustic wave propagation to 3D hypersonic configurations. Model problems pertinent to hypersonic configurations demonstrate the effectiveness of the CESE method in treating flows with shocks, unsteady waves, and separations. Good agreement with exact solutions suggests that the space-time CESE method provides a viable alternative for time-accurate Navier-Stokes calculations of a broad range of problems.

  15. Real coded genetic algorithm for fuzzy time series prediction

    NASA Astrophysics Data System (ADS)

    Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

    2017-10-01

    Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

  16. Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

    PubMed

    Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

    2017-05-01

    physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

  17. Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

    PubMed Central

    Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

    2017-01-01

    Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

  18. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  19. Accurate Sample Time Reconstruction of Inertial FIFO Data.

    PubMed

    Stieber, Sebastian; Dorsch, Rainer; Haubelt, Christian

    2017-12-13

    In the context of modern cyber-physical systems, the accuracy of underlying sensor data plays an increasingly important role in sensor data fusion and feature extraction. The raw events of multiple sensors have to be aligned in time to enable high quality sensor fusion results. However, the growing number of simultaneously connected sensor devices make the energy saving data acquisition and processing more and more difficult. Hence, most of the modern sensors offer a first-in-first-out (FIFO) interface to store multiple data samples and to relax timing constraints, when handling multiple sensor devices. However, using the FIFO interface increases the negative influence of individual clock drifts-introduced by fabrication inaccuracies, temperature changes and wear-out effects-onto the sampling data reconstruction. Furthermore, additional timing offset errors due to communication and software latencies increases with a growing number of sensor devices. In this article, we present an approach for an accurate sample time reconstruction independent of the actual clock drift with the help of an internal sensor timer. Such timers are already available in modern sensors, manufactured in micro-electromechanical systems (MEMS) technology. The presented approach focuses on calculating accurate time stamps using the sensor FIFO interface in a forward-only processing manner as a robust and energy saving solution. The proposed algorithm is able to lower the overall standard deviation of reconstructed sampling periods below 40 μ s, while run-time savings of up to 42% are achieved, compared to single sample acquisition.

  20. A real-time prediction of UTC

    NASA Technical Reports Server (NTRS)

    Thomas, Claudine; Allan, David W.

    1994-01-01

    The reference time scale for all scientific and technologic applications on the Earth, the Universal Coordinated Time (UTC), must be as stable, reliable, and accurate as possible. With this in view the BIPM and before it the BIH, have always calculated and then disseminated UTC with a delay of about 80 days. There are three fundamental reasons for doing this: (1) It takes some weeks for data, gathered from some 200 clocks spread world-wide, to be collected and for errors to be eliminated; (2) changes in clock rates can only be measured with high precision well after the fact; and (3) the measurement noise originating in time links, in particular using Loran-C, is smoothed out only when averaging over an extended period. Until mid-1992, the ultimate stability of UTC was reached at averaging times of about 100 days and corresponded to an Allan deviation sigma(sub y)(tau) of about 1,5x10(exp -14) then compared to the best primary clock in the world, the PTB CS2. For several years now, a predicted UTC has been computed by the USNO through an extrapolation of the values as published in deferred time by the BIPM. This is made available through the USNO Series 4, through the USNO Automated Data Service, and through GPS signals. Due to the instability of UTC, the poor predictability of the available clocks, and the intentional SA degradation of GPS signals, the real-time access to this extrapolated UTC has represented the true deferred-time UTC only to within several hundreds of nanoseconds.

  1. Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

    PubMed

    Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

    2016-01-01

    Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

  2. Competitive Abilities in Experimental Microcosms Are Accurately Predicted by a Demographic Index for R*

    PubMed Central

    Murrell, Ebony G.; Juliano, Steven A.

    2012-01-01

    Resource competition theory predicts that R*, the equilibrium resource amount yielding zero growth of a consumer population, should predict species' competitive abilities for that resource. This concept has been supported for unicellular organisms, but has not been well-tested for metazoans, probably due to the difficulty of raising experimental populations to equilibrium and measuring population growth rates for species with long or complex life cycles. We developed an index (Rindex) of R* based on demography of one insect cohort, growing from egg to adult in a non-equilibrium setting, and tested whether Rindex yielded accurate predictions of competitive abilities using mosquitoes as a model system. We estimated finite rate of increase (λ′) from demographic data for cohorts of three mosquito species raised with different detritus amounts, and estimated each species' Rindex using nonlinear regressions of λ′ vs. initial detritus amount. All three species' Rindex differed significantly, and accurately predicted competitive hierarchy of the species determined in simultaneous pairwise competition experiments. Our Rindex could provide estimates and rigorous statistical comparisons of competitive ability for organisms for which typical chemostat methods and equilibrium population conditions are impractical. PMID:22970128

  3. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    PubMed Central

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

  4. SIFTER search: a web server for accurate phylogeny-based protein function prediction

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

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  5. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    DOE PAGES

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  6. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

    PubMed

    Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

    2013-12-23

    Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

  7. Real-time Tsunami Inundation Prediction Using High Performance Computers

    NASA Astrophysics Data System (ADS)

    Oishi, Y.; Imamura, F.; Sugawara, D.

    2014-12-01

    earthquake occurs took about 2 minutes, which would be sufficient for a practical tsunami inundation predictions. In the presentation, the computational performance of our faster-than-real-time tsunami inundation model will be shown, and preferable tsunami wave source analysis for an accurate inundation prediction will also be discussed.

  8. SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel-Time Prediction and Travel-Time Prediction Uncertainty

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

    Ballard, Sanford; Hipp, James R.; Begnaud, Michael L.

    The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source tomore » receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.« less

  9. SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel-Time Prediction and Travel-Time Prediction Uncertainty

    DOE PAGES

    Ballard, Sanford; Hipp, James R.; Begnaud, Michael L.; ...

    2016-10-11

    The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source tomore » receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.« less

  10. Predicting hepatitis B monthly incidence rates using weighted Markov chains and time series methods.

    PubMed

    Shahdoust, Maryam; Sadeghifar, Majid; Poorolajal, Jalal; Javanrooh, Niloofar; Amini, Payam

    2015-01-01

    Hepatitis B (HB) is a major global mortality. Accurately predicting the trend of the disease can provide an appropriate view to make health policy disease prevention. This paper aimed to apply three different to predict monthly incidence rates of HB. This historical cohort study was conducted on the HB incidence data of Hamadan Province, the west of Iran, from 2004 to 2012. Weighted Markov Chain (WMC) method based on Markov chain theory and two time series models including Holt Exponential Smoothing (HES) and SARIMA were applied on the data. The results of different applied methods were compared to correct percentages of predicted incidence rates. The monthly incidence rates were clustered into two clusters as state of Markov chain. The correct predicted percentage of the first and second clusters for WMC, HES and SARIMA methods was (100, 0), (84, 67) and (79, 47) respectively. The overall incidence rate of HBV is estimated to decrease over time. The comparison of results of the three models indicated that in respect to existing seasonality trend and non-stationarity, the HES had the most accurate prediction of the incidence rates.

  11. Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.

    PubMed

    Li, Hongyang; Panwar, Bharat; Omenn, Gilbert S; Guan, Yuanfang

    2018-02-01

    The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.

  12. Time-Accurate Numerical Simulations of Synthetic Jet Quiescent Air

    NASA Technical Reports Server (NTRS)

    Rupesh, K-A. B.; Ravi, B. R.; Mittal, R.; Raju, R.; Gallas, Q.; Cattafesta, L.

    2007-01-01

    The unsteady evolution of three-dimensional synthetic jet into quiescent air is studied by time-accurate numerical simulations using a second-order accurate mixed explicit-implicit fractional step scheme on Cartesian grids. Both two-dimensional and three-dimensional calculations of synthetic jet are carried out at a Reynolds number (based on average velocity during the discharge phase of the cycle V(sub j), and jet width d) of 750 and Stokes number of 17.02. The results obtained are assessed against PIV and hotwire measurements provided for the NASA LaRC workshop on CFD validation of synthetic jets.

  13. A simple method for HPLC retention time prediction: linear calibration using two reference substances.

    PubMed

    Sun, Lei; Jin, Hong-Yu; Tian, Run-Tao; Wang, Ming-Juan; Liu, Li-Na; Ye, Liu-Ping; Zuo, Tian-Tian; Ma, Shuang-Cheng

    2017-01-01

    Analysis of related substances in pharmaceutical chemicals and multi-components in traditional Chinese medicines needs bulk of reference substances to identify the chromatographic peaks accurately. But the reference substances are costly. Thus, the relative retention (RR) method has been widely adopted in pharmacopoeias and literatures for characterizing HPLC behaviors of those reference substances unavailable. The problem is it is difficult to reproduce the RR on different columns due to the error between measured retention time (t R ) and predicted t R in some cases. Therefore, it is useful to develop an alternative and simple method for prediction of t R accurately. In the present study, based on the thermodynamic theory of HPLC, a method named linear calibration using two reference substances (LCTRS) was proposed. The method includes three steps, procedure of two points prediction, procedure of validation by multiple points regression and sequential matching. The t R of compounds on a HPLC column can be calculated by standard retention time and linear relationship. The method was validated in two medicines on 30 columns. It was demonstrated that, LCTRS method is simple, but more accurate and more robust on different HPLC columns than RR method. Hence quality standards using LCTRS method are easy to reproduce in different laboratories with lower cost of reference substances.

  14. Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

    PubMed

    Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

    2018-03-01

    Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t

  15. Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection.

    PubMed

    Verhofstede, Chris; Fransen, Katrien; Van Den Heuvel, Annelies; Van Laethem, Kristel; Ruelle, Jean; Vancutsem, Ellen; Stoffels, Karolien; Van den Wijngaert, Sigi; Delforge, Marie-Luce; Vaira, Dolores; Hebberecht, Laura; Schauvliege, Marlies; Mortier, Virginie; Dauwe, Kenny; Callens, Steven

    2017-11-29

    There is today no gold standard method to accurately define the time passed since infection at HIV diagnosis. Infection timing and incidence measurement is however essential to better monitor the dynamics of local epidemics and the effect of prevention initiatives. Three methods for infection timing were evaluated using 237 serial samples from documented seroconversions and 566 cross sectional samples from newly diagnosed patients: identification of antibodies against the HIV p31 protein in INNO-LIA, SediaTM BED CEIA and SediaTM LAg-Avidity EIA. A multi-assay decision tree for infection timing was developed. Clear differences in recency window between BED CEIA, LAg-Avidity EIA and p31 antibody presence were observed with a switch from recent to long term infection a median of 169.5, 108.0 and 64.5 days after collection of the pre-seroconversion sample respectively. BED showed high reliability for identification of long term infections while LAg-Avidity is highly accurate for identification of recent infections. Using BED as initial assay to identify the long term infections and LAg-Avidity as a confirmatory assay for those classified as recent infection by BED, explores the strengths of both while reduces the workload. The short recency window of p31 antibodies allows to discriminate very early from early infections based on this marker. BED recent infection results not confirmed by LAg-Avidity are considered to reflect a period more distant from the infection time. False recency predictions in this group can be minimized by elimination of patients with a CD4 count of less than 100 cells/mm3 or without no p31 antibodies. For 566 cross sectional sample the outcome of the decision tree confirmed the infection timing based on the results of all 3 markers but reduced the overall cost from 13.2 USD to 5.2 USD per sample. A step-wise multi assay decision tree allows accurate timing of the HIV infection at diagnosis at affordable effort and cost and can be an important

  16. Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

    PubMed

    Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

  17. Accurate Prediction of Severe Allergic Reactions by a Small Set of Environmental Parameters (NDVI, Temperature)

    PubMed Central

    Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions. PMID:25794106

  18. Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times.

    PubMed

    Mollica, Luca; Theret, Isabelle; Antoine, Mathias; Perron-Sierra, Françoise; Charton, Yves; Fourquez, Jean-Marie; Wierzbicki, Michel; Boutin, Jean A; Ferry, Gilles; Decherchi, Sergio; Bottegoni, Giovanni; Ducrot, Pierre; Cavalli, Andrea

    2016-08-11

    Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

  19. Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

    NASA Astrophysics Data System (ADS)

    Rahmati, Mehdi

    2017-08-01

    Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

  20. Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

    NASA Astrophysics Data System (ADS)

    Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

    2008-11-01

    The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

  1. Reliability of Degree-Day Models to Predict the Development Time of Plutella xylostella (L.) under Field Conditions.

    PubMed

    Marchioro, C A; Krechemer, F S; de Moraes, C P; Foerster, L A

    2015-12-01

    The diamondback moth, Plutella xylostella (L.), is a cosmopolitan pest of brassicaceous crops occurring in regions with highly distinct climate conditions. Several studies have investigated the relationship between temperature and P. xylostella development rate, providing degree-day models for populations from different geographical regions. However, there are no data available to date to demonstrate the suitability of such models to make reliable projections on the development time for this species in field conditions. In the present study, 19 models available in the literature were tested regarding their ability to accurately predict the development time of two cohorts of P. xylostella under field conditions. Only 11 out of the 19 models tested accurately predicted the development time for the first cohort of P. xylostella, but only seven for the second cohort. Five models correctly predicted the development time for both cohorts evaluated. Our data demonstrate that the accuracy of the models available for P. xylostella varies widely and therefore should be used with caution for pest management purposes.

  2. Accuracy of patient's turnover time prediction using RFID technology in an academic ambulatory surgery center.

    PubMed

    Marchand-Maillet, Florence; Debes, Claire; Garnier, Fanny; Dufeu, Nicolas; Sciard, Didier; Beaussier, Marc

    2015-02-01

    Patients flow in outpatient surgical unit is a major issue with regards to resource utilization, overall case load and patient satisfaction. An electronic Radio Frequency Identification Device (RFID) was used to document the overall time spent by the patients between their admission and discharge from the unit. The objective of this study was to evaluate how a RFID-based data collection system could provide an accurate prediction of the actual time for the patient to be discharged from the ambulatory surgical unit after surgery. This is an observational prospective evaluation carried out in an academic ambulatory surgery center (ASC). Data on length of stay at each step of the patient care, from admission to discharge, were recorded by a RFID device and analyzed according to the type of surgical procedure, the surgeon and the anesthetic technique. Based on these initial data (n = 1520), patients were scheduled in a sequential manner according to the expected duration of the previous case. The primary endpoint was the difference between actual and predicted time of discharge from the unit. A total of 414 consecutive patients were prospectively evaluated. One hundred seventy four patients (42%) were discharged at the predicted time ± 30 min. Only 24% were discharged behind predicted schedule. Using an automatic record of patient's length of stay would allow an accurate prediction of the discharge time according to the type of surgery, the surgeon and the anesthetic procedure.

  3. Predicting clinical image delivery time by monitoring PACS queue behavior.

    PubMed

    King, Nelson E; Documet, Jorge; Liu, Brent

    2006-01-01

    The expectation of rapid image retrieval from PACS users contributes to increased information technology (IT) infrastructure investments to increase performance as well as continuing demands upon PACS administrators to respond to "slow" system performance. The ability to provide predicted delivery times to a PACS user may curb user expectations for "fastest" response especially during peak hours. This, in turn, could result in a PACS infrastructure tailored to more realistic performance demands. A PACS with a stand-alone architecture under peak load typically holds study requests in a queue until the DICOM C-Move command can take place. We investigate the contents of a stand-alone architecture PACS RetrieveSend queue and identified parameters and behaviors that enable a more accurate prediction of delivery time. A prediction algorithm for studies delayed in a stand-alone PACS queue can be extendible to other potential bottlenecks such as long-term storage archives. Implications of a queue monitor in other PACS architectures are also discussed.

  4. Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

    NASA Astrophysics Data System (ADS)

    Vašát, Radim; Kodešová, Radka; Borůvka, Luboš

    2017-07-01

    A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest for type 2. The weights to be assigned to individual predictions within the ensemble model (constructed as a weighted average) were determined by an automated procedure that ensured the best solution among all possible was selected. The approach was tested at soil samples taken from surface horizon of four sites differing in the prevailing soil units. By employing the ensemble predictive model the prediction accuracy of SOC improved at all four sites. The coefficient of determination in cross-validation (R2cv) increased from 0.849, 0.611, 0.811 and 0.644 (the best individual predictions) to 0.864, 0.650, 0.824 and 0.698 for Site 1, 2, 3 and 4, respectively. Generally, the ensemble model affected the final prediction so that the maximal deviations of predicted vs. observed values of the individual predictions were reduced, and thus the correlation cloud became thinner as desired.

  5. Assessment of umbilical artery flow and fetal heart rate to predict delivery time in bitches.

    PubMed

    Giannico, Amália Turner; Garcia, Daniela Aparecida Ayres; Gil, Elaine Mayumi Ueno; Sousa, Marlos Gonçalves; Froes, Tilde Rodrigues

    2016-10-15

    The aim of this study was to quantitatively investigate the oscillation of the fetal heart rate (HR) in advance of normal delivery and whether this index could be used to indicate impending delivery. In addition, fetal HR oscillation and umbilical artery resistive index (RI) were correlated to determine if the combination of these parameters provided a more accurate prediction of the time of delivery. Sonographic evaluation was performed in 11 pregnant bitches to evaluate the fetal HR and umbilical artery RI at the following antepartum times: 120 to 96 hours, 72 to 48 hours, 24 to 12 hours, and 12 to 1 hours. Statistical analysis indicated a correlation between the oscillation of fetal HR and the umbilical artery RI. As delivery approached a considerable reduction in the umbilical artery RI was documented and greater oscillations between maximum and minimum HRs occurred. We conclude that the quantitative analysis of fetal HR oscillations may be used to predict the time of delivery in bitches. The combination of fetal HR and umbilical artery RI together may provide more accurate predictions of time of delivery. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Implicit time accurate simulation of unsteady flow

    NASA Astrophysics Data System (ADS)

    van Buuren, René; Kuerten, Hans; Geurts, Bernard J.

    2001-03-01

    Implicit time integration was studied in the context of unsteady shock-boundary layer interaction flow. With an explicit second-order Runge-Kutta scheme, a reference solution to compare with the implicit second-order Crank-Nicolson scheme was determined. The time step in the explicit scheme is restricted by both temporal accuracy as well as stability requirements, whereas in the A-stable implicit scheme, the time step has to obey temporal resolution requirements and numerical convergence conditions. The non-linear discrete equations for each time step are solved iteratively by adding a pseudo-time derivative. The quasi-Newton approach is adopted and the linear systems that arise are approximately solved with a symmetric block Gauss-Seidel solver. As a guiding principle for properly setting numerical time integration parameters that yield an efficient time accurate capturing of the solution, the global error caused by the temporal integration is compared with the error resulting from the spatial discretization. Focus is on the sensitivity of properties of the solution in relation to the time step. Numerical simulations show that the time step needed for acceptable accuracy can be considerably larger than the explicit stability time step; typical ratios range from 20 to 80. At large time steps, convergence problems that are closely related to a highly complex structure of the basins of attraction of the iterative method may occur. Copyright

  7. Towards First Principles-Based Prediction of Highly Accurate Electrochemical Pourbaix Diagrams

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

    Zeng, Zhenhua; Chan, Maria K. Y.; Zhao, Zhi-Jian

    2015-08-13

    Electrochemical potential/pH (Pourbaix) diagrams underpin many aqueous electrochemical processes and are central to the identification of stable phases of metals for processes ranging from electrocatalysis to corrosion. Even though standard DFT calculations are potentially powerful tools for the prediction of such diagrams, inherent errors in the description of transition metal (hydroxy)oxides, together with neglect of van der Waals interactions, have limited the reliability of such predictions for even the simplest pure metal bulk compounds, and corresponding predictions for more complex alloy or surface structures are even more challenging. In the present work, through synergistic use of a Hubbard U correction,more » a state-of-the-art dispersion correction, and a water-based bulk reference state for the calculations, these errors are systematically corrected. The approach describes the weak binding that occurs between hydroxyl-containing functional groups in certain compounds in Pourbaix diagrams, corrects for self-interaction errors in transition metal compounds, and reduces residual errors on oxygen atoms by preserving a consistent oxidation state between the reference state, water, and the relevant bulk phases. The strong performance is illustrated on a series of bulk transition metal (Mn, Fe, Co and Ni) hydroxides, oxyhydroxides, binary, and ternary oxides, where the corresponding thermodynamics of redox and (de)hydration are described with standard errors of 0.04 eV per (reaction) formula unit. The approach further preserves accurate descriptions of the overall thermodynamics of electrochemically-relevant bulk reactions, such as water formation, which is an essential condition for facilitating accurate analysis of reaction energies for electrochemical processes on surfaces. The overall generality and transferability of the scheme suggests that it may find useful application in the construction of a broad array of electrochemical phase diagrams, including

  8. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    PubMed

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

    NASA Technical Reports Server (NTRS)

    Lee, Hanbong

    2017-01-01

    In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

  10. Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

    PubMed

    Cui, Jing; Zhou, Lingjun; Wee, B; Shen, Fengping; Ma, Xiuqiang; Zhao, Jijun

    2014-05-01

    Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

  11. Accurate Time/Frequency Transfer Method Using Bi-Directional WDM Transmission

    NASA Technical Reports Server (NTRS)

    Imaoka, Atsushi; Kihara, Masami

    1996-01-01

    An accurate time transfer method is proposed using b-directional wavelength division multiplexing (WDM) signal transmission along a single optical fiber. This method will be used in digital telecommunication networks and yield a time synchronization accuracy of better than 1 ns for long transmission lines over several tens of kilometers. The method can accurately measure the difference in delay between two wavelength signals caused by the chromatic dispersion of the fiber in conventional simple bi-directional dual-wavelength frequency transfer methods. We describe the characteristics of this difference in delay and then show that the accuracy of the delay measurements can be obtained below 0.1 ns by transmitting 156 Mb/s times reference signals of 1.31 micrometer and 1.55 micrometers along a 50 km fiber using the proposed method. The sub-nanosecond delay measurement using the simple bi-directional dual-wavelength transmission along a 100 km fiber with a wavelength spacing of 1 nm in the 1.55 micrometer range is also shown.

  12. Accurate high-throughput structure mapping and prediction with transition metal ion FRET

    PubMed Central

    Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

    2013-01-01

    Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

  13. Space can substitute for time in predicting climate-change effects on biodiversity.

    PubMed

    Blois, Jessica L; Williams, John W; Fitzpatrick, Matthew C; Jackson, Stephen T; Ferrier, Simon

    2013-06-04

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption--that drivers of spatial gradients of species composition also drive temporal changes in diversity--rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  14. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.

    PubMed

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2008-06-01

    Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides. The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1

  15. Time history prediction of direct-drive implosions on the Omega facility

    DOE PAGES

    Laffite, S.; Bourgade, J. L.; Caillaud, T.; ...

    2016-01-14

    We present in this article direct-drive experiments that were carried out on the Omega facility [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)]. Two different pulse shapes were tested in order to vary the implosion stability of the same target whose parameters, dimensions and composition, remained the same. The direct-drive configuration on the Omega facility allows the accurate time-resolvedmeasurement of the scattered light. We show that, provided the laser coupling is well controlled, the implosion time history, assessed by the “bang-time” and the shell trajectory measurements, can be predicted. This conclusion is independent on the pulse shape. Inmore » contrast, we show that the pulse shape affects the implosion stability, assessed by comparing the target performances between prediction and measurement. For the 1-ns square pulse, the measuredneutron number is about 80% of the prediction. Lastly, for the 2-step 2-ns pulse, we test here that this ratio falls to about 20%.« less

  16. Time history prediction of direct-drive implosions on the Omega facility

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

    Laffite, S.; Bourgade, J. L.; Caillaud, T.

    We present in this article direct-drive experiments that were carried out on the Omega facility [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)]. Two different pulse shapes were tested in order to vary the implosion stability of the same target whose parameters, dimensions and composition, remained the same. The direct-drive configuration on the Omega facility allows the accurate time-resolvedmeasurement of the scattered light. We show that, provided the laser coupling is well controlled, the implosion time history, assessed by the “bang-time” and the shell trajectory measurements, can be predicted. This conclusion is independent on the pulse shape. Inmore » contrast, we show that the pulse shape affects the implosion stability, assessed by comparing the target performances between prediction and measurement. For the 1-ns square pulse, the measuredneutron number is about 80% of the prediction. Lastly, for the 2-step 2-ns pulse, we test here that this ratio falls to about 20%.« less

  17. Time history prediction of direct-drive implosions on the Omega facility

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

    Laffite, S.; Bourgade, J. L.; Caillaud, T.

    We present in this article direct-drive experiments that were carried out on the Omega facility [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)]. Two different pulse shapes were tested in order to vary the implosion stability of the same target whose parameters, dimensions and composition, remained the same. The direct-drive configuration on the Omega facility allows the accurate time-resolved measurement of the scattered light. We show that, provided the laser coupling is well controlled, the implosion time history, assessed by the “bang-time” and the shell trajectory measurements, can be predicted. This conclusion is independent on the pulse shape.more » In contrast, we show that the pulse shape affects the implosion stability, assessed by comparing the target performances between prediction and measurement. For the 1-ns square pulse, the measured neutron number is about 80% of the prediction. For the 2-step 2-ns pulse, we test here that this ratio falls to about 20%.« less

  18. Disambiguating past events: Accurate source memory for time and context depends on different retrieval processes.

    PubMed

    Persson, Bjorn M; Ainge, James A; O'Connor, Akira R

    2016-07-01

    Current animal models of episodic memory are usually based on demonstrating integrated memory for what happened, where it happened, and when an event took place. These models aim to capture the testable features of the definition of human episodic memory which stresses the temporal component of the memory as a unique piece of source information that allows us to disambiguate one memory from another. Recently though, it has been suggested that a more accurate model of human episodic memory would include contextual rather than temporal source information, as humans' memory for time is relatively poor. Here, two experiments were carried out investigating human memory for temporal and contextual source information, along with the underlying dual process retrieval processes, using an immersive virtual environment paired with a 'Remember-Know' memory task. Experiment 1 (n=28) showed that contextual information could only be retrieved accurately using recollection, while temporal information could be retrieved using either recollection or familiarity. Experiment 2 (n=24), which used a more difficult task, resulting in reduced item recognition rates and therefore less potential for contamination by ceiling effects, replicated the pattern of results from Experiment 1. Dual process theory predicts that it should only be possible to retrieve source context from an event using recollection, and our results are consistent with this prediction. That temporal information can be retrieved using familiarity alone suggests that it may be incorrect to view temporal context as analogous to other typically used source contexts. This latter finding supports the alternative proposal that time since presentation may simply be reflected in the strength of memory trace at retrieval - a measure ideally suited to trace strength interrogation using familiarity, as is typically conceptualised within the dual process framework. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break.

    PubMed

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean-Michel; García de Cortázar-Atauri, Iñaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2016-10-01

    The onset of the growing season of trees has been earlier by 2.3 days per decade during the last 40 years in temperate Europe because of global warming. The effect of temperature on plant phenology is, however, not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud endodormancy, and, on the other hand, higher temperatures are necessary to promote bud cell growth afterward. Different process-based models have been developed in the last decades to predict the date of budbreak of woody species. They predict that global warming should delay or compromise endodormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budbreak dates only, with no information on the endodormancy break date because this information is very scarce. Here, we evaluated the efficiency of a set of phenological models to accurately predict the endodormancy break dates of three fruit trees. Our results show that models calibrated solely with budbreak dates usually do not accurately predict the endodormancy break date. Providing endodormancy break date for the model parameterization results in much more accurate prediction of this latter, with, however, a higher error than that on budbreak dates. Most importantly, we show that models not calibrated with endodormancy break dates can generate large discrepancies in forecasted budbreak dates when using climate scenarios as compared to models calibrated with endodormancy break dates. This discrepancy increases with mean annual temperature and is therefore the strongest after 2050 in the southernmost regions. Our results claim for the urgent need of massive measurements of endodormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future. © 2016 John Wiley & Sons Ltd.

  20. Approaches for the accurate definition of geological time boundaries

    NASA Astrophysics Data System (ADS)

    Schaltegger, Urs; Baresel, Björn; Ovtcharova, Maria; Goudemand, Nicolas; Bucher, Hugo

    2015-04-01

    Which strategies lead to the most precise and accurate date of a given geological boundary? Geological units are usually defined by the occurrence of characteristic taxa and hence boundaries between these geological units correspond to dramatic faunal and/or floral turnovers and they are primarily defined using first or last occurrences of index species, or ideally by the separation interval between two consecutive, characteristic associations of fossil taxa. These boundaries need to be defined in a way that enables their worldwide recognition and correlation across different stratigraphic successions, using tools as different as bio-, magneto-, and chemo-stratigraphy, and astrochronology. Sedimentary sequences can be dated in numerical terms by applying high-precision chemical-abrasion, isotope-dilution, thermal-ionization mass spectrometry (CA-ID-TIMS) U-Pb age determination to zircon (ZrSiO4) in intercalated volcanic ashes. But, though volcanic activity is common in geological history, ashes are not necessarily close to the boundary we would like to date precisely and accurately. In addition, U-Pb zircon data sets may be very complex and difficult to interpret in terms of the age of ash deposition. To overcome these difficulties we use a multi-proxy approach we applied to the precise and accurate dating of the Permo-Triassic and Early-Middle Triassic boundaries in South China. a) Dense sampling of ashes across the critical time interval and a sufficiently large number of analysed zircons per ash sample can guarantee the recognition of all system complexities. Geochronological datasets from U-Pb dating of volcanic zircon may indeed combine effects of i) post-crystallization Pb loss from percolation of hydrothermal fluids (even using chemical abrasion), with ii) age dispersion from prolonged residence of earlier crystallized zircon in the magmatic system. As a result, U-Pb dates of individual zircons are both apparently younger and older than the depositional age

  1. Space can substitute for time in predicting climate-change effects on biodiversity

    USGS Publications Warehouse

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-01-01

    “Space-for-time” substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as “time-for-time” predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  2. Impacts of Earth rotation parameters on GNSS ultra-rapid orbit prediction: Derivation and real-time correction

    NASA Astrophysics Data System (ADS)

    Wang, Qianxin; Hu, Chao; Xu, Tianhe; Chang, Guobin; Hernández Moraleda, Alberto

    2017-12-01

    Analysis centers (ACs) for global navigation satellite systems (GNSSs) cannot accurately obtain real-time Earth rotation parameters (ERPs). Thus, the prediction of ultra-rapid orbits in the international terrestrial reference system (ITRS) has to utilize the predicted ERPs issued by the International Earth Rotation and Reference Systems Service (IERS) or the International GNSS Service (IGS). In this study, the accuracy of ERPs predicted by IERS and IGS is analyzed. The error of the ERPs predicted for one day can reach 0.15 mas and 0.053 ms in polar motion and UT1-UTC direction, respectively. Then, the impact of ERP errors on ultra-rapid orbit prediction by GNSS is studied. The methods for orbit integration and frame transformation in orbit prediction with introduced ERP errors dominate the accuracy of the predicted orbit. Experimental results show that the transformation from the geocentric celestial references system (GCRS) to ITRS exerts the strongest effect on the accuracy of the predicted ultra-rapid orbit. To obtain the most accurate predicted ultra-rapid orbit, a corresponding real-time orbit correction method is developed. First, orbits without ERP-related errors are predicted on the basis of ITRS observed part of ultra-rapid orbit for use as reference. Then, the corresponding predicted orbit is transformed from GCRS to ITRS to adjust for the predicted ERPs. Finally, the corrected ERPs with error slopes are re-introduced to correct the predicted orbit in ITRS. To validate the proposed method, three experimental schemes are designed: function extrapolation, simulation experiments, and experiments with predicted ultra-rapid orbits and international GNSS Monitoring and Assessment System (iGMAS) products. Experimental results show that using the proposed correction method with IERS products considerably improved the accuracy of ultra-rapid orbit prediction (except the geosynchronous BeiDou orbits). The accuracy of orbit prediction is enhanced by at least 50

  3. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

    2015-06-04

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

  4. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    PubMed Central

    2015-01-01

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

  5. Accurate seismic phase identification and arrival time picking of glacial icequakes

    NASA Astrophysics Data System (ADS)

    Jones, G. A.; Doyle, S. H.; Dow, C.; Kulessa, B.; Hubbard, A.

    2010-12-01

    A catastrophic lake drainage event was monitored continuously using an array of 6, 4.5 Hz 3 component geophones in the Russell Glacier catchment, Western Greenland. Many thousands of events and arrival time phases (e.g., P- or S-wave) were recorded, often with events occurring simultaneously but at different locations. In addition, different styles of seismic events were identified from 'classical' tectonic earthquakes to tremors usually observed in volcanic regions. The presence of such a diverse and large dataset provides insight into the complex system of lake drainage. One of the most fundamental steps in seismology is the accurate identification of a seismic event and its associated arrival times. However, the collection of such a large and complex dataset makes the manual identification of a seismic event and picking of the arrival time phases time consuming with variable results. To overcome the issues of consistency and manpower, a number of different methods have been developed including short-term and long-term averages, spectrograms, wavelets, polarisation analyses, higher order statistics and auto-regressive techniques. Here we propose an automated procedure which establishes the phase type and accurately determines the arrival times. The procedure combines a number of different automated methods to achieve this, and is applied to the recently acquired lake drainage data. Accurate identification of events and their arrival time phases are the first steps in gaining a greater understanding of the extent of the deformation and the mechanism of such drainage events. A good knowledge of the propagation pathway of lake drainage meltwater through a glacier will have significant consequences for interpretation of glacial and ice sheet dynamics.

  6. The effect of concurrent hand movement on estimated time to contact in a prediction motion task.

    PubMed

    Zheng, Ran; Maraj, Brian K V

    2018-04-27

    In many activities, we need to predict the arrival of an occluded object. This action is called prediction motion or motion extrapolation. Previous researchers have found that both eye tracking and the internal clocking model are involved in the prediction motion task. Additionally, it is reported that concurrent hand movement facilitates the eye tracking of an externally generated target in a tracking task, even if the target is occluded. The present study examined the effect of concurrent hand movement on the estimated time to contact in a prediction motion task. We found different (accurate/inaccurate) concurrent hand movements had the opposite effect on the eye tracking accuracy and estimated TTC in the prediction motion task. That is, the accurate concurrent hand tracking enhanced eye tracking accuracy and had the trend to increase the precision of estimated TTC, but the inaccurate concurrent hand tracking decreased eye tracking accuracy and disrupted estimated TTC. However, eye tracking accuracy does not determine the precision of estimated TTC.

  7. Accurate Drift Time Determination by Traveling Wave Ion Mobility Spectrometry: The Concept of the Diffusion Calibration.

    PubMed

    Kune, Christopher; Far, Johann; De Pauw, Edwin

    2016-12-06

    Ion mobility spectrometry (IMS) is a gas phase separation technique, which relies on differences in collision cross section (CCS) of ions. Ionic clouds of unresolved conformers overlap if the CCS difference is below the instrumental resolution expressed as CCS/ΔCCS. The experimental arrival time distribution (ATD) peak is then a superimposition of the various contributions weighted by their relative intensities. This paper introduces a strategy for accurate drift time determination using traveling wave ion mobility spectrometry (TWIMS) of poorly resolved or unresolved conformers. This method implements through a calibration procedure the link between the peak full width at half-maximum (fwhm) and the drift time of model compounds for wide range of settings for wave heights and velocities. We modified a Gaussian equation, which achieves the deconvolution of ATD peaks where the fwhm is fixed according to our calibration procedure. The new fitting Gaussian equation only depends on two parameters: The apex of the peak (A) and the mean drift time value (μ). The standard deviation parameter (correlated to fwhm) becomes a function of the drift time. This correlation function between μ and fwhm is obtained using the TWIMS calibration procedure which determines the maximum instrumental ion beam diffusion under limited and controlled space charge effect using ionic compounds which are detected as single conformers in the gas phase. This deconvolution process has been used to highlight the presence of poorly resolved conformers of crown ether complexes and peptides leading to more accurate CCS determinations in better agreement with quantum chemistry predictions.

  8. How accurate are resting energy expenditure prediction equations in obese trauma and burn patients?

    PubMed

    Stucky, Chee-Chee H; Moncure, Michael; Hise, Mary; Gossage, Clint M; Northrop, David

    2008-01-01

    While the prevalence of obesity continues to increase in our society, outdated resting energy expenditure (REE) prediction equations may overpredict energy requirements in obese patients. Accurate feeding is essential since overfeeding has been demonstrated to adversely affect outcomes. The first objective was to compare REE calculated by prediction equations to the measured REE in obese trauma and burn patients. Our hypothesis was that an equation using fat-free mass would give a more accurate prediction. The second objective was to consider the effect of a commonly used injury factor on the predicted REE. A retrospective chart review was performed on 28 patients. REE was measured using indirect calorimetry and compared with the Harris-Benedict and Cunningham equations, and an equation using type II diabetes as a factor. Statistical analyses used were paired t test, +/-95% confidence interval, and the Bland-Altman method. Measured average REE in trauma and burn patients was 21.37 +/- 5.26 and 21.81 +/- 3.35 kcal/kg/d, respectively. Harris-Benedict underpredicted REE in trauma and burn patients to the least extent, while the Cunningham equation underpredicted REE in both populations to the greatest extent. Using an injury factor of 1.2, Cunningham continued to underestimate REE in both populations, while the Harris-Benedict and Diabetic equations overpredicted REE in both populations. The measured average REE is significantly less than current guidelines. This finding suggests that a hypocaloric regimen is worth considering for ICU patients. Also, if an injury factor of 1.2 is incorporated in certain equations, patients may be given too many calories.

  9. Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

    PubMed

    Grimes, Tyler; Walker, Alejandro R; Datta, Susmita; Datta, Somnath

    2018-05-30

    Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining these different profiles. The use of RNA-seq data is shown to improve accuracy in comparison to using clinical data alone for predicting overall survival times. Furthermore, clinically high-risk patients can be subclassified based on their predicted overall survival times. In this effort, the best performing model was the elastic net using both transcripts and introns together. This model separated patients into two groups with 2-year overall survival rates of 0.40±0.11 (n=22) versus 0.80±0.05 (n=68). The ensemble approach gave similar results, with groups 0.42±0.10 (n=25) versus 0.82±0.05 (n=65). This suggests that the ensemble is able to effectively combine the individual RNA-seq datasets. Using predicted survival times based on RNA-seq data can provide improved prognosis by subclassifying clinically high-risk neuroblastoma patients. This article was reviewed by Subharup Guha and Isabel Nepomuceno.

  10. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

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

    Visel, Axel; Blow, Matthew J.; Li, Zirong

    2009-02-01

    A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. Wemore » tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.« less

  11. PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

    PubMed

    Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

    2015-07-05

    Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

  12. Time-Accurate Solutions of Incompressible Navier-Stokes Equations for Potential Turbopump Applications

    NASA Technical Reports Server (NTRS)

    Kiris, Cetin; Kwak, Dochan

    2001-01-01

    Two numerical procedures, one based on artificial compressibility method and the other pressure projection method, are outlined for obtaining time-accurate solutions of the incompressible Navier-Stokes equations. The performance of the two method are compared by obtaining unsteady solutions for the evolution of twin vortices behind a at plate. Calculated results are compared with experimental and other numerical results. For an un- steady ow which requires small physical time step, pressure projection method was found to be computationally efficient since it does not require any subiterations procedure. It was observed that the artificial compressibility method requires a fast convergence scheme at each physical time step in order to satisfy incompressibility condition. This was obtained by using a GMRES-ILU(0) solver in our computations. When a line-relaxation scheme was used, the time accuracy was degraded and time-accurate computations became very expensive.

  13. Jump neural network for real-time prediction of glucose concentration.

    PubMed

    Zecchin, Chiara; Facchinetti, Andrea; Sparacino, Giovanni; Cobelli, Claudio

    2015-01-01

    Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.

  14. Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand

    PubMed Central

    Lauer, Stephen A.; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E.; Salje, Henrik; Cummings, Derek A. T.; Lessler, Justin

    2016-01-01

    Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. PMID:27304062

  15. Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand.

    PubMed

    Reich, Nicholas G; Lauer, Stephen A; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E; Salje, Henrik; Cummings, Derek A T; Lessler, Justin

    2016-06-01

    Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making.

  16. Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

    PubMed

    Kim, Scott Y H

    2014-04-01

    The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

  17. Accurate and efficient calculation of response times for groundwater flow

    NASA Astrophysics Data System (ADS)

    Carr, Elliot J.; Simpson, Matthew J.

    2018-03-01

    We study measures of the amount of time required for transient flow in heterogeneous porous media to effectively reach steady state, also known as the response time. Here, we develop a new approach that extends the concept of mean action time. Previous applications of the theory of mean action time to estimate the response time use the first two central moments of the probability density function associated with the transition from the initial condition, at t = 0, to the steady state condition that arises in the long time limit, as t → ∞ . This previous approach leads to a computationally convenient estimation of the response time, but the accuracy can be poor. Here, we outline a powerful extension using the first k raw moments, showing how to produce an extremely accurate estimate by making use of asymptotic properties of the cumulative distribution function. Results are validated using an existing laboratory-scale data set describing flow in a homogeneous porous medium. In addition, we demonstrate how the results also apply to flow in heterogeneous porous media. Overall, the new method is: (i) extremely accurate; and (ii) computationally inexpensive. In fact, the computational cost of the new method is orders of magnitude less than the computational effort required to study the response time by solving the transient flow equation. Furthermore, the approach provides a rigorous mathematical connection with the heuristic argument that the response time for flow in a homogeneous porous medium is proportional to L2 / D , where L is a relevant length scale, and D is the aquifer diffusivity. Here, we extend such heuristic arguments by providing a clear mathematical definition of the proportionality constant.

  18. Accurate electrical prediction of memory array through SEM-based edge-contour extraction using SPICE simulation

    NASA Astrophysics Data System (ADS)

    Shauly, Eitan; Rotstein, Israel; Peltinov, Ram; Latinski, Sergei; Adan, Ofer; Levi, Shimon; Menadeva, Ovadya

    2009-03-01

    The continues transistors scaling efforts, for smaller devices, similar (or larger) drive current/um and faster devices, increase the challenge to predict and to control the transistor off-state current. Typically, electrical simulators like SPICE, are using the design intent (as-drawn GDS data). At more sophisticated cases, the simulators are fed with the pattern after lithography and etch process simulations. As the importance of electrical simulation accuracy is increasing and leakage is becoming more dominant, there is a need to feed these simulators, with more accurate information extracted from physical on-silicon transistors. Our methodology to predict changes in device performances due to systematic lithography and etch effects was used in this paper. In general, the methodology consists on using the OPCCmaxTM for systematic Edge-Contour-Extraction (ECE) from transistors, taking along the manufacturing and includes any image distortions like line-end shortening, corner rounding and line-edge roughness. These measurements are used for SPICE modeling. Possible application of this new metrology is to provide a-head of time, physical and electrical statistical data improving time to market. In this work, we applied our methodology to analyze a small and large array's of 2.14um2 6T-SRAM, manufactured using Tower Standard Logic for General Purposes Platform. 4 out of the 6 transistors used "U-Shape AA", known to have higher variability. The predicted electrical performances of the transistors drive current and leakage current, in terms of nominal values and variability are presented. We also used the methodology to analyze an entire SRAM Block array. Study of an isolation leakage and variability are presented.

  19. Reservoir computer predictions for the Three Meter magnetic field time evolution

    NASA Astrophysics Data System (ADS)

    Perevalov, A.; Rojas, R.; Lathrop, D. P.; Shani, I.; Hunt, B. R.

    2017-12-01

    The source of the Earth's magnetic field is the turbulent flow of liquid metal in the outer core. Our experiment's goal is to create Earth-like dynamo, to explore the mechanisms and to understand the dynamics of the magnetic and velocity fields. Since it is a complicated system, predictions of the magnetic field is a challenging problem. We present results of mimicking the three Meter experiment by a reservoir computer deep learning algorithm. The experiment is a three-meter diameter outer sphere and a one-meter diameter inner sphere with the gap filled with liquid sodium. The spheres can rotate up to 4 and 14 Hz respectively, giving a Reynolds number near to 108. Two external electromagnets apply magnetic fields, while an array of 31 external and 2 internal Hall sensors measure the resulting induced fields. We use this magnetic probe data to train a reservoir computer to predict the 3M time evolution and mimic waves in the experiment. Surprisingly accurate predictions can be made for several magnetic dipole time scales. This shows that such a complicated MHD system's behavior can be predicted. We gratefully acknowledge support from NSF EAR-1417148.

  20. A Comparison of Center/TRACON Automation System and Airline Time of Arrival Predictions

    NASA Technical Reports Server (NTRS)

    Heere, Karen R.; Zelenka, Richard E.

    2000-01-01

    Benefits from information sharing between an air traffic service provider and a major air carrier are evaluated. Aircraft arrival time schedules generated by the NASA/FAA Center/TRACON Automation System (CTAS) were provided to the American Airlines System Operations Control Center in Fort Worth, Texas, during a field trial of a specialized CTAS display. A statistical analysis indicates that the CTAS schedules, based on aircraft trajectories predicted from real-time radar and weather data, are substantially more accurate than the traditional airline arrival time estimates, constructed from flight plans and en route crew updates. The improvement offered by CTAS is especially advantageous during periods of heavy traffic and substantial terminal area delay, allowing the airline to avoid large predictive errors with serious impact on the efficiency and profitability of flight operations.

  1. Kinetic approach to degradation mechanisms in polymer solar cells and their accurate lifetime predictions

    NASA Astrophysics Data System (ADS)

    Arshad, Muhammad Azeem; Maaroufi, AbdelKrim

    2018-07-01

    A beginning has been made in the present study regarding the accurate lifetime predictions of polymer solar cells. Certain reservations about the conventionally employed temperature accelerated lifetime measurements test for its unworthiness of predicting reliable lifetimes of polymer solar cells are brought into light. Critical issues concerning the accelerated lifetime testing include, assuming reaction mechanism instead of determining it, and relying solely on the temperature acceleration of a single property of material. An advanced approach comprising a set of theoretical models to estimate the accurate lifetimes of polymer solar cells is therefore suggested in order to suitably alternate the accelerated lifetime testing. This approach takes into account systematic kinetic modeling of various possible polymer degradation mechanisms under natural weathering conditions. The proposed kinetic approach is substantiated by its applications on experimental aging data-sets of polymer solar materials/solar cells including, P3HT polymer film, bulk heterojunction (MDMO-PPV:PCBM) and dye-sensitized solar cells. Based on the suggested approach, an efficacious lifetime determination formula for polymer solar cells is derived and tested on dye-sensitized solar cells. Some important merits of the proposed method are also pointed out and its prospective applications are discussed.

  2. Prospective versus predictive control in timing of hitting a falling ball.

    PubMed

    Katsumata, Hiromu; Russell, Daniel M

    2012-02-01

    Debate exists as to whether humans use prospective or predictive control to intercept an object falling under gravity (Baurès et al. in Vis Res 47:2982-2991, 2007; Zago et al. in Vis Res 48:1532-1538, 2008). Prospective control involves using continuous information to regulate action. τ, the ratio of the size of the gap to the rate of gap closure, has been proposed as the information used in guiding interceptive actions prospectively (Lee in Ecol Psychol 10:221-250, 1998). This form of control is expected to generate movement modulation, where variability decreases over the course of an action based upon more accurate timing information. In contrast, predictive control assumes that a pre-programmed movement is triggered at an appropriate criterion timing variable. For a falling object it is commonly argued that an internal model of gravitational acceleration is used to predict the motion of the object and determine movement initiation. This form of control predicts fixed duration movements initiated at consistent time-to-contact (TTC), either across conditions (constant criterion operational timing) or within conditions (variable criterion operational timing). The current study sought to test predictive and prospective control hypotheses by disrupting continuous visual information of a falling ball and examining consistency in movement initiation and duration, and evidence for movement modulation. Participants (n = 12) batted a ball dropped from three different heights (1, 1.3 and 1.5 m), under both full-vision and partial occlusion conditions. In the occlusion condition, only the initial ball drop and the final 200 ms of ball flight to the interception point could be observed. The initiation of the swing did not occur at a consistent TTC, τ, or any other timing variable across drop heights, in contrast with previous research. However, movement onset was not impacted by occluding the ball flight for 280-380 ms. This finding indicates that humans did not

  3. Predicting the timing and potential of the spring emergence of overwintered populations of Heliothis spp

    NASA Technical Reports Server (NTRS)

    Hartstack, A. W.; Witz, J. A.; Lopez, J. D. (Principal Investigator)

    1981-01-01

    The current state of knowledge dealing with the prediction of the overwintering population and spring emergence of Heliothis spp., a serious pest of numerous crops is surveyed. Current literature is reviewed in detail. Temperature and day length are the primary factors which program H. spp. larva for possible diapause. Although studies on the interaction of temperature and day length are reported, the complete diapause induction process is not identified sufficiently to allow accurate prediction of diapause timing. Mortality during diapause is reported as highly variable. The factors causing mortality are identified, but only a few are quantified. The spring emergence of overwintering H. spp. adults and mathematical models which predict the timing of emergence are reviewed. Timing predictions compare favorably to observed field data; however, prediction of actual numbers of emerging moths is not possible. The potential for use of spring emergence predictions in pest management applications, as an early warning of potential crop damage, are excellent. Research requirements to develop such an early warning system are discussed.

  4. Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

    PubMed

    Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

    2014-07-01

    With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. © 2013 Wiley Periodicals, Inc.

  5. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

    PubMed Central

    Maturana, Matias I.; Apollo, Nicholas V.; Hadjinicolaou, Alex E.; Garrett, David J.; Cloherty, Shaun L.; Kameneva, Tatiana; Grayden, David B.; Ibbotson, Michael R.; Meffin, Hamish

    2016-01-01

    Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. PMID:27035143

  6. Do dual-route models accurately predict reading and spelling performance in individuals with acquired alexia and agraphia?

    PubMed

    Rapcsak, Steven Z; Henry, Maya L; Teague, Sommer L; Carnahan, Susan D; Beeson, Pélagie M

    2007-06-18

    Coltheart and co-workers [Castles, A., Bates, T. C., & Coltheart, M. (2006). John Marshall and the developmental dyslexias. Aphasiology, 20, 871-892; Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256] have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper, we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing.

  7. A Semi-implicit Method for Time Accurate Simulation of Compressible Flow

    NASA Astrophysics Data System (ADS)

    Wall, Clifton; Pierce, Charles D.; Moin, Parviz

    2001-11-01

    A semi-implicit method for time accurate simulation of compressible flow is presented. The method avoids the acoustic CFL limitation, allowing a time step restricted only by the convective velocity. Centered discretization in both time and space allows the method to achieve zero artificial attenuation of acoustic waves. The method is an extension of the standard low Mach number pressure correction method to the compressible Navier-Stokes equations, and the main feature of the method is the solution of a Helmholtz type pressure correction equation similar to that of Demirdžić et al. (Int. J. Num. Meth. Fluids, Vol. 16, pp. 1029-1050, 1993). The method is attractive for simulation of acoustic combustion instabilities in practical combustors. In these flows, the Mach number is low; therefore the time step allowed by the convective CFL limitation is significantly larger than that allowed by the acoustic CFL limitation, resulting in significant efficiency gains. Also, the method's property of zero artificial attenuation of acoustic waves is important for accurate simulation of the interaction between acoustic waves and the combustion process. The method has been implemented in a large eddy simulation code, and results from several test cases will be presented.

  8. Motor Synchronization in Patients With Schizophrenia: Preserved Time Representation With Abnormalities in Predictive Timing.

    PubMed

    Wilquin, Hélène; Delevoye-Turrell, Yvonne; Dione, Mariama; Giersch, Anne

    2018-01-01

    Objective: Basic temporal dysfunctions have been described in patients with schizophrenia, which may impact their ability to connect and synchronize with the outer world. The present study was conducted with the aim to distinguish between interval timing and synchronization difficulties and more generally the spatial-temporal organization disturbances for voluntary actions. A new sensorimotor synchronization task was developed to test these abilities. Method: Twenty-four chronic schizophrenia patients matched with 27 controls performed a spatial-tapping task in which finger taps were to be produced in synchrony with a regular metronome to six visual targets presented around a virtual circle on a tactile screen. Isochronous (time intervals of 500 ms) and non-isochronous auditory sequences (alternated time intervals of 300/600 ms) were presented. The capacity to produce time intervals accurately versus the ability to synchronize own actions (tap) with external events (tone) were measured. Results: Patients with schizophrenia were able to produce the tapping patterns of both isochronous and non-isochronous auditory sequences as accurately as controls producing inter-response intervals close to the expected interval of 500 and 900 ms, respectively. However, the synchronization performances revealed significantly more positive asynchrony means (but similar variances) in the patient group than in the control group for both types of auditory sequences. Conclusion: The patterns of results suggest that patients with schizophrenia are able to perceive and produce both simple and complex sequences of time intervals but are impaired in the ability to synchronize their actions with external events. These findings suggest a specific deficit in predictive timing, which may be at the core of early symptoms previously described in schizophrenia.

  9. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

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

    Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au

    2013-11-21

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys.more » 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.« less

  10. Efficiency and Accuracy of Time-Accurate Turbulent Navier-Stokes Computations

    NASA Technical Reports Server (NTRS)

    Rumsey, Christopher L.; Sanetrik, Mark D.; Biedron, Robert T.; Melson, N. Duane; Parlette, Edward B.

    1995-01-01

    The accuracy and efficiency of two types of subiterations in both explicit and implicit Navier-Stokes codes are explored for unsteady laminar circular-cylinder flow and unsteady turbulent flow over an 18-percent-thick circular-arc (biconvex) airfoil. Grid and time-step studies are used to assess the numerical accuracy of the methods. Nonsubiterative time-stepping schemes and schemes with physical time subiterations are subject to time-step limitations in practice that are removed by pseudo time sub-iterations. Computations for the circular-arc airfoil indicate that a one-equation turbulence model predicts the unsteady separated flow better than an algebraic turbulence model; also, the hysteresis with Mach number of the self-excited unsteadiness due to shock and boundary-layer separation is well predicted.

  11. Development and Validation of a New Air Carrier Block Time Prediction Model and Methodology

    NASA Astrophysics Data System (ADS)

    Litvay, Robyn Olson

    Commercial airline operations rely on predicted block times as the foundation for critical, successive decisions that include fuel purchasing, crew scheduling, and airport facility usage planning. Small inaccuracies in the predicted block times have the potential to result in huge financial losses, and, with profit margins for airline operations currently almost nonexistent, potentially negate any possible profit. Although optimization techniques have resulted in many models targeting airline operations, the challenge of accurately predicting and quantifying variables months in advance remains elusive. The objective of this work is the development of an airline block time prediction model and methodology that is practical, easily implemented, and easily updated. Research was accomplished, and actual U.S., domestic, flight data from a major airline was utilized, to develop a model to predict airline block times with increased accuracy and smaller variance in the actual times from the predicted times. This reduction in variance represents tens of millions of dollars (U.S.) per year in operational cost savings for an individual airline. A new methodology for block time prediction is constructed using a regression model as the base, as it has both deterministic and probabilistic components, and historic block time distributions. The estimation of the block times for commercial, domestic, airline operations requires a probabilistic, general model that can be easily customized for a specific airline’s network. As individual block times vary by season, by day, and by time of day, the challenge is to make general, long-term estimations representing the average, actual block times while minimizing the variation. Predictions of block times for the third quarter months of July and August of 2011 were calculated using this new model. The resulting, actual block times were obtained from the Research and Innovative Technology Administration, Bureau of Transportation Statistics

  12. How Accurately Can We Predict Eclipses for Algol? (Poster abstract)

    NASA Astrophysics Data System (ADS)

    Turner, D.

    2016-06-01

    (Abstract only) beta Persei, or Algol, is a very well known eclipsing binary system consisting of a late B-type dwarf that is regularly eclipsed by a GK subgiant every 2.867 days. Eclipses, which last about 8 hours, are regular enough that predictions for times of minima are published in various places, Sky & Telescope magazine and The Observer's Handbook, for example. But eclipse minimum lasts for less than a half hour, whereas subtle mistakes in the current ephemeris for the star can result in predictions that are off by a few hours or more. The Algol system is fairly complex, with the Algol A and Algol B eclipsing system also orbited by Algol C with an orbital period of nearly 2 years. Added to that are complex long-term O-C variations with a periodicity of almost two centuries that, although suggested by Hoffmeister to be spurious, fit the type of light travel time variations expected for a fourth star also belonging to the system. The AB sub-system also undergoes mass transfer events that add complexities to its O-C behavior. Is it actually possible to predict precise times of eclipse minima for Algol months in advance given such complications, or is it better to encourage ongoing observations of the star so that O-C variations can be tracked in real time?

  13. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

    PubMed

    Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo

    2017-01-01

    Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have

  14. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

    PubMed Central

    Li, Zhen; Zhang, Renyu

    2017-01-01

    Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact

  15. Obtaining Accurate Probabilities Using Classifier Calibration

    ERIC Educational Resources Information Center

    Pakdaman Naeini, Mahdi

    2016-01-01

    Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

  16. Knotty: Efficient and Accurate Prediction of Complex RNA Pseudoknot Structures.

    PubMed

    Jabbari, Hosna; Wark, Ian; Montemagno, Carlo; Will, Sebastian

    2018-06-01

    The computational prediction of RNA secondary structure by free energy minimization has become an important tool in RNA research. However in practice, energy minimization is mostly limited to pseudoknot-free structures or rather simple pseudoknots, not covering many biologically important structures such as kissing hairpins. Algorithms capable of predicting sufficiently complex pseudoknots (for sequences of length n) used to have extreme complexities, e.g. Pknots (Rivas and Eddy, 1999) has O(n6) time and O(n4) space complexity. The algorithm CCJ (Chen et al., 2009) dramatically improves the asymptotic run time for predicting complex pseudoknots (handling almost all relevant pseudoknots, while being slightly less general than Pknots), but this came at the cost of large constant factors in space and time, which strongly limited its practical application (∼200 bases already require 256GB space). We present a CCJ-type algorithm, Knotty, that handles the same comprehensive pseudoknot class of structures as CCJ with improved space complexity of Θ(n3 + Z)-due to the applied technique of sparsification, the number of "candidates", Z, appears to grow significantly slower than n4 on our benchmark set (which include pseudoknotted RNAs up to 400 nucleotides). In terms of run time over this benchmark, Knotty clearly outperforms Pknots and the original CCJ implementation, CCJ 1.0; Knotty's space consumption fundamentally improves over CCJ 1.0, being on a par with the space-economic Pknots. By comparing to CCJ 2.0, our unsparsified Knotty variant, we demonstrate the isolated effect of sparsification. Moreover, Knotty employs the state-of-the-art energy model of "HotKnots DP09", which results in superior prediction accuracy over Pknots. Our software is available at https://github.com/HosnaJabbari/Knotty. will@tbi.unvie.ac.at. Supplementary data are available at Bioinformatics online.

  17. Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb

    DOE PAGES

    Monti, Remo; Barozzi, Iros; Osterwalder, Marco; ...

    2017-08-21

    Epigenomic mapping of enhancer-associated chromatin modifications facilitates the genome-wide discovery of tissue-specific enhancers in vivo. However, reliance on single chromatin marks leads to high rates of false-positive predictions. More sophisticated, integrative methods have been described, but commonly suffer from limited accessibility to the resulting predictions and reduced biological interpretability. Here we present the Limb-Enhancer Genie (LEG), a collection of highly accurate, genome-wide predictions of enhancers in the developing limb, available through a user-friendly online interface. We predict limb enhancers using a combination of > 50 published limb-specific datasets and clusters of evolutionarily conserved transcription factor binding sites, taking advantage ofmore » the patterns observed at previously in vivo validated elements. By combining different statistical models, our approach outperforms current state-of-the-art methods and provides interpretable measures of feature importance. Our results indicate that including a previously unappreciated score that quantifies tissue-specific nuclease accessibility significantly improves prediction performance. We demonstrate the utility of our approach through in vivo validation of newly predicted elements. Moreover, we describe general features that can guide the type of datasets to include when predicting tissue-specific enhancers genome-wide, while providing an accessible resource to the general biological community and facilitating the functional interpretation of genetic studies of limb malformations.« less

  18. A time series based sequence prediction algorithm to detect activities of daily living in smart home.

    PubMed

    Marufuzzaman, M; Reaz, M B I; Ali, M A M; Rahman, L F

    2015-01-01

    The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included. The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge. The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED. Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.

  19. Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

    PubMed Central

    Garcia Lopez, Sebastian; Kim, Philip M.

    2014-01-01

    Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

  20. Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

    PubMed

    Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

    2011-12-01

    Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy. Copyright © 2011 Wiley-Liss, Inc.

  1. Predicting pornography use over time: Does self-reported "addiction" matter?

    PubMed

    Grubbs, Joshua B; Wilt, Joshua A; Exline, Julie J; Pargament, Kenneth I

    2018-07-01

    In recent years, several works have reported on perceived addiction to internet pornography, or the potential for some individuals to label their own use of pornography as compulsive or out of control. Such works have consistently found that perceived addiction is related to concerning outcomes such as psychological distress, relational distress, and other addictive behaviors. However, very little work has specifically examined whether or not perceived addiction is actually related to increased use of pornography, cross-sectionally or over time. The present work sought to address this deficit in the literature. Using two longitudinal samples (Sample 1, Baseline N = 3988; Sample 2, Baseline N = 1047), a variety of factors (e.g., male gender, lower religiousness, and lower self-control) were found to predict any use of pornography. Among those that acknowledged use (Sample 1, Baseline N = 1352; Sample 2, Baseline N = 793), perceived addiction to pornography consistently predicted greater average daily use of pornography. At subsequent longitudinal follow-ups (Sample 1, Baseline N = 265; Sample 2, One Month Later, N = 410, One Year Later, N = 360), only male gender and baseline average pornography use consistently predicted future use. These findings suggest that perceived addiction to pornography is associated with concurrent use of pornography, but does not appear to predict use over time, suggesting that perceived addiction may not always be an accurate indicator of behavior or addiction. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

    PubMed

    Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

    2018-06-01

    The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  3. MO-G-18C-05: Real-Time Prediction in Free-Breathing Perfusion MRI

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

    Song, H; Liu, W; Ruan, D

    Purpose: The aim is to minimize frame-wise difference errors caused by respiratory motion and eliminate the need for breath-holds in magnetic resonance imaging (MRI) sequences with long acquisitions and repeat times (TRs). The technique is being applied to perfusion MRI using arterial spin labeling (ASL). Methods: Respiratory motion prediction (RMP) using navigator echoes was implemented in ASL. A least-square method was used to extract the respiratory motion information from the 1D navigator. A generalized artificial neutral network (ANN) with three layers was developed to simultaneously predict 10 time points forward in time and correct for respiratory motion during MRI acquisition.more » During the training phase, the parameters of the ANN were optimized to minimize the aggregated prediction error based on acquired navigator data. During realtime prediction, the trained ANN was applied to the most recent estimated displacement trajectory to determine in real-time the amount of spatial Results: The respiratory motion information extracted from the least-square method can accurately represent the navigator profiles, with a normalized chi-square value of 0.037±0.015 across the training phase. During the 60-second training phase, the ANN successfully learned the respiratory motion pattern from the navigator training data. During real-time prediction, the ANN received displacement estimates and predicted the motion in the continuum of a 1.0 s prediction window. The ANN prediction was able to provide corrections for different respiratory states (i.e., inhalation/exhalation) during real-time scanning with a mean absolute error of < 1.8 mm. Conclusion: A new technique enabling free-breathing acquisition during MRI is being developed. A generalized ANN development has demonstrated its efficacy in predicting a continuum of motion profile for volumetric imaging based on navigator inputs. Future work will enhance the robustness of ANN and verify its effectiveness with

  4. Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

    NASA Technical Reports Server (NTRS)

    Voorhies, Coerte V.; Conrad, Joy

    1996-01-01

    The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

  5. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

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

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasetsmore » having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds

  6. Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

    PubMed

    Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

    2017-12-12

    Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

  7. Can recording only the day-time voided volumes predict bladder capacity?

    PubMed

    Cho, Won Yeol; Kim, Seong Cheol; Kim, Sun-Ouck; Park, Sungchan; Lee, Sang Don; Chung, Jae Min; Kim, Kyung Do; Moon, Du Geon; Kim, Young Sig; Kim, Jun Mo

    2018-05-01

    This study aimed to demonstrate a method to easily assess bladder capacity using knowledge of day-time voided volumes, which can be obtained even from patients with nocturnal enuresis where the first morning void cannot accurately predict the bladder capacity due to bladder emptying overnight. We evaluated 177 healthy children from 7 Korean medical centres entered the study between January 2008 and January 2009. Voided volumes measured for more than 48 hours were recorded in the frequency volume chart (FVC). Most voided volumes during day-time were showed between 30% and 80% of the maximal voided volume (MVV). The maximal voided volume during day-time (MVVDT) was significantly less than the MVV (179.5±71.1 mL vs. 227.0±79.2 mL, p<0.001). The correlation coefficients with the MVV were 0.801 for the estimated MVV using the MVVDT (MVVDT×1.25), which suggested a fairly strong relationship between the MVVDT×1.25 and the MVV. The MVV derived from the FVC excluding the FMV was less than if the FMV had been included. When an accurate first morning voided volume cannot be obtained, as in patients with nocturnal enuresis, calculating MVVDT×1.25 allows estimation of the bladder capacity in place of the MVV.

  8. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.

    PubMed

    Jones, David T; Singh, Tanya; Kosciolek, Tomasz; Tetchner, Stuart

    2015-04-01

    Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts-around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

  9. Accurate frequency and time dissemination in the optical domain

    NASA Astrophysics Data System (ADS)

    Khabarova, K. Yu; Kalganova, E. S.; Kolachevsky, N. N.

    2018-02-01

    The development of the optical frequency comb technique has enabled a wide use of atomic optical clocks by allowing frequency conversion from the optical to the radio frequency range. Today, the fractional instability of such clocks has reached the record eighteen-digit level, two orders of magnitude better than for cesium fountains representing the primary frequency standard. This is paralleled by the development of techniques for transferring accurate time and optical frequency signals, including fiber links. With this technology, the fractional instability of transferred frequency can be lowered to below 10‑18 with an averaging time of 1000 s for a 1000 km optical link. At a distance of 500 km, a time signal uncertainty of 250 ps has been achieved. Optical links allow comparing optical clocks and creating a synchronized time and frequency standard network at a new level of precision. Prospects for solving new problems arise, including the determination of the gravitational potential, the measurement of the continental Sagnac effect, and precise tests of fundamental theories.

  10. Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

    PubMed

    Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

    2017-09-01

    Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

  11. WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

    PubMed

    Chi, Sang-Mun; Nam, Dougu

    2012-04-01

    We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

  12. Sex-specific lean body mass predictive equations are accurate in the obese paediatric population

    PubMed Central

    Jackson, Lanier B.; Henshaw, Melissa H.; Carter, Janet; Chowdhury, Shahryar M.

    2015-01-01

    Background The clinical assessment of lean body mass (LBM) is challenging in obese children. A sex-specific predictive equation for LBM derived from anthropometric data was recently validated in children. Aim The purpose of this study was to independently validate these predictive equations in the obese paediatric population. Subjects and methods Obese subjects aged 4–21 were analysed retrospectively. Predicted LBM (LBMp) was calculated using equations previously developed in children. Measured LBM (LBMm) was derived from dual-energy x-ray absorptiometry. Agreement was expressed as [(LBMm-LBMp)/LBMm] with 95% limits of agreement. Results Of 310 enrolled patients, 195 (63%) were females. The mean age was 11.8 ± 3.4 years and mean BMI Z-score was 2.3 ± 0.4. The average difference between LBMm and LBMp was −0.6% (−17.0%, 15.8%). Pearson’s correlation revealed a strong linear relationship between LBMm and LBMp (r=0.97, p<0.01). Conclusion This study validates the use of these clinically-derived sex-specific LBM predictive equations in the obese paediatric population. Future studies should use these equations to improve the ability to accurately classify LBM in obese children. PMID:26287383

  13. Accurate Rapid Lifetime Determination on Time-Gated FLIM Microscopy with Optical Sectioning

    PubMed Central

    Silva, Susana F.; Domingues, José Paulo

    2018-01-01

    Time-gated fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to assess the biochemistry of cells and tissues. When applied to living thick samples, it is hampered by the lack of optical sectioning and the need of acquiring many images for an accurate measurement of fluorescence lifetimes. Here, we report on the use of processing techniques to overcome these limitations, minimizing the acquisition time, while providing optical sectioning. We evaluated the application of the HiLo and the rapid lifetime determination (RLD) techniques for accurate measurement of fluorescence lifetimes with optical sectioning. HiLo provides optical sectioning by combining the high-frequency content from a standard image, obtained with uniform illumination, with the low-frequency content of a second image, acquired using structured illumination. Our results show that HiLo produces optical sectioning on thick samples without degrading the accuracy of the measured lifetimes. We also show that instrument response function (IRF) deconvolution can be applied with the RLD technique on HiLo images, improving greatly the accuracy of the measured lifetimes. These results open the possibility of using the RLD technique with pulsed diode laser sources to determine accurately fluorescence lifetimes in the subnanosecond range on thick multilayer samples, providing that offline processing is allowed. PMID:29599938

  14. Accurate Rapid Lifetime Determination on Time-Gated FLIM Microscopy with Optical Sectioning.

    PubMed

    Silva, Susana F; Domingues, José Paulo; Morgado, António Miguel

    2018-01-01

    Time-gated fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to assess the biochemistry of cells and tissues. When applied to living thick samples, it is hampered by the lack of optical sectioning and the need of acquiring many images for an accurate measurement of fluorescence lifetimes. Here, we report on the use of processing techniques to overcome these limitations, minimizing the acquisition time, while providing optical sectioning. We evaluated the application of the HiLo and the rapid lifetime determination (RLD) techniques for accurate measurement of fluorescence lifetimes with optical sectioning. HiLo provides optical sectioning by combining the high-frequency content from a standard image, obtained with uniform illumination, with the low-frequency content of a second image, acquired using structured illumination. Our results show that HiLo produces optical sectioning on thick samples without degrading the accuracy of the measured lifetimes. We also show that instrument response function (IRF) deconvolution can be applied with the RLD technique on HiLo images, improving greatly the accuracy of the measured lifetimes. These results open the possibility of using the RLD technique with pulsed diode laser sources to determine accurately fluorescence lifetimes in the subnanosecond range on thick multilayer samples, providing that offline processing is allowed.

  15. Accurate prediction of cation-π interaction energy using substituent effects.

    PubMed

    Sayyed, Fareed Bhasha; Suresh, Cherumuttathu H

    2012-06-14

    (M(+))' and ΔV(min). All the Φ-X···M(+) systems showed good agreement between the calculated and predicted E(M(+))() values, suggesting that the ΔV(min) approach to substituent effect is accurate and useful for predicting the interactive behavior of substituted π-systems with cations.

  16. Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications.

    PubMed

    Tatinati, Sivanagaraja; Nazarpour, Kianoush; Tech Ang, Wei; Veluvolu, Kalyana C

    2016-08-01

    Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

  17. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

    PubMed

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  18. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

    PubMed Central

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

  19. Data assimialation for real-time prediction and reanalysis

    NASA Astrophysics Data System (ADS)

    Shprits, Y.; Kellerman, A. C.; Podladchikova, T.; Kondrashov, D. A.; Ghil, M.

    2015-12-01

    We discuss the how data assimilation can be used for the analysis of individual satellite anomalies, development of long-term evolution reconstruction that can be used for the specification models, and use of data assimilation to improve the now-casting and focusing of the radiation belts. We also discuss advanced data assimilation methods such as parameter estimation and smoothing.The 3D data assimilative VERB allows us to blend together data from GOES, RBSP A and RBSP B. Real-time prediction framework operating on our web site based on GOES, RBSP A, B and ACE data and 3D VERB is presented and discussed. In this paper we present a number of application of the data assimilation with the VERB 3D code. 1) Model with data assimilation allows to propagate data to different pitch angles, energies, and L-shells and blends them together with the physics based VERB code in an optimal way. We illustrate how we use this capability for the analysis of the previous events and for obtaining a global and statistical view of the system. 2) The model predictions strongly depend on initial conditions that are set up for the model. Therefore the model is as good as the initial conditions that it uses. To produce the best possible initial condition data from different sources ( GOES, RBSP A, B, our empirical model predictions based on ACE) are all blended together in an optimal way by means of data assimilation as described above. The resulting initial condition does not have gaps. That allows us to make a more accurate predictions.

  20. A time accurate finite volume high resolution scheme for three dimensional Navier-Stokes equations

    NASA Technical Reports Server (NTRS)

    Liou, Meng-Sing; Hsu, Andrew T.

    1989-01-01

    A time accurate, three-dimensional, finite volume, high resolution scheme for solving the compressible full Navier-Stokes equations is presented. The present derivation is based on the upwind split formulas, specifically with the application of Roe's (1981) flux difference splitting. A high-order accurate (up to the third order) upwind interpolation formula for the inviscid terms is derived to account for nonuniform meshes. For the viscous terms, discretizations consistent with the finite volume concept are described. A variant of second-order time accurate method is proposed that utilizes identical procedures in both the predictor and corrector steps. Avoiding the definition of midpoint gives a consistent and easy procedure, in the framework of finite volume discretization, for treating viscous transport terms in the curvilinear coordinates. For the boundary cells, a new treatment is introduced that not only avoids the use of 'ghost cells' and the associated problems, but also satisfies the tangency conditions exactly and allows easy definition of viscous transport terms at the first interface next to the boundary cells. Numerical tests of steady and unsteady high speed flows show that the present scheme gives accurate solutions.

  1. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate resultsmore » from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

  2. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe 3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe 3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yieldmore » accurate results from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

  3. Accurate prediction of collapse temperature using optical coherence tomography-based freeze-drying microscopy.

    PubMed

    Greco, Kristyn; Mujat, Mircea; Galbally-Kinney, Kristin L; Hammer, Daniel X; Ferguson, R Daniel; Iftimia, Nicusor; Mulhall, Phillip; Sharma, Puneet; Kessler, William J; Pikal, Michael J

    2013-06-01

    The objective of this study was to assess the feasibility of developing and applying a laboratory tool that can provide three-dimensional product structural information during freeze-drying and which can accurately characterize the collapse temperature (Tc ) of pharmaceutical formulations designed for freeze-drying. A single-vial freeze dryer coupled with optical coherence tomography freeze-drying microscopy (OCT-FDM) was developed to investigate the structure and Tc of formulations in pharmaceutically relevant products containers (i.e., freeze-drying in vials). OCT-FDM was used to measure the Tc and eutectic melt of three formulations in freeze-drying vials. The Tc as measured by OCT-FDM was found to be predictive of freeze-drying with a batch of vials in a conventional laboratory freeze dryer. The freeze-drying cycles developed using OCT-FDM data, as compared with traditional light transmission freeze-drying microscopy (LT-FDM), resulted in a significant reduction in primary drying time, which could result in a substantial reduction of manufacturing costs while maintaining product quality. OCT-FDM provides quantitative data to justify freeze-drying at temperatures higher than the Tc measured by LT-FDM and provides a reliable upper limit to setting a product temperature in primary drying. Copyright © 2013 Wiley Periodicals, Inc.

  4. Accurate indel prediction using paired-end short reads

    PubMed Central

    2013-01-01

    Background One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives. Results Here, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project ( http://www.1001genomes.org) in Arabidopsis thaliana. Conclusion In this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/. PMID:23442375

  5. Accuration of Time Series and Spatial Interpolation Method for Prediction of Precipitation Distribution on the Geographical Information System

    NASA Astrophysics Data System (ADS)

    Prasetyo, S. Y. J.; Hartomo, K. D.

    2018-01-01

    The Spatial Plan of the Province of Central Java 2009-2029 identifies that most regencies or cities in Central Java Province are very vulnerable to landslide disaster. The data are also supported by other data from Indonesian Disaster Risk Index (In Indonesia called Indeks Risiko Bencana Indonesia) 2013 that suggest that some areas in Central Java Province exhibit a high risk of natural disasters. This research aims to develop an application architecture and analysis methodology in GIS to predict and to map rainfall distribution. We propose our GIS architectural application of “Multiplatform Architectural Spatiotemporal” and data analysis methods of “Triple Exponential Smoothing” and “Spatial Interpolation” as our significant scientific contribution. This research consists of 2 (two) parts, namely attribute data prediction using TES method and spatial data prediction using Inverse Distance Weight (IDW) method. We conduct our research in 19 subdistricts in the Boyolali Regency, Central Java Province, Indonesia. Our main research data is the biweekly rainfall data in 2000-2016 Climatology, Meteorology, and Geophysics Agency (In Indonesia called Badan Meteorologi, Klimatologi, dan Geofisika) of Central Java Province and Laboratory of Plant Disease Observations Region V Surakarta, Central Java. The application architecture and analytical methodology of “Multiplatform Architectural Spatiotemporal” and spatial data analysis methodology of “Triple Exponential Smoothing” and “Spatial Interpolation” can be developed as a GIS application framework of rainfall distribution for various applied fields. The comparison between the TES and IDW methods show that relative to time series prediction, spatial interpolation exhibit values that are approaching actual. Spatial interpolation is closer to actual data because computed values are the rainfall data of the nearest location or the neighbour of sample values. However, the IDW’s main weakness is that some

  6. Review of Thawing Time Prediction Models Depending
on Process Conditions and Product Characteristics

    PubMed Central

    Kluza, Franciszek; Spiess, Walter E. L.; Kozłowicz, Katarzyna

    2016-01-01

    Summary Determining thawing times of frozen foods is a challenging problem as the thermophysical properties of the product change during thawing. A number of calculation models and solutions have been developed. The proposed solutions range from relatively simple analytical equations based on a number of assumptions to a group of empirical approaches that sometimes require complex calculations. In this paper analytical, empirical and graphical models are presented and critically reviewed. The conditions of solution, limitations and possible applications of the models are discussed. The graphical and semi--graphical models are derived from numerical methods. Using the numerical methods is not always possible as running calculations takes time, whereas the specialized software and equipment are not always cheap. For these reasons, the application of analytical-empirical models is more useful for engineering. It is demonstrated that there is no simple, accurate and feasible analytical method for thawing time prediction. Consequently, simplified methods are needed for thawing time estimation of agricultural and food products. The review reveals the need for further improvement of the existing solutions or development of new ones that will enable accurate determination of thawing time within a wide range of practical conditions of heat transfer during processing. PMID:27904387

  7. Accurate and robust genomic prediction of celiac disease using statistical learning.

    PubMed

    Abraham, Gad; Tye-Din, Jason A; Bhalala, Oneil G; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

    2014-02-01

    Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87-0.89) and in independent replication across cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models explained 30-35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases.

  8. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models

    NASA Astrophysics Data System (ADS)

    Blackman, Jonathan; Field, Scott E.; Galley, Chad R.; Szilágyi, Béla; Scheel, Mark A.; Tiglio, Manuel; Hemberger, Daniel A.

    2015-09-01

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic -2Yℓm waveform modes resolved by the NR code up to ℓ=8 . We compare our surrogate model to effective one body waveforms from 50 M⊙ to 300 M⊙ for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  9. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

    PubMed

    Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-09-18

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  10. LPTA: Location Predictive and Time Adaptive Data Gathering Scheme with Mobile Sink for Wireless Sensor Networks

    PubMed Central

    Rodrigues, Joel J. P. C.

    2014-01-01

    This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes. PMID:25302327

  11. Highly accurate pulse-per-second timing distribution over optical fibre network using VCSEL side-mode injection

    NASA Astrophysics Data System (ADS)

    Wassin, Shukree; Isoe, George M.; Gamatham, Romeo R. G.; Leitch, Andrew W. R.; Gibbon, Tim B.

    2017-01-01

    Precise and accurate timing signals distributed between a centralized location and several end-users are widely used in both metro-access and speciality networks for Coordinated Universal Time (UTC), GPS satellite systems, banking, very long baseline interferometry and science projects such as SKA radio telescope. Such systems utilize time and frequency technology to ensure phase coherence among data signals distributed across an optical fibre network. For accurate timing requirements, precise time intervals should be measured between successive pulses. In this paper we describe a novel, all optical method for quantifying one-way propagation times and phase perturbations in the fibre length, using pulse-persecond (PPS) signals. The approach utilizes side mode injection of a 1550nm 10Gbps vertical cavity surface emitting laser (VCSEL) at the remote end. A 125 μs one-way time of flight was accurately measured for 25 km G655 fibre. Since the approach is all-optical, it avoids measurement inaccuracies introduced by electro-optical conversion phase delays. Furthermore, the implementation uses cost effective VCSEL technology and suited to a flexible range of network architectures, supporting a number of end-users conducting measurements at the remote end.

  12. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

    PubMed Central

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-01-01

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car. PMID:26927108

  13. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

    PubMed

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-02-24

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  14. Feedback about More Accurate versus Less Accurate Trials: Differential Effects on Self-Confidence and Activation

    ERIC Educational Resources Information Center

    Badami, Rokhsareh; VaezMousavi, Mohammad; Wulf, Gabriele; Namazizadeh, Mahdi

    2012-01-01

    One purpose of the present study was to examine whether self-confidence or anxiety would be differentially affected by feedback from more accurate rather than less accurate trials. The second purpose was to determine whether arousal variations (activation) would predict performance. On Day 1, participants performed a golf putting task under one of…

  15. Big data prediction of durations for online collective actions based on peak's timing

    NASA Astrophysics Data System (ADS)

    Nie, Shizhao; Wang, Zheng; Pujia, Wangmo; Nie, Yuan; Lu, Peng

    2018-02-01

    Peak Model states that each collective action has a life circle, which contains four periods of "prepare", "outbreak", "peak", and "vanish"; and the peak determines the max energy and the whole process. The peak model's re-simulation indicates that there seems to be a stable ratio between the peak's timing (TP) and the total span (T) or duration of collective actions, which needs further validations through empirical data of collective actions. Therefore, the daily big data of online collective actions is applied to validate the model; and the key is to check the ratio between peak's timing and the total span. The big data is obtained from online data recording & mining of websites. It is verified by the empirical big data that there is a stable ratio between TP and T; furthermore, it seems to be normally distributed. This rule holds for both the general cases and the sub-types of collective actions. Given the distribution of the ratio, estimated probability density function can be obtained, and therefore the span can be predicted via the peak's timing. Under the scenario of big data, the instant span (how long the collective action lasts or when it ends) will be monitored and predicted in real-time. With denser data (Big Data), the estimation of the ratio's distribution gets more robust, and the prediction of collective actions' spans or durations will be more accurate.

  16. Children’s Processing and Comprehension of Complex Sentences Containing Temporal Connectives: The Influence of Memory on the Time Course of Accurate Responses

    PubMed Central

    2016-01-01

    In a touch-screen paradigm, we recorded 3- to 7-year-olds’ (N = 108) accuracy and response times (RTs) to assess their comprehension of 2-clause sentences containing before and after. Children were influenced by order: performance was most accurate when the presentation order of the 2 clauses matched the chronological order of events: “She drank the juice, before she walked in the park” (chronological order) versus “Before she walked in the park, she drank the juice” (reverse order). Differences in RTs for correct responses varied by sentence type: accurate responses were made more speedily for sentences that afforded an incremental processing of meaning. An independent measure of memory predicted this pattern of performance. We discuss these findings in relation to children’s knowledge of connective meaning and the processing requirements of sentences containing temporal connectives. PMID:27690492

  17. Interactions of timing and prediction error learning.

    PubMed

    Kirkpatrick, Kimberly

    2014-01-01

    Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields. Copyright © 2013 Elsevier B.V. All rights reserved.

  18. Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

    NASA Technical Reports Server (NTRS)

    Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

    2011-01-01

    A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

  19. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    PubMed Central

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  20. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    NASA Astrophysics Data System (ADS)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  1. The Space-Time Conservative Schemes for Large-Scale, Time-Accurate Flow Simulations with Tetrahedral Meshes

    NASA Technical Reports Server (NTRS)

    Venkatachari, Balaji Shankar; Streett, Craig L.; Chang, Chau-Lyan; Friedlander, David J.; Wang, Xiao-Yen; Chang, Sin-Chung

    2016-01-01

    Despite decades of development of unstructured mesh methods, high-fidelity time-accurate simulations are still predominantly carried out on structured, or unstructured hexahedral meshes by using high-order finite-difference, weighted essentially non-oscillatory (WENO), or hybrid schemes formed by their combinations. In this work, the space-time conservation element solution element (CESE) method is used to simulate several flow problems including supersonic jet/shock interaction and its impact on launch vehicle acoustics, and direct numerical simulations of turbulent flows using tetrahedral meshes. This paper provides a status report for the continuing development of the space-time conservation element solution element (CESE) numerical and software framework under the Revolutionary Computational Aerosciences (RCA) project. Solution accuracy and large-scale parallel performance of the numerical framework is assessed with the goal of providing a viable paradigm for future high-fidelity flow physics simulations.

  2. Using radiance predicted by the P3 approximation in a spherical geometry to predict tissue optical properties

    NASA Astrophysics Data System (ADS)

    Dickey, Dwayne J.; Moore, Ronald B.; Tulip, John

    2001-01-01

    For photodynamic therapy of solid tumors, such as prostatic carcinoma, to be achieved, an accurate model to predict tissue parameters and light dose must be found. Presently, most analytical light dosimetry models are fluence based and are not clinically viable for tissue characterization. Other methods of predicting optical properties, such as Monet Carlo, are accurate but far too time consuming for clinical application. However, radiance predicted by the P3-Approximation, an anaylitical solution to the transport equation, may be a viable and accurate alternative. The P3-Approximation accurately predicts optical parameters in intralipid/methylene blue based phantoms in a spherical geometry. The optical parameters furnished by the radiance, when introduced into fluence predicted by both P3- Approximation and Grosjean Theory, correlate well with experimental data. The P3-Approximation also predicts the optical properties of prostate tissue, agreeing with documented optical parameters. The P3-Approximation could be the clinical tool necessary to facilitate PDT of solid tumors because of the limited number of invasive measurements required and the speed in which accurate calculations can be performed.

  3. An instrument for rapid, accurate, determination of fuel moisture content

    Treesearch

    Stephen S. Sackett

    1980-01-01

    Moisture contents of dead and living fuels are key variables in fire behavior. Accurate, real-time fuel moisture data are required for prescribed burning and wildfire behavior predictions. The convection oven method has become the standard for direct fuel moisture content determination. Efforts to quantify fuel moisture through indirect methods have not been...

  4. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

    DOE PAGES

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.; ...

    2017-06-05

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy,more » we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  5. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

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

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy,more » we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  6. Predicting Ambulance Time of Arrival to the Emergency Department Using Global Positioning System and Google Maps

    PubMed Central

    Fleischman, Ross J.; Lundquist, Mark; Jui, Jonathan; Newgard, Craig D.; Warden, Craig

    2014-01-01

    Objective To derive and validate a model that accurately predicts ambulance arrival time that could be implemented as a Google Maps web application. Methods This was a retrospective study of all scene transports in Multnomah County, Oregon, from January 1 through December 31, 2008. Scene and destination hospital addresses were converted to coordinates. ArcGIS Network Analyst was used to estimate transport times based on street network speed limits. We then created a linear regression model to improve the accuracy of these street network estimates using weather, patient characteristics, use of lights and sirens, daylight, and rush-hour intervals. The model was derived from a 50% sample and validated on the remainder. Significance of the covariates was determined by p < 0.05 for a t-test of the model coefficients. Accuracy was quantified by the proportion of estimates that were within 5 minutes of the actual transport times recorded by computer-aided dispatch. We then built a Google Maps-based web application to demonstrate application in real-world EMS operations. Results There were 48,308 included transports. Street network estimates of transport time were accurate within 5 minutes of actual transport time less than 16% of the time. Actual transport times were longer during daylight and rush-hour intervals and shorter with use of lights and sirens. Age under 18 years, gender, wet weather, and trauma system entry were not significant predictors of transport time. Our model predicted arrival time within 5 minutes 73% of the time. For lights and sirens transports, accuracy was within 5 minutes 77% of the time. Accuracy was identical in the validation dataset. Lights and sirens saved an average of 3.1 minutes for transports under 8.8 minutes, and 5.3 minutes for longer transports. Conclusions An estimate of transport time based only on a street network significantly underestimated transport times. A simple model incorporating few variables can predict ambulance time of

  7. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    PubMed

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  8. Calculations of steady and transient channel flows with a time-accurate L-U factorization scheme

    NASA Technical Reports Server (NTRS)

    Kim, S.-W.

    1991-01-01

    Calculations of steady and unsteady, transonic, turbulent channel flows with a time accurate, lower-upper (L-U) factorization scheme are presented. The L-U factorization scheme is formally second-order accurate in time and space, and it is an extension of the steady state flow solver (RPLUS) used extensively to solve compressible flows. A time discretization method and the implementation of a consistent boundary condition specific to the L-U factorization scheme are also presented. The turbulence is described by the Baldwin-Lomax algebraic turbulence model. The present L-U scheme yields stable numerical results with the use of much smaller artificial dissipations than those used in the previous steady flow solver for steady and unsteady channel flows. The capability to solve time dependent flows is shown by solving very weakly excited and strongly excited, forced oscillatory, channel flows.

  9. A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

    PubMed Central

    Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

    2013-01-01

    Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

  10. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    PubMed

    Li, Fuyi; Li, Chen; Marquez-Lago, Tatiana T; Leier, André; Akutsu, Tatsuya; Purcell, Anthony W; Smith, A Ian; Lithgow, Trevor; Daly, Roger J; Song, Jiangning; Chou, Kuo-Chen

    2018-06-27

    Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/. Supplementary data are available at Bioinformatics online.

  11. Accurate prediction of retention in hydrophilic interaction chromatography by back calculation of high pressure liquid chromatography gradient profiles.

    PubMed

    Wang, Nu; Boswell, Paul G

    2017-10-20

    Gradient retention times are difficult to project from the underlying retention factor (k) vs. solvent composition (φ) relationships. A major reason for this difficulty is that gradients produced by HPLC pumps are imperfect - gradient delay, gradient dispersion, and solvent mis-proportioning are all difficult to account for in calculations. However, we recently showed that a gradient "back-calculation" methodology can measure these imperfections and take them into account. In RPLC, when the back-calculation methodology was used, error in projected gradient retention times is as low as could be expected based on repeatability in the k vs. φ relationships. HILIC, however, presents a new challenge: the selectivity of HILIC columns drift strongly over time. Retention is repeatable in short time, but selectivity frequently drifts over the course of weeks. In this study, we set out to understand if the issue of selectivity drift can be avoid by doing our experiments quickly, and if there any other factors that make it difficult to predict gradient retention times from isocratic k vs. φ relationships when gradient imperfections are taken into account with the back-calculation methodology. While in past reports, the accuracy of retention projections was >5%, the back-calculation methodology brought our error down to ∼1%. This result was 6-43 times more accurate than projections made using ideal gradients and 3-5 times more accurate than the same retention projections made using offset gradients (i.e., gradients that only took gradient delay into account). Still, the error remained higher in our HILIC projections than in RPLC. Based on the shape of the back-calculated gradients, we suspect the higher error is a result of prominent gradient distortion caused by strong, preferential water uptake from the mobile phase into the stationary phase during the gradient - a factor our model did not properly take into account. It appears that, at least with the stationary phase

  12. Accurate finite difference methods for time-harmonic wave propagation

    NASA Technical Reports Server (NTRS)

    Harari, Isaac; Turkel, Eli

    1994-01-01

    Finite difference methods for solving problems of time-harmonic acoustics are developed and analyzed. Multidimensional inhomogeneous problems with variable, possibly discontinuous, coefficients are considered, accounting for the effects of employing nonuniform grids. A weighted-average representation is less sensitive to transition in wave resolution (due to variable wave numbers or nonuniform grids) than the standard pointwise representation. Further enhancement in method performance is obtained by basing the stencils on generalizations of Pade approximation, or generalized definitions of the derivative, reducing spurious dispersion, anisotropy and reflection, and by improving the representation of source terms. The resulting schemes have fourth-order accurate local truncation error on uniform grids and third order in the nonuniform case. Guidelines for discretization pertaining to grid orientation and resolution are presented.

  13. Real time numerical shake prediction incorporating attenuation structure: a case for the 2016 Kumamoto Earthquake

    NASA Astrophysics Data System (ADS)

    Ogiso, M.; Hoshiba, M.; Shito, A.; Matsumoto, S.

    2016-12-01

    Needless to say, heterogeneous attenuation structure is important for ground motion prediction, including earthquake early warning, that is, real time ground motion prediction. Hoshiba and Ogiso (2015, AGU Fall meeting) showed that the heterogeneous attenuation and scattering structure will lead to earlier and more accurate ground motion prediction in the numerical shake prediction scheme proposed by Hoshiba and Aoki (2015, BSSA). Hoshiba and Ogiso (2015) used assumed heterogeneous structure, and we discuss the effect of them in the case of 2016 Kumamoto Earthquake, using heterogeneous structure estimated by actual observation data. We conducted Multiple Lapse Time Window Analysis (Hoshiba, 1993, JGR) to the seismic stations located on western part of Japan to estimate heterogeneous attenuation and scattering structure. The characteristics are similar to the previous work of Carcole and Sato (2010, GJI), e.g. strong intrinsic and scattering attenuation around the volcanoes located on the central part of Kyushu, and relatively weak heterogeneities in the other area. Real time ground motion prediction simulation for the 2016 Kumamoto Earthquake was conducted using the numerical shake prediction scheme with 474 strong ground motion stations. Comparing the snapshot of predicted and observed wavefield showed a tendency for underprediction around the volcanic area in spite of the heterogeneous structure. These facts indicate the necessity of improving the heterogeneous structure for the numerical shake prediction scheme.In this study, we used the waveforms of Hi-net, K-NET, KiK-net stations operated by the NIED for estimating structure and conducting ground motion prediction simulation. Part of this study was supported by the Earthquake Research Institute, the University of Tokyo cooperative research program and JSPS KAKENHI Grant Number 25282114.

  14. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

    PubMed

    Li, Qiongge; Chan, Maria F

    2017-01-01

    Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.

  15. Interspecies scaling: predicting volumes, mean residence time and elimination half-life. Some suggestions.

    PubMed

    Mahmood, I

    1998-05-01

    Extrapolation of animal data to assess pharmacokinetic parameters in man is an important tool in drug development. Clearance, volume of distribution and elimination half-life are the three most frequently extrapolated pharmacokinetic parameters. Extensive work has been done to improve the predictive performance of allometric scaling for clearance. In general there is good correlation between body weight and volume, hence volume in man can be predicted with reasonable accuracy from animal data. Besides the volume of distribution in the central compartment (Vc), two other volume terms, the volume of distribution by area (Vbeta) and the volume of distribution at steady state (VdSS), are also extrapolated from animals to man. This report compares the predictive performance of allometric scaling for Vc, Vbeta and VdSS in man from animal data. The relationship between elimination half-life (t(1/2)) and body weight across species results in poor correlation, most probably because of the hybrid nature of this parameter. To predict half-life in man from animal data, an indirect method (CL=VK, where CL=clearance, V is volume and K is elimination rate constant) has been proposed. This report proposes another indirect method which uses the mean residence time (MRT). After establishing that MRT can be predicted across species, it was used to predict half-life using the equation MRT=1.44 x t(1/2). The results of the study indicate that Vc is predicted more accurately than Vbeta and VdSS in man. It should be emphasized that for first-time dosing in man, Vc is a more important pharmacokinetic parameter than Vbeta or VdSS. Furthermore, MRT can be predicted reasonably well for man and can be used for prediction of half-life.

  16. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    PubMed

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  17. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

    PubMed

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-02-14

    Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

  18. Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model

    PubMed Central

    DeLeon, Orlando; Hodis, Hagit; O’Malley, Yunxia; Johnson, Jacklyn; Salimi, Hamid; Zhai, Yinjie; Winter, Elizabeth; Remec, Claire; Eichelberger, Noah; Van Cleave, Brandon; Puliadi, Ramya; Harrington, Robert D.; Stapleton, Jack T.; Haim, Hillel

    2017-01-01

    The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify “clues” to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property “volatility” and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small “fluctuations” in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties

  19. SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

    PubMed Central

    2014-01-01

    Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

  20. Does the emergency surgery score accurately predict outcomes in emergent laparotomies?

    PubMed

    Peponis, Thomas; Bohnen, Jordan D; Sangji, Naveen F; Nandan, Anirudh R; Han, Kelsey; Lee, Jarone; Yeh, D Dante; de Moya, Marc A; Velmahos, George C; Chang, David C; Kaafarani, Haytham M A

    2017-08-01

    The emergency surgery score is a mortality-risk calculator for emergency general operation patients. We sought to examine whether the emergency surgery score predicts 30-day morbidity and mortality in a high-risk group of patients undergoing emergent laparotomy. Using the 2011-2012 American College of Surgeons National Surgical Quality Improvement Program database, we identified all patients who underwent emergent laparotomy using (1) the American College of Surgeons National Surgical Quality Improvement Program definition of "emergent," and (2) all Current Procedural Terminology codes denoting a laparotomy, excluding aortic aneurysm rupture. Multivariable logistic regression analyses were performed to measure the correlation (c-statistic) between the emergency surgery score and (1) 30-day mortality, and (2) 30-day morbidity after emergent laparotomy. As sensitivity analyses, the correlation between the emergency surgery score and 30-day mortality was also evaluated in prespecified subgroups based on Current Procedural Terminology codes. A total of 26,410 emergent laparotomy patients were included. Thirty-day mortality and morbidity were 10.2% and 43.8%, respectively. The emergency surgery score correlated well with mortality (c-statistic = 0.84); scores of 1, 11, and 22 correlated with mortalities of 0.4%, 39%, and 100%, respectively. Similarly, the emergency surgery score correlated well with morbidity (c-statistic = 0.74); scores of 0, 7, and 11 correlated with complication rates of 13%, 58%, and 79%, respectively. The morbidity rates plateaued for scores higher than 11. Sensitivity analyses demonstrated that the emergency surgery score effectively predicts mortality in patients undergoing emergent (1) splenic, (2) gastroduodenal, (3) intestinal, (4) hepatobiliary, or (5) incarcerated ventral hernia operation. The emergency surgery score accurately predicts outcomes in all types of emergent laparotomy patients and may prove valuable as a bedside decision

  1. Long-time predictions in nonlinear dynamics

    NASA Technical Reports Server (NTRS)

    Szebehely, V.

    1980-01-01

    It is known that nonintegrable dynamical systems do not allow precise predictions concerning their behavior for arbitrary long times. The available series solutions are not uniformly convergent according to Poincare's theorem and numerical integrations lose their meaningfulness after the elapse of arbitrary long times. Two approaches are the use of existing global integrals and statistical methods. This paper presents a generalized method along the first approach. As examples long-time predictions in the classical gravitational satellite and planetary problems are treated.

  2. Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

    PubMed Central

    2014-01-01

    Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved

  3. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

    2017-04-04

    Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

  4. Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

    NASA Astrophysics Data System (ADS)

    Zhou, Ya-Tong; Fan, Yu; Chen, Zi-Yi; Sun, Jian-Cheng

    2017-05-01

    The contribution of this work is twofold: (1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. (2) An effective sparse hard-cut expectation maximization (SHC-EM) learning algorithm for the GPM model is proposed to improve the prediction performance. SHC-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval. SHC-EM outperforms the traditional variational learning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning. Supported by the National Natural Science Foundation of China under Grant No 60972106, the China Postdoctoral Science Foundation under Grant No 2014M561053, the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108, and the Hebei Province Natural Science Foundation under Grant No E2016202341.

  5. A time-accurate implicit method for chemical non-equilibrium flows at all speeds

    NASA Technical Reports Server (NTRS)

    Shuen, Jian-Shun

    1992-01-01

    A new time accurate coupled solution procedure for solving the chemical non-equilibrium Navier-Stokes equations over a wide range of Mach numbers is described. The scheme is shown to be very efficient and robust for flows with velocities ranging from M less than or equal to 10(exp -10) to supersonic speeds.

  6. NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

    PubMed

    Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

    2013-01-01

    Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

  7. A Time-Accurate Upwind Unstructured Finite Volume Method for Compressible Flow with Cure of Pathological Behaviors

    NASA Technical Reports Server (NTRS)

    Loh, Ching Y.; Jorgenson, Philip C. E.

    2007-01-01

    A time-accurate, upwind, finite volume method for computing compressible flows on unstructured grids is presented. The method is second order accurate in space and time and yields high resolution in the presence of discontinuities. For efficiency, the Roe approximate Riemann solver with an entropy correction is employed. In the basic Euler/Navier-Stokes scheme, many concepts of high order upwind schemes are adopted: the surface flux integrals are carefully treated, a Cauchy-Kowalewski time-stepping scheme is used in the time-marching stage, and a multidimensional limiter is applied in the reconstruction stage. However even with these up-to-date improvements, the basic upwind scheme is still plagued by the so-called "pathological behaviors," e.g., the carbuncle phenomenon, the expansion shock, etc. A solution to these limitations is presented which uses a very simple dissipation model while still preserving second order accuracy. This scheme is referred to as the enhanced time-accurate upwind (ETAU) scheme in this paper. The unstructured grid capability renders flexibility for use in complex geometry; and the present ETAU Euler/Navier-Stokes scheme is capable of handling a broad spectrum of flow regimes from high supersonic to subsonic at very low Mach number, appropriate for both CFD (computational fluid dynamics) and CAA (computational aeroacoustics). Numerous examples are included to demonstrate the robustness of the methods.

  8. Accurate lithography simulation model based on convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

    2017-07-01

    Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

  9. Individualized predictions of time to menopause using multiple measurements of antimüllerian hormone.

    PubMed

    Gohari, Mahmood Reza; Ramezani Tehrani, Fahime; Chenouri, Shojaeddin; Solaymani-Dodaran, Masoud; Azizi, Fereidoun

    2016-08-01

    The ability of antimüllerian hormone (AMH) to predict age at menopause has been reported in several studies, and a decrease in AMH level has been found to increase the probability of menopause. The rate of decline varies among women, and there is also a variability of decline between women's cycles. As a result, individualized evaluation is required to accurately predict time of menopause. To this end, we have used the AMH trajectories of individual women to predict each one's age at menopause. From a cohort study, 266 women (ages 20-50 y) who had regular and predictable menstrual cycles at the initiation of the study were randomly selected from among 1,265 women for multiple AMH measurements. Participants were visited at approximately 3-year intervals and followed for an average of 6.5 years. Individual likelihood of menopause was predicted by fitting the shared random-effects joint model to the baseline covariates and the specific AMH trajectory of each woman. In total, 23.7% of the women reached menopause during the follow-up period. The estimated mean (SD) AMH concentration at the time of menopause was 0.05 ng/mL (0.06 ng/mL), compared with 1.36 ng/mL (1.85 ng/mL) for those with a regular menstrual cycle at their last assessment. The decline rate in the AMH level varied among age groups, and age was a significant prognostic factor for AMH level (P < 0.001). Adjusting for age and body mass index, each woman had her own specific AMH trajectory. Lower AMH and older age had significant effects on the onset of menopause. Individualized prediction of time to menopause was obtained from the fitted model. Longitudinal measurements of AMH will enable physicians to individualize the prediction of menopause, thereby facilitating counseling on the timing of childbearing or medical management of health issues associated with menopause.

  10. Fast and accurate predictions of covalent bonds in chemical space.

    PubMed

    Chang, K Y Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O Anatole

    2016-05-07

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (∼1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H2 (+). Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  11. New analytical model for the ozone electronic ground state potential surface and accurate ab initio vibrational predictions at high energy range.

    PubMed

    Tyuterev, Vladimir G; Kochanov, Roman V; Tashkun, Sergey A; Holka, Filip; Szalay, Péter G

    2013-10-07

    An accurate description of the complicated shape of the potential energy surface (PES) and that of the highly excited vibration states is of crucial importance for various unsolved issues in the spectroscopy and dynamics of ozone and remains a challenge for the theory. In this work a new analytical representation is proposed for the PES of the ground electronic state of the ozone molecule in the range covering the main potential well and the transition state towards the dissociation. This model accounts for particular features specific to the ozone PES for large variations of nuclear displacements along the minimum energy path. The impact of the shape of the PES near the transition state (existence of the "reef structure") on vibration energy levels was studied for the first time. The major purpose of this work was to provide accurate theoretical predictions for ozone vibrational band centres at the energy range near the dissociation threshold, which would be helpful for understanding the very complicated high-resolution spectra and its analyses currently in progress. Extended ab initio electronic structure calculations were carried out enabling the determination of the parameters of a minimum energy path PES model resulting in a new set of theoretical vibrational levels of ozone. A comparison with recent high-resolution spectroscopic data on the vibrational levels gives the root-mean-square deviations below 1 cm(-1) for ozone band centres up to 90% of the dissociation energy. New ab initio vibrational predictions represent a significant improvement with respect to all previously available calculations.

  12. Automatic Earthquake Shear Stress Measurement Method Developed for Accurate Time- Prediction Analysis of Forthcoming Major Earthquakes Along Shallow Active Faults

    NASA Astrophysics Data System (ADS)

    Serata, S.

    2006-12-01

    The Serata Stressmeter has been developed to measure and monitor earthquake shear stress build-up along shallow active faults. The development work made in the past 25 years has established the Stressmeter as an automatic stress measurement system to study timing of forthcoming major earthquakes in support of the current earthquake prediction studies based on statistical analysis of seismological observations. In early 1982, a series of major Man-made earthquakes (magnitude 4.5-5.0) suddenly occurred in an area over deep underground potash mine in Saskatchewan, Canada. By measuring underground stress condition of the mine, the direct cause of the earthquake was disclosed. The cause was successfully eliminated by controlling the stress condition of the mine. The Japanese government was interested in this development and the Stressmeter was introduced to the Japanese government research program for earthquake stress studies. In Japan the Stressmeter was first utilized for direct measurement of the intrinsic lateral tectonic stress gradient G. The measurement, conducted at the Mt. Fuji Underground Research Center of the Japanese government, disclosed the constant natural gradients of maximum and minimum lateral stresses in an excellent agreement with the theoretical value, i.e., G = 0.25. All the conventional methods of overcoring, hydrofracturing and deformation, which were introduced to compete with the Serata method, failed demonstrating the fundamental difficulties of the conventional methods. The intrinsic lateral stress gradient determined by the Stressmeter for the Japanese government was found to be the same with all the other measurements made by the Stressmeter in Japan. The stress measurement results obtained by the major international stress measurement work in the Hot Dry Rock Projects conducted in USA, England and Germany are found to be in good agreement with the Stressmeter results obtained in Japan. Based on this broad agreement, a solid geomechanical

  13. Does mesenteric venous imaging assessment accurately predict pathologic invasion in localized pancreatic ductal adenocarcinoma?

    PubMed

    Clanton, Jesse; Oh, Stephen; Kaplan, Stephen J; Johnson, Emily; Ross, Andrew; Kozarek, Richard; Alseidi, Adnan; Biehl, Thomas; Picozzi, Vincent J; Helton, William S; Coy, David; Dorer, Russell; Rocha, Flavio G

    2018-05-09

    Accurate prediction of mesenteric venous involvement in pancreatic ductal adenocarcinoma (PDAC) is necessary for adequate staging and treatment. A retrospective cohort study was conducted in PDAC patients at a single institution. All patients with resected PDAC and staging CT and EUS between 2003 and 2014 were included and sub-divided into "upfront resected" and "neoadjuvant chemotherapy (NAC)" groups. Independent imaging re-review was correlated to venous resection and venous invasion. Sensitivity, specificity, positive and negative predictive values were then calculated. A total of 109 patients underwent analysis, 60 received upfront resection, and 49 NAC. Venous resection (30%) and vein invasion (13%) was less common in patients resected upfront than those who received NAC (53% and 16%, respectively). Both CT and EUS had poor sensitivity (14-44%) but high specificity (75-95%) for detecting venous resection and vein invasion in patients resected upfront, whereas sensitivity was high (84-100%) and specificity was low (27-44%) after NAC. Preoperative CT and EUS in PDAC have similar efficacy but different predictive capacity in assessing mesenteric venous involvement depending on whether patients are resected upfront or received NAC. Both modalities appear to significantly overestimate true vascular involvement and should be interpreted in the appropriate clinical context. Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

  14. Time scale controversy: Accurate orbital calibration of the early Paleogene

    NASA Astrophysics Data System (ADS)

    Roehl, U.; Westerhold, T.; Laskar, J.

    2012-12-01

    Timing is crucial to understanding the causes and consequences of events in Earth history. The calibration of geological time relies heavily on the accuracy of radioisotopic and astronomical dating. Uncertainties in the computations of Earth's orbital parameters and in radioisotopic dating have hampered the construction of a reliable astronomically calibrated time scale beyond 40 Ma. Attempts to construct a robust astronomically tuned time scale for the early Paleogene by integrating radioisotopic and astronomical dating are only partially consistent. Here, using the new La2010 and La2011 orbital solutions, we present the first accurate astronomically calibrated time scale for the early Paleogene (47-65 Ma) uniquely based on astronomical tuning and thus independent of the radioisotopic determination of the Fish Canyon standard. Comparison with geological data confirms the stability of the new La2011 solution back to 54 Ma. Subsequent anchoring of floating chronologies to the La2011 solution using the very long eccentricity nodes provides an absolute age of 55.530 ± 0.05 Ma for the onset of the Paleocene/Eocene Thermal Maximum (PETM), 54.850 ± 0.05 Ma for the early Eocene ash -17, and 65.250 ± 0.06 Ma for the K/Pg boundary. The new astrochronology presented here indicates that the intercalibration and synchronization of U/Pb and 40Ar/39Ar radioisotopic geochronology is much more challenging than previously thought.

  15. Time scale controversy: Accurate orbital calibration of the early Paleogene

    NASA Astrophysics Data System (ADS)

    Westerhold, Thomas; RöHl, Ursula; Laskar, Jacques

    2012-06-01

    Timing is crucial to understanding the causes and consequences of events in Earth history. The calibration of geological time relies heavily on the accuracy of radioisotopic and astronomical dating. Uncertainties in the computations of Earth's orbital parameters and in radioisotopic dating have hampered the construction of a reliable astronomically calibrated time scale beyond 40 Ma. Attempts to construct a robust astronomically tuned time scale for the early Paleogene by integrating radioisotopic and astronomical dating are only partially consistent. Here, using the new La2010 and La2011 orbital solutions, we present the first accurate astronomically calibrated time scale for the early Paleogene (47-65 Ma) uniquely based on astronomical tuning and thus independent of the radioisotopic determination of the Fish Canyon standard. Comparison with geological data confirms the stability of the new La2011 solution back to ˜54 Ma. Subsequent anchoring of floating chronologies to the La2011 solution using the very long eccentricity nodes provides an absolute age of 55.530 ± 0.05 Ma for the onset of the Paleocene/Eocene Thermal Maximum (PETM), 54.850 ± 0.05 Ma for the early Eocene ash -17, and 65.250 ± 0.06 Ma for the K/Pg boundary. The new astrochronology presented here indicates that the intercalibration and synchronization of U/Pb and 40Ar/39Ar radioisotopic geochronology is much more challenging than previously thought.

  16. Accurate load prediction by BEM with airfoil data from 3D RANS simulations

    NASA Astrophysics Data System (ADS)

    Schneider, Marc S.; Nitzsche, Jens; Hennings, Holger

    2016-09-01

    In this paper, two methods for the extraction of airfoil coefficients from 3D CFD simulations of a wind turbine rotor are investigated, and these coefficients are used to improve the load prediction of a BEM code. The coefficients are extracted from a number of steady RANS simulations, using either averaging of velocities in annular sections, or an inverse BEM approach for determination of the induction factors in the rotor plane. It is shown that these 3D rotor polars are able to capture the rotational augmentation at the inner part of the blade as well as the load reduction by 3D effects close to the blade tip. They are used as input to a simple BEM code and the results of this BEM with 3D rotor polars are compared to the predictions of BEM with 2D airfoil coefficients plus common empirical corrections for stall delay and tip loss. While BEM with 2D airfoil coefficients produces a very different radial distribution of loads than the RANS simulation, the BEM with 3D rotor polars manages to reproduce the loads from RANS very accurately for a variety of load cases, as long as the blade pitch angle is not too different from the cases from which the polars were extracted.

  17. Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

    PubMed Central

    Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

    2015-01-01

    Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

  18. Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.

    PubMed

    Zou, Lingyun; Nan, Chonghan; Hu, Fuquan

    2013-12-15

    Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished. A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.

  19. Towards accurate cosmological predictions for rapidly oscillating scalar fields as dark matter

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

    Ureña-López, L. Arturo; Gonzalez-Morales, Alma X., E-mail: lurena@ugto.mx, E-mail: alma.gonzalez@fisica.ugto.mx

    2016-07-01

    As we are entering the era of precision cosmology, it is necessary to count on accurate cosmological predictions from any proposed model of dark matter. In this paper we present a novel approach to the cosmological evolution of scalar fields that eases their analytic and numerical analysis at the background and at the linear order of perturbations. The new method makes use of appropriate angular variables that simplify the writing of the equations of motion, and which also show that the usual field variables play a secondary role in the cosmological dynamics. We apply the method to a scalar fieldmore » endowed with a quadratic potential and revisit its properties as dark matter. Some of the results known in the literature are recovered, and a better understanding of the physical properties of the model is provided. It is confirmed that there exists a Jeans wavenumber k {sub J} , directly related to the suppression of linear perturbations at wavenumbers k > k {sub J} , and which is verified to be k {sub J} = a √ mH . We also discuss some semi-analytical results that are well satisfied by the full numerical solutions obtained from an amended version of the CMB code CLASS. Finally we draw some of the implications that this new treatment of the equations of motion may have in the prediction of cosmological observables from scalar field dark matter models.« less

  20. Space-Time Earthquake Prediction: The Error Diagrams

    NASA Astrophysics Data System (ADS)

    Molchan, G.

    2010-08-01

    The quality of earthquake prediction is usually characterized by a two-dimensional diagram n versus τ, where n is the rate of failures-to-predict and τ is a characteristic of space-time alarm. Unlike the time prediction case, the quantity τ is not defined uniquely. We start from the case in which τ is a vector with components related to the local alarm times and find a simple structure of the space-time diagram in terms of local time diagrams. This key result is used to analyze the usual 2-d error sets { n, τ w } in which τ w is a weighted mean of the τ components and w is the weight vector. We suggest a simple algorithm to find the ( n, τ w ) representation of all random guess strategies, the set D, and prove that there exists the unique case of w when D degenerates to the diagonal n + τ w = 1. We find also a confidence zone of D on the ( n, τ w ) plane when the local target rates are known roughly. These facts are important for correct interpretation of ( n, τ w ) diagrams when we discuss the prediction capability of the data or prediction methods.

  1. An implicit higher-order spatially accurate scheme for solving time dependent flows on unstructured meshes

    NASA Astrophysics Data System (ADS)

    Tomaro, Robert F.

    1998-07-01

    The present research is aimed at developing a higher-order, spatially accurate scheme for both steady and unsteady flow simulations using unstructured meshes. The resulting scheme must work on a variety of general problems to ensure the creation of a flexible, reliable and accurate aerodynamic analysis tool. To calculate the flow around complex configurations, unstructured grids and the associated flow solvers have been developed. Efficient simulations require the minimum use of computer memory and computational times. Unstructured flow solvers typically require more computer memory than a structured flow solver due to the indirect addressing of the cells. The approach taken in the present research was to modify an existing three-dimensional unstructured flow solver to first decrease the computational time required for a solution and then to increase the spatial accuracy. The terms required to simulate flow involving non-stationary grids were also implemented. First, an implicit solution algorithm was implemented to replace the existing explicit procedure. Several test cases, including internal and external, inviscid and viscous, two-dimensional, three-dimensional and axi-symmetric problems, were simulated for comparison between the explicit and implicit solution procedures. The increased efficiency and robustness of modified code due to the implicit algorithm was demonstrated. Two unsteady test cases, a plunging airfoil and a wing undergoing bending and torsion, were simulated using the implicit algorithm modified to include the terms required for a moving and/or deforming grid. Secondly, a higher than second-order spatially accurate scheme was developed and implemented into the baseline code. Third- and fourth-order spatially accurate schemes were implemented and tested. The original dissipation was modified to include higher-order terms and modified near shock waves to limit pre- and post-shock oscillations. The unsteady cases were repeated using the higher

  2. Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

    PubMed Central

    Sivaraks, Haemwaan

    2015-01-01

    Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

  3. Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding

    PubMed Central

    Nissley, Daniel A.; Sharma, Ajeet K.; Ahmed, Nabeel; Friedrich, Ulrike A.; Kramer, Günter; Bukau, Bernd; O'Brien, Edward P.

    2016-01-01

    The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally—a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process. PMID:26887592

  4. Accurate and scalable social recommendation using mixed-membership stochastic block models.

    PubMed

    Godoy-Lorite, Antonia; Guimerà, Roger; Moore, Cristopher; Sales-Pardo, Marta

    2016-12-13

    With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

  5. Accurate and scalable social recommendation using mixed-membership stochastic block models

    PubMed Central

    Godoy-Lorite, Antonia; Moore, Cristopher

    2016-01-01

    With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. PMID:27911773

  6. Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

    NASA Technical Reports Server (NTRS)

    Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

    2011-01-01

    A surrogate model methodology is described for predicting in real time the residual strength of flight structures with discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. A residual strength test of a metallic, integrally-stiffened panel is simulated to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data would, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high-fidelity fracture simulation framework provide useful tools for adaptive flight technology.

  7. Predicting river travel time from hydraulic characteristics

    USGS Publications Warehouse

    Jobson, H.E.

    2001-01-01

    Predicting the effect of a pollutant spill on downstream water quality is primarily dependent on the water velocity, longitudinal mixing, and chemical/physical reactions. Of these, velocity is the most important and difficult to predict. This paper provides guidance on extrapolating travel-time information from one within bank discharge to another. In many cases, a time series of discharge (such as provided by a U.S. Geological Survey stream gauge) will provide an excellent basis for this extrapolation. Otherwise, the accuracy of a travel time extrapolation based on a resistance equation can be greatly improved by assuming the total flow area is composed of two parts, an active and an inactive area. For 60 reaches of 12 rivers with slopes greater than about 0.0002, travel times could be predicted to within about 10% by computing the active flow area using the Manning equation with n = 0.035 and assuming a constant inactive area for each reach. The predicted travel times were not very sensitive to the assumed values of bed slope or channel width.

  8. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

    NASA Astrophysics Data System (ADS)

    Wessberg, Johan; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung; Biggs, S. James; Srinivasan, Mandayam A.; Nicolelis, Miguel A. L.

    2000-11-01

    Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

  9. Real-time flutter boundary prediction based on time series models

    NASA Astrophysics Data System (ADS)

    Gu, Wenjing; Zhou, Li

    2018-03-01

    For the purpose of predicting the flutter boundary in real time during flutter flight tests, two time series models accompanied with corresponding stability criterion are adopted in this paper. The first method simplifies a long nonstationary response signal as many contiguous intervals and each is considered to be stationary. The traditional AR model is then established to represent each interval of signal sequence. While the second employs a time-varying AR model to characterize actual measured signals in flutter test with progression variable speed (FTPVS). To predict the flutter boundary, stability parameters are formulated by the identified AR coefficients combined with Jury's stability criterion. The behavior of the parameters is examined using both simulated and wind-tunnel experiment data. The results demonstrate that both methods show significant effectiveness in predicting the flutter boundary at lower speed level. A comparison between the two methods is also given in this paper.

  10. Judging the Probability of Hypotheses Versus the Impact of Evidence: Which Form of Inductive Inference Is More Accurate and Time-Consistent?

    PubMed

    Tentori, Katya; Chater, Nick; Crupi, Vincenzo

    2016-04-01

    Inductive reasoning requires exploiting links between evidence and hypotheses. This can be done focusing either on the posterior probability of the hypothesis when updated on the new evidence or on the impact of the new evidence on the credibility of the hypothesis. But are these two cognitive representations equally reliable? This study investigates this question by comparing probability and impact judgments on the same experimental materials. The results indicate that impact judgments are more consistent in time and more accurate than probability judgments. Impact judgments also predict the direction of errors in probability judgments. These findings suggest that human inductive reasoning relies more on estimating evidential impact than on posterior probability. Copyright © 2015 Cognitive Science Society, Inc.

  11. A gene expression biomarker accurately predicts estrogen ...

    EPA Pesticide Factsheets

    The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

  12. Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

    PubMed

    Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

    2014-01-01

    Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

  13. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    PubMed

    Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

    2014-01-01

    Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

  14. Time-Accurate Local Time Stepping and High-Order Time CESE Methods for Multi-Dimensional Flows Using Unstructured Meshes

    NASA Technical Reports Server (NTRS)

    Chang, Chau-Lyan; Venkatachari, Balaji Shankar; Cheng, Gary

    2013-01-01

    With the wide availability of affordable multiple-core parallel supercomputers, next generation numerical simulations of flow physics are being focused on unsteady computations for problems involving multiple time scales and multiple physics. These simulations require higher solution accuracy than most algorithms and computational fluid dynamics codes currently available. This paper focuses on the developmental effort for high-fidelity multi-dimensional, unstructured-mesh flow solvers using the space-time conservation element, solution element (CESE) framework. Two approaches have been investigated in this research in order to provide high-accuracy, cross-cutting numerical simulations for a variety of flow regimes: 1) time-accurate local time stepping and 2) highorder CESE method. The first approach utilizes consistent numerical formulations in the space-time flux integration to preserve temporal conservation across the cells with different marching time steps. Such approach relieves the stringent time step constraint associated with the smallest time step in the computational domain while preserving temporal accuracy for all the cells. For flows involving multiple scales, both numerical accuracy and efficiency can be significantly enhanced. The second approach extends the current CESE solver to higher-order accuracy. Unlike other existing explicit high-order methods for unstructured meshes, the CESE framework maintains a CFL condition of one for arbitrarily high-order formulations while retaining the same compact stencil as its second-order counterpart. For large-scale unsteady computations, this feature substantially enhances numerical efficiency. Numerical formulations and validations using benchmark problems are discussed in this paper along with realistic examples.

  15. Time evolution of predictability of epidemics on networks.

    PubMed

    Holme, Petter; Takaguchi, Taro

    2015-04-01

    Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information-i.e., knowing the state of each individual with respect to the disease-the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

  16. Time evolution of predictability of epidemics on networks

    NASA Astrophysics Data System (ADS)

    Holme, Petter; Takaguchi, Taro

    2015-04-01

    Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information—i.e., knowing the state of each individual with respect to the disease—the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

  17. Predictability of Solar Radiation for Photovoltaics systems over Europe: from short-term to seasonal time-scales

    NASA Astrophysics Data System (ADS)

    De Felice, Matteo; Petitta, Marcello; Ruti, Paolo

    2014-05-01

    Photovoltaic diffusion is steadily growing on Europe, passing from a capacity of almost 14 GWp in 2011 to 21.5 GWp in 2012 [1]. Having accurate forecast is needed for planning and operational purposes, with the possibility to model and predict solar variability at different time-scales. This study examines the predictability of daily surface solar radiation comparing ECMWF operational forecasts with CM-SAF satellite measurements on the Meteosat (MSG) full disk domain. Operational forecasts used are the IFS system up to 10 days and the System4 seasonal forecast up to three months. Forecast are analysed considering average and variance of errors, showing error maps and average on specific domains with respect to prediction lead times. In all the cases, forecasts are compared with predictions obtained using persistence and state-of-art time-series models. We can observe a wide range of errors, with the performance of forecasts dramatically affected by orography and season. Lower errors are on southern Italy and Spain, with errors on some areas consistently under 10% up to ten days during summer (JJA). Finally, we conclude the study with some insight on how to "translate" the error on solar radiation to error on solar power production using available production data from solar power plants. [1] EurObserver, "Baromètre Photovoltaïque, Le journal des énergies renouvables, April 2012."

  18. Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

    NASA Astrophysics Data System (ADS)

    Theologou, I.; Patelaki, M.; Karantzalos, K.

    2015-04-01

    Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

  19. Accurate Time-Dependent Traveling-Wave Tube Model Developed for Computational Bit-Error-Rate Testing

    NASA Technical Reports Server (NTRS)

    Kory, Carol L.

    2001-01-01

    The phenomenal growth of the satellite communications industry has created a large demand for traveling-wave tubes (TWT's) operating with unprecedented specifications requiring the design and production of many novel devices in record time. To achieve this, the TWT industry heavily relies on computational modeling. However, the TWT industry's computational modeling capabilities need to be improved because there are often discrepancies between measured TWT data and that predicted by conventional two-dimensional helical TWT interaction codes. This limits the analysis and design of novel devices or TWT's with parameters differing from what is conventionally manufactured. In addition, the inaccuracy of current computational tools limits achievable TWT performance because optimized designs require highly accurate models. To address these concerns, a fully three-dimensional, time-dependent, helical TWT interaction model was developed using the electromagnetic particle-in-cell code MAFIA (Solution of MAxwell's equations by the Finite-Integration-Algorithm). The model includes a short section of helical slow-wave circuit with excitation fed by radiofrequency input/output couplers, and an electron beam contained by periodic permanent magnet focusing. A cutaway view of several turns of the three-dimensional helical slow-wave circuit with input/output couplers is shown. This has been shown to be more accurate than conventionally used two-dimensional models. The growth of the communications industry has also imposed a demand for increased data rates for the transmission of large volumes of data. To achieve increased data rates, complex modulation and multiple access techniques are employed requiring minimum distortion of the signal as it is passed through the TWT. Thus, intersymbol interference (ISI) becomes a major consideration, as well as suspected causes such as reflections within the TWT. To experimentally investigate effects of the physical TWT on ISI would be

  20. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment.

    PubMed

    Young, Jonathan; Modat, Marc; Cardoso, Manuel J; Mendelson, Alex; Cash, Dave; Ourselin, Sebastien

    2013-01-01

    Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy

  1. A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

    PubMed

    Stacey, R Greg; Skinnider, Michael A; Scott, Nichollas E; Foster, Leonard J

    2017-10-23

    An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. PrInCE is the first fast and easy

  2. Predictable and Adaptable Complex Real-Time Systems

    DTIC Science & Technology

    1993-09-30

    Predictable and Adaptable Complex Real - Time Systems Grant or Contract Number: N00014-92-J-1048 Reporting Period: 1 Oct 91 - 30 Sep 93 1... Real - Time Systems Grant or Contract Number: N00014-92-J-1048 Reporting Period: 1 Oct 91 - 30 Sep 93 2. Summary of Technical Progress Our...cs.umass.edu Grant or Contract Title: Predictable and Adaptable Complex Real - Time Systems Grant or Contract Number: N00014-92-J-1048 Reporting Period: 1 Oct 91

  3. Time Accurate Unsteady Pressure Loads Simulated for the Space Launch System at a Wind Tunnel Condition

    NASA Technical Reports Server (NTRS)

    Alter, Stephen J.; Brauckmann, Gregory J.; Kleb, Bil; Streett, Craig L; Glass, Christopher E.; Schuster, David M.

    2015-01-01

    Using the Fully Unstructured Three-Dimensional (FUN3D) computational fluid dynamics code, an unsteady, time-accurate flow field about a Space Launch System configuration was simulated at a transonic wind tunnel condition (Mach = 0.9). Delayed detached eddy simulation combined with Reynolds Averaged Naiver-Stokes and a Spallart-Almaras turbulence model were employed for the simulation. Second order accurate time evolution scheme was used to simulate the flow field, with a minimum of 0.2 seconds of simulated time to as much as 1.4 seconds. Data was collected at 480 pressure taps at locations, 139 of which matched a 3% wind tunnel model, tested in the Transonic Dynamic Tunnel (TDT) facility at NASA Langley Research Center. Comparisons between computation and experiment showed agreement within 5% in terms of location for peak RMS levels, and 20% for frequency and magnitude of power spectral densities. Grid resolution and time step sensitivity studies were performed to identify methods for improved accuracy comparisons to wind tunnel data. With limited computational resources, accurate trends for reduced vibratory loads on the vehicle were observed. Exploratory methods such as determining minimized computed errors based on CFL number and sub-iterations, as well as evaluating frequency content of the unsteady pressures and evaluation of oscillatory shock structures were used in this study to enhance computational efficiency and solution accuracy. These techniques enabled development of a set of best practices, for the evaluation of future flight vehicle designs in terms of vibratory loads.

  4. Integration of RNA-Seq and RPPA data for survival time prediction in cancer patients.

    PubMed

    Isik, Zerrin; Ercan, Muserref Ece

    2017-10-01

    Integration of several types of patient data in a computational framework can accelerate the identification of more reliable biomarkers, especially for prognostic purposes. This study aims to identify biomarkers that can successfully predict the potential survival time of a cancer patient by integrating the transcriptomic (RNA-Seq), proteomic (RPPA), and protein-protein interaction (PPI) data. The proposed method -RPBioNet- employs a random walk-based algorithm that works on a PPI network to identify a limited number of protein biomarkers. Later, the method uses gene expression measurements of the selected biomarkers to train a classifier for the survival time prediction of patients. RPBioNet was applied to classify kidney renal clear cell carcinoma (KIRC), glioblastoma multiforme (GBM), and lung squamous cell carcinoma (LUSC) patients based on their survival time classes (long- or short-term). The RPBioNet method correctly identified the survival time classes of patients with between 66% and 78% average accuracy for three data sets. RPBioNet operates with only 20 to 50 biomarkers and can achieve on average 6% higher accuracy compared to the closest alternative method, which uses only RNA-Seq data in the biomarker selection. Further analysis of the most predictive biomarkers highlighted genes that are common for both cancer types, as they may be driver proteins responsible for cancer progression. The novelty of this study is the integration of a PPI network with mRNA and protein expression data to identify more accurate prognostic biomarkers that can be used for clinical purposes in the future. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Time-Accurate Simulations and Acoustic Analysis of Slat Free-Shear Layer

    NASA Technical Reports Server (NTRS)

    Khorrami, Mehdi R.; Singer, Bart A.; Berkman, Mert E.

    2001-01-01

    A detailed computational aeroacoustic analysis of a high-lift flow field is performed. Time-accurate Reynolds Averaged Navier-Stokes (RANS) computations simulate the free shear layer that originates from the slat cusp. Both unforced and forced cases are studied. Preliminary results show that the shear layer is a good amplifier of disturbances in the low to mid-frequency range. The Ffowcs-Williams and Hawkings equation is solved to determine the acoustic field using the unsteady flow data from the RANS calculations. The noise radiated from the excited shear layer has a spectral shape qualitatively similar to that obtained from measurements in a corresponding experimental study of the high-lift system.

  6. Control surface hinge moment prediction using computational fluid dynamics

    NASA Astrophysics Data System (ADS)

    Simpson, Christopher David

    The following research determines the feasibility of predicting control surface hinge moments using various computational methods. A detailed analysis is conducted using a 2D GA(W)-1 airfoil with a 20% plain flap. Simple hinge moment prediction methods are tested, including empirical Datcom relations and XFOIL. Steady-state and time-accurate turbulent, viscous, Navier-Stokes solutions are computed using Fun3D. Hinge moment coefficients are computed. Mesh construction techniques are discussed. An adjoint-based mesh adaptation case is also evaluated. An NACA 0012 45-degree swept horizontal stabilizer with a 25% elevator is also evaluated using Fun3D. Results are compared with experimental wind-tunnel data obtained from references. Finally, the costs of various solution methods are estimated. Results indicate that while a steady-state Navier-Stokes solution can accurately predict control surface hinge moments for small angles of attack and deflection angles, a time-accurate solution is necessary to accurately predict hinge moments in the presence of flow separation. The ability to capture the unsteady vortex shedding behavior present in moderate to large control surface deflections is found to be critical to hinge moment prediction accuracy. Adjoint-based mesh adaptation is shown to give hinge moment predictions similar to a globally-refined mesh for a steady-state 2D simulation.

  7. A time-accurate high-resolution TVD scheme for solving the Navier-Stokes equations

    NASA Technical Reports Server (NTRS)

    Kim, Hyun Dae; Liu, Nan-Suey

    1992-01-01

    A total variation diminishing (TVD) scheme has been developed and incorporated into an existing time-accurate high-resolution Navier-Stokes code. The accuracy and the robustness of the resulting solution procedure have been assessed by performing many calculations in four different areas: shock tube flows, regular shock reflection, supersonic boundary layer, and shock boundary layer interactions. These numerical results compare well with corresponding exact solutions or experimental data.

  8. A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

    PubMed

    Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin; Brehm Christensen, Peer; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Mørch, Kristine; Wejstål, Rune; Norkrans, Gunnar; Lindh, Magnus; Färkkilä, Martti; Westin, Johan

    2014-01-01

    Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV) infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI) in the paper, was based on the model: Log-odds (predicting cirrhosis) = -12.17+ (age × 0.11) + (BMI (kg/m(2)) × 0.23) + (D7-lathosterol (μg/100 mg cholesterol)×(-0.013)) + (Platelet count (x10(9)/L) × (-0.018)) + (Prothrombin-INR × 3.69). The area under the ROC curve (AUROC) for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96). The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98). In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

  9. Towards more accurate vegetation mortality predictions

    DOE PAGES

    Sevanto, Sanna Annika; Xu, Chonggang

    2016-09-26

    Predicting the fate of vegetation under changing climate is one of the major challenges of the climate modeling community. Here, terrestrial vegetation dominates the carbon and water cycles over land areas, and dramatic changes in vegetation cover resulting from stressful environmental conditions such as drought feed directly back to local and regional climate, potentially leading to a vicious cycle where vegetation recovery after a disturbance is delayed or impossible.

  10. Real-Time and Accurate Identification of Single Oligonucleotide Photoisomers via an Aerolysin Nanopore.

    PubMed

    Hu, Zheng-Li; Li, Zi-Yuan; Ying, Yi-Lun; Zhang, Junji; Cao, Chan; Long, Yi-Tao; Tian, He

    2018-04-03

    Identification of the configuration for the photoresponsive oligonucleotide plays an important role in the ingenious design of DNA nanomolecules and nanodevices. Due to the limited resolution and sensitivity of present methods, it remains a challenge to determine the accurate configuration of photoresponsive oligonucleotides, much less a precise description of their photoconversion process. Here, we used an aerolysin (AeL) nanopore-based confined space for real-time determination and quantification of the absolute cis/ trans configuration of each azobenzene-modified oligonucleotide (Azo-ODN) with a single molecule resolution. The two completely separated current distributions with narrow peak widths at half height (<0.62 pA) are assigned to cis/ trans-Azo-ODN isomers, respectively. Due to the high current sensitivity, each isomer of Azo-ODN could be undoubtedly identified, which gives the accurate photostationary conversion values of 82.7% for trans-to- cis under UV irradiation and 82.5% for cis-to- trans under vis irradiation. Further real-time kinetic evaluation reveals that the photoresponsive rate constants of Azo-ODN from trans-to- cis and cis-to -trans are 0.43 and 0.20 min -1 , respectively. This study will promote the sophisticated design of photoresponsive ODN to achieve an efficient and applicable photocontrollable process.

  11. A high order accurate finite element algorithm for high Reynolds number flow prediction

    NASA Technical Reports Server (NTRS)

    Baker, A. J.

    1978-01-01

    A Galerkin-weighted residuals formulation is employed to establish an implicit finite element solution algorithm for generally nonlinear initial-boundary value problems. Solution accuracy, and convergence rate with discretization refinement, are quantized in several error norms, by a systematic study of numerical solutions to several nonlinear parabolic and a hyperbolic partial differential equation characteristic of the equations governing fluid flows. Solutions are generated using selective linear, quadratic and cubic basis functions. Richardson extrapolation is employed to generate a higher-order accurate solution to facilitate isolation of truncation error in all norms. Extension of the mathematical theory underlying accuracy and convergence concepts for linear elliptic equations is predicted for equations characteristic of laminar and turbulent fluid flows at nonmodest Reynolds number. The nondiagonal initial-value matrix structure introduced by the finite element theory is determined intrinsic to improved solution accuracy and convergence. A factored Jacobian iteration algorithm is derived and evaluated to yield a consequential reduction in both computer storage and execution CPU requirements while retaining solution accuracy.

  12. Inferential modeling and predictive feedback control in real-time motion compensation using the treatment couch during radiotherapy

    NASA Astrophysics Data System (ADS)

    Qiu, Peng; D'Souza, Warren D.; McAvoy, Thomas J.; Liu, K. J. Ray

    2007-09-01

    Tumor motion induced by respiration presents a challenge to the reliable delivery of conformal radiation treatments. Real-time motion compensation represents the technologically most challenging clinical solution but has the potential to overcome the limitations of existing methods. The performance of a real-time couch-based motion compensation system is mainly dependent on two aspects: the ability to infer the internal anatomical position and the performance of the feedback control system. In this paper, we propose two novel methods for the two aspects respectively, and then combine the proposed methods into one system. To accurately estimate the internal tumor position, we present partial-least squares (PLS) regression to predict the position of the diaphragm using skin-based motion surrogates. Four radio-opaque markers were placed on the abdomen of patients who underwent fluoroscopic imaging of the diaphragm. The coordinates of the markers served as input variables and the position of the diaphragm served as the output variable. PLS resulted in lower prediction errors compared with standard multiple linear regression (MLR). The performance of the feedback control system depends on the system dynamics and dead time (delay between the initiation and execution of the control action). While the dynamics of the system can be inverted in a feedback control system, the dead time cannot be inverted. To overcome the dead time of the system, we propose a predictive feedback control system by incorporating forward prediction using least-mean-square (LMS) and recursive least square (RLS) filtering into the couch-based control system. Motion data were obtained using a skin-based marker. The proposed predictive feedback control system was benchmarked against pure feedback control (no forward prediction) and resulted in a significant performance gain. Finally, we combined the PLS inference model and the predictive feedback control to evaluate the overall performance of the

  13. Renal contrast-enhanced MR angiography: timing errors and accurate depiction of renal artery origins.

    PubMed

    Schmidt, Maria A; Morgan, Robert

    2008-10-01

    To investigate bolus timing artifacts that impair depiction of renal arteries at contrast material-enhanced magnetic resonance (MR) angiography and to determine the effect of contrast agent infusion rates on artifact generation. Renal contrast-enhanced MR angiography was simulated for a variety of infusion schemes, assuming both correct and incorrect timing between data acquisition and contrast agent injection. In addition, the ethics committee approved the retrospective evaluation of clinical breath-hold renal contrast-enhanced MR angiographic studies obtained with automated detection of contrast agent arrival. Twenty-two studies were evaluated for their ability to depict the origin of renal arteries in patent vessels and for any signs of timing errors. Simulations showed that a completely artifactual stenosis or an artifactual overestimation of an existing stenosis at the renal artery origin can be caused by timing errors of the order of 5 seconds in examinations performed with contrast agent infusion rates compatible with or higher than those of hand injections. Lower infusion rates make the studies more likely to accurately depict the origin of the renal arteries. In approximately one-third of all clinical examinations, different contrast agent uptake rates were detected on the left and right sides of the body, and thus allowed us to confirm that it is often impossible to optimize depiction of both renal arteries. In three renal arteries, a signal void was found at the origin in a patent vessel, and delayed contrast agent arrival was confirmed. Computer simulations and clinical examinations showed that timing errors impair the accurate depiction of renal artery origins. (c) RSNA, 2008.

  14. Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector.

    PubMed

    Liu, Shu-Shen; Liu, Yan; Yin, Da-Qian; Wang, Xiao-Dong; Wang, Lian-Sheng

    2006-02-01

    Using the molecular electronegativity distance vector (MEDV) descriptors derived directly from the molecular topological structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were predicted. A five-variable regression equation with the correlation coefficient of 0.9964 and the root mean square errors of 0.0152 was developed. The descriptors included in the equation represent degree of chlorination (nCl), nonortho index (Ino), and interactions between three pairs of atom types, i.e., atom groups -C= and -C=, -C= and >C=, -C= and -Cl. It has been proved that the retention times of all 209 PCB congeners can be accurately predicted as long as there are more than 50 calibration compounds. In the same way, the MEDV descriptors are also used to develop the five- or six-variable models of RRTs of PCBs on other 18 stationary phases and the correlation coefficients in both modeling stage and LOO cross-validation step are not lower than 0.99 except two models.

  15. Predicted osteotomy planes are accurate when using patient-specific instrumentation for total knee arthroplasty in cadavers: a descriptive analysis.

    PubMed

    Kievit, A J; Dobbe, J G G; Streekstra, G J; Blankevoort, L; Schafroth, M U

    2018-06-01

    Malalignment of implants is a major source of failure during total knee arthroplasty. To achieve more accurate 3D planning and execution of the osteotomy cuts during surgery, the Signature (Biomet, Warsaw) patient-specific instrumentation (PSI) was used to produce pin guides for the positioning of the osteotomy blocks by means of computer-aided manufacture based on CT scan images. The research question of this study is: what is the transfer accuracy of osteotomy planes predicted by the Signature PSI system for preoperative 3D planning and intraoperative block-guided pin placement to perform total knee arthroplasty procedures? The transfer accuracy achieved by using the Signature PSI system was evaluated by comparing the osteotomy planes predicted preoperatively with the osteotomy planes seen intraoperatively in human cadaveric legs. Outcomes were measured in terms of translational and rotational errors (varus, valgus, flexion, extension and axial rotation) for both tibia and femur osteotomies. Average translational errors between the osteotomy planes predicted using the Signature system and the actual osteotomy planes achieved was 0.8 mm (± 0.5 mm) for the tibia and 0.7 mm (± 4.0 mm) for the femur. Average rotational errors in relation to predicted and achieved osteotomy planes were 0.1° (± 1.2°) of varus and 0.4° (± 1.7°) of anterior slope (extension) for the tibia, and 2.8° (± 2.0°) of varus and 0.9° (± 2.7°) of flexion and 1.4° (± 2.2°) of external rotation for the femur. The similarity between osteotomy planes predicted using the Signature system and osteotomy planes actually achieved was excellent for the tibia although some discrepancies were seen for the femur. The use of 3D system techniques in TKA surgery can provide accurate intraoperative guidance, especially for patients with deformed bone, tailored to individual patients and ensure better placement of the implant.

  16. CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts.

    PubMed

    Testa, Alison C; Hane, James K; Ellwood, Simon R; Oliver, Richard P

    2015-03-11

    The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against

  17. Evaluation of Fast-Time Wake Vortex Prediction Models

    NASA Technical Reports Server (NTRS)

    Proctor, Fred H.; Hamilton, David W.

    2009-01-01

    Current fast-time wake models are reviewed and three basic types are defined. Predictions from several of the fast-time models are compared. Previous statistical evaluations of the APA-Sarpkaya and D2P fast-time models are discussed. Root Mean Square errors between fast-time model predictions and Lidar wake measurements are examined for a 24 hr period at Denver International Airport. Shortcomings in current methodology for evaluating wake errors are also discussed.

  18. Time accurate application of the MacCormack 2-4 scheme on massively parallel computers

    NASA Technical Reports Server (NTRS)

    Hudson, Dale A.; Long, Lyle N.

    1995-01-01

    Many recent computational efforts in turbulence and acoustics research have used higher order numerical algorithms. One popular method has been the explicit MacCormack 2-4 scheme. The MacCormack 2-4 scheme is second order accurate in time and fourth order accurate in space, and is stable for CFL's below 2/3. Current research has shown that the method can give accurate results but does exhibit significant Gibbs phenomena at sharp discontinuities. The impact of adding Jameson type second, third, and fourth order artificial viscosity was examined here. Category 2 problems, the nonlinear traveling wave and the Riemann problem, were computed using a CFL number of 0.25. This research has found that dispersion errors can be significantly reduced or nearly eliminated by using a combination of second and third order terms in the damping. Use of second and fourth order terms reduced the magnitude of dispersion errors but not as effectively as the second and third order combination. The program was coded using Thinking Machine's CM Fortran, a variant of Fortran 90/High Performance Fortran, and was executed on a 2K CM-200. Simple extrapolation boundary conditions were used for both problems.

  19. Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

    PubMed

    Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

    2015-07-01

    As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  20. Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2002-01-01

    NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

  1. Predictive Mining of Time Series Data

    NASA Astrophysics Data System (ADS)

    Java, A.; Perlman, E. S.

    2002-05-01

    All-sky monitors are a relatively new development in astronomy, and their data represent a largely untapped resource. Proper utilization of this resource could lead to important discoveries not only in the physics of variable objects, but in how one observes such objects. We discuss the development of a Java toolbox for astronomical time series data. Rather than using methods conventional in astronomy (e.g., power spectrum and cross-correlation analysis) we employ rule discovery techniques commonly used in analyzing stock-market data. By clustering patterns found within the data, rule discovery allows one to build predictive models, allowing one to forecast when a given event might occur or whether the occurrence of one event will trigger a second. We have tested the toolbox and accompanying display tool on datasets (representing several classes of objects) from the RXTE All Sky Monitor. We use these datasets to illustrate the methods and functionality of the toolbox. We have found predictive patterns in several ASM datasets. We also discuss problems faced in the development process, particularly the difficulties of dealing with discretized and irregularly sampled data. A possible application would be in scheduling target of opportunity observations where the astronomer wants to observe an object when a certain event or series of events occurs. By combining such a toolbox with an automatic, Java query tool which regularly gathers data on objects of interest, the astronomer or telescope operator could use the real-time datastream to efficiently predict the occurrence of (for example) a flare or other event. By combining the toolbox with dynamic time warping data-mining tools, one could predict events which may happen on variable time scales.

  2. Accurate prediction of cardiorespiratory fitness using cycle ergometry in minimally disabled persons with relapsing-remitting multiple sclerosis.

    PubMed

    Motl, Robert W; Fernhall, Bo

    2012-03-01

    To examine the accuracy of predicting peak oxygen consumption (VO(2peak)) primarily from peak work rate (WR(peak)) recorded during a maximal, incremental exercise test on a cycle ergometer among persons with relapsing-remitting multiple sclerosis (RRMS) who had minimal disability. Cross-sectional study. Clinical research laboratory. Women with RRMS (n=32) and sex-, age-, height-, and weight-matched healthy controls (n=16) completed an incremental exercise test on a cycle ergometer to volitional termination. Not applicable. Measured and predicted VO(2peak) and WR(peak). There were strong, statistically significant associations between measured and predicted VO(2peak) in the overall sample (R(2)=.89, standard error of the estimate=127.4 mL/min) and subsamples with (R(2)=.89, standard error of the estimate=131.3 mL/min) and without (R(2)=.85, standard error of the estimate=126.8 mL/min) multiple sclerosis (MS) based on the linear regression analyses. Based on the 95% confidence limits for worst-case errors, the equation predicted VO(2peak) within 10% of its true value in 95 of every 100 subjects with MS. Peak VO(2) can be accurately predicted in persons with RRMS who have minimal disability as it is in controls by using established equations and WR(peak) recorded from a maximal, incremental exercise test on a cycle ergometer. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  3. Accurate Behavioral Simulator of All-Digital Time-Domain Smart Temperature Sensors by Using SIMULINK

    PubMed Central

    Chen, Chun-Chi; Chen, Chao-Lieh; Lin, You-Ting

    2016-01-01

    This study proposes a new behavioral simulator that uses SIMULINK for all-digital CMOS time-domain smart temperature sensors (TDSTSs) for performing rapid and accurate simulations. Inverter-based TDSTSs offer the benefits of low cost and simple structure for temperature-to-digital conversion and have been developed. Typically, electronic design automation tools, such as HSPICE, are used to simulate TDSTSs for performance evaluations. However, such tools require extremely long simulation time and complex procedures to analyze the results and generate figures. In this paper, we organize simple but accurate equations into a temperature-dependent model (TDM) by which the TDSTSs evaluate temperature behavior. Furthermore, temperature-sensing models of a single CMOS NOT gate were devised using HSPICE simulations. Using the TDM and these temperature-sensing models, a novel simulator in SIMULINK environment was developed to substantially accelerate the simulation and simplify the evaluation procedures. Experiments demonstrated that the simulation results of the proposed simulator have favorable agreement with those obtained from HSPICE simulations, showing that the proposed simulator functions successfully. This is the first behavioral simulator addressing the rapid simulation of TDSTSs. PMID:27509507

  4. Nonstationary time series prediction combined with slow feature analysis

    NASA Astrophysics Data System (ADS)

    Wang, G.; Chen, X.

    2015-07-01

    Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

  5. Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

    ERIC Educational Resources Information Center

    Lin, Jing-Wen

    2016-01-01

    Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

  6. In-Band Asymmetry Compensation for Accurate Time/Phase Transport over Optical Transport Network

    PubMed Central

    Siu, Sammy; Hu, Hsiu-fang; Lin, Shinn-Yan; Liao, Chia-Shu; Lai, Yi-Liang

    2014-01-01

    The demands of precise time/phase synchronization have been increasing recently due to the next generation of telecommunication synchronization. This paper studies the issues that are relevant to distributing accurate time/phase over optical transport network (OTN). Each node and link can introduce asymmetry, which affects the adequate time/phase accuracy over the networks. In order to achieve better accuracy, protocol level full timing support is used (e.g., Telecom-Boundary clock). Due to chromatic dispersion, the use of different wavelengths consequently causes fiber link delay asymmetry. The analytical result indicates that it introduces significant time error (i.e., phase offset) within 0.3397 ns/km in C-band or 0.3943 ns/km in L-band depending on the wavelength spacing. With the proposed scheme in this paper, the fiber link delay asymmetry can be compensated relying on the estimated mean fiber link delay by the Telecom-Boundary clock, while the OTN control plane is responsible for processing the fiber link delay asymmetry to determine the asymmetry compensation in the timing chain. PMID:24982948

  7. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

    NASA Astrophysics Data System (ADS)

    Xie, Tian; Grossman, Jeffrey C.

    2018-04-01

    The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

  8. Prediction of calving time in dairy cattle.

    PubMed

    Mahmoud, Fadul; Christopher, Bogdahn; Maher, Alsaaod; Jürg, Hüsler; Alexander, Starke; Adrian, Steiner; Gaby, Hirsbrunner

    2017-12-01

    This prospective study was carried out to predict the calving time in primiparous (n=11) and multiparous (n=22) Holstein-Friesian cows using the combination of data obtained from the RumiWatch noseband-sensor and 3D-accelerometer. The animals included in the study were fitted with the RumiWatch noseband-sensor and 3D-accelerometer at least 10days before the expected calving day. The calving event was defined as the time of the first appearance of the calves' feet outside the vulva, and this moment was determined by farm staff and/or confirmed by video monitor. As primiparous and multiparous cows behaved differently, two models including data of noseband-sensors and 3D-accelerometers were used to predict the calving time in each group. Lying bouts (LB) increased and rumination chews (RC) decreased similarly in both groups; besides that, boluses (B) decreased and other activities (OA) increased significantly in multiparous and primiparous cows, respectively. The sensitivity (Se) and specificity (Sp) for prediction of the onset of calving within the next 3h were determined with the logistic regression and ROC analysis (Se=88.9%, 85% and Sp=93.3%, 74% for multiparous and primiparous cows, respectively). This pilot study revealed that the RumiWatch system is a useful tool to predict calving time under farm conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. A novel time series link prediction method: Learning automata approach

    NASA Astrophysics Data System (ADS)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  10. Predicting Dynamic Postural Instability Using Center of Mass Time-to-Contact Information

    PubMed Central

    Hasson, Christopher J.; Van Emmerik, Richard E.A.; Caldwell, Graham E.

    2008-01-01

    Our purpose was to determine whether spatiotemporal measures of center of mass motion relative to the base of support boundary could predict stepping strategies after upper-body postural perturbations in humans. We expected that inclusion of center of mass acceleration in such time-to-contact (TtC) calculations would give better predictions and more advanced warning of perturbation severity. TtC measures were compared with traditional postural variables, which don’t consider support boundaries, and with an inverted pendulum model of dynamic stability developed by Hof et al. (2005). A pendulum was used to deliver sequentially increasing perturbations to 10 young adults, who were strapped to a wooden backboard that constrained motion to sagittal plane rotation about the ankle joint. Subjects were instructed to resist the perturbations, stepping only if necessary to prevent a fall. Peak center of mass and center of pressure velocity and acceleration demonstrated linear increases with postural challenge. In contrast, boundary relevant minimum TtC values decreased nonlinearly with postural challenge, enabling prediction of stepping responses using quadratic equations. When TtC calculations incorporated center of mass acceleration, the quadratic fits were better and gave more accurate predictions of the TtC values that would trigger stepping responses. In addition, TtC minima occurred earlier with acceleration inclusion, giving more advanced warning of perturbation severity. Our results were in agreement with TtC predictions based on Hof’s model, and suggest that TtC may function as a control parameter, influencing the postural control system’s decision to transition from a stationary base of support to a stepping strategy. PMID:18556003

  11. Nonstationary time series prediction combined with slow feature analysis

    NASA Astrophysics Data System (ADS)

    Wang, G.; Chen, X.

    2015-01-01

    Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. This paper presents a new technique of combining the driving force of a time series obtained using the Slow Feature Analysis (SFA) approach, then introducing the driving force into a predictive model to predict non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill.

  12. Improved Short-Term Clock Prediction Method for Real-Time Positioning.

    PubMed

    Lv, Yifei; Dai, Zhiqiang; Zhao, Qile; Yang, Sheng; Zhou, Jinning; Liu, Jingnan

    2017-06-06

    The application of real-time precise point positioning (PPP) requires real-time precise orbit and clock products that should be predicted within a short time to compensate for the communication delay or data gap. Unlike orbit correction, clock correction is difficult to model and predict. The widely used linear model hardly fits long periodic trends with a small data set and exhibits significant accuracy degradation in real-time prediction when a large data set is used. This study proposes a new prediction model for maintaining short-term satellite clocks to meet the high-precision requirements of real-time clocks and provide clock extrapolation without interrupting the real-time data stream. Fast Fourier transform (FFT) is used to analyze the linear prediction residuals of real-time clocks. The periodic terms obtained through FFT are adopted in the sliding window prediction to achieve a significant improvement in short-term prediction accuracy. This study also analyzes and compares the accuracy of short-term forecasts (less than 3 h) by using different length observations. Experimental results obtained from International GNSS Service (IGS) final products and our own real-time clocks show that the 3-h prediction accuracy is better than 0.85 ns. The new model can replace IGS ultra-rapid products in the application of real-time PPP. It is also found that there is a positive correlation between the prediction accuracy and the short-term stability of on-board clocks. Compared with the accuracy of the traditional linear model, the accuracy of the static PPP using the new model of the 2-h prediction clock in N, E, and U directions is improved by about 50%. Furthermore, the static PPP accuracy of 2-h clock products is better than 0.1 m. When an interruption occurs in the real-time model, the accuracy of the kinematic PPP solution using 1-h clock prediction product is better than 0.2 m, without significant accuracy degradation. This model is of practical significance

  13. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

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

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.« less

  14. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

    DOE PAGES

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; ...

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.« less

  15. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory.

    PubMed

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; Drisdell, Walter S; Shirley, Eric L; Prendergast, David

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can be rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.

  16. Prediction of induction time for methane hydrate formation in the presence or absence of THF in flow loop system by Natarajan model

    NASA Astrophysics Data System (ADS)

    Talaghat, Mohammad Reza; Jokar, Seyyed Mohammad

    2018-03-01

    The induction time is a time interval to detect the initial hydrate formation, which is counted from the moment when the stirrer is turned on until the first detection of hydrate formation. The main objective of the present work is to predict and measure the induction time of methane hydrate formation in the presence or absence of tetrahydrofuran (THF) as promoter in the flow loop system. A laboratory flow mini-loop apparatus was set up to measure the induction time of methane hydrate formation. The induction time is predicted using developed Kashchiev and Firoozabadi model and modified model of Natarajan for a flow loop system. Furthermore, the effects of volumetric flow rate of the fluid on the induction time were investigated. The results of the models were compared with experimental data. They show that the induction time of hydrate formation in the presence of THF is very short at high pressure and high volumetric flow rate of the fluid. It decreases with increasing pressure and liquid volumetric flow rate. It is also shown that the modified Natarajan model is more accurate than the Kashchiev and Firoozabadi ones in prediction of the induction time.

  17. Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

    NASA Astrophysics Data System (ADS)

    Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

    2012-12-01

    ARMA(1,2) errors were fit to the observations. Preliminary model validation exercises at a 30-day forecast horizon show that the ARMA error models generally improve the predictive skill of the linear regression rating curves. Skill seems to vary based on the ambient hydrologic conditions at the onset of the forecast. For example, ARMA error model forecasts issued before a high flow/turbidity event do not show significant improvements over the rating curve approach. However, ARMA error model forecasts issued during the "falling limb" of the hydrograph are significantly more accurate than rating curves for both single day and accumulated event predictions. In order to assist in reservoir operations decisions associated with turbidity events and general water supply reliability, DEP has initiated design of an Operations Support Tool (OST). OST integrates a reservoir operations model with 2D hydrodynamic water quality models and a database compiling near-real-time data sources and hydrologic forecasts. Currently, OST uses conventional flow-turbidity rating curves and hydrologic forecasts for predictive turbidity inputs. Given the improvements in predictive skill over traditional rating curves, the ARMA error models are currently being evaluated as an addition to DEP's Operations Support Tool.

  18. Large arterial occlusive strokes as a medical emergency: need to accurately predict clot location.

    PubMed

    Vanacker, Peter; Faouzi, Mohamed; Eskandari, Ashraf; Maeder, Philippe; Meuli, Reto; Michel, Patrik

    2017-10-01

    Endovascular treatment for acute ischemic stroke with a large intracranial occlusion was recently shown to be effective. Timely knowledge of the presence, site, and extent of arterial occlusions in the ischemic territory has the potential to influence patient selection for endovascular treatment. We aimed to find predictors of large vessel occlusive strokes, on the basis of available demographic, clinical, radiological, and laboratory data in the emergency setting. Patients enrolled in ASTRAL registry with acute ischemic stroke and computed tomography (CT)-angiography within 12 h of stroke onset were selected and categorized according to occlusion site. Easily accessible variables were used in a multivariate analysis. Of 1645 patients enrolled, a significant proportion (46.2%) had a large vessel occlusion in the ischemic territory. The main clinical predictors of any arterial occlusion were in-hospital stroke [odd ratios (OR) 2.1, 95% confidence interval 1.4-3.1], higher initial National Institute of Health Stroke Scale (OR 1.1, 1.1-1.2), presence of visual field defects (OR 1.9, 1.3-2.6), dysarthria (OR 1.4, 1.0-1.9), or hemineglect (OR 2.0, 1.4-2.8) at admission and atrial fibrillation (OR 1.7, 1.2-2.3). Further, the following radiological predictors were identified: time-to-imaging (OR 0.9, 0.9-1.0), early ischemic changes (OR 2.3, 1.7-3.2), and silent lesions on CT (OR 0.7, 0.5-1.0). The area under curve for this analysis was 0.85. Looking at different occlusion sites, National Institute of Health Stroke Scale and early ischemic changes on CT were independent predictors in all subgroups. Neurological deficits, stroke risk factors, and CT findings accurately identify acute ischemic stroke patients at risk of symptomatic vessel occlusion. Predicting the presence of these occlusions may impact emergency stroke care in regions with limited access to noninvasive vascular imaging.

  19. Accurate prediction of acute fish toxicity of fragrance chemicals with the RTgill-W1 cell assay.

    PubMed

    Natsch, Andreas; Laue, Heike; Haupt, Tina; von Niederhäusern, Valentin; Sanders, Gordon

    2018-03-01

    Testing for acute fish toxicity is an integral part of the environmental safety assessment of chemicals. A true replacement of primary fish tissue was recently proposed using cell viability in a fish gill cell line (RTgill-W1) as a means of predicting acute toxicity, showing good predictivity on 35 chemicals. To promote regulatory acceptance, the predictivity and applicability domain of novel tests need to be carefully evaluated on chemicals with existing high-quality in vivo data. We applied the RTgill-W1 cell assay to 38 fragrance chemicals with a wide range of both physicochemical properties and median lethal concentration (LC50) values and representing a diverse range of chemistries. A strong correlation (R 2  = 0.90-0.94) between the logarithmic in vivo LC50 values, based on fish mortality, and the logarithmic in vitro median effect concentration (EC50) values based on cell viability was observed. A leave-one-out analysis illustrates a median under-/overprediction from in vitro EC50 values to in vivo LC50 values by a factor of 1.5. This assay offers a simple, accurate, and reliable alternative to in vivo acute fish toxicity testing for chemicals, presumably acting mainly by a narcotic mode of action. Furthermore, the present study provides validation of the predictivity of the RTgill-W1 assay on a completely independent set of chemicals that had not been previously tested and indicates that fragrance chemicals are clearly within the applicability domain. Environ Toxicol Chem 2018;37:931-941. © 2017 SETAC. © 2017 SETAC.

  20. An Improved Model Predictive Current Controller of Switched Reluctance Machines Using Time-Multiplexed Current Sensor

    PubMed Central

    Li, Bingchu; Ling, Xiao; Huang, Yixiang; Gong, Liang; Liu, Chengliang

    2017-01-01

    This paper presents a fixed-switching-frequency model predictive current controller using multiplexed current sensor for switched reluctance machine (SRM) drives. The converter was modified to distinguish currents from simultaneously excited phases during the sampling period. The only current sensor installed in the converter was time division multiplexing for phase current sampling. During the commutation stage, the control steps of adjacent phases were shifted so that sampling time was staggered. The maximum and minimum duty ratio of pulse width modulation (PWM) was limited to keep enough sampling time for analog-to-digital (A/D) conversion. Current sensor multiplexing was realized without complex adjustment of either driver circuit nor control algorithms, while it helps to reduce the cost and errors introduced in current sampling due to inconsistency between sensors. The proposed controller is validated by both simulation and experimental results with a 1.5 kW three-phase 12/8 SRM. Satisfied current sampling is received with little difference compared with independent phase current sensors for each phase. The proposed controller tracks the reference current profile as accurately as the model predictive current controller with independent phase current sensors, while having minor tracking errors compared with a hysteresis current controller. PMID:28513554

  1. Towards an accurate real-time locator of infrasonic sources

    NASA Astrophysics Data System (ADS)

    Pinsky, V.; Blom, P.; Polozov, A.; Marcillo, O.; Arrowsmith, S.; Hofstetter, A.

    2017-11-01

    Infrasonic signals propagate from an atmospheric source via media with stochastic and fast space-varying conditions. Hence, their travel time, the amplitude at sensor recordings and even manifestation in the so-called "shadow zones" are random. Therefore, the traditional least-squares technique for locating infrasonic sources is often not effective, and the problem for the best solution must be formulated in probabilistic terms. Recently, a series of papers has been published about Bayesian Infrasonic Source Localization (BISL) method based on the computation of the posterior probability density function (PPDF) of the source location, as a convolution of a priori probability distribution function (APDF) of the propagation model parameters with likelihood function (LF) of observations. The present study is devoted to the further development of BISL for higher accuracy and stability of the source location results and decreasing of computational load. We critically analyse previous algorithms and propose several new ones. First of all, we describe the general PPDF formulation and demonstrate that this relatively slow algorithm might be among the most accurate algorithms, provided the adequate APDF and LF are used. Then, we suggest using summation instead of integration in a general PPDF calculation for increased robustness, but this leads us to the 3D space-time optimization problem. Two different forms of APDF approximation are considered and applied for the PPDF calculation in our study. One of them is previously suggested, but not yet properly used is the so-called "celerity-range histograms" (CRHs). Another is the outcome from previous findings of linear mean travel time for the four first infrasonic phases in the overlapping consecutive distance ranges. This stochastic model is extended here to the regional distance of 1000 km, and the APDF introduced is the probabilistic form of the junction between this travel time model and range-dependent probability

  2. State-space prediction model for chaotic time series

    NASA Astrophysics Data System (ADS)

    Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

    1998-08-01

    A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

  3. A cross-race effect in metamemory: Predictions of face recognition are more accurate for members of our own race

    PubMed Central

    Hourihan, Kathleen L.; Benjamin, Aaron S.; Liu, Xiping

    2012-01-01

    The Cross-Race Effect (CRE) in face recognition is the well-replicated finding that people are better at recognizing faces from their own race, relative to other races. The CRE reveals systematic limitations on eyewitness identification accuracy and suggests that some caution is warranted in evaluating cross-race identification. The CRE is a problem because jurors value eyewitness identification highly in verdict decisions. In the present paper, we explore how accurate people are in predicting their ability to recognize own-race and other-race faces. Caucasian and Asian participants viewed photographs of Caucasian and Asian faces, and made immediate judgments of learning during study. An old/new recognition test replicated the CRE: both groups displayed superior discriminability of own-race faces, relative to other-race faces. Importantly, relative metamnemonic accuracy was also greater for own-race faces, indicating that the accuracy of predictions about face recognition is influenced by race. This result indicates another source of concern when eliciting or evaluating eyewitness identification: people are less accurate in judging whether they will or will not recognize a face when that face is of a different race than they are. This new result suggests that a witness’s claim of being likely to recognize a suspect from a lineup should be interpreted with caution when the suspect is of a different race than the witness. PMID:23162788

  4. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble.

    PubMed

    Zhang, Ming; Xu, Yan; Li, Lei; Liu, Zi; Yang, Xibei; Yu, Dong-Jun

    2018-06-01

    RNA 5-methylcytosine (m 5 C) is an important post-transcriptional modification that plays an indispensable role in biological processes. The accurate identification of m 5 C sites from primary RNA sequences is especially useful for deeply understanding the mechanisms and functions of m 5 C. Due to the difficulty and expensive costs of identifying m 5 C sites with wet-lab techniques, developing fast and accurate machine-learning-based prediction methods is urgently needed. In this study, we proposed a new m 5 C site predictor, called M5C-HPCR, by introducing a novel heuristic nucleotide physicochemical property reduction (HPCR) algorithm and classifier ensemble. HPCR extracts multiple reducts of physical-chemical properties for encoding discriminative features, while the classifier ensemble is applied to integrate multiple base predictors, each of which is trained based on a separate reduct of the physical-chemical properties obtained from HPCR. Rigorous jackknife tests on two benchmark datasets demonstrate that M5C-HPCR outperforms state-of-the-art m 5 C site predictors, with the highest values of MCC (0.859) and AUC (0.962). We also implemented the webserver of M5C-HPCR, which is freely available at http://cslab.just.edu.cn:8080/M5C-HPCR/. Copyright © 2018 Elsevier Inc. All rights reserved.

  5. Studying Individual Differences in Predictability with Gamma Regression and Nonlinear Multilevel Models

    ERIC Educational Resources Information Center

    Culpepper, Steven Andrew

    2010-01-01

    Statistical prediction remains an important tool for decisions in a variety of disciplines. An equally important issue is identifying factors that contribute to more or less accurate predictions. The time series literature includes well developed methods for studying predictability and volatility over time. This article develops…

  6. The NAFLD Index: A Simple and Accurate Screening Tool for the Prediction of Non-Alcoholic Fatty Liver Disease.

    PubMed

    Ichino, Naohiro; Osakabe, Keisuke; Sugimoto, Keiko; Suzuki, Koji; Yamada, Hiroya; Takai, Hiroji; Sugiyama, Hiroko; Yukitake, Jun; Inoue, Takashi; Ohashi, Koji; Hata, Tadayoshi; Hamajima, Nobuyuki; Nishikawa, Toru; Hashimoto, Senju; Kawabe, Naoto; Yoshioka, Kentaro

    2015-01-01

    Non-alcoholic fatty liver disease (NAFLD) is a common debilitating condition in many industrialized countries that increases the risk of cardiovascular disease. The aim of this study was to derive a simple and accurate screening tool for the prediction of NAFLD in the Japanese population. A total of 945 participants, 279 men and 666 women living in Hokkaido, Japan, were enrolled among residents who attended a health check-up program from 2010 to 2014. Participants with an alcohol consumption > 20 g/day and/or a chronic liver disease, such as chronic hepatitis B, chronic hepatitis C or autoimmune hepatitis, were excluded from this study. Clinical and laboratory data were examined to identify predictive markers of NAFLD. A new predictive index for NAFLD, the NAFLD index, was constructed for men and for women. The NAFLD index for men = -15.5693+0.3264 [BMI] +0.0134 [triglycerides (mg/dl)], and for women = -31.4686+0.3683 [BMI] +2.5699 [albumin (g/dl)] +4.6740[ALT/AST] -0.0379 [HDL cholesterol (mg/dl)]. The AUROC of the NAFLD index for men and for women was 0.87(95% CI 0.88-1.60) and 0.90 (95% CI 0.66-1.02), respectively. The cut-off point of -5.28 for men predicted NAFLD with an accuracy of 82.8%. For women, the cut-off point of -7.65 predicted NAFLD with an accuracy of 87.7%. A new index for the non-invasive prediction of NAFLD, the NAFLD index, was constructed using available clinical and laboratory data. This index is a simple screening tool to predict the presence of NAFLD.

  7. Improving the prediction of African savanna vegetation variables using time series of MODIS products

    NASA Astrophysics Data System (ADS)

    Tsalyuk, Miriam; Kelly, Maggi; Getz, Wayne M.

    2017-09-01

    vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

  8. Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

    PubMed

    Kountouris, Petros; Hirst, Jonathan D

    2010-07-31

    Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

  9. Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application.

    PubMed

    El-Assaad, Atlal; Dawy, Zaher; Nemer, Georges; Hajj, Hazem; Kobeissy, Firas H

    2017-01-01

    Degradomics is a novel discipline that involves determination of the proteases/substrate fragmentation profile, called the substrate degradome, and has been recently applied in different disciplines. A major application of degradomics is its utility in the field of biomarkers where the breakdown products (BDPs) of different protease have been investigated. Among the major proteases assessed, calpain and caspase proteases have been associated with the execution phases of the pro-apoptotic and pro-necrotic cell death, generating caspase/calpain-specific cleaved fragments. The distinction between calpain and caspase protein fragments has been applied to distinguish injury mechanisms. Advanced proteomics technology has been used to identify these BDPs experimentally. However, it has been a challenge to identify these BDPs with high precision and efficiency, especially if we are targeting a number of proteins at one time. In this chapter, we present a novel bioinfromatic detection method that identifies BDPs accurately and efficiently with validation against experimental data. This method aims at predicting the consensus sequence occurrences and their variants in a large set of experimentally detected protein sequences based on state-of-the-art sequence matching and alignment algorithms. After detection, the method generates all the potential cleaved fragments by a specific protease. This space and time-efficient algorithm is flexible to handle the different orientations that the consensus sequence and the protein sequence can take before cleaving. It is O(mn) in space complexity and O(Nmn) in time complexity, with N number of protein sequences, m length of the consensus sequence, and n length of each protein sequence. Ultimately, this knowledge will subsequently feed into the development of a novel tool for researchers to detect diverse types of selected BDPs as putative disease markers, contributing to the diagnosis and treatment of related disorders.

  10. A simple tool to predict admission at the time of triage.

    PubMed

    Cameron, Allan; Rodgers, Kenneth; Ireland, Alastair; Jamdar, Ravi; McKay, Gerard A

    2015-03-01

    To create and validate a simple clinical score to estimate the probability of admission at the time of triage. This was a multicentre, retrospective, cross-sectional study of triage records for all unscheduled adult attendances in North Glasgow over 2 years. Clinical variables that had significant associations with admission on logistic regression were entered into a mixed-effects multiple logistic model. This provided weightings for the score, which was then simplified and tested on a separate validation group by receiving operator characteristic (ROC) analysis and goodness-of-fit tests. 215 231 presentations were used for model derivation and 107 615 for validation. Variables in the final model showing clinically and statistically significant associations with admission were: triage category, age, National Early Warning Score (NEWS), arrival by ambulance, referral source and admission within the last year. The resulting 6-variable score showed excellent admission/discharge discrimination (area under ROC curve 0.8774, 95% CI 0.8752 to 0.8796). Higher scores also predicted early returns for those who were discharged: the odds of subsequent admission within 28 days doubled for every 7-point increase (log odds=+0.0933 per point, p<0.0001). This simple, 6-variable score accurately estimates the probability of admission purely from triage information. Most patients could accurately be assigned to 'admission likely', 'admission unlikely', 'admission very unlikely' etc., by setting appropriate cut-offs. This could have uses in patient streaming, bed management and decision support. It also has the potential to control for demographics when comparing performance over time or between departments. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  11. Glass Fibre/Epoxy Resin Interface Life-Time Prediction.

    DTIC Science & Technology

    1983-04-01

    RD-Ai32 26 GLASS FIBRE /POXY RESIN INTERFACE LIFE-TIME PREDICTION 1/1 (U) BRISTOL UNIV (ENGLAND) H H WILLS PHYSICS LAB K H RSHBEE ET AL. APR 83...D 3005-MS GLASS FIBRE /EPOXY RESIN INTERFACE LIFE-TIME PREDICTION - Final Report by K H G Ashbee, Principal Investigator R Ho~l J P Sargent Elizabeth...REPORT h PERIOD COVERED. Glass Fibre /Epoxy Resin Interface Life-time F-inal Technical 11’ port PreictonApril 1981 - A:’ril 1983 6. PERFORMING ORG. REPORT

  12. Dancing to CHANGA: a self-consistent prediction for close SMBH pair formation time-scales following galaxy mergers

    NASA Astrophysics Data System (ADS)

    Tremmel, M.; Governato, F.; Volonteri, M.; Quinn, T. R.; Pontzen, A.

    2018-04-01

    We present the first self-consistent prediction for the distribution of formation time-scales for close supermassive black hole (SMBH) pairs following galaxy mergers. Using ROMULUS25, the first large-scale cosmological simulation to accurately track the orbital evolution of SMBHs within their host galaxies down to sub-kpc scales, we predict an average formation rate density of close SMBH pairs of 0.013 cMpc-3 Gyr-1. We find that it is relatively rare for galaxy mergers to result in the formation of close SMBH pairs with sub-kpc separation and those that do form are often the result of Gyr of orbital evolution following the galaxy merger. The likelihood and time-scale to form a close SMBH pair depends strongly on the mass ratio of the merging galaxies, as well as the presence of dense stellar cores. Low stellar mass ratio mergers with galaxies that lack a dense stellar core are more likely to become tidally disrupted and deposit their SMBH at large radii without any stellar core to aid in their orbital decay, resulting in a population of long-lived `wandering' SMBHs. Conversely, SMBHs in galaxies that remain embedded within a stellar core form close pairs in much shorter time-scales on average. This time-scale is a crucial, though often ignored or very simplified, ingredient to models predicting SMBH mergers rates and the connection between SMBH and star formation activity.

  13. Calculating Path-Dependent Travel Time Prediction Variance and Covariance fro a Global Tomographic P-Velocity Model

    NASA Astrophysics Data System (ADS)

    Ballard, S.; Hipp, J. R.; Encarnacao, A.; Young, C. J.; Begnaud, M. L.; Phillips, W. S.

    2012-12-01

    Seismic event locations can be made more accurate and precise by computing predictions of seismic travel time through high fidelity 3D models of the wave speed in the Earth's interior. Given the variable data quality and uneven data sampling associated with this type of model, it is essential that there be a means to calculate high-quality estimates of the path-dependent variance and covariance associated with the predicted travel times of ray paths through the model. In this paper, we describe a methodology for accomplishing this by exploiting the full model covariance matrix and show examples of path-dependent travel time prediction uncertainty computed from SALSA3D, our global, seamless 3D tomographic P-velocity model. Typical global 3D models have on the order of 1/2 million nodes, so the challenge in calculating the covariance matrix is formidable: 0.9 TB storage for 1/2 of a symmetric matrix, necessitating an Out-Of-Core (OOC) blocked matrix solution technique. With our approach the tomography matrix (G which includes Tikhonov regularization terms) is multiplied by its transpose (GTG) and written in a blocked sub-matrix fashion. We employ a distributed parallel solution paradigm that solves for (GTG)-1 by assigning blocks to individual processing nodes for matrix decomposition update and scaling operations. We first find the Cholesky decomposition of GTG which is subsequently inverted. Next, we employ OOC matrix multiplication methods to calculate the model covariance matrix from (GTG)-1 and an assumed data covariance matrix. Given the model covariance matrix, we solve for the travel-time covariance associated with arbitrary ray-paths by summing the model covariance along both ray paths. Setting the paths equal and taking the square root yields the travel prediction uncertainty for the single path.

  14. Time estimation predicts mathematical intelligence.

    PubMed

    Kramer, Peter; Bressan, Paola; Grassi, Massimo

    2011-01-01

    Performing mental subtractions affects time (duration) estimates, and making time estimates disrupts mental subtractions. This interaction has been attributed to the concurrent involvement of time estimation and arithmetic with general intelligence and working memory. Given the extant evidence of a relationship between time and number, here we test the stronger hypothesis that time estimation correlates specifically with mathematical intelligence, and not with general intelligence or working-memory capacity. Participants performed a (prospective) time estimation experiment, completed several subtests of the WAIS intelligence test, and self-rated their mathematical skill. For five different durations, we found that time estimation correlated with both arithmetic ability and self-rated mathematical skill. Controlling for non-mathematical intelligence (including working memory capacity) did not change the results. Conversely, correlations between time estimation and non-mathematical intelligence either were nonsignificant, or disappeared after controlling for mathematical intelligence. We conclude that time estimation specifically predicts mathematical intelligence. On the basis of the relevant literature, we furthermore conclude that the relationship between time estimation and mathematical intelligence is likely due to a common reliance on spatial ability.

  15. Higher-order accurate space-time schemes for computational astrophysics—Part I: finite volume methods

    NASA Astrophysics Data System (ADS)

    Balsara, Dinshaw S.

    2017-12-01

    As computational astrophysics comes under pressure to become a precision science, there is an increasing need to move to high accuracy schemes for computational astrophysics. The algorithmic needs of computational astrophysics are indeed very special. The methods need to be robust and preserve the positivity of density and pressure. Relativistic flows should remain sub-luminal. These requirements place additional pressures on a computational astrophysics code, which are usually not felt by a traditional fluid dynamics code. Hence the need for a specialized review. The focus here is on weighted essentially non-oscillatory (WENO) schemes, discontinuous Galerkin (DG) schemes and PNPM schemes. WENO schemes are higher order extensions of traditional second order finite volume schemes. At third order, they are most similar to piecewise parabolic method schemes, which are also included. DG schemes evolve all the moments of the solution, with the result that they are more accurate than WENO schemes. PNPM schemes occupy a compromise position between WENO and DG schemes. They evolve an Nth order spatial polynomial, while reconstructing higher order terms up to Mth order. As a result, the timestep can be larger. Time-dependent astrophysical codes need to be accurate in space and time with the result that the spatial and temporal accuracies must be matched. This is realized with the help of strong stability preserving Runge-Kutta schemes and ADER (Arbitrary DERivative in space and time) schemes, both of which are also described. The emphasis of this review is on computer-implementable ideas, not necessarily on the underlying theory.

  16. Prediction-Correction Algorithms for Time-Varying Constrained Optimization

    DOE PAGES

    Simonetto, Andrea; Dall'Anese, Emiliano

    2017-07-26

    This article develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

  17. Towards more accurate and reliable predictions for nuclear applications

    NASA Astrophysics Data System (ADS)

    Goriely, Stephane; Hilaire, Stephane; Dubray, Noel; Lemaître, Jean-François

    2017-09-01

    The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. Nowadays mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenological inputs in the evaluation of nuclear data. The latest achievements to determine nuclear masses within the non-relativistic HFB approach, including the related uncertainties in the model predictions, are discussed. Similarly, recent efforts to determine fission observables within the mean-field approach are described and compared with more traditional existing models.

  18. Cartesian Off-Body Grid Adaption for Viscous Time- Accurate Flow Simulation

    NASA Technical Reports Server (NTRS)

    Buning, Pieter G.; Pulliam, Thomas H.

    2011-01-01

    An improved solution adaption capability has been implemented in the OVERFLOW overset grid CFD code. Building on the Cartesian off-body approach inherent in OVERFLOW and the original adaptive refinement method developed by Meakin, the new scheme provides for automated creation of multiple levels of finer Cartesian grids. Refinement can be based on the undivided second-difference of the flow solution variables, or on a specific flow quantity such as vorticity. Coupled with load-balancing and an inmemory solution interpolation procedure, the adaption process provides very good performance for time-accurate simulations on parallel compute platforms. A method of using refined, thin body-fitted grids combined with adaption in the off-body grids is presented, which maximizes the part of the domain subject to adaption. Two- and three-dimensional examples are used to illustrate the effectiveness and performance of the adaption scheme.

  19. Regional Seismic Travel-Time Prediction, Uncertainty, and Location Improvement in Western Eurasia

    NASA Astrophysics Data System (ADS)

    Flanagan, M. P.; Myers, S. C.

    2004-12-01

    We investigate our ability to improve regional travel-time prediction and seismic event location using an a priori, three-dimensional velocity model of Western Eurasia and North Africa: WENA1.0 [Pasyanos et al., 2004]. Our objective is to improve the accuracy of seismic location estimates and calculate representative location uncertainty estimates. As we focus on the geographic region of Western Eurasia, the Middle East, and North Africa, we develop, test, and validate 3D model-based travel-time prediction models for 30 stations in the study region. Three principal results are presented. First, the 3D WENA1.0 velocity model improves travel-time prediction over the iasp91 model, as measured by variance reduction, for regional Pg, Pn, and P phases recorded at the 30 stations. Second, a distance-dependent uncertainty model is developed and tested for the WENA1.0 model. Third, an end-to-end validation test based on 500 event relocations demonstrates improved location performance over the 1-dimensional iasp91 model. Validation of the 3D model is based on a comparison of approximately 11,000 Pg, Pn, and P travel-time predictions and empirical observations from ground truth (GT) events. Ray coverage for the validation dataset is chosen to provide representative, regional-distance sampling across Eurasia and North Africa. The WENA1.0 model markedly improves travel-time predictions for most stations with an average variance reduction of 25% for all ray paths. We find that improvement is station dependent, with some stations benefiting greatly from WENA1.0 predictions (52% at APA, 33% at BKR, and 32% at NIL), some stations showing moderate improvement (12% at KEV, 14% at BOM, and 12% at TAM), some benefiting only slightly (6% at MOX, and 4% at SVE), and some are degraded (-6% at MLR and -18% at QUE). We further test WENA1.0 by comparing location accuracy with results obtained using the iasp91 model. Again, relocation of these events is dependent on ray paths that evenly

  20. Mass spectrometry in Earth sciences: the precise and accurate measurement of time.

    PubMed

    Schaltegger, Urs; Wotzlaw, Jörn-Frederik; Ovtcharova, Maria; Chiaradia, Massimo; Spikings, Richard

    2014-01-01

    Precise determinations of the isotopic compositions of a variety of elements is a widely applied tool in Earth sciences. Isotope ratios are used to quantify rates of geological processes that occurred during the previous 4.5 billion years, and also at the present time. An outstanding application is geochronology, which utilizes the production of radiogenic daughter isotopes by the radioactive decay of parent isotopes. Geochronological tools, involving isotopic analysis of selected elements from smallest volumes of minerals by thermal ionization mass spectrometry, provide precise and accurate measurements of time throughout the geological history of our planet over nine orders of magnitude, from the accretion of the proto-planetary disk, to the timing of the last glaciation. This article summarizes the recent efforts of the Isotope Geochemistry, Geochronology and Thermochronology research group at the University of Geneva to advance the U-Pb geochronological tool to achieve unprecedented precision and accuracy, and presents two examples of its application to two significant open questions in Earth sciences: what are the triggers and timescales of volcanic supereruptions, and what were the causes of mass extinctions in the geological past, driven by global climatic and environmental deterioration?

  1. Estimating Time-Varying PCB Exposures Using Person-Specific Predictions to Supplement Measured Values: A Comparison of Observed and Predicted Values in Two Cohorts of Norwegian Women

    PubMed Central

    Nøst, Therese Haugdahl; Breivik, Knut; Wania, Frank; Rylander, Charlotta; Odland, Jon Øyvind; Sandanger, Torkjel Manning

    2015-01-01

    Background Studies on the health effects of polychlorinated biphenyls (PCBs) call for an understanding of past and present human exposure. Time-resolved mechanistic models may supplement information on concentrations in individuals obtained from measurements and/or statistical approaches if they can be shown to reproduce empirical data. Objectives Here, we evaluated the capability of one such mechanistic model to reproduce measured PCB concentrations in individual Norwegian women. We also assessed individual life-course concentrations. Methods Concentrations of four PCB congeners in pregnant (n = 310, sampled in 2007–2009) and postmenopausal (n = 244, 2005) women were compared with person-specific predictions obtained using CoZMoMAN, an emission-based environmental fate and human food-chain bioaccumulation model. Person-specific predictions were also made using statistical regression models including dietary and lifestyle variables and concentrations. Results CoZMoMAN accurately reproduced medians and ranges of measured concentrations in the two study groups. Furthermore, rank correlations between measurements and predictions from both CoZMoMAN and regression analyses were strong (Spearman’s r > 0.67). Precision in quartile assignments from predictions was strong overall as evaluated by weighted Cohen’s kappa (> 0.6). Simulations indicated large inter-individual differences in concentrations experienced in the past. Conclusions The mechanistic model reproduced all measurements of PCB concentrations within a factor of 10, and subject ranking and quartile assignments were overall largely consistent, although they were weak within each study group. Contamination histories for individuals predicted by CoZMoMAN revealed variation between study subjects, particularly in the timing of peak concentrations. Mechanistic models can provide individual PCB exposure metrics that could serve as valuable supplements to measurements. Citation Nøst TH, Breivik K, Wania F

  2. Time-Accurate Simulations and Acoustic Analysis of Slat Free-Shear-Layer. Part 2

    NASA Technical Reports Server (NTRS)

    Khorrami, Mehdi R.; Singer, Bart A.; Lockard, David P.

    2002-01-01

    Unsteady computational simulations of a multi-element, high-lift configuration are performed. Emphasis is placed on accurate spatiotemporal resolution of the free shear layer in the slat-cove region. The excessive dissipative effects of the turbulence model, so prevalent in previous simulations, are circumvented by switching off the turbulence-production term in the slat cove region. The justifications and physical arguments for taking such a step are explained in detail. The removal of this excess damping allows the shear layer to amplify large-scale structures, to achieve a proper non-linear saturation state, and to permit vortex merging. The large-scale disturbances are self-excited, and unlike our prior fully turbulent simulations, no external forcing of the shear layer is required. To obtain the farfield acoustics, the Ffowcs Williams and Hawkings equation is evaluated numerically using the simulated time-accurate flow data. The present comparison between the computed and measured farfield acoustic spectra shows much better agreement for the amplitude and frequency content than past calculations. The effect of the angle-of-attack on the slat's flow features radiated acoustic field are also simulated presented.

  3. Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

    PubMed

    Arcon, Juan Pablo; Defelipe, Lucas A; Modenutti, Carlos P; López, Elias D; Alvarez-Garcia, Daniel; Barril, Xavier; Turjanski, Adrián G; Martí, Marcelo A

    2017-04-24

    One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 μs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.

  4. 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.

  5. Automated Prediction of Early Blood Transfusion and Mortality in Trauma Patients

    DTIC Science & Technology

    2014-09-24

    We hypothesized that analysis of pulse oximeter signals could predict blood transfusion and mortality as accurately as conventional vital signs(VSs...to 3-hour transfusion, MT, and mortality no differently from pulse oximeter signals alone. Pulse oximeter features collected in the first 15 minutes...time is an unrealized goal. We hypothesized that analysis of pulse oximeter signals could predict blood transfusion and mortality as accurately as

  6. A solution for measuring accurate reaction time to visual stimuli realized with a programmable microcontroller.

    PubMed

    Ohyanagi, Toshio; Sengoku, Yasuhito

    2010-02-01

    This article presents a new solution for measuring accurate reaction time (SMART) to visual stimuli. The SMART is a USB device realized with a Cypress Programmable System-on-Chip (PSoC) mixed-signal array programmable microcontroller. A brief overview of the hardware and firmware of the PSoC is provided, together with the results of three experiments. In Experiment 1, we investigated the timing accuracy of the SMART in measuring reaction time (RT) under different conditions of operating systems (OSs; Windows XP or Vista) and monitor displays (a CRT or an LCD). The results indicated that the timing error in measuring RT by the SMART was less than 2 msec, on average, under all combinations of OS and display and that the SMART was tolerant to jitter and noise. In Experiment 2, we tested the SMART with 8 participants. The results indicated that there was no significant difference among RTs obtained with the SMART under the different conditions of OS and display. In Experiment 3, we used Microsoft (MS) PowerPoint to present visual stimuli on the display. We found no significant difference in RTs obtained using MS DirectX technology versus using the PowerPoint file with the SMART. We are certain that the SMART is a simple and practical solution for measuring RTs accurately. Although there are some restrictions in using the SMART with RT paradigms, the SMART is capable of providing both researchers and health professionals working in clinical settings with new ways of using RT paradigms in their work.

  7. Estimating energy expenditure in vascular surgery patients: Are predictive equations accurate enough?

    PubMed

    Suen, J; Thomas, J M; Delaney, C L; Spark, J I; Miller, M D

    2016-12-01

    Malnutrition is prevalent in vascular surgical patients who commonly seek tertiary care at advanced stages of disease. Adjunct nutrition support is therefore pertinent to optimise patient outcomes. To negate consequences related to excessive or suboptimal dietary energy intake, it is essential to accurately determine energy expenditure and subsequent requirements. This study aims to compare resting energy expenditure (REE) measured by indirect calorimetry, a commonly used comparator, to REE estimated by predictive equations (Schofield, Harris-Benedict equations and Miller equation) to determine the most suitable equation for vascular surgery patients. Data were collected from four studies that measured REE in 77 vascular surgery patients. Bland-Altman analyses were conducted to explore agreement. Presence of fixed or proportional bias was assessed by linear regression analyses. In comparison to measured REE, on average REE was overestimated when Schofield (+857 kJ/day), Harris-Benedict (+801 kJ/day) and Miller (+71 kJ/day) equations were used. Wide limits of agreement led to an over or underestimation from 1552 to 1755 kJ. Proportional bias was absent in Schofield (R 2  = 0.005, p = 0.54) and Harris-Benedict equations (R 2  = 0.045, p = 0.06) but was present in the Miller equation (R 2  = 0.210, p < 0.01) even after logarithmic transformation (R 2  = 0.213, p < 0.01). Whilst the Miller equation tended to overestimate resting energy expenditure and was affected by proportional bias, the limits of agreement and mean bias were smaller compared to Schofield and Harris-Benedict equations. This suggested that it is the preferred predictive equation for vascular surgery patients. Future research to refine the Miller equation to improve its overall accuracy will better inform the provision of nutritional support for vascular surgery patients and subsequently improve outcomes. Alternatively, an equation might be developed specifically for use with

  8. Improved predictive modeling of white LEDs with accurate luminescence simulation and practical inputs with TracePro opto-mechanical design software

    NASA Astrophysics Data System (ADS)

    Tsao, Chao-hsi; Freniere, Edward R.; Smith, Linda

    2009-02-01

    The use of white LEDs for solid-state lighting to address applications in the automotive, architectural and general illumination markets is just emerging. LEDs promise greater energy efficiency and lower maintenance costs. However, there is a significant amount of design and cost optimization to be done while companies continue to improve semiconductor manufacturing processes and begin to apply more efficient and better color rendering luminescent materials such as phosphor and quantum dot nanomaterials. In the last decade, accurate and predictive opto-mechanical software modeling has enabled adherence to performance, consistency, cost, and aesthetic criteria without the cost and time associated with iterative hardware prototyping. More sophisticated models that include simulation of optical phenomenon, such as luminescence, promise to yield designs that are more predictive - giving design engineers and materials scientists more control over the design process to quickly reach optimum performance, manufacturability, and cost criteria. A design case study is presented where first, a phosphor formulation and excitation source are optimized for a white light. The phosphor formulation, the excitation source and other LED components are optically and mechanically modeled and ray traced. Finally, its performance is analyzed. A blue LED source is characterized by its relative spectral power distribution and angular intensity distribution. YAG:Ce phosphor is characterized by relative absorption, excitation and emission spectra, quantum efficiency and bulk absorption coefficient. Bulk scatter properties are characterized by wavelength dependent scatter coefficients, anisotropy and bulk absorption coefficient.

  9. A fast and accurate method to predict 2D and 3D aerodynamic boundary layer flows

    NASA Astrophysics Data System (ADS)

    Bijleveld, H. A.; Veldman, A. E. P.

    2014-12-01

    A quasi-simultaneous interaction method is applied to predict 2D and 3D aerodynamic flows. This method is suitable for offshore wind turbine design software as it is a very accurate and computationally reasonably cheap method. This study shows the results for a NACA 0012 airfoil. The two applied solvers converge to the experimental values when the grid is refined. We also show that in separation the eigenvalues remain positive thus avoiding the Goldstein singularity at separation. In 3D we show a flow over a dent in which separation occurs. A rotating flat plat is used to show the applicability of the method for rotating flows. The shown capabilities of the method indicate that the quasi-simultaneous interaction method is suitable for design methods for offshore wind turbine blades.

  10. Predicting survival time for cold exposure

    NASA Astrophysics Data System (ADS)

    Tikuisis, Peter

    1995-06-01

    The prediction of survival time (ST) for cold exposure is speculative as reliable controlled data of deep hypothermia are unavailable. At best, guidance can be obtained from case histories of accidental exposure. This study describes the development of a mathematical model for the prediction of ST under sedentary conditions in the cold. The model is based on steady-state heat conduction in a single cylinder comprised of a core and two concentric annular shells representing the fat plus skin and the clothing plus still boundary layer, respectively. The ambient condition can be either air or water; the distinction is made by assigning different values of insulation to the still boundary layer. Metabolic heat production ( M) is comprised of resting and shivering components with the latter predicted by temperature signals from the core and skin. Where the cold exposure is too severe for M to balance heat loss, ST is largely determined by the rate of heat loss from the body. Where a balance occurs, ST is governed by the endurance time for shivering. End of survival is marked by the deep core temperature reaching a value of 30° C. The model was calibrated against survival data of cold water (0 to 20° C) immersion and then applied to cold air exposure. A sampling of ST predictions for the nude exposure of an average healthy male in relatively calm air (1 km/h wind speed) are the following: 1.8, 2.5, 4.1, 9.0, and >24 h for -30, -20, -10, 0, and 10° C, respectively. With two layers of loose clothing (average thickness of 1 mm each) in a 5 km/h wind, STs are 4.0, 5.6, 8.6, 15.4, and >24 h for -50, -40, -30, -20, and -10° C. The predicted STs must be weighted against the extrapolative nature of the model. At present, it would be prudent to use the predictions in a relative sense, that is, to compare or rank-order predicted STs for various combinations of ambient conditions and clothing protection.

  11. Predictive sensor method and apparatus

    NASA Technical Reports Server (NTRS)

    Cambridge, Vivien J.; Koger, Thomas L.

    1993-01-01

    A microprocessor and electronics package employing predictive methodology was developed to accelerate the response time of slowly responding hydrogen sensors. The system developed improved sensor response time from approximately 90 seconds to 8.5 seconds. The microprocessor works in real-time providing accurate hydrogen concentration corrected for fluctuations in sensor output resulting from changes in atmospheric pressure and temperature. Following the successful development of the hydrogen sensor system, the system and predictive methodology was adapted to a commercial medical thermometer probe. Results of the experiment indicate that, with some customization of hardware and software, response time improvements are possible for medical thermometers as well as other slowly responding sensors.

  12. 42 CFR 414.806 - Penalties associated with the failure to submit timely and accurate ASP data.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 42 Public Health 3 2013-10-01 2013-10-01 false Penalties associated with the failure to submit timely and accurate ASP data. 414.806 Section 414.806 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICARE PROGRAM (CONTINUED) PAYMENT FOR PART B MEDICAL AND OTHER HEALTH SERVICES...

  13. 42 CFR 414.806 - Penalties associated with the failure to submit timely and accurate ASP data.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 42 Public Health 3 2012-10-01 2012-10-01 false Penalties associated with the failure to submit timely and accurate ASP data. 414.806 Section 414.806 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICARE PROGRAM (CONTINUED) PAYMENT FOR PART B MEDICAL AND OTHER HEALTH SERVICES...

  14. 42 CFR 414.806 - Penalties associated with the failure to submit timely and accurate ASP data.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 42 Public Health 3 2010-10-01 2010-10-01 false Penalties associated with the failure to submit timely and accurate ASP data. 414.806 Section 414.806 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICARE PROGRAM PAYMENT FOR PART B MEDICAL AND OTHER HEALTH SERVICES Submission of...

  15. 42 CFR 414.806 - Penalties associated with the failure to submit timely and accurate ASP data.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 42 Public Health 3 2011-10-01 2011-10-01 false Penalties associated with the failure to submit timely and accurate ASP data. 414.806 Section 414.806 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICARE PROGRAM PAYMENT FOR PART B MEDICAL AND OTHER HEALTH SERVICES Submission of...

  16. 42 CFR 414.806 - Penalties associated with the failure to submit timely and accurate ASP data.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 42 Public Health 3 2014-10-01 2014-10-01 false Penalties associated with the failure to submit timely and accurate ASP data. 414.806 Section 414.806 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICARE PROGRAM (CONTINUED) PAYMENT FOR PART B MEDICAL AND OTHER HEALTH SERVICES...

  17. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming.

    PubMed

    Magozzi, Sarah; Calosi, Piero

    2015-01-01

    Predicting species vulnerability to global warming requires a comprehensive, mechanistic understanding of sublethal and lethal thermal tolerances. To date, however, most studies investigating species physiological responses to increasing temperature have focused on the underlying physiological traits of either acute or chronic tolerance in isolation. Here we propose an integrative, synthetic approach including the investigation of multiple physiological traits (metabolic performance and thermal tolerance), and their plasticity, to provide more accurate and balanced predictions on species and assemblage vulnerability to both acute and chronic effects of global warming. We applied this approach to more accurately elucidate relative species vulnerability to warming within an assemblage of six caridean prawns occurring in the same geographic, hence macroclimatic, region, but living in different thermal habitats. Prawns were exposed to four incubation temperatures (10, 15, 20 and 25 °C) for 7 days, their metabolic rates and upper thermal limits were measured, and plasticity was calculated according to the concept of Reaction Norms, as well as Q10 for metabolism. Compared to species occupying narrower/more stable thermal niches, species inhabiting broader/more variable thermal environments (including the invasive Palaemon macrodactylus) are likely to be less vulnerable to extreme acute thermal events as a result of their higher upper thermal limits. Nevertheless, they may be at greater risk from chronic exposure to warming due to the greater metabolic costs they incur. Indeed, a trade-off between acute and chronic tolerance was apparent in the assemblage investigated. However, the invasive species P. macrodactylus represents an exception to this pattern, showing elevated thermal limits and plasticity of these limits, as well as a high metabolic control. In general, integrating multiple proxies for species physiological acute and chronic responses to increasing

  18. Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

    PubMed Central

    Xue, Yi; Skrynnikov, Nikolai R

    2014-01-01

    Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15N R1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields. PMID:24452989

  19. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    USGS Publications Warehouse

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  20. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

    PubMed Central

    Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

    2015-01-01

    Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

  1. The phantom robot - Predictive displays for teleoperation with time delay

    NASA Technical Reports Server (NTRS)

    Bejczy, Antal K.; Kim, Won S.; Venema, Steven C.

    1990-01-01

    An enhanced teleoperation technique for time-delayed bilateral teleoperator control is discussed. The control technique selected for time delay is based on the use of a high-fidelity graphics phantom robot that is being controlled in real time (without time delay) against the static task image. Thus, the motion of the phantom robot image on the monitor predicts the motion of the real robot. The real robot's motion will follow the phantom robot's motion on the monitor with the communication time delay implied in the task. Real-time high-fidelity graphics simulation of a PUMA arm is generated and overlaid on the actual camera view of the arm. A simple camera calibration technique is used for calibrated graphics overlay. A preliminary experiment is performed with the predictive display by using a very simple tapping task. The results with this simple task indicate that predictive display enhances the human operator's telemanipulation task performance significantly during free motion when there is a long time delay. It appears, however, that either two-view or stereoscopic predictive displays are necessary for general three-dimensional tasks.

  2. A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics

    PubMed Central

    Stanley, Jeffrey R.; Adkins, Joshua N.; Slysz, Gordon W.; Monroe, Matthew E.; Purvine, Samuel O.; Karpievitch, Yuliya V.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.

    2011-01-01

    Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, as this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referred to as Statistical Tools for AMT tag Confidence (STAC). STAC additionally provides a Uniqueness Probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download as both a command line and a Windows graphical application. PMID:21692516

  3. Development of a New Model for Accurate Prediction of Cloud Water Deposition on Vegetation

    NASA Astrophysics Data System (ADS)

    Katata, G.; Nagai, H.; Wrzesinsky, T.; Klemm, O.; Eugster, W.; Burkard, R.

    2006-12-01

    Scarcity of water resources in arid and semi-arid areas is of great concern in the light of population growth and food shortages. Several experiments focusing on cloud (fog) water deposition on the land surface suggest that cloud water plays an important role in water resource in such regions. A one-dimensional vegetation model including the process of cloud water deposition on vegetation has been developed to better predict cloud water deposition on the vegetation. New schemes to calculate capture efficiency of leaf, cloud droplet size distribution, and gravitational flux of cloud water were incorporated in the model. Model calculations were compared with the data acquired at the Norway spruce forest at the Waldstein site, Germany. High performance of the model was confirmed by comparisons of calculated net radiation, sensible and latent heat, and cloud water fluxes over the forest with measurements. The present model provided a better prediction of measured turbulent and gravitational fluxes of cloud water over the canopy than the Lovett model, which is a commonly used cloud water deposition model. Detailed calculations of evapotranspiration and of turbulent exchange of heat and water vapor within the canopy and the modifications are necessary for accurate prediction of cloud water deposition. Numerical experiments to examine the dependence of cloud water deposition on the vegetation species (coniferous and broad-leaved trees, flat and cylindrical grasses) and structures (Leaf Area Index (LAI) and canopy height) are performed using the presented model. The results indicate that the differences of leaf shape and size have a large impact on cloud water deposition. Cloud water deposition also varies with the growth of vegetation and seasonal change of LAI. We found that the coniferous trees whose height and LAI are 24 m and 2.0 m2m-2, respectively, produce the largest amount of cloud water deposition in all combinations of vegetation species and structures in the

  4. Considerations of the Use of 3-D Geophysical Models to Predict Test Ban Monitoring Observables

    DTIC Science & Technology

    2007-09-01

    predict first P arrival times. Since this is a 3-D model, the travel times are predicted with a 3-D finite-difference code solving the eikonal equations...for the eikonal wave equation should provide more accurate predictions of travel-time from 3D models. These techniques and others are being

  5. Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures

    PubMed Central

    2010-01-01

    Background β-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. Results We have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. Conclusions We have created an accurate predictor of β-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/. PMID:20673368

  6. Commissioning a passive-scattering proton therapy nozzle for accurate SOBP delivery.

    PubMed

    Engelsman, M; Lu, H M; Herrup, D; Bussiere, M; Kooy, H M

    2009-06-01

    Proton radiotherapy centers that currently use passively scattered proton beams do field specific calibrations for a non-negligible fraction of treatment fields, which is time and resource consuming. Our improved understanding of the passive scattering mode of the IBA universal nozzle, especially of the current modulation function, allowed us to re-commission our treatment control system for accurate delivery of SOBPs of any range and modulation, and to predict the output for each of these fields. We moved away from individual field calibrations to a state where continued quality assurance of SOBP field delivery is ensured by limited system-wide measurements that only require one hour per week. This manuscript reports on a protocol for generation of desired SOBPs and prediction of dose output.

  7. Commissioning a passive-scattering proton therapy nozzle for accurate SOBP delivery

    PubMed Central

    Engelsman, M.; Lu, H.-M.; Herrup, D.; Bussiere, M.; Kooy, H. M.

    2009-01-01

    Proton radiotherapy centers that currently use passively scattered proton beams do field specific calibrations for a non-negligible fraction of treatment fields, which is time and resource consuming. Our improved understanding of the passive scattering mode of the IBA universal nozzle, especially of the current modulation function, allowed us to re-commission our treatment control system for accurate delivery of SOBPs of any range and modulation, and to predict the output for each of these fields. We moved away from individual field calibrations to a state where continued quality assurance of SOBP field delivery is ensured by limited system-wide measurements that only require one hour per week. This manuscript reports on a protocol for generation of desired SOBPs and prediction of dose output. PMID:19610306

  8. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging.

    PubMed

    Hughes, Timothy J; Kandathil, Shaun M; Popelier, Paul L A

    2015-02-05

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G(**), B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol(-1), decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol(-1). Copyright © 2013 Elsevier B.V. All rights reserved.

  9. PSO-MISMO modeling strategy for multistep-ahead time series prediction.

    PubMed

    Bao, Yukun; Xiong, Tao; Hu, Zhongyi

    2014-05-01

    Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

  10. Exploring the knowledge behind predictions in everyday cognition: an iterated learning study.

    PubMed

    Stephens, Rachel G; Dunn, John C; Rao, Li-Lin; Li, Shu

    2015-10-01

    Making accurate predictions about events is an important but difficult task. Recent work suggests that people are adept at this task, making predictions that reflect surprisingly accurate knowledge of the distributions of real quantities. Across three experiments, we used an iterated learning procedure to explore the basis of this knowledge: to what extent is domain experience critical to accurate predictions and how accurate are people when faced with unfamiliar domains? In Experiment 1, two groups of participants, one resident in Australia, the other in China, predicted the values of quantities familiar to both (movie run-times), unfamiliar to both (the lengths of Pharaoh reigns), and familiar to one but unfamiliar to the other (cake baking durations and the lengths of Beijing bus routes). While predictions from both groups were reasonably accurate overall, predictions were inaccurate in the selectively unfamiliar domains and, surprisingly, predictions by the China-resident group were also inaccurate for a highly familiar domain: local bus route lengths. Focusing on bus routes, two follow-up experiments with Australia-resident groups clarified the knowledge and strategies that people draw upon, plus important determinants of accurate predictions. For unfamiliar domains, people appear to rely on extrapolating from (not simply directly applying) related knowledge. However, we show that people's predictions are subject to two sources of error: in the estimation of quantities in a familiar domain and extension to plausible values in an unfamiliar domain. We propose that the key to successful predictions is not simply domain experience itself, but explicit experience of relevant quantities.

  11. A time-accurate algorithm for chemical non-equilibrium viscous flows at all speeds

    NASA Technical Reports Server (NTRS)

    Shuen, J.-S.; Chen, K.-H.; Choi, Y.

    1992-01-01

    A time-accurate, coupled solution procedure is described for the chemical nonequilibrium Navier-Stokes equations over a wide range of Mach numbers. This method employs the strong conservation form of the governing equations, but uses primitive variables as unknowns. Real gas properties and equilibrium chemistry are considered. Numerical tests include steady convergent-divergent nozzle flows with air dissociation/recombination chemistry, dump combustor flows with n-pentane-air chemistry, nonreacting flow in a model double annular combustor, and nonreacting unsteady driven cavity flows. Numerical results for both the steady and unsteady flows demonstrate the efficiency and robustness of the present algorithm for Mach numbers ranging from the incompressible limit to supersonic speeds.

  12. Accurate prediction of pregnancy viability by means of a simple scoring system.

    PubMed

    Bottomley, Cecilia; Van Belle, Vanya; Kirk, Emma; Van Huffel, Sabine; Timmerman, Dirk; Bourne, Tom

    2013-01-01

    What is the performance of a simple scoring system to predict whether women will have an ongoing viable intrauterine pregnancy beyond the first trimester? A simple scoring system using demographic and initial ultrasound variables accurately predicts pregnancy viability beyond the first trimester with an area under the curve (AUC) in a receiver operating characteristic curve of 0.924 [95% confidence interval (CI) 0.900-0.947] on an independent test set. Individual demographic and ultrasound factors, such as maternal age, vaginal bleeding and gestational sac size, are strong predictors of miscarriage. Previous mathematical models have combined individual risk factors with reasonable performance. A simple scoring system derived from a mathematical model that can be easily implemented in clinical practice has not previously been described for the prediction of ongoing viability. This was a prospective observational study in a single early pregnancy assessment centre during a 9-month period. A cohort of 1881 consecutive women undergoing transvaginal ultrasound scan at a gestational age <84 days were included. Women were excluded if the first trimester outcome was not known. Demographic features, symptoms and ultrasound variables were tested for their influence on ongoing viability. Logistic regression was used to determine the influence on first trimester viability from demographics and symptoms alone, ultrasound findings alone and then from all the variables combined. Each model was developed on a training data set, and a simple scoring system was derived from this. This scoring system was tested on an independent test data set. The final outcome based on a total of 1435 participants was an ongoing viable pregnancy in 885 (61.7%) and early pregnancy loss in 550 (38.3%) women. The scoring system using significant demographic variables alone (maternal age and amount of bleeding) to predict ongoing viability gave an AUC of 0.724 (95% CI = 0.692-0.756) in the training set

  13. Time-Accurate Computational Fluid Dynamics Simulation of a Pair of Moving Solid Rocket Boosters

    NASA Technical Reports Server (NTRS)

    Strutzenberg, Louise L.; Williams, Brandon R.

    2011-01-01

    Since the Columbia accident, the threat to the Shuttle launch vehicle from debris during the liftoff timeframe has been assessed by the Liftoff Debris Team at NASA/MSFC. In addition to engineering methods of analysis, CFD-generated flow fields during the liftoff timeframe have been used in conjunction with 3-DOF debris transport methods to predict the motion of liftoff debris. Early models made use of a quasi-steady flow field approximation with the vehicle positioned at a fixed location relative to the ground; however, a moving overset mesh capability has recently been developed for the Loci/CHEM CFD software which enables higher-fidelity simulation of the Shuttle transient plume startup and liftoff environment. The present work details the simulation of the launch pad and mobile launch platform (MLP) with truncated solid rocket boosters (SRBs) moving in a prescribed liftoff trajectory derived from Shuttle flight measurements. Using Loci/CHEM, time-accurate RANS and hybrid RANS/LES simulations were performed for the timeframe T0+0 to T0+3.5 seconds, which consists of SRB startup to a vehicle altitude of approximately 90 feet above the MLP. Analysis of the transient flowfield focuses on the evolution of the SRB plumes in the MLP plume holes and the flame trench, impingement on the flame deflector, and especially impingment on the MLP deck resulting in upward flow which is a transport mechanism for debris. The results show excellent qualitative agreement with the visual record from past Shuttle flights, and comparisons to pressure measurements in the flame trench and on the MLP provide confidence in these simulation capabilities.

  14. Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

    PubMed

    Limongi, Roberto; Silva, Angélica M

    2016-11-01

    The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

  15. Estimating Time-Varying PCB Exposures Using Person-Specific Predictions to Supplement Measured Values: A Comparison of Observed and Predicted Values in Two Cohorts of Norwegian Women.

    PubMed

    Nøst, Therese Haugdahl; Breivik, Knut; Wania, Frank; Rylander, Charlotta; Odland, Jon Øyvind; Sandanger, Torkjel Manning

    2016-03-01

    Studies on the health effects of polychlorinated biphenyls (PCBs) call for an understanding of past and present human exposure. Time-resolved mechanistic models may supplement information on concentrations in individuals obtained from measurements and/or statistical approaches if they can be shown to reproduce empirical data. Here, we evaluated the capability of one such mechanistic model to reproduce measured PCB concentrations in individual Norwegian women. We also assessed individual life-course concentrations. Concentrations of four PCB congeners in pregnant (n = 310, sampled in 2007-2009) and postmenopausal (n = 244, 2005) women were compared with person-specific predictions obtained using CoZMoMAN, an emission-based environmental fate and human food-chain bioaccumulation model. Person-specific predictions were also made using statistical regression models including dietary and lifestyle variables and concentrations. CoZMoMAN accurately reproduced medians and ranges of measured concentrations in the two study groups. Furthermore, rank correlations between measurements and predictions from both CoZMoMAN and regression analyses were strong (Spearman's r > 0.67). Precision in quartile assignments from predictions was strong overall as evaluated by weighted Cohen's kappa (> 0.6). Simulations indicated large inter-individual differences in concentrations experienced in the past. The mechanistic model reproduced all measurements of PCB concentrations within a factor of 10, and subject ranking and quartile assignments were overall largely consistent, although they were weak within each study group. Contamination histories for individuals predicted by CoZMoMAN revealed variation between study subjects, particularly in the timing of peak concentrations. Mechanistic models can provide individual PCB exposure metrics that could serve as valuable supplements to measurements.

  16. Clinical time series prediction: towards a hierarchical dynamical system framework

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2014-01-01

    Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

  17. Clinical time series prediction: Toward a hierarchical dynamical system framework.

    PubMed

    Liu, Zitao; Hauskrecht, Milos

    2015-09-01

    Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Application of the accurate mass and time tag approach in studies of the human blood lipidome

    PubMed Central

    Ding, Jie; Sorensen, Christina M.; Jaitly, Navdeep; Jiang, Hongliang; Orton, Daniel J.; Monroe, Matthew E.; Moore, Ronald J.; Smith, Richard D.; Metz, Thomas O.

    2008-01-01

    We report a preliminary demonstration of the accurate mass and time (AMT) tag approach for lipidomics. Initial data-dependent LC-MS/MS analyses of human plasma, erythrocyte, and lymphocyte lipids were performed in order to identify lipid molecular species in conjunction with complementary accurate mass and isotopic distribution information. Identified lipids were used to populate initial lipid AMT tag databases containing 250 and 45 entries for those species detected in positive and negative electrospray ionization (ESI) modes, respectively. The positive ESI database was then utilized to identify human plasma, erythrocyte, and lymphocyte lipids in high-throughput LC-MS analyses based on the AMT tag approach. We were able to define the lipid profiles of human plasma, erythrocytes, and lymphocytes based on qualitative and quantitative differences in lipid abundance. PMID:18502191

  19. Robust and accurate decoding of motoneuron behavior and prediction of the resulting force output.

    PubMed

    Thompson, Christopher K; Negro, Francesco; Johnson, Michael D; Holmes, Matthew R; McPherson, Laura Miller; Powers, Randall K; Farina, Dario; Heckman, Charles J

    2018-05-03

    The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the decerebrate cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation. The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibers represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allows for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the decerebrate cat. Our results showed

  20. Accurate perception of negative emotions predicts functional capacity in schizophrenia.

    PubMed

    Abram, Samantha V; Karpouzian, Tatiana M; Reilly, James L; Derntl, Birgit; Habel, Ute; Smith, Matthew J

    2014-04-30

    Several studies suggest facial affect perception (FAP) deficits in schizophrenia are linked to poorer social functioning. However, whether reduced functioning is associated with inaccurate perception of specific emotional valence or a global FAP impairment remains unclear. The present study examined whether impairment in the perception of specific emotional valences (positive, negative) and neutrality were uniquely associated with social functioning, using a multimodal social functioning battery. A sample of 59 individuals with schizophrenia and 41 controls completed a computerized FAP task, and measures of functional capacity, social competence, and social attainment. Participants also underwent neuropsychological testing and symptom assessment. Regression analyses revealed that only accurately perceiving negative emotions explained significant variance (7.9%) in functional capacity after accounting for neurocognitive function and symptoms. Partial correlations indicated that accurately perceiving anger, in particular, was positively correlated with functional capacity. FAP for positive, negative, or neutral emotions were not related to social competence or social attainment. Our findings were consistent with prior literature suggesting negative emotions are related to functional capacity in schizophrenia. Furthermore, the observed relationship between perceiving anger and performance of everyday living skills is novel and warrants further exploration. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  1. Do measures of surgical effectiveness at 1 year after lumbar spine surgery accurately predict 2-year outcomes?

    PubMed

    Adogwa, Owoicho; Elsamadicy, Aladine A; Han, Jing L; Cheng, Joseph; Karikari, Isaac; Bagley, Carlos A

    2016-12-01

    OBJECTIVE With the recent passage of the Patient Protection and Affordable Care Act, there has been a dramatic shift toward critical analyses of quality and longitudinal assessment of subjective and objective outcomes after lumbar spine surgery. Accordingly, the emergence and routine use of real-world institutional registries have been vital to the longitudinal assessment of quality. However, prospectively obtaining longitudinal outcomes for patients at 24 months after spine surgery remains a challenge. The aim of this study was to assess if 12-month measures of treatment effectiveness accurately predict long-term outcomes (24 months). METHODS A nationwide, multiinstitutional, prospective spine outcomes registry was used for this study. Enrollment criteria included available demographic, surgical, and clinical outcomes data. All patients had prospectively collected outcomes measures and a minimum 2-year follow-up. Patient-reported outcomes instruments (Oswestry Disability Index [ODI], SF-36, and visual analog scale [VAS]-back pain/leg pain) were completed before surgery and then at 3, 6, 12, and 24 months after surgery. The Health Transition Index of the SF-36 was used to determine the 1- and 2-year minimum clinically important difference (MCID), and logistic regression modeling was performed to determine if achieving MCID at 1 year adequately predicted improvement and achievement of MCID at 24 months. RESULTS The study group included 969 patients: 300 patients underwent anterior lumbar interbody fusion (ALIF), 606 patients underwent transforaminal lumbar interbody fusion (TLIF), and 63 patients underwent lateral interbody fusion (LLIF). There was a significant correlation between the 12- and 24-month ODI (r = 0.82; p < 0.0001), SF-36 Physical Component Summary score (r = 0.89; p < 0.0001), VAS-back pain (r = 0.90; p < 0.0001), and VAS-leg pain (r = 0.85; p < 0.0001). For the ALIF cohort, patients achieving MCID thresholds for ODI at 12 months were 13-fold (p < 0

  2. Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task

    PubMed Central

    Carvalho, Fabiana M.; Chaim, Khallil T.; Sanchez, Tiago A.; de Araujo, Draulio B.

    2016-01-01

    The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may

  3. Comparative Studies of Prediction Strategies for Solar X-ray Time Series

    NASA Astrophysics Data System (ADS)

    Muranushi, T.; Hattori, T.; Jin, Q.; Hishinuma, T.; Tominaga, M.; Nakagawa, K.; Fujiwara, Y.; Nakamura, T.; Sakaue, T.; Takahashi, T.; Seki, D.; Namekata, K.; Tei, A.; Ban, M.; Kawamura, A. D.; Hada-Muranushi, Y.; Asai, A.; Nemoto, S.; Shibata, K.

    2016-12-01

    Crucial virtues for operational space weather forecast are real-timeforecast ability, forecast precision and customizability to userneeds. The recent development of deep-learning makes it veryattractive to space weather, because (1) it learns gradually incomingdata, (2) it exhibits superior accuracy over conventional algorithmsin many fields, and (3) it makes the customization of the forecasteasier because it accepts raw images.However, the best deep-learning applications are only attainable bycareful human designers that understands both the mechanism of deeplearning and the application field. Therefore, we need to foster youngresearchers to enter the field of machine-learning aided forecast. So,we have held a seminar every Monday with undergraduate and graduatestudents from May to August 2016.We will review the current status of space weather science and theautomated real-time space weather forecast engine UFCORIN. Then, weintroduce the deep-learning space weather forecast environments wehave set up using Python and Chainer on students' laptop computers.We have started from simple image classification neural network, thenimplemented space-weather neural network that predicts future X-rayflux of the Sun based on the past X-ray lightcurve and magnetic fieldline-of-sight images.In order to perform each forecast faster, we have focused on simplelightcurve-to-lightcurve forecast, and performed comparative surveysby changing following parameters: The size and topology of the neural network Batchsize Neural network hyperparameters such as learning rates to optimize the preduction accuracy, and time for prediction.We have found how to design compact, fast but accurate neural networkto perform forecast. Our forecasters can perform predictionexperiment for four-year timespan in a few minutes, and achieveslog-scale errors of the order of 1. Our studies is ongoing, and inour talk we will review our progress till December.

  4. Hindered rotor models with variable kinetic functions for accurate thermodynamic and kinetic predictions

    NASA Astrophysics Data System (ADS)

    Reinisch, Guillaume; Leyssale, Jean-Marc; Vignoles, Gérard L.

    2010-10-01

    We present an extension of some popular hindered rotor (HR) models, namely, the one-dimensional HR (1DHR) and the degenerated two-dimensional HR (d2DHR) models, allowing for a simple and accurate treatment of internal rotations. This extension, based on the use of a variable kinetic function in the Hamiltonian instead of a constant reduced moment of inertia, is extremely suitable in the case of rocking/wagging motions involved in dissociation or atom transfer reactions. The variable kinetic function is first introduced in the framework of a classical 1DHR model. Then, an effective temperature and potential dependent constant is proposed in the cases of quantum 1DHR and classical d2DHR models. These methods are finally applied to the atom transfer reaction SiCl3+BCl3→SiCl4+BCl2. We show, for this particular case, that a proper accounting of internal rotations greatly improves the accuracy of thermodynamic and kinetic predictions. Moreover, our results confirm (i) that using a suitably defined kinetic function appears to be very adapted to such problems; (ii) that the separability assumption of independent rotations seems justified; and (iii) that a quantum mechanical treatment is not a substantial improvement with respect to a classical one.

  5. PREDICTING EVAPORATION RATES AND TIMES FOR SPILLS OF CHEMICAL MIXTURES

    EPA Science Inventory


    Spreadsheet and short-cut methods have been developed for predicting evaporation rates and evaporation times for spills (and constrained baths) of chemical mixtures. Steady-state and time-varying predictions of evaporation rates can be made for six-component mixtures, includ...

  6. Predictability and prediction of tropical cyclones on daily to interannual time scales

    NASA Astrophysics Data System (ADS)

    Belanger, James Ian

    The spatial and temporal complexity of tropical cyclones (TCs) raises a number of scientific questions regarding their genesis, movement, intensification, and variability. In this dissertation, the principal goal is to determine the current state of predictability for each of these processes using global numerical prediction systems. The predictability findings are then used in conjunction with several new statistical calibration techniques to develop a proof-of-concept, operational forecast system for North Atlantic TCs on daily to intraseasonal time scales. To quantify the current extent of tropical cyclone predictability, we assess probabilistic forecasts from the most advanced global numerical weather prediction system to date, the ECMWF Variable Resolution Ensemble Prediction System (VarEPS; Hamill et al. 2008, Hagedorn et al. 2012). Using a new false alarm clustering technique to maximize the utility of the VarEPS, the ensemble system is shown to provide well-calibrated probabilistic forecasts for TC genesis through a lead-time of one week and pregenesis track forecasts with similar skill compared to the VarEPS's postgenesis track forecasts. These findings provide evidence that skillful real-time TC genesis predictions may be made in the North Indian Ocean—a region that even today has limited forecast warning windows for TCs relative to other ocean basins. To quantify the predictability of TCs on intraseasonal time scales, forecasts from the ECMWF Monthly Forecast System (ECMFS) are examined for the North Atlantic Ocean. From this assessment, dynamically based forecasts from the ECMFS provide forecast skill exceeding climatology out to weeks three and four for portions of the southern Gulf of Mexico, western Caribbean and the Main Development Region. Forecast skill in these regions is traced to the model's ability to capture correctly the variability in deep-layer vertical wind shear as well as the relative frequency of easterly waves moving through these

  7. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

    NASA Technical Reports Server (NTRS)

    He, Yuning

    2015-01-01

    Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

  8. Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

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

    Simmhan, Yogesh; Noor, Muhammad Usman

    2013-10-09

    Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 datamore » points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.« less

  9. Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

    PubMed

    Yadav, Jaydeep; Korzekwa, Ken; Nagar, Swati

    2018-05-07

    Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min -1 , the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min -1 . These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

  10. High-definition endoscopy with digital chromoendoscopy for histologic prediction of distal colorectal polyps.

    PubMed

    Rath, Timo; Tontini, Gian E; Nägel, Andreas; Vieth, Michael; Zopf, Steffen; Günther, Claudia; Hoffman, Arthur; Neurath, Markus F; Neumann, Helmut

    2015-10-22

    Distal diminutive colorectal polyps are common and accurate endoscopic prediction of hyperplastic or adenomatous polyp histology could reduce procedural time, costs and potential risks associated with the resection. Within this study we assessed whether digital chromoendoscopy can accurately predict the histology of distal diminutive colorectal polyps according to the ASGE PIVI statement. In this prospective cohort study, 224 consecutive patients undergoing screening or surveillance colonoscopy were included. Real time histology of 121 diminutive distal colorectal polyps was evaluated using high-definition endoscopy with digital chromoendoscopy and the accuracy of predicting histology with digital chromoendoscopy was assessed. The overall accuracy of digital chromoendoscopy for prediction of adenomatous polyp histology was 90.1 %. Sensitivity, specificity, positive and negative predictive values were 93.3, 88.7, 88.7, and 93.2 %, respectively. In high-confidence predictions, the accuracy increased to 96.3 % while sensitivity, specificity, positive and negative predictive values were calculated as 98.1, 94.4, 94.5, and 98.1 %, respectively. Surveillance intervals with digital chromoendoscopy were correctly predicted with >90 % accuracy. High-definition endoscopy in combination with digital chromoendoscopy allowed real-time in vivo prediction of distal colorectal polyp histology and is accurate enough to leave distal colorectal polyps in place without resection or to resect and discard them without pathologic assessment. This approach has the potential to reduce costs and risks associated with the redundant removal of diminutive colorectal polyps. ClinicalTrials NCT02217449.

  11. Elements of a predictive model for determining beach closures on a real time basis: the case of 63rd Street Beach Chicago

    USGS Publications Warehouse

    Olyphant, Greg A.; Whitman, Richard L.

    2004-01-01

    Data on hydrometeorological conditions and E. coli concentration were simultaneously collected on 57 occasions during the summer of 2000 at 63rd Street Beach, Chicago, Illinois. The data were used to identify and calibrate a statistical regression model aimed at predicting when the bacterial concentration of the beach water was above or below the level considered safe for full body contact. A wide range of hydrological, meteorological, and water quality variables were evaluated as possible predictive variables. These included wind speed and direction, incoming solar radiation (insolation), various time frames of rainfall, air temperature, lake stage and wave height, and water temperature, specific conductance, dissolved oxygen, pH, and turbidity. The best-fit model combined real-time measurements of wind direction and speed (onshore component of resultant wind vector), rainfall, insolation, lake stage, water temperature and turbidity to predict the geometric mean E.coliconcentration in the swimming zone of the beach. The model, which contained both additive and multiplicative (interaction) terms, accounted for 71% of the observed variability in the log E. coliconcentrations. A comparison between model predictions of when the beach should be closed and when the actualbacterial concentrations were above or below the 235 cfu 100 ml-1 threshold value, indicated that the model accurately predicted openingsversus closures 88% of the time.

  12. Real-time forecasting and predictability of catastrophic failure events: from rock failure to volcanoes and earthquakes

    NASA Astrophysics Data System (ADS)

    Main, I. G.; Bell, A. F.; Naylor, M.; Atkinson, M.; Filguera, R.; Meredith, P. G.; Brantut, N.

    2012-12-01

    Accurate prediction of catastrophic brittle failure in rocks and in the Earth presents a significant challenge on theoretical and practical grounds. The governing equations are not known precisely, but are known to produce highly non-linear behavior similar to those of near-critical dynamical systems, with a large and irreducible stochastic component due to material heterogeneity. In a laboratory setting mechanical, hydraulic and rock physical properties are known to change in systematic ways prior to catastrophic failure, often with significant non-Gaussian fluctuations about the mean signal at a given time, for example in the rate of remotely-sensed acoustic emissions. The effectiveness of such signals in real-time forecasting has never been tested before in a controlled laboratory setting, and previous work has often been qualitative in nature, and subject to retrospective selection bias, though it has often been invoked as a basis in forecasting natural hazard events such as volcanoes and earthquakes. Here we describe a collaborative experiment in real-time data assimilation to explore the limits of predictability of rock failure in a best-case scenario. Data are streamed from a remote rock deformation laboratory to a user-friendly portal, where several proposed physical/stochastic models can be analysed in parallel in real time, using a variety of statistical fitting techniques, including least squares regression, maximum likelihood fitting, Markov-chain Monte-Carlo and Bayesian analysis. The results are posted and regularly updated on the web site prior to catastrophic failure, to ensure a true and and verifiable prospective test of forecasting power. Preliminary tests on synthetic data with known non-Gaussian statistics shows how forecasting power is likely to evolve in the live experiments. In general the predicted failure time does converge on the real failure time, illustrating the bias associated with the 'benefit of hindsight' in retrospective analyses

  13. Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions.

    PubMed

    Teklehaimanot, Hailay D; Schwartz, Joel; Teklehaimanot, Awash; Lipsitch, Marc

    2004-11-19

    Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4th-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10-25% more cases at a given sensitivity in cold districts than in hot ones. The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems.

  14. Using timing of ice retreat to predict timing of fall freeze-up in the Arctic

    NASA Astrophysics Data System (ADS)

    Stroeve, Julienne C.; Crawford, Alex D.; Stammerjohn, Sharon

    2016-06-01

    Reliable forecasts of the timing of sea ice advance are needed in order to reduce risks associated with operating in the Arctic as well as planning of human and environmental emergencies. This study investigates the use of a simple statistical model relating the timing of ice retreat to the timing of ice advance, taking advantage of the inherent predictive power supplied by the seasonal ice-albedo feedback and ocean heat uptake. Results show that using the last retreat date to predict the first advance date is applicable in some regions, such as Baffin Bay and the Laptev and East Siberian seas, where a predictive skill is found even after accounting for the long-term trend in both variables. Elsewhere, in the Arctic, there is some predictive skills depending on the year (e.g., Kara and Beaufort seas), but none in regions such as the Barents and Bering seas or the Sea of Okhotsk. While there is some suggestion that the relationship is strengthening over time, this may reflect that higher correlations are expected during periods when the underlying trend is strong.

  15. Time shift in slope failure prediction between unimodal and bimodal modeling approaches

    NASA Astrophysics Data System (ADS)

    Ciervo, Fabio; Casini, Francesca; Nicolina Papa, Maria; Medina, Vicente

    2016-04-01

    within the Bishop stress theory framework (Ciervo et al., 2015). The proposed work tends to emphasize how a more accurate slope stability analysis that accounts dual-structure could be useful to reach a more accurate definition of the stability conditions. The effects in practical analysis may be significant. The highlighted discrepancies between the different approaches in describing the timing processes and strength contribution due to capillary forces may entail no negligible differences in slope stability predictions, especially in those cases where the possibility of a failure in unsaturated terrains is contemplated.

  16. Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone.

    PubMed

    Tailly, Thomas; Larish, Yaniv; Nadeau, Brandon; Violette, Philippe; Glickman, Leonard; Olvera-Posada, Daniel; Alenezi, Husain; Amann, Justin; Denstedt, John; Razvi, Hassan

    2016-04-01

    The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. We identified patients from two centers who had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a computed tomography (CT) available. HUm and HUsd of the stones were compared with ANOVA. Receiver operative characteristic analysis with area under the curve (AUC), Youden index, and likelihood ratio calculations were performed. Data were available for 466 patients. The major components were calcium oxalate monohydrate (COM), uric acid, hydroxyapatite, struvite, brushite, cystine, and CO dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4%, and 4.7% of patients, respectively. The HUm of UA and Br was significantly lower and higher than the HUm of any other stone type, respectively. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851, respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone's mineral composition for all stone types but COM. To the best of our knowledge, this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability.

  17. Folding molecular dynamics simulations accurately predict the effect of mutations on the stability and structure of a vammin-derived peptide.

    PubMed

    Koukos, Panagiotis I; Glykos, Nicholas M

    2014-08-28

    Folding molecular dynamics simulations amounting to a grand total of 4 μs of simulation time were performed on two peptides (with native and mutated sequences) derived from loop 3 of the vammin protein and the results compared with the experimentally known peptide stabilities and structures. The simulations faithfully and accurately reproduce the major experimental findings and show that (a) the native peptide is mostly disordered in solution, (b) the mutant peptide has a well-defined and stable structure, and (c) the structure of the mutant is an irregular β-hairpin with a non-glycine β-bulge, in excellent agreement with the peptide's known NMR structure. Additionally, the simulations also predict the presence of a very small β-hairpin-like population for the native peptide but surprisingly indicate that this population is structurally more similar to the structure of the native peptide as observed in the vammin protein than to the NMR structure of the isolated mutant peptide. We conclude that, at least for the given system, force field, and simulation protocol, folding molecular dynamics simulations appear to be successful in reproducing the experimentally accessible physical reality to a satisfactory level of detail and accuracy.

  18. Program Predicts Time Courses of Human/Computer Interactions

    NASA Technical Reports Server (NTRS)

    Vera, Alonso; Howes, Andrew

    2005-01-01

    CPM X is a computer program that predicts sequences of, and amounts of time taken by, routine actions performed by a skilled person performing a task. Unlike programs that simulate the interaction of the person with the task environment, CPM X predicts the time course of events as consequences of encoded constraints on human behavior. The constraints determine which cognitive and environmental processes can occur simultaneously and which have sequential dependencies. The input to CPM X comprises (1) a description of a task and strategy in a hierarchical description language and (2) a description of architectural constraints in the form of rules governing interactions of fundamental cognitive, perceptual, and motor operations. The output of CPM X is a Program Evaluation Review Technique (PERT) chart that presents a schedule of predicted cognitive, motor, and perceptual operators interacting with a task environment. The CPM X program allows direct, a priori prediction of skilled user performance on complex human-machine systems, providing a way to assess critical interfaces before they are deployed in mission contexts.

  19. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models: 2. Laboratory earthquakes

    NASA Astrophysics Data System (ADS)

    Rubinstein, Justin L.; Ellsworth, William L.; Beeler, Nicholas M.; Kilgore, Brian D.; Lockner, David A.; Savage, Heather M.

    2012-02-01

    The behavior of individual stick-slip events observed in three different laboratory experimental configurations is better explained by a "memoryless" earthquake model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. We make similar findings in the companion manuscript for the behavior of natural repeating earthquakes. Taken together, these results allow us to conclude that the predictions of a characteristic earthquake model that assumes either fixed slip or fixed recurrence interval should be preferred to the predictions of the time- and slip-predictable models for all earthquakes. Given that the fixed slip and recurrence models are the preferred models for all of the experiments we examine, we infer that in an event-to-event sense the elastic rebound model underlying the time- and slip-predictable models does not explain earthquake behavior. This does not indicate that the elastic rebound model should be rejected in a long-term-sense, but it should be rejected for short-term predictions. The time- and slip-predictable models likely offer worse predictions of earthquake behavior because they rely on assumptions that are too simple to explain the behavior of earthquakes. Specifically, the time-predictable model assumes a constant failure threshold and the slip-predictable model assumes that there is a constant minimum stress. There is experimental and field evidence that these assumptions are not valid for all earthquakes.

  20. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

    PubMed

    Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

    2015-11-01

    A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

  1. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

    PubMed Central

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

    2016-01-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

  2. Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

    PubMed Central

    Xie, Weihong; Yu, Yang

    2017-01-01

    Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

  3. Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach.

    PubMed

    Liang, Fan; Xie, Weihong; Yu, Yang

    2017-01-01

    Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively "switch" from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

  4. Uncertainty propagation for statistical impact prediction of space debris

    NASA Astrophysics Data System (ADS)

    Hoogendoorn, R.; Mooij, E.; Geul, J.

    2018-01-01

    Predictions of the impact time and location of space debris in a decaying trajectory are highly influenced by uncertainties. The traditional Monte Carlo (MC) method can be used to perform accurate statistical impact predictions, but requires a large computational effort. A method is investigated that directly propagates a Probability Density Function (PDF) in time, which has the potential to obtain more accurate results with less computational effort. The decaying trajectory of Delta-K rocket stages was used to test the methods using a six degrees-of-freedom state model. The PDF of the state of the body was propagated in time to obtain impact-time distributions. This Direct PDF Propagation (DPP) method results in a multi-dimensional scattered dataset of the PDF of the state, which is highly challenging to process. No accurate results could be obtained, because of the structure of the DPP data and the high dimensionality. Therefore, the DPP method is less suitable for practical uncontrolled entry problems and the traditional MC method remains superior. Additionally, the MC method was used with two improved uncertainty models to obtain impact-time distributions, which were validated using observations of true impacts. For one of the two uncertainty models, statistically more valid impact-time distributions were obtained than in previous research.

  5. Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland.

    PubMed

    Wilson, Chris H; Caughlin, T Trevor; Rifai, Sami W; Boughton, Elizabeth H; Mack, Michelle C; Flory, S Luke

    2017-07-01

    Soil carbon sequestration in agroecosystems could play a key role in climate change mitigation but will require accurate predictions of soil organic carbon (SOC) stocks over spatial scales relevant to land management. Spatial variation in underlying drivers of SOC, such as plant productivity and soil mineralogy, complicates these predictions. Recent advances in the availability of remotely sensed data make it practical to generate multidecadal time series of vegetation indices with high spatial resolution and coverage. However, the utility of such data largely is unknown, only having been tested with shorter (e.g., 1-2 yr) data summaries. Across a 2,000 ha subtropical grassland, we found that a long time series (28 yr) of a vegetation index (Enhanced Vegetation Index; EVI) derived from the Landsat 5 satellite significantly enhanced prediction of spatially varying SOC pools, while a short summary (2 yr) was an ineffective predictor. EVI was the best predictor for surface SOC (0-5 cm depth) and total measured SOC stocks (0-15 cm). The optimum models for SOC in the upper soil layer combined EVI records with elevation and calcium concentration, while deeper SOC was more strongly associated with calcium availability. We demonstrate how data from the open access Landsat archive can predict SOC stocks, a key ecosystem metric, and illustrate the rich variety of analytical approaches that can be applied to long time series of remotely sensed greenness. Overall, our results showed that SOC pools were closely coupled to EVI in this ecosystem, demonstrating that maintenance of higher average green leaf area is correlated with higher SOC. The strong associations of vegetation greenness and calcium concentration with SOC suggest that the ability to sequester additional SOC likely will rely on strategic management of pasture vegetation and soil fertility. © 2017 by the Ecological Society of America.

  6. Analysis of Flight Management System Predictions of Idle-Thrust Descents

    NASA Technical Reports Server (NTRS)

    Stell, Laurel

    2010-01-01

    To enable arriving aircraft to fly optimized descents computed by the flight management system (FMS) in congested airspace, ground automation must accurately predict descent trajectories. To support development of the predictor and its uncertainty models, descents from cruise to the meter fix were executed using vertical navigation in a B737-700 simulator and a B777-200 simulator, both with commercial FMSs. For both aircraft types, the FMS computed the intended descent path for a specified speed profile assuming idle thrust after top of descent (TOD), and then it controlled the avionics without human intervention. The test matrix varied aircraft weight, descent speed, and wind conditions. The first analysis in this paper determined the effect of the test matrix parameters on the FMS computation of TOD location, and it compared the results to those for the current ground predictor in the Efficient Descent Advisor (EDA). The second analysis was similar but considered the time to fly a specified distance to the meter fix. The effects of the test matrix variables together with the accuracy requirements for the predictor will determine the allowable error for the predictor inputs. For the B737, the EDA prediction of meter fix crossing time agreed well with the FMS; but its prediction of TOD location probably was not sufficiently accurate to enable idle-thrust descents in congested airspace, even though the FMS and EDA gave similar shapes for TOD location as a function of the test matrix variables. For the B777, the FMS and EDA gave different shapes for the TOD location function, and the EDA prediction of the TOD location is not accurate enough to fully enable the concept. Furthermore, the differences between the FMS and EDA predictions of meter fix crossing time for the B777 indicated that at least one of them was not sufficiently accurate.

  7. Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment

    PubMed Central

    Liu, Qi; Cai, Weidong; Jin, Dandan; Shen, Jian; Fu, Zhangjie; Liu, Xiaodong; Linge, Nigel

    2016-01-01

    Distributed Computing has achieved tremendous development since cloud computing was proposed in 2006, and played a vital role promoting rapid growth of data collecting and analysis models, e.g., Internet of things, Cyber-Physical Systems, Big Data Analytics, etc. Hadoop has become a data convergence platform for sensor networks. As one of the core components, MapReduce facilitates allocating, processing and mining of collected large-scale data, where speculative execution strategies help solve straggler problems. However, there is still no efficient solution for accurate estimation on execution time of run-time tasks, which can affect task allocation and distribution in MapReduce. In this paper, task execution data have been collected and employed for the estimation. A two-phase regression (TPR) method is proposed to predict the finishing time of each task accurately. Detailed data of each task have drawn interests with detailed analysis report being made. According to the results, the prediction accuracy of concurrent tasks’ execution time can be improved, in particular for some regular jobs. PMID:27589753

  8. Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment.

    PubMed

    Liu, Qi; Cai, Weidong; Jin, Dandan; Shen, Jian; Fu, Zhangjie; Liu, Xiaodong; Linge, Nigel

    2016-08-30

    Distributed Computing has achieved tremendous development since cloud computing was proposed in 2006, and played a vital role promoting rapid growth of data collecting and analysis models, e.g., Internet of things, Cyber-Physical Systems, Big Data Analytics, etc. Hadoop has become a data convergence platform for sensor networks. As one of the core components, MapReduce facilitates allocating, processing and mining of collected large-scale data, where speculative execution strategies help solve straggler problems. However, there is still no efficient solution for accurate estimation on execution time of run-time tasks, which can affect task allocation and distribution in MapReduce. In this paper, task execution data have been collected and employed for the estimation. A two-phase regression (TPR) method is proposed to predict the finishing time of each task accurately. Detailed data of each task have drawn interests with detailed analysis report being made. According to the results, the prediction accuracy of concurrent tasks' execution time can be improved, in particular for some regular jobs.

  9. Accurate thermoelastic tensor and acoustic velocities of NaCl

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

    Marcondes, Michel L., E-mail: michel@if.usp.br; Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455; Shukla, Gaurav, E-mail: shukla@physics.umn.edu

    Despite the importance of thermoelastic properties of minerals in geology and geophysics, their measurement at high pressures and temperatures are still challenging. Thus, ab initio calculations are an essential tool for predicting these properties at extreme conditions. Owing to the approximate description of the exchange-correlation energy, approximations used in calculations of vibrational effects, and numerical/methodological approximations, these methods produce systematic deviations. Hybrid schemes combining experimental data and theoretical results have emerged as a way to reconcile available information and offer more reliable predictions at experimentally inaccessible thermodynamics conditions. Here we introduce a method to improve the calculated thermoelastic tensor bymore » using highly accurate thermal equation of state (EoS). The corrective scheme is general, applicable to crystalline solids with any symmetry, and can produce accurate results at conditions where experimental data may not exist. We apply it to rock-salt-type NaCl, a material whose structural properties have been challenging to describe accurately by standard ab initio methods and whose acoustic/seismic properties are important for the gas and oil industry.« less

  10. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

    PubMed

    Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

    2010-07-08

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  11. Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

    PubMed Central

    Reynolds, Sheila M.; Bilmes, Jeff A.; Noble, William Stafford

    2010-01-01

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence—301 base pairs, centered at the position to be scored—with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  12. Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

    NASA Astrophysics Data System (ADS)

    Yang, Bin

    2017-07-01

    Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

  13. Discovery of a general method of solving the Schrödinger and dirac equations that opens a way to accurately predictive quantum chemistry.

    PubMed

    Nakatsuji, Hiroshi

    2012-09-18

    Just as Newtonian law governs classical physics, the Schrödinger equation (SE) and the relativistic Dirac equation (DE) rule the world of chemistry. So, if we can solve these equations accurately, we can use computation to predict chemistry precisely. However, for approximately 80 years after the discovery of these equations, chemists believed that they could not solve SE and DE for atoms and molecules that included many electrons. This Account reviews ideas developed over the past decade to further the goal of predictive quantum chemistry. Between 2000 and 2005, I discovered a general method of solving the SE and DE accurately. As a first inspiration, I formulated the structure of the exact wave function of the SE in a compact mathematical form. The explicit inclusion of the exact wave function's structure within the variational space allows for the calculation of the exact wave function as a solution of the variational method. Although this process sounds almost impossible, it is indeed possible, and I have published several formulations and applied them to solve the full configuration interaction (CI) with a very small number of variables. However, when I examined analytical solutions for atoms and molecules, the Hamiltonian integrals in their secular equations diverged. This singularity problem occurred in all atoms and molecules because it originates from the singularity of the Coulomb potential in their Hamiltonians. To overcome this problem, I first introduced the inverse SE and then the scaled SE. The latter simpler idea led to immediate and surprisingly accurate solution for the SEs of the hydrogen atom, helium atom, and hydrogen molecule. The free complement (FC) method, also called the free iterative CI (free ICI) method, was efficient for solving the SEs. In the FC method, the basis functions that span the exact wave function are produced by the Hamiltonian of the system and the zeroth-order wave function. These basis functions are called complement

  14. Timebias corrections to predictions

    NASA Technical Reports Server (NTRS)

    Wood, Roger; Gibbs, Philip

    1993-01-01

    The importance of an accurate knowledge of the time bias corrections to predicted orbits to a satellite laser ranging (SLR) observer, especially for low satellites, is highlighted. Sources of time bias values and the optimum strategy for extrapolation are discussed from the viewpoint of the observer wishing to maximize the chances of getting returns from the next pass. What is said may be seen as a commercial encouraging wider and speedier use of existing data centers for mutually beneficial exchange of time bias data.

  15. Financial time series prediction using spiking neural networks.

    PubMed

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  16. Financial Time Series Prediction Using Spiking Neural Networks

    PubMed Central

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. PMID:25170618

  17. Predicting Regional Drought on Sub-Seasonal to Decadal Time Scales

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried; Wang, Hailan; Suarez, Max; Koster, Randal

    2011-01-01

    Drought occurs on a wide range of time scales, and within a variety of different types of regional climates. It is driven foremost by an extended period of reduced precipitation, but it is the impacts on such quantities as soil moisture, streamflow and crop yields that are often most important from a users perspective. While recognizing that different users have different needs for drought information, it is nevertheless important to understand that progress in predicting drought and satisfying such user needs, largely hinges on our ability to improve predictions of precipitation. This talk reviews our current understanding of the physical mechanisms that drive precipitation variations on subseasonal to decadal time scales, and the implications for predictability and prediction skill. Examples are given highlighting the phenomena and mechanisms controlling precipitation on monthly (e.g., stationary Rossby waves, soil moisture), seasonal (ENSO) and decadal time scales (PD and AMO).

  18. Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information.

    PubMed

    Wang, Jianming; Ke, Chunlei; Yu, Zhinuan; Fu, Lei; Dornseif, Bruce

    2016-05-01

    For clinical trials with time-to-event endpoints, predicting the accrual of the events of interest with precision is critical in determining the timing of interim and final analyses. For example, overall survival (OS) is often chosen as the primary efficacy endpoint in oncology studies, with planned interim and final analyses at a pre-specified number of deaths. Often, correlated surrogate information, such as time-to-progression (TTP) and progression-free survival, are also collected as secondary efficacy endpoints. It would be appealing to borrow strength from the surrogate information to improve the precision of the analysis time prediction. Currently available methods in the literature for predicting analysis timings do not consider utilizing the surrogate information. In this article, using OS and TTP as an example, a general parametric model for OS and TTP is proposed, with the assumption that disease progression could change the course of the overall survival. Progression-free survival, related both to OS and TTP, will be handled separately, as it can be derived from OS and TTP. The authors seek to develop a prediction procedure using a Bayesian method and provide detailed implementation strategies under certain assumptions. Simulations are performed to evaluate the performance of the proposed method. An application to a real study is also provided. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  19. 'It is Time to Prepare the Next patient' Real-Time Prediction of Procedure Duration in Laparoscopic Cholecystectomies.

    PubMed

    Guédon, Annetje C P; Paalvast, M; Meeuwsen, F C; Tax, D M J; van Dijke, A P; Wauben, L S G L; van der Elst, M; Dankelman, J; van den Dobbelsteen, J J

    2016-12-01

    Operating Room (OR) scheduling is crucial to allow efficient use of ORs. Currently, the predicted durations of surgical procedures are unreliable and the OR schedulers have to follow the progress of the procedures in order to update the daily planning accordingly. The OR schedulers often acquire the needed information through verbal communication with the OR staff, which causes undesired interruptions of the surgical process. The aim of this study was to develop a system that predicts in real-time the remaining procedure duration and to test this prediction system for reliability and usability in an OR. The prediction system was based on the activation pattern of one single piece of equipment, the electrosurgical device. The prediction system was tested during 21 laparoscopic cholecystectomies, in which the activation of the electrosurgical device was recorded and processed in real-time using pattern recognition methods. The remaining surgical procedure duration was estimated and the optimal timing to prepare the next patient for surgery was communicated to the OR staff. The mean absolute error was smaller for the prediction system (14 min) than for the OR staff (19 min). The OR staff doubted whether the prediction system could take all relevant factors into account but were positive about its potential to shorten waiting times for patients. The prediction system is a promising tool to automatically and objectively predict the remaining procedure duration, and thereby achieve optimal OR scheduling and streamline the patient flow from the nursing department to the OR.

  20. A NEW CLINICAL PREDICTION CRITERION ACCURATELY DETERMINES A SUBSET OF PATIENTS WITH BILATERAL PRIMARY ALDOSTERONISM BEFORE ADRENAL VENOUS SAMPLING.

    PubMed

    Kocjan, Tomaz; Janez, Andrej; Stankovic, Milenko; Vidmar, Gaj; Jensterle, Mojca

    2016-05-01

    Adrenal venous sampling (AVS) is the only available method to distinguish bilateral from unilateral primary aldosteronism (PA). AVS has several drawbacks, so it is reasonable to avoid this procedure when the results would not affect clinical management. Our objective was to identify a clinical criterion that can reliably predict nonlateralized AVS as a surrogate for bilateral PA that is not treated surgically. A retrospective diagnostic cross-sectional study conducted at Slovenian national endocrine referral center included 69 consecutive patients (mean age 56 ± 8 years, 21 females) with PA who underwent AVS. PA was confirmed with the saline infusion test (SIT). AVS was performed sequentially during continuous adrenocorticotrophic hormone (ACTH) infusion. The main outcome measures were variables associated with nonlateralized AVS to derive a clinical prediction rule. Sixty-seven (97%) patients had a successful AVS and were included in the statistical analysis. A total of 39 (58%) patients had nonlateralized AVS. The combined criterion of serum potassium ≥3.5 mmol/L, post-SIT aldosterone <18 ng/dL, and either no or bilateral tumor found on computed tomography (CT) imaging had perfect estimated specificity (and thus 100% positive predictive value) for bilateral PA, saving an estimated 16% of the patients (11/67) from unnecessary AVS. The best overall classification accuracy (50/67 = 75%) was achieved using the post-SIT aldosterone level <18 ng/dL alone, which yielded 74% sensitivity and 75% specificity for predicting nonlateralized AVS. Our clinical prediction criterion appears to accurately determine a subset of patients with bilateral PA who could avoid unnecessary AVS and immediately commence with medical treatment.

  1. Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

    PubMed

    Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

    2015-11-01

    To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

  2. Short time ahead wind power production forecast

    NASA Astrophysics Data System (ADS)

    Sapronova, Alla; Meissner, Catherine; Mana, Matteo

    2016-09-01

    An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

  3. Accurate, predictable, repeatable micro-assembly technology for polymer, microfluidic modules.

    PubMed

    Lee, Tae Yoon; Han, Kyudong; Barrett, Dwhyte O; Park, Sunggook; Soper, Steven A; Murphy, Michael C

    2018-01-01

    A method for the design, construction, and assembly of modular, polymer-based, microfluidic devices using simple micro-assembly technology was demonstrated to build an integrated fluidic system consisting of vertically stacked modules for carrying out multi-step molecular assays. As an example of the utility of the modular system, point mutation detection using the ligase detection reaction (LDR) following amplification by the polymerase chain reaction (PCR) was carried out. Fluid interconnects and standoffs ensured that temperatures in the vertically stacked reactors were within ± 0.2 C° at the center of the temperature zones and ± 1.1 C° overall. The vertical spacing between modules was confirmed using finite element models (ANSYS, Inc., Canonsburg, PA) to simulate the steady-state temperature distribution for the assembly. Passive alignment structures, including a hemispherical pin-in-hole, a hemispherical pin-in-slot, and a plate-plate lap joint, were developed using screw theory to enable accurate exactly constrained assembly of the microfluidic reactors, cover sheets, and fluid interconnects to facilitate the modular approach. The mean mismatch between the centers of adjacent through holes was 64 ± 7.7 μm, significantly reducing the dead volume necessary to accommodate manufacturing variation. The microfluidic components were easily assembled by hand and the assembly of several different configurations of microfluidic modules for executing the assay was evaluated. Temperatures were measured in the desired range in each reactor. The biochemical performance was comparable to that obtained with benchtop instruments, but took less than 45 min to execute, half the time.

  4. Predictability of monthly temperature and precipitation using automatic time series forecasting methods

    NASA Astrophysics Data System (ADS)

    Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

    2018-02-01

    We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

  5. Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

    PubMed

    Wen, Ping-Ping; Shi, Shao-Ping; Xu, Hao-Dong; Wang, Li-Na; Qiu, Jian-Ding

    2016-10-15

    As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights

  6. Accurate prediction of complex free surface flow around a high speed craft using a single-phase level set method

    NASA Astrophysics Data System (ADS)

    Broglia, Riccardo; Durante, Danilo

    2017-11-01

    This paper focuses on the analysis of a challenging free surface flow problem involving a surface vessel moving at high speeds, or planing. The investigation is performed using a general purpose high Reynolds free surface solver developed at CNR-INSEAN. The methodology is based on a second order finite volume discretization of the unsteady Reynolds-averaged Navier-Stokes equations (Di Mascio et al. in A second order Godunov—type scheme for naval hydrodynamics, Kluwer Academic/Plenum Publishers, Dordrecht, pp 253-261, 2001; Proceedings of 16th international offshore and polar engineering conference, San Francisco, CA, USA, 2006; J Mar Sci Technol 14:19-29, 2009); air/water interface dynamics is accurately modeled by a non standard level set approach (Di Mascio et al. in Comput Fluids 36(5):868-886, 2007a), known as the single-phase level set method. In this algorithm the governing equations are solved only in the water phase, whereas the numerical domain in the air phase is used for a suitable extension of the fluid dynamic variables. The level set function is used to track the free surface evolution; dynamic boundary conditions are enforced directly on the interface. This approach allows to accurately predict the evolution of the free surface even in the presence of violent breaking waves phenomena, maintaining the interface sharp, without any need to smear out the fluid properties across the two phases. This paper is aimed at the prediction of the complex free-surface flow field generated by a deep-V planing boat at medium and high Froude numbers (from 0.6 up to 1.2). In the present work, the planing hull is treated as a two-degree-of-freedom rigid object. Flow field is characterized by the presence of thin water sheets, several energetic breaking waves and plungings. The computational results include convergence of the trim angle, sinkage and resistance under grid refinement; high-quality experimental data are used for the purposes of validation, allowing to

  7. Predicting prolonged dose titration in patients starting warfarin.

    PubMed

    Finkelman, Brian S; French, Benjamin; Bershaw, Luanne; Brensinger, Colleen M; Streiff, Michael B; Epstein, Andrew E; Kimmel, Stephen E

    2016-11-01

    Patients initiating warfarin therapy generally experience a dose-titration period of weeks to months, during which time they are at higher risk of both thromboembolic and bleeding events. Accurate prediction of prolonged dose titration could help clinicians determine which patients might be better treated by alternative anticoagulants that, while more costly, do not require dose titration. A prediction model was derived in a prospective cohort of patients starting warfarin (n = 390), using Cox regression, and validated in an external cohort (n = 663) from a later time period. Prolonged dose titration was defined as a dose-titration period >12 weeks. Predictor variables were selected using a modified best subsets algorithm, using leave-one-out cross-validation to reduce overfitting. The final model had five variables: warfarin indication, insurance status, number of doctor's visits in the previous year, smoking status, and heart failure. The area under the ROC curve (AUC) in the derivation cohort was 0.66 (95%CI 0.60, 0.74) using leave-one-out cross-validation, but only 0.59 (95%CI 0.54, 0.64) in the external validation cohort, and varied across clinics. Including genetic factors in the model did not improve the area under the ROC curve (0.59; 95%CI 0.54, 0.65). Relative utility curves indicated that the model was unlikely to provide a clinically meaningful benefit compared with no prediction. Our results suggest that prolonged dose titration cannot be accurately predicted in warfarin patients using traditional clinical, social, and genetic predictors, and that accurate prediction will need to accommodate heterogeneities across clinical sites and over time. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  8. Symplectic geometry spectrum regression for prediction of noisy time series

    NASA Astrophysics Data System (ADS)

    Xie, Hong-Bo; Dokos, Socrates; Sivakumar, Bellie; Mengersen, Kerrie

    2016-05-01

    We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).

  9. The description of a method for accurately estimating creatinine clearance in acute kidney injury.

    PubMed

    Mellas, John

    2016-05-01

    Acute kidney injury (AKI) is a common and serious condition encountered in hospitalized patients. The severity of kidney injury is defined by the RIFLE, AKIN, and KDIGO criteria which attempt to establish the degree of renal impairment. The KDIGO guidelines state that the creatinine clearance should be measured whenever possible in AKI and that the serum creatinine concentration and creatinine clearance remain the best clinical indicators of renal function. Neither the RIFLE, AKIN, nor KDIGO criteria estimate actual creatinine clearance. Furthermore there are no accepted methods for accurately estimating creatinine clearance (K) in AKI. The present study describes a unique method for estimating K in AKI using urine creatinine excretion over an established time interval (E), an estimate of creatinine production over the same time interval (P), and the estimated static glomerular filtration rate (sGFR), at time zero, utilizing the CKD-EPI formula. Using these variables estimated creatinine clearance (Ke)=E/P * sGFR. The method was tested for validity using simulated patients where actual creatinine clearance (Ka) was compared to Ke in several patients, both male and female, and of various ages, body weights, and degrees of renal impairment. These measurements were made at several serum creatinine concentrations in an attempt to determine the accuracy of this method in the non-steady state. In addition E/P and Ke was calculated in hospitalized patients, with AKI, and seen in nephrology consultation by the author. In these patients the accuracy of the method was determined by looking at the following metrics; E/P>1, E/P<1, E=P in an attempt to predict progressive azotemia, recovering azotemia, or stabilization in the level of azotemia respectively. In addition it was determined whether Ke<10 ml/min agreed with Ka and whether patients with AKI on renal replacement therapy could safely terminate dialysis if Ke was greater than 5 ml/min. In the simulated patients there

  10. Interpreting the Strongly Lensed Supernova iPTF16geu: Time Delay Predictions, Microlensing, and Lensing Rates

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

    More, Anupreeta; Oguri, Masamune; More, Surhud

    2017-02-01

    We present predictions for time delays between multiple images of the gravitationally lensed supernova, iPTF16geu, which was recently discovered from the intermediate Palomar Transient Factory (iPTF). As the supernova is of Type Ia where the intrinsic luminosity is usually well known, accurately measured time delays of the multiple images could provide tight constraints on the Hubble constant. According to our lens mass models constrained by the Hubble Space Telescope F814W image, we expect the maximum relative time delay to be less than a day, which is consistent with the maximum of 100 hr reported by Goobar et al. but placesmore » a stringent upper limit. Furthermore, the fluxes of most of the supernova images depart from expected values suggesting that they are affected by microlensing. The microlensing timescales are small enough that they may pose significant problems to measure the time delays reliably. Our lensing rate calculation indicates that the occurrence of a lensed SN in iPTF is likely. However, the observed total magnification of iPTF16geu is larger than expected, given its redshift. This may be a further indication of ongoing microlensing in this system.« less

  11. Hypoglycemia prediction with subject-specific recursive time-series models.

    PubMed

    Eren-Oruklu, Meriyan; Cinar, Ali; Quinn, Lauretta

    2010-01-01

    Avoiding hypoglycemia while keeping glucose within the narrow normoglycemic range (70-120 mg/dl) is a major challenge for patients with type 1 diabetes. Continuous glucose monitors can provide hypoglycemic alarms when the measured glucose decreases below a threshold. However, a better approach is to provide an early alarm that predicts a hypoglycemic episode before it occurs, allowing enough time for the patient to take the necessary precaution to avoid hypoglycemia. We have previously proposed subject-specific recursive models for the prediction of future glucose concentrations and evaluated their prediction performance. In this work, our objective was to evaluate this algorithm further to predict hypoglycemia and provide early hypoglycemic alarms. Three different methods were proposed for alarm decision, where (A) absolute predicted glucose values, (B) cumulative-sum (CUSUM) control chart, and (C) exponentially weighted moving-average (EWMA) control chart were used. Each method was validated using data from the Diabetes Research in Children Network, which consist of measurements from a continuous glucose sensor during an insulin-induced hypoglycemia. Reference serum glucose measurements were used to determine the sensitivity to predict hypoglycemia and the false alarm rate. With the hypoglycemic threshold set to 60 mg/dl, sensitivity of 89, 87.5, and 89% and specificity of 67, 74, and 78% were reported for methods A, B, and C, respectively. Mean values for time to detection were 30 +/- 5.51 (A), 25.8 +/- 6.46 (B), and 27.7 +/- 5.32 (C) minutes. Compared to the absolute value method, both CUSUM and EWMA methods behaved more conservatively before raising an alarm (reduced time to detection), which significantly decreased the false alarm rate and increased the specificity. 2010 Diabetes Technology Society.

  12. Real-time prediction of the occurrence of GLE events

    NASA Astrophysics Data System (ADS)

    Núñez, Marlon; Reyes-Santiago, Pedro J.; Malandraki, Olga E.

    2017-07-01

    A tool for predicting the occurrence of Ground Level Enhancement (GLE) events using the UMASEP scheme is presented. This real-time tool, called HESPERIA UMASEP-500, is based on the detection of the magnetic connection, along which protons arrive in the near-Earth environment, by estimating the lag correlation between the time derivatives of 1 min soft X-ray flux (SXR) and 1 min near-Earth proton fluxes observed by the GOES satellites. Unlike current GLE warning systems, this tool can predict GLE events before the detection by any neutron monitor (NM) station. The prediction performance measured for the period from 1986 to 2016 is presented for two consecutive periods, because of their notable difference in performance. For the 2000-2016 period, this prediction tool obtained a probability of detection (POD) of 53.8% (7 of 13 GLE events), a false alarm ratio (FAR) of 30.0%, and average warning times (AWT) of 8 min with respect to the first NM station's alert and 15 min to the GLE Alert Plus's warning. We have tested the model by replacing the GOES proton data with SOHO/EPHIN proton data, and the results are similar in terms of POD, FAR, and AWT for the same period. The paper also presents a comparison with a GLE warning system.

  13. Mammalian choices: combining fast-but-inaccurate and slow-but-accurate decision-making systems.

    PubMed

    Trimmer, Pete C; Houston, Alasdair I; Marshall, James A R; Bogacz, Rafal; Paul, Elizabeth S; Mendl, Mike T; McNamara, John M

    2008-10-22

    Empirical findings suggest that the mammalian brain has two decision-making systems that act at different speeds. We represent the faster system using standard signal detection theory. We represent the slower (but more accurate) cortical system as the integration of sensory evidence over time until a certain level of confidence is reached. We then consider how two such systems should be combined optimally for a range of information linkage mechanisms. We conclude with some performance predictions that will hold if our representation is realistic.

  14. Real-time Kp predictions from ACE real time solar wind

    NASA Astrophysics Data System (ADS)

    Detman, Thomas; Joselyn, Joann

    1999-06-01

    The Advanced Composition Explorer (ACE) spacecraft provides nearly continuous monitoring of solar wind plasma, magnetic fields, and energetic particles from the Sun-Earth L1 Lagrange point upstream of Earth in the solar wind. The Space Environment Center (SEC) in Boulder receives ACE telemetry from a group of international network of tracking stations. One-minute, and 1-hour averages of solar wind speed, density, temperature, and magnetic field components are posted on SEC's World Wide Web page within 3 to 5 minutes after they are measured. The ACE Real Time Solar Wind (RTSW) can be used to provide real-time warnings and short term forecasts of geomagnetic storms based on the (traditional) Kp index. Here, we use historical data to evaluate the performance of the first real-time Kp prediction algorithm to become operational.

  15. Time Accurate CFD Simulations of the Orion Launch Abort Vehicle in the Transonic Regime

    NASA Technical Reports Server (NTRS)

    Ruf, Joseph; Rojahn, Josh

    2011-01-01

    Significant asymmetries in the fluid dynamics were calculated for some cases in the CFD simulations of the Orion Launch Abort Vehicle through its abort trajectories. The CFD simulations were performed steady state with symmetric boundary conditions and geometries. The trajectory points at issue were in the transonic regime, at 0 and 5 angles of attack with the Abort Motors with and without the Attitude Control Motors (ACM) firing. In some of the cases the asymmetric fluid dynamics resulted in aerodynamic side forces that were large enough that would overcome the control authority of the ACMs. MSFC s Fluid Dynamics Group supported the investigation into the cause of the flow asymmetries with time accurate CFD simulations, utilizing a hybrid RANS-LES turbulence model. The results show that the flow over the vehicle and the subsequent interaction with the AB and ACM motor plumes were unsteady. The resulting instantaneous aerodynamic forces were oscillatory with fairly large magnitudes. Time averaged aerodynamic forces were essentially symmetric.

  16. Accurate procedure for deriving UTI at a submilliarcsecond accuracy from Greenwich Sidereal Time or from the stellar angle

    NASA Astrophysics Data System (ADS)

    Capitaine, N.; Gontier, A.-M.

    1993-08-01

    Present observations using modern astrometric techniques are supposed to provide the Earth orientation parameters, and therefore UT1, with an accuracy better than ±1 mas. In practice, UT1 is determined through the intermediary of Greenwich Sidereal Time (GST), using both the conventional relationship between Greenwich Mean Sidereal Time (GMST) and UTl (Aoki et al. 1982) and the so-called "equation of the equinoxes" limited to the first order terms with respect to the nutation quantities. This highly complex relation between sidereal time and UT1 is not accurate at the milliaresecond level which gives rise to spurious terms of milliaresecond amplitude in the derived UTl. A more complete relationship between GST and UT1 has been recommended by Aoki & Kinoshita (1983) and Aoki (1991) taking into account the second order terms in the difference between GST and GM ST, the largest one having an amplitude of 2.64 mas and a 18.6 yr-period. This paper explains how this complete expansion of GST implicitly uses the concept of "nonrotating origin" (NRO) as proposed by Guinot in 1979 and would, therefore, provide a more accurate value of UTl and consequently of the Earth's angular velocity. This paper shows, moreover, that such a procedure would be simplified and conceptually clarified by the explicit use of the NRO as previously proposed (Guinot 1979; Capitaine et al. 1986). The two corresponding options (implicit or explicit use of the NRO) are shown to be equivalent for defining the specific Earth's angle of rotation and then UT1. The of the use of such an accurate procedure which has been proposed in the new IERS standards (McCarthy 1992a) instead of the usual one are estimated for the practical derivation of UT1.

  17. Managing distribution changes in time series prediction

    NASA Astrophysics Data System (ADS)

    Matias, J. M.; Gonzalez-Manteiga, W.; Taboada, J.; Ordonez, C.

    2006-07-01

    When a problem is modeled statistically, a single distribution model is usually postulated that is assumed to be valid for the entire space. Nonetheless, this practice may be somewhat unrealistic in certain application areas, in which the conditions of the process that generates the data may change; as far as we are aware, however, no techniques have been developed to tackle this problem.This article proposes a technique for modeling and predicting this change in time series with a view to improving estimates and predictions. The technique is applied, among other models, to the hypernormal distribution recently proposed. When tested on real data from a range of stock market indices the technique produces better results that when a single distribution model is assumed to be valid for the entire period of time studied.Moreover, when a global model is postulated, it is highly recommended to select the hypernormal distribution parameter in the same likelihood maximization process.

  18. Alternative predictors in chaotic time series

    NASA Astrophysics Data System (ADS)

    Alves, P. R. L.; Duarte, L. G. S.; da Mota, L. A. C. P.

    2017-06-01

    In the scheme of reconstruction, non-polynomial predictors improve the forecast from chaotic time series. The algebraic manipulation in the Maple environment is the basis for obtaining of accurate predictors. Beyond the different times of prediction, the optional arguments of the computational routines optimize the running and the analysis of global mappings.

  19. Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery

    PubMed Central

    Schwoertzig, Eugénie; Millon, Alexandre

    2016-01-01

    Species occurrence data provide crucial information for biodiversity studies in the current context of global environmental changes. Such studies often rely on a limited number of occurrence data collected in the field and on pseudo-absences arbitrarily chosen within the study area, which reduces the value of these studies. To overcome this issue, we propose an alternative method of prospection using geo-located street view imagery (SVI). Following a standardised protocol of virtual prospection using both vertical (aerial photographs) and horizontal (SVI) perceptions, we have surveyed 1097 randomly selected cells across Spain (0.1x0.1 degree, i.e. 20% of Spain) for the presence of Arundo donax L. (Poaceae). In total we have detected A. donax in 345 cells, thus substantially expanding beyond the now two-centuries-old field-derived record, which described A. donax only 216 cells. Among the field occurrence cells, 81.1% were confirmed by SVI prospection to be consistent with species presence. In addition, we recorded, by SVI prospection, 752 absences, i.e. cells where A. donax was considered absent. We have also compared the outcomes of climatic niche modeling based on SVI data against those based on field data. Using generalized linear models fitted with bioclimatic predictors, we have found SVI data to provide far more compelling results in terms of niche modeling than does field data as classically used in SDM. This original, cost- and time-effective method provides the means to accurately locate highly visible taxa, reinforce absence data, and predict species distribution without long and expensive in situ prospection. At this time, the majority of available SVI data is restricted to human-disturbed environments that have road networks. However, SVI is becoming increasingly available in natural areas, which means the technique has considerable potential to become an important factor in future biodiversity studies. PMID:26751565

  20. Invited article: Time accurate mass flow measurements of solid-fueled systems.

    PubMed

    Olliges, Jordan D; Lilly, Taylor C; Joslyn, Thomas B; Ketsdever, Andrew D

    2008-10-01

    A novel diagnostic method is described that utilizes a thrust stand mass balance (TSMB) to directly measure time-accurate mass flow from a solid-fuel thruster. The accuracy of the TSMB mass flow measurement technique was demonstrated in three ways including the use of an idealized numerical simulation, verifying a fluid mass calibration with high-speed digital photography, and by measuring mass loss in more than 30 hybrid rocket motor firings. Dynamic response of the mass balance was assessed through weight calibration and used to derive spring, damping, and mass moment of inertia coefficients for the TSMB. These dynamic coefficients were used to determine the mass flow rate and total mass loss within an acrylic and gaseous oxygen hybrid rocket motor firing. Intentional variations in the oxygen flow rate resulted in corresponding variations in the total propellant mass flow as expected. The TSMB was optimized to determine mass losses of up to 2.5 g and measured total mass loss to within 2.5% of that calculated by a NIST-calibrated digital scale. Using this method, a mass flow resolution of 0.0011 g/s or 2% of the average mass flow in this study has been achieved.

  1. Invited Article: Time accurate mass flow measurements of solid-fueled systems

    NASA Astrophysics Data System (ADS)

    Olliges, Jordan D.; Lilly, Taylor C.; Joslyn, Thomas B.; Ketsdever, Andrew D.

    2008-10-01

    A novel diagnostic method is described that utilizes a thrust stand mass balance (TSMB) to directly measure time-accurate mass flow from a solid-fuel thruster. The accuracy of the TSMB mass flow measurement technique was demonstrated in three ways including the use of an idealized numerical simulation, verifying a fluid mass calibration with high-speed digital photography, and by measuring mass loss in more than 30 hybrid rocket motor firings. Dynamic response of the mass balance was assessed through weight calibration and used to derive spring, damping, and mass moment of inertia coefficients for the TSMB. These dynamic coefficients were used to determine the mass flow rate and total mass loss within an acrylic and gaseous oxygen hybrid rocket motor firing. Intentional variations in the oxygen flow rate resulted in corresponding variations in the total propellant mass flow as expected. The TSMB was optimized to determine mass losses of up to 2.5 g and measured total mass loss to within 2.5% of that calculated by a NIST-calibrated digital scale. Using this method, a mass flow resolution of 0.0011 g/s or 2% of the average mass flow in this study has been achieved.

  2. Evaluation of a Mobile Application for Multiplier Method Growth and Epiphysiodesis Timing Predictions.

    PubMed

    Wagner, Pablo; Standard, Shawn C; Herzenberg, John E

    The multiplier method (MM) is frequently used to predict limb-length discrepancy and timing of epiphysiodesis. The traditional MM uses complex formulae and requires a calculator. A mobile application was developed in an attempt to simplify and streamline these calculations. We compared the accuracy and speed of using the traditional pencil and paper technique with that using the Multiplier App (MA). After attending a training lecture and a hands-on workshop on the MM and MA, 30 resident surgeons were asked to apply the traditional MM and the MA at different weeks of their rotations. They were randomized as to the method they applied first. Subjects performed calculations for 5 clinical exercises that involved congenital and developmental limb-length discrepancies and timing of epiphysiodesis. The amount of time required to complete the exercises and the accuracy of the answers were evaluated for each subject. The test subjects answered 60% of the questions correctly using the traditional MM and 80% of the questions correctly using the MA (P=0.001). The average amount of time to complete the 5 exercises with the MM and MA was 22 and 8 minutes, respectively (P<0.0001). Several reports state that the traditional MM is quick and easy to use. Nevertheless, even in the most experienced hands, performing the calculations in clinical practice can be time-consuming. Errors may result from choosing the wrong formulae and from performing the calculations by hand. Our data show that the MA is simpler, more accurate, and faster than the traditional MM from a practical standpoint. Level II.

  3. Accurate density functional prediction of molecular electron affinity with the scaling corrected Kohn–Sham frontier orbital energies

    NASA Astrophysics Data System (ADS)

    Zhang, DaDi; Yang, Xiaolong; Zheng, Xiao; Yang, Weitao

    2018-04-01

    Electron affinity (EA) is the energy released when an additional electron is attached to an atom or a molecule. EA is a fundamental thermochemical property, and it is closely pertinent to other important properties such as electronegativity and hardness. However, accurate prediction of EA is difficult with density functional theory methods. The somewhat large error of the calculated EAs originates mainly from the intrinsic delocalisation error associated with the approximate exchange-correlation functional. In this work, we employ a previously developed non-empirical global scaling correction approach, which explicitly imposes the Perdew-Parr-Levy-Balduz condition to the approximate functional, and achieve a substantially improved accuracy for the calculated EAs. In our approach, the EA is given by the scaling corrected Kohn-Sham lowest unoccupied molecular orbital energy of the neutral molecule, without the need to carry out the self-consistent-field calculation for the anion.

  4. Determination of the presence or absence of sulfur materials in drywall using direct analysis in real time in conjunction with an accurate-mass time-of-flight mass spectrometer.

    PubMed

    Curtis, Matthew E; Jones, Patrick R; Sparkman, O David; Cody, Robert B

    2009-11-01

    Based on the concern about the presence of sulfur materials being in drywall (wallboard), a quick and reliable test to confirm the presence or absence of these materials using direct analysis in real time (DART) mass spectrometry in conjunction with an accurate-mass time-of-flight (TOF) mass spectrometer has been developed and is described here.

  5. Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario.

    PubMed

    Shu, Hua; Song, Ci; Pei, Tao; Xu, Lianming; Ou, Yang; Zhang, Libin; Li, Tao

    2016-11-22

    Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals' average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day's WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

  6. Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers

    NASA Astrophysics Data System (ADS)

    Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

    2018-06-01

    Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3-1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that can guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and a highly economical path to determining the RI values for a wide range of organic polymers.

  7. Advanced propeller noise prediction in the time domain

    NASA Technical Reports Server (NTRS)

    Farassat, F.; Dunn, M. H.; Spence, P. L.

    1992-01-01

    The time domain code ASSPIN gives acousticians a powerful technique of advanced propeller noise prediction. Except for nonlinear effects, the code uses exact solutions of the Ffowcs Williams-Hawkings equation with exact blade geometry and kinematics. By including nonaxial inflow, periodic loading noise, and adaptive time steps to accelerate computer execution, the development of this code becomes complete.

  8. Lower NIH stroke scale scores are required to accurately predict a good prognosis in posterior circulation stroke.

    PubMed

    Inoa, Violiza; Aron, Abraham W; Staff, Ilene; Fortunato, Gilbert; Sansing, Lauren H

    2014-01-01

    The NIH stroke scale (NIHSS) is an indispensable tool that aids in the determination of acute stroke prognosis and decision making. Patients with posterior circulation (PC) strokes often present with lower NIHSS scores, which may result in the withholding of thrombolytic treatment from these patients. However, whether these lower initial NIHSS scores predict better long-term prognoses is uncertain. We aimed to assess the utility of the NIHSS at presentation for predicting the functional outcome at 3 months in anterior circulation (AC) versus PC strokes. This was a retrospective analysis of a large prospectively collected database of adults with acute ischemic stroke. Univariate and multivariate analyses were conducted to identify factors associated with outcome. Additional analyses were performed to determine the receiver operating characteristic (ROC) curves for NIHSS scores and outcomes in AC and PC infarctions. Both the optimal cutoffs for maximal diagnostic accuracy and the cutoffs to obtain >80% sensitivity for poor outcomes were determined in AC and PC strokes. The analysis included 1,197 patients with AC stroke and 372 with PC stroke. The median initial NIHSS score for patients with AC strokes was 7 and for PC strokes it was 2. The majority (71%) of PC stroke patients had baseline NIHSS scores ≤4, and 15% of these 'minor' stroke patients had a poor outcome at 3 months. ROC analysis identified that the optimal NIHSS cutoff for outcome prediction after infarction in the AC was 8 and for infarction in the PC it was 4. To achieve >80% sensitivity for detecting patients with a subsequent poor outcome, the NIHSS cutoff for infarctions in the AC was 4 and for infarctions in the PC it was 2. The NIHSS cutoff that most accurately predicts outcomes is 4 points higher in AC compared to PC infarctions. There is potential for poor outcomes in patients with PC strokes and low NIHSS scores, suggesting that thrombolytic treatment should not be withheld from these patients

  9. Can we ease the financial burden of colonoscopy? Using real-time endoscopic assessment of polyp histology to predict surveillance intervals.

    PubMed

    Chandran, S; Parker, F; Lontos, S; Vaughan, R; Efthymiou, M

    2015-12-01

    Polyps identified at colonoscopy are predominantly diminutive (<5 mm) with a small risk (>1%) of high-grade dysplasia or carcinoma; however, the cost of histological assessment is substantial. The aim of this study was to determine whether prediction of colonoscopy surveillance intervals based on real-time endoscopic assessment of polyp histology is accurate and cost effective. A prospective cohort study was conducted across a tertiary care and private community hospital. Ninety-four patients underwent colonoscopy and polypectomy of diminutive (≤5 mm) polyps from October 2012 to July 2013, yielding a total of 159 polyps. Polyps were examined and classified according to the Sano-Emura classification system. The endoscopic assessment (optical diagnosis) of polyp histology was used to predict appropriate colonoscopy surveillance intervals. The main outcome measure was the accuracy of optical diagnosis of diminutive colonic polyps against the gold standard of histological assessment. Optical diagnosis was correct in 105/108 (97.2%) adenomas. This yielded a sensitivity, specificity and positive and negative predictive values (with 95%CI) of 97.2% (92.1-99.4%), 78.4% (64.7-88.7%), 90.5% (83.7-95.2%) and 93% (80.9-98.5%) respectively. Ninety-two (98%) patients were correctly triaged to their repeat surveillance colonoscopy. Based on these findings, a cut and discard approach would have resulted in a saving of $319.77 per patient. Endoscopists within a tertiary care setting can accurately predict diminutive polyp histology and confer an appropriate surveillance interval with an associated financial benefit to the healthcare system. However, limitations to its application in the community setting exist, which may improve with further training and high-definition colonoscopes. © 2015 Royal Australasian College of Physicians.

  10. Time-Accurate Unsteady Pressure Loads Simulated for the Space Launch System at Wind Tunnel Conditions

    NASA Technical Reports Server (NTRS)

    Alter, Stephen J.; Brauckmann, Gregory J.; Kleb, William L.; Glass, Christopher E.; Streett, Craig L.; Schuster, David M.

    2015-01-01

    A transonic flow field about a Space Launch System (SLS) configuration was simulated with the Fully Unstructured Three-Dimensional (FUN3D) computational fluid dynamics (CFD) code at wind tunnel conditions. Unsteady, time-accurate computations were performed using second-order Delayed Detached Eddy Simulation (DDES) for up to 1.5 physical seconds. The surface pressure time history was collected at 619 locations, 169 of which matched locations on a 2.5 percent wind tunnel model that was tested in the 11 ft. x 11 ft. test section of the NASA Ames Research Center's Unitary Plan Wind Tunnel. Comparisons between computation and experiment showed that the peak surface pressure RMS level occurs behind the forward attach hardware, and good agreement for frequency and power was obtained in this region. Computational domain, grid resolution, and time step sensitivity studies were performed. These included an investigation of pseudo-time sub-iteration convergence. Using these sensitivity studies and experimental data comparisons, a set of best practices to date have been established for FUN3D simulations for SLS launch vehicle analysis. To the author's knowledge, this is the first time DDES has been used in a systematic approach and establish simulation time needed, to analyze unsteady pressure loads on a space launch vehicle such as the NASA SLS.

  11. Laser-assisted indocyanine green dye angiography accurately predicts the split-thickness graft timing of integra artificial dermis.

    PubMed

    Fourman, Mitchell S; Phillips, Brett T; Fritz, Jason R; Conkling, Nicole; McClain, Steve A; Simon, Marcia; Dagum, Alexander B

    2014-08-01

    The use of an artificial dermal substitute such as Integra-a bilaminate combination of thin silicone and cross-linked bovine tendon collagen and chondroitin-6-sulfate-has become a popular method to address large surface area wounds or smaller, complex wounds devoid of a vascular bed. The incorporation of Integra depends on a vascular wound bed or periphery and can take 4 weeks or longer to occur. If the Integra has not fully incorporated at the time of placement of the split-thickness graft, complete graft loss may result. The availability of a minimally invasive method to assess the incorporation of Integra would be of great value. Two 5 × 10-cm paraspinal full-thickness wounds were created on 3 female swine. Wounds were randomly assigned full-thickness skin graft or Integra (Plainsboro, NJ) treatment. Both types of grafts were placed after the application of fibrin glue (Tisseel, Deerfield, Ill) to the wound bed. Laser Doppler imaging (LDI) (Moor), indocyanine green dye (ICG) angiography (LifeCell SPY), and clinical scoring were performed weekly for a period of 8 weeks after grafting. At 4 weeks, the silicone layer of the Integra was removed, and a culture of autologous keratinocytes was applied. A 4-mm punch biopsy sample of each graft was taken 1, 2, 4, 6, 7, and 8 weeks postoperatively for histologic analysis. Both ICG angiography and LDI perfusion measurements noted an increase in perfusion at the Integra graft site that peaked 3 weeks after grafting, corresponding with the start of neovascularization and the optimal time for the application of a split-thickness skin graft. indocyanine green dye angiography measurements exhibit greater reproducibility between animals at late time points as compared with LDI. This decrease in LDI precision is directly related to increases in scar tissue thickness of greater than 5 mm as determined via histologic analysis and corresponds with the accepted maximum penetration depth of the LDI laser. Indocyanine green dye

  12. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.

    PubMed

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R; Judson, Richard; Corton, J Christopher

    2016-05-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. Published by Oxford University Press on behalf of the Society of Toxicology 2016. This work is written by US Government employees and is in the public domain in the US.

  13. Simultaneous transmission of accurate time, stable frequency, data, and sensor system over one fiber with ITU 100 GHz grid

    NASA Astrophysics Data System (ADS)

    Horvath, Tomas; Munster, Petr; Vojtech, Josef; Velc, Radek; Oujezsky, Vaclav

    2018-01-01

    Optical fiber is the most used medium for current telecommunication networks. Besides data transmissions, special advanced applications like accurate time or stable frequency transmissions are more common, especially in research and education networks. On the other hand, new applications like distributed sensing are in ISP's interest because e.g. such sensing allows new service: protection of fiber infrastructure. Transmission of all applications in a single fiber can be very cost efficient but it is necessary to evaluate possible interaction before real application and deploying the service, especially if standard 100 GHz grid is considered. We performed laboratory measurement of simultaneous transmission of 100 G data based on DP-QPSK modulation format, accurate time, stable frequency and sensing system based on phase sensitive OTDR through two types of optical fibers, G.655 and G.653. These fibers are less common than G.652 fiber but thanks to their slightly higher nonlinear character, there are suitable for simulation of the worst case which can arise in a real network.

  14. Noise-free accurate count of microbial colonies by time-lapse shadow image analysis.

    PubMed

    Ogawa, Hiroyuki; Nasu, Senshi; Takeshige, Motomu; Funabashi, Hisakage; Saito, Mikako; Matsuoka, Hideaki

    2012-12-01

    Microbial colonies in food matrices could be counted accurately by a novel noise-free method based on time-lapse shadow image analysis. An agar plate containing many clusters of microbial colonies and/or meat fragments was trans-illuminated to project their 2-dimensional (2D) shadow images on a color CCD camera. The 2D shadow images of every cluster distributed within a 3-mm thick agar layer were captured in focus simultaneously by means of a multiple focusing system, and were then converted to 3-dimensional (3D) shadow images. By time-lapse analysis of the 3D shadow images, it was determined whether each cluster comprised single or multiple colonies or a meat fragment. The analytical precision was high enough to be able to distinguish a microbial colony from a meat fragment, to recognize an oval image as two colonies contacting each other, and to detect microbial colonies hidden under a food fragment. The detection of hidden colonies is its outstanding performance in comparison with other systems. The present system attained accuracy for counting fewer than 5 colonies and is therefore of practical importance. Copyright © 2012 Elsevier B.V. All rights reserved.

  15. Model predictive control of P-time event graphs

    NASA Astrophysics Data System (ADS)

    Hamri, H.; Kara, R.; Amari, S.

    2016-12-01

    This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.

  16. Prediction of half-marathon race time in recreational female and male runners.

    PubMed

    Knechtle, Beat; Barandun, Ursula; Knechtle, Patrizia; Zingg, Matthias A; Rosemann, Thomas; Rüst, Christoph A

    2014-01-01

    Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r(2) = 0.42, adjusted r(2) = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) - 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p < 0.0001) to the achieved race time. For women, half-marathon race time might be predicted by the equation (r(2) = 0.68, adjusted r(2) = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) - 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological

  17. Weight Suppression Predicts Time to Remission from Bulimia Nervosa

    ERIC Educational Resources Information Center

    Lowe, Michael R.; Berner, Laura A.; Swanson, Sonja A.; Clark, Vicki L.; Eddy, Kamryn T.; Franko, Debra L.; Shaw, Jena A.; Ross, Stephanie; Herzog, David B.

    2011-01-01

    Objective: To investigate whether, at study entry, (a) weight suppression (WS), the difference between highest past adult weight and current weight, prospectively predicts time to first full remission from bulimia nervosa (BN) over a follow-up period of 8 years, and (b) weight change over time mediates the relationship between WS and time to first…

  18. Shedding light on the variability of optical skin properties: finding a path towards more accurate prediction of light propagation in human cutaneous compartments

    PubMed Central

    Mignon, C.; Tobin, D. J.; Zeitouny, M.; Uzunbajakava, N. E.

    2018-01-01

    Finding a path towards a more accurate prediction of light propagation in human skin remains an aspiration of biomedical scientists working on cutaneous applications both for diagnostic and therapeutic reasons. The objective of this study was to investigate variability of the optical properties of human skin compartments reported in literature, to explore the underlying rational of this variability and to propose a dataset of values, to better represent an in vivo case and recommend a solution towards a more accurate prediction of light propagation through cutaneous compartments. To achieve this, we undertook a novel, logical yet simple approach. We first reviewed scientific articles published between 1981 and 2013 that reported on skin optical properties, to reveal the spread in the reported quantitative values. We found variations of up to 100-fold. Then we extracted the most trust-worthy datasets guided by a rule that the spectral properties should reflect the specific biochemical composition of each of the skin layers. This resulted in the narrowing of the spread in the calculated photon densities to 6-fold. We conclude with a recommendation to use the identified most robust datasets when estimating light propagation in human skin using Monte Carlo simulations. Alternatively, otherwise follow our proposed strategy to screen any new datasets to determine their biological relevance. PMID:29552418

  19. Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration.

    PubMed

    Wittek, Adam; Joldes, Grand; Couton, Mathieu; Warfield, Simon K; Miller, Karol

    2010-12-01

    Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer. Copyright © 2010 Elsevier Ltd. All rights reserved.

  20. Predicting mortality over different time horizons: which data elements are needed?

    PubMed

    Goldstein, Benjamin A; Pencina, Michael J; Montez-Rath, Maria E; Winkelmayer, Wolfgang C

    2017-01-01

    Electronic health records (EHRs) are a resource for "big data" analytics, containing a variety of data elements. We investigate how different categories of information contribute to prediction of mortality over different time horizons among patients undergoing hemodialysis treatment. We derived prediction models for mortality over 7 time horizons using EHR data on older patients from a national chain of dialysis clinics linked with administrative data using LASSO (least absolute shrinkage and selection operator) regression. We assessed how different categories of information relate to risk assessment and compared discrete models to time-to-event models. The best predictors used all the available data (c-statistic ranged from 0.72-0.76), with stronger models in the near term. While different variable groups showed different utility, exclusion of any particular group did not lead to a meaningfully different risk assessment. Discrete time models performed better than time-to-event models. Different variable groups were predictive over different time horizons, with vital signs most predictive for near-term mortality and demographic and comorbidities more important in long-term mortality. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  1. Time travel, Clock Puzzles and Their Experimental Tests

    NASA Astrophysics Data System (ADS)

    Ciufolini, Ignazio

    2013-09-01

    Is time travel possible? What is Einstein's theory of relativity mathematically predicting in that regard? Is time travel related to the so-called clock `paradoxes' of relativity and if so how? Is there any accurate experimental evidence of the phenomena regarding the different flow of time predicted by General Relativity and is there any possible application of the temporal phenomena predicted by relativity to our everyday life? Which temporal phenomena are predicted in the vicinities of a rotating body and of a mass-energy current, and do we have any experimental test of the occurrence of these phenomena near a rotating body? In this paper, we address and answer some of these questions.

  2. Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

    PubMed Central

    Li, Zhencai; Wang, Yang; Liu, Zhen

    2016-01-01

    The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

  3. Predicting Visual Distraction Using Driving Performance Data

    PubMed Central

    Kircher, Katja; Ahlstrom, Christer

    2010-01-01

    Behavioral variables are often used as performance indicators (PIs) of visual or internal distraction induced by secondary tasks. The objective of this study is to investigate whether visual distraction can be predicted by driving performance PIs in a naturalistic setting. Visual distraction is here defined by a gaze based real-time distraction detection algorithm called AttenD. Seven drivers used an instrumented vehicle for one month each in a small scale field operational test. For each of the visual distraction events detected by AttenD, seven PIs such as steering wheel reversal rate and throttle hold were calculated. Corresponding data were also calculated for time periods during which the drivers were classified as attentive. For each PI, means between distracted and attentive states were calculated using t-tests for different time-window sizes (2 – 40 s), and the window width with the smallest resulting p-value was selected as optimal. Based on the optimized PIs, logistic regression was used to predict whether the drivers were attentive or distracted. The logistic regression resulted in predictions which were 76 % correct (sensitivity = 77 % and specificity = 76 %). The conclusion is that there is a relationship between behavioral variables and visual distraction, but the relationship is not strong enough to accurately predict visual driver distraction. Instead, behavioral PIs are probably best suited as complementary to eye tracking based algorithms in order to make them more accurate and robust. PMID:21050615

  4. Effects of Prediction and Contextual Support on Lexical Processing: Prediction takes Precedence

    PubMed Central

    Brothers, Trevor; Swaab, Tamara Y.; Traxler, Matthew J.

    2014-01-01

    Readers may use contextual information to anticipate and pre-activate specific lexical items during reading. However, prior studies have not clearly dissociated the effects of accurate lexical prediction from other forms of contextual facilitation such as plausibility or semantic priming. In this study, we measured electrophysiological responses to predicted and unpredicted target words in passages providing varying levels of contextual support. This method was used to isolate the neural effects of prediction from other potential contextual influences on lexical processing. While both prediction and discourse context influenced ERP amplitudes within the time range of the N400, the effects of prediction occurred much more rapidly, preceding contextual facilitation by approximately 100ms. In addition, a frontal, post-N400 positivity (PNP) was modulated by both prediction accuracy and the overall plausibility of the preceding passage. These results suggest a unique temporal primacy for prediction in facilitating lexical access. They also suggest that the frontal PNP may index the costs of revising discourse representations following an incorrect lexical prediction. PMID:25497522

  5. Microscopic predictions of fission yields based on the time dependent GCM formalism

    NASA Astrophysics Data System (ADS)

    Regnier, D.; Dubray, N.; Schunck, N.; Verrière, M.

    2016-03-01

    Accurate knowledge of fission fragment yields is an essential ingredient of numerous applications ranging from the formation of elements in the r-process to fuel cycle optimization in nuclear energy. The need for a predictive theory applicable where no data is available, together with the variety of potential applications, is an incentive to develop a fully microscopic approach to fission dynamics. One of the most promising theoretical frameworks is the time-dependent generator coordinate method (TDGCM) applied under the Gaussian overlap approximation (GOA). Previous studies reported promising results by numerically solving the TDGCM+GOA equation with a finite difference technique. However, the computational cost of this method makes it difficult to properly control numerical errors. In addition, it prevents one from performing calculations with more than two collective variables. To overcome these limitations, we developed the new code FELIX-1.0 that solves the TDGCM+GOA equation based on the Galerkin finite element method. In this article, we briefly illustrate the capabilities of the solver FELIX-1.0, in particular its validation for n+239Pu low energy induced fission. This work is the result of a collaboration between CEA,DAM,DIF and LLNL on nuclear fission theory.

  6. Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

    PubMed Central

    Shu, Hua; Song, Ci; Pei, Tao; Xu, Lianming; Ou, Yang; Zhang, Libin; Li, Tao

    2016-01-01

    Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas. PMID:27879663

  7. Predicting camber, deflection, and prestress losses in prestressed concrete members.

    DOT National Transportation Integrated Search

    2011-07-01

    Accurate predictions of camber and prestress losses for prestressed concrete bridge girders are essential to minimizing the frequency and cost of construction problems. The time-dependent nature of prestress losses, variable concrete properties, and ...

  8. Hounsfield unit density accurately predicts ESWL success.

    PubMed

    Magnuson, William J; Tomera, Kevin M; Lance, Raymond S

    2005-01-01

    Extracorporeal shockwave lithotripsy (ESWL) is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones. In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. 198 patients were treated at Alaska Urological Associates with ESWL between January 2002 and April 2004. Of these 101 met study inclusion with accessible CT scans and stones ranging from 5-15 mm. Follow-up imaging demonstrated stone freedom in 74.2%. The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different ( 93.61 vs 122.80 p < 0.0001). We determined by receiver operator curve (ROC) that HDV of 93 or less carries a 90% or better chance of stone freedom following ESWL for upper tract calculi between 5-15mm.

  9. Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-BandOpenMote Hardware.

    PubMed

    Daneels, Glenn; Municio, Esteban; Van de Velde, Bruno; Ergeerts, Glenn; Weyn, Maarten; Latré, Steven; Famaey, Jeroen

    2018-02-02

    The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e amendment aims to improve reliability and energy efficiency in industrial and other challenging Internet-of-Things (IoT) environments. This paper presents an accurate and up-to-date energy consumption model for devices using this IEEE 802.15.4e TSCH mode. The model identifies all network-related CPU and radio state changes, thus providing a precise representation of the device behavior and an accurate prediction of its energy consumption. Moreover, energy measurements were performed with a dual-band OpenMote device, running the OpenWSN firmware. This allows the model to be used for devices using 2.4 GHz, as well as 868 MHz. Using these measurements, several network simulations were conducted to observe the TSCH energy consumption effects in end-to-end communication for both frequency bands. Experimental verification of the model shows that it accurately models the consumption for all possible packet sizes and that the calculated consumption on average differs less than 3% from the measured consumption. This deviation includes measurement inaccuracies and the variations of the guard time. As such, the proposed model is very suitable for accurate energy consumption modeling of TSCH networks.

  10. Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-BandOpenMote Hardware

    PubMed Central

    Municio, Esteban; Van de Velde, Bruno; Latré, Steven

    2018-01-01

    The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e amendment aims to improve reliability and energy efficiency in industrial and other challenging Internet-of-Things (IoT) environments. This paper presents an accurate and up-to-date energy consumption model for devices using this IEEE 802.15.4e TSCH mode. The model identifies all network-related CPU and radio state changes, thus providing a precise representation of the device behavior and an accurate prediction of its energy consumption. Moreover, energy measurements were performed with a dual-band OpenMote device, running the OpenWSN firmware. This allows the model to be used for devices using 2.4 GHz, as well as 868 MHz. Using these measurements, several network simulations were conducted to observe the TSCH energy consumption effects in end-to-end communication for both frequency bands. Experimental verification of the model shows that it accurately models the consumption for all possible packet sizes and that the calculated consumption on average differs less than 3% from the measured consumption. This deviation includes measurement inaccuracies and the variations of the guard time. As such, the proposed model is very suitable for accurate energy consumption modeling of TSCH networks. PMID:29393900

  11. Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory

    PubMed Central

    Tao, Qing

    2017-01-01

    Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM. PMID:29391864

  12. Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.

    PubMed

    Yang, Haimin; Pan, Zhisong; Tao, Qing

    2017-01-01

    Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

  13. Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that long-range LD is a major contributing factor.

    PubMed

    Vallejo, Roger L; Silva, Rafael M O; Evenhuis, Jason P; Gao, Guangtu; Liu, Sixin; Parsons, James E; Martin, Kyle E; Wiens, Gregory D; Lourenco, Daniela A L; Leeds, Timothy D; Palti, Yniv

    2018-06-05

    Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium-density single nucleotide polymorphism (SNP) array. Here, the impact of lower-density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)-flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single-step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree-based prediction (0.50-0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL-flanking SNP (0.65-0.72) was similar to the panel with 35K SNP (0.65-0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r 2  ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long-range LD likely contributed to the accurate genomic predictions with the low-density SNP panels. Population structure analysis supported the hypothesis that long-range LD in this population may be caused by admixture. Results suggest that lower-cost, low-density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs. © 2018 The Authors. This article is a U.S. Government work and is in the public domain in the USA. Journal of Animal Breeding and Genetics published by Blackwell Verlag GmbH.

  14. A practical approach for predicting retention time shifts due to pressure and temperature gradients in ultra-high-pressure liquid chromatography.

    PubMed

    Åsberg, Dennis; Chutkowski, Marcin; Leśko, Marek; Samuelsson, Jörgen; Kaczmarski, Krzysztof; Fornstedt, Torgny

    2017-01-06

    Large pressure gradients are generated in ultra-high-pressure liquid chromatography (UHPLC) using sub-2μm particles causing significant temperature gradients over the column due to viscous heating. These pressure and temperature gradients affect retention and ultimately result in important selectivity shifts. In this study, we developed an approach for predicting the retention time shifts due to these gradients. The approach is presented as a step-by-step procedure and it is based on empirical linear relationships describing how retention varies as a function of temperature and pressure and how the average column temperature increases with the flow rate. It requires only four experiments on standard equipment, is based on straightforward calculations, and is therefore easy to use in method development. The approach was rigorously validated against experimental data obtained with a quality control method for the active pharmaceutical ingredient omeprazole. The accuracy of retention time predictions was very good with relative errors always less than 1% and in many cases around 0.5% (n=32). Selectivity shifts observed between omeprazole and the related impurities when changing the flow rate could also be accurately predicted resulting in good estimates of the resolution between critical peak pairs. The approximations which the presented approach are based on were all justified. The retention factor as a function of pressure and temperature was studied in an experimental design while the temperature distribution in the column was obtained by solving the fundamental heat and mass balance equations for the different experimental conditions. We strongly believe that this approach is sufficiently accurate and experimentally feasible for this separation to be a valuable tool when developing a UHPLC method. After further validation with other separation systems, it could become a useful approach in UHPLC method development, especially in the pharmaceutical industry where

  15. Integrating trans-abdominal ultrasonography with fecal steroid metabolite monitoring to accurately diagnose pregnancy and predict the timing of parturition in the red panda (Ailurus fulgens styani).

    PubMed

    Curry, Erin; Browning, Lissa J; Reinhart, Paul; Roth, Terri L

    2017-05-01

    Red pandas (Ailurus fulgens styani) exhibit a variable gestation length and may experience a pseudopregnancy indistinguishable from true pregnancy; therefore, it is not possible to deduce an individual's true pregnancy status and parturition date based on breeding dates or fecal progesterone excretion patterns alone. The goal of this study was to evaluate the use of transabdominal ultrasonography for pregnancy diagnosis in red pandas. Two to three females were monitored over 4 consecutive years, generating a total of seven profiles (four pregnancies, two pseudopregnancies, and one lost pregnancy). Fecal samples were collected and assayed for progesterone (P4) and estrogen conjugate (EC) to characterize patterns associated with breeding activity and parturition events. Animals were trained for voluntary transabdominal ultrasound and examinations were performed weekly. Breeding behaviors and fecal EC data suggest that the estrus cycle of this species is 11-12 days in length. Fecal steroid metabolite analyses also revealed that neither P4 nor EC concentrations were suitable indicators of pregnancy in this species; however, a secondary increase in P4 occurred 69-71 days prior to parturition in all pregnant females, presumably coinciding with embryo implantation. Using ultrasonography, embryos were detected as early as 62 days post-breeding/50 days pre-partum and serial measurements of uterine lumen diameter were documented throughout four pregnancies. Advances in reproductive diagnostics, such as the implementation of ultrasonography, may facilitate improved husbandry of pregnant females and allow for the accurate prediction of parturition. © 2017 Wiley Periodicals, Inc.

  16. pKa prediction of monoprotic small molecules the SMARTS way.

    PubMed

    Lee, Adam C; Yu, Jing-Yu; Crippen, Gordon M

    2008-10-01

    Realizing favorable absorption, distribution, metabolism, elimination, and toxicity profiles is a necessity due to the high attrition rate of lead compounds in drug development today. The ability to accurately predict bioavailability can help save time and money during the screening and optimization processes. As several robust programs already exist for predicting logP, we have turned our attention to the fast and robust prediction of pK(a) for small molecules. Using curated data from the Beilstein Database and Lange's Handbook of Chemistry, we have created a decision tree based on a novel set of SMARTS strings that can accurately predict the pK(a) for monoprotic compounds with R(2) of 0.94 and root mean squared error of 0.68. Leave-some-out (10%) cross-validation achieved Q(2) of 0.91 and root mean squared error of 0.80.

  17. Recording A Sunrise: A Citizen Science Project to Enhance Sunrise/set Prediction Times

    NASA Astrophysics Data System (ADS)

    Wilson, Teresa; Chizek Frouard, Malynda; Bartlett, Jennifer L.

    2017-01-01

    Smartphones, with their ever increasing capabilities, are becoming quite instrumental for data acquisition in a number of fields. Understanding refraction and how it affects what we see on the horizon is no exception. Current algorithms that predict sunrise and sunset times have an error of one to four minutes at mid-latitudes (0° - 55° N/S) due to limitations in the atmospheric models they incorporate. At higher latitudes, slight changes in refraction can cause significant discrepancies, even including difficulties determining when the Sun appears to rise or set. A thorough investigation of the problem requires a substantial data set of observed rise/set times and corresponding meteorological data from around the world, which is currently lacking. We have developed a mobile application so that this data can be taken using smartphones as part of a citizen science project. The app allows the viewer to submit a video of sunrise/set and attaches geographic location along with meteorological data taken from a local weather station. The project will help increase scientific awareness in the public by allowing members of the community to participate in the data-taking process, and give them a greater awareness of the scientific significance of phenomenon they witness every day. The data from the observations will lead to more complete rise/set models that will provide more accurate times to the benefit of astronomers, navigators, and outdoorsmen. The app will be available on the Google Play Store.

  18. Real-time Interplanetary Shock Prediction System

    NASA Astrophysics Data System (ADS)

    Vandegriff, J. D.; Ho, G. C.; Plauger, J. M.

    2002-05-01

    We are creating a system to predict the arrival times and maximum intensities of energetic storm particle (ESP) events at the earth using particle fluxes measured by the EPAM instrument aboard NASA's ACE spacecraft. Real-time flux measurements, consisting of 5 minute averages made available 24 hours per day by the NOAA Space Environment Center, are fed into algorithms looking for characteristic changes in flux, velocity dispersion, and anisotropy. These quantities typically show changes up to 3 hours before shock passage, and thus we expect our system to deliver enhanced probabilities for shock arrival with approximately the same lead time. Forecasting information will be made publicly available through http://sd-www.jhuapl.edu/ACE/EPAM/, the Johns Hopkins University Applied Physics Lab web site for the ACE/EPAM instrument. Early results on the training of our algorithms and comparisons with past shock data will be presented.

  19. Accurate Prediction of Inducible Transcription Factor Binding Intensities In Vivo

    PubMed Central

    Siepel, Adam; Lis, John T.

    2012-01-01

    DNA sequence and local chromatin landscape act jointly to determine transcription factor (TF) binding intensity profiles. To disentangle these influences, we developed an experimental approach, called protein/DNA binding followed by high-throughput sequencing (PB–seq), that allows the binding energy landscape to be characterized genome-wide in the absence of chromatin. We applied our methods to the Drosophila Heat Shock Factor (HSF), which inducibly binds a target DNA sequence element (HSE) following heat shock stress. PB–seq involves incubating sheared naked genomic DNA with recombinant HSF, partitioning the HSF–bound and HSF–free DNA, and then detecting HSF–bound DNA by high-throughput sequencing. We compared PB–seq binding profiles with ones observed in vivo by ChIP–seq and developed statistical models to predict the observed departures from idealized binding patterns based on covariates describing the local chromatin environment. We found that DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in predicting changes in HSF binding affinity. We also investigated the extent to which DNA accessibility, as measured by digital DNase I footprinting data, could be predicted from MNase–seq data and the ChIP–chip profiles for many histone modifications and TFs, and found GAGA element associated factor (GAF), tetra-acetylation of H4, and H4K16 acetylation to be the most predictive covariates. Lastly, we generated an unbiased model of HSF binding sequences, which revealed distinct biophysical properties of the HSF/HSE interaction and a previously unrecognized substructure within the HSE. These findings provide new insights into the interplay between the genomic sequence and the chromatin landscape in determining transcription factor binding intensity. PMID:22479205

  20. A non-contact method based on multiple signal classification algorithm to reduce the measurement time for accurately heart rate detection

    NASA Astrophysics Data System (ADS)

    Bechet, P.; Mitran, R.; Munteanu, M.

    2013-08-01

    Non-contact methods for the assessment of vital signs are of great interest for specialists due to the benefits obtained in both medical and special applications, such as those for surveillance, monitoring, and search and rescue. This paper investigates the possibility of implementing a digital processing algorithm based on the MUSIC (Multiple Signal Classification) parametric spectral estimation in order to reduce the observation time needed to accurately measure the heart rate. It demonstrates that, by proper dimensioning the signal subspace, the MUSIC algorithm can be optimized in order to accurately assess the heart rate during an 8-28 s time interval. The validation of the processing algorithm performance was achieved by minimizing the mean error of the heart rate after performing simultaneous comparative measurements on several subjects. In order to calculate the error the reference value of heart rate was measured using a classic measurement system through direct contact.

  1. A link prediction approach to cancer drug sensitivity prediction.

    PubMed

    Turki, Turki; Wei, Zhi

    2017-10-03

    Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

  2. Time Accurate CFD Simulations of the Orion Launch Abort Vehicle in the Transonic Regime

    NASA Technical Reports Server (NTRS)

    Rojahn, Josh; Ruf, Joe

    2011-01-01

    Significant asymmetries in the fluid dynamics were calculated for some cases in the CFD simulations of the Orion Launch Abort Vehicle through its abort trajectories. The CFD simulations were performed steady state and in three dimensions with symmetric geometries, no freestream sideslip angle, and motors firing. The trajectory points at issue were in the transonic regime, at 0 and +/- 5 angles of attack with the Abort Motors with and without the Attitude Control Motors (ACM) firing. In some of the cases the asymmetric fluid dynamics resulted in aerodynamic side forces that were large enough that would overcome the control authority of the ACMs. MSFC's Fluid Dynamics Group supported the investigation into the cause of the flow asymmetries with time accurate CFD simulations, utilizing a hybrid RANS-LES turbulence model. The results show that the flow over the vehicle and the subsequent interaction with the AB and ACM motor plumes were unsteady. The resulting instantaneous aerodynamic forces were oscillatory with fairly large magnitudes. Time averaged aerodynamic forces were essentially symmetric.

  3. Earthquake prediction in Japan and natural time analysis of seismicity

    NASA Astrophysics Data System (ADS)

    Uyeda, S.; Varotsos, P.

    2011-12-01

    M9 super-giant earthquake with huge tsunami devastated East Japan on 11 March, causing more than 20,000 casualties and serious damage of Fukushima nuclear plant. This earthquake was predicted neither short-term nor long-term. Seismologists were shocked because it was not even considered possible to happen at the East Japan subduction zone. However, it was not the only un-predicted earthquake. In fact, throughout several decades of the National Earthquake Prediction Project, not even a single earthquake was predicted. In reality, practically no effective research has been conducted for the most important short-term prediction. This happened because the Japanese National Project was devoted for construction of elaborate seismic networks, which was not the best way for short-term prediction. After the Kobe disaster, in order to parry the mounting criticism on their no success history, they defiantly changed their policy to "stop aiming at short-term prediction because it is impossible and concentrate resources on fundamental research", that meant to obtain "more funding for no prediction research". The public were and are not informed about this change. Obviously earthquake prediction would be possible only when reliable precursory phenomena are caught and we have insisted this would be done most likely through non-seismic means such as geochemical/hydrological and electromagnetic monitoring. Admittedly, the lack of convincing precursors for the M9 super-giant earthquake has adverse effect for us, although its epicenter was far out off shore of the range of operating monitoring systems. In this presentation, we show a new possibility of finding remarkable precursory signals, ironically, from ordinary seismological catalogs. In the frame of the new time domain termed natural time, an order parameter of seismicity, κ1, has been introduced. This is the variance of natural time kai weighted by normalised energy release at χ. In the case that Seismic Electric Signals

  4. Real-time Adaptive Control Using Neural Generalized Predictive Control

    NASA Technical Reports Server (NTRS)

    Haley, Pam; Soloway, Don; Gold, Brian

    1999-01-01

    The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

  5. FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately.

    PubMed

    Budowski-Tal, Inbal; Nov, Yuval; Kolodny, Rachel

    2010-02-23

    Fast identification of protein structures that are similar to a specified query structure in the entire Protein Data Bank (PDB) is fundamental in structure and function prediction. We present FragBag: An ultrafast and accurate method for comparing protein structures. We describe a protein structure by the collection of its overlapping short contiguous backbone segments, and discretize this set using a library of fragments. Then, we succinctly represent the protein as a "bags-of-fragments"-a vector that counts the number of occurrences of each fragment-and measure the similarity between two structures by the similarity between their vectors. Our representation has two additional benefits: (i) it can be used to construct an inverted index, for implementing a fast structural search engine of the entire PDB, and (ii) one can specify a structure as a collection of substructures, without combining them into a single structure; this is valuable for structure prediction, when there are reliable predictions only of parts of the protein. We use receiver operating characteristic curve analysis to quantify the success of FragBag in identifying neighbor candidate sets in a dataset of over 2,900 structures. The gold standard is the set of neighbors found by six state of the art structural aligners. Our best FragBag library finds more accurate candidate sets than the three other filter methods: The SGM, PRIDE, and a method by Zotenko et al. More interestingly, FragBag performs on a par with the computationally expensive, yet highly trusted structural aligners STRUCTAL and CE.

  6. Accurate interatomic force fields via machine learning with covariant kernels

    NASA Astrophysics Data System (ADS)

    Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro

    2017-06-01

    We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression. This is based on matrix-valued kernel functions, on which we impose the requirements that the predicted force rotates with the target configuration and is independent of any rotations applied to the configuration database entries. We show that such covariant GP kernels can be obtained by integration over the elements of the rotation group SO (d ) for the relevant dimensionality d . Remarkably, in specific cases the integration can be carried out analytically and yields a conservative force field that can be recast into a pair interaction form. Finally, we show that restricting the integration to a summation over the elements of a finite point group relevant to the target system is sufficient to recover an accurate GP. The accuracy of our kernels in predicting quantum-mechanical forces in real materials is investigated by tests on pure and defective Ni, Fe, and Si crystalline systems.

  7. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

    PubMed Central

    Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

    2016-01-01

    In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423

  8. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.

    PubMed

    Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

    2016-01-01

    In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

  9. Healthy Work Revisited: Do Changes in Time Strain Predict Well-Being?

    PubMed Central

    Moen, Phyllis; Kelly, Erin L.; Lam, Jack

    2013-01-01

    Building on Karasek and Theorell (R. Karasek & T. Theorell, 1990, Healthy work: Stress, productivity, and the reconstruction of working life, New York, NY: Basic Books), we theorized and tested the relationship between time strain (work-time demands and control) and seven self-reported health outcomes. We drew on survey data from 550 employees fielded before and 6 months after the implementation of an organizational intervention, the Results Only Work Environment (ROWE) in a white-collar organization. Cross-sectional (Wave 1) models showed psychological time demands and time control measures were related to health outcomes in expected directions. The ROWE intervention did not predict changes in psychological time demands by Wave 2, but did predict increased time control (a sense of time adequacy and schedule control). Statistical models revealed increases in psychological time demands and time adequacy predicted changes in positive (energy, mastery, psychological well-being, self-assessed health) and negative (emotional exhaustion, somatic symptoms, psychological distress) outcomes in expected directions, net of job and home demands and covariates. This study demonstrates the value of including time strain in investigations of the health effects of job conditions. Results encourage longitudinal models of change in psychological time demands as well as time control, along with the development and testing of interventions aimed at reducing time strain in different populations of workers. PMID:23506547

  10. Healthy work revisited: do changes in time strain predict well-being?

    PubMed

    Moen, Phyllis; Kelly, Erin L; Lam, Jack

    2013-04-01

    Building on Karasek and Theorell (R. Karasek & T. Theorell, 1990, Healthy work: Stress, productivity, and the reconstruction of working life, New York, NY: Basic Books), we theorized and tested the relationship between time strain (work-time demands and control) and seven self-reported health outcomes. We drew on survey data from 550 employees fielded before and 6 months after the implementation of an organizational intervention, the results only work environment (ROWE) in a white-collar organization. Cross-sectional (wave 1) models showed psychological time demands and time control measures were related to health outcomes in expected directions. The ROWE intervention did not predict changes in psychological time demands by wave 2, but did predict increased time control (a sense of time adequacy and schedule control). Statistical models revealed increases in psychological time demands and time adequacy predicted changes in positive (energy, mastery, psychological well-being, self-assessed health) and negative (emotional exhaustion, somatic symptoms, psychological distress) outcomes in expected directions, net of job and home demands and covariates. This study demonstrates the value of including time strain in investigations of the health effects of job conditions. Results encourage longitudinal models of change in psychological time demands as well as time control, along with the development and testing of interventions aimed at reducing time strain in different populations of workers.

  11. Fast and accurate Monte Carlo sampling of first-passage times from Wiener diffusion models.

    PubMed

    Drugowitsch, Jan

    2016-02-11

    We present a new, fast approach for drawing boundary crossing samples from Wiener diffusion models. Diffusion models are widely applied to model choices and reaction times in two-choice decisions. Samples from these models can be used to simulate the choices and reaction times they predict. These samples, in turn, can be utilized to adjust the models' parameters to match observed behavior from humans and other animals. Usually, such samples are drawn by simulating a stochastic differential equation in discrete time steps, which is slow and leads to biases in the reaction time estimates. Our method, instead, facilitates known expressions for first-passage time densities, which results in unbiased, exact samples and a hundred to thousand-fold speed increase in typical situations. In its most basic form it is restricted to diffusion models with symmetric boundaries and non-leaky accumulation, but our approach can be extended to also handle asymmetric boundaries or to approximate leaky accumulation.

  12. Prediction of energy expenditure and physical activity in preschoolers

    USDA-ARS?s Scientific Manuscript database

    Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) ...

  13. 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.

  14. Prediction of Time Response of Electrowetting

    NASA Astrophysics Data System (ADS)

    Lee, Seung Jun; Hong, Jiwoo; Kang, Kwan Hyoung

    2009-11-01

    It is very important to predict the time response of electrowetting-based devices, such as liquid lenses, reflective displays, and optical switches. We investigated the time response of electrowetting, based on an analytical and a numerical method, to find out characteristic scales and a scaling law for the switching time. For this, spreading process of a sessile droplet was analyzed based on the domain perturbation method. First, we considered the case of weakly viscous fluids. The analytical result for the spreading process was compared with experimental results, which showed very good agreement in overall time response. It was shown that the overall dynamics is governed by P2 shape mode. We derived characteristic scales combining the droplet volume, density, and surface tension. The overall dynamic process was scaled quite well by the scales. A scaling law was derived from the analytical solution and was verified experimentally. We also suggest a scaling law for highly viscous liquids, based on results of numerical analysis for the electrowetting-actuated spreading process.

  15. Real-time predictive seasonal influenza model in Catalonia, Spain

    PubMed Central

    Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

    2018-01-01

    Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

  16. Predicting nuclear gene coalescence from mitochondrial data: the three-times rule.

    PubMed

    Palumbi, S R; Cipriano, F; Hare, M P

    2001-05-01

    Coalescence theory predicts when genetic drift at nuclear loci will result in fixation of sequence differences to produce monophyletic gene trees. However, the theory is difficult to apply to particular taxa because it hinges on genetically effective population size, which is generally unknown. Neutral theory also predicts that evolution of monophyly will be four times slower in nuclear than in mitochondrial genes primarily because genetic drift is slower at nuclear loci. Variation in mitochondrial DNA (mtDNA) within and between species has been studied extensively, but can these mtDNA data be used to predict coalescence in nuclear loci? Comparison of neutral theories of coalescence of mitochondrial and nuclear loci suggests a simple rule of thumb. The "three-times rule" states that, on average, most nuclear loci will be monophyletic when the branch length leading to the mtDNA sequences of a species is three times longer than the average mtDNA sequence diversity observed within that species. A test using mitochondrial and nuclear intron data from seven species of whales and dolphins suggests general agreement with predictions of the three-times rule. We define the coalescence ratio as the mitochondrial branch length for a species divided by intraspecific mtDNA diversity. We show that species with high coalescence ratios show nuclear monophyly, whereas species with low ratios have polyphyletic nuclear gene trees. As expected, species with intermediate coalescence ratios show a variety of patterns. Especially at very high or low coalescence ratios, the three-times rule predicts nuclear gene patterns that can help detect the action of selection. The three-times rule may be useful as an empirical benchmark for evaluating evolutionary processes occurring at multiple loci.

  17. Accurate Prediction of Protein Contact Maps by Coupling Residual Two-Dimensional Bidirectional Long Short-Term Memory with Convolutional Neural Networks.

    PubMed

    Hanson, Jack; Paliwal, Kuldip; Litfin, Thomas; Yang, Yuedong; Zhou, Yaoqi

    2018-06-19

    Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12, (Schaarschmidt et al., 2018)) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC)>0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'. SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/. Supplementary data is available at Bioinformatics online.

  18. Local Debonding and Fiber Breakage in Composite Materials Modeled Accurately

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2001-01-01

    A prerequisite for full utilization of composite materials in aerospace components is accurate design and life prediction tools that enable the assessment of component performance and reliability. Such tools assist both structural analysts, who design and optimize structures composed of composite materials, and materials scientists who design and optimize the composite materials themselves. NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) software package (http://www.grc.nasa.gov/WWW/LPB/mac) addresses this need for composite design and life prediction tools by providing a widely applicable and accurate approach to modeling composite materials. Furthermore, MAC/GMC serves as a platform for incorporating new local models and capabilities that are under development at NASA, thus enabling these new capabilities to progress rapidly to a stage in which they can be employed by the code's end users.

  19. Urinary Squamous Epithelial Cells Do Not Accurately Predict Urine Culture Contamination, but May Predict Urinalysis Performance in Predicting Bacteriuria.

    PubMed

    Mohr, Nicholas M; Harland, Karisa K; Crabb, Victoria; Mutnick, Rachel; Baumgartner, David; Spinosi, Stephanie; Haarstad, Michael; Ahmed, Azeemuddin; Schweizer, Marin; Faine, Brett

    2016-03-01

    The presence of squamous epithelial cells (SECs) has been advocated to identify urinary contamination despite a paucity of evidence supporting this practice. We sought to determine the value of using quantitative SECs as a predictor of urinalysis contamination. Retrospective cross-sectional study of adults (≥18 years old) presenting to a tertiary academic medical center who had urinalysis with microscopy and urine culture performed. Patients with missing or implausible demographic data were excluded (2.5% of total sample). The primary analysis aimed to determine an SEC threshold that predicted urine culture contamination using receiver operating characteristics (ROC) curve analysis. The a priori secondary analysis explored how demographic variables (age, sex, body mass index) may modify the SEC test performance and whether SECs impacted traditional urinalysis indicators of bacteriuria. A total of 19,328 records were included. ROC curve analysis demonstrated that SEC count was a poor predictor of urine culture contamination (area under the ROC curve = 0.680, 95% confidence interval [CI] = 0.671 to 0.689). In secondary analysis, the positive likelihood ratio (LR+) of predicting bacteriuria via urinalysis among noncontaminated specimens was 4.98 (95% CI = 4.59 to 5.40) in the absence of SECs, but the LR+ fell to 2.35 (95% CI = 2.17 to 2.54) for samples with more than 8 SECs/low-powered field (lpf). In an independent validation cohort, urinalysis samples with fewer than 8 SECs/lpf predicted bacteriuria better (sensitivity = 75%, specificity = 84%) than samples with more than 8 SECs/lpf (sensitivity = 86%, specificity = 70%; diagnostic odds ratio = 17.5 [14.9 to 20.7] vs. 8.7 [7.3 to 10.5]). Squamous epithelial cells are a poor predictor of urine culture contamination, but may predict poor predictive performance of traditional urinalysis measures. © 2016 by the Society for Academic Emergency Medicine.

  20. Accurate pointing of tungsten welding electrodes

    NASA Technical Reports Server (NTRS)

    Ziegelmeier, P.

    1971-01-01

    Thoriated-tungsten is pointed accurately and quickly by using sodium nitrite. Point produced is smooth and no effort is necessary to hold the tungsten rod concentric. The chemically produced point can be used several times longer than ground points. This method reduces time and cost of preparing tungsten electrodes.

  1. Cognitive domains that predict time to diagnosis in prodromal Huntington disease.

    PubMed

    Harrington, Deborah Lynn; Smith, Megan M; Zhang, Ying; Carlozzi, Noelle E; Paulsen, Jane S

    2012-06-01

    Prodromal Huntington's disease (prHD) is associated with a myriad of cognitive changes but the domains that best predict time to clinical diagnosis have not been studied. This is a notable gap because some domains may be more sensitive to cognitive decline, which would inform clinical trials. The present study sought to characterise cognitive domains underlying a large test battery and for the first time, evaluate their ability to predict time to diagnosis. Participants included gene negative and gene positive prHD participants who were enrolled in the PREDICT-HD study. The CAG-age product (CAP) score was the measure of an individual's genetic signature. A factor analysis of 18 tests was performed to identify sets of measures or latent factors that elucidated core constructs of tests. Factor scores were then fit to a survival model to evaluate their ability to predict time to diagnosis. Six factors were identified: (1) speed/inhibition, (2) verbal working memory, (3) motor planning/speed, (4) attention-information integration, (5) sensory-perceptual processing and (6) verbal learning/memory. Factor scores were sensitive to worsening of cognitive functioning in prHD, typically more so than performances on individual tests comprising the factors. Only the motor planning/speed and sensory-perceptual processing factors predicted time to diagnosis, after controlling for CAP scores and motor symptoms. Conclusions The results suggest that motor planning/speed and sensory-perceptual processing are important markers of disease prognosis. The findings also have implications for using composite indices of cognition in preventive Huntington's disease trials where they may be more sensitive than individual tests.

  2. Predicting the Timing of Maturational Spurts in Skeletal Age

    PubMed Central

    Nahhas, Ramzi W.; Sherwood, Richard J.; Chumlea, Wm. Cameron; Towne, Bradford; Duren, Dana L.

    2014-01-01

    Measures of maturity provide windows into the timing and tempo of childhood growth and maturation. Delayed maturation in a single child, or systemically in a population, can result from either genetic or environmental factors. In terms of the skeleton, delayed maturation may result in short stature or indicate another underlying issue. Thus, prediction of the timing of a maturational spurt is often desirable in order to determine the likelihood that a child will catch up to their chronological age peers. Serial data from the Fels Longitudinal Study were used to predict future skeletal age conditional on current skeletal age and to predict the timing of maturational spurts. For children who were delayed relative to their chronological age peers, the like-lihood of catch-up maturation increased through the average age of onset of puberty and decreased prior to the average age of peak height velocity. For boys, the probability of an imminent maturational spurt was higher for those who were less mature. For girls aged 11 to 13 years, however, this probability was higher for those who were more mature, potentially indicating the presence of a skeletal maturation plateau between multiple spurts. The prediction model, available on the web, is most relevant to children of European ancestry living in the Midwestern US. Our model may also provide insight into the tempo of maturation for children in other populations, but must be applied with caution if those populations are known to have high burdens of environmental stressors not typical of the Midwestern US. Am J Phys Anthropol 150:68–75, 2013. PMID:23283666

  3. Prediction of Ripple Properties in Shelf Seas. Mark 2 Predictor for Time Evolution

    DTIC Science & Technology

    2005-12-01

    respectively. It is seen in Figure 15 that the time -evolving ripple predictor manages to predict many of the features seen in the data: the growth from a...UNCLASSIFIED Prediction of Ripple Properties in Shelf Seas Mark 2 Predictor for Time Evolution Final Technical Report Prepared for US Office of Naval...distribution is unlimited j~j HR Wallingford UNCLASSIFIED Prediction of Ripple Properties in Shelf Seas Mark 2 Predictor for Time Evolution

  4. Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts

    NASA Astrophysics Data System (ADS)

    Balasubramanian, A.; Shamsuddin, R.; Prabhakaran, B.; Sawant, A.

    2017-03-01

    Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%) (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable

  5. Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts.

    PubMed

    Balasubramanian, A; Shamsuddin, R; Prabhakaran, B; Sawant, A

    2017-03-07

    Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%); (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable

  6. Remote magnetic navigation for accurate, real-time catheter positioning and ablation in cardiac electrophysiology procedures.

    PubMed

    Filgueiras-Rama, David; Estrada, Alejandro; Shachar, Josh; Castrejón, Sergio; Doiny, David; Ortega, Marta; Gang, Eli; Merino, José L

    2013-04-21

    New remote navigation systems have been developed to improve current limitations of conventional manually guided catheter ablation in complex cardiac substrates such as left atrial flutter. This protocol describes all the clinical and invasive interventional steps performed during a human electrophysiological study and ablation to assess the accuracy, safety and real-time navigation of the Catheter Guidance, Control and Imaging (CGCI) system. Patients who underwent ablation of a right or left atrium flutter substrate were included. Specifically, data from three left atrial flutter and two counterclockwise right atrial flutter procedures are shown in this report. One representative left atrial flutter procedure is shown in the movie. This system is based on eight coil-core electromagnets, which generate a dynamic magnetic field focused on the heart. Remote navigation by rapid changes (msec) in the magnetic field magnitude and a very flexible magnetized catheter allow real-time closed-loop integration and accurate, stable positioning and ablation of the arrhythmogenic substrate.

  7. Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures

    PubMed Central

    Filgueiras-Rama, David; Estrada, Alejandro; Shachar, Josh; Castrejón, Sergio; Doiny, David; Ortega, Marta; Gang, Eli; Merino, José L.

    2013-01-01

    New remote navigation systems have been developed to improve current limitations of conventional manually guided catheter ablation in complex cardiac substrates such as left atrial flutter. This protocol describes all the clinical and invasive interventional steps performed during a human electrophysiological study and ablation to assess the accuracy, safety and real-time navigation of the Catheter Guidance, Control and Imaging (CGCI) system. Patients who underwent ablation of a right or left atrium flutter substrate were included. Specifically, data from three left atrial flutter and two counterclockwise right atrial flutter procedures are shown in this report. One representative left atrial flutter procedure is shown in the movie. This system is based on eight coil-core electromagnets, which generate a dynamic magnetic field focused on the heart. Remote navigation by rapid changes (msec) in the magnetic field magnitude and a very flexible magnetized catheter allow real-time closed-loop integration and accurate, stable positioning and ablation of the arrhythmogenic substrate. PMID:23628883

  8. Ecological prediction with nonlinear multivariate time-frequency functional data models

    USGS Publications Warehouse

    Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

    2013-01-01

    Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

  9. Large-scale structure prediction by improved contact predictions and model quality assessment.

    PubMed

    Michel, Mirco; Menéndez Hurtado, David; Uziela, Karolis; Elofsson, Arne

    2017-07-15

    Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before. Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/ . All programs used here are freely available. arne@bioinfo.se. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  10. The Prediction of Teacher Turnover Employing Time Series Analysis.

    ERIC Educational Resources Information Center

    Costa, Crist H.

    The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

  11. The accuracy of new wheelchair users' predictions about their future wheelchair use.

    PubMed

    Hoenig, Helen; Griffiths, Patricia; Ganesh, Shanti; Caves, Kevin; Harris, Frances

    2012-06-01

    This study examined the accuracy of new wheelchair user predictions about their future wheelchair use. This was a prospective cohort study of 84 community-dwelling veterans provided a new manual wheelchair. The association between predicted and actual wheelchair use was strong at 3 mos (ϕ coefficient = 0.56), with 90% of those who anticipated using the wheelchair at 3 mos still using it (i.e., positive predictive value = 0.96) and 60% of those who anticipated not using it indeed no longer using the wheelchair (i.e., negative predictive value = 0.60, overall accuracy = 0.92). Predictive accuracy diminished over time, with overall accuracy declining from 0.92 at 3 mos to 0.66 at 6 mos. At all time points, and for all types of use, patients better predicted use as opposed to disuse, with correspondingly higher positive than negative predictive values. Accuracy of prediction of use in specific indoor and outdoor locations varied according to location. This study demonstrates the importance of better understanding the potential mismatch between the anticipated and actual patterns of wheelchair use. The findings suggest that users can be relied upon to accurately predict their basic wheelchair-related needs in the short-term. Further exploration is needed to identify characteristics that will aid users and their providers in more accurately predicting mobility needs for the long-term.

  12. Evaluation of a real-time travel time prediction system in a freeway construction work zone : executive summary.

    DOT National Transportation Integrated Search

    2001-03-01

    A real-time travel time prediction system (TIPS) was evaluated in a construction work : zone. TIPS includes changeable message signs (CMSs) displaying the travel time and : distance to the end of the work zone to motorists. The travel times displayed...

  13. Raoult's law revisited: accurately predicting equilibrium relative humidity points for humidity control experiments.

    PubMed

    Bowler, Michael G; Bowler, David R; Bowler, Matthew W

    2017-04-01

    The humidity surrounding a sample is an important variable in scientific experiments. Biological samples in particular require not just a humid atmosphere but often a relative humidity (RH) that is in equilibrium with a stabilizing solution required to maintain the sample in the same state during measurements. The controlled dehydration of macromolecular crystals can lead to significant increases in crystal order, leading to higher diffraction quality. Devices that can accurately control the humidity surrounding crystals while monitoring diffraction have led to this technique being increasingly adopted, as the experiments become easier and more reproducible. Matching the RH to the mother liquor is the first step in allowing the stable mounting of a crystal. In previous work [Wheeler, Russi, Bowler & Bowler (2012). Acta Cryst. F 68 , 111-114], the equilibrium RHs were measured for a range of concentrations of the most commonly used precipitants in macromolecular crystallography and it was shown how these related to Raoult's law for the equilibrium vapour pressure of water above a solution. However, a discrepancy between the measured values and those predicted by theory could not be explained. Here, a more precise humidity control device has been used to determine equilibrium RH points. The new results are in agreement with Raoult's law. A simple argument in statistical mechanics is also presented, demonstrating that the equilibrium vapour pressure of a solvent is proportional to its mole fraction in an ideal solution: Raoult's law. The same argument can be extended to the case where the solvent and solute molecules are of different sizes, as is the case with polymers. The results provide a framework for the correct maintenance of the RH surrounding a sample.

  14. A real-time prediction model for post-irradiation malignant cervical lymph nodes.

    PubMed

    Lo, W-C; Cheng, P-W; Shueng, P-W; Hsieh, C-H; Chang, Y-L; Liao, L-J

    2018-04-01

    To establish a real-time predictive scoring model based on sonographic characteristics for identifying malignant cervical lymph nodes (LNs) in cancer patients after neck irradiation. One-hundred forty-four irradiation-treated patients underwent ultrasonography and ultrasound-guided fine-needle aspirations (USgFNAs), and the resultant data were used to construct a real-time and computerised predictive scoring model. This scoring system was further compared with our previously proposed prediction model. A predictive scoring model, 1.35 × (L axis) + 2.03 × (S axis) + 2.27 × (margin) + 1.48 × (echogenic hilum) + 3.7, was generated by stepwise multivariate logistic regression analysis. Neck LNs were considered to be malignant when the score was ≥ 7, corresponding to a sensitivity of 85.5%, specificity of 79.4%, positive predictive value (PPV) of 82.3%, negative predictive value (NPV) of 83.1%, and overall accuracy of 82.6%. When this new model and the original model were compared, the areas under the receiver operating characteristic curve (c-statistic) were 0.89 and 0.81, respectively (P < .05). A real-time sonographic predictive scoring model was constructed to provide prompt and reliable guidance for USgFNA biopsies to manage cervical LNs after neck irradiation. © 2017 John Wiley & Sons Ltd.

  15. Model Predictive Control application for real time operation of controlled structures for the Water Authority Noorderzijlvest, The Netherlands

    NASA Astrophysics Data System (ADS)

    van Heeringen, Klaas-Jan; Gooijer, Jan; Knot, Floris; Talsma, Jan

    2015-04-01

    In the Netherlands, flood protection has always been a key issue to protect settlements against storm surges and riverine floods. Whereas flood protection traditionally focused on structural measures, nowadays the availability of meteorological and hydrological forecasts enable the application of more advanced real-time control techniques for operating the existing hydraulic infrastructure in an anticipatory and more efficient way. Model Predictive Control (MPC) is a powerful technique to derive optimal control variables with the help of model based predictions evaluated against a control objective. In a project for the regional water authority Noorderzijlvest in the north of the Netherlands, it has been shown that MPC can increase the safety level of the system during flood events by an anticipatory pre-release of water. Furthermore, energy costs of pumps can be reduced by making tactical use of the water storage and shifting pump activities during normal operating conditions to off-peak hours. In this way cheap energy is used in combination of gravity flow through gates during low tide periods. MPC has now been implemented for daily operational use of the whole water system of the water authority Noorderzijlvest. The system developed to a real time decision support system which not only supports the daily operation but is able to directly implement the optimal control settings at the structures. We explain how we set-up and calibrated a prediction model (RTC-Tools) that is accurate and fast enough for optimization purposes, and how we integrated it in the operational flood early warning system (Delft-FEWS). Beside the prediction model, the weights and the factors of the objective function are an important element of MPC, since they shape the control objective. We developed special features in Delft-FEWS to allow the operators to adjust the objective function in order to meet changing requirements and to evaluate different control strategies.

  16. Gaussian mixture models as flux prediction method for central receivers

    NASA Astrophysics Data System (ADS)

    Grobler, Annemarie; Gauché, Paul; Smit, Willie

    2016-05-01

    Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.

  17. An empirical approach to improving tidal predictions using recent real-time tide gauge data

    NASA Astrophysics Data System (ADS)

    Hibbert, Angela; Royston, Samantha; Horsburgh, Kevin J.; Leach, Harry

    2014-05-01

    Classical harmonic methods of tidal prediction are often problematic in estuarine environments due to the distortion of tidal fluctuations in shallow water, which results in a disparity between predicted and observed sea levels. This is of particular concern in the Bristol Channel, where the error associated with tidal predictions is potentially greater due to an unusually large tidal range of around 12m. As such predictions are fundamental to the short-term forecasting of High Water (HW) extremes, it is vital that alternative solutions are found. In a pilot study, using a year-long observational sea level record from the Port of Avonmouth in the Bristol Channel, the UK National Tidal and Sea Level Facility (NTSLF) tested the potential for reducing tidal prediction errors, using three alternatives to the Harmonic Method of tidal prediction. The three methods evaluated were (1) the use of Artificial Neural Network (ANN) models, (2) the Species Concordance technique and (3) a simple empirical procedure for correcting Harmonic Method High Water predictions based upon a few recent observations (referred to as the Empirical Correction Method). This latter method was then successfully applied to sea level records from an additional 42 of the 45 tide gauges that comprise the UK Tide Gauge Network. Consequently, it is to be incorporated into the operational systems of the UK Coastal Monitoring and Forecasting Partnership in order to improve short-term sea level predictions for the UK and in particular, the accurate estimation of HW extremes.

  18. SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

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

    Liu, S; Wooten, H; Wu, Y

    Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motionmore » time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess

  19. Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

    PubMed

    Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

    2016-06-07

    RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

  20. Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

    PubMed Central

    Al-Jumeily, Dhiya; Ghazali, Rozaida; Hussain, Abir

    2014-01-01

    Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques. PMID:25157950