FPGA implementation of predictive degradation model for engine oil lifetime

NASA Astrophysics Data System (ADS)

Idros, M. F. M.; Razak, A. H. A.; Junid, S. A. M. Al; Suliman, S. I.; Halim, A. K.

2018-03-01

This paper presents the implementation of linear regression model for degradation prediction on Register Transfer Logic (RTL) using QuartusII. A stationary model had been identified in the degradation trend for the engine oil in a vehicle in time series method. As for RTL implementation, the degradation model is written in Verilog HDL and the data input are taken at a certain time. Clock divider had been designed to support the timing sequence of input data. At every five data, a regression analysis is adapted for slope variation determination and prediction calculation. Here, only the negative value are taken as the consideration for the prediction purposes for less number of logic gate. Least Square Method is adapted to get the best linear model based on the mean values of time series data. The coded algorithm has been implemented on FPGA for validation purposes. The result shows the prediction time to change the engine oil.

Prediction of area under the curve for a p-glycoprotein, a CYP3A4 and a CYP2C9 substrate using a single time point strategy: assessment using fexofenadine, itraconazole and losartan and metabolites.

PubMed

Srinivas, Nuggehally R

2016-01-01

In the present age of polypharmacy, limited sampling strategy becomes important to verify if drug levels are within the prescribed threshold limits from efficacy and safety considerations. The need to establish reliable single time concentration dependent models to predict exposure becomes important from cost and time perspectives. A simple unweighted linear regression model was developed to describe the relationship between Cmax versus AUC for fexofenadine, losartan, EXP3174, itraconazole and hydroxyitraconazole. The fold difference, defined as the quotient of the observed and predicted AUC values, were evaluated along with statistical comparison of the predicted versus observed values. The correlation between Cmax versus AUC was well established for all the five drugs with a correlation coefficient (r) ranging from 0.9130 to 0.9997. Majority of the predicted values for all the five drugs (77%) were contained within a narrow boundary of 0.75- to 1.5-fold difference. The r values for observed versus predicted AUC were 0.9653 (n = 145), 0.8342 (n = 76), 0.9524 (n = 88), 0.9339 (n = 89) and 0.9452 (n = 66) for fexofenadine, losartan, EXP3174, itraconazole and hydroxyitraconazole, respectively. Cmax versus AUC relationships were established for all drugs and were amenable for limited sampling strategy for AUC prediction. However, fexofenadine, EXP3174 and hydroxyitraconazole may be most relevant for AUC prediction by a single time concentration as judged by the various criteria applied in this study.

Dynamic divisive normalization predicts time-varying value coding in decision-related circuits.

PubMed

Louie, Kenway; LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W

2014-11-26

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. Copyright © 2014 the authors 0270-6474/14/3416046-12$15.00/0.

Application of Neural Network Optimized by Mind Evolutionary Computation in Building Energy Prediction

NASA Astrophysics Data System (ADS)

Song, Chen; Zhong-Cheng, Wu; Hong, Lv

2018-03-01

Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

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

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect lossmore » of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.« less

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.

Anticipating Their Future: Adolescent Values for the Future Predict Adult Behaviors

PubMed Central

Finlay, Andrea; Wray-Lake, Laura; Warren, Michael; Maggs, Jennifer L.

2014-01-01

Adolescent future values – beliefs about what will matter to them in the future – may shape their adult behavior. Utilizing a national longitudinal British sample, this study examined whether adolescent future values in six domains (i.e., family responsibility, full-time job, personal responsibility, autonomy, civic responsibility, and hedonistic privilege) predicted adult social roles, civic behaviors, and alcohol use. Future values positively predicted behaviors within the same domain; fewer cross-domain associations were evident. Civic responsibility positively predicted adult civic behaviors, but negatively predicted having children. Hedonistic privilege positively predicted adult alcohol use and negatively predicted civic behaviors. Results suggest that attention should be paid to how adolescents are thinking about their futures due to the associated links with long-term social and health behaviors. PMID:26279595

Do the EMA accelerated assessment procedure and the FDA priority review ensure a therapeutic added value? 2006-2015: a cohort study.

PubMed

Boucaud-Maitre, Denis; Altman, Jean-Jacques

2016-10-01

The Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have both implemented procedures in order to shorten review time for marketing authorizations with potential therapeutic added value, called priority review and accelerated assessment procedure, respectively. The aim of this study is to compare the new molecular entities (NME) assessed in shorter review time by both agencies and to investigate whether granting a shorter review time status subsequently predicts its therapeutic value attributed by a health technology assessment agency, the French Haute Autorité de Santé (HAS). All NME approved by the EMA and the FDA with a therapeutic added value between 2007 and June 30, 2015 were extracted. We assessed the sensibility, the positive predictive value, and the EMA review time. One hundred seventy-eight NME were approved by the FDA and the EMA and a therapeutic value was available for 160 NME. Eighty-eight (55.0 %) NME were on FDA priority review, 24 (15.0 %) on EMA accelerated procedure and 43 (26.9 %) were considered of high clinical added value. The sensibility was 86.0 % for the FDA and 30.2 % for the EMA. The positive predictive value was, respectively, 42.0 and 54.2 %. Twenty-five NME on FDA priority review and of high therapeutic added value were not on EMA accelerated assessment procedure, leading to a supplementary mean EMA review time of 146 days. The EMA was restrictive to grant a shorten review time status for products with therapeutic interest during the study period.

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

PubMed

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Time-dependent oral absorption models

NASA Technical Reports Server (NTRS)

Higaki, K.; Yamashita, S.; Amidon, G. L.

2001-01-01

The plasma concentration-time profiles following oral administration of drugs are often irregular and cannot be interpreted easily with conventional models based on first- or zero-order absorption kinetics and lag time. Six new models were developed using a time-dependent absorption rate coefficient, ka(t), wherein the time dependency was varied to account for the dynamic processes such as changes in fluid absorption or secretion, in absorption surface area, and in motility with time, in the gastrointestinal tract. In the present study, the plasma concentration profiles of propranolol obtained in human subjects following oral dosing were analyzed using the newly derived models based on mass balance and compared with the conventional models. Nonlinear regression analysis indicated that the conventional compartment model including lag time (CLAG model) could not predict the rapid initial increase in plasma concentration after dosing and the predicted Cmax values were much lower than that observed. On the other hand, all models with the time-dependent absorption rate coefficient, ka(t), were superior to the CLAG model in predicting plasma concentration profiles. Based on Akaike's Information Criterion (AIC), the fluid absorption model without lag time (FA model) exhibited the best overall fit to the data. The two-phase model including lag time, TPLAG model was also found to be a good model judging from the values of sum of squares. This model also described the irregular profiles of plasma concentration with time and frequently predicted Cmax values satisfactorily. A comparison of the absorption rate profiles also suggested that the TPLAG model is better at prediction of irregular absorption kinetics than the FA model. In conclusion, the incorporation of a time-dependent absorption rate coefficient ka(t) allows the prediction of nonlinear absorption characteristics in a more reliable manner.

[Prognostic prediction of the functional capacity and effectiveness of functional improvement program of the musculoskeletal system among users of preventive care service under long-term care insurance].

PubMed

Sone, Toshimasa; Nakaya, Naoki; Tomata, Yasutake; Aida, Jun; Okubo, Ichiro; Ohara, Satoko; Obuchi, Shuichi; Sugiyama, Michiko; Yasumura, Seiji; Suzuki, Takao; Tsuji, Ichiro

2013-01-01

The purpose of this study was to examine the effectiveness of the Functional Improvement Program of the Musculoskeletal System among users of Preventive Care Service under Long-Term Care Insurance. A total of 3,073 subjects were analyzed. We used the prediction formula to estimate the predicted value of the Kihon Checklist after one year, and calculated the measured value minus the predicted value. The subjects were divided into two groups according to the measured value minus predicted value tertiles: the lowest and middle tertile (good-to-fair measured value) and the highest tertile (poor measured value). We used a multiple logistic regression model to calculate the odds ratio (OR) and 95% confidence interval (CI) of the good-to-fair measured values of the Kihon Checklist after one year, according to the Functional Improvement Program of the Musculoskeletal System. In potentially dependent elderly, the multivariate adjusted ORs (95% CI) of the good-to-fair measured values were 2.4 (1.3-4.4) for those who attended the program eight times or more in a month (vs those who attended it three times or less in a month), 1.3 (1.0-1.8) for those who engaged in strength training using machines (vs those who did not train), and 1.4 (1.0-1.9) for those who engaged in endurance training. In this study, among potentially dependent elderly, those who attended the program eight times or more in a month and those who engaged in strength training using machines or endurance training showed a significant improvement of their functional capacity.

Adaptation of clinical prediction models for application in local settings.

PubMed

Kappen, Teus H; Vergouwe, Yvonne; van Klei, Wilton A; van Wolfswinkel, Leo; Kalkman, Cor J; Moons, Karel G M

2012-01-01

When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions. To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice. A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use. Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation. Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.

Predictive Value of Glasgow Coma Score and Full Outline of Unresponsiveness Score on the Outcome of Multiple Trauma Patients.

PubMed

Baratloo, Alireza; Shokravi, Masumeh; Safari, Saeed; Aziz, Awat Kamal

2016-03-01

The Full Outline of Unresponsiveness (FOUR) score was developed to compensate for the limitations of Glasgow coma score (GCS) in recent years. This study aimed to assess the predictive value of GCS and FOUR score on the outcome of multiple trauma patients admitted to the emergency department. The present prospective cross-sectional study was conducted on multiple trauma patients admitted to the emergency department. GCS and FOUR scores were evaluated at the time of admission and at the sixth and twelfth hours after admission. Then the receiver operating characteristic (ROC) curve, sensitivity, specificity, as well as positive and negative predictive value of GCS and FOUR score were evaluated to predict patients' outcome. Patients' outcome was divided into discharge with and without a medical injury (motor deficit, coma or death). Finally, 89 patients were studied. Sensitivity and specificity of GCS in predicting adverse outcome (motor deficit, coma or death) were 84.2% and 88.6% at the time of admission, 89.5% and 95.4% at the sixth hour and 89.5% and 91.5% at the twelfth hour, respectively. These values for the FOUR score were 86.9% and 88.4% at the time of admission, 89.5% and 100% at the sixth hour and 89.5% and 94.4% at the twelfth hour, respectively. Findings of this study indicate that the predictive value of FOUR score and GCS on the outcome of multiple trauma patients admitted to the emergency department is similar.

Improving the Accuracy of a Heliocentric Potential (HCP) Prediction Model for the Aviation Radiation Dose

NASA Astrophysics Data System (ADS)

Hwang, Junga; Yoon, Kyoung-Won; Jo, Gyeongbok; Noh, Sung-Jun

2016-12-01

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

Prosociality: the contribution of traits, values, and self-efficacy beliefs.

PubMed

Caprara, Gian Vittorio; Alessandri, Guido; Eisenberg, Nancy

2012-06-01

The present study examined how agreeableness, self-transcendence values, and empathic self-efficacy beliefs predict individuals' tendencies to engage in prosocial behavior (i.e., prosociality) across time. Participants were 340 young adults, 190 women and 150 men, age approximately 21 years at Time 1 and 25 years at Time 2. Measures of agreeableness, self-transcendence, empathic self-efficacy beliefs, and prosociality were collected at 2 time points. The findings corroborated the posited paths of relations, with agreeableness directly predicting self-transcendence and indirectly predicting empathic self-efficacy beliefs and prosociality. Self-transcendence mediated the relation between agreeableness and empathic self-efficacy beliefs. Empathic self-efficacy beliefs mediated the relation of agreeableness and self-transcendence to prosociality. Finally, earlier prosociality predicted agreeableness and empathic self-efficacy beliefs assessed at Time 2. The posited conceptual model accounted for a significant portion of variance in prosociality and provides guidance to interventions aimed at promoting prosociality. 2012 APA, all rights reserved

[Real-time irrigation forecast of cotton mulched with plastic film under drip irrigation based on meteorological date].

PubMed

Shen, Xiao-jun; Sun, Jing-sheng; Li, Ming-si; Zhang, Ji-yang; Wang, Jing-lei; Li, Dong-wei

2015-02-01

It is important to improve the real-time irrigation forecasting precision by predicting real-time water consumption of cotton mulched with plastic film under drip irrigation based on meteorological data and cotton growth status. The model parameters for calculating ET0 based on Hargreaves formula were determined using historical meteorological data from 1953 to 2008 in Shihezi reclamation area. According to the field experimental data of growing season in 2009-2010, the model of computing crop coefficient Kc was established based on accumulated temperature. On the basis of crop water requirement (ET0) and Kc, a real-time irrigation forecast model was finally constructed, and it was verified by the field experimental data in 2011. The results showed that the forecast model had high forecasting precision, and the average absolute values of relative error between the predicted value and measured value were about 3.7%, 2.4% and 1.6% during seedling, squaring and blossom-boll forming stages, respectively. The forecast model could be used to modify the predicted values in time according to the real-time meteorological data and to guide the water management in local film-mulched cotton field under drip irrigation.

Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.

PubMed

Martínez-Martínez, Víctor; Baladrón, Carlos; Gomez-Gil, Jaime; Ruiz-Ruiz, Gonzalo; Navas-Gracia, Luis M; Aguiar, Javier M; Carro, Belén

2012-10-17

This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.

Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks

PubMed Central

Martínez-Martínez, Víctor; Baladrón, Carlos; Gomez-Gil, Jaime; Ruiz-Ruiz, Gonzalo; Navas-Gracia, Luis M.; Aguiar, Javier M.; Carro, Belén

2012-01-01

This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed. PMID:23202032

Time prediction of failure a type of lamps by using general composite hazard rate model

NASA Astrophysics Data System (ADS)

Riaman; Lesmana, E.; Subartini, B.; Supian, S.

2018-03-01

This paper discusses the basic survival model estimates to obtain the average predictive value of lamp failure time. This estimate is for the parametric model, General Composite Hazard Level Model. The random time variable model used is the exponential distribution model, as the basis, which has a constant hazard function. In this case, we discuss an example of survival model estimation for a composite hazard function, using an exponential model as its basis. To estimate this model is done by estimating model parameters, through the construction of survival function and empirical cumulative function. The model obtained, will then be used to predict the average failure time of the model, for the type of lamp. By grouping the data into several intervals and the average value of failure at each interval, then calculate the average failure time of a model based on each interval, the p value obtained from the tes result is 0.3296.

The predictive power of Japanese candlestick charting in Chinese stock market

NASA Astrophysics Data System (ADS)

Chen, Shi; Bao, Si; Zhou, Yu

2016-09-01

This paper studies the predictive power of 4 popular pairs of two-day bullish and bearish Japanese candlestick patterns in Chinese stock market. Based on Morris' study, we give the quantitative details of definition of long candlestick, which is important in two-day candlestick pattern recognition but ignored by several previous researches, and we further give the quantitative definitions of these four pairs of two-day candlestick patterns. To test the predictive power of candlestick patterns on short-term price movement, we propose the definition of daily average return to alleviate the impact of correlation among stocks' overlap-time returns in statistical tests. To show the robustness of our result, two methods of trend definition are used for both the medium-market-value and large-market-value sample sets. We use Step-SPA test to correct for data snooping bias. Statistical results show that the predictive power differs from pattern to pattern, three of the eight patterns provide both short-term and relatively long-term prediction, another one pair only provide significant forecasting power within very short-term period, while the rest three patterns present contradictory results for different market value groups. For all the four pairs, the predictive power drops as predicting time increases, and forecasting power is stronger for stocks with medium market value than those with large market value.

Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

NASA Technical Reports Server (NTRS)

Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

2015-01-01

Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network

PubMed Central

Tian, Wenliang; Meng, Fandi; Liu, Li; Li, Ying; Wang, Fuhui

2017-01-01

A concept for prediction of organic coatings, based on the alternating hydrostatic pressure (AHP) accelerated tests, has been presented. An AHP accelerated test with different pressure values has been employed to evaluate coating degradation. And a back-propagation artificial neural network (BP-ANN) has been established to predict the service property and the service lifetime of coatings. The pressure value (P), immersion time (t) and service property (impedance modulus |Z|) are utilized as the parameters of the network. The average accuracies of the predicted service property and immersion time by the established network are 98.6% and 84.8%, respectively. The combination of accelerated test and prediction method by BP-ANN is promising to evaluate and predict coating property used in deep sea. PMID:28094340

[The trial of business data analysis at the Department of Radiology by constructing the auto-regressive integrated moving-average (ARIMA) model].

PubMed

Tani, Yuji; Ogasawara, Katsuhiko

2012-01-01

This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.

Context-dependent preferences in starlings: linking ecology, foraging and choice.

PubMed

Vasconcelos, Marco; Monteiro, Tiago; Kacelnik, Alex

2013-01-01

Foraging animals typically encounter opportunities that they either pursue or skip, but occasionally meet several alternatives simultaneously. Behavioural ecologists predict preferences using absolute properties of each option, while decision theorists focus on relative evaluations at the time of choice. We use European starlings (Sturnus vulgaris) to integrate ecological reasoning with decision models, linking and testing hypotheses for value acquisition and choice mechanism. We hypothesise that options' values depend jointly on absolute attributes, learning context, and subject's state. In simultaneous choices, preference could result either from comparing subjective values using deliberation time, or from processing each alternative independently, without relative comparisons. The combination of the value acquisition hypothesis and independent processing at choice time has been called the Sequential Choice Model. We test this model with options equated in absolute properties to exclude the possibility of preference being built at the time of choice. Starlings learned to obtain food by responding to four stimuli in two contexts. In context [AB], they encountered options A5 or B10 in random alternation; in context [CD], they met C10 or D20. Delay to food is denoted, in seconds, by the suffixes. Observed latency to respond (Li) to each option alone (our measure of value) ranked thus: LA≈LC

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

A novel multi-target regression framework for time-series prediction of drug efficacy

PubMed Central

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-01

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

Flight test evaluation of predicted light aircraft drag, performance, and stability

NASA Technical Reports Server (NTRS)

Smetana, F. O.; Fox, S. R.

1979-01-01

A technique was developed which permits simultaneous extraction of complete lift, drag, and thrust power curves from time histories of a single aircraft maneuver such as a pullup (from V sub max to V sub stall) and pushover (to sub V max for level flight.) The technique is an extension to non-linear equations of motion of the parameter identification methods of lliff and Taylor and includes provisions for internal data compatibility improvement as well. The technique was show to be capable of correcting random errors in the most sensitive data channel and yielding highly accurate results. This technique was applied to flight data taken on the ATLIT aircraft. The drag and power values obtained from the initial least squares estimate are about 15% less than the 'true' values. If one takes into account the rather dirty wing and fuselage existing at the time of the tests, however, the predictions are reasonably accurate. The steady state lift measurements agree well with the extracted values only for small values of alpha. The predicted value of the lift at alpha = 0 is about 33% below that found in steady state tests while the predicted lift slope is 13% below the steady state value.

The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series

NASA Astrophysics Data System (ADS)

Du, Kongchang; Zhao, Ying; Lei, Jiaqiang

2017-09-01

In hydrological time series prediction, singular spectrum analysis (SSA) and discrete wavelet transform (DWT) are widely used as preprocessing techniques for artificial neural network (ANN) and support vector machine (SVM) predictors. These hybrid or ensemble models seem to largely reduce the prediction error. In current literature researchers apply these techniques to the whole observed time series and then obtain a set of reconstructed or decomposed time series as inputs to ANN or SVM. However, through two comparative experiments and mathematical deduction we found the usage of SSA and DWT in building hybrid models is incorrect. Since SSA and DWT adopt 'future' values to perform the calculation, the series generated by SSA reconstruction or DWT decomposition contain information of 'future' values. These hybrid models caused incorrect 'high' prediction performance and may cause large errors in practice.

A Quantitative Description of Suicide Inhibition of Dichloroacetic Acid in Rats and Mice

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

Keys, Deborah A.; Schultz, Irv R.; Mahle, Deirdre A.

Dichloroacetic acid (DCA), a minor metabolite of trichloroethylene (TCE) and water disinfection byproduct, remains an important risk assessment issue because of its carcinogenic potency. DCA has been shown to inhibit its own metabolism by irreversibly inactivating glutathione transferase zeta (GSTzeta). To better predict internal dosimetry of DCA, a physiologically based pharmacokinetic (PBPK) model of DCA was developed. Suicide inhibition was described dynamically by varying the rate of maximal GSTzeta mediated metabolism of DCA (Vmax) over time. Resynthesis (zero-order) and degradation (first-order) of metabolic activity were described. Published iv pharmacokinetic studies in native rats were used to estimate an initial Vmaxmore » value, with Km set to an in vitro determined value. Degradation and resynthesis rates were set to estimated values from a published immunoreactive GSTzeta protein time course. The first-order inhibition rate, kd, was estimated to this same time course. A secondary, linear non-GSTzeta-mediated metabolic pathway is proposed to fit DCA time courses following treatment with DCA in drinking water. The PBPK model predictions were validated by comparing predicted DCA concentrations to measured concentrations in published studies of rats pretreated with DCA following iv exposure to 0.05 to 20 mg/kg DCA. The same model structure was parameterized to simulate DCA time courses following iv exposure in native and pretreated mice. Blood and liver concentrations during and postexposure to DCA in drinking water were predicted. Comparisons of PBPK model predicted to measured values were favorable, lending support for the further development of this model for application to DCA or TCE human health risk assessment.« less

Effective Acceleration Model for the Arrival Time of Interplanetary Shocks driven by Coronal Mass Ejections

NASA Astrophysics Data System (ADS)

Paouris, Evangelos; Mavromichalaki, Helen

2017-12-01

In a previous work (Paouris and Mavromichalaki in Solar Phys. 292, 30, 2017), we presented a total of 266 interplanetary coronal mass ejections (ICMEs) with as much information as possible. We developed a new empirical model for estimating the acceleration of these events in the interplanetary medium from this analysis. In this work, we present a new approach on the effective acceleration model (EAM) for predicting the arrival time of the shock that preceds a CME, using data of a total of 214 ICMEs. For the first time, the projection effects of the linear speed of CMEs are taken into account in this empirical model, which significantly improves the prediction of the arrival time of the shock. In particular, the mean value of the time difference between the observed time of the shock and the predicted time was equal to +3.03 hours with a mean absolute error (MAE) of 18.58 hours and a root mean squared error (RMSE) of 22.47 hours. After the improvement of this model, the mean value of the time difference is decreased to -0.28 hours with an MAE of 17.65 hours and an RMSE of 21.55 hours. This improved version was applied to a set of three recent Earth-directed CMEs reported in May, June, and July of 2017, and we compare our results with the values predicted by other related models.

Traveller Information System for Heterogeneous Traffic Condition: A Case Study in Thiruvananthapuram City, India

NASA Astrophysics Data System (ADS)

Satyakumar, M.; Anil, R.; Sreeja, G. S.

2017-12-01

Traffic in Kerala has been growing at a rate of 10-11% every year, resulting severe congestion especially in urban areas. Because of the limitation of spaces it is not always possible to construct new roads. Road users rely on travel time information for journey planning and route choice decisions, while road system managers are increasingly viewing travel time as an important network performance indicator. More recently Advanced Traveler Information Systems (ATIS) are being developed to provide real-time information to roadway users. For ATIS various methodologies have been developed for dynamic travel time prediction. For this work the Kalman Filter Algorithm was selected for dynamic travel time prediction of different modes. The travel time data collected using handheld GPS device were used for prediction. Congestion Index were calculated and Range of CI values were determined according to the percentage speed drop. After prediction using Kalman Filter, the predicted values along with the GPS data was integrated to GIS and using Network Analysis of ArcGIS the offline route navigation guide was prepared. Using this database a program for route navigation based on travel time was developed. This system will help the travelers with pre-trip information.

Predicting Air Quality at First Ingress into Vehicles Visiting the International Space Station.

PubMed

Romoser, Amelia A; Scully, Robert R; Limero, Thomas F; De Vera, Vanessa; Cheng, Patti F; Hand, Jennifer J; James, John T; Ryder, Valerie E

2017-02-01

NASA regularly performs ground-based offgas tests (OGTs), which allow prediction of accumulated volatile pollutant concentrations at first entry on orbit, on whole modules and vehicles scheduled to connect to the International Space Station (ISS). These data guide crew safety operations and allow for estimation of ISS air revitalization systems impact from additional pollutant load. Since volatiles released from vehicle, module, and payload materials can affect crew health and performance, prediction of first ingress air quality is important. To assess whether toxicological risk is typically over or underpredicted, OGT and first ingress samples from 10 vehicles and modules were compared. Samples were analyzed by gas chromatography and gas chromatography-mass spectrometry. The rate of pollutant accumulation was extrapolated over time. Ratios of analytical values and Spacecraft Maximum Allowable Concentrations were used to predict total toxicity values (T-values) at first entry. Results were also compared by compound. Frequently overpredicted was 2-butanone (9/10), whereas propanal (6/10) and ethanol (8/10) were typically underpredicted, but T-values were not substantially affected. Ingress sample collection delay (estimated by octafluoropropane introduced from ISS atmosphere) and T-value prediction accuracy correlated well (R2 = 0.9008), highlighting the importance of immediate air sample collection and accounting for ISS air dilution. Importantly, T-value predictions were conservative 70% of the time. Results also suggest that T-values can be normalized to octafluoropropane levels to adjust for ISS air dilution at first ingress. Finally, OGT and ingress sampling has allowed small leaks in vehicle fluid systems to be recognized and addressed.Romoser AA, Scully RR, Limero TF, De Vera V, Cheng PF, Hand JJ, James JT, Ryder VE. Predicting air quality at first ingress into vehicles visiting the International Space Station. Aerosp Med Hum Perform. 2017; 88(2):104-113.

Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

NASA Astrophysics Data System (ADS)

Anggraeni, Novia Antika

2015-04-01

The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano's inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 - 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between -2.86 up to 5.49 days.

Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

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

Anggraeni, Novia Antika, E-mail: novia.antika.a@gmail.com

The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration ofmore » the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days.« less

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.

Demand theory of gene regulation. II. Quantitative application to the lactose and maltose operons of Escherichia coli.

PubMed Central

Savageau, M A

1998-01-01

Induction of gene expression can be accomplished either by removing a restraining element (negative mode of control) or by providing a stimulatory element (positive mode of control). According to the demand theory of gene regulation, which was first presented in qualitative form in the 1970s, the negative mode will be selected for the control of a gene whose function is in low demand in the organism's natural environment, whereas the positive mode will be selected for the control of a gene whose function is in high demand. This theory has now been further developed in a quantitative form that reveals the importance of two key parameters: cycle time C, which is the average time for a gene to complete an ON/OFF cycle, and demand D, which is the fraction of the cycle time that the gene is ON. Here we estimate nominal values for the relevant mutation rates and growth rates and apply the quantitative demand theory to the lactose and maltose operons of Escherichia coli. The results define regions of the C vs. D plot within which selection for the wild-type regulatory mechanisms is realizable, and these in turn provide the first estimates for the minimum and maximum values of demand that are required for selection of the positive and negative modes of gene control found in these systems. The ratio of mutation rate to selection coefficient is the most relevant determinant of the realizable region for selection, and the most influential parameter is the selection coefficient that reflects the reduction in growth rate when there is superfluous expression of a gene. The quantitative theory predicts the rate and extent of selection for each mode of control. It also predicts three critical values for the cycle time. The predicted maximum value for the cycle time C is consistent with the lifetime of the host. The predicted minimum value for C is consistent with the time for transit through the intestinal tract without colonization. Finally, the theory predicts an optimum value of C that is in agreement with the observed frequency for E. coli colonizing the human intestinal tract. PMID:9691028

[Comparison of two quantitative methods of endobronchial ultrasound real-time elastography for evaluating intrathoracic lymph nodes].

PubMed

Mao, X W; Yang, J Y; Zheng, X X; Wang, L; Zhu, L; Li, Y; Xiong, H K; Sun, J Y

2017-06-12

Objective: To compare the clinical value of two quantitative methods in analyzing endobronchial ultrasound real-time elastography (EBUS-RTE) images for evaluating intrathoracic lymph nodes. Methods: From January 2014 to April 2014, EBUS-RTE examination was performed in patients who received EBUS-TBNA examination in Shanghai Chest Hospital. Each intrathoracic lymph node had a selected EBUS-RTE image. Stiff area ratio and mean hue value of region of interest (ROI) in each image were calculated respectively. The final diagnosis of lymph node was based on the pathologic/microbiologic results of EBUS-TBNA, pathologic/microbiologic results of other examinations and clinical following-up. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy were evaluated for distinguishing malignant and benign lesions. Results: Fifty-six patients and 68 lymph nodes were enrolled in this study, of which 35 lymph nodes were malignant and 33 lymph nodes were benign. The stiff area ratio and mean hue value of benign and malignant lesions were 0.32±0.29, 0.62±0.20 and 109.99±28.13, 141.62±17.52, respectively, and statistical differences were found in both of those two methods ( t =-5.14, P <0.01; t =-5.53, P <0.01). The area under curves was 0.813, 0.814 in stiff area ratio and mean hue value, respectively. The optimal diagnostic cut-off value of stiff area ratio was 0.48, and the sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 82.86%, 81.82%, 82.86%, 81.82% and 82.35%, respectively. The optimal diagnostic cut-off value of mean hue value was 126.28, and the sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 85.71%, 75.76%, 78.95%, 83.33% and 80.88%, respectively. Conclusion: Both the stiff area ratio and mean hue value methods can be used for analyzing EBUS-RTE images quantitatively, having the value of differentiating benign and malignant intrathoracic lymph nodes, and the stiff area ratio is better than the mean hue value between the two methods.

Restoration of acidic mine spoils with sewage sludge: II measurement of solids applied

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

Stucky, D.J.; Zoeller, A.L.

1980-01-01

Sewage sludge was incorporated in acidic strip mine spoils at rates equivalent to 0, 224, 336, and 448 dry metric tons (dmt)/ha and placed in pots in a greenhouse. Spoil parameters were determined 48 hours after sludge incorporation, Time Planting (P), and five months after orchardgrass (Dactylis glomerata L.) was planted, Time Harvest (H), in the pots. Parameters measured were: pH, organic matter content (OM), cation exchange capacity (CEC), electrical conductivity (EC) and yield. Values for each parameter were significantly different at the two sampling times. Correlation coefficient values were calculated for all parameters versus rates of applied sewage sludgemore » and all parameters versus each other. Multiple regressions were performed, stepwise, for all parameters versus rates of applied sewage sludge. Equations to predict amounts of sewage sludge incorporated in spoils were derived for individual and multiple parameters. Generally, measurements made at Time P achieved the highest correlation coefficient and multiple correlation coefficient values; therefore, the authors concluded data from Time P had the greatest predictability value. The most important value measured to predict rate of applied sewage sludge was pH and some additional accuracy was obtained by including CEC in equation. This experiment indicated that soil properties can be used to estimate amounts of sewage sludge solids required to reclaim acidic mine spoils and to estimate quantities incorporated.« less

Time trade-off and attitudes toward euthanasia: implications of using 'death' as an anchor in health state valuation.

PubMed

Augestad, Liv A; Rand-Hendriksen, Kim; Stavem, Knut; Kristiansen, Ivar Sønbø

2013-05-01

Health state values are by convention anchored to 'perfect health' and 'death.' Attitudes toward death may consequently influence the valuations. We used attitudes toward euthanasia (ATE) as a sub-construct for attitudes toward death. We compared the influence on values elicited with time trade-off (TTO), lead-time TTO (LT-TTO) and visual analogue scale (VAS).Since the 'death' anchor is most explicit in TTO, we hypothesized that TTO values would be most influenced by ATE. Respondents valued eight EQ-5D health states with VAS, then TTO (n = 328) or LT-TTO (n = 484). We measured ATE on a scale from -2 (fully disagree) to 2 (fully agree) and used multiple linear regressions to predict VAS, TTO, and LT-TTO values by ATE, sex, age, and education. A one-point increase on the ATE scale predicted a mean TTO value change of -.113 and LT-TTO change of -.072. Demographic variables, but not ATE, predicted VAS values. TTO appears to measure ATE in addition to preferences for health states. Different ways of incorporating death in the valuation may impact substantially on the resulting values. 'Death' is a metaphysically unknown concept, and implications of attitudes toward death should be investigated further to evaluate the appropriateness of using 'death' as an anchor.

How long the singular value decomposed entropy predicts the stock market? - Evidence from the Dow Jones Industrial Average Index

NASA Astrophysics Data System (ADS)

Gu, Rongbao; Shao, Yanmin

2016-07-01

In this paper, a new concept of multi-scales singular value decomposition entropy based on DCCA cross correlation analysis is proposed and its predictive power for the Dow Jones Industrial Average Index is studied. Using Granger causality analysis with different time scales, it is found that, the singular value decomposition entropy has predictive power for the Dow Jones Industrial Average Index for period less than one month, but not for more than one month. This shows how long the singular value decomposition entropy predicts the stock market that extends Caraiani's result obtained in Caraiani (2014). On the other hand, the result also shows an essential characteristic of stock market as a chaotic dynamic system.

Positive predictive value of infective endocarditis in the Danish National Patient Registry: a validation study.

PubMed

Østergaard, Lauge; Adelborg, Kasper; Sundbøll, Jens; Pedersen, Lars; Loldrup Fosbøl, Emil; Schmidt, Morten

2018-05-30

The positive predictive value of an infective endocarditis diagnosis is approximately 80% in the Danish National Patient Registry. However, since infective endocarditis is a heterogeneous disease implying long-term intravenous treatment, we hypothesiszed that the positive predictive value varies by length of hospital stay. A total of 100 patients with first-time infective endocarditis in the Danish National Patient Registry were identified from January 2010 - December 2012 at the University hospital of Aarhus and regional hospitals of Herning and Randers. Medical records were reviewed. We calculated the positive predictive value according to admission length, and separately for patients with a cardiac implantable electronic device and a prosthetic heart valve using the Wilson score method. Among the 92 medical records available for review, the majority of the patients had admission length ⩾2 weeks. The positive predictive value increased with length of admission. In patients with admission length <2 weeks the positive predictive value was 65% while it was 90% for admission length ⩾2 weeks. The positive predictive value was 81% for patients with a cardiac implantable electronic device and 87% for patients with a prosthetic valve. The positive predictive value of the infective endocarditis diagnosis in the Danish National Patient Registry is high for patients with admission length ⩾2 weeks. Using this algorithm, the Danish National Patient Registry provides a valid source for identifying infective endocarditis for research.

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.

Differential encoding of factors influencing predicted reward value in monkey rostral anterior cingulate cortex.

PubMed

Toda, Koji; Sugase-Miyamoto, Yasuko; Mizuhiki, Takashi; Inaba, Kiyonori; Richmond, Barry J; Shidara, Munetaka

2012-01-01

The value of a predicted reward can be estimated based on the conjunction of both the intrinsic reward value and the length of time to obtain it. The question we addressed is how the two aspects, reward size and proximity to reward, influence the responses of neurons in rostral anterior cingulate cortex (rACC), a brain region thought to play an important role in reward processing. We recorded from single neurons while two monkeys performed a multi-trial reward schedule task. The monkeys performed 1-4 sequential color discrimination trials to obtain a reward of 1-3 liquid drops. There were two task conditions, a valid cue condition, where the number of trials and reward amount were associated with visual cues, and a random cue condition, where the cue was picked from the cue set at random. In the valid cue condition, the neuronal firing is strongly modulated by the predicted reward proximity during the trials. Information about the predicted reward amount is almost absent at those times. In substantial subpopulations, the neuronal responses decreased or increased gradually through schedule progress to the predicted outcome. These two gradually modulating signals could be used to calculate the effect of time on the perception of reward value. In the random cue condition, little information about the reward proximity or reward amount is encoded during the course of the trial before reward delivery, but when the reward is actually delivered the responses reflect both the reward proximity and reward amount. Our results suggest that the rACC neurons encode information about reward proximity and amount in a manner that is dependent on utility of reward information. The manner in which the information is represented could be used in the moment-to-moment calculation of the effect of time and amount on predicted outcome value.

Interleukin (IL)-1A and IL-6: Applications to the predictive diagnostic testing of radiation pneumonitis

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

Chen Yuhchyau; Hyrien, Ollivier; Williams, Jacqueline

2005-05-01

Purpose: To explore the application of interleukin (IL)-1{alpha} and IL-6 measurements in the predictive diagnostic testing for symptomatic radiation pneumonitis (RP). Methods and materials: In a prospective protocol investigating RP and cytokines, IL-1{alpha} and IL-6 values were analyzed by enzyme-linked immunosorbent assay from serial weekly blood samples of patients receiving chest radiation. We analyzed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) over selected threshold values for both cytokines in the application to diagnostic testing. Results: The average coefficient of variation was 51% of the weekly mean IL-1{alpha} level and 39% of the weekly mean IL-6 value.more » Interleukin 1{alpha} and IL-6 became positively correlated with time. Specificity for both cytokines was better than sensitivity. IL-6 globally outperformed IL-1{alpha} in predicting RP, with higher PPV and NPV. Conclusions: Our data demonstrate the feasibility of applying IL-1{alpha} and IL-6 measurements of blood specimens to predict RP. Interleukin-6 measurements offer stronger positive predictive value than IL-1{alpha}. This application might be further explored in a larger sample of patients.« less

Cytochrome p450 turnover: regulation of synthesis and degradation, methods for determining rates, and implications for the prediction of drug interactions.

PubMed

Yang, Jiansong; Liao, Mingxiang; Shou, Magang; Jamei, Masoud; Yeo, Karen Rowland; Tucker, Geoffrey T; Rostami-Hodjegan, Amin

2008-06-01

In vivo enzyme levels are governed by the rates of de novo enzyme synthesis and degradation. A current lack of consensus on values of the in vivo turnover half-lives of human cytochrome P450 (CYP) enzymes places a significant limitation on the accurate prediction of changes in drug concentration-time profiles associated with interactions involving enzyme induction and mechanism (time)-based inhibition (MBI). In the case of MBI, the full extent of inhibition is also sensitive to values of enzyme turnover half-life. We review current understanding of CYP regulation, discuss the pros and cons of various in vitro and in vivo approaches used to estimate the turnover of specific CYPs and, by simulation, consider the impact of variability in estimates of CYP turnover on the prediction of enzyme induction and MBI in vivo. In the absence of consensus on values for the in vivo turnover half-lives of key CYPs, a sensitivity analysis of predictions of the pharmacokinetic effects of enzyme induction and MBI to these values should be an integral part of the modelling exercise, and the selective use of values should be avoided.

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.

Choosing the appropriate forecasting model for predictive parameter control.

PubMed

Aleti, Aldeida; Moser, Irene; Meedeniya, Indika; Grunske, Lars

2014-01-01

All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

Determination and prediction of octanol-air partition coefficients for organophosphate flame retardants.

PubMed

Wang, Qingzhi; Zhao, Hongxia; Wang, Yan; Xie, Qing; Chen, Jingwen; Quan, Xie

2017-11-01

Organophosphate flame retardants (OPFRs) have attracted wide concerns due to their toxicities and ubiquitous occurrence in the environment. In this work, Octanol-air partition coefficient (K OA ) for 14 OPFRs including 4 halogenated alkyl-, 5 aryl- and 5 alkyl-OPFRs, were estimated as a function of temperature using a gas chromatographic retention time (GC-RT) method. Their log K OA-GC values and internal energies of phase transfer (Δ OA U/kJmol -1 ) ranged from 8.03 to 13.0 and from 69.7 to 149, respectively. Substitution pattern and molar volume (V M ) were found to be capable of influencing log K OA-GC values of OPFRs. The halogenated alkyl-OPFRs had higher log K OA-GC values than aryl- or alkyl-OPFRs. The bigger the molar volume was, the greater the log K OA-GC values increased. In addition, a predicted model of log K OA-GC versus different relative retention times (RRTs) was developed with a high cross-validated value (Q 2 (cum) ) of 0.951, indicating a good predictive ability and stability. Therefore, the log K OA-GC values of the remaining OPFRs can be predicted by using their RRTs on different GC columns. Copyright © 2017 Elsevier Inc. All rights reserved.

Enhancing the 'real world' prediction of cardiovascular events and major bleeding with the CHA2DS2-VASc and HAS-BLED scores using multiple biomarkers.

PubMed

Roldán, Vanessa; Rivera-Caravaca, José Miguel; Shantsila, Alena; García-Fernández, Amaya; Esteve-Pastor, María Asunción; Vilchez, Juan Antonio; Romera, Marta; Valdés, Mariano; Vicente, Vicente; Marín, Francisco; Lip, Gregory Y H

2018-02-01

Atrial fibrillation (AF)-European guidelines suggest the use of biomarkers to stratify patients for stroke and bleeding risks. We investigated if a multibiomarker strategy improved the predictive performance of CHA 2 DS 2 -VASc and HAS-BLED in anticoagulated AF patients. We included consecutive patients stabilized for six months on vitamin K antagonists (INRs 2.0-3.0). High sensitivity troponin T, NT-proBNP, interleukin-6, von Willebrand factor concentrations and glomerular filtration rate (eGFR; using MDRD-4 formula) were quantified at baseline. Time in therapeutic range (TTR) was recorded at six months after inclusion. Patients were follow-up during a median of 2375 (IQR 1564-2887) days and all adverse events were recorded. In 1361 patients, adding four blood biomarkers, TTR and MDRD-eGFR, the predictive value of CHA 2 DS 2 -VASc increased significantly by c-index (0.63 vs. 0.65; p = .030) and IDI (0.85%; p < .001), but not by NRI (-2.82%; p < .001). The predictive value of HAS-BLED increased up to 1.34% by IDI (p < .001). Nevertheless, the overall predictive value remains modest (c-indexes approximately 0.65) and decision curve analyses found lower net benefit compared with the originals scores. Addition of biomarkers enhanced the predictive value of CHA 2 DS 2 -VASc and HAS-BLED, although the overall improvement was modest and the added predictive advantage over original scores was marginal. Key Messages Recent atrial fibrillation (AF)-European guidelines for the first time suggest the use of biomarkers to stratify patients for stroke and bleeding risks, but their usefulness in real world for risk stratification is still questionable. In this cohort study involving 1361 AF patients optimally anticoagulated with vitamin K antagonists, adding high sensitivity troponin T, N-terminal pro-B-type natriuretic peptide, interleukin 6, von Willebrand factor, glomerular filtration rate (by the MDRD-4 formula) and time in therapeutic range, increased the predictive value of CHA 2 DS 2 -VASc for cardiovascular events, but not the predictive value of HAS-BLED for major bleeding. Reclassification analyses did not show improvement adding multiple biomarkers. Despite the improvement observed, the added predictive advantage is marginal and the clinical usefulness and net benefit over current clinical scores is lower.

Significance of a Behavioral Economic Index of Reward Value in Predicting Drinking Problem Resolution

ERIC Educational Resources Information Center

Tucker, Jalie A.; Vuchinich, Rudy E.; Black, Bethany C.; Rippens, Paula D.

2006-01-01

This study investigated whether a behavioral economic index of the value of rewards available over different time horizons improved prediction of drinking outcomes beyond established biopsychosocial predictors. Preferences for immediate drinking versus more delayed rewards made possible by saving money were determined from expenditures prior to…

An improved method for predicting the evolution of the characteristic parameters of an information system

NASA Astrophysics Data System (ADS)

Dushkin, A. V.; Kasatkina, T. I.; Novoseltsev, V. I.; Ivanov, S. V.

2018-03-01

The article proposes a forecasting method that allows, based on the given values of entropy and error level of the first and second kind, to determine the allowable time for forecasting the development of the characteristic parameters of a complex information system. The main feature of the method under consideration is the determination of changes in the characteristic parameters of the development of the information system in the form of the magnitude of the increment in the ratios of its entropy. When a predetermined value of the prediction error ratio is reached, that is, the entropy of the system, the characteristic parameters of the system and the depth of the prediction in time are estimated. The resulting values of the characteristics and will be optimal, since at that moment the system possessed the best ratio of entropy as a measure of the degree of organization and orderliness of the structure of the system. To construct a method for estimating the depth of prediction, it is expedient to use the maximum principle of the value of entropy.

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.

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

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

Prediction of future asset prices

NASA Astrophysics Data System (ADS)

Seong, Ng Yew; Hin, Pooi Ah; Ching, Soo Huei

2014-12-01

This paper attempts to incorporate trading volumes as an additional predictor for predicting asset prices. Denoting r(t) as the vector consisting of the time-t values of the trading volume and price of a given asset, we model the time-(t+1) asset price to be dependent on the present and l-1 past values r(t), r(t-1), ....., r(t-1+1) via a conditional distribution which is derived from a (2l+1)-dimensional power-normal distribution. A prediction interval based on the 100(α/2)% and 100(1-α/2)% points of the conditional distribution is then obtained. By examining the average lengths of the prediction intervals found by using the composite indices of the Malaysia stock market for the period 2008 to 2013, we found that the value 2 appears to be a good choice for l. With the omission of the trading volume in the vector r(t), the corresponding prediction interval exhibits a slightly longer average length, showing that it might be desirable to keep trading volume as a predictor. From the above conditional distribution, the probability that the time-(t+1) asset price will be larger than the time-t asset price is next computed. When the probability differs from 0 (or 1) by less than 0.03, the observed time-(t+1) increase in price tends to be negative (or positive). Thus the above probability has a good potential of being used as a market indicator in technical analysis.

Comparison of BD GeneOhm Methicillin-Resistant Staphylococcus aureus (MRSA) PCR versus the CHROMagar MRSA Assay for Screening Patients for the Presence of MRSA Strains▿

PubMed Central

Boyce, John M.; Havill, Nancy L.

2008-01-01

We compared the BD GeneOhm methicillin-resistant Staphylococcus aureus (MRSA) real-time PCR assay with the CHROMagar MRSA assay for the detection of MRSA in 286 nasal surveillance specimens. Compared with the CHROMagar MRSA assay, PCR had sensitivity, specificity, positive predictive value, and negative predictive values of 100%, 98.6%, 95.8%, and 100%, respectively. The mean PCR turnaround time was 14.5 h. PMID:18032616

Pulmonary Masses: Initial Results of Cone-beam CT Guidance with Needle Planning Software for Percutaneous Lung Biopsy

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

Braak, Sicco J., E-mail: sjbraak@gmail.com; Herder, Gerarda J. M., E-mail: j.herder@antoniusziekenhuis.nl; Heesewijk, Johannes P. M. van, E-mail: j.heesewijk@antoniusziekenhuis.nl

2012-12-15

Purpose: To evaluate the outcome of percutaneous lung biopsy (PLB) findings using cone-beam computed tomographic (CT) guidance (CBCT guidance) and compared to conventional biopsy guidance techniques. Methods: CBCT guidance is a stereotactic technique for needle interventions, combining 3D soft-tissue cone-beam CT, needle planning software, and real-time fluoroscopy. Between March 2007 and August 2010, we performed 84 Tru-Cut PLBs, where bronchoscopy did not provide histopathologic diagnosis. Mean patient age was 64.6 (range 24-85) years; 57 patients were men, and 25 were women. Records were prospectively collected for calculating sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. We also registeredmore » fluoroscopy time, room time, interventional time, dose-area product (DAP), and complications. Procedures were divided into subgroups (e.g., location, size, operator). Results: Mean lesion diameter was 32.5 (range 3.0-93.0) mm, and the mean number of samples per biopsy procedure was 3.2 (range 1-7). Mean fluoroscopy time was 161 (range 104-551) s, room time was 34 (range 15-79) min, mean DAP value was 25.9 (range 3.9-80.5) Gy{center_dot}cm{sup -2}, and interventional time was 18 (range 5-65) min. Of 84 lesions, 70 were malignant (83.3%) and 14 were benign (16.7%). Seven (8.3%) of the biopsy samples were nondiagnostic. All nondiagnostic biopsied lesions proved to be malignant during surgical resection. The outcome for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 90% (95% confidence interval [CI] 86-96), 100% (95% CI 82-100), 100% (95% CI 96-100), 66.7% (95% CI 55-83), and 91.7% (95% CI 86-96), respectively. Sixteen patients (19%) had minor and 2 (2.4%) had major complications. Conclusion: CBCT guidance is an effective method for PLB, with results comparable to CT/CT fluoroscopy guidance.« less

Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks

PubMed Central

Zhou, Renjie; Yang, Chen; Wan, Jian; Zhang, Wei; Guan, Bo; Xiong, Naixue

2017-01-01

Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques. PMID:28383496

Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks.

PubMed

Zhou, Renjie; Yang, Chen; Wan, Jian; Zhang, Wei; Guan, Bo; Xiong, Naixue

2017-04-06

Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques.

An Improved Method of Predicting Extinction Coefficients for the Determination of Protein Concentration.

PubMed

Hilario, Eric C; Stern, Alan; Wang, Charlie H; Vargas, Yenny W; Morgan, Charles J; Swartz, Trevor E; Patapoff, Thomas W

2017-01-01

Concentration determination is an important method of protein characterization required in the development of protein therapeutics. There are many known methods for determining the concentration of a protein solution, but the easiest to implement in a manufacturing setting is absorption spectroscopy in the ultraviolet region. For typical proteins composed of the standard amino acids, absorption at wavelengths near 280 nm is due to the three amino acid chromophores tryptophan, tyrosine, and phenylalanine in addition to a contribution from disulfide bonds. According to the Beer-Lambert law, absorbance is proportional to concentration and path length, with the proportionality constant being the extinction coefficient. Typically the extinction coefficient of proteins is experimentally determined by measuring a solution absorbance then experimentally determining the concentration, a measurement with some inherent variability depending on the method used. In this study, extinction coefficients were calculated based on the measured absorbance of model compounds of the four amino acid chromophores. These calculated values for an unfolded protein were then compared with an experimental concentration determination based on enzymatic digestion of proteins. The experimentally determined extinction coefficient for the native proteins was consistently found to be 1.05 times the calculated value for the unfolded proteins for a wide range of proteins with good accuracy and precision under well-controlled experimental conditions. The value of 1.05 times the calculated value was termed the predicted extinction coefficient. Statistical analysis shows that the differences between predicted and experimentally determined coefficients are scattered randomly, indicating no systematic bias between the values among the proteins measured. The predicted extinction coefficient was found to be accurate and not subject to the inherent variability of experimental methods. We propose the use of a predicted extinction coefficient for determining the protein concentration of therapeutic proteins starting from early development through the lifecycle of the product. LAY ABSTRACT: Knowing the concentration of a protein in a pharmaceutical solution is important to the drug's development and posology. There are many ways to determine the concentration, but the easiest one to use in a testing lab employs absorption spectroscopy. Absorbance of ultraviolet light by a protein solution is proportional to its concentration and path length; the proportionality constant is the extinction coefficient. The extinction coefficient of a protein therapeutic is usually determined experimentally during early product development and has some inherent method variability. In this study, extinction coefficients of several proteins were calculated based on the measured absorbance of model compounds. These calculated values for an unfolded protein were then compared with experimental concentration determinations based on enzymatic digestion of the proteins. The experimentally determined extinction coefficient for the native protein was 1.05 times the calculated value for the unfolded protein with good accuracy and precision under controlled experimental conditions, so the value of 1.05 times the calculated coefficient was called the predicted extinction coefficient. Comparison of predicted and measured extinction coefficients indicated that the predicted value was very close to the experimentally determined values for the proteins. The predicted extinction coefficient was accurate and removed the variability inherent in experimental methods. © PDA, Inc. 2017.

Facile method for liquid-exfoliated graphene size prediction by UV-visible spectroscopy

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

Ismail, Zulhelmi, E-mail: helmie83@hotmail.com; Yusoh, Kamal, E-mail: kamal@ump.edu.my

2016-07-19

In this work, an application of UV spectroscopy for facile prediction of liquid –exfoliated graphene size is discussed. Dynamic light scattering method was used to estimate the graphene flake size ( whilst UV spectroscopy measurement was carried out for extinction coefficient value (ε) determination. It was found that the value of (ε) decreased gradually as the graphene size was further reduced after intense sonication time (7h). This observation showed the influence of sonication time on electronic structure of graphene. A mathematical equation was derived from log-log graph for correlation between () and (ε) value. Both values can be expressed inmore » a single equation as ( = (3.4 × 10{sup −2}) ε{sup 1.2}).« less

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, Rylander C, Odland JØ, Sandanger TM. 2016. 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. Environ Health Perspect 124:299–305; http://dx.doi.org/10.1289/ehp.1409191 PMID:26186800

Finite-Temperature Behavior of PdH x Elastic Constants Computed by Direct Molecular Dynamics

DOE PAGES

Zhou, X. W.; Heo, T. W.; Wood, B. C.; ...

2017-05-30

In this paper, robust time-averaged molecular dynamics has been developed to calculate finite-temperature elastic constants of a single crystal. We find that when the averaging time exceeds a certain threshold, the statistical errors in the calculated elastic constants become very small. We applied this method to compare the elastic constants of Pd and PdH 0.6 at representative low (10 K) and high (500 K) temperatures. The values predicted for Pd match reasonably well with ultrasonic experimental data at both temperatures. In contrast, the predicted elastic constants for PdH 0.6 only match well with ultrasonic data at 10 K; whereas, atmore » 500 K, the predicted values are significantly lower. We hypothesize that at 500 K, the facile hydrogen diffusion in PdH 0.6 alters the speed of sound, resulting in significantly reduced values of predicted elastic constants as compared to the ultrasonic experimental data. Finally, literature mechanical testing experiments seem to support this hypothesis.« less

Problematic game play: the diagnostic value of playing motives, passion, and playing time in men.

PubMed

Kneer, Julia; Rieger, Diana

2015-04-30

Internet gaming disorder is currently listed in the DSM-not in order to diagnose such a disorder but to encourage research to investigate this phenomenon. Even whether it is still questionable if Internet Gaming Disorder exists and can be judged as a form of addiction, problematic game play is already very well researched to cause problems in daily life. Approaches trying to predict problematic tendencies in digital game play have mainly focused on playing time as a diagnostic criterion. However, motives to engage in digital game play and obsessive passion for game play have also been found to predict problematic game play but have not yet been investigated together. The present study aims at (1) analyzing if obsessive passion can be distinguished from problematic game play as separate concepts, and (2) testing motives of game play, passion, and playing time for their predictive values for problematic tendencies. We found (N = 99 males, Age: M = 22.80, SD = 3.81) that obsessive passion can be conceptually separated from problematic game play. In addition, the results suggest that compared to solely playing time immersion as playing motive and obsessive passion have added predictive value for problematic game play. The implications focus on broadening the criteria in order to diagnose problematic playing.

Problematic Game Play: The Diagnostic Value of Playing Motives, Passion, and Playing Time in Men

PubMed Central

Kneer, Julia; Rieger, Diana

2015-01-01

Internet gaming disorder is currently listed in the DSM—not in order to diagnose such a disorder but to encourage research to investigate this phenomenon. Even whether it is still questionable if Internet Gaming Disorder exists and can be judged as a form of addiction, problematic game play is already very well researched to cause problems in daily life. Approaches trying to predict problematic tendencies in digital game play have mainly focused on playing time as a diagnostic criterion. However, motives to engage in digital game play and obsessive passion for game play have also been found to predict problematic game play but have not yet been investigated together. The present study aims at (1) analyzing if obsessive passion can be distinguished from problematic game play as separate concepts, and (2) testing motives of game play, passion, and playing time for their predictive values for problematic tendencies. We found (N = 99 males, Age: M = 22.80, SD = 3.81) that obsessive passion can be conceptually separated from problematic game play. In addition, the results suggest that compared to solely playing time immersion as playing motive and obsessive passion have added predictive value for problematic game play. The implications focus on broadening the criteria in order to diagnose problematic playing. PMID:25942516

Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy.

PubMed

Kominami, Yoko; Yoshida, Shigeto; Tanaka, Shinji; Sanomura, Yoji; Hirakawa, Tsubasa; Raytchev, Bisser; Tamaki, Toru; Koide, Tetsusi; Kaneda, Kazufumi; Chayama, Kazuaki

2016-03-01

It is necessary to establish cost-effective examinations and treatments for diminutive colorectal tumors that consider the treatment risk and surveillance interval after treatment. The Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) committee of the American Society for Gastrointestinal Endoscopy published a statement recommending the establishment of endoscopic techniques that practice the resect and discard strategy. The aims of this study were to evaluate whether our newly developed real-time image recognition system can predict histologic diagnoses of colorectal lesions depicted on narrow-band imaging and to satisfy some problems with the PIVI recommendations. We enrolled 41 patients who had undergone endoscopic resection of 118 colorectal lesions (45 nonneoplastic lesions and 73 neoplastic lesions). We compared the results of real-time image recognition system analysis with that of narrow-band imaging diagnosis and evaluated the correlation between image analysis and the pathological results. Concordance between the endoscopic diagnosis and diagnosis by a real-time image recognition system with a support vector machine output value was 97.5% (115/118). Accuracy between the histologic findings of diminutive colorectal lesions (polyps) and diagnosis by a real-time image recognition system with a support vector machine output value was 93.2% (sensitivity, 93.0%; specificity, 93.3%; positive predictive value (PPV), 93.0%; and negative predictive value, 93.3%). Although further investigation is necessary to establish our computer-aided diagnosis system, this real-time image recognition system may satisfy the PIVI recommendations and be useful for predicting the histology of colorectal tumors. Copyright © 2016 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

NASA Astrophysics Data System (ADS)

Zheng, Qin; Yang, Zubin; Sha, Jianxin; Yan, Jun

2017-02-01

In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

Nonlinear Prediction As A Tool For Determining Parameters For Phase Space Reconstruction In Meteorology

NASA Astrophysics Data System (ADS)

Miksovsky, J.; Raidl, A.

Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.

Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery

NASA Technical Reports Server (NTRS)

Xie, Hua; Klimesh, Matthew A.

2009-01-01

This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.

Prediction of Tubal Ectopic Pregnancy Using Offline Analysis of 3-Dimensional Transvaginal Ultrasonographic Data Sets: An Interobserver and Diagnostic Accuracy Study.

PubMed

Infante, Fernando; Espada Vaquero, Mercedes; Bignardi, Tommaso; Lu, Chuan; Testa, Antonia C; Fauchon, David; Epstein, Elisabeth; Leone, Francesco P G; Van den Bosch, Thierry; Martins, Wellington P; Condous, George

2018-06-01

To assess interobserver reproducibility in detecting tubal ectopic pregnancies by reading data sets from 3-dimensional (3D) transvaginal ultrasonography (TVUS) and comparing it with real-time 2-dimensional (2D) TVUS. Images were initially classified as showing pregnancies of unknown location or tubal ectopic pregnancies on real time 2D TVUS by an experienced sonologist, who acquired 5 3D volumes. Data sets were analyzed offline by 5 observers who had to classify each case as ectopic pregnancy or pregnancy of unknown location. The interobserver reproducibility was evaluated by the Fleiss κ statistic. The performance of each observer in predicting ectopic pregnancies was compared to that of the experienced sonologist. Women were followed until they were reclassified as follows: (1) failed pregnancy of unknown location; (2) intrauterine pregnancy; (3) ectopic pregnancy; or (4) persistent pregnancy of unknown location. Sixty-one women were included. The agreement between reading offline 3D data sets and the first real-time 2D TVUS was very good (80%-82%; κ = 0.89). The overall interobserver agreement among observers reading offline 3D data sets was moderate (κ = 0.52). The diagnostic performance of experienced observers reading offline 3D data sets had accuracy of 78.3% to 85.0%, sensitivity of 66.7% to 81.3%, specificity of 79.5% to 88.4%, positive predictive value of 57.1% to 72.2%, and negative predictive value of 87.5% to 91.3%, compared to the experienced sonologist's real-time 2D TVUS: accuracy of 94.5%, sensitivity of 94.4%, specificity of 94.5%, positive predictive value of 85.0%, and negative predictive value of 98.1%. The diagnostic accuracy of 3D TVUS by reading offline data sets for predicting ectopic pregnancies is dependent on experience. Reading only static 3D data sets without clinical information does not match the diagnostic performance of real time 2D TVUS combined with clinical information obtained during the scan. © 2017 by the American Institute of Ultrasound in Medicine.

An integrated approach to evaluating alternative risk prediction strategies: a case study comparing alternative approaches for preventing invasive fungal disease.

PubMed

Sadique, Z; Grieve, R; Harrison, D A; Jit, M; Allen, E; Rowan, K M

2013-12-01

This article proposes an integrated approach to the development, validation, and evaluation of new risk prediction models illustrated with the Fungal Infection Risk Evaluation study, which developed risk models to identify non-neutropenic, critically ill adult patients at high risk of invasive fungal disease (IFD). Our decision-analytical model compared alternative strategies for preventing IFD at up to three clinical decision time points (critical care admission, after 24 hours, and end of day 3), followed with antifungal prophylaxis for those judged "high" risk versus "no formal risk assessment." We developed prognostic models to predict the risk of IFD before critical care unit discharge, with data from 35,455 admissions to 70 UK adult, critical care units, and validated the models externally. The decision model was populated with positive predictive values and negative predictive values from the best-fitting risk models. We projected lifetime cost-effectiveness and expected value of partial perfect information for groups of parameters. The risk prediction models performed well in internal and external validation. Risk assessment and prophylaxis at the end of day 3 was the most cost-effective strategy at the 2% and 1% risk threshold. Risk assessment at each time point was the most cost-effective strategy at a 0.5% risk threshold. Expected values of partial perfect information were high for positive predictive values or negative predictive values (£11 million-£13 million) and quality-adjusted life-years (£11 million). It is cost-effective to formally assess the risk of IFD for non-neutropenic, critically ill adult patients. This integrated approach to developing and evaluating risk models is useful for informing clinical practice and future research investment. © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Published by International Society for Pharmacoeconomics and Outcomes Research (ISPOR) All rights reserved.

Estimation of shelf life of natural rubber latex exam-gloves based on creep behavior.

PubMed

Das, Srilekha Sarkar; Schroeder, Leroy W

2008-05-01

Samples of full-length glove-fingers cut from chlorinated and nonchlorinated latex medical examination gloves were aged for various times at several fixed temperatures and 25% relative humidity. Creep testing was performed using an applied stress of 50 kPa on rectangular specimens (10 mm x 8 mm) of aged and unaged glove fingers as an assessment of glove loosening during usage. Variations in creep curves obtained were compared to determine the threshold aging time when the amount of creep became larger than the initial value. These times were then used in various models to estimate shelf lives at lower temperatures. Several different methods of extrapolation were used for shelf-life estimation and comparison. Neither Q-factor nor Arrhenius activation energies, as calculated from 10 degrees C interval shift factors, were constant over the temperature range; in fact, both decreased at lower temperatures. Values of Q-factor and activation energies predicted up to 5 years of shelf life. Predictions are more sensitive to values of activation energy as the storage temperature departs from the experimental aging data. Averaging techniques for prediction of average activation energy predicted the longest shelf life as the curvature is reduced. Copyright 2007 Wiley Periodicals, Inc.

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.

Increasing discomfort tolerance predicts incentive senitization of exercise reinforcement: Preliminary results from a randomized controlled intervention to increase the reinforcing value of exercise in overweight to obese adu

USDA-ARS?s Scientific Manuscript database

Objective: The reinforcing (motivating) value of exercise/physical activity (RRVex) predicts usual exercise behavior and meeting of physical activity guidelines. Recent cross-sectional evidence suggests, for the first time, that greater tolerance for the discomfort experienced during exercise is ass...

Will personal values predict the development of smoking and drinking behaviors? A prospective cohort study of children and adolescents in Taiwan.

PubMed

Nieh, Hsi-Ping; Wu, Wen-Chi; Luh, Dih-Ling; Yen, Lee-Lan; Hurng, Baai-Shyun; Chang, Hsing-Yi

2018-06-01

This study examined how personal values predict the development of smoking and drinking behaviors in adolescence. The longitudinal data of 1545 adolescents over a 6-year period were analyzed. The results showed that adolescents who valued health and academics had similarly lower odds of reporting cigarette and alcohol use and those who valued friends had significantly higher odds. While the odds increased over time, the trend on alcohol use lessened for adolescents who valued academics, while the trend accelerated for those who valued friends. The finding suggests the important role that personal values play in adolescent risk behavioral development.

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

PubMed

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

A comparison of in-cloud HCl concentrations from the NASA/MSFC MDM to measurements for the space shuttle launch

NASA Technical Reports Server (NTRS)

Glasser, M. E.

1981-01-01

The Multilevel Diffusion Model (MDM) Version 5 was modified to include features of more recent versions. The MDM was used to predict in-cloud HCl concentrations for the April 12 launch of the space Shuttle (STS-1). The maximum centerline predictions were compared with measurements of maximum gaseous HCl obtained from aircraft passes through two segments of the fragmented shuttle ground cloud. The model over-predicted the maximum values for gaseous HCl in the lower cloud segment and portrayed the same rate of decay with time as the observed values. However, the decay with time of HCl maximum predicted by the MDM was more rapid than the observed decay for the higher cloud segment, causing the model to under-predict concentrations which were measured late in the life of the cloud. The causes of the tendency for the MDM to be conservative in over-estimating the HCl concentrations in the one case while tending to under-predict concentrations in the other case are discussed.

Interactions between the nucleus accumbens and auditory cortices predict music reward value.

PubMed

Salimpoor, Valorie N; van den Bosch, Iris; Kovacevic, Natasa; McIntosh, Anthony Randal; Dagher, Alain; Zatorre, Robert J

2013-04-12

We used functional magnetic resonance imaging to investigate neural processes when music gains reward value the first time it is heard. The degree of activity in the mesolimbic striatal regions, especially the nucleus accumbens, during music listening was the best predictor of the amount listeners were willing to spend on previously unheard music in an auction paradigm. Importantly, the auditory cortices, amygdala, and ventromedial prefrontal regions showed increased activity during listening conditions requiring valuation, but did not predict reward value, which was instead predicted by increasing functional connectivity of these regions with the nucleus accumbens as the reward value increased. Thus, aesthetic rewards arise from the interaction between mesolimbic reward circuitry and cortical networks involved in perceptual analysis and valuation.

Modeling of exposure to carbon monoxide in fires

NASA Technical Reports Server (NTRS)

Cagliostro, D. E.

1980-01-01

A mathematical model is developed to predict carboxyhemoglobin concentrations in regions of the body for short exposures to carbon monoxide levels expected during escape from aircraft fires. The model includes the respiratory and circulatory dynamics of absorption and distribution of carbon monoxide and carboxyhemoglobin. Predictions of carboxyhemoglobin concentrations are compared to experimental values obtained for human exposures to constant high carbon monoxide levels. Predictions are within 20% of experimental values. For short exposure times, transient concentration effects are predicted. The effect of stress is studied and found to increase carboxyhemoglobin levels substantially compared to a rest state.

[Optimal extraction of effective constituents from Aralia elata by central composite design and response surface methodology].

PubMed

Lv, Shao-Wa; Liu, Dong; Hu, Pan-Pan; Ye, Xu-Yan; Xiao, Hong-Bin; Kuang, Hai-Xue

2010-03-01

To optimize the process of extracting effective constituents from Aralia elata by response surface methodology. The independent variables were ethanol concentration, reflux time and solvent fold, the dependent variable was extraction rate of total saponins in Aralia elata. Linear or no-linear mathematic models were used to estimate the relationship between independent and dependent variables. Response surface methodology was used to optimize the process of extraction. The prediction was carried out through comparing the observed and predicted values. Regression coefficient of binomial fitting complex model was as high as 0.9617, the optimum conditions of extraction process were 70% ethanol, 2.5 hours for reflux, 20-fold solvent and 3 times for extraction. The bias between observed and predicted values was -2.41%. It shows the optimum model is highly predictive.

Validity of Predicting Left Ventricular End Systolic Pressure Changes Following An Acute Bout of Exercise

PubMed Central

Kappus, Rebecca M.; Ranadive, Sushant M.; Yan, Huimin; Lane, Abbi D.; Cook, Marc D.; Hall, Grenita; Harvey, I. Shevon; Wilund, Kenneth R.; Woods, Jeffrey A.; Fernhall, Bo

2012-01-01

Objective Left ventricular end systolic pressure (LV ESP) is important in assessing left ventricular performance. LV ESP is usually derived from prediction equations. It is unknown whether these equations are accurate at rest or following exercise in a young, healthy population. Design We compared measured LV ESP versus LV ESP values from the prediction equations at rest, 15 minutes and 30 minutes following peak aerobic exercise in 60 participants. Methods LV ESP was obtained by applanation tonometry at rest, 15 minutes post and 30 minutes post peak cycle exercise. Results Measured LV ESP was significantly lower (p<0.05) at all time points in comparison to the two calculated values. Measured LV ESP decreased significantly from rest at both the post15 and post30 time points (p<0.05) and changed differently in comparison to the calculated values (significant interaction; p<0.05). The two LV ESP equations were also significantly different from each other (p<0.05) and changed differently over time (significant interaction; p<0.05). Conclusions These data indicate that the two prediction equations commonly used did not accurately predict either resting or post exercise LV ESP in a young, healthy population. Thus, LV ESP needs to be individually determined in young healthy participants. Non-invasive measurement through applanation tonometry appears to allow for a more accurate determination of LV ESP. PMID:22721862

Validity of predicting left ventricular end systolic pressure changes following an acute bout of exercise.

PubMed

Kappus, Rebecca M; Ranadive, Sushant M; Yan, Huimin; Lane, Abbi D; Cook, Marc D; Hall, Grenita; Harvey, I Shevon; Wilund, Kenneth R; Woods, Jeffrey A; Fernhall, Bo

2013-01-01

Left ventricular end systolic pressure (LV ESP) is important in assessing left ventricular performance and is usually derived from prediction equations. It is unknown whether these equations are accurate at rest or following exercise in a young, healthy population. Measured LV ESP vs. LV ESP values from the prediction equations were compared at rest, 15 min and 30 min following peak aerobic exercise in 60 participants. LV ESP was obtained by applanation tonometry at rest, 15 min post and 30 min post peak cycle exercise. Measured LV ESP was significantly lower (p<0.05) at all time points in comparison to the two calculated values. Measured LV ESP decreased significantly from rest at both the post15 and post30 time points (p<0.05) and changed differently in comparison to the calculated values (significant interaction; p<0.05). The two LV ESP equations were also significantly different from each other (p<0.05) and changed differently over time (significant interaction; p<0.05). The two commonly used prediction equations did not accurately predict either resting or post exercise LV ESP in a young, healthy population. Thus, LV ESP needs to be individually determined in young, healthy participants. Non-invasive measurement through applanation tonometry appears to allow for a more accurate determination of LV ESP. Copyright © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Measurement and modeling of diffusion time dependence of apparent diffusion coefficient and fractional anisotropy in prostate tissue ex vivo.

PubMed

Bourne, Roger; Liang, Sisi; Panagiotaki, Eleftheria; Bongers, Andre; Sved, Paul; Watson, Geoffrey

2017-10-01

The purpose of this study was to measure and model the diffusion time dependence of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) derived from conventional prostate diffusion-weighted imaging methods as used in recommended multiparametric MRI protocols. Diffusion tensor imaging (DTI) was performed at 9.4 T with three radical prostatectomy specimens, with diffusion times in the range 10-120 ms and b-values 0-3000 s/mm 2 . ADC and FA were calculated from DTI measurements at b-values of 800 and 1600 s/mm 2 . Independently, a two-component model (restricted isotropic plus Gaussian anisotropic) was used to synthesize DTI data, from which ADC and FA were predicted and compared with the measured values. Measured ADC and FA exhibited a diffusion time dependence, which was closely predicted by the two-component model. ADC decreased by about 0.10-0.15 μm 2 /ms as diffusion time increased from 10 to 120 ms. FA increased with diffusion time at b-values of 800 and 1600 s/mm 2 but was predicted to be independent of diffusion time at b = 3000 s/mm 2 . Both ADC and FA exhibited diffusion time dependence that could be modeled as two unmixed water pools - one having isotropic restricted dynamics, and the other unrestricted anisotropic dynamics. These results highlight the importance of considering and reporting diffusion times in conventional ADC and FA calculations and protocol recommendations, and inform the development of improved diffusion methods for prostate cancer imaging. Copyright © 2017 John Wiley & Sons, Ltd.

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.

SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

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

Joseph, A; Herrera, D; Hijal, T

Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts ofmore » data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. This means that the majority of our estimates are within 6.5 minutes of the actual wait time. Conclusion: The goal of this project was to define an appropriate machine learning algorithm to estimate waiting times based on the collective knowledge and experience learned from previous patients. Our results offer an opportunity to improve the information that is provided to patients and family members regarding the amount of time they can expect to wait for radiotherapy treatment at our centre. AJ acknowledges support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and from the 2014 Q+ Initiative of the McGill University Health Centre.« less

Shock Index Values and Trends in Pediatric Sepsis: Predictors or Therapeutic Targets? A Retrospective Observational Study.

PubMed

Ray, Samiran; Cvetkovic, Mirjana; Brierley, Joe; Lutman, Daniel H; Pathan, Nazima; Ramnarayan, Padmanabhan; Inwald, David P; Peters, Mark J

2016-09-01

Shock index (SI) (heart rate [HR]/systolic blood pressure [SBP]) has been used to predict outcome in both adult and pediatric sepsis within the intensive care unit (ICU). We aimed to evaluate the utility of SI before pediatric ICU (PICU) admission. We conducted a retrospective observational study of children referred to a pediatric intensive care transport service (PICTS) between 2005 and 2011. The predictive value of SI, HR, and blood pressure at three prespecified time points (at referral to PICTS, at PICTS arrival at the referring hospital, and at PICU admission) and changes in SI between the time points were evaluated. Death within the first 48 h of ICU admission (early death) was the primary outcome variable. Over the 7-year period, 633 children with sepsis were referred to the PICTS. Thirty-nine children died before transport to a PICU, whereas 474 were transported alive. Adjusting for age, time points, and time duration in a multilevel regression analysis, SI was significantly higher in those who died early. There was a significant improvement in SI with the transport team in survivors but not in nonsurvivors. However, the predictive value of a change in SI for mortality was no better than either a change in HR or blood pressure. The absolute or change in SI does not predict early death any more than HR and SBP individually in children with sepsis.

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

DOE PAGES

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.; ...

2017-10-30

We simulated the entire month of January, 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model, and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, Sym-H, AL, and cross polar cap potential (CPCP). We find that the model does an excellent job of predicting the Sym-H index, with an RMSE of 17-18 nT. Kp is predicted well during storm-time conditions, but over-predicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonablymore » well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to over-predict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resoution, with the exception of the rate of occurrence for strongly negative AL values. As a result, the use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.« less

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

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

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.

We simulated the entire month of January, 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model, and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, Sym-H, AL, and cross polar cap potential (CPCP). We find that the model does an excellent job of predicting the Sym-H index, with an RMSE of 17-18 nT. Kp is predicted well during storm-time conditions, but over-predicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonablymore » well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to over-predict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resoution, with the exception of the rate of occurrence for strongly negative AL values. As a result, the use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.« less

[Study of the predictive value of detection tests for silent aspirations].

PubMed

Woisard, V; Réhault, E; Brouard, C; Fichaux-Bourin, P; Puech, M; Grand, S

2009-01-01

Screening for aspiration in patients with swallowing disorders is important in preventing complications. The tests used in this regard are insufficient due to silent aspiration relating to abnormal protective reflexes in many patients with swallowing problems. The aim of this study is to determine the predictive values of simple tests in screening for silent aspiration. The reference test used was videofluoroscopic examination on swallowing. In the presence of aspiration (FR+) the presence (ME+) or not (ME-) of a cough of throat clearing was noted. The tests being studied were a nasal test with isotonic saline and swallowing according to a set time. For screening for aspiration the presence of a "wet voice" was considered to be a sign of reduced protective reflexes. 1) During the nasal test, the results are 100% for the positive predictive value (VPp) and 83.3% for the negative predictive value (VPn); 2) These results are respectively 84.6% and 35.9% during the swallowing test. Regarding screening for silent aspiration, 1) during the nasal test, the results are 62.5% for the positive predictive value (VPp) and 36.3% for the negative predictive value (VPn); 2) These results are respectively 54.5% and 26.6% during the swallowing test. This preliminary study points out the lack of predictive value of the nasal test and the swallow test for the silent aspirations. However the results could be useful for other researchers developing other tests in this area.

Time to Positivity and Detection of Growth in Anaerobic Blood Culture Vials Predict the Presence of Candida glabrata in Candidemia: a Two-Center European Cohort Study

PubMed Central

Kaasch, Achim J.; Soriano, Alex; Torres, Jorge-Luis; Vergara, Andrea; Morata, Laura; Zboromyrska, Yuliya; De La Calle, Cristina; Alejo, Izaskun; Hernández, Cristina; Cardozo, Celia; Marco, Franscesc; Del Río, Ana; Almela, Manel; Mensa, Josep; Martínez, José Antonio

2014-01-01

This study shows the accuracy of exclusive or earlier growth in anaerobic vials to predict Candida glabrata in a large series of candidemic patients from two European hospitals using the Bactec 9240 system. Alternatively, C. glabrata can be predicted by a time to positivity cutoff value, which should be determined for each setting. PMID:24899027

Space-Time Urban Air Pollution Forecasts

NASA Astrophysics Data System (ADS)

Russo, A.; Trigo, R. M.; Soares, A.

2012-04-01

Air pollution, like other natural phenomena, may be considered a space-time process. However, the simultaneous integration of time and space is not an easy task to perform, due to the existence of different uncertainties levels and data characteristics. In this work we propose a hybrid method that combines geostatistical and neural models to analyze PM10 time series recorded in the urban area of Lisbon (Portugal) for the 2002-2006 period and to produce forecasts. Geostatistical models have been widely used to characterize air pollution in urban areas, where the pollutant sources are considered diffuse, and also to industrial areas with localized emission sources. It should be stressed however that most geostatistical models correspond basically to an interpolation methodology (estimation, simulation) of a set of variables in a spatial or space-time domain. The temporal prediction of a pollutant usually requires knowledge of the main trends and complex patterns of physical dispersion phenomenon. To deal with low resolution problems and to enhance reliability of predictions, an approach based on neural network short term predictions in the monitoring stations which behave as a local conditioner to a fine grid stochastic simulation model is presented here. After the pollutant concentration is predicted for a given time period at the monitoring stations, we can use the local conditional distributions of observed values, given the predicted value for that period, to perform the spatial simulations for the entire area and consequently evaluate the spatial uncertainty of pollutant concentration. To attain this objective, we propose the use of direct sequential simulations with local distributions. With this approach one succeed to predict the space-time distribution of pollutant concentration that accounts for the time prediction uncertainty (reflecting the neural networks efficiency at each local monitoring station) and the spatial uncertainty as revealed by the spatial variograms. The dataset used consists of PM10 concentrations recorded hourly by 12 monitoring stations within the Lisbon's area, for the period 2002-2006. In addition, meteorological data recorded at 3 monitoring stations and boundary layer height (BLH) daily values from the ECMWF (European Centre for Medium Weather Forecast), ERA Interim, were also used. Based on the large-scale standard pressure fields from the ERA40/ECMWF, prevailing circulation patterns at regional scale where determined and used on the construction of the models. After the daily forecasts were produced, the difference between the average maps based on real observations and predicted values were determined and the model's performance was assessed. Based on the analysis of the results, we conclude that the proposed approach shows to be a very promising alternative for urban air quality characterization because of its good results and simplicity of application.

Implicit Theories, Expectancies, and Values Predict Mathematics Motivation and Behavior across High School and College.

PubMed

Priess-Groben, Heather A; Hyde, Janet Shibley

2017-06-01

Mathematics motivation declines for many adolescents, which limits future educational and career options. The present study sought to identify predictors of this decline by examining whether implicit theories assessed in ninth grade (incremental/entity) predicted course-taking behaviors and utility value in college. The study integrated implicit theory with variables from expectancy-value theory to examine potential moderators and mediators of the association of implicit theories with college mathematics outcomes. Implicit theories and expectancy-value variables were assessed in 165 American high school students (47 % female; 92 % White), who were then followed into their college years, at which time mathematics courses taken, course-taking intentions, and utility value were assessed. Implicit theories predicted course-taking intentions and utility value, but only self-concept of ability predicted courses taken, course-taking intentions, and utility value after controlling for prior mathematics achievement and baseline values. Expectancy for success in mathematics mediated associations between self-concept of ability and college outcomes. This research identifies self-concept of ability as a stronger predictor than implicit theories of mathematics motivation and behavior across several years: math self-concept is critical to sustained engagement in mathematics.

Variation and Grey GM(1, 1) Prediction of Melting Peak Temperature of Polypropylene During Ultraviolet Radiation Aging

NASA Astrophysics Data System (ADS)

Chen, K.; Y Zhang, T.; Zhang, F.; Zhang, Z. R.

2017-12-01

Grey system theory regards uncertain system in which information is known partly and unknown partly as research object, extracts useful information from part known, and thereby revealing the potential variation rule of the system. In order to research the applicability of data-driven modelling method in melting peak temperature (T m) fitting and prediction of polypropylene (PP) during ultraviolet radiation aging, the T m of homo-polypropylene after different ultraviolet radiation exposure time investigated by differential scanning calorimeter was fitted and predicted by grey GM(1, 1) model based on grey system theory. The results show that the T m of PP declines with the prolong of aging time, and fitting and prediction equation obtained by grey GM(1, 1) model is T m = 166.567472exp(-0.00012t). Fitting effect of the above equation is excellent and the maximum relative error between prediction value and actual value of T m is 0.32%. Grey system theory needs less original data, has high prediction accuracy, and can be used to predict aging behaviour of PP.

Growth parameters of Penicillium expansum calculated from mixed inocula as an alternative to account for intraspecies variability.

PubMed

Garcia, Daiana; Ramos, Antonio J; Sanchis, Vicente; Marín, Sonia

2014-09-01

The aim of this work was to compare the radial growth rate (μ) and the lag time (λ) for growth of 25 isolates of Penicillium expansum at 1 and 20 ºC with those of the mixed inoculum of the 25 isolates. Moreover, the evolution of probability of growth through time was also compared for the single strains and mixed inoculum. Working with a mixed inoculum would require less work, time and consumables than if a range of single strains has to be used in order to represent a given species. Suitable predictive models developed for a given species should represent as much as possible the behavior of all strains belonging to this species. The results suggested, on one hand, that the predictions based on growth parameters calculated on the basis of mixed inocula may not accurately predict the behavior of all possible strains but may represent a percentage of them, and the median/mean values of μ and λ obtained by the 25 strains may be substituted by the value obtained with the mixed inoculum. Moreover, the predictions may be biased, in particular, the predictions of λ which may be underestimated (fail-safe). Moreover, the prediction of time for a given probability of growth through a mixed inoculum may not be accurate for all single inocula, but it may represent 92% and 60% of them at 20 and 1 ºC, respectively, and also their overall mean and median values. In conclusion, mixed inoculum could be a good alternative to estimate the mean or median values of high number of isolates, but not to account for those strains with marginal behavior. In particular, estimation of radial growth rate, and time for 0.10 and 0.50 probability of growth using a cocktail inoculum accounted for the estimates of most single isolates tested. For the particular case of probability models, this is an interesting result as for practical applications in the food industry the estimation of t10 or lower probability may be required. Copyright © 2014 Elsevier B.V. All rights reserved.

Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring.

PubMed

Netterberg, Ida; Nielsen, Elisabet I; Friberg, Lena E; Karlsson, Mats O

2017-08-01

To investigate whether a more frequent monitoring of the absolute neutrophil counts (ANC) during myelosuppressive chemotherapy, together with model-based predictions, can improve therapy management, compared to the limited clinical monitoring typically applied today. Daily ANC in chemotherapy-treated cancer patients were simulated from a previously published population model describing docetaxel-induced myelosuppression. The simulated values were used to generate predictions of the individual ANC time-courses, given the myelosuppression model. The accuracy of the predicted ANC was evaluated under a range of conditions with reduced amount of ANC measurements. The predictions were most accurate when more data were available for generating the predictions and when making short forecasts. The inaccuracy of ANC predictions was highest around nadir, although a high sensitivity (≥90%) was demonstrated to forecast Grade 4 neutropenia before it occurred. The time for a patient to recover to baseline could be well forecasted 6 days (±1 day) before the typical value occurred on day 17. Daily monitoring of the ANC, together with model-based predictions, could improve anticancer drug treatment by identifying patients at risk for severe neutropenia and predicting when the next cycle could be initiated.

Parental styles and religious values among teenagers: a 3-year prospective analysis.

PubMed

Heaven, Patrick C L; Ciarrochi, Joseph; Leeson, Peter

2010-01-01

The authors examined the effect of Grade 7 parental styles on Grade 10 religious values. The authors surveyed 784 participants (382 boys, 394 girls; 8 unreported) in Grade 7. The mean age of the group at Time 1 was 12.3 years (SD = 0.5 years). Time 2 occurred 3 years later when students were in Grade 10 (372 boys, 375 girls). In addition to assessing parental styles at Time 1, we also controlled for a number of Time 1 variables thought to possibly influence Time 2 religious values, namely, self-esteem, trait hope, and students' levels of conscientiousness. Time 1 measures (except self-esteem) were significantly correlated with Time 2 religious values, but only parental authoritativeness and hope significantly predicted religious values. The authors discuss these results with reference to the nature of parental styles and hope and their impact on religious values.

Simulation of CO2 Solubility in Polystyrene-b-Polybutadieneb-Polystyrene (SEBS) by artificial intelligence network (ANN) method

NASA Astrophysics Data System (ADS)

Sharudin, R. W.; AbdulBari Ali, S.; Zulkarnain, M.; Shukri, M. A.

2018-05-01

This study reports on the integration of Artificial Neural Network (ANNs) with experimental data in predicting the solubility of carbon dioxide (CO2) blowing agent in SEBS by generating highest possible value for Regression coefficient (R2). Basically, foaming of thermoplastic elastomer with CO2 is highly affected by the CO2 solubility. The ability of ANN in predicting interpolated data of CO2 solubility was investigated by comparing training results via different method of network training. Regards to the final prediction result for CO2 solubility by ANN, the prediction trend (output generate) was corroborated with the experimental results. The obtained result of different method of training showed the trend of output generated by Gradient Descent with Momentum & Adaptive LR (traingdx) required longer training time and required more accurate input to produce better output with final Regression Value of 0.88. However, it goes vice versa with Levenberg-Marquardt (trainlm) technique as it produced better output in quick detention time with final Regression Value of 0.91.

Sensitivity and specificity of subacute computerized neurocognitive testing and symptom evaluation in predicting outcomes after sports-related concussion.

PubMed

Lau, Brian C; Collins, Michael W; Lovell, Mark R

2011-06-01

Concussions affect an estimated 136 000 high school athletes yearly. Computerized neurocognitive testing has been shown to be appropriately sensitive and specific in diagnosing concussions, but no studies have assessed its utility to predict length of recovery. Determining prognosis during subacute recovery after sports concussion will help clinicians more confidently address return-to-play and academic decisions. To quantify the prognostic ability of computerized neurocognitive testing in combination with symptoms during the subacute recovery phase from sports-related concussion. Cohort study (prognosis); Level of evidence, 2. In sum, 108 male high school football athletes completed a computer-based neurocognitive test battery within 2.23 days of injury and were followed until returned to play as set by international guidelines. Athletes were grouped into protracted recovery (>14 days; n = 50) or short-recovery (≤14 days; n = 58). Separate discriminant function analyses were performed using total symptom score on Post-Concussion Symptom Scale, symptom clusters (migraine, cognitive, sleep, neuropsychiatric), and Immediate Postconcussion Assessment and Cognitive Testing neurocognitive scores (verbal memory, visual memory, reaction time, processing speed). Multiple discriminant function analyses revealed that the combination of 4 symptom clusters and 4 neurocognitive composite scores had the highest sensitivity (65.22%), specificity (80.36%), positive predictive value (73.17%), and negative predictive value (73.80%) in predicting protracted recovery. Discriminant function analyses of total symptoms on the Post-Concussion Symptom Scale alone had a sensitivity of 40.81%; specificity, 79.31%; positive predictive value, 62.50%; and negative predictive value, 61.33%. The 4 symptom clusters alone discriminant function analyses had a sensitivity of 46.94%; specificity, 77.20%; positive predictive value, 63.90%; and negative predictive value, 62.86%. Discriminant function analyses of the 4 computerized neurocognitive scores alone had a sensitivity of 53.20%; specificity, 75.44%; positive predictive value, 64.10%; and negative predictive value, 66.15%. The use of computerized neurocognitive testing in conjunction with symptom clusters results improves sensitivity, specificity, positive predictive value, and negative predictive value of predicting protracted recovery compared with each used alone. There is also a net increase in sensitivity of 24.41% when using neurocognitive testing and symptom clusters together compared with using total symptoms on Post-Concussion Symptom Scale alone.

Hunter-gatherer residential mobility and the marginal value of rainforest patches.

PubMed

Venkataraman, Vivek V; Kraft, Thomas S; Dominy, Nathaniel J; Endicott, Kirk M

2017-03-21

The residential mobility patterns of modern hunter-gatherers broadly reflect local resource availability, but the proximate ecological and social forces that determine the timing of camp movements are poorly known. We tested the hypothesis that the timing of such moves maximizes foraging efficiency as hunter-gatherers move across the landscape. The marginal value theorem predicts when a group should depart a camp and its associated foraging area and move to another based on declining marginal return rates. This influential model has yet to be directly applied in a population of hunter-gatherers, primarily because the shape of gain curves (cumulative resource acquisition through time) and travel times between patches have been difficult to estimate in ethnographic settings. We tested the predictions of the marginal value theorem in the context of hunter-gatherer residential mobility using historical foraging data from nomadic, socially egalitarian Batek hunter-gatherers ( n  = 93 d across 11 residential camps) living in the tropical rainforests of Peninsular Malaysia. We characterized the gain functions for all resources acquired by the Batek at daily timescales and examined how patterns of individual foraging related to the emergent property of residential movements. Patterns of camp residence times conformed well with the predictions of the marginal value theorem, indicating that communal perceptions of resource depletion are closely linked to collective movement decisions. Despite (and perhaps because of) a protracted process of deliberation and argument about when to depart camps, Batek residential mobility seems to maximize group-level foraging efficiency.

Hunter-gatherer residential mobility and the marginal value of rainforest patches

PubMed Central

Venkataraman, Vivek V.; Kraft, Thomas S.; Endicott, Kirk M.

2017-01-01

The residential mobility patterns of modern hunter-gatherers broadly reflect local resource availability, but the proximate ecological and social forces that determine the timing of camp movements are poorly known. We tested the hypothesis that the timing of such moves maximizes foraging efficiency as hunter-gatherers move across the landscape. The marginal value theorem predicts when a group should depart a camp and its associated foraging area and move to another based on declining marginal return rates. This influential model has yet to be directly applied in a population of hunter-gatherers, primarily because the shape of gain curves (cumulative resource acquisition through time) and travel times between patches have been difficult to estimate in ethnographic settings. We tested the predictions of the marginal value theorem in the context of hunter-gatherer residential mobility using historical foraging data from nomadic, socially egalitarian Batek hunter-gatherers (n = 93 d across 11 residential camps) living in the tropical rainforests of Peninsular Malaysia. We characterized the gain functions for all resources acquired by the Batek at daily timescales and examined how patterns of individual foraging related to the emergent property of residential movements. Patterns of camp residence times conformed well with the predictions of the marginal value theorem, indicating that communal perceptions of resource depletion are closely linked to collective movement decisions. Despite (and perhaps because of) a protracted process of deliberation and argument about when to depart camps, Batek residential mobility seems to maximize group-level foraging efficiency. PMID:28265058

Modeling and predicting the biofilm formation of Salmonella Virchow with respect to temperature and pH.

PubMed

Ariafar, M Nima; Buzrul, Sencer; Akçelik, Nefise

2016-03-01

Biofilm formation of Salmonella Virchow was monitored with respect to time at three different temperature (20, 25 and 27.5 °C) and pH (5.2, 5.9 and 6.6) values. As the temperature increased at a constant pH level, biofilm formation decreased while as the pH level increased at a constant temperature, biofilm formation increased. Modified Gompertz equation with high adjusted determination coefficient (Radj(2)) and low mean square error (MSE) values produced reasonable fits for the biofilm formation under all conditions. Parameters of the modified Gompertz equation could be described in terms of temperature and pH by use of a second order polynomial function. In general, as temperature increased maximum biofilm quantity, maximum biofilm formation rate and time of acceleration of biofilm formation decreased; whereas, as pH increased; maximum biofilm quantity, maximum biofilm formation rate and time of acceleration of biofilm formation increased. Two temperature (23 and 26 °C) and pH (5.3 and 6.3) values were used up to 24 h to predict the biofilm formation of S. Virchow. Although the predictions did not perfectly match with the data, reasonable estimates were obtained. In principle, modeling and predicting the biofilm formation of different microorganisms on different surfaces under various conditions could be possible.

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.

Limited diagnostic accuracy of magnetic resonance imaging and clinical tests for detecting partial-thickness tears of the rotator cuff.

PubMed

Brockmeyer, Matthias; Schmitt, Cornelia; Haupert, Alexander; Kohn, Dieter; Lorbach, Olaf

2017-12-01

The reliable diagnosis of partial-thickness tears of the rotator cuff is still elusive in clinical practise. Therefore, the purpose of the study was to determine the diagnostic accuracy of MR imaging and clinical tests for detecting partial-thickness tears of the rotator cuff as well as the combination of these parameters. 334 consecutive shoulder arthroscopies for rotator cuff pathologies performed during the time period between 2010 and 2012 were analyzed retrospectively for the findings of common clinical signs for rotator cuff lesions and preoperative MR imaging. These were compared with the intraoperative arthroscopic findings as "gold standard". The reports of the MR imaging were evaluated with regard to the integrity of the rotator cuff. The Ellman Classification was used to define partial-thickness tears of the rotator cuff in accordance with the arthroscopic findings. Descriptive statistics, sensitivity, specificity, positive and negative predictive value were calculated. MR imaging showed 80 partial-thickness and 70 full-thickness tears of the rotator cuff. The arthroscopic examination confirmed 64 partial-thickness tears of which 52 needed debridement or refixation of the rotator cuff. Sensitivity for MR imaging to identify partial-thickness tears was 51.6%, specificity 77.2%, positive predictive value 41.3% and negative predictive value 83.7%. For the Jobe-test, sensitivity was 64.1%, specificity 43.2%, positive predictive value 25.9% and negative predictive value 79.5%. Sensitivity for the Impingement-sign was 76.7%, specificity 46.6%, positive predictive value 30.8% and negative predictive value 86.5%. For the combination of MR imaging, Jobe-test and Impingement-sign sensitivity was 46.9%, specificity 85.4%, positive predictive value 50% and negative predictive value 83.8%. The diagnostic accuracy of MR imaging and clinical tests (Jobe-test and Impingement-sign) alone is limited for detecting partial-thickness tears of the rotator cuff. Additionally, the combination of MR imaging and clinical tests does not improve diagnostic accuracy. Level II, Diagnostic study.

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.

Sensitivity, specificity, positive and negative predictive values: diagnosing purple mange.

PubMed

Collier, Jill; Huebscher, Roxana

2010-04-01

To shed light on several epidemiological terms for better understanding of diagnostic testing measures by using a mythical condition, "purple mange." Scientific literature related to epidemiology and statistical tests. Nurse practitioners (NPs) use the concepts of sensitivity (SEN), specificity (SPEC), positive predictive value (PPV), and negative predictive value (NPV) daily in primary care and specialty areas. In addition, PPV and NPV vary with the prevalence of a condition. At times, NPs misunderstand the meaning of these terms. In order to develop appropriate treatment plans, an understanding of the concepts of SEN, SPEC, PPV, and NPV is important for interpreting test results. The authors have used this mythical condition purple mange as a teaching tool for NP students.

Developing a predictive tropospheric ozone model for Tabriz

NASA Astrophysics Data System (ADS)

Khatibi, Rahman; Naghipour, Leila; Ghorbani, Mohammad A.; Smith, Michael S.; Karimi, Vahid; Farhoudi, Reza; Delafrouz, Hadi; Arvanaghi, Hadi

2013-04-01

Predictive ozone models are becoming indispensable tools by providing a capability for pollution alerts to serve people who are vulnerable to the risks. We have developed a tropospheric ozone prediction capability for Tabriz, Iran, by using the following five modeling strategies: three regression-type methods: Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Gene Expression Programming (GEP); and two auto-regression-type models: Nonlinear Local Prediction (NLP) to implement chaos theory and Auto-Regressive Integrated Moving Average (ARIMA) models. The regression-type modeling strategies explain the data in terms of: temperature, solar radiation, dew point temperature, and wind speed, by regressing present ozone values to their past values. The ozone time series are available at various time intervals, including hourly intervals, from August 2010 to March 2011. The results for MLR, ANN and GEP models are not overly good but those produced by NLP and ARIMA are promising for the establishing a forecasting capability.

The Influence of a Personal Values Intervention on Cold Pressor-Induced Distress Tolerance.

PubMed

Smith, Brooke M; Villatte, Jennifer L; Ong, Clarissa W; Butcher, Grayson M; Twohig, Michael P; Levin, Michael E; Hayes, Steven C

2018-06-01

Research has demonstrated that values and acceptance interventions can increase distress tolerance, but the individual contribution of each remains unclear. The current study examined the isolated effect of a values intervention on immersion time in a cold pressor. Participants randomized to Values ( n = 18) and Control ( n = 14) conditions completed two cold pressor tasks, separated by a 30-min values or control intervention. Immersion time increased 51.06 s for participants in the Values condition and decreased by 10.79 s for those in the Control condition. Increases in self-reported pain and distress predicted decreases in immersion time for Control, but not Values, participants. The best-fitting model accounted for 39% of the variance in immersion time change. Results suggest that a brief isolated values exercise can be used to improve distress tolerance despite increased perceptions of pain and distress, such that values alone may be sufficient to facilitate openness to difficult experiences.

Contrast-enhanced transrectal ultrasound for prediction of prostate cancer aggressiveness: The role of normal peripheral zone time-intensity curves.

PubMed

Huang, Hui; Zhu, Zheng-Qiu; Zhou, Zheng-Guo; Chen, Ling-Shan; Zhao, Ming; Zhang, Yang; Li, Hong-Bo; Yin, Li-Ping

2016-12-08

To assess the role of time-intensity curves (TICs) of the normal peripheral zone (PZ) in the identification of biopsy-proven prostate nodules using contrast-enhanced transrectal ultrasound (CETRUS). This study included 132 patients with 134 prostate PZ nodules. Arrival time (AT), peak intensity (PI), mean transit time (MTT), area under the curve (AUC), time from peak to one half (TPH), wash in slope (WIS) and time to peak (TTP) were analyzed using multivariate linear logistic regression and receiver operating characteristic (ROC) curves to assess whether combining nodule TICs with normal PZ TICs improved the prediction of prostate cancer (PCa) aggressiveness. The PI, AUC (p < 0.001 for both), MTT and TPH (p = 0.011 and 0.040 respectively) values of the malignant nodules were significantly higher than those of the benign nodules. Incorporating the PI and AUC values (both, p < 0.001) of the normal PZ TIC, but not the MTT and TPH values (p = 0.076 and 0.159 respectively), significantly improved the AUC for prediction of malignancy (PI: 0.784-0.923; AUC: 0.758-0.891) and assessment of cancer aggressiveness (p < 0.001). Thus, all these findings indicate that incorporating normal PZ TICs with nodule TICs in CETRUS readings can improve the diagnostic accuracy for PCa and cancer aggressiveness assessment.

A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

NASA Astrophysics Data System (ADS)

Fan, J.; Li, Q.; Hou, J.; Feng, X.; Karimian, H.; Lin, S.

2017-10-01

Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.

Predictive value of early near-infrared spectroscopy monitoring of patients with traumatic brain injury.

PubMed

Vilkė, Alina; Bilskienė, Diana; Šaferis, Viktoras; Gedminas, Martynas; Bieliauskaitė, Dalia; Tamašauskas, Arimantas; Macas, Andrius

2014-01-01

Traumatic brain injury (TBI) is the leading cause of death and disability in young adults. Study aimed to define the predictive value of early near-infrared spectroscopy (NIRS) monitoring of TBI patients in a Lithuanian clinical setting. Data of 61 patients was analyzed. Predictive value of early NIRS monitoring, computed tomography data and regular intensive care unit (ICU) parameters was investigated. Twenty-six patients expressed clinically severe TBI; 14 patients deceased. Patients who survived expressed higher NIRS values at the periods of admission to operative room (75.4%±9.8% vs. 71.0%±20.5%; P=0.013) and 1h after admission to ICU (74.7%±1.5% vs. 61.9%±19.4%; P=0.029). The NIRS values discriminated hospital mortality groups more accurately than admission GCS score, blood sugar or hemoglobin levels. Admission INR value and NIRS value at 1h after admission to ICU were selected by discriminant analysis into the optimal set of features when classifying hospital mortality groups. Average efficiency of classification using this method was 88.9%. When rsO2 values at 1h after admission to ICU did not exceed 68.0% in the left hemisphere and 68.3% in the right hemisphere, the hazard ratio for death increased by 17.7 times (P<0.01) and 5.1 times (P<0.05), respectively. NIRS plays an important role in the clinical care of TBI patients. Regional brain saturation monitoring provides accurate predictive data, which can improve the allocation of scarce medical resources, set the treatment goals and alleviate the early communication with patients' relatives. Copyright © 2014 Lithuanian University of Health Sciences. Production and hosting by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

A look into the relationship between personality traits and basic values: A longitudinal investigation.

PubMed

Vecchione, Michele; Alessandri, Guido; Roccas, Sonia; Caprara, Gian Vittorio

2018-05-27

The present study examines the longitudinal association between basic personal values and the Big Five personality traits. A sample of 546 young adults (57% females) with a mean age of 21.68 years (SD = 1.60) completed the Portrait Values Questionnaire and the Big Five Questionnaire at three-time points, each separated by an interval of four years. Cross-lagged models were used to investigate the possible reciprocal relations between traits and values, after the stability of the variables was taken into account. We found that values did not affect trait development. Traits, by contrast, have some effects on how values change. Specifically, high levels of agreeableness predict an increase over time in the importance assigned to benevolence values. Similarly, high levels of openness predict a later increase in the importance assigned to self-direction values. The same effect was not found for the other traits. Additionally, except for in the case of emotional stability, traits showed synchronous (i.e., within wave) correlations with values, suggesting that part of this relationship is due to common antecedents. Mechanisms underlying the associations between traits and values are discussed. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Airline Transport Pilot Preferences for Predictive Information

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.

1996-01-01

This experiment assessed certain issues about the usefulness of predictive information: (1) the relative time criticality of failures, (2) the subjective utility of predictive information for different parameters or sensors, and (3) the preferred form and prediction time for displaying predictive information. To address these issues, three separate tasks were administered to 22 airline pilots. As shown by the data, these pilots preferred predictive information on parameters they considered vital to the safety of the flight. These parameters were related to the checklists performed first for alert messages. These pilots also preferred to know whether a parameter was changing abnormally and the time to a certain value being reached. Furthermore, they considered this information most useful during the cruise, the climb, and the descent phases of flight. Lastly, these pilots preferred the information to predict as far ahead as possible.

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows.

PubMed

Visentin, G; McDermott, A; McParland, S; Berry, D P; Kenny, O A; Brodkorb, A; Fenelon, M A; De Marchi, M

2015-09-01

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n=713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000cm(-1) were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predictive contribution of neutrophil/lymphocyte ratio in diagnosis of brucellosis.

PubMed

Olt, Serdar; Ergenç, Hasan; Açıkgöz, Seyyid Bilal

2015-01-01

Here we wanted to investigate predictive value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) in the diagnosis of brucellosis. Thirty-two brucellosis patients diagnosed with positive serum agglutination test and thirty-two randomized healthy subjects were enrolled in this study retrospectively. Result with ROC analyzes the baseline NLR and hemoglobin values were found to be significantly associated with brucellosis (P = 0.01, P = 0.01, resp.). Herein we demonstrated for the first time that NLR values were significantly associated with brucellosis. This situation can help clinicians during diagnosis of brucellosis.

Predictive model for survival in patients with gastric cancer.

PubMed

Goshayeshi, Ladan; Hoseini, Benyamin; Yousefli, Zahra; Khooie, Alireza; Etminani, Kobra; Esmaeilzadeh, Abbas; Golabpour, Amin

2017-12-01

Gastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer. This was a historical cohort conducted between 2011 and 2016. Study population were 277 patients suffering from gastric cancer. Data were gathered from the Iranian Cancer Registry and the laboratory of Emam Reza Hospital in Mashhad, Iran. Patients or their relatives underwent interviews where it was needed. Missing values were imputed by data mining techniques. Fifteen factors were analyzed. Survival was addressed as a dependent variable. Then, the predictive model was designed by combining both genetic algorithm and logistic regression. Matlab 2014 software was used to combine them. Of the 277 patients, only survival of 80 patients was available whose data were used for designing the predictive model. Mean ?SD of missing values for each patient was 4.43?.41 combined predictive model achieved 72.57% accuracy. Sex, birth year, age at diagnosis time, age at diagnosis time of patients' family, family history of gastric cancer, and family history of other gastrointestinal cancers were six parameters associated with patient survival. The study revealed that imputing missing values by data mining techniques have a good accuracy. And it also revealed six parameters extracted by genetic algorithm effect on the survival of patients with gastric cancer. Our combined predictive model, with a good accuracy, is appropriate to forecast the survival of patients suffering from Gastric cancer. So, we suggest policy makers and specialists to apply it for prediction of patients' survival.

A proposed mathematical model for sleep patterning.

PubMed

Lawder, R E

1984-01-01

The simple model of a ramp, intersecting a triangular waveform, yields results which conform with seven generalized observations of sleep patterning; including the progressive lengthening of 'rapid-eye-movement' (REM) sleep periods within near-constant REM/nonREM cycle periods. Predicted values of REM sleep time, and of Stage 3/4 nonREM sleep time, can be computed using the observed values of other parameters. The distributions of the actual REM and Stage 3/4 times relative to the predicted values were closer to normal than the distributions relative to simple 'best line' fits. It was found that sleep onset tends to occur at a particular moment in the individual subject's '90-min cycle' (the use of a solar time-scale masks this effect), which could account for a subject with a naturally short sleep/wake cycle synchronizing to a 24-h rhythm. A combined 'sleep control system' model offers quantitative simulation of the sleep patterning of endogenous depressives and, with a different perturbation, qualitative simulation of the symptoms of narcolepsy.

Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

NASA Astrophysics Data System (ADS)

Fu, Y.; Yang, W.; Xu, O.; Zhou, L.; Wang, J.

2017-04-01

To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately.

Disentangling neural representations of value and salience in the human brain

PubMed Central

Kahnt, Thorsten; Park, Soyoung Q; Haynes, John-Dylan; Tobler, Philippe N.

2014-01-01

A large body of evidence has implicated the posterior parietal and orbitofrontal cortex in the processing of value. However, value correlates perfectly with salience when appetitive stimuli are investigated in isolation. Accordingly, considerable uncertainty has remained about the precise nature of the previously identified signals. In particular, recent evidence suggests that neurons in the primate parietal cortex signal salience instead of value. To investigate neural signatures of value and salience, here we apply multivariate (pattern-based) analyses to human functional MRI data acquired during a noninstrumental outcome-prediction task involving appetitive and aversive outcomes. Reaction time data indicated additive and independent effects of value and salience. Critically, we show that multivoxel ensemble activity in the posterior parietal cortex encodes predicted value and salience in superior and inferior compartments, respectively. These findings reinforce the earlier reports of parietal value signals and reconcile them with the recent salience report. Moreover, we find that multivoxel patterns in the orbitofrontal cortex correlate with value. Importantly, the patterns coding for the predicted value of appetitive and aversive outcomes are similar, indicating a common neural scale for appetite and aversive values in the orbitofrontal cortex. Thus orbitofrontal activity patterns satisfy a basic requirement for a neural value signal. PMID:24639493

Estimating cross-validatory predictive p-values with integrated importance sampling for disease mapping models.

PubMed

Li, Longhai; Feng, Cindy X; Qiu, Shi

2017-06-30

An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Dynamic value assessments in oncology supported by the PACE Continuous Innovation Indicators.

PubMed

Paddock, Silvia; Goodman, Clifford; Shortenhaus, Scott; Grainger, David; Zummo, Jacqueline; Thomas, Samuel

2017-10-01

Several recently developed frameworks aim to assess the value of cancer treatments, but the most appropriate metrics remain uncertain. We use data from the Patient Access to Cancer care Excellence Continuous Innovation Indicators to examine the relationship between hazard ratios (HRs) from clinical trials and dynamic therapeutic value accumulating over time. Our analysis shows that HRs from initial clinical trials poorly predict the eventual therapeutic value of cancer treatments. Relying strongly on HRs from registration trials to predict the long-term success of treatments leaves a lot of the variance unexplained. The Continuous Innovation Indicators offer a complementing, dynamic method to track the therapeutic value of cancer treatments and continuously update value assessments as additional evidence accumulates.

Anticipating Their Future: Adolescent Values for the Future Predict Adult Behaviors

ERIC Educational Resources Information Center

Finlay, Andrea K.; Wray-Lake, Laura; Warren, Michael; Maggs, Jennifer

2015-01-01

Adolescent future values--beliefs about what will matter to them in the future--may shape their adult behavior. Utilizing a national longitudinal British sample, this study examined whether adolescent future values in six domains (i.e., family responsibility, full-time job, personal responsibility, autonomy, civic responsibility, and hedonistic…

[Predictive factors of contamination in a blood culture with bacterial growth in an Emergency Department].

PubMed

Hernández-Bou, S; Trenchs Sainz de la Maza, V; Esquivel Ojeda, J N; Gené Giralt, A; Luaces Cubells, C

2015-06-01

The aim of this study is to identify predictive factors of bacterial contamination in positive blood cultures (BC) collected in an emergency department. A prospective, observational and analytical study was conducted on febrile children aged on to 36 months, who had no risk factors of bacterial infection, and had a BC collected in the Emergency Department between November 2011 and October 2013 in which bacterial growth was detected. The potential BC contamination predicting factors analysed were: maximum temperature, time to positivity, initial Gram stain result, white blood cell count, absolute neutrophil count, band count, and C-reactive protein (CRP). Bacteria grew in 169 BC. Thirty (17.8%) were finally considered true positives and 139 (82.2%) false positives. All potential BC contamination predicting factors analysed, except maximum temperature, showed significant differences between true positives and false positives. CRP value, time to positivity, and initial Gram stain result are the best predictors of false positives in BC. The positive predictive values of a CRP value≤30mg/L, BC time to positivity≥16h, and initial Gram stain suggestive of a contaminant in predicting a FP, are 95.1, 96.9 and 97.5%, respectively. When all 3 conditions are applied, their positive predictive value is 100%. Four (8.3%) patients with a false positive BC and discharged to home were revaluated in the Emergency Department. The majority of BC obtained in the Emergency Department that showed positive were finally considered false positives. Initial Gram stain, time to positivity, and CRP results are valuable diagnostic tests in distinguishing between true positives and false positives in BC. The early detection of false positives will allow minimising their negative consequences. Copyright © 2014 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

Valuing hydrological forecasts for a pumped storage assisted hydro facility

NASA Astrophysics Data System (ADS)

Zhao, Guangzhi; Davison, Matt

2009-07-01

SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

Risk Assessment Using Cytochrome P450 Time-Dependent Inhibition Assays at Single Time and Concentration in the Early Stage of Drug Discovery.

PubMed

Kosaka, Mai; Kosugi, Yohei; Hirabayashi, Hideki

2017-09-01

In this article, we proposed a risk assessment strategy for CYP3A time-dependent inhibition (TDI) during drug discovery based on a thorough retrospective study of 13 reference drugs, some of which are known to have in vitro TDI potential but have unknown clinical relevance. First, the traditional parameter k inact /K I , recommended by regulatory authorities for necessity decision making in clinical drug-drug interaction (DDI) studies, was investigated as a predictive index for clinical TDI liability. The cutoff value of 1.1 for k inact /K I , established by the Food and Drug Administration, tended to produce false-positive prediction results for clinical DDI occurrence. The value of 1.25 recommended in the European Medicines Evaluation Agency draft guideline yielded better predictions with only 1 false negative for diltiazem. Second, to enable earlier risk assessment, remaining activity, defined as the residual CYP3A activity in vitro obtained in the screening conditions, was investigated as an alternative index. As a result, the ratios of unbound C max or area under the curve to remaining activity precisely predicted clinical DDI occurrence. In conclusion, we demonstrated the predictive power of k inact /K I and remaining activity values for clinical DDIs. These findings provide insights that enable TDI risk assessment, even during drug discovery. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Mathematical model to predict drivers' reaction speeds.

PubMed

Long, Benjamin L; Gillespie, A Isabella; Tanaka, Martin L

2012-02-01

Mental distractions and physical impairments can increase the risk of accidents by affecting a driver's ability to control the vehicle. In this article, we developed a linear mathematical model that can be used to quantitatively predict drivers' performance over a variety of possible driving conditions. Predictions were not limited only to conditions tested, but also included linear combinations of these tests conditions. Two groups of 12 participants were evaluated using a custom drivers' reaction speed testing device to evaluate the effect of cell phone talking, texting, and a fixed knee brace on the components of drivers' reaction speed. Cognitive reaction time was found to increase by 24% for cell phone talking and 74% for texting. The fixed knee brace increased musculoskeletal reaction time by 24%. These experimental data were used to develop a mathematical model to predict reaction speed for an untested condition, talking on a cell phone with a fixed knee brace. The model was verified by comparing the predicted reaction speed to measured experimental values from an independent test. The model predicted full braking time within 3% of the measured value. Although only a few influential conditions were evaluated, we present a general approach that can be expanded to include other types of distractions, impairments, and environmental conditions.

Multiplier method may be unreliable to predict the timing of temporary hemiepiphysiodesis for coronal angular deformity.

PubMed

Wu, Zhenkai; Ding, Jing; Zhao, Dahang; Zhao, Li; Li, Hai; Liu, Jianlin

2017-07-10

The multiplier method was introduced by Paley to calculate the timing for temporary hemiepiphysiodesis. However, this method has not been verified in terms of clinical outcome measure. We aimed to (1) predict the rate of angular correction per year (ACPY) at the various corresponding ages by means of multiplier method and verify the reliability based on the data from the published studies and (2) screen out risk factors for deviation of prediction. A comprehensive search was performed in the following electronic databases: Cochrane, PubMed, and EMBASE™. A total of 22 studies met the inclusion criteria. If the actual value of ACPY from the collected date was located out of the range of the predicted value based on the multiplier method, it was considered as the deviation of prediction (DOP). The associations of patient characteristics with DOP were assessed with the use of univariate logistic regression. Only one article was evaluated as moderate evidence; the remaining articles were evaluated as poor quality. The rate of DOP was 31.82%. In the detailed individual data of included studies, the rate of DOP was 55.44%. The multiplier method is not reliable in predicting the timing for temporary hemiepiphysiodesis, even though it is prone to be more reliable for the younger patients with idiopathic genu coronal deformity.

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.

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.

Intranasal Pharmacokinetic Data for Triptans Such as Sumatriptan and Zolmitriptan Can Render Area Under the Curve (AUC) Predictions for the Oral Route: Strategy Development and Application.

PubMed

Srinivas, Nuggehally R; Syed, Muzeeb

2016-01-01

Limited pharmacokinetic sampling strategy may be useful for predicting the area under the curve (AUC) for triptans and may have clinical utility as a prospective tool for prediction. Using appropriate intranasal pharmacokinetic data, a Cmax vs. AUC relationship was established by linear regression models for sumatriptan and zolmitriptan. The predictions of the AUC values were performed using published mean/median Cmax data and appropriate regression lines. The quotient of observed and predicted values rendered fold-difference calculation. The mean absolute error (MAE), mean positive error (MPE), mean negative error (MNE), root mean square error (RMSE), correlation coefficient (r), and the goodness of the AUC fold prediction were used to evaluate the two triptans. Also, data from the mean concentration profiles at time points of 1 hour (sumatriptan) and 3 hours (zolmitriptan) were used for the AUC prediction. The Cmax vs. AUC models displayed excellent correlation for both sumatriptan (r = .9997; P < .001) and zolmitriptan (r = .9999; P < .001). Irrespective of the two triptans, the majority of the predicted AUCs (83%-85%) were within 0.76-1.25-fold difference using the regression model. The prediction of AUC values for sumatriptan or zolmitriptan using the concentration data that reflected the Tmax occurrence were in the proximity of the reported values. In summary, the Cmax vs. AUC models exhibited strong correlations for sumatriptan and zolmitriptan. The usefulness of the prediction of the AUC values was established by a rigorous statistical approach.

Timed Stair Climbing is the Single Strongest Predictor of Perioperative Complications in Patients Undergoing Abdominal Surgery

PubMed Central

Reddy, Sushanth; Contreras, Carlo M; Singletary, Brandon; Bradford, T Miller; Waldrop, Mary G; Mims, Andrew H; Smedley, W Andrew; Swords, Jacob A; Thomas N, Wang; Martin J, Heslin

2016-01-01

Background Current methods to predict patients' peri-operative morbidity utilize complex algorithms with multiple clinical variables focusing primarily on organ-specific compromise. The aim of the present study is to determine the value of a timed stair climb (SC) in predicting peri-operative complications for patients undergoing abdominal surgery. Study Design From March 2014 to July 2015, 362 patients attempted SC while being timed prior to undergoing elective abdominal surgery. Vital signs were measured before and after SC. Ninety day post-operative complications were assessed by the Accordion Severity Grading System. The prognostic value of SC was compared to the ACS NSQIP risk calculator. Results A total of 264 (97.4%) patients were able to complete SC. SC time directly correlated to changes in both mean arterial pressure and heart rate as an indicator of stress. An Accordion grade 2 or higher complication occurred in 84 (25%) patients. There were 8 mortalities (2.4%). Patients with slower SC times had an increased complication rate (P<0.0001). In multivariable analysis SC time was the single strongest predictor of complications (OR=1.029, P<0.0001), and no other clinical co-morbidity reached statistical significance. Receiver operative characteristic curves predicting post-operative morbidity by SC time was superior to that of the ACS risk calculator (AUC 0.81 vs. 0.62, P<0.0001). Additionally slower patients had a greater deviation from predicted length of hospital stay (P=0.034) Conclusions SC provides measurable stress, accurately predicts post-operative complications, and is easy to administer in patients undergoing abdominal surgery. Larger patient populations with a diverse group of operations will be needed to further validate the use of SC in risk prediction models. PMID:26920993

Cognitive models of risky choice: parameter stability and predictive accuracy of prospect theory.

PubMed

Glöckner, Andreas; Pachur, Thorsten

2012-04-01

In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are individual differences as measured by model parameters stable enough to improve the ability to predict behavior as compared to modeling without adjustable parameters? We examined this issue in cumulative prospect theory (CPT), arguably the most widely used framework to model decisions under risk. Specifically, we examined (a) the temporal stability of CPT's parameters; and (b) how well different implementations of CPT, varying in the number of adjustable parameters, predict individual choice relative to models with no adjustable parameters (such as CPT with fixed parameters, expected value theory, and various heuristics). We presented participants with risky choice problems and fitted CPT to each individual's choices in two separate sessions (which were 1 week apart). All parameters were correlated across time, in particular when using a simple implementation of CPT. CPT allowing for individual variability in parameter values predicted individual choice better than CPT with fixed parameters, expected value theory, and the heuristics. CPT's parameters thus seem to pick up stable individual differences that need to be considered when predicting risky choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Can a Resident's Publication Record Predict Fellowship Publications?

PubMed Central

Prasad, Vinay; Rho, Jason; Selvaraj, Senthil; Cheung, Mike; Vandross, Andrae; Ho, Nancy

2014-01-01

Background Internal medicine fellowship programs have an incentive to select fellows who will ultimately publish. Whether an applicant's publication record predicts long term publishing remains unknown. Methods Using records of fellowship bound internal medicine residents, we analyzed whether publications at time of fellowship application predict publications more than 3 years (2 years into fellowship) and up to 7 years after fellowship match. We calculate the sensitivity, specificity, positive and negative predictive values and likelihood ratios for every cutoff number of application publications, and plot a receiver operator characteristic curve of this test. Results Of 307 fellowship bound residents, 126 (41%) published at least one article 3 to 7 years after matching, and 181 (59%) of residents do not publish in this time period. The area under the receiver operator characteristic curve is 0.59. No cutoff value for application publications possessed adequate test characteristics. Conclusion The number of publications an applicant has at time of fellowship application is a poor predictor of who publishes in the long term. These findings do not validate the practice of using application publications as a tool for selecting fellows. PMID:24658088

Can a resident's publication record predict fellowship publications?

PubMed

Prasad, Vinay; Rho, Jason; Selvaraj, Senthil; Cheung, Mike; Vandross, Andrae; Ho, Nancy

2014-01-01

Internal medicine fellowship programs have an incentive to select fellows who will ultimately publish. Whether an applicant's publication record predicts long term publishing remains unknown. Using records of fellowship bound internal medicine residents, we analyzed whether publications at time of fellowship application predict publications more than 3 years (2 years into fellowship) and up to 7 years after fellowship match. We calculate the sensitivity, specificity, positive and negative predictive values and likelihood ratios for every cutoff number of application publications, and plot a receiver operator characteristic curve of this test. Of 307 fellowship bound residents, 126 (41%) published at least one article 3 to 7 years after matching, and 181 (59%) of residents do not publish in this time period. The area under the receiver operator characteristic curve is 0.59. No cutoff value for application publications possessed adequate test characteristics. The number of publications an applicant has at time of fellowship application is a poor predictor of who publishes in the long term. These findings do not validate the practice of using application publications as a tool for selecting fellows.

Sensitivity, specificity and predictive values of anterior chamber tap in cases of bacterial endophthalmitis.

PubMed

Sjoholm-Gomez de Liano, Carl; Soberon-Ventura, Vidal F; Salcedo-Villanueva, Guillermo; Santos-Palacios, Abril; Guerrero-Naranjo, Jose Luis; Fromow-Guerra, Jans; García-Aguirre, Gerardo; Morales-Canton, Virgilio; Velez-Montoya, Raul

2017-01-01

To assess the sensitivity, specificity, positive predictive value and negative predictive value of anterior chamber tap for the diagnosis of bacterial endophthalmitis on a population with high prevalence. Retrospective, single centre, case series study. We reviewed all medical records with clinical diagnosis of bacterial endophthalmitis in our hospital from January 1st, 2000 to December 31st 2014. From each record, we documented general demographic data, best corrected visual acuity and vitreous and aqueous tap microbiological results. All cases were further divided according to the endophthalmitis aetiology to perform individual calculations of sensitivity, specificity, positive predictive value, negative predictive value, accuracy and prevalence. We used the results of the vitreous tap as the gold standard for diagnosis of bacterial endophthalmitis. We excluded those records in which the aqueous and vitreous samples were not taken simultaneously or had an incomplete microbiological report. Significance were assessed with chi squared statistics, with an alpha value of 0.05 for statistical significance. A total of 190 cases fulfilled the inclusion/exclusion criteria. Positive culture rate from vitreous samples was 64.74%. Positive culture rate from aqueous sample was 32.11%. Bacteria isolated from aqueous samples matched those isolated from vitreous samples 78.68% of the time. The overall sensitivity was 38.21%, specificity: 75.51%, positive predictive value: 79.66%, negative predictive value: 32.74% ( p  = 0.08). Subgroup analysis showed that anterior chamber taps in cases of post-surgical endophthalmitis had a moderate to low sensitivity (37.73%), high specificity (93%) and high positive predictive value (95%) ( p  < 0.04). The sensitivity and specificity of anterior chamber tap are low and should not be used for critical therapeutic decisions in patients with suspected bacterial endophthalmitis. In cases of post-surgical endophthalmitis, the result of an anterior chamber tap could be used for therapeutic guidance, but only in conjunction with clinical presentation and in the absence of a better method for diagnosis.

Predicting Arrival Of Protons Emitted In Solar Flares

NASA Technical Reports Server (NTRS)

Spagnuolo, John N., Jr.; Schwuttke, Ursula M.; Han, Cecilia S.; Hervias, Felipe

1996-01-01

Visual Utility for Localization of Corona Accelerated Nuclei (VULCAN) computer program provides both advance warnings and insight for post-event analyses of effects of solar flares. Using measurements of peak fluxes, times of detection, flare location, solar wind velocities, and x-ray emissions from Sun, as electronically sent by NOAA (National Oceanographic and Atmospheric Administration), VULCAN predicts resulting intensities of proton fluxes at various user-chosen points (spacecraft or planets) of solar system. Also predicts times of onset of fluxes of protons and peak values of fluxes.

Time to positivity and detection of growth in anaerobic blood culture vials predict the presence of Candida glabrata in candidemia: a two-center European cohort study.

PubMed

Cobos-Trigueros, Nazaret; Kaasch, Achim J; Soriano, Alex; Torres, Jorge-Luis; Vergara, Andrea; Morata, Laura; Zboromyrska, Yuliya; De La Calle, Cristina; Alejo, Izaskun; Hernández, Cristina; Cardozo, Celia; Marco, Franscesc; Del Río, Ana; Almela, Manel; Mensa, Josep; Martínez, José Antonio

2014-08-01

This study shows the accuracy of exclusive or earlier growth in anaerobic vials to predict Candida glabrata in a large series of candidemic patients from two European hospitals using the Bactec 9240 system. Alternatively, C. glabrata can be predicted by a time to positivity cutoff value, which should be determined for each setting. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Value of α-fetoprotein as an early biomarker for treatment response to sorafenib therapy in advanced hepatocellular carcinoma.

PubMed

Sánchez, Ana Isabel Plano; Roces, Lucía Velasco; García, Isabel Zapico; López, Eva Lázaro; Hernandez, Miguel Angel Calleja; Parejo, Maria Isabel Baena; Peña-Díaz, Jaime

2018-06-01

Sorafenib is an oral multikinase inhibitor with antiangiogenic and antiproliferative properties, and is used as the first-line treatment for patients with advanced hepatocellular carcinoma (HCC). Previous studies have identified an improvement in overall survival and progression-free survival in patients with a manageable toxicity profile. α-fetoprotein (AFP) has been revealed to be of great diagnostic and predictive value for tumour staging in multiple studies; however, its role as a predictive factor of response to treatment with sorafenib is not entirely clear. The present study aimed to determine the effectiveness of sorafenib and investigate the value of AFP as a predictive factor of early response to sorafenib in patients with HCC. Effectiveness was analysed based on median overall survival (mOS) time, while to analyse the possible predictive value of AFP, patients were classified into two groups: Non-responders (≤20% AFP reduction) and responders (>20% AFP reduction) at 6-8 weeks of treatment when compared with basal AFP level. For assessment of toxicity, any adverse effects were recorded. A total of 167 patients were included, who collectively exhibited a mOS time of 11 months with a median treatment duration of 5 months. The mOS time was significantly higher for patients with better hepatic function (12 months in cases of Child-Pugh score A vs. 8 months in cases of Child-Pugh score B; P=0.03) and with basal AFP values ≤200 ng/ml (14 months vs. 8 months in patients with AFP levels >200 ng/ml; P=0.01). A >20% reduction of AFP at 6-8 weeks was determined to be a positive predictive factor upon multivariate analysis (P=0.002), obtaining, for the responder patients, an mOS of 18 months compared with 10 months (P=0.004) for the non-responders. The main adverse reactions were hand-foot syndrome (35/167; 21%), diarrhoea (39/167; 23.4%), anorexia (29/167; 17.4%) and arterial hypertension (30/167; 18%). In conclusion, a >20% drop in AFP at 6-8 weeks may be useful as a predictive factor of response to sorafenib, as indicated by its association with longer survival times in patients with advanced HCC following treatment with sorafenib in the present study.

Dopamine Reward Prediction Error Responses Reflect Marginal Utility

PubMed Central

Stauffer, William R.; Lak, Armin; Schultz, Wolfram

2014-01-01

Summary Background Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. Results In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions’ shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. Conclusions These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). PMID:25283778

Predictive value of cognition for different domains of outcome in recent-onset schizophrenia.

PubMed

Holthausen, Esther A E; Wiersma, Durk; Cahn, Wiepke; Kahn, René S; Dingemans, Peter M; Schene, Aart H; van den Bosch, Robert J

2007-01-15

The aim of this study was to see whether and how cognition predicts outcome in recent-onset schizophrenia in a large range of domains such as course of illness, self-care, interpersonal functioning, vocational functioning and need for care. At inclusion, 115 recent-onset patients were tested on a cognitive battery and 103 patients participated in the follow-up 2 years after inclusion. Differences in outcome between cognitively normal and cognitively impaired patients were also analysed. Cognitive measures at inclusion did not predict number of relapses, activities of daily living and interpersonal functioning. Time in psychosis or in full remission, as well as need for care, were partly predicted by specific cognitive measures. Although statistically significant, the predictive value of cognition with regard to clinical outcome was limited. There was a significant difference between patients with and without cognitive deficits in competitive employment status and vocational functioning. The predictive value of cognition for different social outcome domains varies. It seems that cognition most strongly predicts work performance, where having a cognitive deficit, regardless of the nature of the deficit, acts as a rate-limiting factor.

Prediction possibilities of Arosa total ozone

NASA Astrophysics Data System (ADS)

Kane, R. P.

1987-01-01

Using the periodicities obtained by a Maximum Entropy Spectral Analysis (MESA) of the Arosa total ozone data ( CC') series for 1932 1971, the values predicted for 1972 onwards were compared with the observed values of the ( AD) series. A change of level was noticed, with the observed ( AD) values lower by about 7 D.U. Also, the matching was poor in 1980, 1981, 1982. In the monthly values, the most prominent periodicity was the annual wave, comprising some 80% variance. In the 12 month running averages, the annual wave was eliminated and the most prominent periodicity was T=3.7 years, encompassing roundly 20% variance. This and other periodicities at T=4.7, 5.4, 6.2, 10 and 16 years were all statistically significant at a 3.5δ a priori i.e., 2δ a posteriori level. However, the predictions from these were unsatisfactory, probably because some of these periodicities may be transient i.e., changing amplitudes and/or phases with time. Thus, no meaningful prediction seem possible for Arosa total ozone.

Predictive value of modeled AUC(AFP-hCG), a dynamic kinetic parameter characterizing serum tumor marker decline in patients with nonseminomatous germ cell tumor.

PubMed

You, Benoit; Fronton, Ludivine; Boyle, Helen; Droz, Jean-Pierre; Girard, Pascal; Tranchand, Brigitte; Ribba, Benjamin; Tod, Michel; Chabaud, Sylvie; Coquelin, Henri; Fléchon, Aude

2010-08-01

The early decline profile of alpha-fetoprotein (AFP) and human chorionic gonadotropin (hCG) in patients with nonseminomatous germ cell tumors (NSGCT) treated with chemotherapy may be related to the risk of relapse. We assessed the predictive values of areas under the curve of hCG (AUC(hCG)) and AFP (AUC(AFP)) of modeled concentration-time equations on progression-free survival (PFS). Single-center retrospective analysis of hCG and AFP time-points from 65 patients with IGCCCG intermediate-poor risk NSGCT treated with 4 cycles of bleomycin-etoposide-cisplatin (BEP). To determine AUC(hCG) and AUC(AFP) for D0-D42, AUCs for D0-D7 were calculated using the trapezoid rule and AUCs for D7-D42 were calculated using the mathematic integrals of equations modeled with NONMEM. Combining AUC(AFP) and AUC(hCG) enabled us to define 2 predictive groups: namely, patients with favorable and unfavorable AUC(AFP-hCG). Survival analyses and ROC curves assessed the predictive values of AUC(AFP-hCG) groups regarding progression-free survival (PFS) and compared them with those of half-life (HL) and time-to-normalization (TTN). Mono-exponential models best fit the patterns of marker decreases. Patients with a favorable AUC(AFP-hCG) had a significantly better PFS (100% vs 71.5%, P = .014). ROC curves confirmed the encouraging predictive accuracy of AUC(AFP-hCG) against HL or TTN regarding progression risk (ROC AUCs = 79.6 vs 71.9 and 70.2 respectively). Because of the large number of patients with missing data, multivariate analysis could not be performed. AUC(AFP-hCG) is a dynamic parameter characterizing tumor marker decline in patients with NSGCT during BEP treatment. Its value as a promising predictive factor should be validated. Copyright 2010 Elsevier Inc. All rights reserved.

Assessment of right ventricular adaptability to loading conditions can improve the timing of listing to transplantation in patients with pulmonary arterial hypertension.

PubMed

Dandel, Michael; Knosalla, Christoph; Kemper, Dagmar; Stein, Julia; Hetzer, Roland

2015-03-01

Right ventricle (RV) performance is load dependent, and right-sided heart failure (RHF) is the main cause of death in pulmonary arterial hypertension (PAH). Prediction of RV worsening for timely identification of patients needing transplantation (Tx) is paramount. Assessment of RV adaptability to load has proved useful in certain clinical circumstances. This study assessed its predictive value for RHF-free and Tx-free outcome with PAH. Between 2006 and 2012, all potential Tx candidates with PAH, without RHF at the first evaluation, were selected for follow-up (except congenital heart diseases). At selection and at each follow-up, N-terminal prohormone brain natriuretic peptide (NT-proBNP) and the 6-minute walk distance were measured, and RV adaptability to load was assessed by echocardiography. Collected data were tested for the ability to predict RV stability and Tx-free survival. During a 12-month to 92-month follow-up, RHF developed in 23 of 79 evaluated patients, despite similar medication and no differences in initial RV size and ejection fraction compared with the patients who remained stable. However, unstable patients had an initially lower RV load-adaptation index and afterload-corrected peak global systolic longitudinal strain-rate values as well as higher RV dyssynchrony, tricuspid regurgitation, and NT-proBNP levels (p ≤ 0.01). At certain cutoff values, these variables appeared predictive for 1-year and 3-year freedom from RHF and 3-year Tx-free survival. An RV load-adaptation index reduction of ≥20% showed the highest predictive value (90.0%) for short-term (≤1 year) RV decompensation. Assessment of RV adaptability to load allows prediction of RV function and Tx-free survival with severe PAH during the next 1 to 3 years. This can improve the timing of listing for Tx. Copyright © 2015 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.

Evaluation of AUC(0-4) predictive methods for cyclosporine in kidney transplant patients.

PubMed

Aoyama, Takahiko; Matsumoto, Yoshiaki; Shimizu, Makiko; Fukuoka, Masamichi; Kimura, Toshimi; Kokubun, Hideya; Yoshida, Kazunari; Yago, Kazuo

2005-05-01

Cyclosporine (CyA) is the most commonly used immunosuppressive agent in patients who undergo kidney transplantation. Dosage adjustment of CyA is usually based on trough levels. Recently, trough levels have been replacing the area under the concentration-time curve during the first 4 h after CyA administration (AUC(0-4)). The aim of this study was to compare the predictive values obtained using three different methods of AUC(0-4) monitoring. AUC(0-4) was calculated from 0 to 4 h in early and stable renal transplant patients using the trapezoidal rule. The predicted AUC(0-4) was calculated using three different methods: the multiple regression equation reported by Uchida et al.; Bayesian estimation for modified population pharmacokinetic parameters reported by Yoshida et al.; and modified population pharmacokinetic parameters reported by Cremers et al. The predicted AUC(0-4) was assessed on the basis of predictive bias, precision, and correlation coefficient. The predicted AUC(0-4) values obtained using three methods through measurement of three blood samples showed small differences in predictive bias, precision, and correlation coefficient. In the prediction of AUC(0-4) measurement of one blood sample from stable renal transplant patients, the performance of the regression equation reported by Uchida depended on sampling time. On the other hand, the performance of Bayesian estimation with modified pharmacokinetic parameters reported by Yoshida through measurement of one blood sample, which is not dependent on sampling time, showed a small difference in the correlation coefficient. The prediction of AUC(0-4) using a regression equation required accurate sampling time. In this study, the prediction of AUC(0-4) using Bayesian estimation did not require accurate sampling time in the AUC(0-4) monitoring of CyA. Thus Bayesian estimation is assumed to be clinically useful in the dosage adjustment of CyA.

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

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

NASA Astrophysics Data System (ADS)

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.; Morley, Steven K.; Ozturk, Dogacan Su

2017-12-01

We simulated the entire month of January 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, SYM-H, AL, and cross-polar cap potential (CPCP). We find that the model does an excellent job of predicting the SYM-H index, with a root-mean-square error (RMSE) of 17-18 nT. Kp is predicted well during storm time conditions but overpredicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonably well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to overpredict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resolution, with the exception of the rate of occurrence for strongly negative AL values. The use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.

Prediction of the optimum hybridization conditions of dot-blot-SNP analysis using estimated melting temperature of oligonucleotide probes.

PubMed

Shiokai, Sachiko; Kitashiba, Hiroyasu; Nishio, Takeshi

2010-08-01

Although the dot-blot-SNP technique is a simple cost-saving technique suitable for genotyping of many plant individuals, optimization of hybridization and washing conditions for each SNP marker requires much time and labor. For prediction of the optimum hybridization conditions for each probe, we compared T (m) values estimated from nucleotide sequences using the DINAMelt web server, measured T (m) values, and hybridization conditions yielding allele-specific signals. The estimated T (m) values were comparable to the measured T (m) values with small differences of less than 3 degrees C for most of the probes. There were differences of approximately 14 degrees C between the specific signal detection conditions and estimated T (m) values. Change of one level of SSC concentrations of 0.1, 0.2, 0.5, and 1.0x SSC corresponded to a difference of approximately 5 degrees C in optimum signal detection temperature. Increasing the sensitivity of signal detection by shortening the exposure time to X-ray film changed the optimum hybridization condition for specific signal detection. Addition of competitive oligonucleotides to the hybridization mixture increased the suitable hybridization conditions by 1.8. Based on these results, optimum hybridization conditions for newly produced dot-blot-SNP markers will become predictable.

The closure problem for turbulence in meteorology and oceanography

NASA Technical Reports Server (NTRS)

Pierson, W. J., Jr.

1985-01-01

The dependent variables used for computer based meteorological predictions and in plans for oceanographic predictions are wave number and frequency filtered values that retain only scales resolvable by the model. Scales unresolvable by the grid in use become 'turbulence'. Whether or not properly processed data are used for initial values is important, especially for sparce data. Fickian diffusion with a constant eddy diffusion is used as a closure for many of the present models. A physically realistic closure based on more modern turbulence concepts, especially one with a reverse cascade at the right times and places, could help improve predictions.

Diagnostic accuracy of quantitative real-time PCR assay versus clinical and Gram stain identification of bacterial vaginosis.

PubMed

Menard, J-P; Mazouni, C; Fenollar, F; Raoult, D; Boubli, L; Bretelle, F

2010-12-01

The purpose of this investigation was to determine the diagnostic accuracy of quantitative real-time polymerase chain reaction (PCR) assay in diagnosing bacterial vaginosis versus the standard methods, the Amsel criteria and the Nugent score. The Amsel criteria, the Nugent score, and results from the molecular tool were obtained independently from vaginal samples of 163 pregnant women who reported abnormal vaginal symptoms before 20 weeks gestation. To determine the performance of the molecular tool, we calculated the kappa value, sensitivity, specificity, and positive and negative predictive values. Either or both of the Amsel criteria (≥3 criteria) and the Nugent score (score ≥7) indicated that 25 women (15%) had bacterial vaginosis, and the remaining 138 women did not. DNA levels of Gardnerella vaginalis or Atopobium vaginae exceeded 10(9) copies/mL or 10(8) copies/mL, respectively, in 34 (21%) of the 163 samples. Complete agreement between both reference methods and high concentrations of G. vaginalis and A. vaginae was found in 94.5% of women (154/163 samples, kappa value = 0.81, 95% confidence interval 0.70-0.81). The nine samples with discordant results were categorized as intermediate flora by the Nugent score. The molecular tool predicted bacterial vaginosis with a sensitivity of 100%, a specificity of 93%, a positive predictive value of 73%, and a negative predictive value of 100%. The quantitative real-time PCR assay shows excellent agreement with the results of both reference methods for the diagnosis of bacterial vaginosis.

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

PubMed Central

Serrancolí, Gil; Kinney, Allison L.; Fregly, Benjamin J.; Font-Llagunes, Josep M.

2016-01-01

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking. PMID:27210105

Detection of physiological noise in resting state fMRI using machine learning.

PubMed

Ash, Tom; Suckling, John; Walter, Martin; Ooi, Cinly; Tempelmann, Claus; Carpenter, Adrian; Williams, Guy

2013-04-01

We present a technique for predicting cardiac and respiratory phase on a time point by time point basis, from fMRI image data. These predictions have utility in attempts to detrend effects of the physiological cycles from fMRI image data. We demonstrate the technique both in the case where it can be trained on a subject's own data, and when it cannot. The prediction scheme uses a multiclass support vector machine algorithm. Predictions are demonstrated to have a close fit to recorded physiological phase, with median Pearson correlation scores between recorded and predicted values of 0.99 for the best case scenario (cardiac cycle trained on a subject's own data) down to 0.83 for the worst case scenario (respiratory predictions trained on group data), as compared to random chance correlation score of 0.70. When predictions were used with RETROICOR--a popular physiological noise removal tool--the effects are compared to using recorded phase values. Using Fourier transforms and seed based correlation analysis, RETROICOR is shown to produce similar effects whether recorded physiological phase values are used, or they are predicted using this technique. This was seen by similar levels of noise reduction noise in the same regions of the Fourier spectra, and changes in seed based correlation scores in similar regions of the brain. This technique has a use in situations where data from direct monitoring of the cardiac and respiratory cycles are incomplete or absent, but researchers still wish to reduce this source of noise in the image data. Copyright © 2011 Wiley Periodicals, Inc.

Progress towards a more predictive model for hohlraum radiation drive and symmetry

NASA Astrophysics Data System (ADS)

Jones, O. S.; Suter, L. J.; Scott, H. A.; Barrios, M. A.; Farmer, W. A.; Hansen, S. B.; Liedahl, D. A.; Mauche, C. W.; Moore, A. S.; Rosen, M. D.; Salmonson, J. D.; Strozzi, D. J.; Thomas, C. A.; Turnbull, D. P.

2017-05-01

For several years, we have been calculating the radiation drive in laser-heated gold hohlraums using flux-limited heat transport with a limiter of 0.15, tabulated values of local thermodynamic equilibrium gold opacity, and an approximate model for not in a local thermodynamic equilibrium (NLTE) gold emissivity (DCA_2010). This model has been successful in predicting the radiation drive in vacuum hohlraums, but for gas-filled hohlraums used to drive capsule implosions, the model consistently predicts too much drive and capsule bang times earlier than measured. In this work, we introduce a new model that brings the calculated bang time into better agreement with the measured bang time. The new model employs (1) a numerical grid that is fully converged in space, energy, and time, (2) a modified approximate NLTE model that includes more physics and is in better agreement with more detailed offline emissivity models, and (3) a reduced flux limiter value of 0.03. We applied this model to gas-filled hohlraum experiments using high density carbon and plastic ablator capsules that had hohlraum He fill gas densities ranging from 0.06 to 1.6 mg/cc and hohlraum diameters of 5.75 or 6.72 mm. The new model predicts bang times to within ±100 ps for most experiments with low to intermediate fill densities (up to 0.85 mg/cc). This model predicts higher temperatures in the plasma than the old model and also predicts that at higher gas fill densities, a significant amount of inner beam laser energy escapes the hohlraum through the opposite laser entrance hole.

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.

Predicting the cosmological constant with the scale-factor cutoff measure

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

De Simone, Andrea; Guth, Alan H.; Salem, Michael P.

2008-09-15

It is well known that anthropic selection from a landscape with a flat prior distribution of cosmological constant {lambda} gives a reasonable fit to observation. However, a realistic model of the multiverse has a physical volume that diverges with time, and the predicted distribution of {lambda} depends on how the spacetime volume is regulated. A very promising method of regulation uses a scale-factor cutoff, which avoids a number of serious problems that arise in other approaches. In particular, the scale-factor cutoff avoids the 'youngness problem' (high probability of living in a much younger universe) and the 'Q and G catastrophes'more » (high probability for the primordial density contrast Q and gravitational constant G to have extremely large or small values). We apply the scale-factor cutoff measure to the probability distribution of {lambda}, considering both positive and negative values. The results are in good agreement with observation. In particular, the scale-factor cutoff strongly suppresses the probability for values of {lambda} that are more than about 10 times the observed value. We also discuss qualitatively the prediction for the density parameter {omega}, indicating that with this measure there is a possibility of detectable negative curvature.« less

Validation of a dye stain assay for vaginally inserted HEC-filled microbicide applicators

PubMed Central

Katzen, Lauren L.; Fernández-Romero, José A.; Sarna, Avina; Murugavel, Kailapuri G.; Gawarecki, Daniel; Zydowsky, Thomas M.; Mensch, Barbara S.

2011-01-01

Background The reliability and validity of self-reports of vaginal microbicide use are questionable given the explicit understanding that participants are expected to comply with study protocols. Our objective was to optimize the Population Council's previously validated dye stain assay (DSA) and related procedures, and establish predictive values for the DSA's ability to identify vaginally inserted single-use, low-density polyethylene microbicide applicators filled with hydroxyethylcellulose gel. Methods Applicators, inserted by 252 female sex workers enrolled in a microbicide feasibility study in Southern India, served as positive controls for optimization and validation experiments. Prior to validation, optimal dye concentration and staining time were ascertained. Three validation experiments were conducted to determine sensitivity, specificity, negative predictive values and positive predictive values. Results The dye concentration of 0.05% (w/v) FD&C Blue No. 1 Granular Food Dye and staining time of five seconds were determined to be optimal and were used for the three validation experiments. There were a total of 1,848 possible applicator readings across validation experiments; 1,703 (92.2%) applicator readings were correct. On average, the DSA performed with 90.6% sensitivity, 93.9% specificity, and had a negative predictive value of 93.8% and a positive predictive value of 91.0%. No statistically significant differences between experiments were noted. Conclusions The DSA was optimized and successfully validated for use with single-use, low-density polyethylene applicators filled with hydroxyethylcellulose (HEC) gel. We recommend including the DSA in future microbicide trials involving vaginal gels in order to identify participants who have low adherence to dosing regimens. In doing so, we can develop strategies to improve adherence as well as investigate the association between product use and efficacy. PMID:21992983

Real time detection of farm-level swine mycobacteriosis outbreak using time series modeling of the number of condemned intestines in abattoirs.

PubMed

Adachi, Yasumoto; Makita, Kohei

2015-09-01

Mycobacteriosis in swine is a common zoonosis found in abattoirs during meat inspections, and the veterinary authority is expected to inform the producer for corrective actions when an outbreak is detected. The expected value of the number of condemned carcasses due to mycobacteriosis therefore would be a useful threshold to detect an outbreak, and the present study aims to develop such an expected value through time series modeling. The model was developed using eight years of inspection data (2003 to 2010) obtained at 2 abattoirs of the Higashi-Mokoto Meat Inspection Center, Japan. The resulting model was validated by comparing the predicted time-dependent values for the subsequent 2 years with the actual data for 2 years between 2011 and 2012. For the modeling, at first, periodicities were checked using Fast Fourier Transformation, and the ensemble average profiles for weekly periodicities were calculated. An Auto-Regressive Integrated Moving Average (ARIMA) model was fitted to the residual of the ensemble average on the basis of minimum Akaike's information criterion (AIC). The sum of the ARIMA model and the weekly ensemble average was regarded as the time-dependent expected value. During 2011 and 2012, the number of whole or partial condemned carcasses exceeded the 95% confidence interval of the predicted values 20 times. All of these events were associated with the slaughtering of pigs from three producers with the highest rate of condemnation due to mycobacteriosis.

Predictive Contribution of Neutrophil/Lymphocyte Ratio in Diagnosis of Brucellosis

PubMed Central

Olt, Serdar; Ergenç, Hasan; Açıkgöz, Seyyid Bilal

2015-01-01

Here we wanted to investigate predictive value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) in the diagnosis of brucellosis. Thirty-two brucellosis patients diagnosed with positive serum agglutination test and thirty-two randomized healthy subjects were enrolled in this study retrospectively. Result with ROC analyzes the baseline NLR and hemoglobin values were found to be significantly associated with brucellosis (P = 0.01, P = 0.01, resp.). Herein we demonstrated for the first time that NLR values were significantly associated with brucellosis. This situation can help clinicians during diagnosis of brucellosis. PMID:25722970

Reality Check Algorithm for Complex Sources in Early Warning

NASA Astrophysics Data System (ADS)

Karakus, G.; Heaton, T. H.

2013-12-01

In almost all currently operating earthquake early warning (EEW) systems, presently available seismic data are used to predict future shaking. In most cases, location and magnitude are estimated. We are developing an algorithm to test the goodness of that prediction in real time. We monitor envelopes of acceleration, velocity, and displacement; if they deviate significantly from the envelope predicted by Cua's envelope gmpe's then we declare an overfit (perhaps false alarm) or an underfit (possibly a larger event has just occurred). This algorithm is designed to provide a robust measure and to work as quickly as possible in real-time. We monitor the logarithm of the ratio between the envelopes of the ongoing observed event and the envelopes derived from the predicted envelopes of channels of ground motion of the Virtual Seismologist (VS) (Cua, G. and Heaton, T.). Then, we recursively filter this result with a simple running median (de-spiking operator) to minimize the effect of one single high value. Depending on the result of the filtered value we make a decision such as if this value is large enough (e.g., >1), then we would declare, 'that a larger event is in progress', or similarly if this value is small enough (e.g., <-1), then we would declare a false alarm. We design the algorithm to work at a wide range of amplitude scales; that is, it should work for both small and large events.

Signal-averaged P wave in patients with paroxysmal atrial fibrillation.

PubMed

Rosenheck, S

1997-10-01

The theoretical and experimental rational of atrial signal-averaged ECG in patients with AF is delay in the intra-atrial and interatrial conduction. Similar to the ventricular signal-averaged ECG, the atrial signal-averaged ECG is an averaging of a high number of consecutive P waves that match the template created earlier P wave triggering is preferred over QRS triggering because of more accurate aligning. However, the small amplitude of the atrial ECG and its gradual increase from the isoelectric line may create difficulties in defining the start point if P wave triggering is used. Studies using P wave triggering and those using QRS triggering demonstrate a prolonged P wave duration in patients with paroxysmal AF. The negative predictive value of this test is relatively high at 60%-80%. The positive predictive value of atrial signal-averaged ECGs in predicting the risk of AF is considerably lower than the negative predictive value. All the data accumulated prospectively on the predictive value of P wave signal-averaging was determined only in patients undergoing coronary bypass surgery or following MI; its value in other patients with paroxysmal AF is still not determined. The clinical role of frequency-domain analysis (alone or added to time-domain analysis) remains undefined. Because of this limited knowledge on the predictive value of P wave signal-averaging, it is still not clinical medicine, and further research is needed before atrial signal-averaged ECG will be part of clinical testing.

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.

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

2018-05-01

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

[Relationships between motivational regulation strategies, motivational factors, and learning behaviors outside the classroom].

PubMed

Umemoto, Takatoyo; Tanaka, Kenshiro

2017-04-01

This study examined the relationships among motivational regulation strategies, motivational factors, and learning behaviors outside the classroom. There are three subtypes of motivational regulation strategies: autonomous regulation strategies, cooperative strategies, and performance-focused strategies. Motivational factors included in the investigation were self-efficacy and task value, while behavioral and emotional engagement and study time were selected as learning behaviors outside the classroom. A self-report questionnaire was administered to 322 undergraduates from two universities. Multiple regression analysis revealed the use of autonomous regulation strategies, and that task value was positively correlated with engagement and study time. Moreover, self-efficacy positively predicted study time. In contrast, the use of performance strategies negatively predicted engagement. The use of cooperative strategies did not predict learning behaviors. These results indicate that motivation, as well as the regulation of motivation, were important for learning outside the classroom. The effects of regulation of motivation and motivation on learning outside the classroom are discussed in light of the current findings.

Analysis of loss of time value during road maintenance project

NASA Astrophysics Data System (ADS)

Sudarsana, Dewa Ketut; Sanjaya, Putu Ari

2017-06-01

Lane closure is frequently performed in the execution of the road maintenance project. It has a negative impact on road users such as the loss of vehicle operating costs and the loss of time value. Nevertheless, analysis on loss of time value in Indonesia has not been carried out. The parameter of time value for the road users was the minimum wage city/region approach. Vehicle speed of pre-construction was obtained by observation, while the speed during the road maintenance project was predicted by the speed of the pre-construction by multiplying it with the speed adjustment factor. In the case of execution of the National road maintenance project in the two-lane two-way urban and interurban road types in the fiscal year of 2015 in Bali province, the loss of time value was at the average of IDR 12,789,000/day/link road. The relationship of traffic volume and loss of time value of the road users was obtained by a logarithm model.

Implications of the difference between true and predicted breeding values for the study of natural selection and micro-evolution.

PubMed

Postma, E

2006-03-01

The ability to predict individual breeding values in natural populations with known pedigrees has provided a powerful tool to separate phenotypic values into their genetic and environmental components in a nonexperimental setting. This has allowed sophisticated analyses of selection, as well as powerful tests of evolutionary change and differentiation. To date, there has, however, been no evaluation of the reliability or potential limitations of the approach. In this article, I address these gaps. In particular, I emphasize the differences between true and predicted breeding values (PBVs), which as yet have largely been ignored. These differences do, however, have important implications for the interpretation of, firstly, the relationship between PBVs and fitness, and secondly, patterns in PBVs over time. I subsequently present guidelines I believe to be essential in the formulation of the questions addressed in studies using PBVs, and I discuss possibilities for future research.

Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

Statistical Approaches for Spatiotemporal Prediction of Low Flows

NASA Astrophysics Data System (ADS)

Fangmann, A.; Haberlandt, U.

2017-12-01

An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be problematic. Spatiotemporal prediction of L-moments appeared highly uncertain for higher-order moments resulting in unrealistic future low flow values. All in all, the results promote an inclusion of simple statistical methods in climate change impact assessment.

HIV RNA testing in the context of nonoccupational postexposure prophylaxis.

PubMed

Roland, Michelle E; Elbeik, Tarek A; Kahn, James O; Bamberger, Joshua D; Coates, Thomas J; Krone, Melissa R; Katz, Mitchell H; Busch, Michael P; Martin, Jeffrey N

2004-08-01

The specificity and positive predictive value of human immunodeficiency virus (HIV) RNA assays have not been evaluated in the setting of postexposure prophylaxis (PEP). Plasma from subjects enrolled in a nonoccupational PEP study was tested with 2 branched-chain DNA (bDNA) assays, 2 polymerase chain reaction (PCR) assays, and a transcription-mediated amplification (TMA) assay. Assay specificity and positive predictive value were determined for subjects who remained negative for HIV antibody for >or=3 months. In 329 subjects examined, the lowest specificities (90.1%-93.7%) were seen for bDNA testing performed in real time. The highest specificities were seen with batched bDNA version 3.0 (99.1%), standard PCR (99.4%), ultrasensitive PCR (100%), and TMA (99.6%) testing. Only the 2 assays with the highest specificities had positive predictive values >40%. For the bDNA assays, increasing the cutoff point at which a test is called positive (e.g., from 50 copies/mL to 500 copies/mL for version 3.0) increased both specificity and positive predictive values to 100%. The positive predictive value of HIV RNA assays in individuals presenting for PEP is unacceptably low for bDNA-based testing and possibly acceptable for PCR- and TMA-based testing. Routine use of HIV RNA assays in such individuals is not recommended.

Hydrodynamic correlation functions of hard-sphere fluids at short times

NASA Astrophysics Data System (ADS)

Leegwater, Jan A.; van Beijeren, Henk

1989-11-01

The short-time behavior of the coherent intermediate scattering function for a fluid of hard-sphere particles is calculated exactly through order t 4, and the other hydrodynamic correlation functions are calculated exactly through order t 2. It is shown that for all of the correlation functions considered the Enskog theory gives a fair approximation. Also, the initial time behavior of various Green-Kubo integrands is studied. For the shear-viscosity integrand it is found that at density nσ3=0.837 the prediction of the Enskog theory is 32% too low. The initial value of the bulk viscosity integrand is nonzero, in contrast to the Enskog result. The initial value of the thermal conductivity integrand at high densities is predicted well by Enskog theory.

Fuzzy time series forecasting model with natural partitioning length approach for predicting the unemployment rate under different degree of confidence

NASA Astrophysics Data System (ADS)

Ramli, Nazirah; Mutalib, Siti Musleha Ab; Mohamad, Daud

2017-08-01

Fuzzy time series forecasting model has been proposed since 1993 to cater for data in linguistic values. Many improvement and modification have been made to the model such as enhancement on the length of interval and types of fuzzy logical relation. However, most of the improvement models represent the linguistic term in the form of discrete fuzzy sets. In this paper, fuzzy time series model with data in the form of trapezoidal fuzzy numbers and natural partitioning length approach is introduced for predicting the unemployment rate. Two types of fuzzy relations are used in this study which are first order and second order fuzzy relation. This proposed model can produce the forecasted values under different degree of confidence.

Support vector machines for TEC seismo-ionospheric anomalies detection

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-02-01

Using time series prediction methods, it is possible to pursue the behaviors of earthquake precursors in the future and to announce early warnings when the differences between the predicted value and the observed value exceed the predefined threshold value. Support Vector Machines (SVMs) are widely used due to their many advantages for classification and regression tasks. This study is concerned with investigating the Total Electron Content (TEC) time series by using a SVM to detect seismo-ionospheric anomalous variations induced by the three powerful earthquakes of Tohoku (11 March 2011), Haiti (12 January 2010) and Samoa (29 September 2009). The duration of TEC time series dataset is 49, 46 and 71 days, for Tohoku, Haiti and Samoa earthquakes, respectively, with each at time resolution of 2 h. In the case of Tohoku earthquake, the results show that the difference between the predicted value obtained from the SVM method and the observed value reaches the maximum value (i.e., 129.31 TECU) at earthquake time in a period of high geomagnetic activities. The SVM method detected a considerable number of anomalous occurrences 1 and 2 days prior to the Haiti earthquake and also 1 and 5 days before the Samoa earthquake in a period of low geomagnetic activities. In order to show that the method is acting sensibly with regard to the results extracted during nonevent and event TEC data, i.e., to perform some null-hypothesis tests in which the methods would also be calibrated, the same period of data from the previous year of the Samoa earthquake date has been taken into the account. Further to this, in this study, the detected TEC anomalies using the SVM method were compared to the previous results (Akhoondzadeh and Saradjian, 2011; Akhoondzadeh, 2012) obtained from the mean, median, wavelet and Kalman filter methods. The SVM detected anomalies are similar to those detected using the previous methods. It can be concluded that SVM can be a suitable learning method to detect the novelty changes of a nonlinear time series such as variations of earthquake precursors.

A Case for Transforming the Criterion of a Predictive Validity Study

ERIC Educational Resources Information Center

Patterson, Brian F.; Kobrin, Jennifer L.

2011-01-01

This study presents a case for applying a transformation (Box and Cox, 1964) of the criterion used in predictive validity studies. The goals of the transformation were to better meet the assumptions of the linear regression model and to reduce the residual variance of fitted (i.e., predicted) values. Using data for the 2008 cohort of first-time,…

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.

Methods of sequential estimation for determining initial data in numerical weather prediction. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Cohn, S. E.

1982-01-01

Numerical weather prediction (NWP) is an initial-value problem for a system of nonlinear differential equations, in which initial values are known incompletely and inaccurately. Observational data available at the initial time must therefore be supplemented by data available prior to the initial time, a problem known as meteorological data assimilation. A further complication in NWP is that solutions of the governing equations evolve on two different time scales, a fast one and a slow one, whereas fast scale motions in the atmosphere are not reliably observed. This leads to the so called initialization problem: initial values must be constrained to result in a slowly evolving forecast. The theory of estimation of stochastic dynamic systems provides a natural approach to such problems. For linear stochastic dynamic models, the Kalman-Bucy (KB) sequential filter is the optimal data assimilation method, for linear models, the optimal combined data assimilation-initialization method is a modified version of the KB filter.

An original approach was used to better evaluate the capacity of a prognostic marker using published survival curves.

PubMed

Dantan, Etienne; Combescure, Christophe; Lorent, Marine; Ashton-Chess, Joanna; Daguin, Pascal; Classe, Jean-Marc; Giral, Magali; Foucher, Yohann

2014-04-01

Predicting chronic disease evolution from a prognostic marker is a key field of research in clinical epidemiology. However, the prognostic capacity of a marker is not systematically evaluated using the appropriate methodology. We proposed the use of simple equations to calculate time-dependent sensitivity and specificity based on published survival curves and other time-dependent indicators as predictive values, likelihood ratios, and posttest probability ratios to reappraise prognostic marker accuracy. The methodology is illustrated by back calculating time-dependent indicators from published articles presenting a marker as highly correlated with the time to event, concluding on the high prognostic capacity of the marker, and presenting the Kaplan-Meier survival curves. The tools necessary to run these direct and simple computations are available online at http://www.divat.fr/en/online-calculators/evalbiom. Our examples illustrate that published conclusions about prognostic marker accuracy may be overoptimistic, thus giving potential for major mistakes in therapeutic decisions. Our approach should help readers better evaluate clinical articles reporting on prognostic markers. Time-dependent sensitivity and specificity inform on the inherent prognostic capacity of a marker for a defined prognostic time. Time-dependent predictive values, likelihood ratios, and posttest probability ratios may additionally contribute to interpret the marker's prognostic capacity. Copyright © 2014 Elsevier Inc. All rights reserved.

Predictive Values of the New Sarcopenia Index by the Foundation for the National Institutes of Health Sarcopenia Project for Mortality among Older Korean Adults.

PubMed

Moon, Joon Ho; Kim, Kyoung Min; Kim, Jung Hee; Moon, Jae Hoon; Choi, Sung Hee; Lim, Soo; Lim, Jae-Young; Kim, Ki Woong; Park, Kyong Soo; Jang, Hak Chul

2016-01-01

We evaluated the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project's recommended criteria for sarcopenia's association with mortality among older Korean adults. We conducted a community-based prospective cohort study which included 560 (285 men and 275 women) older Korean adults aged ≥65 years. Muscle mass (appendicular skeletal muscle mass-to-body mass index ratio (ASM/BMI)), handgrip strength, and walking velocity were evaluated in association with all-cause mortality during 6-year follow-up. Both the lowest quintile for each parameter (ethnic-specific cutoff) and FNIH-recommended values were used as cutoffs. Forty men (14.0%) and 21 women (7.6%) died during 6-year follow-up. The deceased subjects were older and had lower ASM, handgrip strength, and walking velocity. Sarcopenia defined by both low lean mass and weakness had a 4.13 (95% CI, 1.69-10.11) times higher risk of death, and sarcopenia defined by a combination of low lean mass, weakness, and slowness had a 9.56 (3.16-28.90) times higher risk of death after adjusting for covariates in men. However, these significant associations were not observed in women. In terms of cutoffs of each parameter, using the lowest quintile showed better predictive values in mortality than using the FNIH-recommended values. Moreover, new muscle mass index, ASM/BMI, provided better prognostic values than ASM/height2 in all associations. New sarcopenia definition by FNIH was better able to predict 6-year mortality among Korean men. Moreover, ethnic-specific cutoffs, the lowest quintile for each parameter, predicted the higher risk of mortality than the FNIH-recommended values.

Predictive Values of the New Sarcopenia Index by the Foundation for the National Institutes of Health Sarcopenia Project for Mortality among Older Korean Adults

PubMed Central

Kim, Jung Hee; Moon, Jae Hoon; Choi, Sung Hee; Lim, Soo; Lim, Jae-Young; Kim, Ki Woong; Park, Kyong Soo; Jang, Hak Chul

2016-01-01

Objective We evaluated the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project’s recommended criteria for sarcopenia’s association with mortality among older Korean adults. Methods We conducted a community-based prospective cohort study which included 560 (285 men and 275 women) older Korean adults aged ≥65 years. Muscle mass (appendicular skeletal muscle mass-to-body mass index ratio (ASM/BMI)), handgrip strength, and walking velocity were evaluated in association with all-cause mortality during 6-year follow-up. Both the lowest quintile for each parameter (ethnic-specific cutoff) and FNIH-recommended values were used as cutoffs. Results Forty men (14.0%) and 21 women (7.6%) died during 6-year follow-up. The deceased subjects were older and had lower ASM, handgrip strength, and walking velocity. Sarcopenia defined by both low lean mass and weakness had a 4.13 (95% CI, 1.69–10.11) times higher risk of death, and sarcopenia defined by a combination of low lean mass, weakness, and slowness had a 9.56 (3.16–28.90) times higher risk of death after adjusting for covariates in men. However, these significant associations were not observed in women. In terms of cutoffs of each parameter, using the lowest quintile showed better predictive values in mortality than using the FNIH-recommended values. Moreover, new muscle mass index, ASM/BMI, provided better prognostic values than ASM/height2 in all associations. Conclusions New sarcopenia definition by FNIH was better able to predict 6-year mortality among Korean men. Moreover, ethnic-specific cutoffs, the lowest quintile for each parameter, predicted the higher risk of mortality than the FNIH-recommended values. PMID:27832145

Predictable waves of sequential forest degradation and biodiversity loss spreading from an African city.

PubMed

Ahrends, Antje; Burgess, Neil D; Milledge, Simon A H; Bulling, Mark T; Fisher, Brendan; Smart, James C R; Clarke, G Philip; Mhoro, Boniface E; Lewis, Simon L

2010-08-17

Tropical forest degradation emits carbon at a rate of approximately 0.5 Pgxy(-1), reduces biodiversity, and facilitates forest clearance. Understanding degradation drivers and patterns is therefore crucial to managing forests to mitigate climate change and reduce biodiversity loss. Putative patterns of degradation affecting forest stocks, carbon, and biodiversity have variously been described previously, but these have not been quantitatively assessed together or tested systematically. Economic theory predicts a systematic allocation of land to its highest use value in response to distance from centers of demand. We tested this theory to see if forest exploitation would expand through time and space as concentric waves, with each wave targeting lower value products. We used forest data along a transect from 10 to 220 km from Dar es Salaam (DES), Tanzania, collected at two points in time (1991 and 2005). Our predictions were confirmed: high-value logging expanded 9 kmxy(-1), and an inner wave of lower value charcoal production 2 kmxy(-1). This resource utilization is shown to reduce the public goods of carbon storage and species richness, which significantly increased with each kilometer from DES [carbon, 0.2 Mgxha(-1); 0.1 species per sample area (0.4 ha)]. Our study suggests that tropical forest degradation can be modeled and predicted, with its attendant loss of some public goods. In sub-Saharan Africa, an area experiencing the highest rate of urban migration worldwide, coupled with a high dependence on forest-based resources, predicting the spatiotemporal patterns of degradation can inform policies designed to extract resources without unsustainably reducing carbon storage and biodiversity.

Dopamine reward prediction error responses reflect marginal utility.

PubMed

Stauffer, William R; Lak, Armin; Schultz, Wolfram

2014-11-03

Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Protecting Astronaut Health at First Entry into Vehicles Visiting the international Space Station: Insights from Whole-Module Offgas Testing

NASA Technical Reports Server (NTRS)

Meyers, Valerie

2014-01-01

NASA has accumulated considerable experience in offgas testing of whole modules prior to their docking with the International Space Station (ISS). Since 1998, the Space Toxicology Office has performed offgas testing of the Lab module, both MPLM modules, US Airlock, Node 1, Node 2, Node 3, ATV1, HTV1, and three commercial vehicles. The goal of these tests is twofold: first, to protect the crew from adverse health effects of accumulated volatile pollutants when they first enter the module on orbit, and secondly, to determine the additional pollutant load that the ISS air revitalization systems must handle. In order to predict the amount of accumulated pollutants, the module is sealed for at least 1/5th the worst-case time interval that could occur between the last clean air purge and final hatch closure on the ground and the crew's first entry on orbit. This time can range from a few days to a few months. Typically, triplicate samples are taken at pre-planned times throughout the test. Samples are then analyzed by gas chromatography and mass spectrometry, and the rate of accumulation of pollutants is then extrapolated over time. The analytical values are indexed against 7-day spacecraft maximum allowable concentrations (SMACs) to provide a prediction of the total toxicity value (T-value) at the time of first entry. This T-value and the toxicological effects of specific pollutants that contribute most to the overall toxicity are then used to guide first entry operations. Finally, results are compared to first entry samples collected on orbit to determine the predictive ability of the ground-based offgas test.

Reply to “Statistical evaluation of the VAN Method using the historic earthquake catalog in Greece,” by Richard L. Aceves, Stephen K. Park and David J. Strauss

NASA Astrophysics Data System (ADS)

Varotsos, P.; Lazaridou, M.

The pioneering calculation by Aceves et al. [1996] shed light on the main question of this debate, i.e., on whether “VAN predictions can be ascribed to chance.” Aceves et al. [1996] conclude that “the VAN method has resulted in a significantly higher prediction rate than randomly sampling a PDF (probability density function) map generated from a 25 year history of earthquakes.” After investigating the totality of VAN predictions issued during the period 1987-1989, Aceves et al. [1996] found: “The prediction rate for the VAN method clearly exceeds that from the random model at all time lags between 5-22 days. At a 5 day time lag, the VAN prediction rate of 35.7% has a P-value of less than 0.06%. This means that a random model does as well as does the VAN method less than 0.06% of the time. At 22 days, the prediction rate of 67.9% has a P-value of less than 0.07%.” These conclusions basically coincide with those of Hamada [1993] although Aceves et al. [1996] followed different procedures. They are also in fundamental agreement with the results of Honkura and Tanaka [1996]. Another important conclusion of Aceves et al. [1996] is that, after declustering the earthquake catalog and prediction list from aftershocks, “VAN method is still formally significant.”

Fetal gender prediction based on maternal plasma testosterone and insulin-like peptide 3 concentrations at midgestation and late gestation in cattle.

PubMed

Kibushi, M; Kawate, N; Kaminogo, Y; Hannan, M A; Weerakoon, W W P N; Sakase, M; Fukushima, M; Seyama, T; Inaba, T; Tamada, H

2016-10-15

We compared maternal plasma testosterone and insulin-like peptide 3 (INSL3) concentrations between dams carrying a male versus female fetus from early to late gestation and examined the application of maternal hormonal concentrations to fetal gender prediction in dairy and beef cattle. Blood samples were collected from Holstein cows or heifers (N = 31) and Japanese Black beef cows (N = 33) at 1-month intervals at 2 to 8 months of gestation. Fetal gender was confirmed by visual observation of external genitalia of calves just after birth. Plasma testosterone and INSL3 concentrations were determined by enzyme-immunoassay. Fetal genders were judged based on cutoff values of maternal testosterone and INSL3 concentrations (male, if it was ≥ cutoff value; female, if < cutoff value), which we set for each hormone at each gestational month using receiver operating characteristic curves. Plasma testosterone concentrations were higher for dams with a male fetus than those with a female at 4, 5, 7, and 8 months for the dairy cattle (P < 0.05) and at 4, 5, 6, and 8 months for the beef cows (P < 0.05). Plasma INSL3 concentrations were higher for dams with a male fetus than those with a female at 2 and 6 months for the dairy cattle (P < 0.05) and at 4 to 8 months for the beef cows (P < 0.05). The predictive values and detection rates for fetal gender prediction based on maternal testosterone concentrations were 75.8% to 79.3% for dairy cattle at 5 and 7 months and for beef cows at 5 and 6 months, whereas those values by maternal INSL3 concentrations were 71.0% to 72.4% for the dairy cattle at 6 months and beef cows at 4 and 8 months. When multiple time points of testosterone and INSL3 concentrations at several midgestation and late gestation months were considered for fetal gender prediction, predictive values were 89.3% (5-7 months) and 85.7% to 88.0% (4-6, 8 months) for the dairy and beef breeds, respectively. Maternal testosterone and INSL3 concentrations in dams carrying a male fetus were higher than those carrying a female at midgestation and/or late gestation in Holstein and Japanese Black beef cattle. Nearly, 80% accuracy was obtained for fetal gender prediction by a single time point of maternal plasma testosterone concentrations at midgestation. Nearly 90% accuracy for the prediction was obtained when multiple time points of testosterone and INSL3 concentrations from midgestation to late gestation were considered. Copyright © 2016 Elsevier Inc. All rights reserved.

The prediction of speech intelligibility in classrooms using computer models

NASA Astrophysics Data System (ADS)

Dance, Stephen; Dentoni, Roger

2005-04-01

Two classrooms were measured and modeled using the industry standard CATT model and the Web model CISM. Sound levels, reverberation times and speech intelligibility were predicted in these rooms using data for 7 octave bands. It was found that overall sound levels could be predicted to within 2 dB by both models. However, overall reverberation time was found to be accurately predicted by CATT 14% prediction error, but not by CISM, 41% prediction error. This compared to a 30% prediction error using classical theory. As for STI: CATT predicted within 11%, CISM to within 3% and Sabine to within 28% of the measured value. It should be noted that CISM took approximately 15 seconds to calculate, while CATT took 15 minutes. CISM is freely available on-line at www.whyverne.co.uk/acoustics/Pages/cism/cism.html

An interactive dynamic analysis and decision support software for MR mammography.

PubMed

Ertaş, Gökhan; Gülçür, H Ozcan; Tunaci, Mehtap

2008-06-01

A fully automated software is introduced to facilitate MR mammography (MRM) examinations and overcome subjectiveness in diagnosis using normalized maximum intensity-time ratio (nMITR) maps. These maps inherently suppress enhancements due to normal parenchyma and blood vessels that surround lesions and have natural tolerance to small field inhomogeneities and motion artifacts. The classifier embedded within the software is trained with normalized complexity and maximum nMITR of 22 lesions and tested with the features of remaining 22 lesions. Achieved diagnostic performances are 92% sensitivity, 90% specificity, 91% accuracy, 92% positive predictive value and 90% negative predictive value. DynaMammoAnalyst shortens evaluation time considerably and reduces inter and intra-observer variability by providing decision support.

A CBR-Based and MAHP-Based Customer Value Prediction Model for New Product Development

PubMed Central

Zhao, Yu-Jie; Luo, Xin-xing; Deng, Li

2014-01-01

In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts' workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor's effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers' transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM's three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment. PMID:25162050

A CBR-based and MAHP-based customer value prediction model for new product development.

PubMed

Zhao, Yu-Jie; Luo, Xin-xing; Deng, Li

2014-01-01

In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts' workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor's effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers' transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM's three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment.

A function approximation approach to anomaly detection in propulsion system test data

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Hoyt, W. A.

1993-01-01

Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

The electrochemistry of carbon steel in simulated concrete pore water in boom clay repository environments

NASA Astrophysics Data System (ADS)

MacDonald, D. D.; Saleh, A.; Lee, S. K.; Azizi, O.; Rosas-Camacho, O.; Al-Marzooqi, A.; Taylor, M.

2011-04-01

The prediction of corrosion damage of canisters to experimentally inaccessible times is vitally important in assessing various concepts for the disposal of High Level Nuclear Waste. Such prediction can only be made using deterministic models, whose predictions are constrained by the time-invariant natural laws. In this paper, we describe the measurement of experimental electrochemical data that will allow the prediction of damage to the carbon steel overpack of the super container in Belgium's proposed Boom Clay repository by using the Point Defect Model (PDM). PDM parameter values are obtained by optimizing the model on experimental, wide-band electrochemical impedance spectroscopy data.

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms.

PubMed

Doble, Brett; Lorgelly, Paula

2016-04-01

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

Validation study of the SCREENIVF: an instrument to screen women or men on risk for emotional maladjustment before the start of a fertility treatment.

PubMed

Ockhuijsen, Henrietta D L; van Smeden, Maarten; van den Hoogen, Agnes; Boivin, Jacky

2017-06-01

To examine construct and criterion validity of the Dutch SCREENIVF among women and men undergoing a fertility treatment. A prospective longitudinal study nested in a randomized controlled trial. University hospital. Couples, 468 women and 383 men, undergoing an IVF/intracytoplasmic sperm injection (ICSI) treatment in a fertility clinic, completed the SCREENIVF. Construct and criteria validity of the SCREENIVF. The comparative fit index and root mean square error of approximation for women and men show a good fit of the factor model. Across time, the sensitivity for Hospital Anxiety and Depression Scale subscale in women ranged from 61%-98%, specificity 53%-65%, predictive value of a positive test (PVP) 13%-56%, predictive value of a negative test (PVN) 70%-99%. The sensitivity scores for men ranged from 38%-100%, specificity 71%-75%, PVP 9%-27%, PVN 92%-100%. A prediction model revealed that for women 68.7% of the variance in the Hospital Anxiety and Depression Scale on time 1 and 42.5% at time 2 and 38.9% at time 3 was explained by the predictors, the sum score scales of the SCREENIVF. For men, 58.1% of the variance in the Hospital Anxiety and Depression Scale on time 1 and 46.5% at time 2 and 37.3% at time 3 was explained by the predictors, the sum score scales of the SCREENIVF. The SCREENIVF has good construct validity but the concurrent validity is better than the predictive validity. SCREENIVF will be most effectively used in fertility clinics at the start of treatment and should not be used as a predictive tool. Copyright © 2017 American Society for Reproductive Medicine. All rights reserved.

A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

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

Kevin McCarthy; Milos Manic

Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presentsmore » an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.« less

A New Hybrid-Multiscale SSA Prediction of Non-Stationary Time Series

NASA Astrophysics Data System (ADS)

Ghanbarzadeh, Mitra; Aminghafari, Mina

2016-02-01

Singular spectral analysis (SSA) is a non-parametric method used in the prediction of non-stationary time series. It has two parameters, which are difficult to determine and very sensitive to their values. Since, SSA is a deterministic-based method, it does not give good results when the time series is contaminated with a high noise level and correlated noise. Therefore, we introduce a novel method to handle these problems. It is based on the prediction of non-decimated wavelet (NDW) signals by SSA and then, prediction of residuals by wavelet regression. The advantages of our method are the automatic determination of parameters and taking account of the stochastic structure of time series. As shown through the simulated and real data, we obtain better results than SSA, a non-parametric wavelet regression method and Holt-Winters method.

The Hubble Constant from SN Refsdal

NASA Astrophysics Data System (ADS)

Vega-Ferrero, J.; Diego, J. M.; Miranda, V.; Bernstein, G. M.

2018-02-01

Hubble Space Telescope observations from 2015 December 11 detected the expected fifth counter-image of supernova (SN) Refsdal at z = 1.49. In this Letter, we compare the time-delay predictions from numerous models with the measured value derived by Kelly et al. from very early data in the light curve of the SN Refsdal and find a best value for {H}0={64}-11+9 {km} {{{s}}}-1 {{Mpc}}-1 (68% CL), in excellent agreement with predictions from cosmic microwave background and recent weak lensing data + baryon acoustic oscillations + Big Bang nucleosynthesis (from the DES Collaboration). This is the first constraint on H 0 derived from time delays between multiple-lensed SN images, and the first with a galaxy cluster lens, subject to systematic effects different from other time-delay H 0 estimates. Additional time-delay measurements from new multiply imaged SNe will allow derivation of competitive constraints on H 0.

Determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for established and alternative flame retardants.

PubMed

Stenzel, Angelika; Goss, Kai-Uwe; Endo, Satoshi

2013-02-05

Polyparameter linear free energy relationships (pp-LFERs) can predict partition coefficients for a multitude of environmental and biological phases with high accuracy. In this work, the pp-LFER substance descriptors of 40 established and alternative flame retardants (e.g., polybrominated diphenyl ethers, hexabromocyclododecane, bromobenzenes, trialkyl phosphates) were determined experimentally. In total, 251 data for gas-chromatographic (GC) retention times and liquid/liquid partition coefficients (K) were measured and used to calibrate the pp-LFER substance descriptors. Substance descriptors were validated through a comparison between predicted and experimental log K for the systems octanol/water (K(ow)), water/air (K(wa)), organic carbon/water (K(oc)) and liposome/water (K(lipw)), revealing a high reliability of pp-LFER predictions based on our descriptors. For instance, the difference between predicted and experimental log K(ow) was <0.3 log units for 17 out of 21 compounds for which experimental values were available. Moreover, we found an indication that the H-bond acceptor value (B) depends on the solvent for some compounds. Thus, for predicting environmentally relevant partition coefficients it is important to determine B values using measurements in aqueous systems. The pp-LFER descriptors calibrated in this study can be used to predict partition coefficients for which experimental data are unavailable, and the predicted values can serve as references for further experimental measurements.

The statistical properties and possible causes of polar motion prediction errors

NASA Astrophysics Data System (ADS)

Kosek, Wieslaw; Kalarus, Maciej; Wnek, Agnieszka; Zbylut-Gorska, Maria

2015-08-01

The pole coordinate data predictions from different prediction contributors of the Earth Orientation Parameters Combination of Prediction Pilot Project (EOPCPPP) were studied to determine the statistical properties of polar motion forecasts by looking at the time series of differences between them and the future IERS pole coordinates data. The mean absolute errors, standard deviations as well as the skewness and kurtosis of these differences were computed together with their error bars as a function of prediction length. The ensemble predictions show a little smaller mean absolute errors or standard deviations however their skewness and kurtosis values are similar as the for predictions from different contributors. The skewness and kurtosis enable to check whether these prediction differences satisfy normal distribution. The kurtosis values diminish with the prediction length which means that the probability distribution of these prediction differences is becoming more platykurtic than letptokurtic. Non zero skewness values result from oscillating character of these differences for particular prediction lengths which can be due to the irregular change of the annual oscillation phase in the joint fluid (atmospheric + ocean + land hydrology) excitation functions. The variations of the annual oscillation phase computed by the combination of the Fourier transform band pass filter and the Hilbert transform from pole coordinates data as well as from pole coordinates model data obtained from fluid excitations are in a good agreement.

The variability and IRI2007-predictability of hmF2 over South Africa

NASA Astrophysics Data System (ADS)

Mbambo, M. C.; McKinnell, Lee-Anne; Habarulema, J. B.

2013-11-01

This paper presents an investigation into the variability and predictability of the maximum height of the ionospheric F2 layer, hmF2 over the South African region. Data from three South African stations, namely Madimbo (22.4°S, 26.5°E, dip angle: -61.47°), Grahamstown (33.3°S, 26.5°E, dip angle: -64.08°) and Louisvale (28.5°S, 21.2°E, dip angle: -65.44°) were used in this study. The results indicate that hmF2 shows a larger variability around midnight than during the daytime for all seasons. Monthly median hmF2 values were used in all cases and were compared with predictions from the IRI-2007 model, using the URSI (Union Radio-Scientifique Internationale) coefficient option. The analysis covers the diurnal and seasonal hourly hmF2 values for the selected months and time sectors e.g. January, July, April and October for 2003 and 2005. The time ranges between (03h00-23h00 UT; LT = UT + 2h) representing the local sunrise, midday, sunset and midnight hours. The time covers sunrise, midday, sunrise, and midnight hours (03-06h00 UT, 07-11h00 UT, sunrise 16-18h00 UT and 22-23h00 UT; LT = UT + 2h). The dependence of the results on solar activity levels was also investigated. The IRI-2007 predictions follow fairly well the diurnal and seasonal variation patterns of the observed hmF2 values at all the stations. However, the IRI-2007 model overestimates and underestimates the hmF2 value during different months for all the solar activity periods.

Persistent hemifacial spasm after microvascular decompression: a risk assessment model.

PubMed

Shah, Aalap; Horowitz, Michael

2017-06-01

Microvascular decompression (MVD) for hemifacial spasm (HFS) provides resolution of disabling symptoms such as eyelid twitching and muscle contractions of the entire hemiface. The primary aim of this study was to evaluate the predictive value of patient demographics and spasm characteristics on long-term outcomes, with or without intraoperative lateral spread response (LSR) as an additional variable in a risk assessment model. A retrospective study was undertaken to evaluate the associations of pre-operative patient characteristics, as well as intraoperative LSR and need for a staged procedure on the presence of persistent or recurrent HFS at the time of hospital discharge and at follow-up. A risk assessment model was constructed with the inclusion of six clinically or statistically significant variables from the univariate analyses. A receiving operator characteristic curve was generated, and area under the curve was calculated to determine the strength of the predictive model. A risk assessment model was first created consisting of significant pre-operative variables (Model 1) (age >50, female gender, history of botulinum toxin use, platysma muscle involvement). This model demonstrated borderline predictive value for persistent spasm at discharge (AUC .60; p=.045) and fair predictive value at follow-up (AUC .75; p=.001). Intraoperative variables (e.g. LSR persistence) demonstrated little additive value (Model 2) (AUC .67). Patients with a higher risk score (three or greater) demonstrated greater odds of persistent HFS at the time of discharge (OR 1.5 [95%CI 1.16-1.97]; p=.035), as well as greater odds of persistent or recurrent spasm at the time of follow-up (OR 3.0 [95%CI 1.52-5.95]; p=.002) Conclusions: A risk assessment model consisting of pre-operative clinical characteristics is useful in prognosticating HFS persistence at follow-up.

Can cell kinetic parameters predict the response of tumours to radiotherapy?

PubMed

McNally, N J

1989-11-01

Three potential predictive assays of the repopulation component in tumour response to therapy are considered. (1) The DNA index can easily be measured. It is of prognostic value for cancers of certain sites, aneuploidy being a bad prognostic indicator. It is not strictly an indicator of cell proliferation. (2) The in vitro labelling index is of predictive value in early stage operable breast cancer and in head and neck cancer. In the former a high pretreatment labelling index can identify patients who could benefit from adjuvant chemotherapy. (3) The tumour potential doubling time (Tpot) can be measured rapidly following in vivo labelling with bromodeoxyuridine or iododeoxyuridine. We have measured Tpot in over 100 solid tumours with a success rate of about 75 per cent. Nearly 50 per cent of the tumours have a pre-treatment potential doubling time of 5 days or less. These would be suitable candidates for accelerated fractionation.

Ability of crime, demographic and business data to forecast areas of increased violence.

PubMed

Bowen, Daniel A; Mercer Kollar, Laura M; Wu, Daniel T; Fraser, David A; Flood, Charles E; Moore, Jasmine C; Mays, Elizabeth W; Sumner, Steven A

2018-05-24

Identifying geographic areas and time periods of increased violence is of considerable importance in prevention planning. This study compared the performance of multiple data sources to prospectively forecast areas of increased interpersonal violence. We used 2011-2014 data from a large metropolitan county on interpersonal violence (homicide, assault, rape and robbery) and forecasted violence at the level of census block-groups and over a one-month moving time window. Inputs to a Random Forest model included historical crime records from the police department, demographic data from the US Census Bureau, and administrative data on licensed businesses. Among 279 block groups, a model utilizing all data sources was found to prospectively improve the identification of the top 5% most violent block-group months (positive predictive value = 52.1%; negative predictive value = 97.5%; sensitivity = 43.4%; specificity = 98.2%). Predictive modelling with simple inputs can help communities more efficiently focus violence prevention resources geographically.

[Determination and prediction for vapor pressures of organophosphate flame retardants by gas chromatography].

PubMed

Wang, Qingzhi; Zhao, Hongxia; Wang, Yan; Xie, Qing; Chen, Jingwen; Quan, Xie

2017-09-08

Organophosphate flame retardants (OPFRs) are ubiquitous in the environment. To better understand and predict their environmental transport and fate, well-defined physicochemical properties are required. Vapor pressures ( P ) of 14 OPFRs were estimated as a function of temperature ( T ) by gas chromatography (GC), while 1,1,1-trichioro-2,2-bis (4-chlorophenyl) ethane ( p,p '-DDT) was acted as a reference substance. Their log P GC values and internal energies of phase transfer (△ vap H ) ranged from -6.17 to -1.25 and 74.1 kJ/mol to 122 kJ/mol, respectively. Substitution pattern and molar volume ( V M ) were found to be capable of influencing log P GC values of the OPFRs. The halogenated alkyl-OPFRs had lower log P GC values than aryl-or alkyl-OPFRs. The bigger the molar volume was, the smaller the log P GC value was. In addition, a quantitative structure-property relationship (QSPR) model of log P GC versus different relative retention times (RRTs) was developed with a high cross-validated value ( Q 2 cum ) of 0.946, indicating a good predictive ability and stability. Therefore, the log P GC values of the OPFRs without standard substance can be predicted by using their RRTs on different GC columns.

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud.

PubMed

Zia Ullah, Qazi; Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers.

Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study

PubMed Central

Le Strat, Yann

2017-01-01

The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package surveillance and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances. PMID:28715489

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud

PubMed Central

Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers. PMID:28811819

SIM_ADJUST -- A computer code that adjusts simulated equivalents for observations or predictions

USGS Publications Warehouse

Poeter, Eileen P.; Hill, Mary C.

2008-01-01

This report documents the SIM_ADJUST computer code. SIM_ADJUST surmounts an obstacle that is sometimes encountered when using universal model analysis computer codes such as UCODE_2005 (Poeter and others, 2005), PEST (Doherty, 2004), and OSTRICH (Matott, 2005; Fredrick and others (2007). These codes often read simulated equivalents from a list in a file produced by a process model such as MODFLOW that represents a system of interest. At times values needed by the universal code are missing or assigned default values because the process model could not produce a useful solution. SIM_ADJUST can be used to (1) read a file that lists expected observation or prediction names and possible alternatives for the simulated values; (2) read a file produced by a process model that contains space or tab delimited columns, including a column of simulated values and a column of related observation or prediction names; (3) identify observations or predictions that have been omitted or assigned a default value by the process model; and (4) produce an adjusted file that contains a column of simulated values and a column of associated observation or prediction names. The user may provide alternatives that are constant values or that are alternative simulated values. The user may also provide a sequence of alternatives. For example, the heads from a series of cells may be specified to ensure that a meaningful value is available to compare with an observation located in a cell that may become dry. SIM_ADJUST is constructed using modules from the JUPITER API, and is intended for use on any computer operating system. SIM_ADJUST consists of algorithms programmed in Fortran90, which efficiently performs numerical calculations.

Prognosis of Pregnant Women with One Abnormal Value on 75g OGTT.

PubMed

Kozuma, Yutaka; Inoue, Shigeru; Horinouchi, Takashi; Shinagawa, Takaaki; Nakayama, Hitomi; Kawaguchi, Atsushi; Hori, Daizo; Kamura, Toshiharu; Yamada, Kentaro; Ushijima, Kimio

2015-01-01

The aim of this study was to identify risk factors to allow us to detect patients at high risk of requiring insulin therapy, among Japanese pregnant women with one abnormal value (OAV) on a 75-g oral glucose tolerance test (75-g OGTT). A total of 118 pregnant women with OAV on a previous 75-g OGTT between 1997 and 2010 were studied. We identified the factors which can predict patients at high risk of requiring insulin therapy among Japanese pregnant women with OAV, by comparing severe abnormal glucose tolerance (insulin treatment; n=17) with mild glucose tolerance patients (diet only; n=101). The following factors were examined; plasma level of glucose (PG) and immunoreactive insulin (IRI) at fasting, 0.5, 1 and 2 hours after loading glucose, insulinogenic index, homeostasis model assessment insulin resistance (HOMA-IR), insulin sensitivity index-composite (ISI composite), and HbA1c at the time of the 75-g OGTT. Univariate analysis showed a positive correlation between insulin therapy and 2-h PG value, 0.5-h and 1-h IRI values, AUC-IRI and insulinogenic index (p<0.05). Multivariate analysis showed that the PG 2-h value and insulinogenic index were independent predictive factors of insulin therapy. A 2-h PG ≥153 mg / dl and an insulinogenic index of <0.42 had a sensitivity of 81.8%, a specificity of 83.8%, a positive predictive value of 60.0% and a negative predictive value of 93.9% for the prediction of patients who required insulin therapy among pregnant women with OAV. These results suggest that a level of 2-h PG ≥153 mg/dl and an insulinogenic index of <0.42 on 75-g OGTT are predictive factors for insulin therapy in Japanese pregnant women with OAV.

Prognostic value of three-dimensional ultrasound for fetal hydronephrosis

PubMed Central

WANG, JUNMEI; YING, WEIWEN; TANG, DAXING; YANG, LIMING; LIU, DONGSHENG; LIU, YUANHUI; PAN, JIAOE; XIE, XING

2015-01-01

The present study evaluated the prognostic value of three-dimensional ultrasound for fetal hydronephrosis. Pregnant females with fetal hydronephrosis were enrolled and a novel three-dimensional ultrasound indicator, renal parenchymal volume/kidney volume, was introduced to predict the postnatal prognosis of fetal hydronephrosis in comparison with commonly used ultrasound indicators. All ultrasound indicators of fetal hydronephrosis could predict whether postnatal surgery was required for fetal hydronephrosis; however, the predictive performance of renal parenchymal volume/kidney volume measurements as an individual indicator was the highest. In conclusion, ultrasound is important in predicting whether postnatal surgery is required for fetal hydronephrosis, and the three-dimensional ultrasound indicator renal parenchymal volume/kidney volume has a high predictive performance. Furthermore, the majority of cases of fetal hydronephrosis spontaneously regress subsequent to birth, and the regression time is closely associated with ultrasound indicators. PMID:25667626

Ability of a 5-minute electrocardiography (ECG) for predicting arrhythmias in Doberman Pinschers with cardiomyopathy in comparison with a 24-hour ambulatory ECG.

PubMed

Wess, G; Schulze, A; Geraghty, N; Hartmann, K

2010-01-01

Ventricular premature contractions (VPCs) are common in the occult stage of cardiomyopathy in Doberman Pinschers. Although the gold standard for detecting arrhythmia is the 24-hour ambulatory electrocardiography (ECG) (Holter), this method is more expensive, time-consuming and often not as readily available as common ECG. Comparison of 5-minute ECGs with Holter examinations. Eight hundred and seventy-five 5-minute ECGs and Holter examinations of 431 Doberman Pinschers. Each examination included a 5-minute ECG and Holter examination. A cut-off value of > 100 VPCs/24 hours using Holter was considered diagnostic for the presence of cardiomyopathy. Statistical evaluation included calculation of sensitivity, specificity, positive predictive value, and negative predictive value. Holter examinations revealed > 100 VPCs/24 hours in 204/875 examinations. At least 1 VPC during a 5-minute ECG was detected in 131 (64.2%) of these 204 examinations. No VPCs were found in the 5-minute ECG in 73 (35.8%) examinations of affected Doberman Pinschers. A 5-minute ECG with at least 1 VPC as cut-off had a sensitivity of 64.2%, a specificity of 96.7%, a positive predictive value of 85.6% and a negative predictive value of 89.9% for the presence of > 100 VPCs/24 hours. A 5-minute ECG is a rather insensitive method for detecting arrhythmias in Doberman Pinschers. However, the occurrence of at least 1 VPC in 5 minutes strongly warrants further examination of the dog, because specificity (96.7%) and positive predictive value (85.6%) are high and could suggest occult cardiomyopathy.

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Within-and among-year germination in Sonoran Desert winter annuals: bet hedging and predictive germination in a variable environment.

PubMed

Gremer, Jennifer R; Kimball, Sarah; Venable, D Lawrence

2016-10-01

In variable environments, organisms must have strategies to ensure fitness as conditions change. For plants, germination can time emergence with favourable conditions for later growth and reproduction (predictive germination), spread the risk of unfavourable conditions (bet hedging) or both (integrated strategies). Here we explored the adaptive value of within- and among-year germination timing for 12 species of Sonoran Desert winter annual plants. We parameterised models with long-term demographic data to predict optimal germination fractions and compared them to observed germination. At both temporal scales we found that bet hedging is beneficial and that predicted optimal strategies corresponded well with observed germination. We also found substantial fitness benefits to varying germination timing, suggesting some degree of predictive germination in nature. However, predictive germination was imperfect, calling for some degree of bet hedging. Together, our results suggest that desert winter annuals have integrated strategies combining both predictive plasticity and bet hedging. © 2016 John Wiley & Sons Ltd/CNRS.

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

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.

Prediction of Classroom Reverberation Time using Neural Network

NASA Astrophysics Data System (ADS)

Liyana Zainudin, Fathin; Kadir Mahamad, Abd; Saon, Sharifah; Nizam Yahya, Musli

2018-04-01

In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE < 0.005 and R > 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds.

Dopamine reward prediction errors reflect hidden state inference across time

PubMed Central

Starkweather, Clara Kwon; Babayan, Benedicte M.; Uchida, Naoshige; Gershman, Samuel J.

2017-01-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a ‘belief state’). In this work, we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling exhibited a striking difference between two tasks that differed only with respect to whether reward was delivered deterministically. Our results favor an associative learning rule that combines cached values with hidden state inference. PMID:28263301

Dopamine reward prediction errors reflect hidden-state inference across time.

PubMed

Starkweather, Clara Kwon; Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2017-04-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a 'belief state'). Here we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling showed a notable difference between two tasks that differed only with respect to whether reward was delivered in a deterministic manner. Our results favor an associative learning rule that combines cached values with hidden-state inference.

A maintenance time prediction method considering ergonomics through virtual reality simulation.

PubMed

Zhou, Dong; Zhou, Xin-Xin; Guo, Zi-Yue; Lv, Chuan

2016-01-01

Maintenance time is a critical quantitative index in maintainability prediction. An efficient maintenance time measurement methodology plays an important role in early stage of the maintainability design. While traditional way to measure the maintenance time ignores the differences between line production and maintenance action. This paper proposes a corrective MOD method considering several important ergonomics factors to predict the maintenance time. With the help of the DELMIA analysis tools, the influence coefficient of several factors are discussed to correct the MOD value and the designers can measure maintenance time by calculating the sum of the corrective MOD time of each maintenance therbligs. Finally a case study is introduced, by maintaining the virtual prototype of APU motor starter in DELMIA, designer obtains the actual maintenance time by the proposed method, and the result verifies the effectiveness and accuracy of the proposed method.

Lung Ultrasound for Diagnosing Pneumothorax in the Critically Ill Neonate.

PubMed

Raimondi, Francesco; Rodriguez Fanjul, Javier; Aversa, Salvatore; Chirico, Gaetano; Yousef, Nadya; De Luca, Daniele; Corsini, Iuri; Dani, Carlo; Grappone, Lidia; Orfeo, Luigi; Migliaro, Fiorella; Vallone, Gianfranco; Capasso, Letizia

2016-08-01

To evaluate the accuracy of lung ultrasound for the diagnosis of pneumothorax in the sudden decompensating patient. In an international, prospective study, sudden decompensation was defined as a prolonged significant desaturation (oxygen saturation <65% for more than 40 seconds) and bradycardia or sudden increase of oxygen requirement by at least 50% in less than 10 minutes with a final fraction of inspired oxygen ≥0.7 to keep stable saturations. All eligible patients had an ultrasound scan before undergoing a chest radiograph, which was the reference standard. Forty-two infants (birth weight = 1531 ± 812 g; gestational age = 31 ± 3.5 weeks) were enrolled in 6 centers; pneumothorax was detected in 26 (62%). Lung ultrasound accuracy in diagnosing pneumothorax was as follows: sensitivity 100%, specificity 100%, positive predictive value 100%, and negative predictive value 100%. Clinical evaluation of pneumothorax showed sensitivity 84%, specificity 56%, positive predictive value 76%, and negative predictive value 69%. After sudden decompensation, a lung ultrasound scan was performed in an average time of 5.3 ± 5.6 minutes vs 19 ± 11.7 minutes required for a chest radiography. Emergency drainage was performed after an ultrasound scan but before radiography in 9 cases. Lung ultrasound shows high accuracy in detecting pneumothorax in the critical infant, outperforming clinical evaluation and reducing time to imaging diagnosis and drainage. Copyright © 2016 Elsevier Inc. All rights reserved.

Diagnostic value of the flow profile in the distal descending aorta by phase-contrast magnetic resonance for predicting severe coarctation of the aorta.

PubMed

Muzzarelli, Stefano; Ordovas, Karen Gomes; Hope, Michael D; Meadows, Jeffery J; Higgins, Charles B; Meadows, Alison Knauth

2011-06-01

To compare aortic flow profiles at the level of the proximal descending (PDAo) and distal descending aorta (DDAo) in patients investigated for coarctation of the aorta (CoA), and compare their respective diagnostic value for predicting severe CoA. Diastolic flow decay in the PDAo predicts severe CoA, but flow measurements at this level are limited by flow turbulence, aliasing, and stent-related artifacts. We studied 49 patients evaluated for CoA with phase contrast magnetic resonance imaging (PC-MRI). Parameters of diastolic flow decay in the PDAo and DDAo were compared. Their respective diagnostic value was compared with the standard reference of transcatheter peak gradient ≥20 mmHg. Flow measurement in the PDAo required repeated acquisition with adjustment of encoding velocity or location of the imaging plane in 69% of patients; measurement in the DDAo was achieved in single acquisition in all cases. Parameters of diastolic flow decay in the PDAo and DDAo, including rate-corrected (RC) deceleration time and RC flow deceleration yielded a good correlation (r = 0.78; P < 0.01, and r = 0.92; P < 0.01), and a similar diagnostic value for predicting severe CoA. The highest diagnostic accuracy was achieved by RC deceleration time at DDAo (sensitivity 85%, specificity 85%). Characterization of aortic flow profiles at the DDAo offers a quick and reliable noninvasive means of assessing hemodynamically significant CoA. Copyright © 2011 Wiley-Liss, Inc.

Task Values and Ability Beliefs as Predictors of High School Literacy Choices: A Developmental Analysis

ERIC Educational Resources Information Center

Durik, Amanda M.; Vida, Mina; Eccles, Jacquelynne S.

2005-01-01

This study examines how competence beliefs and task values predict high school achievement choices related to literacy. Students' task beliefs (self-concept of ability, intrinsic value, and importance) about reading in the 4th grade and English in the 10th grade were tracked over time. Task beliefs, school performance, and gender were used to…

Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks.

PubMed

Hajmeer, M; Basheer, I; Cliver, D O

2006-09-01

Using artificial neural networks (ANNs), a highly accurate model was developed to simulate survival curves of Listeria monocytogenes in chorizos as affected by the initial water activity (a(w0)) of the sausage formulation, temperature (T), and air inflow velocity (F) where the sausages are stored. The ANN-based survival model (R(2)=0.970) outperformed the regression-based cubic model (R(2)=0.851), and as such was used to derive other models (using regression) that allow prediction of the times needed to drop count by 1, 2, 3, and 4 logs (i.e., nD-values, n=1, 2, 3, 4). The nD-value regression models almost perfectly predicted the various times derived from a number of simulated survival curves exhibiting a wide variety of the operating conditions (R(2)=0.990-0.995). The nD-values were found to decrease with decreasing a(w0), and increasing T and F. The influence of a(w0) on nD-values seems to become more significant at some critical value of a(w0), below which the variation is negligible (0.93 for 1D-value, 0.90 for 2D-value, and <0.85 for 3D- and 4D-values). There is greater influence of storage T and F on 3D- and 4D-values than on 1D- and 2D-values.

Forecasting Enrollments with Fuzzy Time Series.

ERIC Educational Resources Information Center

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

Impaction and Prediction: Does Ureteral Wall Thickness Affect the Success of Medical Expulsive Therapy in Pediatric Ureteral Stones?

PubMed

Tuerxun, Aierken; Batuer, Abudukahaer; Erturhan, Sakip; Eryildirim, Bilal; Camur, Emre; Sarica, Kemal

2017-01-01

The study aimed to evaluate the predictive value of ureteral wall thickness (UWT) and stone-related parameters for medical expulsive therapy (MET) success with an alpha blocker in pediatric upper ureteral stones. A total of 35 children receiving MET ureteral stones (<10 mm) were evaluated. Patients were divided into 2 subgroups where MET was successful in 18 children (51.4%) and unsuccessful in 17 children (48.6%). Prior to management, stone size, stone density (in Hounsfield unit), degree of hydronephrosis, and UWT were evaluated with patient demographics and recorded. The possible predictive value of these parameters in success rates and time to stone expulsion were evaluated in a comparative manner between the 2 groups. The overall mean patient age and stone size values were 5.40 ± 0.51 years and 6.24 ± 0.28 mm, respectively. Regarding the predictive values of these parameters for the success of MET, while stone size and UWT were found to be highly predictive for MET success, patients age, body mass index, stone density, and degree of hydronephrosis had no predictive value on this aspect. Our findings indicated that some stone and anatomical factors may be used to predict the success of MET in pediatric ureteral stones in an effective manner. With this approach, unnecessary use of these drugs that may cause a delay in removing the stone will be avoided, and the possible adverse effects of obstruction as well as stone-related clinical symptoms could be minimized. © 2017 S. Karger AG, Basel.

Russian State Time and Earth Rotation Service: Observations, Eop Series, Prediction

NASA Astrophysics Data System (ADS)

Kaufman, M.; Pasynok, S.

2010-01-01

Russian State Time, Frequency and Earth Rotation Service provides the official EOP data and time for use in scientific, technical and metrological works in Russia. The observations of GLONASS and GPS on 30 stations in Russia, and also the Russian and worldwide observations data of VLBI (35 stations) and SLR (20 stations) are used now. To these three series of EOP the data calculated in two other Russian analysis centers are added: IAA (VLBI, GPS and SLR series) and MCC (SLR). Joint processing of these 7 series is carried out every day (the operational EOP data for the last day and the predicted values for 50 days). The EOP values are weekly refined and systematic errors of every individual series are corrected. The combined results become accessible on the VNIIFTRI server (ftp.imvp.ru) approximately at 6h UT daily.

TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

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

Magome, T; Haga, A; Igaki, H

Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyomore » Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R{sup 2}) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R{sup 2} between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R{sup 2} was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by the JSPS Core-to-Core Program(No. 23003) and Grant-in-aid from the JSPS Fellows.« less

Human values and beliefs and concern about climate change: a Bayesian longitudinal analysis.

PubMed

Prati, Gabriele; Pietrantoni, Luca; Albanesi, Cinzia

2018-01-01

The aim of this study was to investigate the influence of human values on beliefs and concern about climate change using a longitudinal design and Bayesian analysis. A sample of 298 undergraduate/master students filled out the same questionnaire on two occasions at an interval of 2 months. The questionnaire included measures of beliefs and concern about climate change (i.e., perceived consequences, risk perception, and skepticism) and human values (i.e., the Portrait Values Questionnaire). After controlling for gender and the respective baseline score, universalism at Time 1 was associated with higher levels of perceived consequences of climate change and lower levels of climate change skepticism. Self-direction at Time 1 predicted Time 2 climate change risk perception and perceived consequences of climate change. Hedonism at Time 1 was associated with Time 2 climate change risk perception. The other human values at Time 1 were not associated with any of the measures of beliefs and concern about climate change at Time 2. The results of this study suggest that a focus on universalism and self-direction values seems to be a more successful approach to stimulate public engagement with climate change than a focus on other human values.

A double-blinded, prospective study to define antigenemia and quantitative real-time polymerase chain reaction cutoffs to start preemptive therapy in low-risk, seropositive, renal transplanted recipients.

PubMed

David-Neto, Elias; Triboni, Ana H K; Paula, Flavio J; Vilas Boas, Lucy S; Machado, Clarisse M; Agena, Fabiana; Latif, Acram Z A; Alencar, Cecília S; Pierrotti, Ligia C; Nahas, William C; Caiaffa-Filho, Helio H; Pannuti, Claudio S

2014-11-27

Cytomegalovirus (CMV) disease occurs in 16% to 20% of low-risk, CMV-positive renal transplant recipients. The cutoffs for quantitative real-time polymerase chain reaction (qPCR) or phosphoprotein (pp65) antigenemia (pp65emia) for starting preemptive therapy have not been well established. We measured qPCR and pp65emia weekly from day 7 to day 120 after transplantation, in anti-CMV immunoglobulin G–positive donor and recipient pairs. Patients and physicians were blinded to the test results. Suspicion of CMV disease led to the order of new tests. In asymptomatic viremic patients, the highest pp65emia and qPCR values were used, whereas we considered the last value before diagnosis in those with CMV disease. We collected a total of 1,481 blood samples from 102 adult patients. Seventeen patients developed CMV disease, 54 presented at least one episode of viremia that cleared spontaneously, and 31 never presented viremia. Five patients developed CMV disease after the end of the study period. The median (95% confidence interval) pp65emia and qPCR values were higher before CMV disease than during asymptomatic viremia (6 [9–82] vs. 3 [1–14] cells/10(6) cells; P<0.001 and 3,080 [1,263–15,605] vs. 258 [258–1,679] copies/mL; P=0.008, respectively). The receiver operating characteristic curve showed that pp65emia 4 cells/10(6) cells or greater showed a sensitivity and specificity to predict CMV disease of 69% and 81%, respectively (area, 0.769; P=0.001), with a positive predictive value of 37% and a negative predictive value of 93%. For qPCR 2,000 copies/mL or higher, the positive predictive value and negative predictive value were 57% and 91%, respectively (receiver operating characteristic area, 0.782; P=0.000). With these cutoffs, both methods are appropriate for detecting CMV disease.

The predictive value of quantitative DCE metrics for immediate therapeutic response of high-intensity focused ultrasound ablation (HIFU) of symptomatic uterine fibroids.

PubMed

Wei, Chao; Fang, Xin; Wang, Chuan-Bin; Chen, Yu; Xu, Xiao; Dong, Jiang-Ning

2017-12-04

The aim of this study was to investigate the value of quantitative DCE-MRI parameters for predicting the immediate non-perfused volume ratio (NPVR) of HIFU therapy in the treatment of symptomatic uterine fibroids. A total of 78 symptomatic uterine fibroids in 65 female patients were treated with US-HIFU therapy. All patients underwent conventional MRI and DCE-MRI scans 1 day before and 3 days after HIFU treatment. Permeability parameters K trans , K ep , V e , and V p and T1 perfusion parameters BF and BV of pretreatment were measured as a baseline, while NPVR was used to assess immediate ablation efficiency. Data were assigned to NPVR ≧ 70% and NPVR < 70% groups. Then, the predictive performances of different parameters for ablation efficacy were studied to seek the optimal cut-off value, and the length of time to calculate the variable parameters in each case was recorded. (1) It was observed that the pretreatment K trans , K ep , V e , and BF values of the NPVR ≧ 70% group were significantly lower compared to the NPVR < 70% group (p < 0.05). (2) The immediate NPVR was negatively correlated with the K trans , BF, and BV values before HIFU treatment (r = - 0.561, - 0.712, and - 0.528, respectively, p < 0.05 for all). (3) The AUCs of pretreatment K trans , BF, BV values, and K trans combined with BF used to predict the immediate NPVR were 0.810, 0.909, 0.795, and 0.922, respectively (p < 0.05 for all). (4) The mean time to calculate the variable parameters in each case was 7.5 min. Higher K trans , BF, and BV values at baseline DCE-MRI suggested a poor ablation efficacy of HIFU therapy for symptomatic uterine fibroids, while the pretreatment DCE-MRI parameters could be useful biomarkers for predicting the ablation efficacy in select cases. The software used to calculate DCE-MRI parameters was simpler, quicker, and easier to incorporate into clinical practice.

The diagnostic value of troponin T testing in the community setting.

PubMed

Planer, David; Leibowitz, David; Paltiel, Ora; Boukhobza, Rina; Lotan, Chaim; Weiss, Teddy A

2006-03-08

Many patients presenting with chest pain to their family physician are referred to the emergency room, in part, due to lack of accurate objective diagnostic tools. This study aimed to assess the diagnostic value of bedside troponin T kit testing in patients presenting with chest pain to their family physician. Prospective, multi-center study. Consecutive subjects with chest pain were recruited from 44 community clinics in Jerusalem. Following clinical assessment by the family physician, qualitative troponin kit testing was performed. Patients with a negative clinical assessment and negative troponin kit were sent home and all others were referred to the emergency room. The final diagnosis at the time of hospital discharge was recorded and telephone follow up was performed after 60 days. Positive predictive value, negative predictive value, sensitivity and specificity of troponin kit for myocardial infarction diagnosis and of family physician for hospitalization, were assessed. Of 392 patients enrolled, 349 (89%) were included in the final analysis. The prevalence of myocardial infarction was 1.7%. The positive and negative predictive values of the troponin kit for myocardial infarction diagnosis were 100% and 99.7%, respectively. The positive and negative predictive values of the family physician's assessment to predict hospitalization were 41.4% and 94.1%, respectively. Troponin kit testing is an important tool to assist the family physician in the assessment of patients with chest pain in the community setting. Troponin kit testing may identify otherwise undiagnosed cases of myocardial infarctions, and reduce unnecessary referrals to the emergency room.

Estimation of the base temperature and growth phase duration in terms of thermal time for four grapevine cultivars

NASA Astrophysics Data System (ADS)

Zapata, D.; Salazar, M.; Chaves, B.; Keller, M.; Hoogenboom, G.

2015-12-01

Thermal time models have been used to predict the development of many different species, including grapevine ( Vitis vinifera L.). These models normally assume that there is a linear relationship between temperature and plant development. The goal of this study was to estimate the base temperature and duration in terms of thermal time for predicting veraison for four grapevine cultivars. Historical phenological data for four cultivars that were collected in the Pacific Northwest were used to develop the thermal time model. Base temperatures ( T b) of 0 and 10 °C and the best estimated T b using three different methods were evaluated for predicting veraison in grapevine. Thermal time requirements for each individual cultivar were evaluated through analysis of variance, and means were compared using the Fisher's test. The methods that were applied to estimate T b for the development of wine grapes included the least standard deviation in heat units, the regression coefficient, and the development rate method. The estimated T b varied among methods and cultivars. The development rate method provided the lowest T b values for all cultivars. For the three methods, Chardonnay had the lowest T b ranging from 8.7 to 10.7 °C, while the highest T b values were obtained for Riesling and Cabernet Sauvignon with 11.8 and 12.8 °C, respectively. Thermal time also differed among cultivars, when either the fixed or estimated T b was used. Predictions of the beginning of ripening with the estimated temperature resulted in the lowest variation in real days when compared with predictions using T b = 0 or 10 °C, regardless of the method that was used to estimate the T b.

Comparison of ionospheric F2 peak parameters foF2 and hmF2 with IRI2001 at Hainan

NASA Astrophysics Data System (ADS)

Wang, X.; Shi, J. K.; Wang, G. J.; Gong, Y.

2009-06-01

Monthly median values of foF2, hmF2 and M(3000)F2 parameters, with quarter-hourly time interval resolution for the diurnal variation, obtained with DPS4 digisonde at Hainan (19.5°N, 109.1°E; Geomagnetic coordinates: 178.95°E, 8.1°N) are used to investigate the low-latitude ionospheric variations and comparisons with the International Reference Ionosphere (IRI) model predictions. The data used for the present study covers the period from February 2002 to April 2007, which is characterized by a wide range of solar activity, ranging from high solar activity (2002) to low solar activity (2007). The results show that (1) Generally, IRI predictions follow well the diurnal and seasonal variation patterns of the experimental values of foF2, especially in the summer of 2002. However, there are systematic deviation between experimental values and IRI predictions with either CCIR or URSI coefficients. Generally IRI model greatly underestimate the values of foF2 from about noon to sunrise of next day, especially in the afternoon, and slightly overestimate them from sunrise to about noon. It seems that there are bigger deviations between IRI Model predictions and the experimental observations for the moderate solar activity. (2) Generally the IRI-predicted hmF2 values using CCIR M(3000)F2 option shows a poor agreement with the experimental results, but there is a relatively good agreement in summer at low solar activity. The deviation between the IRI-predicted hmF2 using CCIR M(3000)F2 and observed hmF2 is bigger from noon to sunset and around sunrise especially at high solar activity. The occurrence time of hmF2 peak (about 1200 LT) of the IRI model predictions is earlier than that of observations (around 1500 LT). The agreement between the IRI hmF2 obtained with the measured M(3000)F2 and the observed hmF2 is very good except that IRI overestimates slightly hmF2 in the daytime in summer at high solar activity and underestimates it in the nighttime with lower values near sunrise at low solar activity.

Algorithm for Training a Recurrent Multilayer Perceptron

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Rais, Omar T.; Menon, Sunil K.; Atiya, Amir F.

2004-01-01

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system.

Continuous Amplitude-Integrated Electroencephalographic Monitoring Is a Useful Prognostic Tool for Hypothermia-Treated Cardiac Arrest Patients.

PubMed

Oh, Sang Hoon; Park, Kyu Nam; Shon, Young-Min; Kim, Young-Min; Kim, Han Joon; Youn, Chun Song; Kim, Soo Hyun; Choi, Seung Pill; Kim, Seok Chan

2015-09-22

Modern treatments have improved the survival rate following cardiac arrest, but prognostication remains a challenge. We examined the prognostic value of continuous electroencephalography according to time by performing amplitude-integrated electroencephalography on patients with cardiac arrest receiving therapeutic hypothermia. We prospectively studied 130 comatose patients treated with hypothermia from September 2010 to April 2013. We evaluated the time to normal trace (TTNT) as a neurological outcome predictor and determined the prognostic value of burst suppression and status epilepticus, with a particular focus on their time of occurrence. Fifty-five patients exhibited a cerebral performance category score of 1 to 2. The area under the curve for TTNT was 0.97 (95% confidence interval, 0.92-0.99), and the sensitivity and specificity of TTNT<24 hours after resuscitation as a threshold for predicting good neurological outcome were 94.6% (95% confidence interval, 84.9%-98.9%) and 90.7% (95% confidence interval, 81.7%-96.2%), respectively. The threshold displaying 100% specificity for predicting poor neurological outcome was TTNT>36 hours. Burst suppression and status epilepticus predicted poor neurological outcome (positive predictive value of 98.3% and 96.4%, respectively). The combination of these factors predicted a negative outcome at a median of 6.2 hours after resuscitation (sensitivity and specificity of 92.0% and 96.4%, respectively). A TTNT<24 hours was associated with good neurological outcome. The lack of normal trace development within 36 hours, status epilepticus, and burst suppression were predictors of poor outcome. The combination of these negative predictors may improve their prognostic performance at an earlier stage. © 2015 The Authors.

Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning

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

Martin, Luis; Marchante, Ruth; Cony, Marco

2010-10-15

Due to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production. This work presents a comparisons of statistical models based on time seriesmore » applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models. (author)« less

Physiologic volume of phosphorus during hemodialysis: predictions from a pseudo one-compartment model.

PubMed

Leypoldt, John K; Akonur, Alp; Agar, Baris U; Culleton, Bruce F

2012-10-01

The kinetics of plasma phosphorus concentrations during hemodialysis (HD) are complex and cannot be described by conventional one- or two-compartment kinetic models. It has recently been shown by others that the physiologic (or apparent distribution) volume for phosphorus (Vr-P) increases with increasing treatment time and shows a large variation among patients treated by thrice weekly and daily HD. Here, we describe the dependence of Vr-P on treatment time and predialysis plasma phosphorus concentration as predicted by a novel pseudo one-compartment model. The kinetics of plasma phosphorus during conventional and six times per week daily HD were simulated as a function of treatment time per session for various dialyzer phosphate clearances and patient-specific phosphorus mobilization clearances (K(M)). Vr-P normalized to extracellular volume from these simulations were reported and compared with previously published empirical findings. Simulated results were relatively independent of dialyzer phosphate clearance and treatment frequency. In contrast, Vr-P was strongly dependent on treatment time per session; the increase in Vr-P with treatment time was larger for higher values of K(M). Vr-P was inversely dependent on predialysis plasma phosphorus concentration. There was significant variation among predicted Vr-P values, depending largely on the value of K(M). We conclude that a pseudo one-compartment model can describe the empirical dependence of the physiologic volume of phosphorus on treatment time and predialysis plasma phosphorus concentration. Further, the variation in physiologic volume of phosphorus among HD patients is largely due to differences in patient-specific phosphorus mobilization clearance. © 2012 The Authors. Hemodialysis International © 2012 International Society for Hemodialysis.

Utility of Clinical Parameters and Multiparametric MRI as Predictive Factors for Differentiating Uterine Sarcoma From Atypical Leiomyoma.

PubMed

Bi, Qiu; Xiao, Zhibo; Lv, Fajin; Liu, Yao; Zou, Chunxia; Shen, Yiqing

2018-02-05

The objective of this study was to find clinical parameters and qualitative and quantitative magnetic resonance imaging (MRI) features for differentiating uterine sarcoma from atypical leiomyoma (ALM) preoperatively and to calculate predictive values for uterine sarcoma. Data from 60 patients with uterine sarcoma and 88 patients with ALM confirmed by surgery and pathology were collected. Clinical parameters, qualitative MRI features, diffusion-weighted imaging with apparent diffusion coefficient values, and quantitative parameters of dynamic contrast-enhanced MRI of these two tumor types were compared. Predictive values for uterine sarcoma were calculated using multivariable logistic regression. Patient clinical manifestations, tumor locations, margins, T2-weighted imaging signals, mean apparent diffusion coefficient values, minimum apparent diffusion coefficient values, and time-signal intensity curves of solid tumor components were obvious significant parameters for distinguishing between uterine sarcoma and ALM (all P <.001). Abnormal vaginal bleeding, tumors located mainly in the uterine cavity, ill-defined tumor margins, and mean apparent diffusion coefficient values of <1.272 × 10 -3  mm 2 /s were significant preoperative predictors of uterine sarcoma. When the overall scores of these four predictors were greater than or equal to 7 points, the sensitivity, the specificity, the accuracy, and the positive and negative predictive values were 88.9%, 99.9%, 95.7%, 97.0%, and 95.1%, respectively. The use of clinical parameters and multiparametric MRI as predictive factors was beneficial for diagnosing uterine sarcoma preoperatively. These findings could be helpful for guiding treatment decisions. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods

NASA Astrophysics Data System (ADS)

Szeląg, Bartosz; Barbusiński, Krzysztof; Studziński, Jan; Bartkiewicz, Lidia

2017-11-01

In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values measured in previous time steps and daily wastewater inflows. Also, independent prediction systems that can be used in case of monitoring devices malfunction are provided. Models of wastewater quality indicators were developed using MARS (multivariate adaptive regression spline) method, artificial neural networks (ANN) of the multilayer perceptron type combined with the classification model (SOM) and cascade neural networks (CNN). The lowest values of absolute and relative errors were obtained using ANN+SOM, whereas the MARS method produced the highest error values. It was shown that for the analysed WWTP it is possible to obtain continuous prediction of selected wastewater quality indicators using the two developed independent prediction systems. Such models can ensure reliable WWTP work when wastewater quality monitoring systems become inoperable, or are under maintenance.

Motor Development and Physical Activity: A Longitudinal Discordant Twin-Pair Study.

PubMed

Aaltonen, Sari; Latvala, Antti; Rose, Richard J; Pulkkinen, Lea; Kujala, Urho M; Kaprio, Jaakko; Silventoinen, Karri

2015-10-01

Previous longitudinal research suggests that motor proficiency in early life predicts physical activity in adulthood. Familial effects including genetic and environmental factors could explain the association, but no long-term follow-up studies have taken into account potential confounding by genetic and social family background. The present twin study investigated whether childhood motor skill development is associated with leisure-time physical activity levels in adulthood independent of family background. Altogether, 1550 twin pairs from the FinnTwin12 study and 1752 twin pairs from the FinnTwin16 study were included in the analysis. Childhood motor development was assessed by the parents' report of whether one of the co-twins had been ahead of the other in different indicators of motor skill development in childhood. Leisure-time physical activity (MET·h·d) was self-reported by the twins in young adulthood and adulthood. Statistical analyses included conditional and ordinary linear regression models within twin pairs. Using all activity-discordant twin pairs, the within-pair difference in a sum score of motor development in childhood predicted the within-pair difference in the leisure-time physical activity level in young adulthood (P < 0.001). Within specific motor development indicators, learning to stand unaided earlier in infancy predicted higher leisure-time MET values in young adulthood statistically significantly in both samples (FinnTwin12, P = 0.02; and FinnTwin16, P = 0.001) and also in the pooled data set of the FinnTwin12 and FinnTwin16 studies (P < 0.001). Having been more agile than the co-twin as a child predicted higher leisure-time MET values up to adulthood (P = 0.03). More advanced childhood motor development is associated with higher leisure-time MET values in young adulthood at least partly independent of family background in both men and women.

MOTOR DEVELOPMENT AND PHYSICAL ACTIVITY: A LONGITUDINAL DISCORDANT TWIN-PAIR STUDY

PubMed Central

Aaltonen, Sari; Latvala, Antti; Rose, Richard J.; Pulkkinen, Lea; Kujala, Urho M.; Kaprio, Jaakko; Silventoinen, Karri

2015-01-01

Introduction Previous longitudinal research suggests that motor proficiency in early life predicts physical activity in adulthood. Familial effects including genetic and environmental factors could explain the association, but no long-term follow-up studies have taken into account potential confounding by genetic and social family background. The present twin study investigated whether childhood motor skill development is associated with leisure-time physical activity levels in adulthood independent of family background. Methods Altogether, 1 550 twin pairs from the FinnTwin12 study and 1 752 twin pairs from the FinnTwin16 study were included in the analysis. Childhood motor development was assessed by the parents’ report of whether one of the co-twins had been ahead of the other in different indicators of motor skill development in childhood. Leisure-time physical activity (MET hours/day) was self-reported by the twins in young adulthood and adulthood. Statistical analyses included conditional and ordinary linear regression models within twin pairs. Results Using all activity-discordant twin pairs, the within-pair difference in a sum score of motor development in childhood predicted the within-pair difference in the leisure-time physical activity level in young adulthood (p<0.001). Within specific motor development indicators, learning to stand unaided earlier in infancy predicted higher leisure-time MET values in young adulthood statistically significantly in both samples (FinnTwin12 p=0.02, FinnTwin16 p=0.001) and also in the pooled dataset of the FinnTwin12 and FinnTwin16 studies (p<0.001). Having been more agile than the co-twin as a child predicted higher leisure-time MET values up to adulthood (p=0.03). Conclusions More advanced childhood motor development is associated with higher leisure-time MET values in young adulthood at least partly independent of family background, in both men and women. PMID:26378945

A thermal NO(x) prediction model - Scalar computation module for CFD codes with fluid and kinetic effects

NASA Technical Reports Server (NTRS)

Mcbeath, Giorgio; Ghorashi, Bahman; Chun, Kue

1993-01-01

A thermal NO(x) prediction model is developed to interface with a CFD, k-epsilon based code. A converged solution from the CFD code is the input to the postprocessing model for prediction of thermal NO(x). The model uses a decoupled analysis to estimate the equilibrium level of (NO(x))e which is the constant rate limit. This value is used to estimate the flame (NO(x)) and in turn predict the rate of formation at each node using a two-step Zeldovich mechanism. The rate is fixed on the NO(x) production rate plot by estimating the time to reach equilibrium by a differential analysis based on the reaction: O + N2 = NO + N. The rate is integrated in the nonequilibrium time space based on the residence time at each node in the computational domain. The sum of all nodal predictions yields the total NO(x) level.

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values

PubMed Central

Huang, Hairong; Xu, Zanzan; Shao, Xianhong; Wismeijer, Daniel; Sun, Ping; Wang, Jingxiao

2017-01-01

Objectives This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice. Methods We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval. Results The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2. Conclusions These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice. PMID:29084260

Modeling changes in biomass composition during microwave-based alkali pretreatment of switchgrass.

PubMed

Keshwani, Deepak R; Cheng, Jay J

2010-01-01

This study used two different approaches to model changes in biomass composition during microwave-based pretreatment of switchgrass: kinetic modeling using a time-dependent rate coefficient, and a Mamdani-type fuzzy inference system. In both modeling approaches, the dielectric loss tangent of the alkali reagent and pretreatment time were used as predictors for changes in amounts of lignin, cellulose, and xylan during the pretreatment. Training and testing data sets for development and validation of the models were obtained from pretreatment experiments conducted using 1-3% w/v NaOH (sodium hydroxide) and pretreatment times ranging from 5 to 20 min. The kinetic modeling approach for lignin and xylan gave comparable results for training and testing data sets, and the differences between the predictions and experimental values were within 2%. The kinetic modeling approach for cellulose was not as effective, and the differences were within 5-7%. The time-dependent rate coefficients of the kinetic models estimated from experimental data were consistent with the heterogeneity of individual biomass components. The Mamdani-type fuzzy inference was shown to be an effective approach to model the pretreatment process and yielded predictions with less than 2% deviation from the experimental values for lignin and with less than 3% deviation from the experimental values for cellulose and xylan. The entropies of the fuzzy outputs from the Mamdani-type fuzzy inference system were calculated to quantify the uncertainty associated with the predictions. Results indicate that there is no significant difference between the entropies associated with the predictions for lignin, cellulose, and xylan. It is anticipated that these models could be used in process simulations of bioethanol production from lignocellulosic materials.

Heart rate time series characteristics for early detection of infections in critically ill patients.

PubMed

Tambuyzer, T; Guiza, F; Boonen, E; Meersseman, P; Vervenne, H; Hansen, T K; Bjerre, M; Van den Berghe, G; Berckmans, D; Aerts, J M; Meyfroidt, G

2017-04-01

It is difficult to make a distinction between inflammation and infection. Therefore, new strategies are required to allow accurate detection of infection. Here, we hypothesize that we can distinguish infected from non-infected ICU patients based on dynamic features of serum cytokine concentrations and heart rate time series. Serum cytokine profiles and heart rate time series of 39 patients were available for this study. The serum concentration of ten cytokines were measured using blood sampled every 10 min between 2100 and 0600 hours. Heart rate was recorded every minute. Ten metrics were used to extract features from these time series to obtain an accurate classification of infected patients. The predictive power of the metrics derived from the heart rate time series was investigated using decision tree analysis. Finally, logistic regression methods were used to examine whether classification performance improved with inclusion of features derived from the cytokine time series. The AUC of a decision tree based on two heart rate features was 0.88. The model had good calibration with 0.09 Hosmer-Lemeshow p value. There was no significant additional value of adding static cytokine levels or cytokine time series information to the generated decision tree model. The results suggest that heart rate is a better marker for infection than information captured by cytokine time series when the exact stage of infection is not known. The predictive value of (expensive) biomarkers should always be weighed against the routinely monitored data, and such biomarkers have to demonstrate added value.

Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later

PubMed Central

Fagan, Anne M.; Grant, Elizabeth A.; Hassenstab, Jason; Moulder, Krista L.; Maue Dreyfus, Denise; Sutphen, Courtney L.; Benzinger, Tammie L.S.; Mintun, Mark A.; Holtzman, David M.; Morris, John C.

2013-01-01

Objectives: We compared the ability of molecular biomarkers for Alzheimer disease (AD), including amyloid imaging and CSF biomarkers (Aβ42, tau, ptau181, tau/Aβ42, ptau181/Aβ42), to predict time to incident cognitive impairment among cognitively normal adults aged 45 to 88 years and followed for up to 7.5 years. Methods: Longitudinal data from Knight Alzheimer's Disease Research Center participants (N = 201) followed for a mean of 3.70 years (SD = 1.46 years) were used. Participants with amyloid imaging and CSF collection within 1 year of a clinical assessment indicating normal cognition were eligible. Cox proportional hazards models tested whether the individual biomarkers were related to time to incident cognitive impairment. “Expanded” models were developed using the biomarkers and participant demographic variables. The predictive values of the models were compared. Results: Abnormal levels of all biomarkers were associated with faster time to cognitive impairment, and some participants with abnormal biomarker levels remained cognitively normal for up to 6.6 years. No differences in predictive value were found between the individual biomarkers (p > 0.074), nor did we find differences between the expanded biomarker models (p > 0.312). Each expanded model better predicted incident cognitive impairment than the model containing the biomarker alone (p < 0.005). Conclusions: Our results indicate that all AD biomarkers studied here predicted incident cognitive impairment, and support the hypothesis that biomarkers signal underlying AD pathology at least several years before the appearance of dementia symptoms. PMID:23576620

Ultimate pier and contraction scour prediction in cohesive soils at selected bridges in Illinois

USGS Publications Warehouse

Straub, Timothy D.; Over, Thomas M.; Domanski, Marian M.

2013-01-01

The Scour Rate In COhesive Soils-Erosion Function Apparatus (SRICOS-EFA) method includes an ultimate scour prediction that is the equilibrium maximum pier and contraction scour of cohesive soils over time. The purpose of this report is to present the results of testing the ultimate pier and contraction scour methods for cohesive soils on 30 bridge sites in Illinois. Comparison of the ultimate cohesive and noncohesive methods, along with the Illinois Department of Transportation (IDOT) cohesive soil reduction-factor method and measured scour are presented. Also, results of the comparison of historic IDOT laboratory and field values of unconfined compressive strength of soils (Qu) are presented. The unconfined compressive strength is used in both ultimate cohesive and reduction-factor methods, and knowing how the values from field methods compare to the laboratory methods is critical to the informed application of the methods. On average, the non-cohesive method results predict the highest amount of scour, followed by the reduction-factor method results; and the ultimate cohesive method results predict the lowest amount of scour. The 100-year scour predicted for the ultimate cohesive, noncohesive, and reduction-factor methods for each bridge site and soil are always larger than observed scour in this study, except 12% of predicted values that are all within 0.4 ft of the observed scour. The ultimate cohesive scour prediction is smaller than the non-cohesive scour prediction method for 78% of bridge sites and soils. Seventy-six percent of the ultimate cohesive predictions show a 45% or greater reduction from the non-cohesive predictions that are over 10 ft. Comparing the ultimate cohesive and reduction-factor 100-year scour predictions methods for each bridge site and soil, the scour predicted by the ultimate cohesive scour prediction method is less than the reduction-factor 100-year scour prediction method for 51% of bridge sites and soils. Critical shear stress remains a needed parameter in the ultimate scour prediction for cohesive soils. The unconfined soil compressive strength measured by IDOT in the laboratory was found to provide a good prediction of critical shear stress, as measured by using the erosion function apparatus in a previous study. Because laboratory Qu analyses are time-consuming and expensive, the ability of field-measured Rimac data to estimate unconfined soil strength in the critical shear–soil strength relation was tested. A regression analysis was completed using a historic IDOT dataset containing 366 data pairs of laboratory Qu and field Rimac measurements from common sites with cohesive soils. The resulting equations provide a point prediction of Qu, given any Rimac value with the 90% confidence interval. The prediction equations are not significantly different from the identity Qu = Rimac. The alternative predictions of ultimate cohesive scour presented in this study assume Qu will be estimated using Rimac measurements that include computed uncertainty. In particular, the ultimate cohesive predicted scour is greater than observed scour for the entire 90% confidence interval range for predicting Qu at the bridges and soils used in this study, with the exception of the six predicted values that are all within 0.6 ft of the observed scour.

Some aspects of forecasting the post-mining substratum deformation for evaluation of its influence on constructions

NASA Astrophysics Data System (ADS)

Strzałkowski, Piotr; Ścigała, Roman; Szafulera, Katarzyna

2018-04-01

Some problems have been discussed, connected with performing predictions of post-mining terrain deformations. Especially problems occur with the summation of horizontal strain over long time intervals as well as predictions of linear discontinuous deformations. Of great importance in recent years is the problem of taking into account transient values of deformations associated with the development of extraction field. The exemplary analysis has been presented of planned extraction influences on two characteristic locations of building structure. The proposal has been shown of calculations with using transient deformation model allowing to describe the influence of extraction advance influence on the value of coefficient of extraction rate c (time factor), according to own original empirical formula.

Predictable waves of sequential forest degradation and biodiversity loss spreading from an African city

PubMed Central

Burgess, Neil D.; Milledge, Simon A. H.; Bulling, Mark T.; Fisher, Brendan; Smart, James C. R.; Clarke, G. Philip; Mhoro, Boniface E.; Lewis, Simon L.

2010-01-01

Tropical forest degradation emits carbon at a rate of ~0.5 Pg·y−1, reduces biodiversity, and facilitates forest clearance. Understanding degradation drivers and patterns is therefore crucial to managing forests to mitigate climate change and reduce biodiversity loss. Putative patterns of degradation affecting forest stocks, carbon, and biodiversity have variously been described previously, but these have not been quantitatively assessed together or tested systematically. Economic theory predicts a systematic allocation of land to its highest use value in response to distance from centers of demand. We tested this theory to see if forest exploitation would expand through time and space as concentric waves, with each wave targeting lower value products. We used forest data along a transect from 10 to 220 km from Dar es Salaam (DES), Tanzania, collected at two points in time (1991 and 2005). Our predictions were confirmed: high-value logging expanded 9 km·y−1, and an inner wave of lower value charcoal production 2 km·y−1. This resource utilization is shown to reduce the public goods of carbon storage and species richness, which significantly increased with each kilometer from DES [carbon, 0.2 Mg·ha−1; 0.1 species per sample area (0.4 ha)]. Our study suggests that tropical forest degradation can be modeled and predicted, with its attendant loss of some public goods. In sub-Saharan Africa, an area experiencing the highest rate of urban migration worldwide, coupled with a high dependence on forest-based resources, predicting the spatiotemporal patterns of degradation can inform policies designed to extract resources without unsustainably reducing carbon storage and biodiversity. PMID:20679200

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Predictive value of early changes in triglycerides and weight for longer-term changes in metabolic measures during olanzapine, ziprasidone or aripiprazole treatment for schizophrenia and schizoaffective disorder post hoc analyses of 3 randomized, controlled clinical trials.

PubMed

Hoffmann, Vicki P; Case, Michael; Stauffer, Virginia L; Jacobson, Jennie G; Conley, Robert R

2010-12-01

The objective of this study was to determine if early changes in triglycerides and weight may be useful in predicting longer-term changes in weight and other metabolic parameters. Data were from three 24- to 28-week randomized, controlled studies comparing olanzapine to ziprasidone or aripiprazole for treatment of schizophrenia. Analyses were restricted to completers with fasting laboratory data at all protocol specified time points. Analyses were primarily descriptive and included mean changes and categorical outcomes. In all treatment groups, participants who did not experience a 20 mg/dL or greater increase in triglycerides at early time points were unlikely to experience a change of 50 mg/dL or more in triglycerides after 6 months. Negative predictive values were 83% to 95%. However, early change in triglycerides was not useful for predicting later change in glucose, cholesterol, or weight. Similarly, early weight change gave robust negative predictive values for longer-term weight change (≥10 kg), but not for change in glucose or cholesterol. Lack of early elevation in triglyceride concentrations was predictive of later lack of substantial increase in triglycerides in olanzapine-, ziprasidone-, and aripiprazole-treated participants. Lack of early elevation in weight was predictive of later lack of substantial increase in weight in all 3 treatment groups. Early monitoring of triglyceride concentrations and weight may help clinicians assess risk that individuals will experience significant increase in triglycerides or weight gain, allowing assessments of potential risks and benefits earlier in treatment. Clinical monitoring is advised throughout treatment for all patients.

Improved disturbance rejection for predictor-based control of MIMO linear systems with input delay

NASA Astrophysics Data System (ADS)

Shi, Shang; Liu, Wenhui; Lu, Junwei; Chu, Yuming

2018-02-01

In this paper, we are concerned with the predictor-based control of multi-input multi-output (MIMO) linear systems with input delay and disturbances. By taking the future values of disturbances into consideration, a new improved predictive scheme is proposed. Compared with the existing predictive schemes, our proposed predictive scheme can achieve a finite-time exact state prediction for some smooth disturbances including the constant disturbances, and a better disturbance attenuation can also be achieved for a large class of other time-varying disturbances. The attenuation of mismatched disturbances for second-order linear systems with input delay is also investigated by using our proposed predictor-based controller.

Neural evidence of motivational conflict between social values.

PubMed

Leszkowicz, Emilia; Linden, David E J; Maio, Gregory R; Ihssen, Niklas

2017-10-01

Motivational interdependence is an organizing principle in Schwartz's circumplex model of social values, which has received abundant cross-cultural support. We used fMRI to test whether motivational relations between social values predict different brain responses in a situation of choice between values. We hypothesized that differences in brain responses would become evident when the more important value had to be selected in pairs of congruent (e.g., wealth and success) as opposed to incongruent (e.g., curiosity and stability) values as they are described in Schwartz's model, because the former serve mutually facilitating motives, whereas the latter serve mutually inhibiting motives. Consistent with the model, choosing between congruent values led to longer response times and more activation in conflict-related brain regions (e.g., the supplementary motor area, dorsolateral prefrontal cortex) than selecting between incongruent values. These results provide novel neural evidence supporting the circumplex model's predictions about motivational interdependence between social values. In particular, our results show that the neural networks underlying social values are organized in a way that allows activation patterns related to motivational similarity between congruent values to be dissociated from those related to incongruent values.

Probabilistic seismic loss estimation via endurance time method

NASA Astrophysics Data System (ADS)

Tafakori, Ehsan; Pourzeynali, Saeid; Estekanchi, Homayoon E.

2017-01-01

Probabilistic Seismic Loss Estimation is a methodology used as a quantitative and explicit expression of the performance of buildings using terms that address the interests of both owners and insurance companies. Applying the ATC 58 approach for seismic loss assessment of buildings requires using Incremental Dynamic Analysis (IDA), which needs hundreds of time-consuming analyses, which in turn hinders its wide application. The Endurance Time Method (ETM) is proposed herein as part of a demand propagation prediction procedure and is shown to be an economical alternative to IDA. Various scenarios were considered to achieve this purpose and their appropriateness has been evaluated using statistical methods. The most precise and efficient scenario was validated through comparison against IDA driven response predictions of 34 code conforming benchmark structures and was proven to be sufficiently precise while offering a great deal of efficiency. The loss values were estimated by replacing IDA with the proposed ETM-based procedure in the ATC 58 procedure and it was found that these values suffer from varying inaccuracies, which were attributed to the discretized nature of damage and loss prediction functions provided by ATC 58.

A simplified heat transfer model for predicting temperature change inside food package kept in cold room.

PubMed

Raval, A H; Solanki, S C; Yadav, Rajvir

2013-04-01

A simple analytical heat flow model for a closed rectangular food package containing fruits or vegetables is proposed for predicting time temperature distribution during transient cooling in a controlled environment cold room. It is based on the assumption of only conductive heat transfer inside a closed food package with effective thermal properties, and convective and radiative heat transfer at the outside of the package. The effective thermal conductivity of the food package is determined by evaluating its effective thermal resistance to heat conduction in the packages. Food packages both as an infinite slab and a finite slab have been investigated. The finite slab solution has been obtained as the product of three infinite slab solutions describe in ASHRAE guide and data book. Time temperature variation has been determined and is presented graphically. The cooling rate and the half cooling time were also obtained. These predicted values, are compared with the experimentally measured values for both the finite and infinite closed packages containing oranges. An excellent agreement between them validated the simple proposed model.

Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

NASA Astrophysics Data System (ADS)

Osman, Marisol; Vera, C. S.

2017-10-01

This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to those associated with the signal, especially at the extratropics.

Incremental Value of Repeated Risk Factor Measurements for Cardiovascular Disease Prediction in Middle-Aged Korean Adults: Results From the NHIS-HEALS (National Health Insurance System-National Health Screening Cohort).

PubMed

Cho, In-Jeong; Sung, Ji Min; Chang, Hyuk-Jae; Chung, Namsik; Kim, Hyeon Chang

2017-11-01

Increasing evidence suggests that repeatedly measured cardiovascular disease (CVD) risk factors may have an additive predictive value compared with single measured levels. Thus, we evaluated the incremental predictive value of incorporating periodic health screening data for CVD prediction in a large nationwide cohort with periodic health screening tests. A total of 467 708 persons aged 40 to 79 years and free from CVD were randomly divided into development (70%) and validation subcohorts (30%). We developed 3 different CVD prediction models: a single measure model using single time point screening data; a longitudinal average model using average risk factor values from periodic screening data; and a longitudinal summary model using average values and the variability of risk factors. The development subcohort included 327 396 persons who had 3.2 health screenings on average and 25 765 cases of CVD over 12 years. The C statistics (95% confidence interval [CI]) for the single measure, longitudinal average, and longitudinal summary models were 0.690 (95% CI, 0.682-0.698), 0.695 (95% CI, 0.687-0.703), and 0.752 (95% CI, 0.744-0.760) in men and 0.732 (95% CI, 0.722-0.742), 0.735 (95% CI, 0.725-0.745), and 0.790 (95% CI, 0.780-0.800) in women, respectively. The net reclassification index from the single measure model to the longitudinal average model was 1.78% in men and 1.33% in women, and the index from the longitudinal average model to the longitudinal summary model was 32.71% in men and 34.98% in women. Using averages of repeatedly measured risk factor values modestly improves CVD predictability compared with single measurement values. Incorporating the average and variability information of repeated measurements can lead to great improvements in disease prediction. URL: https://www.clinicaltrials.gov. Unique identifier: NCT02931500. © 2017 American Heart Association, Inc.

Near Real-Time Event Detection & Prediction Using Intelligent Software Agents

DTIC Science & Technology

2006-03-01

value was 0.06743. Multiple autoregressive integrated moving average ( ARIMA ) models were then build to see if the raw data, differenced data, or...slight improvement. The best adjusted r^2 value was found to be 0.1814. Successful results were not expected from linear or ARIMA -based modelling ...appear, 2005. [63] Mora-Lopez, L., Mora, J., Morales-Bueno, R., et al. Modelling time series of climatic parameters with probabilistic finite

Sample entropy predicts lifesaving interventions in trauma patients with normal vital signs.

PubMed

Naraghi, L; Mejaddam, A Y; Birkhan, O A; Chang, Y; Cropano, C M; Mesar, T; Larentzakis, A; Peev, M; Sideris, A C; Van der Wilden, G M; Imam, A M; Hwabejire, J O; Velmahos, G C; Fagenholz, P J; Yeh, D; de Moya, M A; King, D R

2015-08-01

Heart rate complexity, commonly described as a "new vital sign," has shown promise in predicting injury severity, but its use in clinical practice is not yet widely adopted. We previously demonstrated the ability of this noninvasive technology to predict lifesaving interventions (LSIs) in trauma patients. This study was conducted to prospectively evaluate the utility of real-time, automated, noninvasive, instantaneous sample entropy (SampEn) analysis to predict the need for an LSI in a trauma alert population presenting with normal vital signs. Prospective enrollment of patients who met criteria for trauma team activation and presented with normal vital signs was conducted at a level I trauma center. High-fidelity electrocardiogram recording was used to calculate SampEn and SD of the normal-to-normal R-R interval (SDNN) continuously in real time for 2 hours with a portable, handheld device. Patients who received an LSI were compared to patients without any intervention (non-LSI). Multivariable analysis was performed to control for differences between the groups. Treating clinicians were blinded to results. Of 129 patients enrolled, 38 (29%) received 136 LSIs within 24 hours of hospital arrival. Initial systolic blood pressure was similar in both groups. Lifesaving intervention patients had a lower Glasgow Coma Scale. The mean SampEn on presentation was 0.7 (0.4-1.2) in the LSI group compared to 1.5 (1.1-2.0) in the non-LSI group (P < .0001). The area under the curve with initial SampEn alone was 0.73 (95% confidence interval [CI], 0.64-0.81) and increased to 0.93 (95% CI, 0.89-0.98) after adding sedation to the model. Sample entropy of less than 0.8 yields sensitivity, specificity, negative predictive value, and positive predictive value of 58%, 86%, 82%, and 65%, respectively, with an overall accuracy of 76% for predicting an LSI. SD of the normal-to-normal R-R interval had no predictive value. In trauma patients with normal presenting vital signs, decreased SampEn is an independent predictor of the need for LSI. Real-time SampEn analysis may be a useful adjunct to standard vital signs monitoring. Adoption of real-time, instantaneous SampEn monitoring for trauma patients, especially in resource-constrained environments, should be considered. Copyright © 2015 Elsevier Inc. All rights reserved.

Sun Series program for the REEDA System. [predicting orbital lifetime using sunspot values

NASA Technical Reports Server (NTRS)

Shankle, R. W.

1980-01-01

Modifications made to data bases and to four programs in a series of computer programs (Sun Series) which run on the REEDA HP minicomputer system to aid NASA's solar activity predictions used in orbital life time predictions are described. These programs utilize various mathematical smoothing technique and perform statistical and graphical analysis of various solar activity data bases residing on the REEDA System.

Decay of intestinal enterococci concentrations in high-energy estuarine and coastal waters: towards real-time T90 values for modelling faecal indicators in recreational waters.

PubMed

Kay, D; Stapleton, C M; Wyer, M D; McDonald, A T; Crowther, J; Paul, N; Jones, K; Francis, C; Watkins, J; Wilkinson, J; Humphrey, N; Lin, B; Yang, L; Falconer, R A; Gardner, S

2005-02-01

Intestinal enterococci are the principal 'health-evidence-based' parameter recommended by WHO for the assessment of marine recreational water compliance. Understanding the survival characteristics of these organisms in nearshore waters is central to public health protection using robust modelling to effect real-time prediction of water quality at recreation sites as recently suggested by WHO and the Commission of the European Communities Previous models have more often focused on the coliform parameters and assumed two static day-time and night-time T90 values to characterise the decay process. The principal driver for enterococci survival is the received dose of irradiance from sunlight. In the water column, transmission of irradiance is determined by turbidity produced by suspended material. This paper reports the results of irradiated microcosm experiments using simulated sunlight to investigate the decay of intestinal enterococci in relatively turbid estuarine and coastal waters collected from the Severn Estuary and Bristol Channel, UK. High-turbidity estuarine waters produced a T90 value of 39.5 h. Low-turbidity coastal waters produced a much shorter T90 value of 6.6 h. In experiments receiving no irradiation, high-turbidity estuarine waters also produced a longer T90 of 65.1 h compared with corresponding low-turbidity coastal waters, T90 24.8 h. Irradiated T90 values were correlated with salinity, turbidity and suspended solids (r>0.8, p<0.001). The results suggest that enterococci decay in irradiated experiments with turbidity >200 NTU is similar to decay observed under dark conditions. Most significantly, these results suggest that modelling turbidity and or suspended solids offers a potential means of predicting T90 values in 'real-time' for discrete cells of a hydrodynamic model.

Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs.

PubMed

Lado, Bettina; Battenfield, Sarah; Guzmán, Carlos; Quincke, Martín; Singh, Ravi P; Dreisigacker, Susanne; Peña, R Javier; Fritz, Allan; Silva, Paula; Poland, Jesse; Gutiérrez, Lucía

2017-07-01

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (V) or assuming linkage equilibrium (V). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat ( L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses. Copyright © 2017 Crop Science Society of America.

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN).

PubMed

Titah, Harmin Sulistiyaning; Halmi, Mohd Izuan Effendi Bin; Abdullah, Siti Rozaimah Sheikh; Hasan, Hassimi Abu; Idris, Mushrifah; Anuar, Nurina

2018-06-07

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg -1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R 2 (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.

The predictive value of respiratory function tests for non-invasive ventilation in amyotrophic lateral sclerosis.

PubMed

Tilanus, T B M; Groothuis, J T; TenBroek-Pastoor, J M C; Feuth, T B; Heijdra, Y F; Slenders, J P L; Doorduin, J; Van Engelen, B G; Kampelmacher, M J; Raaphorst, J

2017-07-25

Non-invasive ventilation (NIV) improves survival and quality of life in amyotrophic lateral sclerosis (ALS) patients. The timing of referral to a home ventilation service (HVS), which is in part based on respiratory function tests, has shown room for improvement. It is currently unknown which respiratory function test predicts an appropriate timing of the initiation of NIV. We analysed, retrospectively, serial data of five respiratory function tests: forced vital capacity (FVC), peak cough flow (PCF), maximum inspiratory and expiratory pressure (MIP and MEP) and sniff nasal inspiratory pressure (SNIP) in patients with ALS. Patients who had had at least one assessment of respiratory function and one visit at the HVS, were included. Our aim was to detect the test with the highest predictive value for the need for elective NIV in the following 3 months. We analysed time curves, currently used cut-off values for referral, and respiratory function test results between 'NIV indication' and 'no-NIV indication' patients. One hundred ten patients with ALS were included of whom 87 received an NIV indication; 11.5% had one assessment before receiving an NIV indication, 88.5% had two or more assessments. The NIV indication was based on complaints of hypoventilation and/or proven (nocturnal) hypercapnia. The five respiratory function tests showed a descending trend during disease progression, where SNIP showed the greatest decline within the latest 3 months before NIV indication (mean = -22%). PCF at the time of referral to the HVS significantly discriminated between the groups 'NIV-indication' and 'no NIV-indication yet' patients at the first HVS visit: 259 (±92) vs. 348 (±137) L/min, p = 0.019. PCF and SNIP showed the best predictive characteristics in terms of sensitivity. SNIP showed the greatest decline prior to NIV indication and PCF significantly differentiated 'NIV-indication' from 'no NIV-indication yet' patients with ALS. Currently used cut-off values might be adjusted and other respiratory function tests such as SNIP and PCF may become part of routine care in patients with ALS in order to avoid non-timely initiation of (non-invasive) ventilation.

Predicting Early School Achievement with the EDI: A Longitudinal Population-Based Study

ERIC Educational Resources Information Center

Forget-Dubois, Nadine; Lemelin, Jean-Pascal; Boivin, Michel; Dionne, Ginette; Seguin, Jean R.; Vitaro, Frank; Tremblay, Richard E.

2007-01-01

School readiness tests are significant predictors of early school achievement. Measuring school readiness on a large scale would be necessary for the implementation of intervention programs at the community level. However, assessment of school readiness is costly and time consuming. This study assesses the predictive value of a school readiness…

Prediction of toxicity and comparison of alternatives using WebTEST (Web-services Toxicity Estimation Software Tool)

EPA Science Inventory

A Java-based web service is being developed within the US EPA’s Chemistry Dashboard to provide real time estimates of toxicity values and physical properties. WebTEST can generate toxicity predictions directly from a simple URL which includes the endpoint, QSAR method, and ...

Prediction of toxicity and comparison of alternatives using WebTEST (Web-services Toxicity Estimation Software Tool)(Bled Slovenia)

EPA Science Inventory

A Java-based web service is being developed within the US EPA’s Chemistry Dashboard to provide real time estimates of toxicity values and physical properties. WebTEST can generate toxicity predictions directly from a simple URL which includes the endpoint, QSAR method, and ...

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC-TOF-MS.

PubMed

Noorizadeh, Hadi; Farmany, Abbas; Narimani, Hojat; Noorizadeh, Mehrab

2013-05-01

A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN. Copyright © 2011 John Wiley & Sons, Ltd.

Predicting cloud-to-ground lightning with neural networks

NASA Technical Reports Server (NTRS)

Barnes, Arnold A., Jr.; Frankel, Donald; Draper, James Stark

1991-01-01

A neural network is being trained to predict lightning at Cape Canaveral for periods up to two hours in advance. Inputs consist of ground based field mill data, meteorological tower data, lightning location data, and radiosonde data. High values of the field mill data and rapid changes in the field mill data, offset in time, provide the forecasts or desired output values used to train the neural network through backpropagation. Examples of input data are shown and an example of data compression using a hidden layer in the neural network is discussed.

The predictive power of singular value decomposition entropy for stock market dynamics

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2014-01-01

We use a correlation-based approach to analyze financial data from the US stock market, both daily and monthly observations from the Dow Jones. We compute the entropy based on the singular value decomposition of the correlation matrix for the components of the Dow Jones Industrial Index. Based on a moving window, we derive time varying measures of entropy for both daily and monthly data. We find that the entropy has a predictive ability with respect to stock market dynamics as indicated by the Granger causality tests.

The prediction of three-dimensional liquid-propellant rocket nozzle admittances

NASA Technical Reports Server (NTRS)

Bell, W. A.; Zinn, B. T.

1973-01-01

Crocco's three-dimensional nozzle admittance theory is extended to be applicable when the amplitudes of the combustor and nozzle oscillations increase or decrease with time. An analytical procedure and a computer program for determining nozzle admittance values from the extended theory are presented and used to compute the admittances of a family of liquid-propellant rocket nozzles. The calculated results indicate that the nozzle geometry entrance Mach number and temporal decay coefficient significantly affect the nozzle admittance values. The theoretical predictions are shown to be in good agreement with available experimental data.

VARTM Process Modeling of Aerospace Composite Structures

NASA Technical Reports Server (NTRS)

Song, Xiao-Lan; Grimsley, Brian W.; Hubert, Pascal; Cano, Roberto J.; Loos, Alfred C.

2003-01-01

A three-dimensional model was developed to simulate the VARTM composite manufacturing process. The model considers the two important mechanisms that occur during the process: resin flow, and compaction and relaxation of the preform. The model was used to simulate infiltration of a carbon preform with an epoxy resin by the VARTM process. The model predicted flow patterns and preform thickness changes agreed qualitatively with the measured values. However, the predicted total infiltration times were much longer than measured most likely due to the inaccurate preform permeability values used in the simulation.

The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT

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

Li, Heyse, E-mail: heyse.li@mail.utoronto.ca; Becker, Nathan; Raman, Srinivas

2015-08-15

Purpose: There is evidence that computed tomography (CT) and positron emission tomography (PET) imaging metrics are prognostic and predictive in nonsmall cell lung cancer (NSCLC) treatment outcomes. However, few studies have explored the use of standardized uptake value (SUV)-based image features of nodal regions as predictive features. The authors investigated and compared the use of tumor and node image features extracted from the radiotherapy target volumes to predict relapse in a cohort of NSCLC patients undergoing chemoradiation treatment. Methods: A prospective cohort of 25 patients with locally advanced NSCLC underwent 4DPET/4DCT imaging for radiation planning. Thirty-seven image features were derivedmore » from the CT-defined volumes and SUVs of the PET image from both the tumor and nodal target regions. The machine learning methods of logistic regression and repeated stratified five-fold cross-validation (CV) were used to predict local and overall relapses in 2 yr. The authors used well-known feature selection methods (Spearman’s rank correlation, recursive feature elimination) within each fold of CV. Classifiers were ranked on their Matthew’s correlation coefficient (MCC) after CV. Area under the curve, sensitivity, and specificity values are also presented. Results: For predicting local relapse, the best classifier found had a mean MCC of 0.07 and was composed of eight tumor features. For predicting overall relapse, the best classifier found had a mean MCC of 0.29 and was composed of a single feature: the volume greater than 0.5 times the maximum SUV (N). Conclusions: The best classifier for predicting local relapse had only tumor features. In contrast, the best classifier for predicting overall relapse included a node feature. Overall, the methods showed that nodes add value in predicting overall relapse but not local relapse.« less

Dynamo theory prediction of solar activity

NASA Technical Reports Server (NTRS)

Schatten, Kenneth H.

1988-01-01

The dynamo theory technique to predict decadal time scale solar activity variations is introduced. The technique was developed following puzzling correlations involved with geomagnetic precursors of solar activity. Based upon this, a dynamo theory method was developed to predict solar activity. The method was used successfully in solar cycle 21 by Schatten, Scherrer, Svalgaard, and Wilcox, after testing with 8 prior solar cycles. Schatten and Sofia used the technique to predict an exceptionally large cycle, peaking early (in 1990) with a sunspot value near 170, likely the second largest on record. Sunspot numbers are increasing, suggesting that: (1) a large cycle is developing, and (2) that the cycle may even surpass the largest cycle (19). A Sporer Butterfly method shows that the cycle can now be expected to peak in the latter half of 1989, consistent with an amplitude comparable to the value predicted near the last solar minimum.

Irreversibility inversions in two-dimensional turbulence

NASA Astrophysics Data System (ADS)

Bragg, Andrew D.; De Lillo, Filippo; Boffetta, Guido

2018-02-01

In this paper, we consider a recent theoretical prediction [Bragg et al., Phys. Fluids 28, 013305 (2016), 10.1063/1.4939694] that for inertial particles in two-dimensional (2D) turbulence, the nature of the irreversibility of the particle-pair dispersion inverts when the particle inertia exceeds a certain value. In particular, when the particle Stokes number, St , is below a certain value, the forward-in-time (FIT) dispersion should be faster than the backward-in-time (BIT) dispersion, but for St above this value, this should invert so that BIT becomes faster than FIT dispersion. This nontrivial behavior arises because of the competition between two physically distinct irreversibility mechanisms that operate in different regimes of St . In three-dimensional (3D) turbulence, both mechanisms act to produce faster BIT than FIT dispersion, but in 2D turbulence, the two mechanisms have opposite effects because of the flux of energy from the small to the large scales. We supplement the qualitative argument given by Bragg et al. [Phys. Fluids 28, 013305 (2016), 10.1063/1.4939694] by deriving quantitative predictions of this effect in the short time limit. We confirm the theoretical predictions using results of inertial particle dispersion in a direct numerical simulation of 2D turbulence. A more general finding of this analysis is that in turbulent flows with an inverse energy flux, inertial particles may yet exhibit a net downscale flux of kinetic energy because of their nonlocal-in-time dynamics.

To walk or to fly? How birds choose among foraging modes

PubMed Central

Bautista, Luis M.; Tinbergen, Joost; Kacelnik, Alejandro

2001-01-01

We test the predictive value of the main energetic currencies used in foraging theory using starlings that choose between two foraging modes (walking versus flying). Walking is low-cost, low-yield, whereas flying is the opposite. We fixed experimentally, at 11 different values, the amount of flight required to get one food reward, and for each flight cost value, we titrated the amount of walking until the birds showed indifference between foraging modes. We then compared the indifference points to those predicted by gross rate of gain over time, net rate of gain over time, and the ratio of gain to expenditure (efficiency). The results for the choice between modes show strong qualitative and quantitative support for net rate of gain over time over the alternatives. However, the birds foraged for only a fraction of the available time, indicating that the choice between foraging and resting could not be explained by any of these currencies. We suggest that this discrepancy could be accounted for functionally because nonenergetic factors such as predation risk may differ between resting and foraging in any mode but may not differ much between foraging modes, hence releasing the choice between foraging modes from the influence of such factors. Alternatively, the discrepancy may be attributable to the use of predictable (rather than stochastic) ratios of effort per prey in our experiment, and it may thus be better understood with mechanistic rather than functional arguments. PMID:11158599

To walk or to fly? How birds choose among foraging modes.

PubMed

Bautista, L M; Tinbergen, J; Kacelnik, A

2001-01-30

We test the predictive value of the main energetic currencies used in foraging theory using starlings that choose between two foraging modes (walking versus flying). Walking is low-cost, low-yield, whereas flying is the opposite. We fixed experimentally, at 11 different values, the amount of flight required to get one food reward, and for each flight cost value, we titrated the amount of walking until the birds showed indifference between foraging modes. We then compared the indifference points to those predicted by gross rate of gain over time, net rate of gain over time, and the ratio of gain to expenditure (efficiency). The results for the choice between modes show strong qualitative and quantitative support for net rate of gain over time over the alternatives. However, the birds foraged for only a fraction of the available time, indicating that the choice between foraging and resting could not be explained by any of these currencies. We suggest that this discrepancy could be accounted for functionally because nonenergetic factors such as predation risk may differ between resting and foraging in any mode but may not differ much between foraging modes, hence releasing the choice between foraging modes from the influence of such factors. Alternatively, the discrepancy may be attributable to the use of predictable (rather than stochastic) ratios of effort per prey in our experiment, and it may thus be better understood with mechanistic rather than functional arguments.

Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System.

PubMed

Norouzi, Jamshid; Yadollahpour, Ali; Mirbagheri, Seyed Ahmad; Mazdeh, Mitra Mahdavi; Hosseini, Seyed Ahmad

2016-01-01

Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m(2) of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.

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

Sileghem, L.; Wallner, T.; Verhelst, S.

As knock is one of the main factors limiting the efficiency of spark-ignition engines, the introduction of alcohol blends could help to mitigate knock concerns due to the elevated knock resistance of these blends. A model that can accurately predict their autoignition behavior would be of great value to engine designers. The current work aims to develop such a model for alcohol–gasoline blends. First, a mixing rule for the autoignition delay time of alcohol–gasoline blends is proposed. Subsequently, this mixing rule is used together with an autoignition delay time correlation of gasoline and an autoignition delay time cor-relation of methanolmore » in a knock integral model that is implemented in a two-zone engine code. The pre-dictive performance of the resulting model is validated through comparison against experimental measurements on a CFR engine for a range of gasoline–methanol blends. The knock limited spark advance, the knock intensity, the knock onset crank angle and the value of the knock integral at the experimental knock onset have been simulated and compared to the experimental values derived from in-cylinder pressure measurements.« less

The value of time-averaged serum high-sensitivity C-reactive protein in prediction of mortality and dropout in peritoneal dialysis patients.

PubMed

Liu, Shou-Hsuan; Chen, Chao-Yu; Li, Yi-Jung; Wu, Hsin-Hsu; Lin, Chan-Yu; Chen, Yung-Chang; Chang, Ming-Yang; Hsu, Hsiang-Hao; Ku, Cheng-Lung; Tian, Ya-Chung

2017-01-01

C-reactive protein (CRP) is a useful biomarker for prediction of long-term outcomes in patients undergoing chronic dialysis. This observational cohort study evaluated whether the time-averaged serum high-sensitivity CRP (HS-CRP) level was a better predictor of clinical outcomes than a single HS-CRP level in patients undergoing peritoneal dialysis (PD). We classified 335 patients into three tertiles according to the time-averaged serum HS-CRP level and followed up regularly from January 2010 to December 2014. Clinical outcomes such as cardiovascular events, infection episodes, newly developed malignancy, encapsulating peritoneal sclerosis (EPS), dropout (death plus conversion to hemodialysis), and mortality were assessed. During a 5-year follow-up, 164 patients (49.0%) ceased PD; this included 52 patient deaths (15.5%), 100 patients (29.9%) who converted to hemodialysis, and 12 patients (3.6%) who received a kidney transplantation. The Kaplan-Meier survival analysis and log-rank test revealed a significantly worse survival accumulation in patients with high time-average HS-CRP levels. A multivariate Cox regression analysis revealed that a higher time-averaged serum HS-CRP level, older age, and the occurrence of cardiovascular events were independent mortality predictors. A higher time-averaged serum HS-CRP level, the occurrence of cardiovascular events, infection episodes, and EPS were important predictors of dropout. The receiver operating characteristic analysis verified that the value of the time-average HS-CRP level in predicting the 5-year mortality and dropout was superior to a single serum baseline HS-CRP level. This study shows that the time-averaged serum HS-CRP level is a better marker than a single baseline measurement in predicting the 5-year mortality and dropout in PD patients.

The silver effect of admission glucose level on excellent outcome in thrombolysed stroke patients.

PubMed

Rosso, Charlotte; Baronnet, Flore; Diaz, Belen; Le Bouc, Raphael; Frasca Polara, Giulia; Moulton, Eric Jr; Deltour, Sandrine; Leger, Anne; Crozier, Sophie; Samson, Yves

2018-05-18

Higher admission glucose levels (AGL) are associated with less favorable outcome in thrombolysis. But, could AGL's impact on outcome vary by onset-to-treatment (OTT) time? Is hyperglycemia associated with a shorter therapeutic time window for excellent outcome for thrombolysed stroke patients? We assessed predictive values of AGL, baseline NIHSS, age, and OTT time quartiles on excellent outcome (3-month modified Rankin score of 0-1) in 773 patients treated by rt-Pa. We added the AGL × OTT time quartile interaction in the model and separately analyzed the predictive values of AGL, age, and NIHSS for each OTT time quartile if the interaction was significant. AGL, baseline NIHSS, age, and OTT time quartiles were significant predictors. When added in the model, the AGL × OTT interaction was significant (OR: 0.96, 95% CI: 0.94-0.99, p: 0.0009). AGL was predictive only during the third OTT time quartile (181-224 min). During this period, the predicted rate of excellent outcome was 16% for AGL = 6.5 mmol/L and 8% for AGL = 8 mmol/L. The rate of excellent outcome was not decreased in hyperglycemic patients for OTT time ≤ 180 min (20 vs. 24.5% p: 0.37), but was decreased for OTT time > 180 min (9.6 vs. 26.7% p: 0.00001). Similar results were found in patients with MCA recanalization, but not in patients without recanalization. The therapeutic time window for excellent outcome is shortened in hyperglycemic patients. This would support the design of "freezing penumbra" randomized trials based on ultra-early AGL control.

Do intensive care data on respiratory infections reflect influenza epidemics?

PubMed

Koetsier, Antonie; van Asten, Liselotte; Dijkstra, Frederika; van der Hoek, Wim; Snijders, Bianca E; van den Wijngaard, Cees C; Boshuizen, Hendriek C; Donker, Gé A; de Lange, Dylan W; de Keizer, Nicolette F; Peek, Niels

2013-01-01

Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics. We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003-2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI. Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51. ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities.

Nomogram to Predict Postoperative Readmission in Patients Who Undergo General Surgery.

PubMed

Tevis, Sarah E; Weber, Sharon M; Kent, K Craig; Kennedy, Gregory D

2015-06-01

The Centers for Medicare and Medicaid Services have implemented penalties for hospitals with above-average readmission rates under the Hospital Readmissions Reductions Program. These changes will likely be extended to affect postoperative readmissions in the future. To identify variables that place patients at risk for readmission, develop a predictive nomogram, and validate this nomogram. Retrospective review and prospective validation of a predictive nomogram. A predictive nomogram was developed with the linear predictor method using the American College of Surgeons National Surgical Quality Improvement Program database paired with institutional billing data for patients who underwent nonemergent inpatient general surgery procedures. The nomogram was developed from August 1, 2006, through December 31, 2011, in 2799 patients and prospectively validated from November 1, 2013, through December 19, 2013, in 255 patients at a single academic institution. Area under the curve and positive and negative predictive values were calculated. The outcome of interest was readmission within 30 days of discharge following an index hospitalization for a surgical procedure. Bleeding disorder (odds ratio, 2.549; 95% CI, 1.464-4.440), long operative time (odds ratio, 1.601; 95% CI, 1.186-2.160), in-hospital complications (odds ratio, 16.273; 95% CI, 12.028-22.016), dependent functional status, and the need for a higher level of care at discharge (odds ratio, 1.937; 95% CI, 1.176-3.190) were independently associated with readmission. The nomogram accurately predicted readmission (C statistic = 0.756) in a prospective evaluation. The negative predictive value was 97.9% in the prospective validation, while the positive predictive value was 11.1%. Development of an online calculator using this predictive model will allow us to identify patients who are at high risk for readmission at the time of discharge. Patients with increased risk may benefit from more intensive postoperative follow-up in the outpatient setting.

Wet tropospheric delays forecast based on Vienna Mapping Function time series analysis

NASA Astrophysics Data System (ADS)

Rzepecka, Zofia; Kalita, Jakub

2016-04-01

It is well known that the dry part of the zenith tropospheric delay (ZTD) is much easier to model than the wet part (ZTW). The aim of the research is applying stochastic modeling and prediction of ZTW using time series analysis tools. Application of time series analysis enables closer understanding of ZTW behavior as well as short-term prediction of future ZTW values. The ZTW data used for the studies were obtained from the GGOS service hold by Vienna technical University. The resolution of the data is six hours. ZTW for the years 2010 -2013 were adopted for the study. The International GNSS Service (IGS) permanent stations LAMA and GOPE, located in mid-latitudes, were admitted for the investigations. Initially the seasonal part was separated and modeled using periodic signals and frequency analysis. The prominent annual and semi-annual signals were removed using sines and consines functions. The autocorrelation of the resulting signal is significant for several days (20-30 samples). The residuals of this fitting were further analyzed and modeled with ARIMA processes. For both the stations optimal ARMA processes based on several criterions were obtained. On this basis predicted ZTW values were computed for one day ahead, leaving the white process residuals. Accuracy of the prediction can be estimated at about 3 cm.

Value of IgA tTG in Predicting Mucosal Recovery in Children with Celiac Disease on a Gluten Free Diet

PubMed Central

Leonard, Maureen M.; Weir, Dascha C.; DeGroote, Maya; Mitchell, Paul D.; Singh, Prashant; Silvester, Jocelyn A.; Leichtner, Alan M.; Fasano, Alessio

2017-01-01

Objective Our objective was to determine the rate of mucosal recovery in pediatric patients with celiac disease on a gluten free diet. We also sought to determine whether IgA tissue transglutaminase (tTG) correlates with mucosal damage at the time of a repeat endoscopy with duodenal biopsy in these patients. Methods We performed a retrospective chart review of one-hundred and three pediatric patients, under 21 years of age, with a diagnosis of celiac disease defined as Marsh 3 histology, and who underwent a repeat endoscopy with duodenal biopsy at least twelve months after initiating a gluten free diet. Results We found that 19% of pediatric patients treated with a gluten free diet had persistent enteropathy. At the time of the repeat biopsy, tTG was elevated in 43% of cases with persistent enteropathy and 32% of cases in which there was mucosal recovery. Overall the positive predictive value of the autoantibody tissue transglutaminase was 25% and the negative predictive value was 83% in patients on a gluten free diet for a median of 2.4 years. Conclusions Nearly one in five children with celiac disease in our population had persistent enteropathy despite maintaining a gluten free diet and IgA tTG was not an accurate marker of mucosal recovery. Neither the presence of symptoms nor positive serology were predictive of a patient’s histology at the time of repeat biopsy. These findings suggest a revisitation of monitoring and management criteria of celiac disease in childhood. PMID:28112686

Evaluation of coffee roasting degree by using electronic nose and artificial neural network for off-line quality control.

PubMed

Romani, Santina; Cevoli, Chiara; Fabbri, Angelo; Alessandrini, Laura; Dalla Rosa, Marco

2012-09-01

An electronic nose (EN) based on an array of 10 metal oxide semiconductor sensors was used, jointly with an artificial neural network (ANN), to predict coffee roasting degree. The flavor release evolution and the main physicochemical modifications (weight loss, density, moisture content, and surface color: L*, a*), during the roasting process of coffee, were monitored at different cooking times (0, 6, 8, 10, 14, 19 min). Principal component analysis (PCA) was used to reduce the dimensionality of sensors data set (600 values per sensor). The selected PCs were used as ANN input variables. Two types of ANN methods (multilayer perceptron [MLP] and general regression neural network [GRNN]) were used in order to estimate the EN signals. For both neural networks the input values were represented by scores of sensors data set PCs, while the output values were the quality parameter at different roasting times. Both the ANNs were able to well predict coffee roasting degree, giving good prediction results for both roasting time and coffee quality parameters. In particular, GRNN showed the highest prediction reliability. Actually the evaluation of coffee roasting degree is mainly a manned operation, substantially based on the empirical final color observation. For this reason it requires well-trained operators with a long professional skill. The coupling of e-nose and artificial neural networks (ANNs) may represent an effective possibility to roasting process automation and to set up a more reproducible procedure for final coffee bean quality characterization. © 2012 Institute of Food Technologists®

Tailoff thrust and impulse imbalance between pairs of Space Shuttle solid rocket motors

NASA Technical Reports Server (NTRS)

Jacobs, E. P.; Yeager, J. M.

1975-01-01

The tailoff thrust and impulse imbalance between pairs of solid rocket motors is of particular interest for the Space Shuttle Vehicle because of the potential control problems that exist with this asymmetric configuration. Although a similar arrangement of solid rocket motors was utilized for the Titan Program, they produced less than one-half the thrust level of the Space Shuttle at web action time, and the overall vehicle was symmetric. Since the Titan Program does provide the most applicable actual test data, 23 flight pairs were analyzed to determine the actual tailoff thrust and impulse imbalance experienced. The results were scaled up using the predicted web action time thrust and tailoff time to arrive at values for the Space Shuttle. These values were then statistically treated to obtain a prediction of the maximum imbalance one could expect to experience during the Shuttle Program.

Predictability of Landslide Timing From Quasi-Periodic Precursory Earthquakes

NASA Astrophysics Data System (ADS)

Bell, Andrew F.

2018-02-01

Accelerating rates of geophysical signals are observed before a range of material failure phenomena. They provide insights into the physical processes controlling failure and the basis for failure forecasts. However, examples of accelerating seismicity before landslides are rare, and their behavior and forecasting potential are largely unknown. Here I use a Bayesian methodology to apply a novel gamma point process model to investigate a sequence of quasiperiodic repeating earthquakes preceding a large landslide at Nuugaatsiaq in Greenland in June 2017. The evolution in earthquake rate is best explained by an inverse power law increase with time toward failure, as predicted by material failure theory. However, the commonly accepted power law exponent value of 1.0 is inconsistent with the data. Instead, the mean posterior value of 0.71 indicates a particularly rapid acceleration toward failure and suggests that only relatively short warning times may be possible for similar landslides in future.

Discovering urban mobility patterns with PageRank based traffic modeling and prediction

NASA Astrophysics Data System (ADS)

Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

2017-11-01

Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.

The efficacy of real-time colour Doppler flow imaging on endoscopic ultrasonography for differential diagnosis between neoplastic and non-neoplastic gallbladder polyps.

PubMed

Kim, Su Young; Cho, Jae Hee; Kim, Eui Joo; Chung, Dong Hae; Kim, Kun Kuk; Park, Yeon Ho; Kim, Yeon Suk

2018-05-01

We evaluated the usefulness of real-time colour Doppler flow (CDF) endoscopic ultrasonography (EUS) for differentiating neoplastic gallbladder (GB) polyps from non-neoplastic polyps. Between August 2014 and December 2016, a total of 233 patients with GB polyps who underwent real-time CDF-EUS were consecutively enrolled in this prospective study. CDF imaging was subjectively categorized for each patient as: strong CDF pattern, weak CDF pattern and no CDF pattern. Of the 233 patients, 115 underwent surgical resection. Of these, there were 90 cases of non-neoplastic GB polyps and 23 cases of neoplastic GB polyps. In a multivariate analysis, a strong CDF pattern was the most significant predictive factor for neoplastic polyps; sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 52.2 %, 79.4 %, 38.7 %, 86.9 % and 73.9 %, respectively. Solitary polyp and polyp size were associated with an increased risk of neoplasm. The presence of a strong CDF pattern as well as solitary and larger polyps on EUS may be predictive of neoplastic GB polyps. As real-time CDF-EUS poses no danger to the patient and requires no additional equipment, it is likely to become a supplemental tool for the differential diagnosis of GB polyps. • Differential diagnosis between neoplastic polyps and non-neoplastic polyps of GB is limited. • The use of real-time CDF-EUS was convenient, with high agreement between operators. • The real-time CDF-EUS is helpful in differential diagnosis of GB polyps.

Assessment, analysis and appraisal of road traffic noise pollution in Rourkela city, India.

PubMed

Goswami, Shreerup; Swain, Bijay Kumar; Panda, Santosh Kumar

2013-09-01

The problem of road traffic noise pollution has become a concern for both the public and the policy makers. Noise level was assessed in 12 different squares of Rourkela city during different specified times (7-10 a.m., 11 a.m.-2 p.m., 3-6 p.m., 7-10 p.m., 10 p.m.-12 midnight and 4-6 a.m.). Noise descriptors such as L,eq, traffic noise index, noise pollution level, noise climate, Lday, Levening, Lnight and Lden were assessed to reveal the extent of noise pollution due to heavy traffic in this city. The equivalent noise levels of all the 12 squares were found to be much beyond the permissible limit (70dB during day time and 55dB during night time). Appallingly, even the minimum L eq and NPL values were more than 82 dB and 96 dB during day time and 69 dB and 91 dB during night time respectively. Lden values of investigated squares ranged from 83.4 to 86.1 dB and were even more than the day time permissible limit of traffic noise. The prediction model was used in the present study to predict noise pollution level instead of Leq. Comparison of predicted with that of the actual measured data demonstrated that the model used for the prediction has the ability to calibrate the multicomponent traffic noise and yield reliable results close to that by direct measurement. Lastly, it is inferred that the dimension of the traffic generated noise pollution in Rourkela is critical.

Enhancing user experience by using multi-sensor data fusion to predict phone's luminance

NASA Astrophysics Data System (ADS)

Marhoubi, Asmaa H.

2017-09-01

The movement of a phone in an environment with different brightness, makes the luminance prediction challenging. The ambient light sensor takes time to modify the brightness of the screen based on the environment it is placed in. This causes an unsatisfactory user experience and delays in adjustment of the screen brightness. In this research, a method is proposed for enhancing the prediction of luminance using accelerometer, gyroscope and speed measurement technique. The speed of the phone is identified using Sum-of-Sine parameters. The lux values are then fused with the accelerometer and gyroscope data to present more accurate luminance values for the ALS based on the movement of the phone. An investigation is made during the movement of the user in a standard lighting environment. This enhances the user experience and improves the screen brightness precision. The accuracy has given an R-Square value of up to 0.97.

Evaluation of a Mysis bioenergetics model

USGS Publications Warehouse

Chipps, S.R.; Bennett, D.H.

2002-01-01

Direct approaches for estimating the feeding rate of the opossum shrimp Mysis relicta can be hampered by variable gut residence time (evacuation rate models) and non-linear functional responses (clearance rate models). Bioenergetics modeling provides an alternative method, but the reliability of this approach needs to be evaluated using independent measures of growth and food consumption. In this study, we measured growth and food consumption for M. relicta and compared experimental results with those predicted from a Mysis bioenergetics model. For Mysis reared at 10??C, model predictions were not significantly different from observed values. Moreover, decomposition of mean square error indicated that 70% of the variation between model predictions and observed values was attributable to random error. On average, model predictions were within 12% of observed values. A sensitivity analysis revealed that Mysis respiration and prey energy density were the most sensitive parameters affecting model output. By accounting for uncertainty (95% CLs) in Mysis respiration, we observed a significant improvement in the accuracy of model output (within 5% of observed values), illustrating the importance of sensitive input parameters for model performance. These findings help corroborate the Mysis bioenergetics model and demonstrate the usefulness of this approach for estimating Mysis feeding rate.

FIEFDom: A Transparent Domain Boundary Recognition System using a Fuzzy Mean Operator

DTIC Science & Technology

2008-12-04

to search for matching fragments by running the PSI-BLAST program a second time. During this step, the expectation value threshold ( e -value) is set at...statistical significance (or low e -value), and therefore have low scores. Finally, the domain boundaries (if any) are predicted using the scored...neighbor (match) is weighted by its e -value, the relative contribution of each neighbor is apparent. This is contrary to black-box models in which the

Adolescents' perceptions of socializers' beliefs, career-related conversations, and motivation in mathematics.

PubMed

Lazarides, Rebecca; Rubach, Charlott; Ittel, Angela

2017-03-01

Research based on the Eccles model of parent socialization demonstrated that parents are an important source of value and ability information for their children. Little is known, however, about the bidirectional effects between students' perceptions of their parents' beliefs and behaviors and the students' own domain-specific values. This study analyzed how students' perceptions of parents' beliefs and behaviors and students' mathematics values and mathematics-related career plans affect each other bidirectionally, and analyzed the role of students' gender as a moderator of these relations. Data from 475 students in 11th and 12th grade (girls: 50.3%; 31 classrooms; 12 schools), who participated in 2 waves of the study, were analyzed. Results of longitudinal structural equation models demonstrated that students' perceptions of their parents' mathematics value beliefs at Time 1 affected the students' own mathematics utility value at Time 2. Bidirectional effects were not shown in the full sample but were identified for boys. The paths within the tested model varied for boys and girls. For example, boys', not girls', mathematics intrinsic value predicted their reported conversations with their fathers about future occupational plans. Boys', not girls', perceived parents' mathematics value predicted the mathematics utility value. Findings are discussed in relation to their implications for parents and teachers, as well as in relation to gendered motivational processes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

PubMed

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Earthquake recurrence models fail when earthquakes fail to reset the stress field

USGS Publications Warehouse

Tormann, Thessa; Wiemer, Stefan; Hardebeck, Jeanne L.

2012-01-01

Parkfield's regularly occurring M6 mainshocks, about every 25 years, have over two decades stoked seismologists' hopes to successfully predict an earthquake of significant size. However, with the longest known inter-event time of 38 years, the latest M6 in the series (28 Sep 2004) did not conform to any of the applied forecast models, questioning once more the predictability of earthquakes in general. Our study investigates the spatial pattern of b-values along the Parkfield segment through the seismic cycle and documents a stably stressed structure. The forecasted rate of M6 earthquakes based on Parkfield's microseismicity b-values corresponds well to observed rates. We interpret the observed b-value stability in terms of the evolution of the stress field in that area: the M6 Parkfield earthquakes do not fully unload the stress on the fault, explaining why time recurrent models fail. We present the 1989 M6.9 Loma Prieta earthquake as counter example, which did release a significant portion of the stress along its fault segment and yields a substantial change in b-values.

Reported maternal tendencies predict the reward value of infant facial cuteness, but not cuteness detection

PubMed Central

Hahn, Amanda C.; DeBruine, Lisa M.; Jones, Benedict C.

2015-01-01

The factors that contribute to individual differences in the reward value of cute infant facial characteristics are poorly understood. Here we show that the effect of cuteness on a behavioural measure of the reward value of infant faces is greater among women reporting strong maternal tendencies. By contrast, maternal tendencies did not predict women's subjective ratings of the cuteness of these infant faces. These results show, for the first time, that the reward value of infant facial cuteness is greater among women who report being more interested in interacting with infants, implicating maternal tendencies in individual differences in the reward value of infant cuteness. Moreover, our results indicate that the relationship between maternal tendencies and the reward value of infant facial cuteness is not due to individual differences in women's ability to detect infant cuteness. This latter result suggests that individual differences in the reward value of infant cuteness are not simply a by-product of low-cost, functionless biases in the visual system. PMID:25740842

The use of genomic information increases the accuracy of breeding value predictions for sea louse (Caligus rogercresseyi) resistance in Atlantic salmon (Salmo salar).

PubMed

Correa, Katharina; Bangera, Rama; Figueroa, René; Lhorente, Jean P; Yáñez, José M

2017-01-31

Sea lice infestations caused by Caligus rogercresseyi are a main concern to the salmon farming industry due to associated economic losses. Resistance to this parasite was shown to have low to moderate genetic variation and its genetic architecture was suggested to be polygenic. The aim of this study was to compare accuracies of breeding value predictions obtained with pedigree-based best linear unbiased prediction (P-BLUP) methodology against different genomic prediction approaches: genomic BLUP (G-BLUP), Bayesian Lasso, and Bayes C. To achieve this, 2404 individuals from 118 families were measured for C. rogercresseyi count after a challenge and genotyped using 37 K single nucleotide polymorphisms. Accuracies were assessed using fivefold cross-validation and SNP densities of 0.5, 1, 5, 10, 25 and 37 K. Accuracy of genomic predictions increased with increasing SNP density and was higher than pedigree-based BLUP predictions by up to 22%. Both Bayesian and G-BLUP methods can predict breeding values with higher accuracies than pedigree-based BLUP, however, G-BLUP may be the preferred method because of reduced computation time and ease of implementation. A relatively low marker density (i.e. 10 K) is sufficient for maximal increase in accuracy when using G-BLUP or Bayesian methods for genomic prediction of C. rogercresseyi resistance in Atlantic salmon.

The predictive value of the heart-rate-variability derived Analgesia Nociception Index in children anaesthetised with sevoflurane - an observational pilot-study.

PubMed

Weber, Frank; Geerts, Noortje J E; Roeleveld, Hilde G; Warmenhoven, Annejet T; Liebrand, Chantal A

2018-05-13

The heart rate variability (HRV) derived Analgesia Nociception Index (ANI ™ ) is a continuous non-invasive tool to assess the nociception/anti-nociception balance in unconscious patients. It has been shown to be superior to hemodynamic variables in detecting insufficient anti-nociception in children, while little is known about its predictive value. The primary objective of this prospective observational pilot study in paediatric surgical patients under sevoflurane anaesthesia, was to compare the predictive value of the ANI and heart rate to help decide to give additional opioids. The paediatric anaesthesiologist in charge was blinded to ANI values. In patients with an ANI value <50 (indicating insufficient anti-nociception) at the moment of decision, ANI values dropped from ±55 (indicating sufficient anti-nociception) to ±35, starting 60 sec. before decision. Within 120 sec. after administration of fentanyl (1 mcg/kg), ANI values returned to ±60. This phenomenon was only observed in the ANI values derived from HRV data averaged over 2 min. Heart rate remained unchanged. In patients with ANI values ≥50 at the time of decision, opioid administration had no effect on ANI or heart rate. The same accounts for morphine for postoperative analgesia and fentanyl in case of intraoperative movement. This study provides evidence of a better predictive value of the ANI in detecting insufficient anti-nociception in paediatric surgical patients than heart rate. The same accounts for depicting re-establishment of sufficient anti-nociception after opioid drug administration. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A Two-Stage Process Model of Sensory Discrimination: An Alternative to Drift-Diffusion

PubMed Central

Landy, Michael S.

2016-01-01

Discrimination of the direction of motion of a noisy stimulus is an example of sensory discrimination under uncertainty. For stimuli that are extended in time, reaction time is quicker for larger signal values (e.g., discrimination of opposite directions of motion compared with neighboring orientations) and larger signal strength (e.g., stimuli with higher contrast or motion coherence, that is, lower noise). The standard model of neural responses (e.g., in lateral intraparietal cortex) and reaction time for discrimination is drift-diffusion. This model makes two clear predictions. (1) The effects of signal strength and value on reaction time should interact multiplicatively because the diffusion process depends on the signal-to-noise ratio. (2) If the diffusion process is interrupted, as in a cued-response task, the time to decision after the cue should be independent of the strength of accumulated sensory evidence. In two experiments with human participants, we show that neither prediction holds. A simple alternative model is developed that is consistent with the results. In this estimate-then-decide model, evidence is accumulated until estimation precision reaches a threshold value. Then, a decision is made with duration that depends on the signal-to-noise ratio achieved by the first stage. SIGNIFICANCE STATEMENT Sensory decision-making under uncertainty is usually modeled as the slow accumulation of noisy sensory evidence until a threshold amount of evidence supporting one of the possible decision outcomes is reached. Furthermore, it has been suggested that this accumulation process is reflected in neural responses, e.g., in lateral intraparietal cortex. We derive two behavioral predictions of this model and show that neither prediction holds. We introduce a simple alternative model in which evidence is accumulated until a sufficiently precise estimate of the stimulus is achieved, and then that estimate is used to guide the discrimination decision. This model is consistent with the behavioral data. PMID:27807167

A Two-Stage Process Model of Sensory Discrimination: An Alternative to Drift-Diffusion.

PubMed

Sun, Peng; Landy, Michael S

2016-11-02

Discrimination of the direction of motion of a noisy stimulus is an example of sensory discrimination under uncertainty. For stimuli that are extended in time, reaction time is quicker for larger signal values (e.g., discrimination of opposite directions of motion compared with neighboring orientations) and larger signal strength (e.g., stimuli with higher contrast or motion coherence, that is, lower noise). The standard model of neural responses (e.g., in lateral intraparietal cortex) and reaction time for discrimination is drift-diffusion. This model makes two clear predictions. (1) The effects of signal strength and value on reaction time should interact multiplicatively because the diffusion process depends on the signal-to-noise ratio. (2) If the diffusion process is interrupted, as in a cued-response task, the time to decision after the cue should be independent of the strength of accumulated sensory evidence. In two experiments with human participants, we show that neither prediction holds. A simple alternative model is developed that is consistent with the results. In this estimate-then-decide model, evidence is accumulated until estimation precision reaches a threshold value. Then, a decision is made with duration that depends on the signal-to-noise ratio achieved by the first stage. Sensory decision-making under uncertainty is usually modeled as the slow accumulation of noisy sensory evidence until a threshold amount of evidence supporting one of the possible decision outcomes is reached. Furthermore, it has been suggested that this accumulation process is reflected in neural responses, e.g., in lateral intraparietal cortex. We derive two behavioral predictions of this model and show that neither prediction holds. We introduce a simple alternative model in which evidence is accumulated until a sufficiently precise estimate of the stimulus is achieved, and then that estimate is used to guide the discrimination decision. This model is consistent with the behavioral data. Copyright © 2016 the authors 0270-6474/16/3611259-16$15.00/0.

Some critical issues in the characterization of nanoscale thermal conductivity by molecular dynamics analysis

NASA Astrophysics Data System (ADS)

Ehsan Khaled, Mohammad; Zhang, Liangchi; Liu, Weidong

2018-07-01

The nanoscale thermal conductivity of a material can be significantly different from its value at the macroscale. Although a number of studies using the equilibrium molecular dynamics (EMD) with Green–Kubo (GK) formula have been conducted for nano-conductivity predictions, there are many problems in the analysis that have made the EMD results unreliable or misleading. This paper aims to clarify such critical issues through a thorough investigation on the effect and determination of the vital physical variables in the EMD-GK analysis, using the prediction of the nanoscale thermal conductivity of Si as an example. The study concluded that to have a reliable prediction, quantum correction, time step, simulation time, correlation time and system size are all crucial.

Earth Observing System/Advanced Microwave Sounding Unit-A (EOS/AMSU-A): Reliability prediction report for module A1 (channels 3 through 15) and module A2 (channels 1 and 2)

NASA Technical Reports Server (NTRS)

Geimer, W.

1995-01-01

This report documents the final reliability prediction performed on the Earth Observing System/Advanced Microwave Sounding Unit-A (EOS/AMSU-A). The A1 Module contains Channels 3 through 15, and is referred to herein as 'EOS/AMSU-A1'. The A2 Module contains Channels 1 and 2, and is referred herein as 'EOS/AMSU-A2'. The 'specified' figures were obtained from Aerojet Reports 8897-1 and 9116-1. The predicted reliability figure for the EOS/AMSU-A1 meets the specified value and provides a Mean Time Between Failures (MTBF) of 74,390 hours. The predicted reliability figure for the EOS/AMSU-A2 meets the specified value and provides a MTBF of 193,110 hours.

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis.

PubMed

Quintela-del-Río, Alejandro; Francisco-Fernández, Mario

2011-02-01

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Validation of an internal hardwood log defect prediction model

Treesearch

R. Edward Thomas

2011-01-01

The type, size, and location of internal defects dictate the grade and value of lumber sawn from hardwood logs. However, acquiring internal defect knowledge with x-ray/computed-tomography or magnetic-resonance imaging technology can be expensive both in time and cost. An alternative approach uses prediction models based on correlations among external defect indicators...

Main Trend Extraction Based on Irregular Sampling Estimation and Its Application in Storage Volume of Internet Data Center

PubMed Central

Dou, Chao

2016-01-01

The storage volume of internet data center is one of the classical time series. It is very valuable to predict the storage volume of a data center for the business value. However, the storage volume series from a data center is always “dirty,” which contains the noise, missing data, and outliers, so it is necessary to extract the main trend of storage volume series for the future prediction processing. In this paper, we propose an irregular sampling estimation method to extract the main trend of the time series, in which the Kalman filter is used to remove the “dirty” data; then the cubic spline interpolation and average method are used to reconstruct the main trend. The developed method is applied in the storage volume series of internet data center. The experiment results show that the developed method can estimate the main trend of storage volume series accurately and make great contribution to predict the future volume value. 
 PMID:28090205

Main Trend Extraction Based on Irregular Sampling Estimation and Its Application in Storage Volume of Internet Data Center.

PubMed

Miao, Beibei; Dou, Chao; Jin, Xuebo

2016-01-01

The storage volume of internet data center is one of the classical time series. It is very valuable to predict the storage volume of a data center for the business value. However, the storage volume series from a data center is always "dirty," which contains the noise, missing data, and outliers, so it is necessary to extract the main trend of storage volume series for the future prediction processing. In this paper, we propose an irregular sampling estimation method to extract the main trend of the time series, in which the Kalman filter is used to remove the "dirty" data; then the cubic spline interpolation and average method are used to reconstruct the main trend. The developed method is applied in the storage volume series of internet data center. The experiment results show that the developed method can estimate the main trend of storage volume series accurately and make great contribution to predict the future volume value. 
 .

Combination of three-dimensional ultrasound measurement of foetal adrenal gland enlargement and placental alpha microglobulin-1 for the prediction of the timing of delivery within seven days in women with threatened preterm labour and preterm labour.

PubMed

Santipap, Monchai; Phupong, Vorapong

2018-03-23

The aim of this study was to predict the timing of delivery within seven days in singleton pregnant women with threatened preterm labour and preterm labour by using a three-dimensional (3D) ultrasound measurement of foetal adrenal gland volume enlargement, a foetal zone enlargement and cervicovaginal placental alpha microglobulin-1 (PAMG-1) test. This prospective cohort study included singleton pregnant women at 22-36 +6  weeks of gestation who presented with threatened preterm labour and with preterm labour. Transabdominal 3D ultrasound measurement of the whole foetal adrenal gland and of the foetal adrenal zone were performed. Qualitative cervicovaginal PAMG-1 detection was performed at the same time. One hundred and fifty-four pregnant women were included into the study. Eighty-four pregnant women had threatened preterm labour and seventy pregnant women had preterm labour. Twenty-nine pregnant women (18%) delivered within seven days. Use of foetal adrenal gland volume enlargement, foetal zone enlargement and the PAMG-1 test in combination increased sensitivity; if one parameter was positive, the sensitivity, specificity, positive predictive value and negative predictive value were 82.8%, 27.2%, 20.9% and 87.2%, respectively, in the prediction of the timing of delivery within seven days. The combination of foetal adrenal gland enlargement and PAMG-1 increased sensitivity for the prediction of the timing of delivery within seven days in pregnant women presenting with threatened preterm labour and preterm labour. Impact Statement What is already known on this subject? An increased foetal adrenal gland volume is significantly correlated with the risk of preterm birth. What do the results of this study add? The combination of a foetal adrenal gland enlargement and a placental alpha microglobulin-1 increased sensitivity for the prediction of the timing of delivery within seven days in pregnant women presenting with threatened preterm labour and preterm labour. What are the implications of these findings for clinical practice and/or further research? The combination of a foetal adrenal gland enlargement and placental alpha microglobulin-1 may be used for the prediction of the timing of delivery within seven days in pregnant women presenting with threatened preterm labour and with preterm labour.

Comparison of Dst Forecast Models for Intense Geomagnetic Storms

NASA Technical Reports Server (NTRS)

Ji, Eun-Young; Moon, Y.-J.; Gopalswamy, N.; Lee, D.-H.

2012-01-01

We have compared six disturbance storm time (Dst) forecast models using 63 intense geomagnetic storms (Dst <=100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (Delta Dst(sub min)) and the difference in the absolute value of Dst minimum time (Delta t(sub Dst)) between the observed and the predicted. As a result, we found that the model by Temerin and Li gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the Delta Dst(sub min) of 7.7 nT, and the absolute value of Delta t(sub Dst) of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee is better than the other models for the events having 100 <= Dst < 200 nT, and three recent models (the model of Wang et al., the model of Temerin and Li, and the model of Boynton et al.) are better than the other three models for the events having Dst <= 200 nT.

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

PubMed

Puntel, Laila A; Sawyer, John E; Barker, Daniel W; Thorburn, Peter J; Castellano, Michael J; Moore, Kenneth J; VanLoocke, Andrew; Heaton, Emily A; Archontoulis, Sotirios V

2018-01-01

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time ( R 2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity ( R 2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined ( n = 31) with an average error range of ±38 kg N ha -1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

PubMed Central

Puntel, Laila A.; Sawyer, John E.; Barker, Daniel W.; Thorburn, Peter J.; Castellano, Michael J.; Moore, Kenneth J.; VanLoocke, Andrew; Heaton, Emily A.; Archontoulis, Sotirios V.

2018-01-01

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost. PMID:29706974

Detecting evidence of luteal activity by least-squares quantitative basal temperature analysis against urinary progesterone metabolites and the effect of wake-time variability.

PubMed

Bedford, Jennifer L; Prior, Jerilynn C; Hitchcock, Christine L; Barr, Susan I

2009-09-01

To assess computerised least-squares analysis of quantitative basal temperature (LS-BT) against urinary pregnanediol glucuronide (PdG) as an indirect measure of ovulation, and to evaluate the stability of LS-QBT to wake-time variation. Cross-sectional study of 40 healthy, normal-weight, regularly menstruating women aged 19-34. Participants recorded basal temperature and collected first void urine daily for one complete menstrual cycle. Evidence of luteal activity (ELA), an indirect ovulation indicator, was assessed using Kassam's PdG algorithm, which identifies a sustained 3-day PdG rise, and the LS-QBT algorithm, by determining whether the temperature curve is significantly biphasic. Cycles were classified as ELA(+) or ELA(-). We explored the need to pre-screen for wake-time variations by repeating the analysis using: (A) all recorded temperatures, (B) wake-time adjusted temperatures, (C) temperatures within 2h of average wake-time, and (D) expert reviewed temperatures. Relative to PdG, classification of cycles as ELA(+) was 35 of 36 for LS-QBT methods A and B, 33 of 34 (method C) and 30 of 31 (method D). Classification of cycles as ELA(-) was 1 of 4 (methods A and B) and 0 of 3 (methods C and D). Positive predictive value was 92% for methods A-C and 91% for method D. Negative predictive value was 50% for methods A and B and 0% for methods C and D. Overall accuracy was 90% for methods A and B, 89% for method C and 88% for method D. The day of a significant temperature increase by LS-QBT and the first day of a sustained PdG rise were correlated (r=0.803, 0.741, 0.651, 0.747 for methods A-D, respectively, all p<0.001). LS-QBT showed excellent detection of ELA(+) cycles (sensitivity, positive predictive value) but poor detection of ELA(-) cycles (specificity, negative predictive value) relative to urinary PdG. Correlations between the methods and overall accuracy were good and similar for all analyses. Findings suggest that LS-QBT is robust to wake-time variability and that expert interpretation is unnecessary. This method shows promise for use as an epidemiological tool to document cyclic progesterone increase. Further validation relative to daily transvaginal ultrasound is required.

International normalized ratio stability in warfarin-experienced patients with nonvalvular atrial fibrillation.

PubMed

Nelson, Winnie W; Desai, Sunita; Damaraju, Chandrasekharrao V; Lu, Lang; Fields, Larry E; Wildgoose, Peter; Schein, Jeffery R

2015-06-01

Maintaining stable levels of anticoagulation using warfarin therapy is challenging. Few studies have examined the stability of the international normalized ratio (INR) in patients with nonvalvular atrial fibrillation (NVAF) who have had ≥6 months' exposure to warfarin anticoagulation for stroke prevention. Our objective was to describe INR control in NVAF patients who had been receiving warfarin for at least 6 months. Using retrospective patient data from the CoagClinic™ database, we analyzed data from NVAF patients treated with warfarin to assess the quality of INR control and possible predictors of poor INR control. Time within, above, and below the recommended INR range (2.0-3.0) was calculated for patients who had received warfarin for ≥6 months and had three or more INR values. The analysis also assessed INR patterns and resource utilization of patients with an INR >4.0. Logistic regression models were used to determine factors associated with poor INR control. Patients (n = 9433) had an average of 1.6 measurements per 30 days. Mean follow-up time was 544 days. Approximately 39% of INR values were out of range, with 23% of INR values being <2.0 and 16% being >3.0. Mean percent time with INR in therapeutic range was 67%; INR <2.0 was 19% and INR >3.0 was 14%. Patients with more than one reading of INR >4.0 (~39%) required an average of one more visit and took 3 weeks to return to an in-range INR. Male sex and age >75 years were predictive of better INR control, whereas a history of heart failure or diabetes were predictive of out-of-range INR values. However, patient characteristics did not predict the likelihood of INR >4.0. Out-of-range INR values remain frequent in patients with NVAF treated with warfarin. Exposure to high INR values was common, resulting in increased resource utilization.

Chitosan based grey wastewater treatment--a statistical design approach.

PubMed

Thirugnanasambandham, K; Sivakumar, V; Prakash Maran, J; Kandasamy, S

2014-01-01

In this present study, grey wastewater was treated under different operating conditions such as agitation time (1-3 min), pH (2.5-5.5), chitosan dose (0.3-0.6g/l) and settling time (10-20 min) using response surface methodology (RSM). Four factors with three levels Box-Behnken response surface design (BBD) were employed to optimize and investigate the effect of process variables on the responses such as turbidity, BOD and COD removal. The results were analyzed by Pareto analysis of variance (ANOVA) and second order polynomial models were developed in order to predict the responses. Under the optimum conditions, experimental values such as turbidity (96%), BOD (91%) and COD (73%) removals are closely agreed with predicted values. Copyright © 2013 Elsevier Ltd. All rights reserved.

Potential Value of Coagulation Parameters for Suggesting Preeclampsia During the Third Trimester of Pregnancy.

PubMed

Chen, Ying; Lin, Li

2017-07-01

Preeclampsia is a relatively common complication of pregnancy and considered to be associated with different degrees of coagulation dysfunction. This study was developed to evaluate the potential value of coagulation parameters for suggesting preeclampsia during the third trimester of pregnancy. Data from 188 healthy pregnant women, 125 patients with preeclampsia in the third trimester and 120 age-matched nonpregnant women were analyzed. Prothrombin time, prothrombin activity, activated partial thromboplastin time, fibrinogen (Fg), antithrombin, platelet count, mean platelet volume, platelet distribution width and plateletcrit were tested. All parameters, excluding prothrombin time, platelet distribution width and plateletcrit, differed significantly between healthy pregnant women and those with preeclampsia. Platelet count, antithrombin and Fg were significantly lower and mean platelet volume and prothrombin activity were significantly higher in patients with preeclampsia (P < 0.001). Among these parameters, the largest area under the receiver operating characteristic curve for preeclampsia was 0.872 for Fg with an optimal cutoff value of ≤2.87g/L (sensitivity = 0.68 and specificity = 0.98). For severe preeclampsia, the area under the curve for Fg reached up to 0.922 with the same optimal cutoff value (sensitivity = 0.84, specificity = 0.98, positive predictive value = 0.96 and negative predictive value = 0.93). Fg is a biomarker suggestive of preeclampsia in the third trimester of pregnancy, and our data provide a potential cutoff value of Fg ≤ 2.87g/L for screening preeclampsia, especially severe preeclampsia. Copyright © 2017 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.

Optimization of torrefaction conditions of coffee industry residues using desirability function approach.

PubMed

Buratti, C; Barbanera, M; Lascaro, E; Cotana, F

2018-03-01

The aim of the present study is to analyze the influence of independent process variables such as temperature, residence time, and heating rate on the torrefaction process of coffee chaff (CC) and spent coffee grounds (SCGs). Response surface methodology and a three-factor and three-level Box-Behnken design were used in order to evaluate the effects of the process variables on the weight loss (W L ) and the Higher Heating Value (HHV) of the torrefied materials. Results showed that the effects of the three factors on both responses were sequenced as follows: temperature>residence time>heating rate. Data obtained from the experiments were analyzed by analysis of variance (ANOVA) and fitted to second-order polynomial models by using multiple regression analysis. Predictive models were determined, able to obtain satisfactory fittings of the experimental data, with coefficient of determination (R 2 ) values higher than 0.95. An optimization study using Derringer's desired function methodology was also carried out and the optimal torrefaction conditions were found: temperature 271.7°C, residence time 20min, heating rate 5°C/min for CC and 256.0°C, 20min, 25°C/min for SCGs. The experimental values closely agree with the corresponding predicted values. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

An algorithm of Saxena-Easo on fuzzy time series forecasting

NASA Astrophysics Data System (ADS)

Ramadhani, L. C.; Anggraeni, D.; Kamsyakawuni, A.; Hadi, A. F.

2018-04-01

This paper presents a forecast model of Saxena-Easo fuzzy time series prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy time series doesn’t need stationarity like conventional forecasting method, capable of dealing with the value of time series which are linguistic and has the advantage of reducing the calculation, time and simplifying the calculation process. Generally it’s focus on percentage change as the universe discourse, interval partition and defuzzification. The result indicate that between the actual data and the forecast data are close enough with Root Mean Square Error (RMSE) = 1.5289.

Is the Ratio of Observed X-ray Luminosity to Bolometric Luminosity in Early-type Stars Really a Constant?

NASA Technical Reports Server (NTRS)

Waldron, W. L.

1985-01-01

The observed X-ray emission from early-type stars can be explained by the recombination stellar wind model (or base coronal model). The model predicts that the true X-ray luminosity from the base coronal zone can be 10 to 1000 times greater than the observed X-ray luminosity. From the models, scaling laws were found for the true and observed X-ray luminosities. These scaling laws predict that the ratio of the observed X-ray luminosity to the bolometric luminosity is functionally dependent on several stellar parameters. When applied to several other O and B stars, it is found that the values of the predicted ratio agree very well with the observed values.

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.

Predicting moisture and economic value of solid forest fuel piles for improving the profitability of bioenergy use

NASA Astrophysics Data System (ADS)

Lauren, Ari; Kinnunen, Jyrki-Pekko; Sikanen, Lauri

2016-04-01

Bioenergy contributes 26 % of the total energy use in Finland, and 60 % of this is provided by solid forest fuel consisting of small stems and logging residues such as tops, branches, roots and stumps. Typically the logging residues are stored as piles on site before transporting to regional combined heat and power plants for combustion. Profitability of forest fuel use depends on smart control of the feedstock. Fuel moisture, dry matter loss, and the rate of interest during the storing are the key variables affecting the economic value of the fuel. The value increases with drying, but decreases with wetting, dry matter loss and positive rate of interest. We compiled a simple simulation model computing the moisture change, dry matter loss, transportation costs and present value of feedstock piles. The model was used to predict the time of the maximum value of the stock, and to compose feedstock allocation strategies under the question: how should we choose the piles and the combustion time so that total energy yield and the economic value of the energy production is maximized? The question was assessed concerning the demand of the energy plant. The model parameterization was based on field scale studies. The initial moisture, and the rates of daily moisture change and dry matter loss in the feedstock piles depended on the day of the year according to empirical field measurements. Time step of the computation was one day. Effects of pile use timing on the total energy yield and profitability was studied using combinatorial optimization. Results show that the storing increases the pile maximum value if the natural drying onsets soon after the harvesting; otherwise dry matter loss and the capital cost of the storing overcome the benefits gained by drying. Optimized timing of the pile use can improve slightly the profitability, based on the increased total energy yield and because the energy unit based transportation costs decrease when water content in the biomass is decreased.

OPR-PPR, a Computer Program for Assessing Data Importance to Model Predictions Using Linear Statistics

USGS Publications Warehouse

Tonkin, Matthew J.; Tiedeman, Claire; Ely, D. Matthew; Hill, Mary C.

2007-01-01

The OPR-PPR program calculates the Observation-Prediction (OPR) and Parameter-Prediction (PPR) statistics that can be used to evaluate the relative importance of various kinds of data to simulated predictions. The data considered fall into three categories: (1) existing observations, (2) potential observations, and (3) potential information about parameters. The first two are addressed by the OPR statistic; the third is addressed by the PPR statistic. The statistics are based on linear theory and measure the leverage of the data, which depends on the location, the type, and possibly the time of the data being considered. For example, in a ground-water system the type of data might be a head measurement at a particular location and time. As a measure of leverage, the statistics do not take into account the value of the measurement. As linear measures, the OPR and PPR statistics require minimal computational effort once sensitivities have been calculated. Sensitivities need to be calculated for only one set of parameter values; commonly these are the values estimated through model calibration. OPR-PPR can calculate the OPR and PPR statistics for any mathematical model that produces the necessary OPR-PPR input files. In this report, OPR-PPR capabilities are presented in the context of using the ground-water model MODFLOW-2000 and the universal inverse program UCODE_2005. The method used to calculate the OPR and PPR statistics is based on the linear equation for prediction standard deviation. Using sensitivities and other information, OPR-PPR calculates (a) the percent increase in the prediction standard deviation that results when one or more existing observations are omitted from the calibration data set; (b) the percent decrease in the prediction standard deviation that results when one or more potential observations are added to the calibration data set; or (c) the percent decrease in the prediction standard deviation that results when potential information on one or more parameters is added.

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.

Predicting pneumococcal community-acquired pneumonia in the emergency department: evaluation of clinical parameters.

PubMed

Huijts, S M; Boersma, W G; Grobbee, D E; Gruber, W C; Jansen, K U; Kluytmans, J A J W; Kuipers, B A F; Palmen, F; Pride, M W; Webber, C; Bonten, M J M

2014-12-01

The aim of this study was to quantify the value of clinical predictors available in the emergency department (ED) in predicting Streptococcus pneumoniae as the cause of community-acquired pneumonia (CAP). A prospective, observational, cohort study of patients with CAP presenting in the ED was performed. Pneumococcal aetiology of CAP was based on either bacteraemia, or S. pneumoniae being cultured from sputum, or urinary immunochromatographic assay positivity, or positivity of a novel serotype-specific urinary antigen detection test. Multivariate logistic regression was used to identify independent predictors and various cut-off values of probability scores were used to evaluate the usefulness of the model. Three hundred and twenty-eight (31.0%) of 1057 patients with CAP had pneumococcal CAP. Nine independent predictors for pneumococcal pneumonia were identified, but the clinical utility of this prediction model was disappointing, because of low positive predictive values or a small yield. Clinical criteria have insufficient diagnostic capacity to predict pneumococcal CAP. Rapid antigen detection tests are needed to diagnose S. pneumoniae at the time of hospital admission. © 2014 The Authors Clinical Microbiology and Infection © 2014 European Society of Clinical Microbiology and Infectious Diseases.

Predictive value of uterine Doppler waveform during pregnancies complicated by diabetes.

PubMed

Haddad, B; Uzan, M; Tchobroutsky, C; Uzan, S; Papiernik-Berkhauer, E

1993-01-01

Diabetes, whether or not it is insulin deficient, is frequently associated with vascular complications during pregnancies. It is accepted nowadays that the uterine artery velocity waveform is predictive concerning pregnancy-induced hypertension (PIH) and its complications. It thus seemed interesting to analyse the predictivity of vascular complications of diabetes by using uterine artery velocity waveforms. We have thus explored 37 diabetic patients [group 1: insulin-deficient diabetes (IDD), n = 10; group 2: gestational IDD, n = 6; and gestational non-IDD, n = 21). We have found vascular complications for 10 patients, divided between all 2 groups: 2 pre-eclampsia, 2 fetal suffering before any labour, 2 cases of intra-uterine growth retardation (including a trisomy 18) and 5 PIH. The uterine artery velocimetry measurement has been found to be pathological 5 times, and always in patients who later developed vascular complications. Among this selected population and excluding the trisomy 18, the sensitivity is of 44.5%, the specificity of 100%, the positive predictive value of 100%, and the negative predictive value of 84.3%. If these results are confirmed, this examination could be an excellent marker of the vascular risk and thus would have its place during systematic survey of pregnancies complicated by diabetes.

A neighborhood statistics model for predicting stream pathogen indicator levels.

PubMed

Pandey, Pramod K; Pasternack, Gregory B; Majumder, Mahbubul; Soupir, Michelle L; Kaiser, Mark S

2015-03-01

Because elevated levels of water-borne Escherichia coli in streams are a leading cause of water quality impairments in the U.S., water-quality managers need tools for predicting aqueous E. coli levels. Presently, E. coli levels may be predicted using complex mechanistic models that have a high degree of unchecked uncertainty or simpler statistical models. To assess spatio-temporal patterns of instream E. coli levels, herein we measured E. coli, a pathogen indicator, at 16 sites (at four different times) within the Squaw Creek watershed, Iowa, and subsequently, the Markov Random Field model was exploited to develop a neighborhood statistics model for predicting instream E. coli levels. Two observed covariates, local water temperature (degrees Celsius) and mean cross-sectional depth (meters), were used as inputs to the model. Predictions of E. coli levels in the water column were compared with independent observational data collected from 16 in-stream locations. The results revealed that spatio-temporal averages of predicted and observed E. coli levels were extremely close. Approximately 66 % of individual predicted E. coli concentrations were within a factor of 2 of the observed values. In only one event, the difference between prediction and observation was beyond one order of magnitude. The mean of all predicted values at 16 locations was approximately 1 % higher than the mean of the observed values. The approach presented here will be useful while assessing instream contaminations such as pathogen/pathogen indicator levels at the watershed scale.

The mathematics of morality for neonatal resuscitation.

PubMed

Meadow, William; Lagatta, Joanne; Andrews, Bree; Lantos, John

2012-12-01

This article discusses the ethical issues surrounding the resuscitation of infants who are at great risk to die or survive with significant morbidity. Data are introduced regarding money, outcomes, and prediction. Gestational age influences some of the outcomes after birth more than others do. Prediction is possible at four stages of the resuscitation process. Data suggest that antenatal and delivery room predictions are inadequate, and prediction at the time of discharge is too late. The predictive value (>95%) for the outcome of death or survival with neurodevelopmental impairment is discussed. Copyright © 2012 Elsevier Inc. All rights reserved.

A battery power model for the EUVE spacecraft

NASA Technical Reports Server (NTRS)

Yen, Wen L.; Littlefield, Ronald G.; Mclean, David R.; Tuchman, Alan; Broseghini, Todd A.; Page, Brenda J.

1993-01-01

This paper describes a battery power model that has been developed to simulate and predict the behavior of the 50 ampere-hour nickel-cadmium battery that supports the Extreme Ultraviolet Explorer (EUVE) spacecraft in its low Earth orbit. First, for given orbit, attitude, solar array panel and spacecraft load data, the model calculates minute-by-minute values for the net power available for charging the battery for a user-specified time period (usually about two weeks). Next, the model is used to calculate minute-by-minute values for the battery voltage, current and state-of-charge for the time period. The model's calculations are explained for its three phases: sunrise charging phase, constant voltage phase, and discharge phase. A comparison of predicted model values for voltage, current and state-of-charge with telemetry data for a complete charge-discharge cycle shows good correlation. This C-based computer model will be used by the EUVE Flight Operations Team for various 'what-if' scheduling analyses.

Groundwater Pollution Source Identification using Linked ANN-Optimization Model

NASA Astrophysics Data System (ADS)

Ayaz, Md; Srivastava, Rajesh; Jain, Ashu

2014-05-01

Groundwater is the principal source of drinking water in several parts of the world. Contamination of groundwater has become a serious health and environmental problem today. Human activities including industrial and agricultural activities are generally responsible for this contamination. Identification of groundwater pollution source is a major step in groundwater pollution remediation. Complete knowledge of pollution source in terms of its source characteristics is essential to adopt an effective remediation strategy. Groundwater pollution source is said to be identified completely when the source characteristics - location, strength and release period - are known. Identification of unknown groundwater pollution source is an ill-posed inverse problem. It becomes more difficult for real field conditions, when the lag time between the first reading at observation well and the time at which the source becomes active is not known. We developed a linked ANN-Optimization model for complete identification of an unknown groundwater pollution source. The model comprises two parts- an optimization model and an ANN model. Decision variables of linked ANN-Optimization model contain source location and release period of pollution source. An objective function is formulated using the spatial and temporal data of observed and simulated concentrations, and then minimized to identify the pollution source parameters. In the formulation of the objective function, we require the lag time which is not known. An ANN model with one hidden layer is trained using Levenberg-Marquardt algorithm to find the lag time. Different combinations of source locations and release periods are used as inputs and lag time is obtained as the output. Performance of the proposed model is evaluated for two and three dimensional case with error-free and erroneous data. Erroneous data was generated by adding uniformly distributed random error (error level 0-10%) to the analytically computed concentration values. The main advantage of the proposed model is that it requires only upper half of the breakthrough curve and is capable of predicting source parameters when the lag time is not known. Linking of ANN model with proposed optimization model reduces the dimensionality of the decision variables of the optimization model by one and hence complexity of optimization model is reduced. The results show that our proposed linked ANN-Optimization model is able to predict the source parameters for the error-free data accurately. The proposed model was run several times to obtain the mean, standard deviation and interval estimate of the predicted parameters for observations with random measurement errors. It was observed that mean values as predicted by the model were quite close to the exact values. An increasing trend was observed in the standard deviation of the predicted values with increasing level of measurement error. The model appears to be robust and may be efficiently utilized to solve the inverse pollution source identification problem.

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

PubMed Central

Barrett, Jessica; Pennells, Lisa; Sweeting, Michael; Willeit, Peter; Di Angelantonio, Emanuele; Gudnason, Vilmundur; Nordestgaard, Børge G.; Psaty, Bruce M; Goldbourt, Uri; Best, Lyle G; Assmann, Gerd; Salonen, Jukka T; Nietert, Paul J; Verschuren, W. M. Monique; Brunner, Eric J; Kronmal, Richard A; Salomaa, Veikko; Bakker, Stephan J L; Dagenais, Gilles R; Sato, Shinichi; Jansson, Jan-Håkan; Willeit, Johann; Onat, Altan; de la Cámara, Agustin Gómez; Roussel, Ronan; Völzke, Henry; Dankner, Rachel; Tipping, Robert W; Meade, Tom W; Donfrancesco, Chiara; Kuller, Lewis H; Peters, Annette; Gallacher, John; Kromhout, Daan; Iso, Hiroyasu; Knuiman, Matthew; Casiglia, Edoardo; Kavousi, Maryam; Palmieri, Luigi; Sundström, Johan; Davis, Barry R; Njølstad, Inger; Couper, David; Danesh, John; Thompson, Simon G; Wood, Angela

2017-01-01

Abstract The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962–2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction. PMID:28549073

Liver Stiffness Measured by Two-Dimensional Shear-Wave Elastography: Prognostic Value after Radiofrequency Ablation for Hepatocellular Carcinoma.

PubMed

Lee, Dong Ho; Lee, Jeong Min; Yoon, Jung-Hwan; Kim, Yoon Jun; Lee, Jeong-Hoon; Yu, Su Jong; Han, Joon Koo

2018-03-01

To evaluate the prognostic value of liver stiffness (LS) measured using two-dimensional (2D) shear-wave elastography (SWE) in patients with hepatocellular carcinoma (HCC) treated by radiofrequency ablation (RFA). The Institutional Review Board approved this retrospective study and informed consent was obtained from all patients. A total of 134 patients with up to 3 HCCs ≤5 cm who had undergone pre-procedural 2D-SWE prior to RFA treatment between January 2012 and December 2013 were enrolled. LS values were measured using real-time 2D-SWE before RFA on the procedural day. After a mean follow-up of 33.8 ± 9.9 months, we analyzed the overall survival after RFA using the Kaplan-Meier method and Cox proportional hazard regression model. The optimal cutoff LS value to predict overall survival was determined using the minimal p value approach. During the follow-up period, 22 patients died, and the estimated 1- and 3-year overall survival rates were 96.4 and 85.8%, respectively. LS measured by 2D-SWE was found to be a significant predictive factor for overall survival after RFA of HCCs, as was the presence of extrahepatic metastases. As for the optimal cutoff LS value for the prediction of overall survival, it was determined to be 13.3 kPa. In our study, 71 patients had LS values ≥13.3 kPa, and the estimated 3-year overall survival was 76.8% compared to 96.3% in 63 patients with LS values <13.3 kPa. This difference was statistically significant (hazard ratio = 4.30 [1.26-14.7]; p = 0.020). LS values measured by 2D-SWE was a significant predictive factor for overall survival after RFA for HCC.

Application of empirical Bayes methods to predict the rate of decline in ERG at the individual level among patients with retinitis pigmentosa.

PubMed

Qiu, Weiliang; Sandberg, Michael A; Rosner, Bernard

2018-05-31

Retinitis pigmentosa is one of the most common forms of inherited retinal degeneration. The electroretinogram (ERG) can be used to determine the severity of retinitis pigmentosa-the lower the ERG amplitude, the more severe the disease is. In practice for career, lifestyle, and treatment counseling, it is of interest to predict the ERG amplitude of a patient at a future time. One approach is prediction based on the average rate of decline for individual patients. However, there is considerable variation both in initial amplitude and in rate of decline. In this article, we propose an empirical Bayes (EB) approach to incorporate the variations in initial amplitude and rate of decline for the prediction of ERG amplitude at the individual level. We applied the EB method to a collection of ERGs from 898 patients with 3 or more visits over 5 or more years of follow-up tested in the Berman-Gund Laboratory and observed that the predicted values at the last (kth) visit obtained by using the proposed method based on data for the first k-1 visits are highly correlated with the observed values at the kth visit (Spearman correlation =0.93) and have a higher correlation with the observed values than those obtained based on either the population average decline rate or those obtained based on the individual decline rate. The mean square errors for predicted values obtained by the EB method are also smaller than those predicted by the other methods. Copyright © 2018 John Wiley & Sons, Ltd.

Use of Fuzzy rainfall-runoff predictions for claypan watersheds with conservation buffers in Northeast Missouri

NASA Astrophysics Data System (ADS)

Anomaa Senaviratne, G. M. M. M.; Udawatta, Ranjith P.; Anderson, Stephen H.; Baffaut, Claire; Thompson, Allen

2014-09-01

Fuzzy rainfall-runoff models are often used to forecast flood or water supply in large catchments and applications at small/field scale agricultural watersheds are limited. The study objectives were to develop, calibrate, and validate a fuzzy rainfall-runoff model using long-term data of three adjacent field scale row crop watersheds (1.65-4.44 ha) with intermittent discharge in the claypan soils of Northeast Missouri. The watersheds were monitored for a six-year calibration period starting 1991 (pre-buffer period). Thereafter, two of them were treated with upland contour grass and agroforestry (tree + grass) buffers (4.5 m wide, 36.5 m apart) to study water quality benefits. The fuzzy system was based on Mamdani method using MATLAB 7.10.0. The model predicted event-based runoff with model performance coefficients of r2 and Nash-Sutcliffe Coefficient (NSC) values greater than 0.65 for calibration and validation. The pre-buffer fuzzy system predicted event-based runoff for 30-50 times larger corn/soybean watersheds with r2 values of 0.82 and 0.68 and NSC values of 0.77 and 0.53, respectively. The runoff predicted by the fuzzy system closely agreed with values predicted by physically-based Agricultural Policy Environmental eXtender model (APEX) for the pre-buffer watersheds. The fuzzy rainfall-runoff model has the potential for runoff predictions at field-scale watersheds with minimum input. It also could up-scale the predictions for large-scale watersheds to evaluate the benefits of conservation practices.

Predictive Value of Performance Criteria for First-Time Sophomore Resident Assistants

ERIC Educational Resources Information Center

Severance, Dana A.

2015-01-01

Housing professionals are increasingly compelled to consider hiring resident assistants (RAs) from a pool of applicants that includes students with less college experience than has traditionally been expected. The purpose of the study is to determine if the success of first-time sophomore RAs differs from that of first-time upper-class RAs…

On Geomagnetism and Paleomagnetism

NASA Technical Reports Server (NTRS)

Voorhies, Coerte V.

1998-01-01

A statistical description of Earth's broad scale, core-source magnetic field has been developed and tested. The description features an expected, or mean, spatial magnetic power spectrum that is neither "flat" nor "while" at any depth, but is akin to spectra advanced by Stevenson and McLeod. This multipole spectrum describes the magnetic energy range; it is not steep enough for Gubbins' magnetic dissipation range. Natural variations of core multipole powers about their mean values are to be expected over geologic time and are described via trial probability distribution functions that neither require nor prohibit magnetic isotropy. The description is thus applicable to core-source dipole and low degree non-dipole fields despite axial dipole anisotropy. The description is combined with main field models of modem satellite and surface geomagnetic measurements to make testable predictions of: (1) the radius of Earth's core, (2) mean paleomagnetic field intensity, and (3) the mean rates and durations of both dipole power excursions and durable axial dipole reversals. The predicted core radius is 0.7% above the 3480 km seismologic value. The predicted root mean square paleointensity (35.6 mu T) and mean Virtual Axial Dipole Moment (about 6.2 lx 1022 Am(exp 2)) are within the range of various mean paleointensity estimates. The predicted mean rate of dipole power excursions, as defined by an absolute dipole moment <20% of the 1980 value, is 9.04/Myr and 14% less than obtained by analysis of a 4 Myr paleointensity record. The predicted mean rate of durable axial dipole reversals (2.26/Myr) is 2.3% more than established by the polarity time-scale for the past 84 Myr. The predicted mean duration of axial dipole reversals (5533 yr) is indistinguishable from an observational value. The accuracy of these predictions demonstrates the power and utility of the description, which is thought to merit further development and testing. It is suggested that strong stable stratification of Earth's uppermost outer core leads to a geologically long interval of no dipole reversals and a very nearly axisymmetric field outside the core. Statistical descriptions of other planetary magnetic fields are outlined.

Evaluation of approaches for estimating the accuracy of genomic prediction in plant breeding

PubMed Central

2013-01-01

Background In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other. Results The size of the estimated genetic variance and hence heritability exerted the strongest influence on the variation in the estimated predictive accuracy. Increasing the number of genotypes considerably increases the time required to compute predictive accuracy by all the seven methods, most notably for the five methods that require cross-validation (Methods 1, 2, 3, 4 and 6). A new method that we propose (Method 5) and an existing method (Method 7) used in animal breeding programs were the fastest and gave the least biased, most precise and stable estimates of predictive accuracy. Of the methods that use cross-validation Methods 4 and 6 were often the best. Conclusions The estimated genetic variance and the number of genotypes had the greatest influence on predictive accuracy. Methods 5 and 7 were the fastest and produced the least biased, the most precise, robust and stable estimates of predictive accuracy. These properties argue for routinely using Methods 5 and 7 to assess predictive accuracy in genomic selection studies. PMID:24314298

Evaluation of approaches for estimating the accuracy of genomic prediction in plant breeding.

PubMed

Ould Estaghvirou, Sidi Boubacar; Ogutu, Joseph O; Schulz-Streeck, Torben; Knaak, Carsten; Ouzunova, Milena; Gordillo, Andres; Piepho, Hans-Peter

2013-12-06

In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other. The size of the estimated genetic variance and hence heritability exerted the strongest influence on the variation in the estimated predictive accuracy. Increasing the number of genotypes considerably increases the time required to compute predictive accuracy by all the seven methods, most notably for the five methods that require cross-validation (Methods 1, 2, 3, 4 and 6). A new method that we propose (Method 5) and an existing method (Method 7) used in animal breeding programs were the fastest and gave the least biased, most precise and stable estimates of predictive accuracy. Of the methods that use cross-validation Methods 4 and 6 were often the best. The estimated genetic variance and the number of genotypes had the greatest influence on predictive accuracy. Methods 5 and 7 were the fastest and produced the least biased, the most precise, robust and stable estimates of predictive accuracy. These properties argue for routinely using Methods 5 and 7 to assess predictive accuracy in genomic selection studies.

An application of a relational database system for high-throughput prediction of elemental compositions from accurate mass values.

PubMed

Sakurai, Nozomu; Ara, Takeshi; Kanaya, Shigehiko; Nakamura, Yukiko; Iijima, Yoko; Enomoto, Mitsuo; Motegi, Takeshi; Aoki, Koh; Suzuki, Hideyuki; Shibata, Daisuke

2013-01-15

High-accuracy mass values detected by high-resolution mass spectrometry analysis enable prediction of elemental compositions, and thus are used for metabolite annotations in metabolomic studies. Here, we report an application of a relational database to significantly improve the rate of elemental composition predictions. By searching a database of pre-calculated elemental compositions with fixed kinds and numbers of atoms, the approach eliminates redundant evaluations of the same formula that occur in repeated calculations with other tools. When our approach is compared with HR2, which is one of the fastest tools available, our database search times were at least 109 times shorter than those of HR2. When a solid-state drive (SSD) was applied, the search time was 488 times shorter at 5 ppm mass tolerance and 1833 times at 0.1 ppm. Even if the search by HR2 was performed with 8 threads in a high-spec Windows 7 PC, the database search times were at least 26 and 115 times shorter without and with the SSD. These improvements were enhanced in a low spec Windows XP PC. We constructed a web service 'MFSearcher' to query the database in a RESTful manner. Available for free at http://webs2.kazusa.or.jp/mfsearcher. The web service is implemented in Java, MySQL, Apache and Tomcat, with all major browsers supported. sakurai@kazusa.or.jp Supplementary data are available at Bioinformatics online.

Sensitivity study on durability variables of marine concrete structures

NASA Astrophysics Data System (ADS)

Zhou, Xin'gang; Li, Kefei

2013-06-01

In order to study the influence of parameters on durability of marine concrete structures, the parameter's sensitivity analysis was studied in this paper. With the Fick's 2nd law of diffusion and the deterministic sensitivity analysis method (DSA), the sensitivity factors of apparent surface chloride content, apparent chloride diffusion coefficient and its time dependent attenuation factor were analyzed. The results of the analysis show that the impact of design variables on concrete durability was different. The values of sensitivity factor of chloride diffusion coefficient and its time dependent attenuation factor were higher than others. Relative less error in chloride diffusion coefficient and its time dependent attenuation coefficient induces a bigger error in concrete durability design and life prediction. According to probability sensitivity analysis (PSA), the influence of mean value and variance of concrete durability design variables on the durability failure probability was studied. The results of the study provide quantitative measures of the importance of concrete durability design and life prediction variables. It was concluded that the chloride diffusion coefficient and its time dependent attenuation factor have more influence on the reliability of marine concrete structural durability. In durability design and life prediction of marine concrete structures, it was very important to reduce the measure and statistic error of durability design variables.

Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Lijun; Zhang, Jimin

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

Modeling polyvinyl chloride Plasma Modification by Neural Networks

NASA Astrophysics Data System (ADS)

Wang, Changquan

2018-03-01

Neural networks model were constructed to analyze the connection between dielectric barrier discharge parameters and surface properties of material. The experiment data were generated from polyvinyl chloride plasma modification by using uniform design. Discharge voltage, discharge gas gap and treatment time were as neural network input layer parameters. The measured values of contact angle were as the output layer parameters. A nonlinear mathematical model of the surface modification for polyvinyl chloride was developed based upon the neural networks. The optimum model parameters were obtained by the simulation evaluation and error analysis. The results of the optimal model show that the predicted value is very close to the actual test value. The prediction model obtained here are useful for discharge plasma surface modification analysis.

RELATIVISTIC MEASUREMENTS FROM TIMING THE BINARY PULSAR PSR B1913+16

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

Weisberg, J. M.; Huang, Y., E-mail: jweisber@carleton.edu

2016-09-20

We present relativistic analyses of 9257 measurements of times-of-arrival from the first binary pulsar, PSR B1913+16, acquired over the last 35 years. The determination of the “Keplerian” orbital elements plus two relativistic terms completely characterizes the binary system, aside from an unknown rotation about the line of sight, leading to a determination of the masses of the pulsar and its companion: 1.438 ± 0.001 M {sub ☉} and 1.390 ± 0.001 M {sub ☉}, respectively. In addition, the complete system characterization allows for the creation of relativistic gravitation test by comparing measured and predicted sizes of various relativistic phenomena. Wemore » find that the ratio of the observed orbital period decrease caused by gravitational wave damping (corrected by a kinematic term) to the general relativistic prediction is 0.9983 ± 0.0016, thereby confirms the existence and strength of gravitational radiation as predicted by general relativity. For the first time in this system, we have also successfully measured the two parameters characterizing the Shapiro gravitational propagation delay, and found that their values are consistent with general relativistic predictions. For the first time in any system, we have also measured the relativistic shape correction to the elliptical orbit, δ {sub θ} , although its intrinsic value is obscured by currently unquantified pulsar emission beam aberration. We have also marginally measured the time derivative of the projected semimajor axis, which, when improved in combination with beam aberration modeling from geodetic precession observations, should ultimately constrain the pulsar’s moment of inertia.« less

Forecasting Kp from solar wind data: input parameter study using 3-hour averages and 3-hour range values

NASA Astrophysics Data System (ADS)

Wintoft, Peter; Wik, Magnus; Matzka, Jürgen; Shprits, Yuri

2017-11-01

We have developed neural network models that predict Kp from upstream solar wind data. We study the importance of various input parameters, starting with the magnetic component Bz, particle density n, and velocity V and then adding total field B and the By component. As we also notice a seasonal and UT variation in average Kp we include functions of day-of-year and UT. Finally, as Kp is a global representation of the maximum range of geomagnetic variation over 3-hour UT intervals we conclude that sudden changes in the solar wind can have a big effect on Kp, even though it is a 3-hour value. Therefore, 3-hour solar wind averages will not always appropriately represent the solar wind condition, and we introduce 3-hour maxima and minima values to some degree address this problem. We find that introducing total field B and 3-hour maxima and minima, derived from 1-minute solar wind data, have a great influence on the performance. Due to the low number of samples for high Kp values there can be considerable variation in predicted Kp for different networks with similar validation errors. We address this issue by using an ensemble of networks from which we use the median predicted Kp. The models (ensemble of networks) provide prediction lead times in the range 20-90 min given by the time it takes a solar wind structure to travel from L1 to Earth. Two models are implemented that can be run with real time data: (1) IRF-Kp-2017-h3 uses the 3-hour averages of the solar wind data and (2) IRF-Kp-2017 uses in addition to the averages, also the minima and maxima values. The IRF-Kp-2017 model has RMS error of 0.55 and linear correlation of 0.92 based on an independent test set with final Kp covering 2 years using ACE Level 2 data. The IRF-Kp-2017-h3 model has RMSE = 0.63 and correlation = 0.89. We also explore the errors when tested on another two-year period with real-time ACE data which gives RMSE = 0.59 for IRF-Kp-2017 and RMSE = 0.73 for IRF-Kp-2017-h3. The errors as function of Kp and for different years are also studied.

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

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

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.

Optimization of chitin yield from shrimp shell waste by Bacillus subtilis and impact of gamma irradiation on production of low molecular weight chitosan.

PubMed

Gamal, Rawia F; El-Tayeb, Tarek S; Raffat, Enas I; Ibrahim, Haytham M M; Bashandy, A S

2016-10-01

Chitin and chitosan have been produced from the exoskeletons of crustacean shells such as shrimps. In this study, seventy bacterial isolates, isolated from soil, were tested for proteolytic enzymes production. The most efficient one, identified as Bacillus subtilis, was employed to extract chitin from shrimp shell waste (SSW). Following one-variable-at-a-time approach, the relevant factors affecting deproteinization (DP) and demineralization (DM) were sucrose concentration (10%, w/v), SSW concentration (5%, w/v), inoculum size (15%, v/v), and fermentation time (6days). These factors were optimized subsequently using Box-Behnken design and response surface methodology. Maximum DP (97.65%) and DM (82.94%) were predicted at sucrose concentration (5%), SSW concentration (12.5%), inoculum size (10%, containing 35×10(8) CFU/mL), and fermentation time (7days). The predicted optimum values were verified by additional experiment. The values of DP (96.0%) and DM (82.1%) obtained experimentally correlated to the predicted values which justify the authenticity of optimum points. Overall 1.3-fold increase in DP% and DM% was obtained compared with 75.27% and 63.50%, respectively, before optimization. Gamma-irradiation (35kGy) reduced deacetylation time of irradiated chitin by 4.5-fold compared with non-irradiated chitin. The molecular weight of chitosan was decreased from 1.9×10(6) (non-irradiated) to 3.7×10(4)g/mol (at 35kGy). Copyright © 2016 Elsevier B.V. All rights reserved.

CGM-measured glucose values have a strong correlation with C-peptide, HbA1c and IDAAC, but do poorly in predicting C-peptide levels in the two years following onset of diabetes.

PubMed

Buckingham, Bruce; Cheng, Peiyao; Beck, Roy W; Kollman, Craig; Ruedy, Katrina J; Weinzimer, Stuart A; Slover, Robert; Bremer, Andrew A; Fuqua, John; Tamborlane, William

2015-06-01

The aim of this work was to assess the association between continuous glucose monitoring (CGM) data, HbA1c, insulin-dose-adjusted HbA1c (IDAA1c) and C-peptide responses during the first 2 years following diagnosis of type 1 diabetes. A secondary analysis was conducted of data collected from a randomised trial assessing the effect of intensive management initiated within 1 week of diagnosis of type 1 diabetes, in which mixed-meal tolerance tests were performed at baseline and at eight additional time points through 24 months. CGM data were collected at each visit. Among 67 study participants (mean age [± SD] 13.3 ± 5.7 years), HbA1c was inversely correlated with C-peptide at each time point (p < 0.001), as were changes in each measure between time points (p < 0.001). However, C-peptide at one visit did not predict the change in HbA1c at the next visit and vice versa. Higher C-peptide levels correlated with increased proportion of CGM glucose values between 3.9 and 7.8 mmol/l and lower CV (p = 0.001 and p = 0.02, respectively) but not with CGM glucose levels <3.9 mmol/l. Virtually all participants with IDAA1c < 9 retained substantial insulin secretion but when evaluated together with CGM, time in the range of 3.9-7.8 mmol/l and CV did not provide additional value in predicting C-peptide levels. In the first 2 years after diagnosis of type 1 diabetes, higher C-peptide levels are associated with increased sensor glucose levels in the target range and with lower glucose variability but not hypoglycaemia. CGM metrics do not provide added value over the IDAA1c in predicting C-peptide levels.

Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability

NASA Astrophysics Data System (ADS)

Liu, Sijun; Chen, Jiaping; Wang, Jianming; Wu, Zhuchao; Wu, Weihua; Xu, Zhiwei; Hu, Wenbiao; Xu, Fei; Tong, Shilu; Shen, Hongbing

2017-10-01

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.

Charting the Eccles' expectancy-value model from mothers' beliefs in childhood to youths' activities in adolescence.

PubMed

Simpkins, Sandra D; Fredricks, Jennifer A; Eccles, Jacquelynne S

2012-07-01

The Eccles' expectancy-value model posits that a cascade of mechanisms explain associations between parents' beliefs and youths' achievement-related behaviors. Specifically, parents' beliefs predict parents' behaviors; in turn, parents' behaviors predict youths' motivational beliefs, and youths' motivational beliefs predict their behaviors. This investigation focused on testing this model with mothers in sports, music, math, and reading over a 12-year period. Data were drawn from mother, youth, and teacher questionnaires collected as part of Childhood and Beyond Study (92% European American; N = 723). Mothers' beliefs in sports, music, and math positively predicted their behaviors in these areas 1 year later, which predicted youths' self-concepts of ability and values (i.e., their motivational beliefs) in these domains 1 year later. Adolescents' motivational beliefs predicted time spent in organized sport activities, playing music, and reading after school measured 4 years later as well as the number of math courses taken in high school. Furthermore, except in reading, mothers' behaviors mediated the relations between mothers' and youths' beliefs, and youths' beliefs mediated the relations between mothers' behaviors and youths' behaviors. Although there were mean-level differences in several indicators based on child gender, in most cases the relations among these indicators did not significantly vary by child gender. This study highlights the processes by which mothers' beliefs during their children's childhood can predict children's activities in adolescence.

Evaluation of stratospheric temperature simulation results by the global GRAPES model

NASA Astrophysics Data System (ADS)

Liu, Ningwei; Wang, Yangfeng; Ma, Xiaogang; Zhang, Yunhai

2017-12-01

Global final analysis (FNL) products and the general circulation spectral model (ECHAM) were used to evaluate the simulation of stratospheric temperature by the global assimilation and prediction system (GRAPES). Through a series of comparisons, it was shown that the temperature variations at 50 hPa simulated by GRAPES were significantly elevated in the southern hemisphere, whereas simulations by ECHAM and FNL varied little over time. The regional warming predicted by GRAPES seemed to be too distinct and uncontrolled to be reasonable. The temperature difference between GRAPES and FNL (GRAPES minus FNL) was small at the start time on the global scale. Over time, the positive values became larger in more locations, especially in parts of the southern hemisphere, where the warming predicted by GRAPES was dominant, with a maximal value larger than 24 K. To determine the reasons for the stratospheric warming, we considered the model initial conditions and ozone data to be possible factors; however, a comparison and sensitivity test indicated that the errors produced by GRAPES were not significantly related to either factor. Further research focusing on the impact of factors such as vapor, heating rate, and the temperature tendency on GRAPES simulations will be conducted.

A simple model of the effects of the mid-latitude total ion trough in the bottomside F layer on HF radiowave propagation

NASA Astrophysics Data System (ADS)

Lockwood, M.

1981-06-01

Observations of the amplitudes and Doppler shifts of received HF radio waves are compared with model predictions made using a two-dimensional ray-tracing program. The signals are propagated over a sub-auroral path, which is shown to lie along the latitudes of the mid-latitude trough at times of low geomagnetic activity. Generalizing the predictions to include a simple model of the trough in the density and height of the F2 peak enables the explanation of the anomalous observed diurnal variations. The behavior of received amplitude, Doppler shift, and signal-to-noise ratio as a function of the K sub p index value, the time of day, and the season (in 17 months of continuous recording) is found to agree closely with that predicted using the statistical position of the trough as deduced from 8 years of Alouette satellite soundings. The variation in the times of the observation of large signal amplitudes with the K sub p value and the complete absence of such amplitudes when it exceeds 2.75 are two features that implicate the trough in these effects.

Correlation of MFOLD-predicted DNA secondary structures with separation patterns obtained by capillary electrophoresis single-strand conformation polymorphism (CE-SSCP) analysis.

PubMed

Glavac, Damjan; Potocnik, Uros; Podpecnik, Darja; Zizek, Teofil; Smerkolj, Sava; Ravnik-Glavac, Metka

2002-04-01

We have studied 57 different mutations within three beta-globin gene promoter fragments with sizes 52 bp, 77 bp, and 193 bp by fluorescent capillary electrophoresis CE-SSCP analysis. For each mutation and wild type, energetically most-favorable predicted secondary structures were calculated for sense and antisense strands using the MFOLD DNA-folding algorithm in order to investigate if any correlation exists between predicted DNA structures and actual CE migration time shifts. The overall CE-SSCP detection rate was 100% for all mutations in three studied DNA fragments. For shorter 52 bp and 77 bp DNA fragments we obtained a positive correlation between the migration time shifts and difference in free energy values of predicted secondary structures at all temperatures. For longer 193 bp beta-globin gene fragments with 46 mutations MFOLD predicted different secondary structures for 89% of mutated strands at 25 degrees C and 40 degrees C. However, the magnitude of the mobility shifts did not necessarily correlate with their secondary structures and free energy values except for the sense strand at 40 degrees C where this correlation was statistically significant (r = 0.312, p = 0.033). Results of this study provided more direct insight into the mechanism of CE-SSCP and showed that MFOLD prediction could be helpful in making decisions about the running temperatures and in prediction of CE-SSCP data patterns, especially for shorter (50-100 bp) DNA fragments. Copyright 2002 Wiley-Liss, Inc.

3-D sonography for diagnosis of disk dislocation of the temporomandibular joint compared with MRI.

PubMed

Landes, Constantin A; Goral, Wojciech A; Sader, Robert; Mack, Martin G

2006-05-01

This study determines the value of three-dimensional (3-D) sonography for the assessment of disk dislocation of the temporomandibular joint (TMJ). Sixty-eight patients (i.e.,136 TMJ) with clinical dysfunction were examined by 272 sonographic 3-D scans. An 8- to 12.5-MHz transducer, angulated by step-motor, was used after picking a volume box on 2-D scan; magnetic resonance imaging followed immediately. Every TMJ was scrutinized in closed- and open-mouth position for normal or dislocated disk position. Fifty-three patients had complete data sets, i.e., 106 TMJ, 212 examinations. Sonographic examination took 5 min, with 74% specificity (62% closed-mouth; 85% open-mouth); sensitivity 53% (62/43%); accuracy 70% (62/77%); positive predictive value 49% (57/41%); and negative predictive value 77% (67/86%). This study encourages more research on the diagnostic capacity of 3-D TMJ sonography, with the advantage of multidimensional joint visualization. Although fair in specificity and negative predictive value, sensitivity and accuracy may ameliorate with future higher-sound frequency, real-time 3-D viewing and automated image analysis.

The use of ECDIS equipment to achieve an optimum value for energy efficiency operation index

NASA Astrophysics Data System (ADS)

Acomi, N.; Acomi, O. C.; Stanca, C.

2015-11-01

To reduce air pollution produced by ships, the International Maritime Organization has developed a set of technical, operational and management measures. The subject of our research addresses the operational measures for minimizing CO2 air emissions and the way how the emission value could be influenced by external factors regardless of ship-owners’ will. This study aims to analyse the air emissions for a loaded voyage leg performed by an oil tanker. The formula that allows us to calculate the predicted Energy Efficiency Operational Index involves the estimation of distance and fuel consumption, while the quantity of cargo is known. The electronic chart display and information system, ECDIS Simulation Software, will be used for adjusting the passage plan in real time, given the predicted severe environmental conditions. The distance will be determined using ECDIS, while the prediction of the fuel consumption will consider the sea trial and the vessel experience records. That way it will be possible to compare the estimated EEOI value in the case of great circle navigation in adverse weather condition with the estimated EEOI value for weather navigation.

Time series and recurrence interval models to predict the vulnerability of streams to episodic acidification in Shenandoah National Park, Virginia

USGS Publications Warehouse

Deviney, Frank A.; Rice, Karen C.; Hornberger, George M.

2006-01-01

Acid rain affects headwater streams by temporarily reducing the acid‐neutralizing capacity (ANC) of the water, a process termed episodic acidification. The increase in acidic components in stream water can have deleterious effects on the aquatic biota. Although acidic deposition is uniform across Shenandoah National Park (SNP) in north central Virginia, the stream water quality response during rain events varies substantially. This response is a function of the catchment's underlying geology and topography. Geologic and topographic data for SNP's 231 catchments are readily available; however, long‐term measurements (tens of years) of ANC and accompanying discharge are not and would be prohibitively expensive to collect. Transfer function time series models were developed to predict hourly ANC from discharge for five SNP catchments with long‐term water‐quality and discharge records. Hourly ANC predictions over short time periods (≤1 week) were averaged, and distributions of the recurrence intervals of annual water‐year minimum ANC values were model‐simulated for periods of 6, 24, 72, and 168 hours. The distributions were extrapolated to the rest of the SNP catchments on the basis of catchment geology and topography. On the basis of the models, large numbers of SNP streams have 6‐ to 168‐hour periods of low‐ANC values, which may stress resident fish populations. Smaller catchments are more vulnerable to episodic acidification than larger catchments underlain by the same bedrock. Catchments with similar topography and size are more vulnerable if underlain by less basaltic/carbonate bedrock. Many catchments are predicted to have successive years of low‐ANC values potentially sufficient to extirpate some species.

Comparison between presepsin and procalcitonin in early diagnosis of neonatal sepsis.

PubMed

Iskandar, Agustin; Arthamin, Maimun Z; Indriana, Kristin; Anshory, Muhammad; Hur, Mina; Di Somma, Salvatore

2018-05-09

Neonatal sepsis remains worldwide one of the leading causes of morbidity and mortality in both term and preterm infants. Lower mortality rates are related to timely diagnostic evaluation and prompt initiation of empiric antibiotic therapy. Blood culture, as gold standard examination for sepsis, has several limitations for early diagnosis, so that sepsis biomarkers could play an important role in this regard. This study was aimed to compare the value of the two biomarkers presepsin and procalcitonin in early diagnosis of neonatal sepsis. This was a prospective cross-sectional study performed, in Saiful Anwar General Hospital Malang, Indonesia, in 51 neonates that fulfill the criteria of systemic inflammatory response syndrome (SIRS) with blood culture as diagnostic gold standard for sepsis. At reviewer operating characteristic (ROC) curve analyses, using a presepsin cutoff of 706,5 pg/mL, the obtained area under the curve (AUCs) were: sensitivity = 85.7%, specificity = 68.8%, positive predictive value = 85.7%, negative predictive value = 68.8%, positive likelihood ratio = 2.75, negative likelihood ratio = 0.21, and accuracy = 80.4%. On the other hand, with a procalcitonin cutoff value of 161.33 pg/mL the obtained AUCs showed: sensitivity = 68.6%, specificity = 62.5%, positive predictive value = 80%, negative predictive value = 47.6%, positive likelihood ratio = 1.83, the odds ratio negative = 0.5, and accuracy = 66.7%. In early diagnosis of neonatal sepsis, compared with procalcitonin, presepsin seems to provide better early diagnostic value with consequent possible faster therapeutical decision making and possible positive impact on outcome of neonates.

Gradient retention prediction of acid-base analytes in reversed phase liquid chromatography: a simplified approach for acetonitrile-water mobile phases.

PubMed

Andrés, Axel; Rosés, Martí; Bosch, Elisabeth

2014-11-28

In previous work, a two-parameter model to predict chromatographic retention of ionizable analytes in gradient mode was proposed. However, the procedure required some previous experimental work to get a suitable description of the pKa change with the mobile phase composition. In the present study this previous experimental work has been simplified. The analyte pKa values have been calculated through equations whose coefficients vary depending on their functional group. Forced by this new approach, other simplifications regarding the retention of the totally neutral and totally ionized species also had to be performed. After the simplifications were applied, new prediction values were obtained and compared with the previously acquired experimental data. The simplified model gave pretty good predictions while saving a significant amount of time and resources. Copyright © 2014 Elsevier B.V. All rights reserved.

Cortisol Secretion and Change in Sleep Problems in Early Childhood: Moderation by Maternal Overcontrol

PubMed Central

Kiel, Elizabeth J.; Hummel, Alexandra C.; Luebbe, Aaron M.

2015-01-01

Childhood sleep problems are prevalent and relate to a wide range of negative psychological outcomes. However, it remains unclear how biological processes, such as HPA activity, may predict sleep problems over time in childhood in the context of certain parenting environments. Fifty-one mothers and their 18–20 month-old toddlers participated in a short-term longitudinal study assessing how shared variance among morning levels, diurnal change, and nocturnal change in toddlers’ cortisol secretion predicted change in sleep problems in the context of maternal overprotection and critical control. A composite characterized by low variability in, and, to a lesser extent, high morning values of cortisol, predicted increasing sleep problems from age 2 to age 3 when mothers reported high critical control. Results suggest value in assessing shared variance among different indices of cortisol secretion patterns and the interaction between cortisol and the environment in predicting sleep problems in early childhood. PMID:25766262

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

Saaban, Azizan; Zainudin, Lutfi; Bakar, Mohd Nazari Abu

This paper intends to reveal the ability of the linear interpolation method to predict missing values in solar radiation time series. Reliable dataset is equally tends to complete time series observed dataset. The absence or presence of radiation data alters long-term variation of solar radiation measurement values. Based on that change, the opportunities to provide bias output result for modelling and the validation process is higher. The completeness of the observed variable dataset has significantly important for data analysis. Occurrence the lack of continual and unreliable time series solar radiation data widely spread and become the main problematic issue. However,more » the limited number of research quantity that has carried out to emphasize and gives full attention to estimate missing values in the solar radiation dataset.« less

Accelerated convergence for synchronous approximate agreement

NASA Technical Reports Server (NTRS)

Kearns, J. P.; Park, S. K.; Sjogren, J. A.

1988-01-01

The protocol for synchronous approximate agreement presented by Dolev et. al. exhibits the undesirable property that a faulty processor, by the dissemination of a value arbitrarily far removed from the values held by good processors, may delay the termination of the protocol by an arbitrary amount of time. Such behavior is clearly undesirable in a fault tolerant dynamic system subject to hard real-time constraints. A mechanism is presented by which editing data suspected of being from Byzantine-failed processors can lead to quicker, predictable, convergence to an agreement value. Under specific assumptions about the nature of values transmitted by failed processors relative to those transmitted by good processors, a Monte Carlo simulation is presented whose qualitative results illustrate the trade-off between accelerated convergence and the accuracy of the value agreed upon.

Automatic burst detection for the EEG of the preterm infant.

PubMed

Jennekens, Ward; Ruijs, Loes S; Lommen, Charlotte M L; Niemarkt, Hendrik J; Pasman, Jaco W; van Kranen-Mastenbroek, Vivianne H J M; Wijn, Pieter F F; van Pul, Carola; Andriessen, Peter

2011-10-01

To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.

Systematic Evaluation of Wajima Superposition (Steady-State Concentration to Mean Residence Time) in the Estimation of Human Intravenous Pharmacokinetic Profile.

PubMed

Lombardo, Franco; Berellini, Giuliano; Labonte, Laura R; Liang, Guiqing; Kim, Sean

2016-03-01

We present a systematic evaluation of the Wajima superpositioning method to estimate the human intravenous (i.v.) pharmacokinetic (PK) profile based on a set of 54 marketed drugs with diverse structure and range of physicochemical properties. We illustrate the use of average of "best methods" for the prediction of clearance (CL) and volume of distribution at steady state (VDss) as described in our earlier work (Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):178-191; Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):167-177). These methods provided much more accurate prediction of human PK parameters, yielding 88% and 70% of the prediction within 2-fold error for VDss and CL, respectively. The prediction of human i.v. profile using Wajima superpositioning of rat, dog, and monkey time-concentration profiles was tested against the observed human i.v. PK using fold error statistics. The results showed that 63% of the compounds yielded a geometric mean of fold error below 2-fold, and an additional 19% yielded a geometric mean of fold error between 2- and 3-fold, leaving only 18% of the compounds with a relatively poor prediction. Our results showed that good superposition was observed in any case, demonstrating the predictive value of the Wajima approach, and that the cause of poor prediction of human i.v. profile was mainly due to the poorly predicted CL value, while VDss prediction had a minor impact on the accuracy of human i.v. profile prediction. Copyright © 2016. Published by Elsevier Inc.

Properties of biochar-amended soils and their sorption of imidacloprid, isoproturon, and atrazine.

PubMed

Jin, Jie; Kang, Mingjie; Sun, Ke; Pan, Zezhen; Wu, Fengchang; Xing, Baoshan

2016-04-15

Biochars produced from rice straw, wheat straw and swine manure at 300, 450 and 600°C were added to soil at 1, 5, 10, or 20% levels to determine whether they would predictably reduce the pore water concentration of imidacloprid, isoproturon, and atrazine. The sorption capacity of the mixtures increased with increasing biochar amounts. The enhanced sorption capacity could be attributed to the increased organic carbon (OC) content and surface area (SA) as well as the decreased hydrophobicity. Biochar dominated the overall sorption when its content was above 5%. The OC contents of the mixtures with 10% and 20% biochar were generally lower than the predicted values. This implies possible interaction between soil components and biochar and/or the effect of biochar oxidation. For soils amended with biochars produced at 300°C, the N2 SA (N2-SA) values were underestimated. The predicted CO2 SA (CO2-SA) values of the mixtures at the biochar content of 10% and 20% were generally higher than the experimental values. Sorption of imidacloprid to the soils amended with biochar at 10% and 20% levels, excluding the soils amended with rice (SR300) and wheat (SW300) straw-derived biochar produced at 300°C, was lower than the predicted value. For SR300 and SW300, the intrinsic sorption capacity of biochar was enhanced by 1.3-5.6 times, depending on the biochar, solute concentration, and biochar dose. This study indicates that biochars would be helpful to stabilize the soil contaminated with imidacloprid, isoproturon, and atrazine, but the sorption capacity of the mixtures could exceed or fall short of predicted values without assuming a cross-effect between soil and biochar. Copyright © 2016 Elsevier B.V. All rights reserved.

The use of multiple time point dynamic positron emission tomography/computed tomography in patients with oral/head and neck cancer does not predictably identify metastatic cervical lymph nodes.

PubMed

Carlson, Eric R; Schaefferkoetter, Josh; Townsend, David; McCoy, J Michael; Campbell, Paul D; Long, Misty

2013-01-01

To determine whether the time course of 18-fluorine fluorodeoxyglucose (18F-FDG) activity in multiple consecutively obtained 18F-FDG positron emission tomography (PET)/computed tomography (CT) scans predictably identifies metastatic cervical adenopathy in patients with oral/head and neck cancer. It is hypothesized that the activity will increase significantly over time only in those lymph nodes harboring metastatic cancer. A prospective cohort study was performed whereby patients with oral/head and neck cancer underwent consecutive imaging at 9 time points with PET/CT from 60 to 115 minutes after injection with (18)F-FDG. The primary predictor variable was the status of the lymph nodes based on dynamic PET/CT imaging. Metastatic lymph nodes were defined as those that showed an increase greater than or equal to 10% over the baseline standard uptake values. The primary outcome variable was the pathologic status of the lymph node. A total of 2,237 lymph nodes were evaluated histopathologically in the 83 neck dissections that were performed in 74 patients. A total of 119 lymph nodes were noted to have hypermetabolic activity on the 90-minute (static) portion of the study and were able to be assessed by time points. When we compared the PET/CT time point (dynamic) data with the histopathologic analysis of the lymph nodes, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 60.3%, 70.5%, 66.0%, 65.2%, and 65.5%, respectively. The use of dynamic PET/CT imaging does not permit the ablative surgeon to depend only on the results of the PET/CT study to determine which patients will benefit from neck dissection. As such, we maintain that surgeons should continue to rely on clinical judgment and maintain a low threshold for executing neck dissection in patients with oral/head and neck cancer, including those patients with N0 neck designations. Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Mining CRRES IDM Pulse Data and CRRES Environmental Data to Improve Spacecraft Charging/Discharging Models and Guidelines

NASA Technical Reports Server (NTRS)

Brautigam, D. H.; Frederickson, A. R.

2004-01-01

One can truly predict the charging and pulsing in space over a year's time using only the physics that worked for periods of an hour and less in prior publications. All portions of the task were achieved, including the optional portion of determining a value for conductivity that best .t the data. Fortran statements were developed that are required for the NUMIT runs to work with this kind of data from space. In addition to developing the Fortran for NUMIT, simple correlations between the IDM pulsing history and the space radiation were observed because we now have a better characterization of the space radiation. The study showed that: (1) the new methods for measurement of charge storage and conduction in insulators provide the correct values to use for prediction of charging and pulsing in space; (2) the methods in NUMIT that worked well for time durations less than hours now work well for durations of months; (3) an average spectrum such as AE8 is probably not a good guide for predicting pulsing in space one must take time dependence into account in order to understand insulator pulsing; and (4) the old method for predicting pulse rates in space that was based on the CRRES data could be improved to include dependencies on material parameters.

The value of the injury severity score in pediatric trauma: Time for a new definition of severe injury?

PubMed

Brown, Joshua B; Gestring, Mark L; Leeper, Christine M; Sperry, Jason L; Peitzman, Andrew B; Billiar, Timothy R; Gaines, Barbara A

2017-06-01

The Injury Severity Score (ISS) is the most commonly used injury scoring system in trauma research and benchmarking. An ISS greater than 15 conventionally defines severe injury; however, no studies evaluate whether ISS performs similarly between adults and children. Our objective was to evaluate ISS and Abbreviated Injury Scale (AIS) to predict mortality and define optimal thresholds of severe injury in pediatric trauma. Patients from the Pennsylvania trauma registry 2000-2013 were included. Children were defined as younger than 16 years. Logistic regression predicted mortality from ISS for children and adults. The optimal ISS cutoff for mortality that maximized diagnostic characteristics was determined in children. Regression also evaluated the association between mortality and maximum AIS in each body region, controlling for age, mechanism, and nonaccidental trauma. Analysis was performed in single and multisystem injuries. Sensitivity analyses with alternative outcomes were performed. Included were 352,127 adults and 50,579 children. Children had similar predicted mortality at ISS of 25 as adults at ISS of 15 (5%). The optimal ISS cutoff in children was ISS greater than 25 and had a positive predictive value of 19% and negative predictive value of 99% compared to a positive predictive value of 7% and negative predictive value of 99% for ISS greater than 15 to predict mortality. In single-system-injured children, mortality was associated with head (odds ratio, 4.80; 95% confidence interval, 2.61-8.84; p < 0.01) and chest AIS (odds ratio, 3.55; 95% confidence interval, 1.81-6.97; p < 0.01), but not abdomen, face, neck, spine, or extremity AIS (p > 0.05). For multisystem injury, all body region AIS scores were associated with mortality except extremities. Sensitivity analysis demonstrated ISS greater than 23 to predict need for full trauma activation, and ISS greater than 26 to predict impaired functional independence were optimal thresholds. An ISS greater than 25 may be a more appropriate definition of severe injury in children. Pattern of injury is important, as only head and chest injury drive mortality in single-system-injured children. These findings should be considered in benchmarking and performance improvement efforts. Epidemiologic study, level III.

Shortened acquisition protocols for the quantitative assessment of the 2-tissue-compartment model using dynamic PET/CT 18F-FDG studies.

PubMed

Strauss, Ludwig G; Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2011-03-01

(18)F-FDG kinetics are quantified by a 2-tissue-compartment model. The routine use of dynamic PET is limited because of this modality's 1-h acquisition time. We evaluated shortened acquisition protocols up to 0-30 min regarding the accuracy for data analysis with the 2-tissue-compartment model. Full dynamic series for 0-60 min were analyzed using a 2-tissue-compartment model. The time-activity curves and the resulting parameters for the model were stored in a database. Shortened acquisition data were generated from the database using the following time intervals: 0-10, 0-16, 0-20, 0-25, and 0-30 min. Furthermore, the impact of adding a 60-min uptake value to the dynamic series was evaluated. The datasets were analyzed using dedicated software to predict the results of the full dynamic series. The software is based on a modified support vector machines (SVM) algorithm and predicts the compartment parameters of the full dynamic series. The SVM-based software provides user-independent results and was accurate at predicting the compartment parameters of the full dynamic series. If a squared correlation coefficient of 0.8 (corresponding to 80% explained variance of the data) was used as a limit, a shortened acquisition of 0-16 min was accurate at predicting the 60-min 2-tissue-compartment parameters. If a limit of 0.9 (90% explained variance) was used, a dynamic series of at least 0-20 min together with the 60-min uptake values is required. Shortened acquisition protocols can be used to predict the parameters of the 2-tissue-compartment model. Either a dynamic PET series of 0-16 min or a combination of a dynamic PET/CT series of 0-20 min and a 60-min uptake value is accurate for analysis with a 2-tissue-compartment model.

Prediction of successful memory encoding based on single-trial rhinal and hippocampal phase information.

PubMed

Höhne, Marlene; Jahanbekam, Amirhossein; Bauckhage, Christian; Axmacher, Nikolai; Fell, Juergen

2016-10-01

Mediotemporal EEG characteristics are closely related to long-term memory formation. It has been reported that rhinal and hippocampal EEG measures reflecting the stability of phases across trials are better suited to distinguish subsequently remembered from forgotten trials than event-related potentials or amplitude-based measures. Theoretical models suggest that the phase of EEG oscillations reflects neural excitability and influences cellular plasticity. However, while previous studies have shown that the stability of phase values across trials is indeed a relevant predictor of subsequent memory performance, the effect of absolute single-trial phase values has been little explored. Here, we reanalyzed intracranial EEG recordings from the mediotemporal lobe of 27 epilepsy patients performing a continuous word recognition paradigm. Two-class classification using a support vector machine was performed to predict subsequently remembered vs. forgotten trials based on individually selected frequencies and time points. We demonstrate that it is possible to successfully predict single-trial memory formation in the majority of patients (23 out of 27) based on only three single-trial phase values given by a rhinal phase, a hippocampal phase, and a rhinal-hippocampal phase difference. Overall classification accuracy across all subjects was 69.2% choosing frequencies from the range between 0.5 and 50Hz and time points from the interval between -0.5s and 2s. For 19 patients, above chance prediction of subsequent memory was possible even when choosing only time points from the prestimulus interval (overall accuracy: 65.2%). Furthermore, prediction accuracies based on single-trial phase surpassed those based on single-trial power. Our results confirm the functional relevance of mediotemporal EEG phase for long-term memory operations and suggest that phase information may be utilized for memory enhancement applications based on deep brain stimulation. Copyright © 2016 Elsevier Inc. All rights reserved.

Experimental system for measurement of radiologists' performance by visual search task.

PubMed

Maeda, Eriko; Yoshikawa, Takeharu; Nakashima, Ryoichi; Kobayashi, Kazufumi; Yokosawa, Kazuhiko; Hayashi, Naoto; Masutani, Yoshitaka; Yoshioka, Naoki; Akahane, Masaaki; Ohtomo, Kuni

2013-01-01

Detective performance of radiologists for "obvious" targets should be evaluated by visual search task instead of ROC analysis, but visual task have not been applied to radiology studies. The aim of this study was to set up an environment that allows visual search task in radiology, to evaluate its feasibility, and to preliminarily investigate the effect of career on the performance. In a darkroom, ten radiologists were asked to answer the type of lesion by pressing buttons, when images without lesions, with bulla, ground-glass nodule, and solid nodule were randomly presented on a display. Differences in accuracy and reaction times depending on board certification were investigated. The visual search task was successfully and feasibly performed. Radiologists were found to have high sensitivity, specificity, positive predictive values and negative predictive values in non-board and board groups. Reaction time was under 1 second for all target types in both groups. Board radiologists were significantly faster in answering for bulla, but there were no significant differences for other targets and values. We developed an experimental system that allows visual search experiment in radiology. Reaction time for detection of bulla was shortened with experience.

Predictive value of the present-on-admission indicator for hospital-acquired venous thromboembolism.

PubMed

Khanna, Raman R; Kim, Sharon B; Jenkins, Ian; El-Kareh, Robert; Afsarmanesh, Nasim; Amin, Alpesh; Sand, Heather; Auerbach, Andrew; Chia, Catherine Y; Maynard, Gregory; Romano, Patrick S; White, Richard H

2015-04-01

Hospital-acquired venous thromboembolic (HA-VTE) events are an important, preventable cause of morbidity and death, but accurately identifying HA-VTE events requires labor-intensive chart review. Administrative diagnosis codes and their associated "present-on-admission" (POA) indicator might allow automated identification of HA-VTE events, but only if VTE codes are accurately flagged "not present-on-admission" (POA=N). New codes were introduced in 2009 to improve accuracy. We identified all medical patients with at least 1 VTE "other" discharge diagnosis code from 5 academic medical centers over a 24-month period. We then sampled, within each center, patients with VTE codes flagged POA=N or POA=U (insufficient documentation) and POA=Y or POA=W (timing clinically uncertain) and abstracted each chart to clarify VTE timing. All events that were not clearly POA were classified as HA-VTE. We then calculated predictive values of the POA=N/U flags for HA-VTE and the POA=Y/W flags for non-HA-VTE. Among 2070 cases with at least 1 "other" VTE code, we found 339 codes flagged POA=N/U and 1941 flagged POA=Y/W. Among 275 POA=N/U abstracted codes, 75.6% (95% CI, 70.1%-80.6%) were HA-VTE; among 291 POA=Y/W abstracted events, 73.5% (95% CI, 68.0%-78.5%) were non-HA-VTE. Extrapolating from this sample, we estimated that 59% of actual HA-VTE codes were incorrectly flagged POA=Y/W. POA indicator predictive values did not improve after new codes were introduced in 2009. The predictive value of VTE events flagged POA=N/U for HA-VTE was 75%. However, sole reliance on this flag may substantially underestimate the incidence of HA-VTE.

Evaluating a slope-stability model for shallow rain-induced landslides using gage and satellite data

USGS Publications Warehouse

Yatheendradas, S.; Kirschbaum, D.; Baum, Rex L.; Godt, Jonathan W.

2014-01-01

Improving prediction of landslide early warning systems requires accurate estimation of the conditions that trigger slope failures. This study tested a slope-stability model for shallow rainfall-induced landslides by utilizing rainfall information from gauge and satellite records. We used the TRIGRS model (Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis) for simulating the evolution of the factor of safety due to rainfall infiltration. Using a spatial subset of a well-characterized digital landscape from an earlier study, we considered shallow failure on a slope adjoining an urban transportation roadway near the Seattle area in Washington, USA.We ran the TRIGRS model using high-quality rain gage and satellite-based rainfall data from the Tropical Rainfall Measuring Mission (TRMM). Preliminary results with parameterized soil depth values suggest that the steeper slope values in this spatial domain have factor of safety values that are extremely close to the failure limit within an extremely narrow range of values, providing multiple false alarms. When the soil depths were constrained using a back analysis procedure to ensure that slopes were stable under initial condtions, the model accurately predicted the timing and location of the landslide observation without false alarms over time for gage rain data. The TRMM satellite rainfall data did not show adequately retreived rainfall peak magnitudes and accumulation over the study period, and as a result failed to predict the landslide event. These preliminary results indicate that more accurate and higher-resolution rain data (e.g., the upcoming Global Precipitation Measurement (GPM) mission) are required to provide accurate and reliable landslide predictions in ungaged basins.

Dynamic hub load predicts cognitive decline after resective neurosurgery.

PubMed

Carbo, Ellen W S; Hillebrand, Arjan; van Dellen, Edwin; Tewarie, Prejaas; de Witt Hamer, Philip C; Baayen, Johannes C; Klein, Martin; Geurts, Jeroen J G; Reijneveld, Jaap C; Stam, Cornelis J; Douw, Linda

2017-02-07

Resective neurosurgery carries the risk of postoperative cognitive deterioration. The concept of 'hub (over)load', caused by (over)use of the most important brain regions, has been theoretically postulated in relation to symptomatology and neurological disease course, but lacks experimental confirmation. We investigated functional hub load and postsurgical cognitive deterioration in patients undergoing lesion resection. Patients (n = 28) underwent resting-state magnetoencephalography and neuropsychological assessments preoperatively and 1-year after lesion resection. We calculated stationary hub load score (SHub) indicating to what extent brain regions linked different subsystems; high SHub indicates larger processing pressure on hub regions. Dynamic hub load score (DHub) assessed its variability over time; low values, particularly in combination with high SHub values, indicate increased load, because of consistently high usage of hub regions. Hypothetically, increased SHub and decreased DHub relate to hub overload and thus poorer/deteriorating cognition. Between time points, deteriorating verbal memory performance correlated with decreasing upper alpha DHub. Moreover, preoperatively low DHub values accurately predicted declining verbal memory performance. In summary, dynamic hub load relates to cognitive functioning in patients undergoing lesion resection: postoperative cognitive decline can be tracked and even predicted using dynamic hub load, suggesting it may be used as a prognostic marker for tailored treatment planning.

Predicting long-term catchment nutrient export: the use of nonlinear time series models

NASA Astrophysics Data System (ADS)

Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

2010-05-01

After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the ARMA class. In most cases the relative improvement of SETAR models against AR models of first order was low ranging between 1% and 4% with the exception of the three-regime model for the River Stour time-series where the improvement was 48.9%. In comparison, the relative improvement of MSW models was between 44.6% and 52.5 for two-regime and from 60.4% to 75% for three-regime models. However, the visual assessment of models plotted against original datasets showed that despite a high value of RSS, some ARMA models could describe the analyzed time-series better than AR, MA and SETAR models with lower values of RSS. In both datasets MSW models provided a very good visual fit describing most of the extreme values.

Exponential analysis of the lung pressure-volume curve in patients with chronic pigeon-breeder's lung.

PubMed

Sansores, R; Perez-Padilla, R; Paré, P D; Selman, M

1992-05-01

Pigeon-breeder's lung (PBL) is extremely common in Mexico City and often progresses to irreversible pulmonary fibrosis. The exponential analysis of the lung pressure-volume (PV) curve (V = A - Be-kp) has been suggested as a method to separate the lung restriction caused by inflammation from that caused by pulmonary fibrosis; a significantly decreased value for the exponential constant, k, suggests a change in the mechanical properties of the functioning lung parenchyma, while a normal value accompanied by restriction suggests subtraction of lung units without a change in the mechanical properties of the functioning units. We measured lung volumes and static PV curves in 29 patients who had persistent lung restriction following a biopsy-proven diagnosis of PBL. Mean values in the 29 subjects were as follows: age, 43 +/- 13 years; TLC, 61 +/- 15 percent of predicted; VC, 46 +/- 19 percent of predicted; and k, 55 +/- 17 percent of predicted. Twenty-four of the 29 patients had values for k that were below the 95 percent confidence level, and five had "normal" values. There was no difference in TLC and VC (percent of predicted) between those with or without a decreased value for k. Four of five patients with a normal value for k improved subsequent to diagnosis, while only one of 21 patients with a decreased k improved. We conclude that increased lung elasticity manifested by a low value for k is common in patients with chronic PBL. These results support the observation of frequent irreversible lung fibrosis in these patients. Measurements of k could prove a good prognostic indicator at the time of initial diagnosis.

Prediction of the total cycle 24 of solar activity by several autoregressive methods and by the precursor method

NASA Astrophysics Data System (ADS)

Ozheredov, V. A.; Breus, T. K.; Obridko, V. N.

2012-12-01

As follows from the statement of the Third Official Solar Cycle 24 Prediction Panel created by the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the International Space Environment Service (ISES) based on the results of an analysis of many solar cycle 24 predictions, there has been no consensus on the amplitude and time of the maximum. There are two different scenarios: 90 units and August 2012 or 140 units and October 2011. The aim of our study is to revise the solar cycle 24 predictions by a comparative analysis of data obtained by three different methods: the singular spectral method, the nonlinear neural-based method, and the precursor method. As a precursor for solar cycle 24, we used the dynamics of the solar magnetic fields forming solar spots with Wolf numbers Rz. According to the prediction on the basis of the neural-based approach, it was established that the maximum of solar cycle 24 is expected to be 70. The precursor method predicted 50 units for the amplitude and April of 2012 for the time of the maximum. In view of the fact that the data used in the precursor method were averaged over 4.4 years, the amplitude of the maximum can be 20-30% larger (i.e., around 60-70 units), which is close to the values predicted by the neural-based method. The protracted minimum of solar cycle 23 and predicted low values of the maximum of solar cycle 24 are reminiscent of the historical Dalton minimum.

[ETAP: A smoking scale for Primary Health Care].

PubMed

González Romero, Pilar María; Cuevas Fernández, Francisco Javier; Marcelino Rodríguez, Itahisa; Rodríguez Pérez, María Del Cristo; Cabrera de León, Antonio; Aguirre-Jaime, Armando

2016-05-01

To obtain a scale of tobacco exposure to address smoking cessation. Follow-up of a cohort. Scale validation. Primary Care Research Unit. Tenerife. A total of 6729 participants from the "CDC de Canarias" cohort. A scale was constructed under the assumption that the time of exposure to tobacco is the key factor to express accumulated risk. Discriminant validity was tested on prevalent cases of acute myocardial infarction (AMI; n=171), and its best cut-off for preventive screening was obtained. Its predictive validity was tested with incident cases of AMI (n=46), comparing the predictive power with markers (age, sex) and classic risk factors of AMI (hypertension, diabetes, dyslipidaemia), including the pack-years index (PYI). The scale obtained was the sum of three times the years that they had smoked plus years exposed to smoking at home and at work. The frequency of AMI increased with the values of the scale, with the value 20 years of exposure being the most appropriate cut-off for preventive action, as it provided adequate predictive values for incident AMI. The scale surpassed PYI in predicting AMI, and competed with the known markers and risk factors. The proposed scale allows a valid measurement of exposure to smoking and provides a useful and simple approach that can help promote a willingness to change, as well as prevention. It still needs to demonstrate its validity, taking as reference other problems associated with smoking. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

Verification of an ensemble prediction system for storm surge forecast in the Adriatic Sea

NASA Astrophysics Data System (ADS)

Mel, Riccardo; Lionello, Piero

2014-12-01

In the Adriatic Sea, storm surges present a significant threat to Venice and to the flat coastal areas of the northern coast of the basin. Sea level forecast is of paramount importance for the management of daily activities and for operating the movable barriers that are presently being built for the protection of the city. In this paper, an EPS (ensemble prediction system) for operational forecasting of storm surge in the northern Adriatic Sea is presented and applied to a 3-month-long period (October-December 2010). The sea level EPS is based on the HYPSE (hydrostatic Padua Sea elevation) model, which is a standard single-layer nonlinear shallow water model, whose forcings (mean sea level pressure and surface wind fields) are provided by the ensemble members of the ECMWF (European Center for Medium-Range Weather Forecasts) EPS. Results are verified against observations at five tide gauges located along the Croatian and Italian coasts of the Adriatic Sea. Forecast uncertainty increases with the predicted value of the storm surge and with the forecast lead time. The EMF (ensemble mean forecast) provided by the EPS has a rms (root mean square) error lower than the DF (deterministic forecast), especially for short (up to 3 days) lead times. Uncertainty for short lead times of the forecast and for small storm surges is mainly caused by uncertainty of the initial condition of the hydrodynamical model. Uncertainty for large lead times and large storm surges is mainly caused by uncertainty in the meteorological forcings. The EPS spread increases with the rms error of the forecast. For large lead times the EPS spread and the forecast error substantially coincide. However, the EPS spread in this study, which does not account for uncertainty in the initial condition, underestimates the error during the early part of the forecast and for small storm surge values. On the contrary, it overestimates the rms error for large surge values. The PF (probability forecast) of the EPS has a clear skill in predicting the actual probability distribution of sea level, and it outperforms simple "dressed" PF methods. A probability estimate based on the single DF is shown to be inadequate. However, a PF obtained with a prescribed Gaussian distribution and centered on the DF value performs very similarly to the EPS-based PF.

Prediction of hemoglobin in blood donors using a latent class mixed-effects transition model.

PubMed

Nasserinejad, Kazem; van Rosmalen, Joost; de Kort, Wim; Rizopoulos, Dimitris; Lesaffre, Emmanuel

2016-02-20

Blood donors experience a temporary reduction in their hemoglobin (Hb) value after donation. At each visit, the Hb value is measured, and a too low Hb value leads to a deferral for donation. Because of the recovery process after each donation as well as state dependence and unobserved heterogeneity, longitudinal data of Hb values of blood donors provide unique statistical challenges. To estimate the shape and duration of the recovery process and to predict future Hb values, we employed three models for the Hb value: (i) a mixed-effects models; (ii) a latent-class mixed-effects model; and (iii) a latent-class mixed-effects transition model. In each model, a flexible function was used to model the recovery process after donation. The latent classes identify groups of donors with fast or slow recovery times and donors whose recovery time increases with the number of donations. The transition effect accounts for possible state dependence in the observed data. All models were estimated in a Bayesian way, using data of new entrant donors from the Donor InSight study. Informative priors were used for parameters of the recovery process that were not identified using the observed data, based on results from the clinical literature. The results show that the latent-class mixed-effects transition model fits the data best, which illustrates the importance of modeling state dependence, unobserved heterogeneity, and the recovery process after donation. The estimated recovery time is much longer than the current minimum interval between donations, suggesting that an increase of this interval may be warranted. Copyright © 2015 John Wiley & Sons, Ltd.

Corrections to the Shapiro Equation used to Predict Sweating and Water Requirements

DTIC Science & Technology

2008-01-01

Nishi, Y., and A. P. Gagge. Effective temperature scale useful for hypobaric and hyperbaric environments. Aviat. Space Environ. Med. 48: 97-107, 1977...time series predictions of specific variables (35). Comparison of the original Shapiro equation predicting sweat loss and water requirements was...40 60 80 100 % O ff (+ ,m od el u nd er pr ed ic ts ;-, ov er pr ed ic ts ) It is clear from Figure 2’s plot of the residual values ( comparison

Predicting Sepsis Risk Using the "Sniffer" Algorithm in the Electronic Medical Record.

PubMed

Olenick, Evelyn M; Zimbro, Kathie S; DʼLima, Gabrielle M; Ver Schneider, Patricia; Jones, Danielle

The Sepsis "Sniffer" Algorithm (SSA) has merit as a digital sepsis alert but should be considered an adjunct to versus an alternative for the Nurse Screening Tool (NST), given lower specificity and positive predictive value. The SSA reduced the risk of incorrectly categorizing patients at low risk for sepsis, detected sepsis high risk in half the time, and reduced redundant NST screens by 70% and manual screening hours by 64% to 72%. Preserving nurse hours expended on manual sepsis alerts may translate into time directed toward other patient priorities.

CA-125 AUC as a predictor for epithelial ovarian cancer relapse.

PubMed

Mano, António; Falcão, Amílcar; Godinho, Isabel; Santos, Jorge; Leitão, Fátima; de Oliveira, Carlos; Caramona, Margarida

2008-01-01

The aim of the present work was to evaluate the usefulness of CA-125 normalized in time area under the curve (CA-125 AUC) to signalise epithelial ovarian cancer relapse. Data from a hundred and eleven patients were submitted to two different approaches based on CA-125 AUC increase values to predict patient relapse. In Criterion A total CA-125 AUC normalized in time value (AUC(i)) was compared with the immediately previous one (AUC(i-1)) using the formulae AUC(i) > or = F * AUC(i-1) (several F values were tested) to find the appropriate close related increment associated to patient relapse. In Criterion B total CA-125 AUC normalised in time was calculated and several cut-off values were correlated with patient relapse prediction capacity. In Criterion A the best accuracy was achieved with a factor (F) of 1.25 (increment of 25% from the previous status), while in Criterion B the best accuracies were achieved with cut-offs of 25, 50, 75 and 100 IU/mL. The mean lead time to relapse achieved with Criterion A was 181 days, while with Criterion B they were, respectively, 131, 111, 63 and 11 days. Based on our results we believe that conjugation and sequential application of both criteria in patient relapse detection should be highly advisable. CA-125 AUC rapid burst in asymptomatic patients should be firstly evaluated using Criterion A with a high accuracy (0.85) and with a substantial mean lead time to relapse (181 days). If a negative answer was obtained then Criterion B should performed to confirm the absence of relapse.

Clinical evaluation of β-tubulin real-time PCR for rapid diagnosis of dermatophytosis, a comparison with mycological methods.

PubMed

Motamedi, Marjan; Mirhendi, Hossein; Zomorodian, Kamiar; Khodadadi, Hossein; Kharazi, Mahboobeh; Ghasemi, Zeinab; Shidfar, Mohammad Reza; Makimura, Koichi

2017-10-01

Following our previous report on evaluation of the beta tubulin real-time PCR for detection of dermatophytosis, this study aimed to compare the real-time PCR assay with conventional methods for the clinical assessment of its diagnostic performance. Samples from a total of 853 patients with suspected dermatophyte lesions were subjected to direct examination (all samples), culture (499 samples) and real-time PCR (all samples). Fungal DNA was extracted directly from clinical samples using a conical steel bullet, followed by purification with a commercial kit and subjected to the Taq-Man probe-based real-time PCR. The study showed that among the 499 specimens for which all three methods were used, 156 (31.2%), 128 (25.6%) and 205 (41.0%) were found to be positive by direct microscopy, culture and real-time PCR respectively. Real-time PCR significantly increased the detection rate of dermatophytes compared with microscopy (288 vs 229) with 87% concordance between the two methods. The sensitivity, specificity, positive predictive value, and negative predictive value of the real-time PCR was 87.5%, 85%, 66.5% and 95.2% respectively. Although real-time PCR performed better on skin than on nail samples, it should not yet fully replace conventional diagnosis. © 2017 Blackwell Verlag GmbH.

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.

Evaluation of mRNA markers for estimating blood deposition time: Towards alibi testing from human forensic stains with rhythmic biomarkers.

PubMed

Lech, Karolina; Liu, Fan; Ackermann, Katrin; Revell, Victoria L; Lao, Oscar; Skene, Debra J; Kayser, Manfred

2016-03-01

Determining the time a biological trace was left at a scene of crime reflects a crucial aspect of forensic investigations as - if possible - it would permit testing the sample donor's alibi directly from the trace evidence, helping to link (or not) the DNA-identified sample donor with the crime event. However, reliable and robust methodology is lacking thus far. In this study, we assessed the suitability of mRNA for the purpose of estimating blood deposition time, and its added value relative to melatonin and cortisol, two circadian hormones we previously introduced for this purpose. By analysing 21 candidate mRNA markers in blood samples from 12 individuals collected around the clock at 2h intervals for 36h under real-life, controlled conditions, we identified 11 mRNAs with statistically significant expression rhythms. We then used these 11 significantly rhythmic mRNA markers, with and without melatonin and cortisol also analysed in these samples, to establish statistical models for predicting day/night time categories. We found that although in general mRNA-based estimation of time categories was less accurate than hormone-based estimation, the use of three mRNA markers HSPA1B, MKNK2 and PER3 together with melatonin and cortisol generally enhanced the time prediction accuracy relative to the use of the two hormones alone. Our data best support a model that by using these five molecular biomarkers estimates three time categories, i.e. night/early morning, morning/noon, and afternoon/evening with prediction accuracies expressed as AUC values of 0.88, 0.88, and 0.95, respectively. For the first time, we demonstrate the value of mRNA for blood deposition timing and introduce a statistical model for estimating day/night time categories based on molecular biomarkers, which shall be further validated with additional samples in the future. Moreover, our work provides new leads for molecular approaches on time of death estimation using the significantly rhythmic mRNA markers established here. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Validation of the Predictive Value of Modeled Human Chorionic Gonadotrophin Residual Production in Low-Risk Gestational Trophoblastic Neoplasia Patients Treated in NRG Oncology/Gynecologic Oncology Group-174 Phase III Trial.

PubMed

You, Benoit; Deng, Wei; Hénin, Emilie; Oza, Amit; Osborne, Raymond

2016-01-01

In low-risk gestational trophoblastic neoplasia, chemotherapy effect is monitored and adjusted with serum human chorionic gonadotrophin (hCG) levels. Mathematical modeling of hCG kinetics may allow prediction of methotrexate (MTX) resistance, with production parameter "hCGres." This approach was evaluated using the GOG-174 (NRG Oncology/Gynecologic Oncology Group-174) trial database, in which weekly MTX (arm 1) was compared with dactinomycin (arm 2). Database (210 patients, including 78 with resistance) was split into 2 sets. A 126-patient training set was initially used to estimate model parameters. Patient hCG kinetics from days 7 to 45 were fit to: [hCG(time)] = hCG7 * exp(-k * time) + hCGres, where hCGres is residual hCG tumor production, hCG7 is the initial hCG level, and k is the elimination rate constant. Receiver operating characteristic (ROC) analyses defined putative hCGRes predictor of resistance. An 84-patient test set was used to assess prediction validity. The hCGres was predictive of outcome in both arms, with no impact of treatment arm on unexplained variability of kinetic parameter estimates. The best hCGres cutoffs to discriminate resistant versus sensitive patients were 7.7 and 74.0 IU/L in arms 1 and 2, respectively. By combining them, 2 predictive groups were defined (ROC area under the curve, 0.82; sensitivity, 93.8%; specificity, 70.5%). The predictive value of hCGres-based groups regarding resistance was reproducible in test set (ROC area under the curve, 0.81; sensitivity, 88.9%; specificity, 73.1%). Both hCGres and treatment arm were associated with resistance by logistic regression analysis. The early predictive value of the modeled kinetic parameter hCGres regarding resistance seems promising in the GOG-174 study. This is the second positive evaluation of this approach. Prospective validation is warranted.

The Added Value of Collecting Information on Pain Experience When Predicting Time on Benefits for Injured Workers with Back Pain.

PubMed

Steenstra, Ivan A; Franche, Renée-Louise; Furlan, Andrea D; Amick, Ben; Hogg-Johnson, Sheilah

2016-06-01

Objectives Some injured workers with work-related, compensated back pain experience a troubling course in return to work. A prediction tool was developed in an earlier study, using administrative data only. This study explored the added value of worker reported data in identifying those workers with back pain at higher risk of being on benefits for a longer period of time. Methods This was a cohort study of workers with compensated back pain in 2005 in Ontario. Workplace Safety and Insurance Board (WSIB) data was used. As well, we examined the added value of patient-reported prognostic factors obtained from a prospective cohort study. Improvement of model fit was determined by comparing area under the curve (AUC) statistics. The outcome measure was time on benefits during a first workers' compensation claim for back pain. Follow-up was 2 years. Results Among 1442 workers with WSIB data still on full benefits at 4 weeks, 113 were also part of the prospective cohort study. Model fit of an established rule in the smaller dataset of 113 workers was comparable to the fit previously established in the larger dataset. Adding worker rating of pain at baseline improved the rule substantially (AUC = 0.80, 95 % CI 0.68, 0.91 compared to benefit status at 180 days, AUC = 0.88, 95 % CI 0.74, 1.00 compared to benefits status at 360 days). Conclusion Although data routinely collected by workers' compensation boards show some ability to predict prolonged time on benefits, adding information on experienced pain reported by the worker improves the predictive ability of the model from 'fairly good' to 'good'. In this study, a combination of prognostic factors, reported by multiple stakeholders, including the worker, could identify those at high risk of extended duration on disability benefits and in potentially in need of additional support at the individual level.

Optimization of Bleaching Parameters in Refining Process of Kenaf Seed Oil with a Central Composite Design Model.

PubMed

Chew, Sook Chin; Tan, Chin Ping; Nyam, Kar Lin

2017-07-01

Kenaf seed oil has been suggested to be used as nutritious edible oil due to its unique fatty acid composition and nutritional value. The objective of this study was to optimize the bleaching parameters of the chemical refining process for kenaf seed oil, namely concentration of bleaching earth (0.5 to 2.5% w/w), temperature (30 to 110 °C) and time (5 to 65 min) based on the responses of total oxidation value (TOTOX) and color reduction using response surface methodology. The results indicated that the corresponding response surface models were highly statistical significant (P < 0.0001) and sufficient to describe and predict TOTOX value and color reduction with R 2 of 0.9713 and 0.9388, respectively. The optimal parameters in the bleaching stage of kenaf seed oil were: 1.5% w/w of the concentration of bleaching earth, temperature of 70 °C, and time of 40 min. These optimum parameters produced bleached kenaf seed oil with TOTOX value of 8.09 and color reduction of 32.95%. There were no significant differences (P > 0.05) between experimental and predicted values, indicating the adequacy of the fitted models. © 2017 Institute of Food Technologists®.

Application of a time-magnitude prediction model for earthquakes

NASA Astrophysics Data System (ADS)

An, Weiping; Jin, Xueshen; Yang, Jialiang; Dong, Peng; Zhao, Jun; Zhang, He

2007-06-01

In this paper we discuss the physical meaning of the magnitude-time model parameters for earthquake prediction. The gestation process for strong earthquake in all eleven seismic zones in China can be described by the magnitude-time prediction model using the computations of the parameters of the model. The average model parameter values for China are: b = 0.383, c=0.154, d = 0.035, B = 0.844, C = -0.209, and D = 0.188. The robustness of the model parameters is estimated from the variation in the minimum magnitude of the transformed data, the spatial extent, and the temporal period. Analysis of the spatial and temporal suitability of the model indicates that the computation unit size should be at least 4° × 4° for seismic zones in North China, at least 3° × 3° in Southwest and Northwest China, and the time period should be as long as possible.

The value of forecasting key-decision variables for rain-fed farming

NASA Astrophysics Data System (ADS)

Winsemius, Hessel; Werner, Micha

2013-04-01

Rain-fed farmers are highly vulnerable to variability in rainfall. Timely knowledge of the onset of the rainy season, the expected amount of rainfall and the occurrence of dry spells can help rain-fed farmers to plan the cropping season. Seasonal probabilistic weather forecasts may provide such information to farmers, but need to provide reliable forecasts of key variables with which farmers can make decisions. In this contribution, we present a new method to evaluate the value of meteorological forecasts in predicting these key variables. The proposed method measures skill by assessing whether a forecast was useful to this decision. This is done by taking into account the required accuracy of timing of the event to make the decision useful. The method progresses the estimate of forecast skill to forecast value by taking into account the required accuracy that is needed to make the decision valuable, based on the cost/loss ratio of possible decisions. The method is applied over the Limpopo region in Southern Africa. We demonstrate the method using the example of temporary water harvesting techniques. Such techniques require time to construct and must be ready long enough before the occurrence of a dry spell to be effective. The value of the forecasts to the decision used as an example is shown to be highly sensitive to the accuracy in the timing of forecasted dry spells, and the tolerance in the decision to timing error. The skill with which dry spells can be predicted is shown to be higher in some parts of the basin, indicating that these forecasts have higher value for the decision in those parts than in others. Through assessing the skill of forecasting key decision variables to the farmers we show that it is easier to understand if the forecasts have value in reducing risk, or if other adaptation strategies should be implemented.

Predictive model for the growth kinetics of Staphylococcus aureus in raw pork developed using Integrated Pathogen Modeling Program (IPMP) 2013.

PubMed

Lee, Yong Ju; Jung, Byeong Su; Kim, Kee-Tae; Paik, Hyun-Dong

2015-09-01

A predictive model was performed to describe the growth of Staphylococcus aureus in raw pork by using Integrated Pathogen Modeling Program 2013 and a polynomial model as a secondary predictive model. S. aureus requires approximately 180 h to reach 5-6 log CFU/g at 10 °C. At 15 °C and 25 °C, approximately 48 and 20 h, respectively, are required to cause food poisoning. Predicted data using the Gompertz model was the most accurate in this study. For lag time (LT) model, bias factor (Bf) and accuracy factor (Af) values were both 1.014, showing that the predictions were within a reliable range. For specific growth rate (SGR) model, Bf and Af were 1.188 and 1.190, respectively. Additionally, both Bf and Af values of the LT and SGR models were close to 1, indicating that IPMP Gompertz model is more adequate for predicting the growth of S. aureus on raw pork than other models. Copyright © 2015 Elsevier Ltd. All rights reserved.

Academic motivation, self-concept, engagement, and performance in high school: key processes from a longitudinal perspective.

PubMed

Green, Jasmine; Liem, Gregory Arief D; Martin, Andrew J; Colmar, Susan; Marsh, Herbert W; McInerney, Dennis

2012-10-01

The study tested three theoretically/conceptually hypothesized longitudinal models of academic processes leading to academic performance. Based on a longitudinal sample of 1866 high-school students across two consecutive years of high school (Time 1 and Time 2), the model with the most superior heuristic value demonstrated: (a) academic motivation and self-concept positively predicted attitudes toward school; (b) attitudes toward school positively predicted class participation and homework completion and negatively predicted absenteeism; and (c) class participation and homework completion positively predicted test performance whilst absenteeism negatively predicted test performance. Taken together, these findings provide support for the relevance of the self-system model and, particularly, the importance of examining the dynamic relationships amongst engagement factors of the model. The study highlights implications for educational and psychological theory, measurement, and intervention. Copyright © 2012 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Progress in space weather predictions and applications

NASA Astrophysics Data System (ADS)

Lundstedt, H.

The methods of today's predictions of space weather and effects are so much more advanced and yesterday's statistical methods are now replaced by integrated knowledge-based neuro-computing models and MHD methods. Within the ESA Space Weather Programme Study a real-time forecast service has been developed for space weather and effects. This prototype is now being implemented for specific users. Today's applications are not only so many more but also so much more advanced and user-oriented. A scientist needs real-time predictions of a global index as input for an MHD model calculating the radiation dose for EVAs. A power company system operator needs a prediction of the local value of a geomagnetically induced current. A science tourist needs to know whether or not aurora will occur. Soon we might even be able to predict the tropospheric climate changes and weather caused by the space weather.

Development and validation of an electronic phenotyping algorithm for chronic kidney disease

PubMed Central

Nadkarni, Girish N; Gottesman, Omri; Linneman, James G; Chase, Herbert; Berg, Richard L; Farouk, Samira; Nadukuru, Rajiv; Lotay, Vaneet; Ellis, Steve; Hripcsak, George; Peissig, Peggy; Weng, Chunhua; Bottinger, Erwin P

2014-01-01

Twenty-six million Americans are estimated to have chronic kidney disease (CKD) with increased risk for cardiovascular disease and end stage renal disease. CKD is frequently undiagnosed and patients are unaware, hampering intervention. A tool for accurate and timely identification of CKD from electronic medical records (EMR) could improve healthcare quality and identify patients for research. As members of eMERGE (electronic medical records and genomics) Network, we developed an automated phenotyping algorithm that can be deployed to identify rapidly diabetic and/or hypertensive CKD cases and controls in health systems with EMRs It uses diagnostic codes, laboratory results, medication and blood pressure records, and textual information culled from notes. Validation statistics demonstrated positive predictive values of 96% and negative predictive values of 93.3. Similar results were obtained on implementation by two independent eMERGE member institutions. The algorithm dramatically outperformed identification by ICD-9-CM codes with 63% positive and 54% negative predictive values, respectively. PMID:25954398

Development of a predictive limited sampling strategy for estimation of mycophenolic acid area under the concentration time curve in patients receiving concomitant sirolimus or cyclosporine.

PubMed

Figurski, Michal J; Nawrocki, Artur; Pescovitz, Mark D; Bouw, Rene; Shaw, Leslie M

2008-08-01

Limited sampling strategies for estimation of the area under the concentration time curve (AUC) for mycophenolic acid (MPA) co-administered with sirolimus (SRL) have not been previously evaluated. The authors developed and validated 68 regression models for estimation of MPA AUC for two groups of patients, one with concomitant SRL (n = 24) and the second with concomitant cyclosporine (n=14), using various combinations of time points between 0 and 4 hours after drug administration. To provide as robust a model as possible, a dataset-splitting method similar to a bootstrap was used. In this method, the dataset was randomly split in half 100 times. Each time, one half of the data was used to estimate the equation coefficients, and the other half was used to test and validate the models. Final models were obtained by calculating the median values of the coefficients. Substantial differences were found in the pharmacokinetics of MPA between these groups. The mean MPA AUC as well as the standard deviation was much greater in the SRL group, 56.4 +/- 23.5 mg.h/L, compared with 30.4 +/- 11.0 mg.h/L in the cyclosporine group (P < 0.001). Mean maximum concentration was also greater in the SRL group: 16.4 +/- 7.7 mg/L versus 11.7 +/- 7.1mg/L (P < 0.005). The second absorption peak in the pharmacokinetic profile, presumed to result from enterohepatic recycling of glucuronide MPA, was observed in 70% of the profiles in the SRL group and in 35% of profiles from the cyclosporine group. Substantial differences in the predictive performance of the regression models, based on the same time points, were observed between the two groups. The best model for the SRL group was based on 0 (trough) and 40 minutes and 4 hour time points with R2, root mean squared error, and predictive performance values of 0.82, 10.0, and 78%, respectively. In the cyclosporine group, the best model was 0 and 40 minutes and 2 hours, with R2, RMSE, and predictive performance values of 0.86, 4.1, and 83%, respectively. The model with 2 hours as the last time point is also recommended for the SRL group for practical reasons, with the above parameters of 0.77, 11.3, and 69%, respectively.

Parse, simulation, and prediction of NOx emission across the Midwestern United States

NASA Astrophysics Data System (ADS)

Fang, H.; Michalski, G. M.; Spak, S.

2017-12-01

Accurately constraining N emissions in space and time has been a challenge for atmospheric scientists. It has been suggested that 15N isotopes may be a way of tracking N emission sources across various spatial and temporal scales. However, the complexity of multiple N sources that can quickly change in intensity has made this a difficult problem. We have used a SMOKE emission model to parse NOx emission across the Midwestern United States for a one-year simulation. An isotope mass balance methods was used to assign 15N values to road, non-road, point, and area sources. The SMOKE emissions and isotope mass balance were then combined to predict the 15N of NOx emissions (Figure 1). This ^15N of NOx emissions model was then incorporated into CMAQ to assess the role of transport and chemistry would impact the 15N value of NOx due to mixing and removal processes. The predicted 15N value of NOx was compared to those in recent measurements of NOx and atmospheric nitrate.

Freeze-dried, mucoadhesive system for vaginal delivery of the HIV microbicide, dapivirine: optimisation by an artificial neural network.

PubMed

Woolfson, A David; Umrethia, Manish L; Kett, Victoria L; Malcolm, R Karl

2010-03-30

Dapivirine mucoadhesive gels and freeze-dried tablets were prepared using a 3x3x2 factorial design. An artificial neural network (ANN) with multi-layer perception was used to investigate the effect of hydroxypropyl-methylcellulose (HPMC): polyvinylpyrrolidone (PVP) ratio (X1), mucoadhesive concentration (X2) and delivery system (gel or freeze-dried mucoadhesive tablet, X3) on response variables; cumulative release of dapivirine at 24h (Q(24)), mucoadhesive force (F(max)) and zero-rate viscosity. Optimisation was performed by minimising the error between the experimental and predicted values of responses by ANN. The method was validated using check point analysis by preparing six formulations of gels and their corresponding freeze-dried tablets randomly selected from within the design space of contour plots. Experimental and predicted values of response variables were not significantly different (p>0.05, two-sided paired t-test). For gels, Q(24) values were higher than their corresponding freeze-dried tablets. F(max) values for freeze-dried tablets were significantly different (2-4 times greater, p>0.05, two-sided paired t-test) compared to equivalent gels. Freeze-dried tablets having lower values for X1 and higher values for X2 components offered the best compromise between effective dapivirine release, mucoadhesion and viscosity such that increased vaginal residence time was likely to be achieved. Copyright (c) 2009 Elsevier B.V. All rights reserved.

New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs

PubMed Central

Beckman, Robert A.; Chen, Cong

2013-01-01

Predictive biomarkers are important to the future of oncology; they can be used to identify patient populations who will benefit from therapy, increase the value of cancer medicines, and decrease the size and cost of clinical trials while increasing their chance of success. But predictive biomarkers do not always work. When unsuccessful, they add cost, complexity, and time to drug development. This perspective describes phases 2 and 3 development methods that efficiently and adaptively check the ability of a biomarker to predict clinical outcomes. In the end, the biomarker is emphasized to the extent that it can actually predict. PMID:23489587

How important is it to my parents? Transmission of STEM academic values: the role of parents' values and practices and children's perceptions of parental influences

NASA Astrophysics Data System (ADS)

Šimunović, Mara; Reić Ercegovac, Ina; Burušić, Josip

2018-06-01

The success of science education in classroom and out-of-school settings can be influenced by parents' behaviours and STEM-related values. The present study investigated pathways in parent-to-child transmission of STEM (science, technology, engineering, mathematics) values by examining at same time parents' values and behaviours, along with their children's perceptions of these parental influences. The study included 1071 students (Mage = 12.15) and the same number of their parents. Path analysis revealed that children's importance value of the STEM school fields was best explained by their perceptions of parental values and behaviours in STEM. On the other hand, parents' self-reported values and behaviours had a weak effect in predicting children's values, which can be explained by inaccurate children's perceptions of their parents. The results suggest that parents more easily convey beliefs about the utility than the attainment value of STEM. Namely, parents' utility value had a larger effect in predicting children's value, partly mediated through children's perception of parents' encouragement of STEM interests. The study highlights the role of children's perceptions of their parents' beliefs and behaviours and the importance of communicating STEM-related values within the family. Practical implications for parents and science educators are discussed.

Thermal inactivation of H5N1 high pathogenicity avian influenza virus in naturally infected chicken meat.

PubMed

Thomas, Colleen; Swayne, David E

2007-03-01

Thermal inactivation of the H5N1 high pathogenicity avian influenza (HPAI) virus strain A/chicken/Korea/ES/2003 (Korea/03) was quantitatively measured in thigh and breast meat harvested from infected chickens. The Korea/03 titers were recorded as the mean embryo infectious dose (EID50) and were 10(8.0) EID50/g in uncooked thigh samples and 10(7.5) EID50/g in uncooked breast samples. Survival curves were constructed for Korea/03 in chicken thigh and breast meat at 1 degrees C intervals for temperatures of 57 to 61 degrees C. Although some curves had a slightly biphasic shape, a linear model provided a fair-to-good fit at all temperatures, with R2 values of 0.85 to 0.93. Stepwise linear regression revealed that meat type did not contribute significantly to the regression model and generated a single linear regression equation for z-value calculations and D-value predictions for Korea/03 in both meat types. The z-value and the upper limit of the 95% confidence interval for the z-value were 4.64 and 5.32 degrees C, respectively. From the lowest temperature to the highest, the predicted D-values and the upper limits of their 95% prediction intervals (conservative D-values) for 57 to 61 degrees C were 241.2 and 321.1 s, 146.8 and 195.4 s, 89.3 and 118.9 s, 54.4 and 72.4 s, and 33.1 and 44.0 s. D-values and conservative D-values predicted for higher temperatures were 0.28 and 0.50 s for 70 degrees C and 0.041 and 0.073 s for 73.9 degrees C. Calculations with the conservative D-values predicted that cooking chicken meat according to current U.S. Department of Agriculture Food Safety and Inspection Service time-temperature guidelines will inactivate Korea/03 in a heavily contaminated meat sample, such as those tested in this study, with a large margin of safety.

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

The sFlt-1/PlGF ratio associates with prolongation and adverse outcome of pregnancy in women with (suspected) preeclampsia: analysis of a high-risk cohort.

PubMed

Saleh, Langeza; Verdonk, Koen; Jan Danser, A H; Steegers, Eric A P; Russcher, Henk; van den Meiracker, Anton H; Visser, Willy

2016-04-01

To evaluate the additive value of the sFlt-1/PlGF ratio for diagnosing preeclampsia (PE) and predicting prolongation of pregnancy and adverse outcome in a cohort of women with PE or at high risk of PE. Patients with suspected or confirmed clinical PE were recruited. At time of inclusion blood for measurement of sFlt-1and PlGF was taken. Values were determined after delivery. A cut-off ratio of ≥85 was defined as a positive test. A total of 107 patients were included. Of the patients, 62 (58%) met the clinical criteria of PE at time of blood sampling. In 10% of these patients (n=6) the ratio was <85 (false negative), whereas in 7% (n=3) of patients without clinical PE the ratio was ≥85 (false positive), resulting in positive and negative predictive values of 95% and 88% respectively. One patient with false positive ratio developed superimposed PE and 2 developed gestational hypertension, and adverse outcome occurred in all three. An adverse pregnancy outcome was only encountered in 1 of the 6 patients with a false negative ratio. Using a binary regression model with adjustment for gestational age <34 weeks, the adverse outcome risk was 11 times increased on the basis of clinical PE, and 30 times on the basis of an elevated ratio (P=0.036). The additive value of an increased ratio for diagnosing PE is limited since most patients with clinical PE also have a positive ratio. However, an elevated ratio is superior to the clinical diagnosis of PE for predicting an adverse pregnancy outcome. Furthermore, irrespective of clinical PE, a low ratio is inversely correlated with prolongation of pregnancy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Determination of Aerobic Power Through a Specific Test for Taekwondo - A Predictive Equation Model

PubMed Central

Louro, Hugo; Matias, Ricardo; Brito, João; Costa, Aldo M.

2016-01-01

Abstract Our aim was to verify the concurrent validity of a maximal taekwondo specific test (TST) to predict VO2max through an explanatory model. Seventeen elite male taekwondo athletes (age: 17.59 ± 4.34 years; body height: 1.72 ± 6.5 m; body mass: 61.3 ± 8.7 kg) performed two graded maximal exercise tests on different days: a 20 m multistage shuttle run test (SRT) and an incremental TST. We recorded test time, VO2max, ventilation, a heart rate and time to exhaustion. Significant differences were found between observed and estimated VO2max values [F (2, 16) = 5.77, p < 0.01]; post-hoc subgroup analysis revealed the existence of significant differences (p = 0.04) between the estimated VO2max value in the SRT and the observed value recorded in the TST (58.4 ± 6.4 ml/kg/min and 52.6 ± 5.2 ml/kg/min, respectively). Our analysis also revealed a moderate correlation between both testing protocols regarding VO2max (r = 0.70; p = 0.005), test time (r = 0.77; p = 0.02) and ventilation (r = 0.69; p = 0.03). There was no proportional bias in the mean difference (t = -1.04; p = 0.313), and there was a level of agreement between both tests. An equation/model was used to estimate VO2max during the TST based on the mean heart rate, test time, body height and mass, which explained 74.3% of the observed VO2max variability. A moderate correlation was found between the observed and predicted VO2max values in the taekwondo TST (r = 0.74, p = 0.001). Our results suggest that an incremental specific test estimates VO2max of elite taekwondo athletes with acceptable concurrent validity. PMID:28149417

Solar flux forecasting using mutual information with an optimal delay

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Conway, D.; Rokni, M.; Sperling, R.; Roszman, L.; Cooley, J.

1993-01-01

Solar flux F(sub 10.7) directly affects the atmospheric density, thereby changing the lifetime and prediction of satellite orbits. For this reason, accurate forecasting of F(sub 10.7) is crucial for orbit determination of spacecraft. Our attempts to model and forecast F(sub 10.7) uncovered highly entangled dynamics. We concluded that the general lack of predictability in solar activity arises from its nonlinear nature. Nonlinear dynamics allow us to predict F(sub 10.7) more accurately than is possible using stochastic methods for time scales shorter than a characteristic horizon, and with about the same accuracy as using stochastic techniques when the forecasted data exceed this horizon. The forecast horizon is a function of two dynamical invariants: the attractor dimension and the Lyapunov exponent. In recent years, estimation of the attractor dimension reconstructed from a time series has become an important tool in data analysis. In calculating the invariants of the system, the first necessary step is the reconstruction of the attractor for the system from the time-delayed values of the time series. The choice of the time delay is critical for this reconstruction. For an infinite amount of noise-free data, the time delay can, in principle, be chosen almost arbitrarily. However, the quality of the phase portraits produced using the time-delay technique is determined by the value chosen for the delay time. Fraser and Swinney have shown that a good choice for this time delay is the one suggested by Shaw, which uses the first local minimum of the mutual information rather than the autocorrelation function to determine the time delay. This paper presents a refinement of this criterion and applies the refined technique to solar flux data to produce a forecast of the solar activity.

Predictive value of dysregulation profile trajectories in childhood for symptoms of ADHD, anxiety and depression in late adolescence.

PubMed

Wang, B; Brueni, L G; Isensee, C; Meyer, T; Bock, N; Ravens-Sieberer, U; Klasen, F; Schlack, R; Becker, A; Rothenberger, A

2018-06-01

We examined whether there are certain dysregulation profile trajectories in childhood that may predict an elevated risk for mental disorders in later adolescence. Participants (N = 554) were drawn from a representative community sample of German children, 7-11 years old, who were followed over four measurement points (baseline, 1, 2 and 6 years later). Dysregulation profile, derived from the parent report of the Strengths and Difficulties Questionnaire, was measured at the first three measurement points, while symptoms of attention deficit hyperactivity disorder (ADHD), anxiety and depression were assessed at the fourth measurement point. We used latent class growth analysis to investigate developmental trajectories in the development of the dysregulation profile. The predictive value of dysregulation profile trajectories for later ADHD, anxiety and depression was examined by linear regression. For descriptive comparison, the predictive value of a single measurement (baseline) was calculated. Dysregulation profile was a stable trait during childhood. Boys and girls had similar levels of dysregulation profile over time. Two developmental subgroups were identified, namely the low dysregulation profile and the high dysregulation profile trajectory. The group membership in the high dysregulation profile trajectory (n = 102) was best predictive of later ADHD, regardless of an individual's gender and age. It explained 11% of the behavioural variance. For anxiety this was 8.7% and for depression 5.6%, including some gender effects. The single-point measurement was less predictive. An enduring high dysregulation profile in childhood showed some predictive value for psychological functioning 4 years later. Hence, it might be helpful in the preventive monitoring of children at risk.

Estimating tree grades for Southern Appalachian natural forest stands

Treesearch

Jeffrey P. Prestemon

1998-01-01

Log prices can vary significantly by grade: grade 1 logs are often several times the price per unit of grade 3 logs. Because tree grading rules derive from log grading rules, a model that predicts tree grades based on tree and stand-level variables might be useful for predicting stand values. The model could then assist in the modeling of timber supply and in economic...

Disparities between Ophthalmologists and Patients in Estimating Quality of Life Associated with Diabetic Retinopathy

PubMed Central

Zou, Haidong; Xu, Xun; Zhang, Xi

2015-01-01

Background This study aimed to evaluate and compare the utility values associated with diabetic retinopathy (DR) in a sample of Chinese patients and ophthalmologists. Methods Utility values were evaluated by both the time trade-off (TTO) and rating scale (RS) methods for 109 eligible patients with DR and 2 experienced ophthalmologists. Patients were stratified by Snellen best-corrected visual acuity (BCVA) in the better-seeing eye. The correlations between the utility values and general vision-related health status measures were analyzed. These utility values were compared with data from two other studies. Results The mean utility values elicited from the patients themselves with the TTO (0.81; SD 0.10) and RS (0.81; SD 0.11) methods were both statistically lower than the mean utility values assessed by ophthalmologists. Significant predictors of patients’ TTO and RS utility values were both LogMAR BCVA in the affected eye and average weighted LogMAR BCVA. DR grade and duration of visual dysfunction were also variables that significantly predicted patients’ TTO utility values. For ophthalmologists, patients’ LogMAR BCVA in the affected eye and in the better eye were the variables that significantly predicted both the TTO and RS utility values. Patients’ education level was also a variable that significantly predicted RS utility values. Moreover, both diabetic macular edema and employment status were significant predictors of TTO and RS utility values, whether from patients or ophthalmologists. There was no difference in mean TTO utility values compared to our American and Canadian patients. Conclusions DR caused a substantial decrease in Chinese patients’ utility values, and ophthalmologists substantially underestimated its effect on patient quality of life. PMID:26630653

Public-Interest Values and Program Sustainability: Some Implications for Evaluation Practice

ERIC Educational Resources Information Center

Chelimsky, Eleanor

2014-01-01

Evaluating the longer-term sustainability of government programs and policies seems in many ways to go beyond the boundaries of typical evaluation practice. Not only have intervention failures over time been difficult to predict, but the question of sustainability itself tends to fall outside current evaluation thinking, timing and functions. This…

Method for Predicting and Optimizing System Parameters for Electrospinning System

NASA Technical Reports Server (NTRS)

Wincheski, Russell A. (Inventor)

2011-01-01

An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.

Developmental relations between sympathy, moral emotion attributions, moral reasoning, and social justice values from childhood to early adolescence.

PubMed

Daniel, Ella; Dys, Sebastian P; Buchmann, Marlis; Malti, Tina

2014-10-01

This study examined the development of sympathy, moral emotion attributions (MEA), moral reasoning, and social justice values in a representative sample of Swiss children (N = 1273) at 6 years of age (Time 1), 9 years of age (Time 2), and 12 years of age (Time 3). Cross-lagged panel analyses revealed that sympathy predicted subsequent increases in MEA and moral reasoning, but not vice versa. In addition, sympathy and moral reasoning at 6 and 9 years of age were associated with social justice values at 12 years of age. The results point to increased integration of affect and cognition in children's morality from middle childhood to early adolescence, as well as to the role of moral development in the emergence of social justice values. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Delayed risk stratification, to include the response to initial treatment (surgery and radioiodine ablation), has better outcome predictivity in differentiated thyroid cancer patients.

PubMed

Castagna, Maria Grazia; Maino, Fabio; Cipri, Claudia; Belardini, Valentina; Theodoropoulou, Alexandra; Cevenini, Gabriele; Pacini, Furio

2011-09-01

After initial treatment, differentiated thyroid cancer (DTC) patients are stratified as low and high risk based on clinical/pathological features. Recently, a risk stratification based on additional clinical data accumulated during follow-up has been proposed. To evaluate the predictive value of delayed risk stratification (DRS) obtained at the time of the first diagnostic control (8-12 months after initial treatment). We reviewed 512 patients with DTC whose risk assessment was initially defined according to the American (ATA) and European Thyroid Association (ETA) guidelines. At the time of the first control, 8-12 months after initial treatment, patients were re-stratified according to their clinical status: DRS. Using DRS, about 50% of ATA/ETA intermediate/high-risk patients moved to DRS low-risk category, while about 10% of ATA/ETA low-risk patients moved to DRS high-risk category. The ability of the DRS to predict the final outcome was superior to that of ATA and ETA. Positive and negative predictive values for both ATA (39.2 and 90.6% respectively) and ETA (38.4 and 91.3% respectively) were significantly lower than that observed with the DRS (72.8 and 96.3% respectively, P<0.05). The observed variance in predicting final outcome was 25.4% for ATA, 19.1% for ETA, and 62.1% for DRS. Delaying the risk stratification of DTC patients at a time when the response to surgery and radioiodine ablation is evident allows to better define individual risk and to better modulate the subsequent follow-up.

Changes in beta cell function during the proximate post-diagnosis period in persons with type 1 diabetes.

PubMed

DiMeglio, Linda A; Cheng, Peiyao; Beck, Roy W; Kollman, Craig; Ruedy, Katrina J; Slover, Robert; Aye, Tandy; Weinzimer, Stuart A; Bremer, Andrew A; Buckingham, Bruce

2016-06-01

Prior studies examining beta-cell preservation in type 1 diabetes have predominantly assessed stimulated C-peptide concentrations approximately 10 wk after diagnosis. We examined whether earlier assessments might aid in prediction of beta cell function over time. Using data from a multi-center randomized trial assessing the effect of intensive diabetes management initiated within 1 wk of diagnosis, we assessed which clinical factors predicted 90-min mixed-meal tolerance test (MMTT) stimulated C-peptide values obtained 2 and 6 wk after diagnosis. We also studied associations of these factors with C-peptide values at 1- and 2-year post-diagnosis. Data from intervention and control groups were pooled. Among 67 study participants (mean age 13.3 ± 5.7 yr, range 7.8-45.7 yr) in multivariable analyses, C-peptide increased from baseline to 2 wks and then 6 wk. C-peptide levels at these times were significantly correlated with 1- and 2-yr C-peptide concentrations (all p < 0.001), with the strongest observed associations between 6-wk C-peptide and the 1- and 2-yr values (r = 0.66 and r = 0.61, respectively). In multivariable analyses, greater baseline and 6-wk C-peptide, and older age independently predicted greater 1- and 2-yr C-peptide concentrations. C-peptide assessments close to diagnosis were predictive of subsequent C-peptide production. Our data demonstrate a clear increase in C-peptide over the initial 6 wk after diabetes diagnosis followed by a plateau. Our data do not suggest that MMTT assessments performed closer to diagnosis than 6 wk would improve prediction of subsequent residual beta cell function. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.

PubMed

Hidalgo, J Ignacio; Colmenar, J Manuel; Kronberger, Gabriel; Winkler, Stephan M; Garnica, Oscar; Lanchares, Juan

2017-08-08

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.

Prediction of Chemoresistance in Women Undergoing Neo-Adjuvant Chemotherapy for Locally Advanced Breast Cancer: Volumetric Analysis of First-Order Textural Features Extracted from Multiparametric MRI

PubMed Central

Losio, C.; Della Corte, A.; Venturini, E.; Ambrosi, A.; Panizza, P.; De Cobelli, F.

2018-01-01

Purpose To assess correlations between volumetric first-order texture parameters on baseline MRI and pathological response after neoadjuvant chemotherapy (NAC) for locally advanced breast cancer (BC). Materials and Methods 69 patients with locally advanced BC candidate to neoadjuvant chemotherapy underwent MRI within 4 weeks from the start of therapeutic regimen. T2, DWI, and DCE sequences were analyzed and maps were generated for Apparent Diffusion Coefficient (ADC), T2 signal intensity, and the following dynamic parameters: k-trans, peak enhancement, area under curve (AUC), time to maximal enhancement (TME), wash-in rate, and washout rate. Volumetric analysis of these parameters was performed, yielding a histogram analysis including first-order texture kinetics (percentiles, maximum value, minimum value, range, standard deviation, mean, median, mode, skewness, and kurtosis). Finally, correlations between these values and response to NAC (evaluated on the surgical specimen according to RECIST 1.1 criteria) were assessed. Results Out of 69 tumors, 33 (47.8%) achieved complete pathological response, 26 (37.7%) partial response, and 10 (14.5%) no response. Higher levels of AUCmax (p value = 0.0338), AUCrange (p value = 0.0311), and TME75 (p value = 0.0452) and lower levels of washout10 (p value = 0.0417), washout20 (p value = 0.0138), washout25 (p value = 0.0114), and washout30 (p value = 0.05) were predictive of noncomplete response. Conclusion Histogram-derived texture analysis of MRI images allows finding quantitative parameters predictive of nonresponse to NAC in women affected by locally advanced BC. PMID:29853811

Prediction of Chemoresistance in Women Undergoing Neo-Adjuvant Chemotherapy for Locally Advanced Breast Cancer: Volumetric Analysis of First-Order Textural Features Extracted from Multiparametric MRI.

PubMed

Panzeri, M M; Losio, C; Della Corte, A; Venturini, E; Ambrosi, A; Panizza, P; De Cobelli, F

2018-01-01

To assess correlations between volumetric first-order texture parameters on baseline MRI and pathological response after neoadjuvant chemotherapy (NAC) for locally advanced breast cancer (BC). 69 patients with locally advanced BC candidate to neoadjuvant chemotherapy underwent MRI within 4 weeks from the start of therapeutic regimen. T2, DWI, and DCE sequences were analyzed and maps were generated for Apparent Diffusion Coefficient (ADC), T2 signal intensity, and the following dynamic parameters: k -trans, peak enhancement, area under curve (AUC), time to maximal enhancement (TME), wash-in rate, and washout rate. Volumetric analysis of these parameters was performed, yielding a histogram analysis including first-order texture kinetics (percentiles, maximum value, minimum value, range, standard deviation, mean, median, mode, skewness, and kurtosis). Finally, correlations between these values and response to NAC (evaluated on the surgical specimen according to RECIST 1.1 criteria) were assessed. Out of 69 tumors, 33 (47.8%) achieved complete pathological response, 26 (37.7%) partial response, and 10 (14.5%) no response. Higher levels of AUCmax ( p value = 0.0338), AUCrange ( p value = 0.0311), and TME 75 ( p value = 0.0452) and lower levels of washout 10 ( p value = 0.0417), washout 20 ( p value = 0.0138), washout 25 ( p value = 0.0114), and washout 30 ( p value = 0.05) were predictive of noncomplete response. Histogram-derived texture analysis of MRI images allows finding quantitative parameters predictive of nonresponse to NAC in women affected by locally advanced BC.

[Establishing a noninvasive prediction model for type 2 diabetes mellitus based on a rural Chinese population].

PubMed

Zhang, H Y; Shi, W H; Zhang, M; Yin, L; Pang, C; Feng, T P; Zhang, L; Ren, Y C; Wang, B Y; Yang, X Y; Zhou, J M; Han, C Y; Zhao, Y; Zhao, J Z; Hu, D S

2016-05-01

To provide a noninvasive type 2 diabetes mellitus (T2DM) prediction model for a rural Chinese population. From July to August, 2007 and July to August, 2008, a total of 20 194 participants aged ≥18 years were selected by cluster sampling technique from a rural population in two townships of Henan province, China. Data were collected by questionnaire interview, anthropometric measurement, and fasting plasma glucose and lipid profile examination. A total 17 265 participants were followed up from July to August, 2013 and July to October, 2014. Finally, 12 285 participants were selected for analysis. Data for these participants were randomly divided into a derivation group (derivation dataset, n= 6 143) and validation group (validation dataset, n=6 142) by 1∶1, respectively. Randomization was carried out by the use of computer-generated random numbers. A Cox regression model was used to analyze risk factors of T2DM in the derivation dataset. A T2DM prediction model was established by multiplying β by 10 for each significant variable. After the total score was calculated by the model, analysis of the receiver operating characteristic (ROC) curve was performed. The area under the ROC curve (AUC) was used for evaluating model predictability. Furthermore, the model's predictability was validated in the validation dataset and compared with the Finnish Diabetes Risk Score (FINDRISC) model. A total 779 of 12 285 participants developed T2DM during the 6-year study period. The incidence rate was 6.12% in the derivation dataset (n=376) and 6.56% in the validation dataset (n=403). The difference was not statistically significant (χ(2)=1.00, P=0.316). A total of four noninvasive T2DM prediction models were established using the Cox regression model. The ROCs of the risk score calculated by the prediction models indicated that the AUCs of these models were similar (0.67-0.70). The AUC and Youden index of model 4 was the highest. The optimal cut-off value, sensitivity, specificity, and Youden index were scores of 25, 65.96%, 66.47%, and 0.32, respectively. Age, sleep time, BMI, waist circumference, and hypertension were selected as predictive variables. Using age<30 years as reference, β values were 1.07, 1.58, and 1.67 and assigned scores were 11, 16, and 17 for age groups 30-44, 45-59, and ≥60 years, respectively. Using sleep time<8.0 h/d as reference, the β value and assigned score were 0.27 and 3, respectively, for sleep time ≥10.0 h/d. Using BMI 18.5-23.9 kg/m(2) as reference, β values were 0.53 and 1.00 and assigned scores 5 and 10, respectively, for BMI 24.0-27.9 kg/m(2), and ≥28.0 kg/m(2). Using waist circumference <85 cm for males/< 80 cm for females as reference, β values were 0.44 and 0.65 and assigned scores 4 and 7, respectively, for 85 cm ≤ waist circumference <90 cm for males/80 cm≤ waist circumference <85 cm for females, and waist circumference ≥90 cm for males/≥85 cm for females. Using nonhypertension as reference, the respective β value and assigned score were 0.34 and 3 for hypertension. The AUC performance of this model and the FINDRISC model was 0.66 and 0.64 (P=0.135), respectively, in the validation dataset. Based on this cohort study, a noninvasive prediction model that included age, sleep time, BMI, waist circumference, and hypertension was established, which is equivalent to the FINDRISC model and applicable to a rural Chinese population.

Linear prediction and single-channel recording.

PubMed

Carter, A A; Oswald, R E

1995-08-01

The measurement of individual single-channel events arising from the gating of ion channels provides a detailed data set from which the kinetic mechanism of a channel can be deduced. In many cases, the pattern of dwells in the open and closed states is very complex, and the kinetic mechanism and parameters are not easily determined. Assuming a Markov model for channel kinetics, the probability density function for open and closed time dwells should consist of a sum of decaying exponentials. One method of approaching the kinetic analysis of such a system is to determine the number of exponentials and the corresponding parameters which comprise the open and closed dwell time distributions. These can then be compared to the relaxations predicted from the kinetic model to determine, where possible, the kinetic constants. We report here the use of a linear technique, linear prediction/singular value decomposition, to determine the number of exponentials and the exponential parameters. Using simulated distributions and comparing with standard maximum-likelihood analysis, the singular value decomposition techniques provide advantages in some situations and are a useful adjunct to other single-channel analysis techniques.

Acoustic energy relations in Mudejar-Gothic churches.

PubMed

Zamarreño, Teófilo; Girón, Sara; Galindo, Miguel

2007-01-01

Extensive objective energy-based parameters have been measured in 12 Mudejar-Gothic churches in the south of Spain. Measurements took place in unoccupied churches according to the ISO-3382 standard. Monoaural objective measures in the 125-4000 Hz frequency range and in their spatial distributions were obtained. Acoustic parameters: clarity C80, definition D50, sound strength G and center time Ts have been deduced using impulse response analysis through a maximum length sequence measurement system in each church. These parameters spectrally averaged according to the most extended criteria in auditoria in order to consider acoustic quality were studied as a function of source-receiver distance. The experimental results were compared with predictions given by classical and other existing theoretical models proposed for concert halls and churches. An analytical semi-empirical model based on the measured values of the C80 parameter is proposed in this work for these spaces. The good agreement between predicted values and experimental data for definition, sound strength, and center time in the churches analyzed shows that the model can be used for design predictions and other purposes with reasonable accuracy.

Improved protocol and data analysis for accelerated shelf-life estimation of solid dosage forms.

PubMed

Waterman, Kenneth C; Carella, Anthony J; Gumkowski, Michael J; Lukulay, Patrick; MacDonald, Bruce C; Roy, Michael C; Shamblin, Sheri L

2007-04-01

To propose and test a new accelerated aging protocol for solid-state, small molecule pharmaceuticals which provides faster predictions for drug substance and drug product shelf-life. The concept of an isoconversion paradigm, where times in different temperature and humidity-controlled stability chambers are set to provide a critical degradant level, is introduced for solid-state pharmaceuticals. Reliable estimates for temperature and relative humidity effects are handled using a humidity-corrected Arrhenius equation, where temperature and relative humidity are assumed to be orthogonal. Imprecision is incorporated into a Monte-Carlo simulation to propagate the variations inherent in the experiment. In early development phases, greater imprecision in predictions is tolerated to allow faster screening with reduced sampling. Early development data are then used to design appropriate test conditions for more reliable later stability estimations. Examples are reported showing that predicted shelf-life values for lower temperatures and different relative humidities are consistent with the measured shelf-life values at those conditions. The new protocols and analyses provide accurate and precise shelf-life estimations in a reduced time from current state of the art.

Genomic selection for slaughter age in pigs using the Cox frailty model.

PubMed

Santos, V S; Martins Filho, S; Resende, M D V; Azevedo, C F; Lopes, P S; Guimarães, S E F; Glória, L S; Silva, F F

2015-10-19

The aim of this study was to compare genomic selection methodologies using a linear mixed model and the Cox survival model. We used data from an F2 population of pigs, in which the response variable was the time in days from birth to the culling of the animal and the covariates were 238 markers [237 single nucleotide polymorphism (SNP) plus the halothane gene]. The data were corrected for fixed effects, and the accuracy of the method was determined based on the correlation of the ranks of predicted genomic breeding values (GBVs) in both models with the corrected phenotypic values. The analysis was repeated with a subset of SNP markers with largest absolute effects. The results were in agreement with the GBV prediction and the estimation of marker effects for both models for uncensored data and for normality. However, when considering censored data, the Cox model with a normal random effect (S1) was more appropriate. Since there was no agreement between the linear mixed model and the imputed data (L2) for the prediction of genomic values and the estimation of marker effects, the model S1 was considered superior as it took into account the latent variable and the censored data. Marker selection increased correlations between the ranks of predicted GBVs by the linear and Cox frailty models and the corrected phenotypic values, and 120 markers were required to increase the predictive ability for the characteristic analyzed.

Predictive value of sperm morphology and progressively motile sperm count for pregnancy outcomes in intrauterine insemination.

PubMed

Lemmens, Louise; Kos, Snjezana; Beijer, Cornelis; Brinkman, Jacoline W; van der Horst, Frans A L; van den Hoven, Leonie; Kieslinger, Dorit C; van Trooyen-van Vrouwerff, Netty J; Wolthuis, Albert; Hendriks, Jan C M; Wetzels, Alex M M

2016-06-01

To investigate the value of sperm parameters to predict an ongoing pregnancy outcome in couples treated with intrauterine insemination (IUI), during a methodologically stable period of time. Retrospective, observational study with logistic regression analyses. University hospital. A total of 1,166 couples visiting the fertility laboratory for their first IUI episode, including 4,251 IUI cycles. None. Sperm morphology, total progressively motile sperm count (TPMSC), and number of inseminated progressively motile spermatozoa (NIPMS); odds ratios (ORs) of the sperm parameters after the first IUI cycle and the first finished IUI episode; discriminatory accuracy of the multivariable model. None of the sperm parameters was of predictive value for pregnancy after the first IUI cycle. In the first finished IUI episode, a positive relationship was found for ≤4% of morphologically normal spermatozoa (OR 1.39) and a moderate NIPMS (5-10 million; OR 1.73). Low NIPMS showed a negative relation (≤1 million; OR 0.42). The TPMSC had no predictive value. The multivariable model (i.e., sperm morphology, NIPMS, female age, male age, and the number of cycles in the episode) had a moderate discriminatory accuracy (area under the curve 0.73). Intrauterine insemination is especially relevant for couples with moderate male factor infertility (sperm morphology ≤4%, NIPMS 5-10 million). In the multivariable model, however, the predictive power of these sperm parameters is rather low. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Time-to-positivity, type of culture media and oxidase test performed on positive blood culture vials to predict Pseudomonas aeruginosa in patients with Gram-negative bacilli bacteraemia.

PubMed

Cobos-Triguero, N; Zboromyrska, Y; Morata, L; Alejo, I; De La Calle, C; Vergara, A; Cardozo, C; Arcas, M P; Soriano, A; Marco, F; Mensa, J; Almela, M; Martínez, J A

2017-02-01

The aim of this study was to determine the usefulness of oxidase test and time-to-positivity (TTP) in aerobic and anaerobic blood culture vials to detect the presence of Pseudomonas aeruginosa in patients with Gram-negative bacilli (GNB) bacteraemia. TTP was recorded for each aerobic and anaerobic blood culture vial of monomicrobial bacteraemia due to GNB. Oxidase test was performed in a pellet of the centrifuged content of the positive blood culture. An algorithm was developed in order to perform the oxidase test efficiently taking into account TTP and type of vial. A total of 341 episodes of GNB bacteraemia were analysed. Sensitivity, specificity, positive predictive value and negative predictive value of the oxidase test performed on positive vials with GNB to predict P. aeruginosa were 95%, 99%, 91%, and 99%, respectively. When growth was first or exclusively detected in anaerobic vials, P. aeruginosa was never identified hence the performance of the oxidase test could be avoided. When growth was only or first detected in aerobic vials, a TTP≥8h predicted P. aeruginosa in 37% or cases (63 of 169), therefore oxidase test is highly recommended. Oxidase test performed onto positive blood culture vials previously selected by TTP and type of vials is an easy and inexpensive way to predict P. aeruginosa. In most cases, this can lead to optimization of treatment in less than 24 hours.

The role of parents in the ontogeny of achievement-related motivation and behavioral choices.

PubMed

Simpkins, Sandra D

2015-06-01

Parents believe what they do matters. But, how does it matter? How do parents' beliefs about their children early on translate into the choices those children make as adolescents? The Eccles' expectancy–value model asserts that parents' beliefs about their children during childhood predict adolescents' achievement-related choices through a sequence of processes that operate in a cumulative, cascading fashion over time. Specifically, parents' beliefs predict parents' behaviors that predict their children's motivational beliefs. Those beliefs predict children's subsequent choices. Using data from the Childhood and Beyond Study (92% European American; N = 723), we tested these predictions in the activity domains of sports, instrumental music, mathematics, and reading across a 12-year period. In testing these predictions, we looked closely at the idea of reciprocal influences and at the role of child gender as a moderator. The cross-lagged models generally supported the bidirectional influences described in Eccles' expectancy-value model. Furthermore, the findings demonstrated that: (a) these relations were stronger in the leisure domains than in the academic domains, (b) these relations did not consistently vary based on youth gender, (c) parents were stronger predictors of their children's beliefs than vice versa, and (d) adolescents' beliefs were stronger predictors of their behaviors than the reverse. The findings presented in this monograph extend our understanding of the complexity of families, developmental processes that unfold over time, and the extent to which these processes are universal across domains and child gender.

Simulation of hydrocephalus condition in infant head

NASA Astrophysics Data System (ADS)

Wijayanti, Erna; Arif, Idam

2014-03-01

Hydrocephalus is a condition of an excessive of cerebrospinal fluid in brain. In this paper, we try to simulate the behavior of hydrocephalus conditions in infant head by using a hydro-elastic model which is combined with orthotropic elastic skull and with the addition of suture that divide the skull into two lobes. The model then gives predictions for the case of stenosis aqueduct by varying the cerebral aqueduct diameter, time constant and brain elastic modulus. The hydrocephalus condition which is shown by the significant value of ventricle displacement, as the result shows, is occurred when the aqueduct is as resistant as brain parenchyma for the flow of cerebrospinal fluid. The decrement of brain elastic modulus causes brain parenchyma displacement value approach ventricle displacement value. The smaller of time constant value causes the smaller value of ventricle displacement.

Correlating Structural Order with Structural Rearrangement in Dusty Plasma Liquids: Can Structural Rearrangement be Predicted by Static Structural Information?

NASA Astrophysics Data System (ADS)

Su, Yen-Shuo; Liu, Yu-Hsuan; I, Lin

2012-11-01

Whether the static microstructural order information is strongly correlated with the subsequent structural rearrangement (SR) and their predicting power for SR are investigated experimentally in the quenched dusty plasma liquid with microheterogeneities. The poor local structural order is found to be a good alarm to identify the soft spot and predict the short term SR. For the site with good structural order, the persistent time for sustaining the structural memory until SR has a large mean value but a broad distribution. The deviation of the local structural order from that averaged over nearest neighbors serves as a good second alarm to further sort out the short time SR sites. It has the similar sorting power to that using the temporal fluctuation of the local structural order over a small time interval.

Comparison of the Diagnostic Value Between Real-Time Reverse Transcription-Polymerase Chain Reaction Assay and Histopathologic Examination in Sentinel Lymph Nodes for Patients With Gastric Carcinoma.

PubMed

Kwak, Yoonjin; Nam, Soo Kyung; Shin, Eun; Ahn, Sang-Hoon; Lee, Hee Eun; Park, Do Joong; Kim, Woo Ho; Kim, Hyung-Ho; Lee, Hye Seung

2016-05-01

Sentinel lymph node (SLN)-based diagnosis in gastric cancers has shown varied sensitivities and false-negative rates in several studies. Application of the reverse transcription-polymerase chain reaction (RT-PCR) in SLN diagnosis has recently been proposed. A total of 155 SLNs from 65 patients with cT1-2, N0 gastric cancer were examined. The histopathologic results were compared with results obtained by real-time RT-PCR for detecting molecular RNA (mRNA) of cytokeratin (CK)19, carcinoembryonic antigen (CEA), and CK20. The sensitivity and specificity of the multiple marker RT-PCR assay standardized against the results of the postoperative histological examination were 0.778 (95% confidence interval [CI], 0.577-0.914) and 0.781 (95% CI, 0.700-0.850), respectively. In comparison, the sensitivity and specificity of intraoperative diagnosis were 0.819 (95% CI, 0.619-0.937) and 1.000 (95% CI, 0.972-1.000), respectively. The positive predictive value of the multiple-marker RT-PCR assay was 0.355 (95% CI, 0.192-0.546) for predicting non-SLN metastasis, which was lower than that of intraoperative diagnosis (0.813, 95% CI, 0.544-0.960). The real-time RT-PCR assay could detect SLN metastasis in gastric cancer. However, the predictive value of the real-time RT-PCR assay was lower than that of precise histopathologic examination and did not outweigh that of our intraoperative SLN diagnosis. © American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The Satellite Clock Bias Prediction Method Based on Takagi-Sugeno Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Cai, C. L.; Yu, H. G.; Wei, Z. C.; Pan, J. D.

2017-05-01

The continuous improvement of the prediction accuracy of Satellite Clock Bias (SCB) is the key problem of precision navigation. In order to improve the precision of SCB prediction and better reflect the change characteristics of SCB, this paper proposes an SCB prediction method based on the Takagi-Sugeno fuzzy neural network. Firstly, the SCB values are pre-treated based on their characteristics. Then, an accurate Takagi-Sugeno fuzzy neural network model is established based on the preprocessed data to predict SCB. This paper uses the precise SCB data with different sampling intervals provided by IGS (International Global Navigation Satellite System Service) to realize the short-time prediction experiment, and the results are compared with the ARIMA (Auto-Regressive Integrated Moving Average) model, GM(1,1) model, and the quadratic polynomial model. The results show that the Takagi-Sugeno fuzzy neural network model is feasible and effective for the SCB short-time prediction experiment, and performs well for different types of clocks. The prediction results for the proposed method are better than the conventional methods obviously.

A statistical forecast model using the time-scale decomposition technique to predict rainfall during flood period over the middle and lower reaches of the Yangtze River Valley

NASA Astrophysics Data System (ADS)

Hu, Yijia; Zhong, Zhong; Zhu, Yimin; Ha, Yao

2018-04-01

In this paper, a statistical forecast model using the time-scale decomposition method is established to do the seasonal prediction of the rainfall during flood period (FPR) over the middle and lower reaches of the Yangtze River Valley (MLYRV). This method decomposites the rainfall over the MLYRV into three time-scale components, namely, the interannual component with the period less than 8 years, the interdecadal component with the period from 8 to 30 years, and the interdecadal component with the period larger than 30 years. Then, the predictors are selected for the three time-scale components of FPR through the correlation analysis. At last, a statistical forecast model is established using the multiple linear regression technique to predict the three time-scale components of the FPR, respectively. The results show that this forecast model can capture the interannual and interdecadal variation of FPR. The hindcast of FPR during 14 years from 2001 to 2014 shows that the FPR can be predicted successfully in 11 out of the 14 years. This forecast model performs better than the model using traditional scheme without time-scale decomposition. Therefore, the statistical forecast model using the time-scale decomposition technique has good skills and application value in the operational prediction of FPR over the MLYRV.

Optimizing the learning rate for adaptive estimation of neural encoding models

PubMed Central

2018-01-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069

Optimizing the learning rate for adaptive estimation of neural encoding models.

PubMed

Hsieh, Han-Lin; Shanechi, Maryam M

2018-05-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

Assessment of general movements and heart rate variability in prediction of neurodevelopmental outcome in preterm infants.

PubMed

Dimitrijević, Lidija; Bjelaković, Bojko; Čolović, Hristina; Mikov, Aleksandra; Živković, Vesna; Kocić, Mirjana; Lukić, Stevo

2016-08-01

Adverse neurologic outcome in preterm infants could be associated with abnormal heart rate (HR) characteristics as well as with abnormal general movements (GMs) in the 1st month of life. To demonstrate to what extent GMs assessment can predict neurological outcome in preterm infants in our clinical setting; and to assess the clinical usefulness of time-domain indices of heart rate variability (HRV) in improving predictive value of poor repertoire (PR) GMs in writhing period. Qualitative assessment of GMs at 1 and 3 months corrected age; 24h electrocardiography (ECG) recordings and analyzing HRV at 1 month corrected age. Seventy nine premature infants at risk of neurodevelopmental impairments were included prospectively. Neurodevelopmental outcome was assessed at the age of 2 years corrected. Children were classified as having normal neurodevelopmental status, minor neurologic dysfunction (MND), or cerebral palsy (CP). We found that GMs in writhing period (1 month corrected age) predicted CP at 2 years with sensitivity of 100%, and specificity of 72.1%. Our results demonstrated the excellent predictive value of cramped synchronized (CS) GMs, but not of PR pattern. Analyzing separately a group of infants with PR GMs we found significantly lower values of HRV parameters in infants who later developed CP or MND vs. infants with PR GMs who had normal outcome. The quality of GMs was predictive for neurodevelopmental outcome at 2 years. Prediction of PR GMs was significantly enhanced with analyzing HRV parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Self-rated health and mortality: could clinical and performance-based measures of health and functioning explain the association?

PubMed

Lyyra, Tiina-Mari; Heikkinen, Eino; Lyyra, Anna-Liisa; Jylhä, Marja

2006-01-01

It is well established that self-rated health (SRH) predicts mortality even when other indicators of health status are taken into account. It has been suggested that SRH measures a wide array of mortality-related physiological and pathological characteristics not captured by the covariates included in the analyses. Our aim was to test this hypothesis by examining the predictive value of SRH on mortality controlling for different measurements of body structure, performance-based functioning and diagnosed diseases with a population-based, prospective study over an 18-year follow-up. Subjects consisted of 257 male residents of the city of Jyväskylä, central Finland, aged 51-55 and 71-75 years. Among the 71-75-year-olds the association between SRH and mortality was weaker over the longer compared to shorter follow-up period. In the multivariate Cox regression models with an 18-year follow-up time for middle-aged and a10-year follow-up time for older men, SRH predicted mortality even when the anthropometrics, clinical chemistry and performance-based measures of functioning were controlled for, but not when the number of chronic diseases was included. Although our results confirm the hypothesis that the predictive value of SRH can be explained by diagnosed diseases, its predictive power remained, when the clinical and performance-based measures of health and functioning were controlled.

Listening In on the Past: What Can Otolith δ18O Values Really Tell Us about the Environmental History of Fishes?

PubMed Central

Darnaude, Audrey M.; Sturrock, Anna; Trueman, Clive N.; Mouillot, David; EIMF; Campana, Steven E.; Hunter, Ewan

2014-01-01

Oxygen isotope ratios from fish otoliths are used to discriminate marine stocks and reconstruct past climate, assuming that variations in otolith δ18O values closely reflect differences in temperature history of fish when accounting for salinity induced variability in water δ18O. To investigate this, we exploited the environmental and migratory data gathered from a decade using archival tags to study the behaviour of adult plaice (Pleuronectes platessa L.) in the North Sea. Based on the tag-derived monthly distributions of the fish and corresponding temperature and salinity estimates modelled across three consecutive years, we first predicted annual otolith δ18O values for three geographically discrete offshore sub-stocks, using three alternative plausible scenarios for otolith growth. Comparison of predicted vs. measured annual δ18O values demonstrated >96% correct prediction of sub-stock membership, irrespective of the otolith growth scenario. Pronounced inter-stock differences in δ18O values, notably in summer, provide a robust marker for reconstructing broad-scale plaice distribution in the North Sea. However, although largely congruent, measured and predicted annual δ18O values of did not fully match. Small, but consistent, offsets were also observed between individual high-resolution otolith δ18O values measured during tag recording time and corresponding δ18O predictions using concomitant tag-recorded temperatures and location-specific salinity estimates. The nature of the shifts differed among sub-stocks, suggesting specific vital effects linked to variation in physiological response to temperature. Therefore, although otolith δ18O in free-ranging fish largely reflects environmental temperature and salinity, we counsel prudence when interpreting otolith δ18O data for stock discrimination or temperature reconstruction until the mechanisms underpinning otolith δ18O signature acquisition, and associated variation, are clarified. PMID:25279667

A comparison of methods for converting DCE values onto the full health-dead QALY scale.

PubMed

Rowen, Donna; Brazier, John; Van Hout, Ben

2015-04-01

Preference elicitation techniques such as time trade-off (TTO) and standard gamble (SG) receive criticism for their complexity and difficulties of use. Ordinal techniques such as discrete choice experiment (DCE) are arguably easier to understand but generate values that are not anchored onto the full health-dead 1-0 quality-adjusted life-year (QALY) scale required for use in economic evaluation. This article compares existing methods for converting modeled DCE latent values onto the full health-dead QALY scale: 1) anchoring DCE values using dead as valued in the DCE and 2) anchoring DCE values using TTO value for worst state to 2 new methods: 3) mapping DCE values onto TTO and 4) combining DCE and TTO data in a hybrid model. Models are compared using their ability to predict mean TTO health state values. We use postal DCE data (n = 263) and TTO data (n = 307) collected by interview in a general population valuation study of an asthma condition-specific measure (AQL-5D). New methods 3 and 4 using mapping and hybrid models are better able to predict mean TTO health state values (mean absolute difference [MAD], 0.052-0.084) than the anchor-based methods (MAD, 0.075-0.093) and were better able to predict mean TTO health state values even when using in their estimation a subsample of the available TTO data. These new mapping and hybrid methods have a potentially useful role for producing values on the QALY scale from data elicited using ordinal techniques such as DCE for use in economic evaluation that makes best use of the desirable properties of each elicitation technique and elicited data. Further research is encouraged. © The Author(s) 2014.

[Open narcissism, covered narcissism and personality disorders as predictive factors of treatment response in an out-patient Drug Addiction Unit].

PubMed

Salazar-Fraile, José; Ripoll-Alandes, Carmen; Bobes, Julio

2010-01-01

Although a high prevalence of personality disorders has been reported in substance users, the literature on their value for predicting treatment response is controversial. On the other hand, while the predictive validity of personality traits as predictors of response to drug abuse or dependence has been studied, research on the validity of narcissistic personality traits is scarce. To study the predictive value of personality disorders, narcissistic personality traits and self-esteem for predicting treatment response. We assessed 78 patients attended at an addiction treatment unit using personality disorder diagnoses and measures of self-esteem, narcissism and covert (hypersensitive) narcissism. These variables were used in a Cox survival model as predictive variables of time to relapse into drug use. Hypersensitive (covert) narcissism and borderline and passive-aggressive personality disorders were risk factors for relapse into drug use, while open narcissism was a protective factor. Self-esteem did not show predictive validity. Personality disorders characterized by impulsivity-instability and passivity-resentfulness show higher risk of relapse into drug abuse. Personality traits characterized by high sensitivity to humiliation increase the risk of relapse, whereas pride and self-confidence are protective factors.

Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

PubMed

Wagner, Christian; Pan, Yuzhuo; Hsu, Vicky; Grillo, Joseph A; Zhang, Lei; Reynolds, Kellie S; Sinha, Vikram; Zhao, Ping

2015-01-01

The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI. This evaluation was based on 15 substrate PBPK models submitted by nine sponsors between 2009 and 2013. For these 15 models, a total of 26 DDI studies (cases) with various CYP inhibitors were available. Sponsors developed the PBPK models, reportedly without considering clinical DDI data. Inhibitor models were either developed by sponsors or provided by PBPK software developers and applied with minimal or no modification. The metric for assessing predictive performance of the sponsors' PBPK approach was the R predicted/observed value (R predicted/observed = [predicted mean exposure ratio]/[observed mean exposure ratio], with the exposure ratio defined as [C max (maximum plasma concentration) or AUC (area under the plasma concentration-time curve) in the presence of CYP inhibition]/[C max or AUC in the absence of CYP inhibition]). In 81 % (21/26) and 77 % (20/26) of cases, respectively, the R predicted/observed values for AUC and C max ratios were within a pre-defined threshold of 1.25-fold of the observed data. For all cases, the R predicted/observed values for AUC and C max were within a 2-fold range. These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.

Rainfall Prediction of Indian Peninsula: Comparison of Time Series Based Approach and Predictor Based Approach using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Dash, Y.; Mishra, S. K.; Panigrahi, B. K.

2017-12-01

Prediction of northeast/post monsoon rainfall which occur during October, November and December (OND) over Indian peninsula is a challenging task due to the dynamic nature of uncertain chaotic climate. It is imperative to elucidate this issue by examining performance of different machine leaning (ML) approaches. The prime objective of this research is to compare between a) statistical prediction using historical rainfall observations and global atmosphere-ocean predictors like Sea Surface Temperature (SST) and Sea Level Pressure (SLP) and b) empirical prediction based on a time series analysis of past rainfall data without using any other predictors. Initially, ML techniques have been applied on SST and SLP data (1948-2014) obtained from NCEP/NCAR reanalysis monthly mean provided by the NOAA ESRL PSD. Later, this study investigated the applicability of ML methods using OND rainfall time series for 1948-2014 and forecasted up to 2018. The predicted values of aforementioned methods were verified using observed time series data collected from Indian Institute of Tropical Meteorology and the result revealed good performance of ML algorithms with minimal error scores. Thus, it is found that both statistical and empirical methods are useful for long range climatic projections.

A strategy for early-risk predictions of clinical drug-drug interactions involving the GastroPlusTM DDI module for time-dependent CYP inhibitors.

PubMed

Sohlenius-Sternbeck, Anna-Karin; Meyerson, Gabrielle; Hagbjörk, Ann-Louise; Juric, Sanja; Terelius, Ylva

2018-04-01

1. A set of reference compounds for time-dependent inhibition (TDI) of cytochrome P450 with available literature data for k inact and K I was used to predict clinical implications using the GastroPlus TM software. Comparisons were made to in vivo literature interaction data. 2. The predicted AUC ratios (AUC +inhibitor /AUC control ) could be compared with the observed ratios from literature for all compounds with detailed information about in vivo administration, pharmacokinetics and in vivo interactions (N = 21). For this dataset, the difference between predicted and observed AUC ratios for interactions with midazolam was within twofold for all compounds except one (telaprevir, for which non-CYP-mediated metabolism likely plays a role after multiple dosing). 3. The sensitivity, specificity and accuracy of the GastroPlus TM predictions using a binary classification as no-to-weak interaction versus moderate-to-strong interaction for all compounds with available in vivo interaction data, were 80%, 82% and 81%, respectively (N = 31). 4. As a result of our evaluations of the DDI module in GastroPlus TM , we have implemented an early TDI risk assessment decision tree for our drug discovery projects involving in vitro screening and early GastroPlus TM predictions. Shifted IC 50 values are determined and k inact /K I estimated (by using a regression line established with in house-shifted IC 50 values and literature k inact /K I ratios), followed by GastroPlus TM predictions.

Variations in and predictors of the occurrence of depressive symptoms and mood symptoms in multiple sclerosis: a longitudinal two-year study.

PubMed

Johansson, Sverker; Gottberg, Kristina; Kierkegaard, Marie; Ytterberg, Charlotte

2016-03-05

There is limited knowledge regarding how depressive symptoms and a cluster of specific mood symptoms in people with multiple sclerosis (MS) vary over time and how they are influenced by contributing factors. Therefore, the aims of this study were a) to describe variations over 2 years in the occurrence of depressive symptoms and mood symptoms in a sample of people with MS, and b) to investigate the predictive value of sex, age, coping capacity, work status, disease severity, disease course, fatigue, cognition, frequency of social/lifestyle activities, and perceived impact of MS on health, on the occurrence of depressive symptoms and mood symptoms. Through using a protocol of measures of functioning and perceived impact of MS on health, comprising of the Beck Depression Inventory, 219 people with MS were assessed at 0, 12 and 24 months. Predictive values were explored with Generalised Estimating Equations. Proportions with depressive symptoms varied significantly (p < 0.001) from 21 to 30% between the three time points. Proportions with mood symptoms varied significantly (p < 0.001) from 14 to 17% between the three time points. Weak coping capacity and reduced frequency of social/lifestyle activities predicted the occurrence of depressive symptoms and mood symptoms, as did the psychological impact of MS on health in interaction with time. For people with MS of working age, not working predicted the occurrence of depressive symptoms and mood symptoms, as did the physical impact of MS on health on the occurrence of mood symptoms. The occurrence of depressive symptoms and mood symptoms in people with MS vary over a 2-year time period; almost half have depressive symptoms at least once. Health care services should develop strategies aimed at identifying people with MS who are depressed or who develop depressive symptoms. Interventions for alleviating depressive symptoms should consider the individual's coping capacity and perceived impact of MS on health, and facilitate their ability to maintain participation in valued everyday activities.

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 intensity, and mean geomagnetic dipole power excursion and axial dipole reversal frequencies. We conclude that McLeod's Rule helps unify geo-paleomagnetism, correctly relates theoretically predictable statistical properties of the core geodynamo to magnetic observation, and provides a priori information required for stochastic inversion of paleo-, archeo-, and/or historical geomagnetic measurements.

The reliability of the clinical examination in predicting hemodynamic status in acute febrile illness in a tropical, resource-limited setting.

PubMed

Moek, Felix; Poe, Poe; Charunwatthana, Prakaykaew; Pan-Ngum, Wirichada; Wattanagoon, Yupaporn; Chierakul, Wirongrong

2018-05-19

The clinical examination alone is widely considered unreliable when assessing fluid responsiveness in critically ill patients. Little evidence exists on the performance of the clinical examination to predict other hemodynamic derangements or more complex hemodynamic states. Patients with acute febrile illness were assessed on admission, both clinically and per non-invasive hemodynamic measurement. Correlations between clinical signs and hemodynamics patterns were analyzed, and the predictive capacity of the clinical signs was examined. Seventy-one patients were included; the most common diagnoses were bacterial sepsis, scrub typhus and dengue infection. Correlations between clinical signs and hemodynamic parameters were only statistically significant for Cardiac Index (r=0.75, p-value <0.01), Systemic Vascular Resistance Index (r=0.79, p-value <0.01) and flow time corrected (r=0.44, p-value 0.03). When assessing the predictive accuracy of clinical signs, the model identified only 62% of hemodynamic states correctly, even less if there was more than one hemodynamic abnormality. The clinical examination is not reliable to assess a patient's hemodynamic status in acute febrile illness. Fluid responsiveness, cardiodepression and more complex hemodynamic states are particularly easily missed.

Long-term Recall of Time to Pregnancy.

PubMed

Jukic, Anne Marie Z; McConnaughey, D Robert; Weinberg, Clarice R; Wilcox, Allen J; Baird, Donna D

2016-09-01

Despite the widespread use of retrospectively reported time to pregnancy to evaluate fertility either as an outcome or as a risk factor for chronic disease, only two small studies have directly compared prospective data with later recall. The North Carolina Early Pregnancy Study (1982-1986) collected prospective time-to-pregnancy data from the beginning of participants' pregnancy attempt. In 2010, (24-28 years later) women were sent a questionnaire including lifetime reproductive history that asked about all prior times to pregnancy. Of the 202 women with prospective time-to-pregnancy data, 76% provided recalled time to pregnancy. A lower proportion of women with times to pregnancy ≥3 cycles provided a recalled time to pregnancy than women with times to pregnancy <3 cycles. Also, high gravidity or parity was associated with a lower likelihood of providing a recalled time to pregnancy. Women with very short or very long times to pregnancy (1 cycle or ≥13 cycles) had good recall of time to pregnancy. Positive predictive values of 1 or ≥13 cycles were 73% and 68%, respectively, while positive predictive values for other categories of time to pregnancy ranged from 38% to 58%. The weighted kappa statistic for recalled versus prospective time to pregnancy was 0.72 (95% confidence interval: 0.65, 0.79). Recalled time to pregnancy showed good agreement with prospective time to pregnancy. Informative missingness must be considered when imputing recalled time to pregnancy. Associations observed in future studies can be corrected for misclassification.

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

Predicting physical time series using dynamic ridge polynomial neural networks.

PubMed

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.

Social and monetary reward learning engage overlapping neural substrates.

PubMed

Lin, Alice; Adolphs, Ralph; Rangel, Antonio

2012-03-01

Learning to make choices that yield rewarding outcomes requires the computation of three distinct signals: stimulus values that are used to guide choices at the time of decision making, experienced utility signals that are used to evaluate the outcomes of those decisions and prediction errors that are used to update the values assigned to stimuli during reward learning. Here we investigated whether monetary and social rewards involve overlapping neural substrates during these computations. Subjects engaged in two probabilistic reward learning tasks that were identical except that rewards were either social (pictures of smiling or angry people) or monetary (gaining or losing money). We found substantial overlap between the two types of rewards for all components of the learning process: a common area of ventromedial prefrontal cortex (vmPFC) correlated with stimulus value at the time of choice and another common area of vmPFC correlated with reward magnitude and common areas in the striatum correlated with prediction errors. Taken together, the findings support the hypothesis that shared anatomical substrates are involved in the computation of both monetary and social rewards. © The Author (2011). Published by Oxford University Press.

Religion and Substance Use among Youths of Mexican Heritage: A Social Capital Perspective

PubMed Central

Hodge, David R.; Marsiglia, Flavio F.; Nieri, Tanya

2011-01-01

Despite elevated levels of substance use among many Latino youths, there has been little research on protective factors against such use. In keeping with federal commitments to address health disparities, this prospective study examined the protective influence of religion on substance use among a school-based sample (N = 804) of youths of Mexican heritage in the American Southwest. Drawing from the social capital literature, the authors posited that both integration into religious networks and trust in religious values at time 1 (Tl) would predict less likelihood of using substances at time 2 (T2) but that exposure to religious norms at Tl would not predict subsequent substance use at T2. The hypotheses regarding religious networks and religious norms were largely confirmed, whereas little support emerged for the hypothesis regarding religious values. The results are discussed in light of the various pathways through which religion may exhibit a protective influence. PMID:22140302

[Value of DC and DRs in prediction of cardiovascular events in acute myocardial infarction patients].

PubMed

Gao, L; Chen, Y D; Shi, Y J; Xue, H; Wang, J L

2016-05-24

To investigate the value of deceleration capacity of rate (DC) and heart rate deceleration runs(DRs) in predicting cardiovascular events in patient with acute myocardial infarction (AMI). This study included 166 patients with AMI, who underwent ECG with sinus rhythm.These patients were followed-up for major adverse cardiac events (MACE). The receiver operating characteristic curve (ROC) was drawn to determine the best values for estimating the MACE. The mean follow-up time was (20.5±2.8) months, with 13 cases of cardiac death.There was statistically significant difference of DC, DRs and standard diviation of NN intervals(SDNN-24) between the death group and survival group.The area under the curve (AUC) of DC, DR4 and DR8 were larger than SDNN-24 (0.874, 0.804 vs 0.727). The values of DC, DR2, DR4 and root mean square of the successive differences(RMSSD) in the group of patients who underwent cardiac adverse events were smaller than the group of patients who didn't, and the AUC of DC was slightly higher than that of RMSSD. DC and DRs have important predictive value for cardiac death and MACE and can screen high-risk patients in patients with AMI.

Intrinsic Work Value-Reward Dissonance and Work Satisfaction during Young Adulthood

PubMed Central

Porfeli, Erik J.; Mortimer, Jeylan T.

2010-01-01

Previous research suggests that discrepancies between work values and rewards are indicators of dissonance that induce change in both to reduce such dissonance over time. The present study elaborates this model to suggest parallels with the first phase of the extension- and-strain curve. Small discrepancies or small increases in extension are presumed to be almost unnoticeable, while increasingly large discrepancies are thought to yield exponentially increasing strain. Work satisfaction is a principal outcome of dissonance; hence, work value-reward discrepancies are predicted to diminish work satisfaction in an exponential fashion. Findings from the work and family literature, however, lead to the prediction that this curvilinear association will be moderated by gender and family roles. Using longitudinal data spanning the third decade of life, the results suggest that intrinsic work value-reward discrepancies, as predicted, are increasingly associated, in a negative curvilinear fashion, with work satisfaction. This pattern, however, differs as a function of gender and family roles. Females who established family roles exhibited the expected pattern while other gender by family status groups did not. The results suggest that gender and family roles moderate the association between intrinsic work value-reward dissonance and satisfaction. In addition, women who remained unmarried and childless exhibited the strongest associations between occupational rewards and satisfaction. PMID:20526434

Intrinsic Work Value-Reward Dissonance and Work Satisfaction during Young Adulthood.

PubMed

Porfeli, Erik J; Mortimer, Jeylan T

2010-06-01

Previous research suggests that discrepancies between work values and rewards are indicators of dissonance that induce change in both to reduce such dissonance over time. The present study elaborates this model to suggest parallels with the first phase of the extension- and-strain curve. Small discrepancies or small increases in extension are presumed to be almost unnoticeable, while increasingly large discrepancies are thought to yield exponentially increasing strain. Work satisfaction is a principal outcome of dissonance; hence, work value-reward discrepancies are predicted to diminish work satisfaction in an exponential fashion. Findings from the work and family literature, however, lead to the prediction that this curvilinear association will be moderated by gender and family roles. Using longitudinal data spanning the third decade of life, the results suggest that intrinsic work value-reward discrepancies, as predicted, are increasingly associated, in a negative curvilinear fashion, with work satisfaction. This pattern, however, differs as a function of gender and family roles. Females who established family roles exhibited the expected pattern while other gender by family status groups did not. The results suggest that gender and family roles moderate the association between intrinsic work value-reward dissonance and satisfaction. In addition, women who remained unmarried and childless exhibited the strongest associations between occupational rewards and satisfaction.

Integrated Logistics Support Analysis of the International Space Station Alpha, Background and Summary of Mathematical Modeling and Failure Density Distributions Pertaining to Maintenance Time Dependent Parameters

NASA Technical Reports Server (NTRS)

Sepehry-Fard, F.; Coulthard, Maurice H.

1995-01-01

The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.

The valuation of the EQ-5D in Portugal.

PubMed

Ferreira, Lara N; Ferreira, Pedro L; Pereira, Luis N; Oppe, Mark

2014-03-01

The EQ-5D is a preference-based measure widely used in cost-utility analysis (CUA). Several countries have conducted surveys to derive value sets, but this was not the case for Portugal. The purpose of this study was to estimate a value set for the EQ-5D for Portugal using the time trade-off (TTO). A representative sample of the Portuguese general population (n = 450) stratified by age and gender valued 24 health states. Face-to-face interviews were conducted by trained interviewers. Each respondent ranked and valued seven health states using the TTO. Several models were estimated at both the individual and aggregated levels to predict health state valuations. Alternative functional forms were considered to account for the skewed distribution of these valuations. The models were analyzed in terms of their coefficients, overall fit and the ability for predicting the TTO values. Random effects models were estimated using generalized least squares and were robust across model specification. The results are generally consistent with other value sets. This research provides the Portuguese EQ-5D value set based on the preferences of the Portuguese general population as measured by the TTO. This value set is recommended for use in CUA conducted in Portugal.

Can the biomass-ratio hypothesis predict mixed-species litter decomposition along a climatic gradient?

PubMed Central

Tardif, Antoine; Shipley, Bill; Bloor, Juliette M. G.; Soussana, Jean-François

2014-01-01

Background and Aims The biomass-ratio hypothesis states that ecosystem properties are driven by the characteristics of dominant species in the community. In this study, the hypothesis was operationalized as community-weighted means (CWMs) of monoculture values and tested for predicting the decomposition of multispecies litter mixtures along an abiotic gradient in the field. Methods Decomposition rates (mg g−1 d−1) of litter from four herb species were measured using litter-bed experiments with the same soil at three sites in central France along a correlated climatic gradient of temperature and precipitation. All possible combinations from one to four species mixtures were tested over 28 weeks of incubation. Observed mixture decomposition rates were compared with those predicted by the biomass-ratio hypothesis. Variability of the prediction errors was compared with the species richness of the mixtures, across sites, and within sites over time. Key Results Both positive and negative prediction errors occurred. Despite this, the biomass-ratio hypothesis was true as an average claim for all sites (r = 0·91) and for each site separately, except for the climatically intermediate site, which showed mainly synergistic deviations. Variability decreased with increasing species richness and in less favourable climatic conditions for decomposition. Conclusions Community-weighted mean values provided good predictions of mixed-species litter decomposition, converging to the predicted values with increasing species richness and in climates less favourable to decomposition. Under a context of climate change, abiotic variability would be important to take into account when predicting ecosystem processes. PMID:24482152

A fluctuating plume dispersion model for the prediction of odour-impact frequencies from continuous stationary sources

NASA Astrophysics Data System (ADS)

Mussio, P.; Gnyp, A. W.; Henshaw, P. F.

A fluctuating plume dispersion model has been developed to facilitate the prediction of odour-impact frequencies in the communities surrounding elevated point sources. The model was used to predict the frequencies of occurrence of odours of various magnitudes for 1 h periods. In addition, the model predicted the maximum odour level. The model was tested with an extensive set of data collected in the residential areas surrounding the paint shop of an automotive assembly plant. Most of the perceived odours in the vicinity of the 64, 46 m high stacks ranged between 2 and 7 odour units and generally persisted for less than 30 s. Ninety-eight different field determinations of odour impact frequencies within 1 km of the plant were conducted during the course of the study. To simplify evaluation, the frequencies of occurrence of different odour levels were summed to give the total frequency of occurrence of all readily detectable (>2 OU) odours. The model provided excellent simulation of the total frequencies of occurrence where the odour was frequent (i.e . readily detectable more than 30% of the time). At lower frequencies of occurrence the model prediction was poor. The stability class did not seem to affect the model's ability to predict field frequency values. However, the model provided excellent predictions of the maximum odour levels without being sensitive to either stability class or distance from the source. Ninety-five percent of the predicted maximum values were within a factor of two of the measured field maximum values.

Black Hole Sign: Novel Imaging Marker That Predicts Hematoma Growth in Patients With Intracerebral Hemorrhage.

PubMed

Li, Qi; Zhang, Gang; Xiong, Xin; Wang, Xing-Chen; Yang, Wen-Song; Li, Ke-Wei; Wei, Xiao; Xie, Peng

2016-07-01

Early hematoma growth is a devastating neurological complication after intracerebral hemorrhage. We aim to report and evaluate the usefulness of computed tomography (CT) black hole sign in predicting hematoma growth in patients with intracerebral hemorrhage. Patients with intracerebral hemorrhage were screened for the presence of CT black hole sign on admission head CT performed within 6 hours after onset of symptoms. The black hole sign was defined as hypoattenuatting area encapsulated within the hyperattenuating hematoma with a clearly defined border. The sensitivity, specificity, and positive and negative predictive values of CT black hole sign in predicting hematoma expansion were calculated. Logistic regression analyses were used to assess the presence of the black hole sign and early hematoma growth. A total of 206 patients were enrolled. Black hole sign was found in 30 (14.6%) of 206 patients on the baseline CT scan. The black hole sign was more common in patients with hematoma growth (31.9%) than those without hematoma growth (5.8%; P<0.001). The sensitivity, specificity, positive predictive value, and negative predictive value of back hole sign in predicting early hematoma growth were 31.9%, 94.1%, 73.3%, and 73.2%, respectively. The time-to-admission CT scan, baseline hematoma volume, and the presence of black hole sign on admission CT independently predict hematoma growth in multivariate model. The CT black hole sign could be used as a simple and easy-to-use predictor for early hematoma growth in patients with intracerebral hemorrhage. © 2016 American Heart Association, Inc.

Geomagnetic storm forecasting service StormFocus: 5 years online

NASA Astrophysics Data System (ADS)

Podladchikova, Tatiana; Petrukovich, Anatoly; Yermolaev, Yuri

2018-04-01

Forecasting geomagnetic storms is highly important for many space weather applications. In this study, we review performance of the geomagnetic storm forecasting service StormFocus during 2011-2016. The service was implemented in 2011 at SpaceWeather.Ru and predicts the expected strength of geomagnetic storms as measured by Dst index several hours ahead. The forecast is based on L1 solar wind and IMF measurements and is updated every hour. The solar maximum of cycle 24 is weak, so most of the statistics are on rather moderate storms. We verify quality of selection criteria, as well as reliability of real-time input data in comparison with the final values, available in archives. In real-time operation 87% of storms were correctly predicted while the reanalysis running on final OMNI data predicts successfully 97% of storms. Thus the main reasons for prediction errors are discrepancies between real-time and final data (Dst, solar wind and IMF) due to processing errors, specifics of datasets.

An MEG signature corresponding to an axiomatic model of reward prediction error.

PubMed

Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

2012-01-02

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.

Prediction of the blowout of jet diffusion flames in a coflowing stream of air

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

Karbasi, M.; Wierzba, I.

1995-12-31

The blowout limits of a lifted diffusion flame in a coflowing stream of air are estimated using a simple model for extinction, for a range of fuels, jet diameters and co-flowing stream velocities. The proposed model uses a parameter which relates to the ratio of a time associated with the mixing processes in a turbulent jet to a characteristic chemical time. The Kolmogorov microscale of time is used as time scale in this model. It is shown that turbulent diffusion flames are quenched by excessive turbulence for a critical value of this parameter. The predicted blowout velocity of diffusion flamesmore » obtained using this model is in good agreement with the available experimental data.« less

A comparison of LOD and UT1-UTC forecasts by different combined prediction techniques

NASA Astrophysics Data System (ADS)

Kosek, W.; Kalarus, M.; Johnson, T. J.; Wooden, W. H.; McCarthy, D. D.; Popiński, W.

Stochastic prediction techniques including autocovariance, autoregressive, autoregressive moving average, and neural networks were applied to the UT1-UTC and Length of Day (LOD) International Earth Rotation and Reference Systems Servive (IERS) EOPC04 time series to evaluate the capabilities of each method. All known effects such as leap seconds and solid Earth zonal tides were first removed from the observed values of UT1-UTC and LOD. Two combination procedures were applied to predict the resulting LODR time series: 1) the combination of the least-squares (LS) extrapolation with a stochastic predition method, and 2) the combination of the discrete wavelet transform (DWT) filtering and a stochastic prediction method. The results of the combination of the LS extrapolation with different stochastic prediction techniques were compared with the results of the UT1-UTC prediction method currently used by the IERS Rapid Service/Prediction Centre (RS/PC). It was found that the prediction accuracy depends on the starting prediction epochs, and for the combined forecast methods, the mean prediction errors for 1 to about 70 days in the future are of the same order as those of the method used by the IERS RS/PC.

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 human subjects. Research supported by National Institutes of Health National Cancer Institute Grant R01 CA159471-01.« less

Rapid and non-destructive determination of rancidity levels in butter cookies by multi-spectral imaging.

PubMed

Xia, Qing; Liu, Changhong; Liu, Jinxia; Pan, Wenjuan; Lu, Xuzhong; Yang, Jianbo; Chen, Wei; Zheng, Lei

2016-03-30

Rancidity is an important attribute for quality assessment of butter cookies, while traditional methods for rancidity measurement are usually laborious, destructive and prone to operational error. In the present paper, the potential of applying multi-spectral imaging (MSI) technology with 19 wavelengths in the range of 405-970 nm to evaluate the rancidity in butter cookies was investigated. Moisture content, acid value and peroxide value were determined by traditional methods and then related with the spectral information by partial least squares regression (PLSR) and back-propagation artificial neural network (BP-ANN). The optimal models for predicting moisture content, acid value and peroxide value were obtained by PLSR. The correlation coefficient (r) obtained by PLSR models revealed that MSI had a perfect ability to predict moisture content (r = 0.909), acid value (r = 0.944) and peroxide value (r = 0.971). The study demonstrated that the rancidity level of butter cookies can be continuously monitored and evaluated in real-time by the multi-spectral imaging, which is of great significance for developing online food safety monitoring solutions. © 2015 Society of Chemical Industry.

Math-related career aspirations and choices within Eccles et al.'s expectancy-value theory of achievement-related behaviors.

PubMed

Lauermann, Fani; Tsai, Yi-Miau; Eccles, Jacquelynne S

2017-08-01

Which occupation to pursue is one of the more consequential decisions people make and represents a key developmental task. Yet the underlying developmental processes associated with either individual or group differences in occupational choices are still not well understood. This study contributes toward filling this gap, focusing in particular on the math domain. We examined two aspects of Eccles et al.'s (1983) expectancy-value theory of achievement-related behaviors: (a) the reciprocal associations between adolescents' expectancy and subjective task value beliefs and adolescents' career plans and (b) the multiplicative association between expectancies and values in predicting occupational outcomes in the math domain. Our analyses indicate that adolescents' expectancy and subjective task value beliefs about math and their math- or science-related career plans reported at the beginning and end of high school predict each other over time, with the exception of intrinsic interest in math. Furthermore, multiplicative associations between adolescents' expectancy and subjective task value beliefs about math predict math-related career attainment approximately 15 years after graduation from high school. Gender differences emerged regarding career-related beliefs and career attainment, with male students being more likely than female to both pursue and attain math-related careers. These gender differences could not be explained by differences in beliefs about math as an academic subject. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Pre-operative labs: Wasted dollars or predictors of post-operative cardiac and septic events in orthopaedic trauma patients?

PubMed

Lakomkin, Nikita; Sathiyakumar, Vasanth; Dodd, Ashley C; Jahangir, A Alex; Whiting, Paul S; Obremskey, William T; Sethi, Manish K

2016-06-01

As US healthcare expenditures continue to rise, there is significant pressure to reduce the cost of inpatient medical services. Studies have estimated that over 70% of routine labs may not yield clinical benefits while adding over $300 in costs per day for every inpatient. Although orthopaedic trauma patients tend to have longer inpatient stays and hip fractures have been associated with significant morbidity, there is a dearth of data examining pre-operative labs in predicting post-operative adverse events in these populations. The purpose of this study was to assess whether pre-operative labs significantly predict post-operative cardiac and septic complications in orthopaedic trauma and hip fracture patients. Between 2006 and 2013, 56,336 (15.6%) orthopaedic trauma patients were identified and 27,441 patients (7.6%) were diagnosed with hip fractures. Pre-operative labs included sodium, BUN, creatinine, albumin, bilirubin, SGOT, alkaline phosphatase, white count, hematocrit, platelet count, prothrombin time, INR, and partial thromboplastin time. For each of these labs, patients were deemed to have normal or abnormal values. Patients were noted to have developed cardiac or septic complications if they sustained (1) myocardial infarction (MI), (2) cardiac arrest, or (3) septic shock within 30 days after surgery. Separate regressions incorporating over 40 patient characteristics including age, gender, pre-operative comorbidities, and labs were performed for orthopaedic trauma patients in order to determine whether pre-operative labs predicted adverse cardiac or septic outcomes. 749 (1.3%) orthopaedic trauma patients developed cardiac complications and 311 (0.6%) developed septic shock. Multivariate regression demonstrated that abnormal pre-operative platelet values were significantly predictive of post-operative cardiac arrest (OR: 11.107, p=0.036), and abnormal bilirubin levels were predictive (OR: 8.487, p=0.008) of the development of septic shock in trauma patients. In the hip fracture cohort, abnormal partial thromboplastin time was significantly associated with post-operative myocardial infarction (OR: 15.083, p=0.046), and abnormal bilirubin (OR: 58.674, p=0.002) significantly predicted the onset of septic shock. This is the first study to demonstrate the utility of pre-operative labs in predicting perioperative cardiac and septic adverse events in orthopaedic trauma and hip fracture patients. Particular attention should be paid to haematologic/coagulation labs (platelets, PTT) and bilirubin values. Prognostic Level II. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP.

PubMed

Peak, Jasmine; Goranitis, Ilias; Day, Ed; Copello, Alex; Freemantle, Nick; Frew, Emma

2018-05-30

Economic evaluation normally requires information to be collected on outcome improvement using utility values. This is often not collected during the treatment of substance use disorders making cost-effectiveness evaluations of therapy difficult. One potential solution is the use of mapping to generate utility values from clinical measures. This study develops and evaluates mapping algorithms that could be used to predict the EuroQol-5D (EQ-5D-5 L) and the ICEpop CAPability measure for Adults (ICECAP-A) from the three commonly used clinical measures; the CORE-OM, the LDQ and the TOP measures. Models were estimated using pilot trial data of heroin users in opiate substitution treatment. In the trial the EQ-5D-5 L, ICECAP-A, CORE-OM, LDQ and TOP were administered at baseline, three and twelve month time intervals. Mapping was conducted using estimation and validation datasets. The normal estimation dataset, which comprised of baseline sample data, used ordinary least squares (OLS) and tobit regression methods. Data from the baseline and three month time periods were combined to create a pooled estimation dataset. Cluster and mixed regression methods were used to map from this dataset. Predictive accuracy of the models was assessed using the root mean square error (RMSE) and the mean absolute error (MAE). Algorithms were validated using sample data from the follow-up time periods. Mapping algorithms can be used to predict the ICECAP-A and the EQ-5D-5 L in the context of opiate dependence. Although both measures can be predicted, the ICECAP-A was better predicted by the clinical measures. There were no advantages of pooling the data. There were 6 chosen mapping algorithms, which had MAE scores ranging from 0.100 to 0.138 and RMSE scores ranging from 0.134 to 0.178. It is possible to predict the scores of the ICECAP-A and the EQ-5D-5 L with the use of mapping. In the context of opiate dependence, these algorithms provide the possibility of generating utility values from clinical measures and thus enabling economic evaluation of alternative therapy options. ISRCTN22608399 . Date of registration: 27/04/2012. Date of first randomisation: 14/08/2012.

SU-G-JeP4-02: An Investigation of Respiratory Surrogate Motion Data Requirements for Multiple-Step Ahead Prediction

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

Zawisza, I; Ren, L; Yin, F

Purpose: Respiratory-gated radiotherapy and dynamic tracking employ real-time imaging and surrogate motion-monitoring methods with tumor motion prediction in advance of real-time. This study investigated respiratory motion data length on prediction accuracy of tumor motion. Methods: Predictions generated from the algorithm are validated against a one-dimensional surrogate signal of amplitude versus time. Prediction consists of three major components: extracting top-ranked subcomponents from training data matching the last respiratory cycle; calculating weighting factors from best-matched subcomponents; fusing data proceeding best-matched subcomponents with respective weighting factors to form predictions. Predictions for one respiratory cycle (∼3-6seconds) were assessed using 351 patient data from themore » respiratory management device. Performance was evaluated for correlation coefficient and root mean square error (RMSE) between prediction and final respiratory cycle. Results: Respiratory prediction results fell into two classes, where best predictions for 70 cycles or less performed using relative prediction and greater than 70 cycles are predicted similarly using relative and derivative relative. For 70 respiratory cycles or less, the average correlation between prediction and final respiratory cycle was 0.9999±0.0001, 0.9999±0.0001, 0.9988±0.0003, 0.9985±0.0023, and 0.9981±0.0023 with RMSE values of 0.0091±0.0030, 0.0091±0.0030, 0.0305±0.0051, 0.0299±0.0259, and 0.0299±0.0259 for equal, relative, pattern, derivative equal and derivative relative weighting methods, respectively. Respectively, the total best prediction for each method was 37, 65, 20, 22, and 22. For data with greater than 70 cycles average correlation was 0.9999±0.0001, 0.9999±0.0001, 0.9988±0.0004, 0.9988±0.0020, and 0.9988±0.0020 with RMSE values of 0.0081±0.0031, 0.0082±0.0033, 0.0306±0.0056, 0.0218±0.0222, and 0.0218±0.0222 for equal, relative, pattern, derivative equal and derivative relative weighting methods, respectively. Respectively, the total best prediction for each method was 24, 44, 42, 30, and 45. Conclusion: The prediction algorithms are effective in estimating surrogate motion in advance. These results indicate an advantage in using relative prediction for shorter data and either relative or derivative relative prediction for longer data.« less

Real-time SWMF-Geospace at CCMC: assessing the quality of output from continuous operational simulations

NASA Astrophysics Data System (ADS)

Liemohn, M. W.; Welling, D. T.; De Zeeuw, D.; Kuznetsova, M. M.; Rastaetter, L.; Ganushkina, N. Y.; Ilie, R.; Toth, G.; Gombosi, T. I.; van der Holst, B.

2016-12-01

The ground-based magnetometer index Dst is a decent measure of the near-Earth current systems, in particular those in the storm-time inner magnetosphere. The ability of a large-scale, physics-based model to reproduce, or even predict, this index is therefore a tangible measure of the overall validity of the code for space weather research and space weather operational usage. Experimental real-time simulations of the Space Weather Modeling Framework (SWMF) are conducted at the Community Coordinated Modeling Center (CCMC), with results available there (http://ccmc.gsfc.nasa.gov/realtime.php), through the CCMC Integrated Space Weather Analysis (iSWA) site (http://iswa.ccmc.gsfc.nasa.gov/IswaSystemWebApp/), and the Michigan SWMF site (http://csem.engin.umich.edu/realtime). Presently, two configurations of the SWMF are running in real time at CCMC, both focusing on the geospace modules, using the BATS-R-US magnetohydrodynamic model, the Ridley Ionosphere Model, and with and without the Rice Convection Model for inner magnetospheric drift physics. While both have been running for several years, nearly continuous results are available since July 2015. Dst from the model output is compared against the Kyoto real-time Dst. Various quantitative measures are presented to assess the goodness of fit between the models and observations. In particular, correlation coefficients, RMSE and prediction efficiency are calculated and discussed. In addition, contingency tables are presented, demonstrating the ability of the model to predict "disturbed times" as defined by Dst values below some critical threshold. It is shown that the SWMF run with the inner magnetosphere model is significantly better at reproducing storm-time values, with prediction efficiencies above 0.25 and Heidke skill scores above 0.5. This work was funded by NASA and NSF grants, and the European Union's Horizon 2020 research and innovation programme under grant agreement 637302 PROGRESS.

Limiting the Effects of Earthquake Shaking on Gravitational-Wave Interferometers

NASA Astrophysics Data System (ADS)

Perry, M. R.; Earle, P. S.; Guy, M. R.; Harms, J.; Coughlin, M.; Biscans, S.; Buchanan, C.; Coughlin, E.; Fee, J.; Mukund, N.

2016-12-01

Second-generation ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to high-amplitude waves from teleseismic events, which can cause astronomical detectors to fall out of mechanical lock (lockloss). This causes the data to be useless for gravitational wave detection around the time of the seismic arrivals and for several hours thereafter while the detector stabilizes enough to return to the locked state. The down time can be reduced if advance warning of impending shaking is received and the impact is suppressed in the isolation system with the goal of maintaining lock even at the expense of increased instrumental noise. Here we describe an early warning system for modern gravitational-wave observatories. The system relies on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). Hypocenter and magnitude information is typically available within 5 to 20 minutes of the origin time of significant earthquakes, generally before the arrival of high-amplitude waves from these teleseisms at LIGO. These alerts are used to estimate arrival times and ground velocities at the gravitational wave detectors. In general, 94% of the predictions for ground-motion amplitude are within a factor of 5 of measured values. The error in both arrival time and ground-motion prediction introduced by using preliminary, rather than final, hypocenter and magnitude information is minimal with about 90% of the events falling within a factor of 2 of the final predicted value. By using a Machine Learning Algorithm, we develop a lockloss prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could save lockloss from 40-100 earthquake events in a 6-month time-period.

Equilibration and aging of dense soft-sphere glass-forming liquids

NASA Astrophysics Data System (ADS)

Sánchez-Díaz, Luis Enrique; Ramírez-González, Pedro; Medina-Noyola, Magdaleno

2013-05-01

The recently developed nonequilibrium extension of the self-consistent generalized Langevin equation theory of irreversible relaxation [Ramírez-González and Medina-Noyola, Phys. Rev. E10.1103/PhysRevE.82.061503 82, 061503 (2010); Ramírez-González and Medina-Noyola, Phys. Rev. E10.1103/PhysRevE.82.061504 82, 061504 (2010)] is applied to the description of the irreversible process of equilibration and aging of a glass-forming soft-sphere liquid that follows a sudden temperature quench, within the constraint that the local mean particle density remains uniform and constant. For these particular conditions, this theory describes the nonequilibrium evolution of the static structure factor S(k;t) and of the dynamic properties, such as the self-intermediate scattering function FS(k,τ;t), where τ is the correlation delay time and t is the evolution or waiting time after the quench. Specific predictions are presented for the deepest quench (to zero temperature). The predicted evolution of the α-relaxation time τα(t) as a function of t allows us to define the equilibration time teq(ϕ), as the time after which τα(t) has attained its equilibrium value ταeq(ϕ). It is predicted that both, teq(ϕ) and ταeq(ϕ), diverge as ϕ→ϕ(a), where ϕ(a) is the hard-sphere dynamic-arrest volume fraction ϕ(a)(≈0.582), thus suggesting that the measurement of equilibrium properties at and above ϕ(a) is experimentally impossible. The theory also predicts that for fixed finite waiting times t, the plot of τα(t;ϕ) as a function of ϕ exhibits two regimes, corresponding to samples that have fully equilibrated within this waiting time (ϕ≤ϕ(c)(t)), and to samples for which equilibration is not yet complete (ϕ≥ϕ(c)(t)). The crossover volume fraction ϕ(c)(t) increases with t but saturates to the value ϕ(a).

The importance of negative predictive value (NPV) of vulnerable elderly survey (VES 13) as a pre-screening test in older patients with cancer.

PubMed

Castagneto, B; Di Pietrantonj, C; Stevani, I; Anfossi, A; Arzese, M; Giorcelli, L; Giaretto, L

2013-12-01

The importance of prognostic value of the comprehensive geriatric assessment (CGA) is well known in geriatric oncology, but there is no consensus on the use of alternative abbreviated screening methods for the evaluation of older patient disabilities. The participants in this study underwent vulnerable elderly survey 13 (VES 13) at first entry in Oncology Department and were later assessed by a geriatrician according to CGA. A score >3 for VES 13 identified patients as vulnerable. Aim of this study was to evaluate the specificity, sensibility, positive predictive value (PPV), and negative predictive value (NPV) of VES 13 versus cumulative illness rating scale (CIRS), activities of daily living (ADL), instrumental activities of daily living (IADL), and short portable mental status questionnaire (SPMSQ). Hundred and seventeen patients (mean age 78.8 years) entered the study. The NPV of VES was 74.6% for CIRS, 90.1% for IADL, 93.0% for ADL, and 100% for SPMSQ. As for PPV, the VES 13 showed no accuracy. We can conclude that VES 13 demonstrated sufficient accuracy as a screening test in identifying elderly "fit" patients in order to spare the more time-consuming CGA.

Role of procalcitonin in predicting dilating vesicoureteral reflux in young children hospitalized with a first febrile urinary tract infection.

PubMed

Sun, Hai-Lun; Wu, Kang-Hsi; Chen, Shan-Ming; Chao, Yu-Hua; Ku, Min-Sho; Hung, Tong-Wei; Liao, Pen-Fen; Lue, Ko-Huang; Sheu, Ji-Nan

2013-09-01

The aim of this article was to assess the usefulness of procalcitonin (PCT) as a marker for predicting dilating (grades III-V) vesicoureteral reflux (VUR) in young children with a first febrile urinary tract infection. Children ≤2 years of age with a first febrile urinary tract infection were prospectively evaluated. Serum samples were tested for PCT at the time of admission to a tertiary hospital. All children underwent renal ultrasonography (US), Tc-dimercaptosuccinic acid renal scan, and voiding cystourethrography. The diagnostic characteristics of PCT test for acute pyelonephritis and dilating VUR were calculated. Of 272 children analyzed (168 boys and 104 girls; median age, 5 months), 169 (62.1%) had acute pyelonephritis. There was VUR demonstrated in 97 (35.7%), including 70 (25.7%) with dilating VUR. The median PCT value was significantly higher in children with VUR than in those without (P < 0.001). Using a PCT cutoff value of ≥1.0 ng/mL, the sensitivity and negative predictive value for predicting dilating VUR were 94.3% and 95.4%, respectively, for PCT, and 97.1% and 97.8%, respectively, for the combined PCT and US studies, whereas the positive and negative likelihood ratios were 2.03 and 0.107, respectively, for PCT, and 1.72 and 0.067, respectively, for the combined studies. By multivariate analysis, high PCT values and abnormalities on US were independent predictors of dilating VUR. PCT is useful for diagnosing acute pyelonephritis and predicting dilating VUR in young children with a first febrile urinary tract infection. A voiding cystourethrography is indicated only in children with high PCT values (≥1.0 ng/mL) and/or abnormalities found on a US.

Should gram stains have a role in diagnosing hip arthroplasty infections?

PubMed

Johnson, Aaron J; Zywiel, Michael G; Stroh, D Alex; Marker, David R; Mont, Michael A

2010-09-01

The utility of Gram stains in diagnosing periprosthetic infections following total hip arthroplasty has recently been questioned. Several studies report low sensitivity of the test, and its poor ability to either confirm or rule out infection in patients undergoing revision total hip arthroplasty. Despite this, many institutions including that of the senior author continue to perform Gram stains during revision total hip arthroplasty. We assessed the sensitivity, specificity, accuracy, and positive and negative predictive values of Gram stains from surgical-site samplings taken from procedures on patients with both infected and aseptic revision total hip arthroplasties. A review was performed on patients who underwent revision total hip arthroplasty between 2000 and 2007. Eighty-two Gram stains were performed on patients who had infected total hip arthroplasties and underwent revision procedures. Additionally, of the 410 revision total hip arthroplasties performed on patients who were confirmed infection-free, 120 Gram stains were performed. Patients were diagnosed as infected using multiple criteria at the time of surgery. Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated from these Gram stain results. The Gram stain demonstrated a sensitivity and specificity of 9.8% and 100%, respectively. In this series, the Gram stain had a negative predictive value of 62%, a positive predictive value of 100%, and an accuracy of 63%. Gram stains obtained from surgical-site samples had poor sensitivity and poor negative predictive value. Based on these findings, as well as those of other authors, we believe that Gram stains should no longer be considered for diagnosing infections in revision total hip arthroplasty. Level III, diagnostic study. See Guidelines for Authors for a complete description of levels of evidence.

Rapid detection and ruling out of neonatal sepsis by PCR coupled with Electrospray Ionization Mass Spectrometry (PCR/ESI-MS).

PubMed

Delcò, Cristina; Karam, Oliver; Pfister, Riccardo; Gervaix, Alain; Renzi, Gesuele; Emonet, Stéphane; Schrenzel, Jacques; Posfay-Barbe, Klara M

2017-05-01

Sepsis is an important cause of morbidity and mortality in neonates and clinicians are typically required to administer empiric antibiotics while waiting for blood culture results. However, prolonged and inappropriate use of antibiotics is associated with various complications and adverse events. Better tools to rapidly rule out bacterial infections are therefore needed. We aimed to assess the negative predictive value of PCR coupled with Electrospray Ionization Mass Spectrometry (PCR/ESI-MS) compared to conventional blood cultures in neonatal sepsis. Prospective observational study. All consecutive neonates (<28days old) with clinical suspicion of sepsis. Samples for PCR/ESI-MS analysis were collected at the same time as samples for the blood culture, before the initiation of antibiotics. Our primary objective was to evaluate the negative predictive value of PCR/ESI-MS for the detection of bacteria in the bloodstream of newborns with suspected sepsis. Our secondary objective was the evaluation of the sensitivity, specificity and positive predictive value of the PCR/ESI-MS in such a neonatal population. We analysed 114 samples over 14months. The median age and weight were 32weeks+3days and 1840g, respectively. Two patients had negative PCR/ESI-MS results, but positive blood cultures. Overall, the negative predictive value was 98% (95%CI: 92% to 100%). Based on these results, PCR/ESI-MS analysis of blood samples of neonates with suspected sepsis appears to have a very good negative predictive value when compared to blood cultures as gold standard. This novel test might allow for early reassessment of the need for antibiotics. Copyright © 2017 Elsevier B.V. All rights reserved.

Duration of untreated psychosis: Impact of the definition of treatment onset on its predictive value over three years of treatment.

PubMed

Golay, Philippe; Alameda, Luis; Baumann, Philipp; Elowe, Julien; Progin, Pierre; Polari, Andrea; Conus, Philippe

2016-06-01

While reduction of DUP (Duration of Untreated Psychosis) is a key goal in early intervention strategies, the predictive value of DUP on outcome has been questioned. We planned this study in order to explore the impact of three different definition of "treatment initiation" on the predictive value of DUP on outcome in an early psychosis sample. 221 early psychosis patients aged 18-35 were followed-up prospectively over 36 months. DUP was measured using three definitions for treatment onset: Initiation of antipsychotic medication (DUP1); engagement in a specialized programme (DUP2) and combination of engagement in a specialized programme and adherence to medication (DUP3). 10% of patients never reached criteria for DUP3 and therefore were never adequately treated over the 36-month period of care. While DUP1 and DUP2 had a limited predictive value on outcome, DUP3, based on a more restrictive definition for treatment onset, was a better predictor of positive and negative symptoms, as well as functional outcome at 12, 24 and 36 months. Globally, DUP3 explained 2 to 5 times more of the variance than DUP1 and DUP2, with effect sizes falling in the medium range according to Cohen. The limited predictive value of DUP on outcome in previous studies may be linked to problems of definitions that do not take adherence to treatment into account. While they need replication, our results suggest effort to reduce DUP should continue and aim both at early detection and development of engagement strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Variable life-adjusted display (VLAD) for hip fracture patients: a prospective trial.

PubMed

Williams, H; Gwyn, R; Smith, A; Dramis, A; Lewis, J

2015-08-01

With restructuring within the NHS, there is increased public and media interest in surgical outcomes. The Nottingham Hip Fracture Score (NHFS) is a well-validated tool in predicting 30-day mortality in hip fractures. VLAD provides a visual plot in real time of the difference between the cumulative expected mortality and the actual death occurring. Survivors are incorporated as a positive value equal to 1 minus the probability of survival and deaths as a negative value equal to the probability of survival. Downward deflections indicate mortality and potentially suboptimal care. We prospectively included every hip fracture admitted to UHW that underwent surgery from January-August 2014. NHFS was then calculated and predicted survival identified. A VLAD plot was then produced comparing the predicted with the actual 30-day mortality. Two hundred and seventy-seven patients have completed the 30-day follow-up, and initial results showed that the actual 30-day mortality (7.2 %) was much lower than that predicted by the NHFS (8.0 %). This was reflected by a positive trend on the VLAD plot. Variable life-adjusted display provides an easy-to-use graphical representation of risk-adjusted survival over time and can act as an "early warning" system to identify trends in mortality for hip fractures.

Predicting trace organic compound attenuation by ozone oxidation: Development of indicator and surrogate models.

PubMed

Park, Minkyu; Anumol, Tarun; Daniels, Kevin D; Wu, Shimin; Ziska, Austin D; Snyder, Shane A

2017-08-01

Ozone oxidation has been demonstrated to be an effective treatment process for the attenuation of trace organic compounds (TOrCs); however, predicting TOrC attenuation by ozone processes is challenging in wastewaters. Since ozone is rapidly consumed, determining the exposure times of ozone and hydroxyl radical proves to be difficult. As direct potable reuse schemes continue to gain traction, there is an increasing need for the development of real-time monitoring strategies for TOrC abatement in ozone oxidation processes. Hence, this study is primarily aimed at developing indicator and surrogate models for the prediction of TOrC attenuation by ozone oxidation. To this end, the second-order kinetic equations with a second-phase R ct value (ratio of hydroxyl radical exposure to molecular ozone exposure) were used to calculate comparative kinetics of TOrC attenuation and the reduction of indicator and spectroscopic surrogate parameters, including UV absorbance at 254 nm (UVA 254 ) and total fluorescence (TF). The developed indicator model using meprobamate as an indicator compound and the surrogate models with UVA 254 and TF exhibited good predictive power for the attenuation of 13 kinetically distinct TOrCs in five filtered and unfiltered wastewater effluents (R 2 values > 0.8). This study is intended to help provide a guideline for the implementation of indicator/surrogate models for real-time monitoring of TOrC abatement with ozone processes and integrate them into a regulatory framework in water reuse. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sci-Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning

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

Joseph, Ackeem; Herrera, David; Hijal, Tarek

We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting inmore » the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.« less

A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

PubMed Central

Ye, Min; Nagar, Swati; Korzekwa, Ken

2015-01-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

Weak Measurement and Quantum Smoothing of a Superconducting Qubit

NASA Astrophysics Data System (ADS)

Tan, Dian

In quantum mechanics, the measurement outcome of an observable in a quantum system is intrinsically random, yielding a probability distribution. The state of the quantum system can be described by a density matrix rho(t), which depends on the information accumulated until time t, and represents our knowledge about the system. The density matrix rho(t) gives probabilities for the outcomes of measurements at time t. Further probing of the quantum system allows us to refine our prediction in hindsight. In this thesis, we experimentally examine a quantum smoothing theory in a superconducting qubit by introducing an auxiliary matrix E(t) which is conditioned on information obtained from time t to a final time T. With the complete information before and after time t, the pair of matrices [rho(t), E(t)] can be used to make smoothed predictions for the measurement outcome at time t. We apply the quantum smoothing theory in the case of continuous weak measurement unveiling the retrodicted quantum trajectories and weak values. In the case of strong projective measurement, while the density matrix rho(t) with only diagonal elements in a given basis |n〉 may be treated as a classical mixture, we demonstrate a failure of this classical mixture description in determining the smoothed probabilities for the measurement outcome at time t with both diagonal rho(t) and diagonal E(t). We study the correlations between quantum states and weak measurement signals and examine aspects of the time symmetry of continuous quantum measurement. We also extend our study of quantum smoothing theory to the case of resonance fluorescence of a superconducting qubit with homodyne measurement and observe some interesting effects such as the modification of the excited state probabilities, weak values, and evolution of the predicted and retrodicted trajectories.

HMI Data Driven Magnetohydrodynamic Model Predicted Active Region Photospheric Heating Rates: Their Scale Invariant, Flare Like Power Law Distributions, and Their Possible Association With Flares

NASA Technical Reports Server (NTRS)

Goodman, Michael L.; Kwan, Chiman; Ayhan, Bulent; Shang, Eric L.

2017-01-01

A data driven, near photospheric, 3 D, non-force free magnetohydrodynamic model predicts time series of the complete current density, and the resistive heating rate Q at the photosphere in neutral line regions (NLRs) of 14 active regions (ARs). The model is driven by time series of the magnetic field B observed by the Helioseismic and Magnetic Imager on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series for B in every AR pixel. Errors in B due to these periods can be significant. The number of occurrences N(q) of values of Q > or = q for each AR time series is found to be a scale invariant power law distribution, N(Q) / Q-s, above an AR dependent threshold value of Q, where 0.3952 < or = s < or = 0.5298 with mean and standard deviation of 0.4678 and 0.0454, indicating little variation between ARs. Observations show that the number of occurrences N(E) of coronal flares with a total energy released > or = E obeys the same type of distribution, N(E) / E-S, above an AR dependent threshold value of E, with 0.38 < or approx. S < or approx. 0.60, also with little variation among ARs. Within error margins the ranges of s and S are nearly identical. This strong similarity between N(Q) and N(E) suggests a fundamental connection between the process that drives coronal flares and the process that drives photospheric NLR heating rates in ARs. In addition, results suggest it is plausible that spikes in Q, several orders of magnitude above background values, are correlated with times of the subsequent occurrence of M or X flares.

Multicomponent seismic reservoir characterization of a steam-assisted gravity drainage (SAGD) heavy oil project, Athabasca oil sands, Alberta

NASA Astrophysics Data System (ADS)

Schiltz, Kelsey Kristine

Steam-assisted gravity drainage (SAGD) is an in situ heavy oil recovery method involving the injection of steam in horizontal wells. Time-lapse seismic analysis over a SAGD project in the Athabasca oil sands deposit of Alberta reveals that the SAGD steam chamber has not developed uniformly. Core data confirm the presence of low permeability shale bodies within the reservoir. These shales can act as barriers and baffles to steam and limit production by prohibiting steam from accessing the full extent of the reservoir. Seismic data can be used to identify these shale breaks prior to siting new SAGD well pairs in order to optimize field development. To identify shale breaks in the study area, three types of seismic inversion and a probabilistic neural network prediction were performed. The predictive value of each result was evaluated by comparing the position of interpreted shales with the boundaries of the steam chamber determined through time-lapse analysis. The P-impedance result from post-stack inversion did not contain enough detail to be able to predict the vertical boundaries of the steam chamber but did show some predictive value in a spatial sense. P-impedance from pre-stack inversion exhibited some meaningful correlations with the steam chamber but was misleading in many crucial areas, particularly the lower reservoir. Density estimated through the application of a probabilistic neural network (PNN) trained using both PP and PS attributes identified shales most accurately. The interpreted shales from this result exhibit a strong relationship with the boundaries of the steam chamber, leading to the conclusion that the PNN method can be used to make predictions about steam chamber growth. In this study, reservoir characterization incorporating multicomponent seismic data demonstrated a high predictive value and could be useful in evaluating future well placement.

Strike-Slip Fault Patterns on Europa: Obliquity or Polar Wander?

NASA Technical Reports Server (NTRS)

Rhoden, Alyssa Rose; Hurford, Terry A.; Manga, Michael

2011-01-01

Variations in diurnal tidal stress due to Europa's eccentric orbit have been considered as the driver of strike-slip motion along pre-existing faults, but obliquity and physical libration have not been taken into account. The first objective of this work is to examine the effects of obliquity on the predicted global pattern of fault slip directions based on a tidal-tectonic formation model. Our second objective is to test the hypothesis that incorporating obliquity can reconcile theory and observations without requiring polar wander, which was previously invoked to explain the mismatch found between the slip directions of 192 faults on Europa and the global pattern predicted using the eccentricity-only model. We compute predictions for individual, observed faults at their current latitude, longitude, and azimuth with four different tidal models: eccentricity only, eccentricity plus obliquity, eccentricity plus physical libration, and a combination of all three effects. We then determine whether longitude migration, presumably due to non-synchronous rotation, is indicated in observed faults by repeating the comparisons with and without obliquity, this time also allowing longitude translation. We find that a tidal model including an obliquity of 1.2?, along with longitude migration, can predict the slip directions of all observed features in the survey. However, all but four faults can be fit with only 1? of obliquity so the value we find may represent the maximum departure from a lower time-averaged obliquity value. Adding physical libration to the obliquity model improves the accuracy of predictions at the current locations of the faults, but fails to predict the slip directions of six faults and requires additional degrees of freedom. The obliquity model with longitude migration is therefore our preferred model. Although the polar wander interpretation cannot be ruled out from these results alone, the obliquity model accounts for all observations with a value consistent with theoretical expectations and cycloid modeling.

Assessment of insulin resistance in Chinese PCOS patients with normal glucose tolerance.

PubMed

Gao, Jing; Zhou, Li; Hong, Jie; Chen, Chen

2017-11-01

The study aimed to investigate insulin resistance (IR) status in polycystic ovary syndrome (PCOS) women with normal glucose tolerance (NGT), and further to evaluate feasible diagnostic method for those patients. Three hundred and twenty-five PCOS women with NGT and ninety-five healthy age-matched controls were recruited with Rotterdam criterion and 75 g oral glucose tolerance test (OGTT). IR status was estimated following a glycemic and insulinemic OGTT (0, 30, 60, 120, and 180 min). A modified HOMA-IR formula was applied to each time-course value of glycemia and insulinemia. The predictive performance of each IR index was analyzed with the use of ROC curves. Compared with healthy controls, both non-obese and obese PCOS patients with NGT had a higher BMI, serum glucose, insulin value (p < .05). The best predictive index of IR in non-obese PCOS-NGT was a HOMA-M30 value of 20.36 or more (AUC: 0.753). In obese PCOS-NGT population, the best predictive performance was obtained by a HOMA-M60 value of 32.17 or more (AUC: 0.868). IR was common in Chinese PCOS women with NGT, and the early assessment of IR should be heeded. We recommended HOMA-M30 (Cutoff: 20.36) and HOMA-M60 (Cutoff: 32.17) as the best predictive parameters for non-obese and obese PCOS-NGT patients, respectively.

Stability of Gradient Field Corrections for Quantitative Diffusion MRI.

PubMed

Rogers, Baxter P; Blaber, Justin; Welch, E Brian; Ding, Zhaohua; Anderson, Adam W; Landman, Bennett A

2017-02-11

In magnetic resonance diffusion imaging, gradient nonlinearity causes significant bias in the estimation of quantitative diffusion parameters such as diffusivity, anisotropy, and diffusion direction in areas away from the magnet isocenter. This bias can be substantially reduced if the scanner- and coil-specific gradient field nonlinearities are known. Using a set of field map calibration scans on a large (29 cm diameter) phantom combined with a solid harmonic approximation of the gradient fields, we predicted the obtained b-values and applied gradient directions throughout a typical field of view for brain imaging for a typical 32-direction diffusion imaging sequence. We measured the stability of these predictions over time. At 80 mm from scanner isocenter, predicted b-value was 1-6% different than intended due to gradient nonlinearity, and predicted gradient directions were in error by up to 1 degree. Over the course of one month the change in these quantities due to calibration-related factors such as scanner drift and variation in phantom placement was <0.5% for b-values, and <0.5 degrees for angular deviation. The proposed calibration procedure allows the estimation of gradient nonlinearity to correct b-values and gradient directions ahead of advanced diffusion image processing for high angular resolution data, and requires only a five-minute phantom scan that can be included in a weekly or monthly quality assurance protocol.

Experimental design and response surface modelling for optimization of vat dye from water by nano zero valent iron (NZVI).

PubMed

Arabi, Simin; Sohrabi, Mahmoud Reza

2013-01-01

In this study, NZVI particles was prepared and studied for the removal of vat green 1 dye from aqueous solution. A four-factor central composite design (CCD) combined with response surface modeling (RSM) to evaluate the combined effects of variables as well as optimization was employed for maximizing the dye removal by prepared NZVI based on 30 different experimental data obtained in a batch study. Four independent variables, viz. NZVI dose (0.1-0.9 g/L), pH (1.5-9.5), contact time (20-100 s), and initial dye concentration (10-50 mg/L) were transform to coded values and quadratic model was built to predict the responses. The significant of independent variables and their interactions were tested by the analysis of variance (ANOVA). Adequacy of the model was tested by the correlation between experimental and predicted values of the response and enumeration of prediction errors. The ANOVA results indicated that the proposed model can be used to navigate the design space. Optimization of the variables for maximum adsorption of dye by NZVI particles was performed using quadratic model. The predicted maximum adsorption efficiency (96.97%) under the optimum conditions of the process variables (NZVI dose 0.5 g/L, pH 4, contact time 60 s, and initial dye concentration 30 mg/L) was very close to the experimental value (96.16%) determined in batch experiment. In the optimization, R2 and R2adj correlation coefficients for the model were evaluated as 0.95 and 0.90, respectively.

Retention modeling under organic modifier gradient conditions in ion-pair reversed-phase chromatography. Application to the separation of a set of underivatized amino acids.

PubMed

Pappa-Louisi, A; Agrafiotou, P; Papachristos, K

2010-07-01

The combined effect of the ion-pairing reagent concentration, C(ipr), and organic modifier content, phi, on the retention under phi-gradient conditions at different constant C(ipr) was treated in this study by using two approaches. In the first approach, the prediction of the retention time of a sample solute is based on a direct fitting procedure of a proper retention model to 3-D phi-gradient retention data obtained under the same phi-linear variation but with different slope and time duration of the initial isocratic part and in the presence of various constant C(ipr) values in the eluent. The second approach is based on a retention model describing the combined effect of C(ipr) and phi on the retention of solutes in isocratic mode and consequently analyzes isocratic data obtained in mobile phases containing different C(ipr) values. The effectiveness of the above approaches was tested in the retention prediction of a mixture of 16 underivatized amino acids using mobile phases containing acetonitrile as organic modifier and sodium dodecyl sulfate as ion-pairing reagent. From these approaches, only the first one gives satisfactory predictions and can be successfully used in optimization of ion-pair chromatographic separations under gradient conditions. The failure of the second approach to predict the retention of solutes in the gradient elution mode in the presence of different C(ipr) values was attributed to slow changes in the distribution equilibrium of ion-pairing reagents caused by phi-variation.