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.

Sensitivity and positive predictive values of presurgical clinical diagnosis of excised benign and malignant skin tumors: a prospective study of 835 lesions in 778 patients.

PubMed

Har-Shai, Y; Hai, N; Taran, A; Mayblum, S; Barak, A; Tzur, E; Schafer, I; David, R; David, E; Linn, S

2001-12-01

This article reports on the sensitivity and positive predictive value of clinical diagnosis of benign and malignant skin tumors by expert plastic surgeons in an Israeli clinic. Most published reports have focused on the sensitivity of clinicians' diagnoses, a general measure of the physician's skill that does not predict the rate of accuracy of a physician's diagnoses. Our study of 835 lesions in 778 patients, one of the largest Israeli series, assesses the clinical diagnosis of malignant and benign skin tumors and is one of the few that provide information on the positive predictive value, the measure that is of interest to both physicians and patients. The majority of tumors were benign (56.8 percent), 31.6 percent were malignant, and 11.6 percent were premalignant. Among the 474 benign lesions, 46 percent were nevi. The most common nevi subclass was compound nevi (53 percent), 9 percent of the nevi were dysplastic, and 5 percent were blue nevi. The most common malignant tumor was basal cell carcinoma, accounting for 78 percent of malignant tumors. Although sensitivity for clinical diagnosis of malignancy was 91.3 percent, the positive predictive value for clinical diagnosis of malignancy was 71.3 percent. The sensitivity rate for clinically diagnosing premalignant tumors was 42.3 percent, whereas the positive predictive value for these diagnoses was higher (64.1 percent). The sensitivity rate for diagnosis of all benign lesions was 85.9 percent, and the positive predictive value was 94.2 percent. The sensitivity rate for diagnosis of all nevi was 87.6 percent, and the positive predictive value was 85.7 percent: i.e., only seven of the 218 pathologically proven diagnoses of nevi (3.2 percent) were falsely diagnosed as malignant lesions. Even more interestingly, five of the 223 clinical diagnoses of nevi (2.2 percent) were pathologically proven to be malignant melanomas, and seven were found to be premalignant lesions (3.1 percent). It was concluded that publications which report only on the sensitivity neglect to provide information of interest regarding the positive predictive value. Often, positive predictive value is qualitatively different from the sensitivity, and thus relying only on the sensitivity may lead to incorrect evaluation of a clinical judgment, which may result in erroneous surgical decisions.

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.

Adjustment of regional regression equations for urban storm-runoff quality using at-site data

USGS Publications Warehouse

Barks, C.S.

1996-01-01

Regional regression equations have been developed to estimate urban storm-runoff loads and mean concentrations using a national data base. Four statistical methods using at-site data to adjust the regional equation predictions were developed to provide better local estimates. The four adjustment procedures are a single-factor adjustment, a regression of the observed data against the predicted values, a regression of the observed values against the predicted values and additional local independent variables, and a weighted combination of a local regression with the regional prediction. Data collected at five representative storm-runoff sites during 22 storms in Little Rock, Arkansas, were used to verify, and, when appropriate, adjust the regional regression equation predictions. Comparison of observed values of stormrunoff loads and mean concentrations to the predicted values from the regional regression equations for nine constituents (chemical oxygen demand, suspended solids, total nitrogen as N, total ammonia plus organic nitrogen as N, total phosphorus as P, dissolved phosphorus as P, total recoverable copper, total recoverable lead, and total recoverable zinc) showed large prediction errors ranging from 63 percent to more than several thousand percent. Prediction errors for 6 of the 18 regional regression equations were less than 100 percent and could be considered reasonable for water-quality prediction equations. The regression adjustment procedure was used to adjust five of the regional equation predictions to improve the predictive accuracy. For seven of the regional equations the observed and the predicted values are not significantly correlated. Thus neither the unadjusted regional equations nor any of the adjustments were appropriate. The mean of the observed values was used as a simple estimator when the regional equation predictions and adjusted predictions were not appropriate.

Predictive models of alcohol use based on attitudes and individual values.

PubMed

García del Castillo Rodríguez, José A; López-Sánchez, Carmen; Quiles Soler, M Carmen; García del Castillo-López, Alvaro; Gázquez Pertusa, Mónica; Marzo Campos, Juan Carlos; Inglés, Candido J

2013-01-01

Two predictive models are developed in this article: the first is designed to predict people's attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The questionnaire used obtained information on participants' alcohol use, attitudes and personal values. The results show that the attitudes model correctly classifies 76.3% of cases. Likewise, the model for level of alcohol use correctly classifies 82% of cases. According to our results, we can conclude that there are a series of individual values that influence drinking and attitudes to alcohol use, which therefore provides us with a potentially powerful instrument for developing preventive intervention programs.

The value of identity: olfactory notes on orbitofrontal cortex function.

PubMed

Gottfried, Jay A; Zelano, Christina

2011-12-01

Neuroscientific research has emphatically promoted the idea that the key function of the orbitofrontal cortex (OFC) is to encode value. Associative learning studies indicate that OFC representations of stimulus cues reflect the predictive value of expected outcomes. Neuroeconomic studies suggest that the OFC distills abstract representations of value from discrete commodities to optimize choice. Although value-based models provide good explanatory power for many different findings, these models are typically disconnected from the very stimuli and commodities giving rise to those value representations. Little provision is made, either theoretically or empirically, for the necessary cooperative role of object identity, without which value becomes orphaned from its source. As a step toward remediating the value of identity, this review provides a focused olfactory survey of OFC research, including new work from our lab, to highlight the elemental involvement of this region in stimulus-specific predictive coding of both perceptual outcomes and expected values. © 2011 New York Academy of Sciences.

Progress in the prediction of pKa values in proteins

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

Alexov, Emil; Mehler, Ernest L.; Baker, Nathan A.

2011-12-15

The pKa-cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pKa values and protein electrostatics in general. The first round of the pKa -cooperative, which challenged computational labs to carry out blind predictions against pKas experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6-10, 2009). This paper serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here we briefly outline existing approaches for pKa calculations, emphasizing methodsmore » that were used by the participants in calculating the blind pKa values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pKa calculations.« less

Research showcase, summer 2014 : the value of roadside vegetation, hydroplane prediction tool, gearing up for automated vehicles.

DOT National Transportation Integrated Search

2014-01-01

This issue of Research Showcase highlights the value of roadside vegetation, from stabilizing soil, : which protects infrastructure and provides safe clear zones for errant vehicles, to providing habitat : for wildlife and crop pollinators. Recent FD...

Predictive Validity of a Cigarette Purchase Task in a Randomized Controlled Trial of Contingent Vouchers for Smoking in Individuals With Substance Use Disorders

PubMed Central

Mackillop, James; Murphy, Cara M.; Martin, Rosemarie A.; Stojek, Monika; Tidey, Jennifer W.; Colby, Suzanne M.

2016-01-01

Abstract Introduction: A cigarette purchase task (CPT) is a behavioral economic measure of the reinforcing value of smoking in monetary terms (ie, cigarette demand). This study investigated whether cigarette demand predicted response to contingent monetary rewards for abstinence among individuals with substance use disorders. It also sought to replicate evidence for greater price sensitivity at whole-dollar pack price transitions (ie, left-digit effects). Methods: Participants ( N = 338) were individuals in residential substance use disorder treatment who participated in a randomized controlled trial that compared contingent vouchers to noncontingent vouchers for smoking abstinence. Baseline demand indices were used to predict number of abstinent days during the 14-day voucher period (after the reduction lead-in) and at 1 and 3 months afterward. Results: Demand indices correlated with measures of smoking and nicotine dependence. As measured by elasticity, intensity and Omax , higher demand significantly predicted fewer abstinent exhaled carbon monoxide readings during voucher period for individuals in the noncontingent vouchers condition. Breakpoint exhibited a trend-level association with abstinent exhaled carbon monoxide readings. Demand indices did not predict abstinence in the contingent vouchers group, and did not predict abstinence at 1- and 3-month follow-ups. Left-digit price transitions were associated with significantly greater reductions in consumption. Conclusions: The association of cigarette demand with smoking behavior only in the group for whom abstinence was not incentivized indicates that CPT assesses the value of smoking more than the value of money per se and that vouchers counteract the effects of the intrinsic reinforcing value of cigarettes. Results provide initial short-term evidence of predictive validity for the CPT indices. Implications: This study provides the first evidence of the validity of the CPT for predicting early response to brief advice for smoking cessation plus nicotine replacement in smokers with substance dependence. However, demand for cigarettes did not predict voucher-based treatment response, indicating that incentives serve as a powerful motivator not to smoke that acts in opposition to the intrinsic reinforcing value of cigarettes and that the indices reflect the value of smoking more than the value of money per se. PMID:26498173

Predicting Stability Constants for Uranyl Complexes Using Density Functional Theory

DOE PAGES

Vukovic, Sinisa; Hay, Benjamin P.; Bryantsev, Vyacheslav S.

2015-04-02

The ability to predict the equilibrium constants for the formation of 1:1 uranyl:ligand complexes (log K 1 values) provides the essential foundation for the rational design of ligands with enhanced uranyl affinity and selectivity. We also use density functional theory (B3LYP) and the IEFPCM continuum solvation model to compute aqueous stability constants for UO 2 2+ complexes with 18 donor ligands. Theoretical calculations permit reasonably good estimates of relative binding strengths, while the absolute log K 1 values are significantly overestimated. Accurate predictions of the absolute log K 1 values (root mean square deviation from experiment < 1.0 for logmore » K 1 values ranging from 0 to 16.8) can be obtained by fitting the experimental data for two groups of mono and divalent negative oxygen donor ligands. The utility of correlations is demonstrated for amidoxime and imide dioxime ligands, providing a useful means of screening for new ligands with strong chelate capability to uranyl.« less

Dopamine prediction error responses integrate subjective value from different reward dimensions

PubMed Central

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

2014-01-01

Prediction error signals enable us to learn through experience. These experiences include economic choices between different rewards that vary along multiple dimensions. Therefore, an ideal way to reinforce economic choice is to encode a prediction error that reflects the subjective value integrated across these reward dimensions. Previous studies demonstrated that dopamine prediction error responses reflect the value of singular reward attributes that include magnitude, probability, and delay. Obviously, preferences between rewards that vary along one dimension are completely determined by the manipulated variable. However, it is unknown whether dopamine prediction error responses reflect the subjective value integrated from different reward dimensions. Here, we measured the preferences between rewards that varied along multiple dimensions, and as such could not be ranked according to objective metrics. Monkeys chose between rewards that differed in amount, risk, and type. Because their choices were complete and transitive, the monkeys chose “as if” they integrated different rewards and attributes into a common scale of value. The prediction error responses of single dopamine neurons reflected the integrated subjective value inferred from the choices, rather than the singular reward attributes. Specifically, amount, risk, and reward type modulated dopamine responses exactly to the extent that they influenced economic choices, even when rewards were vastly different, such as liquid and food. This prediction error response could provide a direct updating signal for economic values. PMID:24453218

An application of quantile random forests for predictive mapping of forest attributes

Treesearch

E.A. Freeman; G.G. Moisen

2015-01-01

Increasingly, random forest models are used in predictive mapping of forest attributes. Traditional random forests output the mean prediction from the random trees. Quantile regression forests (QRF) is an extension of random forests developed by Nicolai Meinshausen that provides non-parametric estimates of the median predicted value as well as prediction quantiles. It...

A two-component rain model for the prediction of attenuation and diversity improvement

NASA Technical Reports Server (NTRS)

Crane, R. K.

1982-01-01

A new model was developed to predict attenuation statistics for a single Earth-satellite or terrestrial propagation path. The model was extended to provide predictions of the joint occurrences of specified or higher attenuation values on two closely spaced Earth-satellite paths. The joint statistics provide the information required to obtain diversity gain or diversity advantage estimates. The new model is meteorologically based. It was tested against available Earth-satellite beacon observations and terrestrial path measurements. The model employs the rain climate region descriptions of the Global rain model. The rms deviation between the predicted and observed attenuation values for the terrestrial path data was 35 percent, a result consistent with the expectations of the Global model when the rain rate distribution for the path is not used in the calculation. Within the United States the rms deviation between measurement and prediction was 36 percent but worldwide it was 79 percent.

Beyond SaGMRotI: Conversion to SaArb, SaSN, and SaMaxRot

USGS Publications Warehouse

Watson-Lamprey, J. A.; Boore, D.M.

2007-01-01

In the seismic design of structures, estimates of design forces are usually provided to the engineer in the form of elastic response spectra. Predictive equations for elastic response spectra are derived from empirical recordings of ground motion. The geometric mean of the two orthogonal horizontal components of motion is often used as the response value in these predictive equations, although it is not necessarily the most relevant estimate of forces within the structure. For some applications it is desirable to estimate the response value on a randomly chosen single component of ground motion, and in other applications the maximum response in a single direction is required. We give adjustment factors that allow converting the predictions of geometric-mean ground-motion predictions into either of these other two measures of seismic ground-motion intensity. In addition, we investigate the relation of the strike-normal component of ground motion to the maximum response values. We show that the strike-normal component of ground motion seldom corresponds to the maximum horizontal-component response value (in particular, at distances greater than about 3 km from faults), and that focusing on this case in exclusion of others can result in the underestimation of the maximum component. This research provides estimates of the maximum response value of a single component for all cases, not just near-fault strike-normal components. We provide modification factors that can be used to convert predictions of ground motions in terms of the geometric mean to the maximum spectral acceleration (SaMaxRot) and the random component of spectral acceleration (SaArb). Included are modification factors for both the mean and the aleatory standard deviation of the logarithm of the motions.

FEV1/FVC and FEV1 for the assessment of chronic airflow obstruction in prevalence studies: do prediction equations need revision?

PubMed

Roche, Nicolas; Dalmay, François; Perez, Thierry; Kuntz, Claude; Vergnenègre, Alain; Neukirch, Françoise; Giordanella, Jean-Pierre; Huchon, Gérard

2008-11-01

Little is known on the long-term validity of reference equations used in the calculation of FEV(1) and FEV(1)/FVC predicted values. This survey assessed the prevalence of chronic airflow obstruction in a population-based sample and how it is influenced by: (i) the definition of airflow obstruction; and (ii) equations used to calculate predicted values. Subjects aged 45 or more were recruited in health prevention centers, performed spirometry and fulfilled a standardized ECRHS-derived questionnaire. Previously diagnosed cases and risk factors were identified. Prevalence of airflow obstruction was calculated using: (i) ATS-GOLD definition (FEV(1)/FVC<0.70); and (ii) ERS definition (FEV(1)/FVC

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.

Spatial Pattern of Standing Timber Value across the Brazilian Amazon

PubMed Central

Ahmed, Sadia E.; Ewers, Robert M.

2012-01-01

The Amazon is a globally important system, providing a host of ecosystem services from climate regulation to food sources. It is also home to a quarter of all global diversity. Large swathes of forest are removed each year, and many models have attempted to predict the spatial patterns of this forest loss. The spatial patterns of deforestation are determined largely by the patterns of roads that open access to frontier areas and expansion of the road network in the Amazon is largely determined by profit seeking logging activities. Here we present predictions for the spatial distribution of standing value of timber across the Amazon. We show that the patterns of timber value reflect large-scale ecological gradients, determining the spatial distribution of functional traits of trees which are, in turn, correlated with timber values. We expect that understanding the spatial patterns of timber value across the Amazon will aid predictions of logging movements and thus predictions of potential future road developments. These predictions in turn will be of great use in estimating the spatial patterns of deforestation in this globally important biome. PMID:22590520

Polar body based aneuploidy screening is poorly predictive of embryo ploidy and reproductive potential.

PubMed

Salvaggio, C N; Forman, E J; Garnsey, H M; Treff, N R; Scott, R T

2014-09-01

Polar body (polar body) biopsy represents one possible solution to performing comprehensive chromosome screening (CCS). This study adds to what is known about the predictive value of polar body based testing for the genetic status of the resulting embryo, but more importantly, provides the first evaluation of the predictive value for actual clinical outcomes after embryo transfer. SNP array was performed on first polar body, second polar body, and either a blastomere or trophectoderm biopsy, or the entire arrested embryo. Concordance of the polar body-based prediction with the observed diagnoses in the embryos was assessed. In addition, the predictive value of the polar body -based diagnosis for the specific clinical outcome of transferred embryos was evaluated through the use of DNA fingerprinting to track individual embryos. There were 459 embryos analyzed from 96 patients with a mean maternal age of 35.3. The polar body-based predictive value for the embryo based diagnosis was 70.3%. The blastocyst implantation predictive value of a euploid trophectoderm was higher than from euploid polar bodies (51% versus 40%). The cleavage stage embryo implantation predictive value of a euploid blastomere was also higher than from euploid polar bodies (31% versus 22%). Polar body based aneuploidy screening results were less predictive of actual clinical outcomes than direct embryo assessment and may not be adequate to improve sustained implantation rates. In nearly one-third of cases the polar body based analysis failed to predict the ploidy of the embryo. This imprecision may hinder efforts for polar body based CCS to improve IVF clinical outcomes.

Prediction of daily sea surface temperature using efficient neural networks

NASA Astrophysics Data System (ADS)

Patil, Kalpesh; Deo, Makaranad Chintamani

2017-04-01

Short-term prediction of sea surface temperature (SST) is commonly achieved through numerical models. Numerical approaches are more suitable for use over a large spatial domain than in a specific site because of the difficulties involved in resolving various physical sub-processes at local levels. Therefore, for a given location, a data-driven approach such as neural networks may provide a better alternative. The application of neural networks, however, needs a large experimentation in their architecture, training methods, and formation of appropriate input-output pairs. A network trained in this manner can provide more attractive results if the advances in network architecture are additionally considered. With this in mind, we propose the use of wavelet neural networks (WNNs) for prediction of daily SST values. The prediction of daily SST values was carried out using WNN over 5 days into the future at six different locations in the Indian Ocean. First, the accuracy of site-specific SST values predicted by a numerical model, ROMS, was assessed against the in situ records. The result pointed out the necessity for alternative approaches. First, traditional networks were tried and after noticing their poor performance, WNN was used. This approach produced attractive forecasts when judged through various error statistics. When all locations were viewed together, the mean absolute error was within 0.18 to 0.32 °C for a 5-day-ahead forecast. The WNN approach was thus found to add value to the numerical method of SST prediction when location-specific information is desired.

Multimethod, Multi-Informant Agreement, and Positive Predictive Value in the Identification of Child Anxiety Disorders Using the SCAS and ADIS-C

ERIC Educational Resources Information Center

Brown-Jacobsen, Amy M.; Wallace, Dustin P.; Whiteside, Stephen P. H.

2011-01-01

The current study sought to provide practical information for the clinical use of child and parent reports of child anxiety symptoms by investigating agreement between parent, child, and clinician as well as the predictive value of this information. Examining 88 anxious children and their parents, the study compared agreement by correlating parent…

The in-training examination: an analysis of its predictive value on performance on the general pediatrics certification examination.

PubMed

Althouse, Linda A; McGuinness, Gail A

2008-09-01

This study investigates the predictive validity of the In-Training Examination (ITE). Although studies have confirmed the predictive validity of ITEs in other medical specialties, no study has been done for general pediatrics. Each year, residents in accredited pediatric training programs take the ITE as a self-assessment instrument. The ITE is similar to the American Board of Pediatrics General Pediatrics Certifying Examination. First-time takers of the certifying examination over a 5-year period who took at least 1 ITE examination were included in the sample. Regression models analyzed the predictive value of the ITE. The predictive power of the ITE in the first training year is minimal. However, the predictive power of the ITE increases each year, providing the greatest power in the third year of training. Even though ITE scores provide information regarding the likelihood of passing the certification examination, the data should be used with caution, particularly in the first training year. Other factors also must be considered when predicting performance on the certification examination. This study continues to support the ITE as an assessment tool for program directors, as well as a means of providing residents with feedback regarding their acquisition of pediatric knowledge.

Predictive value of first fasting plasma glucose compared with admission plasma glucose for undiagnosed diabetes in a stable cardiology population.

PubMed

Wen, Zhu-zhi; Zhang, Xin-mei; Mai, Zun; Geng, Deng-feng; Wang, Jing-feng

2012-09-01

The study compared the predictive value of admission plasma glucose (APG) and first fasting plasma glucose (FPG) in stratifying patients meriting an oral glucose tolerance test (OGTT). Characteristics of APG, FPG and OGTT 2-hour glucose as well as other blood measurements, physical examinations and medical information were assessed in 994 patients without known diabetes. The prevalences of diabetes and impaired glucose tolerance were 24.6% and 37.9%, according to an OGTT, respectively. The first FPG demonstrated stronger predictive value in diagnosing diabetes than APG did both in overall and in patients with less clinical value. Compared to the first FPG, APG provided less value to coronary artery disease, hypertension and high-sensitivity C-reactive protein for diabetes screening. The first FPG exerted more predictive value than APG did and was still a preferable reference prior to APG in stratifying patients for undiagnosed diabetes by an OGTT. Copyright © 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Predictive value of ventilatory inflection points determined under field conditions.

PubMed

Heyde, Christian; Mahler, Hubert; Roecker, Kai; Gollhofer, Albert

2016-01-01

The aim of this study was to evaluate the predictive potential provided by two ventilatory inflection points (VIP1 and VIP2) examined in field without using gas analysis systems and uncomfortable facemasks. A calibrated respiratory inductance plethysmograph (RIP) and a computerised routine were utilised, respectively, to derive ventilation and to detect VIP1 and VIP2 during a standardised field ramp test on a 400 m running track on 81 participants. In addition, average running speed of a competitive 1000 m run (S1k) was observed as criterion. The predictive value of running speed at VIP1 (SVIP1) and the speed range between VIP1 and VIP2 in relation to VIP2 (VIPSPAN) was analysed via regression analysis. VIPSPAN rather than running speed at VIP2 (SVIP2) was operationalised as a predictor to consider the covariance between SVIP1 and SVIP2. SVIP1 and VIPSPAN, respectively, provided 58.9% and 22.9% of explained variance in regard to S1k. Considering covariance, the timing of two ventilatory inflection points provides predictive value in regard to a competitive 1000 m run. This is the first study to apply computerised detection of ventilatory inflection points in a field setting independent on measurements of the respiratory gas exchange and without using any facemasks.

A threshold-free summary index of prediction accuracy for censored time to event data.

PubMed

Yuan, Yan; Zhou, Qian M; Li, Bingying; Cai, Hengrui; Chow, Eric J; Armstrong, Gregory T

2018-05-10

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a prespecified future time t 0 . Accuracy measures such as positive predictive values have been recommended for evaluating the predictive performance. However, for commonly used continuous or ordinal risk score systems, these measures require a subjective cutoff threshold value that dichotomizes the risk scores. The need for a cutoff value created barriers for practitioners and researchers. In this paper, we propose a threshold-free summary index of positive predictive values that accommodates time-dependent event status and competing risks. We develop a nonparametric estimator and provide an inference procedure for comparing this summary measure between 2 risk scores for censored time to event data. We conduct a simulation study to examine the finite-sample performance of the proposed estimation and inference procedures. Lastly, we illustrate the use of this measure on a real data example, comparing 2 risk score systems for predicting heart failure in childhood cancer survivors. Copyright © 2018 John Wiley & Sons, Ltd.

Probabilistic Reasoning and Prediction with Young Children

ERIC Educational Resources Information Center

Kinnear, Virginia; Clark, Julie

2014-01-01

This paper reports findings from a classroom based study with 5 year old children in their first term of school. A data modelling activity contextualised by a picture story book was used to present a prediction problem. A data table with numerical data values provided for three consecutive days of rubbish collection was provided, with a fourth day…

Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

PubMed Central

Cavanagh, Sean E; Wallis, Joni D; Kennerley, Steven W; Hunt, Laurence T

2016-01-01

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations. DOI: http://dx.doi.org/10.7554/eLife.18937.001 PMID:27705742

Can administrative health utilisation data provide an accurate diabetes prevalence estimate for a geographical region?

PubMed

Chan, Wing Cheuk; Papaconstantinou, Dean; Lee, Mildred; Telfer, Kendra; Jo, Emmanuel; Drury, Paul L; Tobias, Martin

2018-05-01

To validate the New Zealand Ministry of Health (MoH) Virtual Diabetes Register (VDR) using longitudinal laboratory results and to develop an improved algorithm for estimating diabetes prevalence at a population level. The assigned diabetes status of individuals based on the 2014 version of the MoH VDR is compared to the diabetes status based on the laboratory results stored in the Auckland regional laboratory result repository (TestSafe) using the New Zealand diabetes diagnostic criteria. The existing VDR algorithm is refined by reviewing the sensitivity and positive predictive value of the each of the VDR algorithm rules individually and as a combination. The diabetes prevalence estimate based on the original 2014 MoH VDR was 17% higher (n = 108,505) than the corresponding TestSafe prevalence estimate (n = 92,707). Compared to the diabetes prevalence based on TestSafe, the original VDR has a sensitivity of 89%, specificity of 96%, positive predictive value of 76% and negative predictive value of 98%. The modified VDR algorithm has improved the positive predictive value by 6.1% and the specificity by 1.4% with modest reductions in sensitivity of 2.2% and negative predictive value of 0.3%. At an aggregated level the overall diabetes prevalence estimated by the modified VDR is 5.7% higher than the corresponding estimate based on TestSafe. The Ministry of Health Virtual Diabetes Register algorithm has been refined to provide a more accurate diabetes prevalence estimate at a population level. The comparison highlights the potential value of a national population long term condition register constructed from both laboratory results and administrative data. Copyright © 2018 Elsevier B.V. All rights reserved.

Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers.

PubMed

Da, Yang

2015-12-18

The amount of functional genomic information has been growing rapidly but remains largely unused in genomic selection. Genomic prediction and estimation using haplotypes in genome regions with functional elements such as all genes of the genome can be an approach to integrate functional and structural genomic information for genomic selection. Towards this goal, this article develops a new haplotype approach for genomic prediction and estimation. A multi-allelic haplotype model treating each haplotype as an 'allele' was developed for genomic prediction and estimation based on the partition of a multi-allelic genotypic value into additive and dominance values. Each additive value is expressed as a function of h - 1 additive effects, where h = number of alleles or haplotypes, and each dominance value is expressed as a function of h(h - 1)/2 dominance effects. For a sample of q individuals, the limit number of effects is 2q - 1 for additive effects and is the number of heterozygous genotypes for dominance effects. Additive values are factorized as a product between the additive model matrix and the h - 1 additive effects, and dominance values are factorized as a product between the dominance model matrix and the h(h - 1)/2 dominance effects. Genomic additive relationship matrix is defined as a function of the haplotype model matrix for additive effects, and genomic dominance relationship matrix is defined as a function of the haplotype model matrix for dominance effects. Based on these results, a mixed model implementation for genomic prediction and variance component estimation that jointly use haplotypes and single markers is established, including two computing strategies for genomic prediction and variance component estimation with identical results. The multi-allelic genetic partition fills a theoretical gap in genetic partition by providing general formulations for partitioning multi-allelic genotypic values and provides a haplotype method based on the quantitative genetics model towards the utilization of functional and structural genomic information for genomic prediction and estimation.

Driver's mental workload prediction model based on physiological indices.

PubMed

Yan, Shengyuan; Tran, Cong Chi; Wei, Yingying; Habiyaremye, Jean Luc

2017-09-15

Developing an early warning model to predict the driver's mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new drivers' MWL and their work performance, regarding the number of errors. Additionally, the group method of data handling is used to establish the driver's MWL predictive model based on subjective rating (NASA task load index [NASA-TLX]) and six physiological indices. The results indicate that the NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an R 2 value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver's work performance.

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

PubMed

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

2016-12-01

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

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.

Using Toxicological Evidence from QSAR Models in Practice

EPA Science Inventory

The new generation of QSAR models provides supporting documentation in addition to the predicted toxicological value. Such information enables the toxicologist to explore the properties of chemical substances and to review and increase the reliability of toxicity predictions. Thi...

Using Long-Short-Term-Memory Recurrent Neural Networks to Predict Aviation Engine Vibrations

NASA Astrophysics Data System (ADS)

ElSaid, AbdElRahman Ahmed

This thesis examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The different networks are trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a "memory" of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 1, 5, 10, and 20 seconds in the future, with 2.84% 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.

Using instrumental (CIE and reflectance) measures to predict consumers' acceptance of beef colour.

PubMed

Holman, Benjamin W B; van de Ven, Remy J; Mao, Yanwei; Coombs, Cassius E O; Hopkins, David L

2017-05-01

We aimed to establish colorimetric thresholds based upon the capacity for instrumental measures to predict consumer satisfaction with beef colour. A web-based survey was used to distribute standardised photographs of beef M. longissimus lumborum with known colorimetrics (L*, a*, b*, hue, chroma, ratio of reflectance at 630nm and 580nm, and estimated deoxymyoglobin, oxymyoglobin and metmyoglobin concentrations) for scrutiny. Consumer demographics and perceived importance of colour to beef value were also evaluated. It was found that a* provided the most simple and robust prediction of beef colour acceptability. Beef colour was considered acceptable (with 95% acceptance) when a* values were equal to or above 14.5. Demographic effects on this threshold were negligible, but consumer nationality and gender did contribute to variation in the relative importance of colour to beef value. These results provide future beef colour studies with context to interpret objective colour measures in terms of consumer acceptance and market appeal. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

A reexamination of age-related variation in body weight and morphometry of Maryland nutria

USGS Publications Warehouse

Sherfy, M.H.; Mollett, T.A.; McGowan, K.R.; Daugherty, S.L.

2006-01-01

Age-related variation in morphometry has been documented for many species. Knowledge of growth patterns can be useful for modeling energetics, detecting physiological influences on populations, and predicting age. These benefits have shown value in understanding population dynamics of invasive species, particularly in developing efficient control and eradication programs. However, development and evaluation of descriptive and predictive models is a critical initial step in this process. Accordingly, we used data from necropsies of 1,544 nutria (Myocastor coypus) collected in Maryland, USA, to evaluate the accuracy of previously published models for prediction of nutria age from body weight. Published models underestimated body weights of our animals, especially for ages <3. We used cross-validation procedures to develop and evaluate models for describing nutria growth patterns and for predicting nutria age. We derived models from a randomly selected model-building data set (n = 192-193 M, 217-222 F) and evaluated them with the remaining animals (n = 487-488 M, 642-647 F). We used nonlinear regression to develop Gompertz growth-curve models relating morphometric variables to age. Predicted values of morphometric variables fell within the 95% confidence limits of their true values for most age classes. We also developed predictive models for estimating nutria age from morphometry, using linear regression of log-transformed age on morphometric variables. The evaluation data set corresponded with 95% prediction intervals from the new models. Predictive models for body weight and length provided greater accuracy and less bias than models for foot length and axillary girth. Our growth models accurately described age-related variation in nutria morphometry, and our predictive models provided accurate estimates of ages from morphometry that will be useful for live-captured individuals. Our models offer better accuracy and precision than previously published models, providing a capacity for modeling energetics and growth patterns of Maryland nutria as well as an empirical basis for determining population age structure from live-captured animals.

Comparing quantitative values of two generations of laser-assisted indocyanine green dye angiography systems: can we predict necrosis?

PubMed

Phillips, Brett T; Fourman, Mitchell S; Rivara, Andrew; Dagum, Alexander B; Huston, Tara L; Ganz, Jason C; Bui, Duc T; Khan, Sami U

2014-01-01

Several devices exist today to assist the intraoperative determination of skin flap perfusion. Laser-Assisted Indocyanine Green Dye Angiography (LAICGA) has been shown to accurately predict mastectomy skin flap necrosis using quantitative perfusion values. The laser properties of the latest LAICGA device (SPY Elite) differ significantly from its predecessor system (SPY 2001), preventing direct translation of previous published data. The purpose of this study was to establish a mathematical relationship of perfusion values between these 2 devices. Breast reconstruction patients were prospectively enrolled into a clinical trial where skin flap evaluation and excision was based on quantitative SPY Q values previously established in the literature. Initial study patients underwent mastectomy skin flap evaluation using both SPY systems simultaneously. Absolute perfusion unit (APU) values at identical locations on the breast were then compared graphically. 210 data points were identified on the same patients (n = 4) using both SPY systems. A linear relationship (y = 2.9883x + 12.726) was identified with a high level or correlation (R(2) = 0.744). Previously published values using SPY 2001 (APU 3.7) provided a value of 23.8 APU on the SPY Elite. In addition, postoperative necrosis in these patients correlated to regions of skin identified with the SPY Elite with APU less than 23.8. Intraoperative comparison of LAICGA systems has provided direct correlation of perfusion values predictive of necrosis that were previously established in the literature. An APU value of 3.7 from the SPY 2001 correlates to a SPY Elite APU value of 23.8.

Comparing Quantitative Values of Two Generations of Laser-Assisted Indocyanine Green Dye Angiography Systems: Can We Predict Necrosis?

PubMed Central

Fourman, Mitchell S.; Rivara, Andrew; Dagum, Alexander B.; Huston, Tara L.; Ganz, Jason C.; Bui, Duc T.; Khan, Sami U.

2014-01-01

Objective: Several devices exist today to assist the intraoperative determination of skin flap perfusion. Laser-Assisted Indocyanine Green Dye Angiography (LAICGA) has been shown to accurately predict mastectomy skin flap necrosis using quantitative perfusion values. The laser properties of the latest LAICGA device (SPY Elite) differ significantly from its predecessor system (SPY 2001), preventing direct translation of previous published data. The purpose of this study was to establish a mathematical relationship of perfusion values between these 2 devices. Methods: Breast reconstruction patients were prospectively enrolled into a clinical trial where skin flap evaluation and excision was based on quantitative SPY Q values previously established in the literature. Initial study patients underwent mastectomy skin flap evaluation using both SPY systems simultaneously. Absolute perfusion unit (APU) values at identical locations on the breast were then compared graphically. Results: 210 data points were identified on the same patients (n = 4) using both SPY systems. A linear relationship (y = 2.9883x + 12.726) was identified with a high level or correlation (R2 = 0.744). Previously published values using SPY 2001 (APU 3.7) provided a value of 23.8 APU on the SPY Elite. In addition, postoperative necrosis in these patients correlated to regions of skin identified with the SPY Elite with APU less than 23.8. Conclusion: Intraoperative comparison of LAICGA systems has provided direct correlation of perfusion values predictive of necrosis that were previously established in the literature. An APU value of 3.7 from the SPY 2001 correlates to a SPY Elite APU value of 23.8. PMID:25525483

Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs.

PubMed

Ahmadi, Hamed; Rodehutscord, Markus

2017-01-01

In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. The MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values. The results revealed that the developed ANN [ R 2  = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM ( R 2  = 0.95; RMSE = 0.21 MJ/kg of dry matter) models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR ( R 2  = 0.89; RMSE = 0.27 MJ/kg of dry matter). The developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel ® calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.

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.

Adjusting Quality index Log Values to Represent Local and Regional Commercial Sawlog Product Values

Treesearch

Orris D. McCauley; Joseph J. Mendel; Joseph J. Mendel

1969-01-01

The primary purpose of this paper is not only to report the results of a comparative analysis as to how well the Q.I. method predicts log product values when compared to commercial sawmill log output values, but also to develop a methodology which will facilitate the comparison and provide the adjustments needed by the sawmill operator.

Potential uncertainty reduction in model-averaged benchmark dose estimates informed by an additional dose study.

PubMed

Shao, Kan; Small, Mitchell J

2011-10-01

A methodology is presented for assessing the information value of an additional dosage experiment in existing bioassay studies. The analysis demonstrates the potential reduction in the uncertainty of toxicity metrics derived from expanded studies, providing insights for future studies. Bayesian methods are used to fit alternative dose-response models using Markov chain Monte Carlo (MCMC) simulation for parameter estimation and Bayesian model averaging (BMA) is used to compare and combine the alternative models. BMA predictions for benchmark dose (BMD) are developed, with uncertainty in these predictions used to derive the lower bound BMDL. The MCMC and BMA results provide a basis for a subsequent Monte Carlo analysis that backcasts the dosage where an additional test group would have been most beneficial in reducing the uncertainty in the BMD prediction, along with the magnitude of the expected uncertainty reduction. Uncertainty reductions are measured in terms of reduced interval widths of predicted BMD values and increases in BMDL values that occur as a result of this reduced uncertainty. The methodology is illustrated using two existing data sets for TCDD carcinogenicity, fitted with two alternative dose-response models (logistic and quantal-linear). The example shows that an additional dose at a relatively high value would have been most effective for reducing the uncertainty in BMA BMD estimates, with predicted reductions in the widths of uncertainty intervals of approximately 30%, and expected increases in BMDL values of 5-10%. The results demonstrate that dose selection for studies that subsequently inform dose-response models can benefit from consideration of how these models will be fit, combined, and interpreted. © 2011 Society for Risk Analysis.

Predicting charmonium and bottomonium spectra with a quark harmonic oscillator

NASA Technical Reports Server (NTRS)

Norbury, J. W.; Badavi, F. F.; Townsend, L. W.

1986-01-01

The nonrelativistic quark model is applied to heavy (nonrelativistic) meson (two-body) systems to obtain sufficiently accurate predictions of the spin-averaged mass levels of the charmonium and bottomonium spectra as an example of the three-dimensional harmonic oscillator. The present calculations do not include any spin dependence, but rather, mass values are averaged for different spins. Results for a charmed quark mass value of 1500 MeV/c-squared show that the simple harmonic oscillator model provides good agreement with experimental values for 3P states, and adequate agreement for the 3S1 states.

Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

DOEpatents

Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

2006-11-14

A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

Algorithm for Lossless Compression of Calibrated Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Kiely, Aaron B.; Klimesh, Matthew A.

2010-01-01

A two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.

Development of an evidence-based approach to external quality assurance for breast cancer hormone receptor immunohistochemistry: comparison of reference values.

PubMed

Makretsov, Nikita; Gilks, C Blake; Alaghehbandan, Reza; Garratt, John; Quenneville, Louise; Mercer, Joel; Palavdzic, Dragana; Torlakovic, Emina E

2011-07-01

External quality assurance and proficiency testing programs for breast cancer predictive biomarkers are based largely on traditional ad hoc design; at present there is no universal consensus on definition of a standard reference value for samples used in external quality assurance programs. To explore reference values for estrogen receptor and progesterone receptor immunohistochemistry in order to develop an evidence-based analytic platform for external quality assurance. There were 31 participating laboratories, 4 of which were previously designated as "expert" laboratories. Each participant tested a tissue microarray slide with 44 breast carcinomas for estrogen receptor and progesterone receptor and submitted it to the Canadian Immunohistochemistry Quality Control Program for analysis. Nuclear staining in 1% or more of the tumor cells was a positive score. Five methods for determining reference values were compared. All reference values showed 100% agreement for estrogen receptor and progesterone receptor scores, when indeterminate results were excluded. Individual laboratory performance (agreement rates, test sensitivity, test specificity, positive predictive value, negative predictive value, and κ value) was very similar for all reference values. Identification of suboptimal performance by all methods was identical for 30 of 31 laboratories. Estrogen receptor assessment of 1 laboratory was discordant: agreement was less than 90% for 3 of 5 reference values and greater than 90% with the use of 2 other reference values. Various reference values provide equivalent laboratory rating. In addition to descriptive feedback, our approach allows calculation of technical test sensitivity and specificity, positive and negative predictive values, agreement rates, and κ values to guide corrective actions.

Real-ear-to-coupler difference predictions as a function of age for two coupling procedures.

PubMed

Bagatto, Marlene P; Scollie, Susan D; Seewald, Richard C; Moodie, K Shane; Hoover, Brenda M

2002-09-01

The predicted real-ear-to-coupler difference (RECD) values currently used in pediatric hearing instrument prescription methods are based on 12-month age range categories and were derived from measures using standard acoustic immittance probe tips. Consequently, the purpose of this study was to develop normative RECD predicted values for foam/acoustic immittance tips and custom earmolds across the age continuum. To this end, RECD data were collected on 392 infants and children (141 with acoustic immittance tips, 251 with earmolds) to develop normative regression equations for use in deriving continuous age predictions of RECDs for foam/acoustic immittance tips and earmolds. Owing to the substantial between-subject variability observed in the data, the predictive equations of RECDs by age (in months) resulted in only gross estimates of RECD values (i.e., within +/- 4.4 dB for 95% of acoustic immittance tip measures; within +/- 5.4 dB in 95% of measures with custom earmolds) across frequency. Thus, it is concluded that the estimates derived from this study should not be used to replace the more precise individual RECD measurements. Relative to previously available normative RECD values for infants and young children, however, the estimates derived through this study provide somewhat more accurate predicted values for use under those circumstances for which individual RECD measurements cannot be made.

[Diagnostic value of a predictive model for complete ruptures of the rotator cuff associated to subacromial impingement].

PubMed

Águila-Ledesma, I R; Córdova-Fonseca, J L; Medina-Pontaza, O; Núñez-Gómez, D A; Calvache-García, C; Pérez-Atanasio, J M; Torres-González, R

2017-01-01

Pathology related to the rotator cuff remains among the most prevalent musculoskeletal diseases. There is an increasing need for imaging studies (MRI, US, arthroscopy) to test the diagnostic performance of the medical history and physical examination. To prove the diagnostic value of a clinical-radiographic predictive model to find complete ruptures of the rotator cuff. Descriptive, observational, prospective, transversal and analytical study. Fifty-five patients with preoperative shoulder pain were evaluated with 13 predictive variables: age > 50 years, nocturnal pain, muscle weakness, clinical signs of Neer, Hawkins, Jobe, external rotation lag (ERLS), belly-press, bear hug, and lift-off, radiographic measurement of subacromial space, acromial index and critical shoulder angle. Sensitivity, specificity, and positive and negative predictive values were measured in each variable, comparing the results of each one against the postoperative findings. Of the 55 patients evaluated, 42 had a complete rupture of the rotator cuff in the postoperative period. The eight variables with a higher diagnostic value were selected and a ROC curve was performed, providing an area under the curve of 0.88. This predictive model uses eight variables (age > 50 years, nocturnal pain, muscle weakness, Jobe, Hawkins, ERLS, subacromial space ≤ 6 mm, and critical shoulder angle > 35°), which together add the predictive value of 0.88 (AUC) to diagnose complete ruptures of the supraspinatus tendon.

Glycated hemoglobin measurement and prediction of cardiovascular disease.

PubMed

Di Angelantonio, Emanuele; Gao, Pei; Khan, Hassan; Butterworth, Adam S; Wormser, David; Kaptoge, Stephen; Kondapally Seshasai, Sreenivasa Rao; Thompson, Alex; Sarwar, Nadeem; Willeit, Peter; Ridker, Paul M; Barr, Elizabeth L M; Khaw, Kay-Tee; Psaty, Bruce M; Brenner, Hermann; Balkau, Beverley; Dekker, Jacqueline M; Lawlor, Debbie A; Daimon, Makoto; Willeit, Johann; Njølstad, Inger; Nissinen, Aulikki; Brunner, Eric J; Kuller, Lewis H; Price, Jackie F; Sundström, Johan; Knuiman, Matthew W; Feskens, Edith J M; Verschuren, W M M; Wald, Nicholas; Bakker, Stephan J L; Whincup, Peter H; Ford, Ian; Goldbourt, Uri; Gómez-de-la-Cámara, Agustín; Gallacher, John; Simons, Leon A; Rosengren, Annika; Sutherland, Susan E; Björkelund, Cecilia; Blazer, Dan G; Wassertheil-Smoller, Sylvia; Onat, Altan; Marín Ibañez, Alejandro; Casiglia, Edoardo; Jukema, J Wouter; Simpson, Lara M; Giampaoli, Simona; Nordestgaard, Børge G; Selmer, Randi; Wennberg, Patrik; Kauhanen, Jussi; Salonen, Jukka T; Dankner, Rachel; Barrett-Connor, Elizabeth; Kavousi, Maryam; Gudnason, Vilmundur; Evans, Denis; Wallace, Robert B; Cushman, Mary; D'Agostino, Ralph B; Umans, Jason G; Kiyohara, Yutaka; Nakagawa, Hidaeki; Sato, Shinichi; Gillum, Richard F; Folsom, Aaron R; van der Schouw, Yvonne T; Moons, Karel G; Griffin, Simon J; Sattar, Naveed; Wareham, Nicholas J; Selvin, Elizabeth; Thompson, Simon G; Danesh, John

2014-03-26

The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. Analysis of individual-participant data available from 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5% to <7.5%), and high (≥ 7.5%) risk. During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20,840 incident fatal and nonfatal CVD outcomes (13,237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.

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.

Optimal weighted combinatorial forecasting model of QT dispersion of ECGs in Chinese adults.

PubMed

Wen, Zhang; Miao, Ge; Xinlei, Liu; Minyi, Cen

2016-07-01

This study aims to provide a scientific basis for unifying the reference value standard of QT dispersion of ECGs in Chinese adults. Three predictive models including regression model, principal component model, and artificial neural network model are combined to establish the optimal weighted combination model. The optimal weighted combination model and single model are verified and compared. Optimal weighted combinatorial model can reduce predicting risk of single model and improve the predicting precision. The reference value of geographical distribution of Chinese adults' QT dispersion was precisely made by using kriging methods. When geographical factors of a particular area are obtained, the reference value of QT dispersion of Chinese adults in this area can be estimated by using optimal weighted combinatorial model and reference value of the QT dispersion of Chinese adults anywhere in China can be obtained by using geographical distribution figure as well.

Models of Affective Decision Making: How Do Feelings Predict Choice?

PubMed

Charpentier, Caroline J; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P; Sharot, Tali

2016-06-01

Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. © The Author(s) 2016.

Revisiting Grodzins systematics of B(E2) values

DOE PAGES

Pritychenko, B.; Birch, M.; Singh, B.

2017-04-03

Using Grodzins formalism, we analyze systematics of our latest evaluated B(E2) data for all the even–even nuclei in Z=2–104. The analysis indicates a low predictive power of systematics for a large number of cases, and a strong correlation between B(E2) fit values and nuclear structure effects. These findings provide a strong rationale for introduction of individual or elemental (grouped by Z) fit parameters. The current estimates of quadrupole collectivities for systematics of even–even nuclei yield complementary values for comparison with experimental results and theoretical calculations. Furthermore, the lists of fit parameters and predicted B(E2) values are given and possible implicationsmore » are discussed.« less

Can non‐clinical repolarization assays predict the results of clinical thorough QT studies? Results from a research consortium

PubMed Central

Park, Eunjung; Gintant, Gary A; Bi, Daoqin; Kozeli, Devi; Pettit, Syril D; Skinner, Matthew; Willard, James; Wisialowski, Todd; Koerner, John; Valentin, Jean‐Pierre

2018-01-01

Background and Purpose Translation of non‐clinical markers of delayed ventricular repolarization to clinical prolongation of the QT interval corrected for heart rate (QTc) (a biomarker for torsades de pointes proarrhythmia) remains an issue in drug discovery and regulatory evaluations. We retrospectively analysed 150 drug applications in a US Food and Drug Administration database to determine the utility of established non‐clinical in vitro IKr current human ether‐à‐go‐go‐related gene (hERG), action potential duration (APD) and in vivo (QTc) repolarization assays to detect and predict clinical QTc prolongation. Experimental Approach The predictive performance of three non‐clinical assays was compared with clinical thorough QT study outcomes based on free clinical plasma drug concentrations using sensitivity and specificity, receiver operating characteristic (ROC) curves, positive (PPVs) and negative predictive values (NPVs) and likelihood ratios (LRs). Key Results Non‐clinical assays demonstrated robust specificity (high true negative rate) but poor sensitivity (low true positive rate) for clinical QTc prolongation at low‐intermediate (1×–30×) clinical exposure multiples. The QTc assay provided the most robust PPVs and NPVs (ability to predict clinical QTc prolongation). ROC curves (overall test accuracy) and LRs (ability to influence post‐test probabilities) demonstrated overall marginal performance for hERG and QTc assays (best at 30× exposures), while the APD assay demonstrated minimal value. Conclusions and Implications The predictive value of hERG, APD and QTc assays varies, with drug concentrations strongly affecting translational performance. While useful in guiding preclinical candidates without clinical QT prolongation, hERG and QTc repolarization assays provide greater value compared with the APD assay. PMID:29181850

Value and role of intensive care unit outcome prediction models in end-of-life decision making.

PubMed

Barnato, Amber E; Angus, Derek C

2004-07-01

In the United States, intensive care unit (ICU) admission at the end of life is commonplace. What is the value and role of ICU mortality prediction models for informing the utility of ICU care?In this article, we review the history, statistical underpinnings,and current deployment of these models in clinical care. We conclude that the use of outcome prediction models to ration care that is unlikely to provide an expected benefit is hampered by imperfect performance, the lack of real-time availability, failure to consider functional outcomes beyond survival, and physician resistance to the use of probabilistic information when death is guaranteed by the decision it informs. Among these barriers, the most important technical deficiency is the lack of automated information systems to provide outcome predictions to decision makers, and the most important research and policy agenda is to understand and address our national ambivalence toward rationing care based on any criterion.

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding

PubMed Central

Billings, John; Georghiou, Theo; Blunt, Ian; Bardsley, Martin

2013-01-01

Objectives To test the performance of new variants of models to identify people at risk of an emergency hospital admission. We compared (1) the impact of using alternative data sources (hospital inpatient, A&E, outpatient and general practitioner (GP) electronic medical records) (2) the effects of local calibration on the performance of the models and (3) the choice of population denominators. Design Multivariate logistic regressions using person-level data adding each data set sequentially to test value of additional variables and denominators. Setting 5 Primary Care Trusts within England. Participants 1 836 099 people aged 18–95 registered with GPs on 31 July 2009. Main outcome measures Models to predict hospital admission and readmission were compared in terms of the positive predictive value and sensitivity for various risk strata and with the receiver operating curve C statistic. Results The addition of each data set showed moderate improvement in the number of patients identified with little or no loss of positive predictive value. However, even with inclusion of GP electronic medical record information, the algorithms identified only a small number of patients with no emergency hospital admissions in the previous 2 years. The model pooled across all sites performed almost as well as the models calibrated to local data from just one site. Using population denominators from GP registers led to better case finding. Conclusions These models provide a basis for wider application in the National Health Service. Each of the models examined produces reasonably robust performance and offers some predictive value. The addition of more complex data adds some value, but we were unable to conclude that pooled models performed less well than those in individual sites. Choices about model should be linked to the intervention design. Characteristics of patients identified by the algorithms provide useful information in the design/costing of intervention strategies to improve care coordination/outcomes for these patients. PMID:23980068

NUCLEAR AND HEAVY ION PHYSICS: α-decay half-lives of superheavy nuclei and general predictions

NASA Astrophysics Data System (ADS)

Dong, Jian-Min; Zhang, Hong-Fei; Wang, Yan-Zhao; Zuo, Wei; Su, Xin-Ning; Li, Jun-Qing

2009-08-01

The generalized liquid drop model (GLDM) and the cluster model have been employed to calculate the α-decay half-lives of superheavy nuclei (SHN) using the experimental α-decay Q values. The results of the cluster model are slightly poorer than those from the GLDM if experimental Q values are used. The prediction powers of these two models with theoretical Q values from Audi et al. (QAudi) and Muntian et al. (QM) have been tested to find that the cluster model with QAudi and QM could provide reliable results for Z > 112 but the GLDM with QAudi for Z <= 112. The half-lives of some still unknown nuclei are predicted by these two models and these results may be useful for future experimental assignment and identification.

Bispectral index to predict neurological outcome early after cardiac arrest.

PubMed

Stammet, Pascal; Collignon, Olivier; Werer, Christophe; Sertznig, Claude; Devaux, Yvan

2014-12-01

To address the value of continuous monitoring of bispectral index (BIS) to predict neurological outcome after cardiac arrest. In this prospective observational study in adult comatose patients treated by therapeutic hypothermia after cardiac arrest we measured bispectral index (BIS) during the first 24 hours of intensive care unit stay. A blinded neurological outcome assessment by cerebral performance category (CPC) was done 6 months after cardiac arrest. Forty-six patients (48%) had a good neurological outcome at 6-month, as defined by a cerebral performance category (CPC) 1-2, and 50 patients (52%) had a poor neurological outcome (CPC 3-5). Over the 24h of monitoring, mean BIS values over time were higher in the good outcome group (38 ± 9) compared to the poor outcome group (17 ± 12) (p<0.001). Analysis of BIS recorded every 30 minutes provided an optimal prediction after 12.5h, with an area under the receiver operating characteristic curve (AUC) of 0.89, a specificity of 89% and a sensitivity of 86% using a cut-off value of 23. With a specificity fixed at 100% (sensitivity 26%) the cut-off BIS value was 2.4 over the first 271 minutes. In multivariable analyses including clinical characteristics, mean BIS value over the first 12.5h was a predictor of neurological outcome (p = 6E-6) and provided a continuous net reclassification index of 1.28% (p = 4E-10) and an integrated discrimination improvement of 0.31 (p=1E-10). Mean BIS value calculated over the first 12.5h after ICU admission potentially predicts 6-months neurological outcome after cardiac arrest. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Prediction of Response to Sorafenib in Hepatocellular Carcinoma: A Putative Marker Panel by Multiple Reaction Monitoring-Mass Spectrometry (MRM-MS).

PubMed

Kim, Hyunsoo; Yu, Su Jong; Yeo, Injun; Cho, Young Youn; Lee, Dong Hyeon; Cho, Yuri; Cho, Eun Ju; Lee, Jeong-Hoon; Kim, Yoon Jun; Lee, Sungyoung; Jun, Jongsoo; Park, Taesung; Yoon, Jung-Hwan; Kim, Youngsoo

2017-07-01

Sorafenib is the only standard treatment for unresectable hepatocellular carcinoma (HCC), but it provides modest survival benefits over placebo, necessitating predictive biomarkers of the response to sorafenib. Serum samples were obtained from 115 consecutive patients with HCC before sorafenib treatment and analyzed by multiple reaction monitoring-mass spectrometry (MRM-MS) and ELISA to quantify candidate biomarkers. We verified a triple-marker panel to be predictive of the response to sorafenib by MRM-MS, comprising CD5 antigen-like (CD5L), immunoglobulin J (IGJ), and galectin-3-binding protein (LGALS3BP), in HCC patients. This panel was a significant predictor (AUROC > 0.950) of the response to sorafenib treatment, having the best cut-off value (0.4) by multivariate analysis. In the training set, patients who exceeded this cut-off value had significantly better overall survival (median, 21.4 months) than those with lower values (median, 8.6 months; p = 0.001). Further, a value that was lower than this cutoff was an independent predictor of poor overall survival [hazard ratio (HR), 2.728; 95% confidence interval (CI), 1.312-5.672; p = 0.007] and remained an independent predictive factor of rapid progression (HR, 2.631; 95% CI, 1.448-4.780; p = 0.002). When applied to the independent validation set, levels of the cut-off value for triple-marker panel maintained their prognostic value for poor clinical outcomes. On the contrast, the triple-marker panel was not a prognostic factor for patients who were treated with transarterial chemoembolization (TACE). The discriminatory signature of a triple-marker panel provides new insights into targeted proteomic biomarkers for individualized sorafenib therapy. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

PubMed Central

Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A.

2012-01-01

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. PMID:22991575

The application of SEAT values for predicting how compliant seats with backrests influence vibration discomfort.

PubMed

Basri, Bazil; Griffin, Michael J

2014-11-01

The extent to which a seat can provide useful attenuation of vehicle vibration depends on three factors: the characteristics of the vehicle motion, the vibration transmissibility of the seat, and the sensitivity of the body to vibration. The 'seat effective amplitude transmissibility' (i.e., SEAT value) reflects how these three factors vary with the frequency and the direction of vibration so as to predict the vibration isolation efficiency of a seat. The SEAT value is mostly used to select seat cushions or seat suspensions based on the transmission of vertical vibration to the principal supporting surface of a seat. This study investigated the accuracy of SEAT values in predicting how seats with backrests influence the discomfort caused by multiple-input vibration. Twelve male subjects participated in a four-part experiment to determine equivalent comfort contours, the relative discomfort, the location of discomfort, and seat transmissibility with three foam seats and a rigid reference seat at 14 frequencies of vibration in the range 1-20 Hz at magnitudes of vibration from 0.2 to 1.6 ms(-2) r.m.s. The 'measured seat dynamic discomfort' (MSDD) was calculated for each foam seat from the ratio of the vibration acceleration required to cause similar discomfort with the foam seat and with the rigid reference seat. Using the frequency weightings in current standards, the SEAT values of each seat were calculated from the ratio of overall ride values with the foam seat to the overall ride values with the rigid reference seat, and compared to the corresponding MSDD at each frequency. The SEAT values provided good predictions of how the foam seats increased vibration discomfort at frequencies around the 4-Hz resonance but reduced vibration discomfort at frequencies greater than about 6.3 Hz, with discrepancies explained by a known limitation of the frequency weightings. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Sensitivity, Specificity, PPV, and NPV for Predictive Biomarkers

PubMed Central

2015-01-01

Molecularly targeted cancer drugs are often developed with companion diagnostics that attempt to identify which patients will have better outcome on the new drug than the control regimen. Such predictive biomarkers are playing an increasingly important role in precision oncology. For diagnostic tests, sensitivity, specificity, positive predictive value, and negative predictive are usually used as performance measures. This paper discusses these indices for predictive biomarkers, provides methods for their calculation with survival or response endpoints, and describes assumptions involved in their use. PMID:26109105

Evaluation of predictive capacities of biomarkers based on research synthesis.

PubMed

Hattori, Satoshi; Zhou, Xiao-Hua

2016-11-10

The objective of diagnostic studies or prognostic studies is to evaluate and compare predictive capacities of biomarkers. Suppose we are interested in evaluation and comparison of predictive capacities of continuous biomarkers for a binary outcome based on research synthesis. In analysis of each study, subjects are often classified into two groups of the high-expression and low-expression groups according to a cut-off value, and statistical analysis is based on a 2 × 2 table defined by the response and the high expression or low expression of the biomarker. Because the cut-off is study specific, it is difficult to interpret a combined summary measure such as an odds ratio based on the standard meta-analysis techniques. The summary receiver operating characteristic curve is a useful method for meta-analysis of diagnostic studies in the presence of heterogeneity of cut-off values to examine discriminative capacities of biomarkers. We develop a method to estimate positive or negative predictive curves, which are alternative to the receiver operating characteristic curve based on information reported in published papers of each study. These predictive curves provide a useful graphical presentation of pairs of positive and negative predictive values and allow us to compare predictive capacities of biomarkers of different scales in the presence of heterogeneity in cut-off values among studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

DOE PAGES

Alderman, Phillip D.; Stanfill, Bryan

2016-10-06

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relativemore » contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. Here, this study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.« less

Reward-based training of recurrent neural networks for cognitive and value-based tasks

PubMed Central

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-01

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

The application of the statistical theory of extreme values to gust-load problems

NASA Technical Reports Server (NTRS)

Press, Harry

1950-01-01

An analysis is presented which indicates that the statistical theory of extreme values is applicable to the problems of predicting the frequency of encountering the larger gust loads and gust velocities for both specific test conditions as well as commercial transport operations. The extreme-value theory provides an analytic form for the distributions of maximum values of gust load and velocity. Methods of fitting the distribution are given along with a method of estimating the reliability of the predictions. The theory of extreme values is applied to available load data from commercial transport operations. The results indicate that the estimates of the frequency of encountering the larger loads are more consistent with the data and more reliable than those obtained in previous analyses. (author)

A Review On Missing Value Estimation Using Imputation Algorithm

NASA Astrophysics Data System (ADS)

Armina, Roslan; Zain, Azlan Mohd; Azizah Ali, Nor; Sallehuddin, Roselina

2017-09-01

The presence of the missing value in the data set has always been a major problem for precise prediction. The method for imputing missing value needs to minimize the effect of incomplete data sets for the prediction model. Many algorithms have been proposed for countermeasure of missing value problem. In this review, we provide a comprehensive analysis of existing imputation algorithm, focusing on the technique used and the implementation of global or local information of data sets for missing value estimation. In addition validation method for imputation result and way to measure the performance of imputation algorithm also described. The objective of this review is to highlight possible improvement on existing method and it is hoped that this review gives reader better understanding of imputation method trend.

The reliability, validity, sensitivity, specificity and predictive values of the Chinese version of the Rowland Universal Dementia Assessment Scale.

PubMed

Chen, Chia-Wei; Chu, Hsin; Tsai, Chia-Fen; Yang, Hui-Ling; Tsai, Jui-Chen; Chung, Min-Huey; Liao, Yuan-Mei; Chi, Mei-Ju; Chou, Kuei-Ru

2015-11-01

The purpose of this study was to translate the Rowland Universal Dementia Assessment Scale into Chinese and to evaluate the psychometric properties (reliability and validity) and the diagnostic properties (sensitivity, specificity and predictive values) of the Chinese version of the Rowland Universal Dementia Assessment Scale. The accurate detection of early dementia requires screening tools with favourable cross-cultural linguistic and appropriate sensitivity, specificity, and predictive values, particularly for Chinese-speaking populations. This was a cross-sectional, descriptive study. Overall, 130 participants suspected to have cognitive impairment were enrolled in the study. A test-retest for determining reliability was scheduled four weeks after the initial test. Content validity was determined by five experts, whereas construct validity was established by using contrasted group technique. The participants' clinical diagnoses were used as the standard in calculating the sensitivity, specificity, positive predictive value and negative predictive value. The study revealed that the Chinese version of the Rowland Universal Dementia Assessment Scale exhibited a test-retest reliability of 0.90, an internal consistency reliability of 0.71, an inter-rater reliability (kappa value) of 0.88 and a content validity index of 0.97. Both the patients and healthy contrast group exhibited significant differences in their cognitive ability. The optimal cut-off points for the Chinese version of the Rowland Universal Dementia Assessment Scale in the test for mild cognitive impairment and dementia were 24 and 22, respectively; moreover, for these two conditions, the sensitivities of the scale were 0.79 and 0.76, the specificities were 0.91 and 0.81, the areas under the curve were 0.85 and 0.78, the positive predictive values were 0.99 and 0.83 and the negative predictive values were 0.96 and 0.91 respectively. The Chinese version of the Rowland Universal Dementia Assessment Scale exhibited sound reliability, validity, sensitivity, specificity and predictive values. This scale can help clinical staff members to quickly and accurately diagnose cognitive impairment and provide appropriate treatment as early as possible. © 2015 John Wiley & Sons Ltd.

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

Diagnostic value and cost utility analysis for urine Gram stain and urine microscopic examination as screening tests for urinary tract infection.

PubMed

Wiwanitkit, Viroj; Udomsantisuk, Nibhond; Boonchalermvichian, Chaiyaporn

2005-06-01

The aim of this study was to evaluate the diagnostic properties of urine Gram stain and urine microscopic examination for screening for urinary tract infection (UTI), and to perform an additional cost utility analysis. This descriptive study was performed on 95 urine samples sent for urine culture to the Department of Microbiology, Faculty of Medicine, Chulalongkorn University. The first part of the study was to determine the diagnostic properties of two screening tests (urine Gram stain and urine microscopic examination). Urine culture was set as the gold standard and the results from both methods were compared to this. The second part of this study was to perform a cost utility analysis. The sensitivity of urine Gram stain was 96.2%, the specificity 93.0%, the positive predictive value 94.3% and the negative predictive value 95.2%. False positives occurred with a frequency of 7.0% and false negatives 3.8%. For the microscopic examination, the sensitivity was 65.4%, specificity 74.4%, positive predictive value 75.6% and negative predictive value 64.0%. False positives occurred with a frequency of 25.6% and false negatives 34.6%. Combining urine Gram stain and urine microscopic examination, the sensitivity was 98.1%, specificity 74.4%, positive predictive value 82.3% and negative predictive value 97.0%. False positives occurred with a frequency of 25.6% and false negatives 1.9%. However, the cost per utility of the combined method was higher than either urine microscopic examination or urine Gram stain alone. Urine Gram stain provided the lowest cost per utility. Economically, urine Gram stain is the proper screening tool for presumptive diagnosis of UTI.

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.

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

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

Lee, Taewoo, E-mail: taewoo.lee@utoronto.ca; Hammad, Muhannad; Chan, Timothy C. Y.

2013-12-15

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

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

Lee, Taewoo, E-mail: taewoo.lee@utoronto.ca; Hammad, Muhannad; Chan, Timothy C. Y.

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less

The information value of early career productivity in mathematics: a ROC analysis of prediction errors in bibliometricly informed decision making.

PubMed

Lindahl, Jonas; Danell, Rickard

The aim of this study was to provide a framework to evaluate bibliometric indicators as decision support tools from a decision making perspective and to examine the information value of early career publication rate as a predictor of future productivity. We used ROC analysis to evaluate a bibliometric indicator as a tool for binary decision making. The dataset consisted of 451 early career researchers in the mathematical sub-field of number theory. We investigated the effect of three different definitions of top performance groups-top 10, top 25, and top 50 %; the consequences of using different thresholds in the prediction models; and the added prediction value of information on early career research collaboration and publications in prestige journals. We conclude that early career performance productivity has an information value in all tested decision scenarios, but future performance is more predictable if the definition of a high performance group is more exclusive. Estimated optimal decision thresholds using the Youden index indicated that the top 10 % decision scenario should use 7 articles, the top 25 % scenario should use 7 articles, and the top 50 % should use 5 articles to minimize prediction errors. A comparative analysis between the decision thresholds provided by the Youden index which take consequences into consideration and a method commonly used in evaluative bibliometrics which do not take consequences into consideration when determining decision thresholds, indicated that differences are trivial for the top 25 and the 50 % groups. However, a statistically significant difference between the methods was found for the top 10 % group. Information on early career collaboration and publication strategies did not add any prediction value to the bibliometric indicator publication rate in any of the models. The key contributions of this research is the focus on consequences in terms of prediction errors and the notion of transforming uncertainty into risk when we are choosing decision thresholds in bibliometricly informed decision making. The significance of our results are discussed from the point of view of a science policy and management.

Comparing the outcomes of two strategies for colorectal tumor detection: policy-promoted screening program versus health promotion service.

PubMed

Wu, Ping-Hsiu; Lin, Yu-Min; Liao, Chao-Sheng; Chang, Hung-Chuen; Chen, Yu-Hung; Yang, Kuo-Ching; Shih, Chia-Hui

2013-06-01

The Taiwanese government has proposed a population-based colorectal tumor detection program for the average-risk population. This study's objectives were to understand the outcomes of these screening policies and to evaluate the effectiveness of the program. We compared two databases compiled in one medical center. The "policy-promoted cancer screening" (PPS) database was built on the basis of the policy of the Taiwan Bureau of National Health Insurance for cancer screening. The "health promotion service" (HPS) database was built to provide health check-ups for self-paid volunteers. Both the PPS and HPS databases employ the immunochemical fecal occult blood test (iFOBT) and colonoscopy for colorectal tumor screening using different strategies. A comparison of outcomes between the PPS and HPS included: (1) quality indicators-compliance rate, cecum reaching rate, and tumor detection rate; and (2) validity indicators-sensitivity, specificity, positive, and negative predictive values for detecting colorectal neoplasms. A total of 10,563 and 1481 individuals were enrolled in PPS and HPS, respectively. Among quality indicators, there was no statistically significant difference in the cecum reaching rate between PPS and HPS. The compliance rates were 56.1% for PPS and 91.8% for HPS (p < 0.001). The advanced adenoma detection rates of PPS and HPS were 1.0% and 3.6%, respectively (p < 0.01). The carcinoma detection rates were 0.3% and 0.4%, respectively (p = 0.59). For validity indicators, PPS provides only a positive predictive value for colorectal tumor detection. HPS provides additional validity indicators, including sensitivity, specificity, positive predictive value, and negative predictive value, for colorectal tumor screening. In comparison with the outcomes of the HPS database, the screening efficacy of the PPS database is even for detecting colorectal carcinoma but is limited in detecting advanced adenoma. HPS may provide comprehensive validity indicators and will be helpful in adjusting current policies for improving screening performance. Copyright © 2013. Published by Elsevier B.V.

WallGen, software to construct layered cellulose-hemicellulose networks and predict their small deformation mechanics.

PubMed

Kha, Hung; Tuble, Sigrid C; Kalyanasundaram, Shankar; Williamson, Richard E

2010-02-01

We understand few details about how the arrangement and interactions of cell wall polymers produce the mechanical properties of primary cell walls. Consequently, we cannot quantitatively assess if proposed wall structures are mechanically reasonable or assess the effectiveness of proposed mechanisms to change mechanical properties. As a step to remedying this, we developed WallGen, a Fortran program (available on request) building virtual cellulose-hemicellulose networks by stochastic self-assembly whose mechanical properties can be predicted by finite element analysis. The thousands of mechanical elements in the virtual wall are intended to have one-to-one spatial and mechanical correspondence with their real wall counterparts of cellulose microfibrils and hemicellulose chains. User-defined inputs set the properties of the two polymer types (elastic moduli, dimensions of microfibrils and hemicellulose chains, hemicellulose molecular weight) and their population properties (microfibril alignment and volume fraction, polymer weight percentages in the network). This allows exploration of the mechanical consequences of variations in nanostructure that might occur in vivo and provides estimates of how uncertainties regarding certain inputs will affect WallGen's mechanical predictions. We summarize WallGen's operation and the choice of values for user-defined inputs and show that predicted values for the elastic moduli of multinet walls subject to small displacements overlap measured values. "Design of experiment" methods provide systematic exploration of how changed input values affect mechanical properties and suggest that changing microfibril orientation and/or the number of hemicellulose cross-bridges could change wall mechanical anisotropy.

Effects of Economy Type and Nicotine on the Essential Value of Food in Rats

ERIC Educational Resources Information Center

Cassidy, Rachel N.; Dallery, Jesse

2012-01-01

The exponential demand equation proposed by Hursh and Silberberg (2008) provides an estimate of the essential value of a good as a function of price. The model predicts that essential value should remain constant across changes in the magnitude of a reinforcer, but may change as a function of motivational operations. In Experiment 1, rats' demand…

[Geographical distribution of the Serum creatinine reference values of healthy adults].

PubMed

Wei, De-Zhi; Ge, Miao; Wang, Cong-Xia; Lin, Qian-Yi; Li, Meng-Jiao; Li, Peng

2016-11-20

To explore the relationship between serum creatinine (Scr) reference values in healthy adults and geographic factors and provide evidence for establishing Scr reference values in different regions. We collected 29 697 Scr reference values from healthy adults measured by 347 medical facilities from 23 provinces, 4 municipalities and 5 autonomous regions. We chose 23 geographical factors and analyzed their correlation with Scr reference values to identify the factors correlated significantly with Scr reference values. According to the Principal component analysis and Ridge regression analysis, two predictive models were constructed and the optimal model was chosen after comparison of the two model's fitting degree of predicted results and measured results. The distribution map of Scr reference values was drawn using the Kriging interpolation method. Seven geographic factors, including latitude, annual sunshine duration, annual average temperature, annual average relative humidity, annual precipitation, annual temperature range and topsoil (silt) cation exchange capacity were found to correlate significantly with Scr reference values. The overall distribution of Scr reference values featured a pattern that the values were high in the south and low in the north, varying consistently with the latitude change. The data of the geographic factors in a given region allows the prediction of the Scr values in healthy adults. Analysis of these geographical factors can facilitate the determination of the reference values specific to a region to improve the accuracy for clinical diagnoses.

Demands, values, and burnout

PubMed Central

Leiter, Michael P.; Frank, Erica; Matheson, Timothy J.

2009-01-01

OBJECTIVE T o explore the interaction between workload and values congruence (personal values with health care system values) in the context of burnout and physician engagement and to explore the relative importance of these factors by sex, given the distinct work patterns of male and female physicians. DESIGN National mailed survey. SETTING Canada. PARTICIPANTS A random sample of 8100 Canadian physicians (response rate 40%, N = 3213); 2536 responses (from physicians working more than 35 hours per week) were analyzed. MAIN OUTCOME MEASURES Levels of burnout, values congruence, and workload, by sex, measured by the Maslach Burnout Inventory—General Scale and the Areas of Worklife Scale. RESULTS Results showed a moderate level of burnout among Canadian physicians, with relatively positive scores on exhaustion, average scores on cynicism, and mildly negative scores on professional efficacy. A series of multiple regression analyses confirmed parallel main effect contributions from manageable workload and values congruence. Both workload and values congruence predicted exhaustion and cynicism for men and women (P = .001). Only values congruence provided a significant prediction of professional efficacy for both men and women (P = .001) These predictors interacted for women on all 3 aspects of burnout (exhaustion, cynicism, and diminished efficacy). Howevever, overall levels of the burnout indicators departed only modestly from normative levels. CONCLUSION W orkload and values congruence make distinct contributions to physician burnout. Work overload contributes to predicting exhaustion and cynicism; professional values crises contribute to predicting exhaustion, cynicism, and low professional efficacy. The interaction of values and workload for women in particular has implications for the distinct work-life patterns of male and female physicians. Specifically, the congruence of individual values with values inherent in the health care system appeared to be of greater consequence for women than for men. PMID:20008605

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil

2010-01-01

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

An image based method for crop yield prediction using remotely sensed and crop canopy data: the case of Paphos district, western Cyprus

NASA Astrophysics Data System (ADS)

Papadavid, G.; Hadjimitsis, D.

2014-08-01

Remote sensing techniques development have provided the opportunity for optimizing yields in the agricultural procedure and moreover to predict the forthcoming yield. Yield prediction plays a vital role in Agricultural Policy and provides useful data to policy makers. In this context, crop and soil parameters along with NDVI index which are valuable sources of information have been elaborated statistically to test if a) Durum wheat yield can be predicted and b) when is the actual time-window to predict the yield in the district of Paphos, where Durum wheat is the basic cultivation and supports the rural economy of the area. 15 plots cultivated with Durum wheat from the Agricultural Research Institute of Cyprus for research purposes, in the area of interest, have been under observation for three years to derive the necessary data. Statistical and remote sensing techniques were then applied to derive and map a model that can predict yield of Durum wheat in this area. Indeed the semi-empirical model developed for this purpose, with very high correlation coefficient R2=0.886, has shown in practice that can predict yields very good. Students T test has revealed that predicted values and real values of yield have no statistically significant difference. The developed model can and will be further elaborated with more parameters and applied for other crops in the near future.

Performance Characteristics of the Cepheid Xpert vanA Assay for Rapid Identification of Patients at High Risk for Carriage of Vancomycin-Resistant Enterococci

PubMed Central

Gilhuley, Kathleen; Cianciminio-Bordelon, Diane; Tang, Yi-Wei

2012-01-01

We compared the performance characteristics of culture and the Cepheid Xpert vanA assay for routine surveillance of vancomycin-resistant enterococci (VRE) from rectal swabs in patients at high risk for VRE carriage. The Cepheid Xpert vanA assay had a limit of detection of 100 CFU/ml and correctly detected 101 well-characterized clinical VRE isolates with no cross-reactivity in 27 non-VRE and related culture isolates. The clinical sensitivity, specificity, positive predictive value, and negative predictive value of the Xpert vanA PCR assay were 100%, 96.9%, 91.3%, and 100%, respectively, when tested on 300 consecutively collected rectal swabs. This assay provides excellent predictive values for prompt identification of VRE-colonized patients in hospitals with relatively high rates of VRE carriage. PMID:22972822

Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24 827 people: systematic review and meta-analysis.

PubMed

Fazel, Seena; Singh, Jay P; Doll, Helen; Grann, Martin

2012-07-24

To investigate the predictive validity of tools commonly used to assess the risk of violence, sexual, and criminal behaviour. Systematic review and tabular meta-analysis of replication studies following PRISMA guidelines. PsycINFO, Embase, Medline, and United States Criminal Justice Reference Service Abstracts. We included replication studies from 1 January 1995 to 1 January 2011 if they provided contingency data for the offending outcome that the tools were designed to predict. We calculated the diagnostic odds ratio, sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, the number needed to detain to prevent one offence, as well as a novel performance indicator-the number safely discharged. We investigated potential sources of heterogeneity using metaregression and subgroup analyses. Risk assessments were conducted on 73 samples comprising 24,847 participants from 13 countries, of whom 5879 (23.7%) offended over an average of 49.6 months. When used to predict violent offending, risk assessment tools produced low to moderate positive predictive values (median 41%, interquartile range 27-60%) and higher negative predictive values (91%, 81-95%), and a corresponding median number needed to detain of 2 (2-4) and number safely discharged of 10 (4-18). Instruments designed to predict violent offending performed better than those aimed at predicting sexual or general crime. Although risk assessment tools are widely used in clinical and criminal justice settings, their predictive accuracy varies depending on how they are used. They seem to identify low risk individuals with high levels of accuracy, but their use as sole determinants of detention, sentencing, and release is not supported by the current evidence. Further research is needed to examine their contribution to treatment and management.

A Global Meta-Analysis of the Value of Ecosystem Services Provided by Lakes.

PubMed

Reynaud, Arnaud; Lanzanova, Denis

2017-07-01

This study presents the first meta-analysis on the economic value of ecosystem services delivered by lakes. A worldwide data set of 699 observations drawn from 133 studies combines information reported in primary studies with geospatial data. The meta-analysis explores antagonisms and synergies between ecosystem services. This is the first meta-analysis to incorporate simultaneously external geospatial data and ecosystem service interactions. We first show that it is possible to reliably predict the value of ecosystem services provided by lakes based on their physical and geographic characteristics. Second, we demonstrate that interactions between ecosystem services appear to be significant for explaining lake ecosystem service values. Third, we provide an estimation of the average value of ecosystem services provided by lakes: between 106 and 140 USD$2010 per respondent per year for non-hedonic price studies and between 169 and 403 USD$2010 per property per year for hedonic price studies.

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.

Health fair screening: the clinical utility of the comprehensive metabolic profile.

PubMed

Alpert, Jeffrey P; Greiner, Allen; Hall, Sandra

2004-01-01

Health fairs are a common method used by providers and health care organizations to provide screening tests, including comprehensive metabolic profiles (CMPs), to asymptomatic individuals. No national organizations currently recommend the complete CMP as a screening test for asymptomatic individuals in primary care settings. This study evaluated the value of CMPs in a health fair setting by measuring the ability of a health fair CMP to predict new medical diagnoses among residents of a sparsely populated rural county. Volunteer participants submitted fasting blood samples at a health fair conducted by a county health center in a county with 2,531 total residents. CMP values were determined to be "normal" or "abnormal" based on laboratory reference ranges and clinical judgment of the health center physicians. Medical records were reviewed 4 months later to determine if participants with abnormal CMP values had been diagnosed with new medical conditions as a result of the screening tests. Analysis was conducted to evaluate CMP test characteristics and determine whether demographic factors or specific CMP values predicted new medical diagnoses in the participants. Out of 478 health fair participants, 73 individuals had at least one abnormal CMP value. The most frequently occurring abnormal value was an elevated glucose level, with Hispanic participants significantly more likely to have this abnormality than whites. After all evaluation was completed, only about 1% of tested subjects had a new diagnosis as a result of the screening CMP test; most abnormal CMP tests did not result in a new diagnosis. The positive predictive value for an abnormal test resulting in a new medical diagnosis was 0.356. Comprehensive metabolic profiles have limited value as a screening tool in asymptomatic populations at health fairs.

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

Stable isotope signatures and trophic-step fractionation factors of fish tissues collected as non-lethal surrogates of dorsal muscle.

PubMed

Busst, Georgina M A; Bašić, Tea; Britton, J Robert

2015-08-30

Dorsal white muscle is the standard tissue analysed in fish trophic studies using stable isotope analyses. As muscle is usually collected destructively, fin tissues and scales are often used as non-lethal surrogates; we examined the utility of scales and fin tissue as muscle surrogates. The muscle, fin and scale δ(15) N and δ(13) C values from 10 cyprinid fish species determined with an elemental analyser coupled with an isotope ratio mass spectrometer were compared. The fish comprised (1) samples from the wild, and (2) samples from tank aquaria, using six species held for 120 days and fed a single food resource. Relationships between muscle, fin and scale isotope ratios were examined for each species and for the entire dataset, with the efficacy of four methods of predicting muscle isotope ratios from fin and scale values being tested. The fractionation factors between the three tissues of the laboratory fishes and their food resource were then calculated and applied to Bayesian mixing models to assess their effect on fish diet predictions. The isotopic data of the three tissues per species were distinct, but were significantly related, enabling estimations of muscle values from the two surrogates. Species-specific equations provided the least erroneous corrections of scale and fin isotope ratios (errors < 0.6‰). The fractionation factors for δ(15) N values were in the range obtained for other species, but were often higher for δ(13) C values. Their application to data from two fish populations in the mixing models resulted in significant alterations in diet predictions. Scales and fin tissue are strong surrogates of dorsal muscle in food web studies as they can provide estimates of muscle values within an acceptable level of error when species-specific methods are used. Their derived fractionation factors can also be applied to models predicting fish diet composition from δ(15) N and δ(13) C values. Copyright © 2015 John Wiley & Sons, Ltd.

Measuring the value of accurate link prediction for network seeding.

PubMed

Wei, Yijin; Spencer, Gwen

2017-01-01

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

Increasing the Quality and Value of Conferences, Seminars, and Workshops

ERIC Educational Resources Information Center

Hoyt, Jeff E.; Whyte, Chrystine

2011-01-01

The purpose of this best practices article is to provide continuing education administrators with a reliable participant evaluation that measures factors predictive of not only satisfaction, but also perceived value: adequacy of topics, customer service, learning, quality of facilities, image, and quality of presentations, among other variables.…

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.

Structural features that predict real-value fluctuations of globular proteins.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-05-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

Structural features that predict real-value fluctuations of globular proteins

PubMed Central

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-01-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics trajectories of non-homologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real-value of residue fluctuations using the support vector regression. It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in molecular dynamics trajectories. Moreover, support vector regression that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson’s correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed for the prediction by the Gaussian network model. An advantage of the developed method over the Gaussian network models is that the former predicts the real-value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. PMID:22328193

Moral Attitudes Predict Cheating and Gamesmanship Behaviors Among Competitive Tennis Players

PubMed Central

Lucidi, Fabio; Zelli, Arnaldo; Mallia, Luca; Nicolais, Giampaolo; Lazuras, Lambros; Hagger, Martin S.

2017-01-01

Background: The present study tested Lee et al.’s (2008) model of moral attitudes and cheating behavior in sports in an Italian sample of young tennis players and extended it to predict behavior in actual match play. In the first phase of the study we proposed that moral, competence and status values would predict prosocial and antisocial moral attitudes directly, and indirectly through athletes’ goal orientations. In the second phase, we hypothesized that moral attitudes would directly predict actual cheating behavior observed during match play. Method: Adolescent competitive tennis players (N = 314, 76.75% males, M age = 14.36 years, SD = 1.50) completed measures of values, goal orientations, and moral attitudes. A sub-sample (n = 90) was observed in 45 competitive tennis matches by trained observers who recorded their cheating and gamesmanship behaviors on a validated checklist. Results: Consistent with hypotheses, athletes’ values predicted their moral attitudes through the effects of goal orientations. Anti-social attitudes directly predicted cheating behavior in actual match play providing support for a direct link between moral attitude and actual behavior. Conclusion: The present study findings support key propositions of Lee and colleagues’ model, and extended its application to competitive athletes in actual match play. PMID:28446891

Comparative values of medical school assessments in the prediction of internship performance.

PubMed

Lee, Ming; Vermillion, Michelle

2018-02-01

Multiple undergraduate achievements have been used for graduate admission consideration. Their relative values in the prediction of residency performance are not clear. This study compared the contributions of major undergraduate assessments to the prediction of internship performance. Internship performance ratings of the graduates of a medical school were collected from 2012 to 2015. Hierarchical multiple regression analyses were used to examine the predictive values of undergraduate measures assessing basic and clinical sciences knowledge and clinical performances, after controlling for differences in the Medical College Admission Test (MCAT). Four hundred eighty (75%) graduates' archived data were used in the study. Analyses revealed that clinical competencies, assessed by the USMLE Step 2 CK, NBME medicine exam, and an eight-station objective structured clinical examination (OSCE), were strong predictors of internship performance. Neither the USMLE Step 1 nor the inpatient internal medicine clerkship evaluation predicted internship performance. The undergraduate assessments as a whole showed a significant collective relationship with internship performance (ΔR 2  = 0.12, p < 0.001). The study supports the use of clinical competency assessments, instead of pre-clinical measures, in graduate admission consideration. It also provides validity evidence for OSCE scores in the prediction of workplace performance.

When in doubt, seize the day? Security values, prosocial values, and proactivity under ambiguity.

PubMed

Grant, Adam M; Rothbard, Nancy P

2013-09-01

Researchers have suggested that both ambiguity and values play important roles in shaping employees' proactive behaviors, but have not theoretically or empirically integrated these factors. Drawing on theories of situational strength and values, we propose that ambiguity constitutes a weak situation that strengthens the relationship between the content of employees' values and their proactivity. A field study of 204 employees and their direct supervisors in a water treatment plant provided support for this contingency perspective. Ambiguity moderated the relationship between employees' security and prosocial values and supervisor ratings of proactivity. Under high ambiguity, security values predicted lower proactivity, whereas prosocial values predicted higher proactivity. Under low ambiguity, values were not associated with proactivity. We replicated these findings in a laboratory experiment with 232 participants in which we measured proactivity objectively as initiative taken to correct errors: Participants with strong security values were less proactive, and participants with strong prosocial values were more proactive, but only when performance expectations were ambiguous. We discuss theoretical implications for research on proactivity, values, and ambiguity and uncertainty. PsycINFO Database Record (c) 2013 APA, all rights reserved

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.

Consumer product chemical weight fractions from ingredient lists.

PubMed

Isaacs, Kristin K; Phillips, Katherine A; Biryol, Derya; Dionisio, Kathie L; Price, Paul S

2018-05-01

Assessing human exposures to chemicals in consumer products requires composition information. However, comprehensive composition data for products in commerce are not generally available. Many consumer products have reported ingredient lists that are constructed using specific guidelines. A probabilistic model was developed to estimate quantitative weight fraction (WF) values that are consistent with the rank of an ingredient in the list, the number of reported ingredients, and labeling rules. The model provides the mean, median, and 95% upper and lower confidence limit WFs for ingredients of any rank in lists of any length. WFs predicted by the model compared favorably with those reported on Material Safety Data Sheets. Predictions for chemicals known to provide specific functions in products were also found to reasonably agree with reported WFs. The model was applied to a selection of publicly available ingredient lists, thereby estimating WFs for 1293 unique ingredients in 1123 products in 81 product categories. Predicted WFs, although less precise than reported values, can be estimated for large numbers of product-chemical combinations and thus provide a useful source of data for high-throughput or screening-level exposure assessments.

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.

Quantitative structure-toxicity relationship of the aquatic toxicity for various narcotic pollutants using the norm indexes.

PubMed

Wang, Qiang; Jia, Qingzhu; Yan, Lihong; Xia, Shuqian; Ma, Peisheng

2014-08-01

The aquatic toxicity value of hazardous contaminants plays an important role in the risk assessments of aquatic ecosystems. The following study presents a stable and accurate structure-toxicity relationship model based on the norm indexes for the prediction of toxicity value (log(LC50)) for 190 diverse narcotic pollutants (96 h LC50 data for Poecilia reticulata). Research indicates that this new model is very efficient and provides satisfactory results. The suggested prediction model is evidenced by R(2) (square correlation coefficient) and ARD (average relative difference) values of 0.9376 and 10.45%, respectively, for the training set, and 0.9264 and 13.90% for the testing set. Comparison results with reference models demonstrate that this new method, based on the norm indexes proposed in this work, results in significant improvements, both in accuracy and stability for predicting aquatic toxicity values of narcotic pollutants. Copyright © 2014 Elsevier Ltd. All rights reserved.

Couples' cultural values, shared parenting, and family emotional climate within Mexican American families.

PubMed

Sotomayor-Peterson, Marcela; Figueredo, Aurelio J; Christensen, Donna H; Taylor, Angela R

2012-06-01

This study tested a model of shared parenting as its centerpiece that incorporates cultural values as predictors and family emotional climate as the outcome variable of interest. We aimed to assess the predictive power of the Mexican cultural values of familismo and simpatia over couples' shared parenting practices. We anticipated that higher levels of shared parenting would predict family emotional climate. The participants were 61 Mexican American, low income couples, with at least one child between 3 and 4 years of age, recruited from a home-based Head Start program. The predictive model demonstrated excellent goodness of fit, supporting the hypothesis that a positive emotional climate within the family is fostered when Mexican American couples practice a sufficient level of shared parenting. Empirical evidence was previously scarce on this proposition. The findings also provide evidence for the role of cultural values, highlighting the importance of family solidarity and avoidance of confrontation as a pathway to shared parenting within Mexican American couples. © FPI, Inc.

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.

Fractional flow reserve by computerized tomography and subsequent coronary revascularization

PubMed Central

Packard, René R. Sevag; Li, Dong; Budoff, Matthew J.; Karlsberg, Ronald P.

2017-01-01

Aims Fractional flow reserve by computerized tomography (FFR-CT) provides non-invasive functional assessment of the hemodynamic significance of coronary artery stenosis. We determined the FFR-CT values, receiver operator characteristic (ROC) curves, and predictive ability of FFR-CT for actual standard of care guided coronary revascularization. Methods and results Consecutive outpatients who underwent coronary CT angiography (coronary CTA) followed by invasive angiography over a 24-month period from 2012 to 2014 were identified. Studies that fit inclusion criteria (n = 75 patients, mean age 66, 75% males) were sent for FFR-CT analysis, and results stratified by coronary artery calcium (CAC) scores. Coronary CTA studies were re-interpreted in a blinded manner, and baseline FFR-CT values were obtained retrospectively. Therefore, results did not interfere with clinical decision-making. Median FFR-CT values were 0.70 in revascularized (n = 69) and 0.86 in not revascularized (n = 138) coronary arteries (P < 0.001). Using clinically established significance cut-offs of FFR-CT ≤0.80 and coronary CTA ≥70% stenosis for the prediction of clinical decision-making and subsequent coronary revascularization, the positive predictive values were 74 and 88% and negative predictive values were 96 and 84%, respectively. The area under the curve (AUC) for all studied territories was 0.904 for coronary CTA, 0.920 for FFR-CT, and 0.941 for coronary CTA combined with FFR-CT (P = 0.001). With increasing CAC scores, the AUC decreased for coronary CTA but remained higher for FFR-CT (P < 0.05). Conclusion The addition of FFR-CT provides a complementary role to coronary CTA and increases the ability of a CT-based approach to identify subsequent standard of care guided coronary revascularization. PMID:27469588

Sound transmission loss of composite sandwich panels

NASA Astrophysics Data System (ADS)

Zhou, Ran

Light composite sandwich panels are increasingly used in automobiles, ships and aircraft, because of the advantages they offer of high strength-to-weight ratios. However, the acoustical properties of these light and stiff structures can be less desirable than those of equivalent metal panels. These undesirable properties can lead to high interior noise levels. A number of researchers have studied the acoustical properties of honeycomb and foam sandwich panels. Not much work, however, has been carried out on foam-filled honeycomb sandwich panels. In this dissertation, governing equations for the forced vibration of asymmetric sandwich panels are developed. An analytical expression for modal densities of symmetric sandwich panels is derived from a sixth-order governing equation. A boundary element analysis model for the sound transmission loss of symmetric sandwich panels is proposed. Measurements of the modal density, total loss factor, radiation loss factor, and sound transmission loss of foam-filled honeycomb sandwich panels with different configurations and thicknesses are presented. Comparisons between the predicted sound transmission loss values obtained from wave impedance analysis, statistical energy analysis, boundary element analysis, and experimental values are presented. The wave impedance analysis model provides accurate predictions of sound transmission loss for the thin foam-filled honeycomb sandwich panels at frequencies above their first resonance frequencies. The predictions from the statistical energy analysis model are in better agreement with the experimental transmission loss values of the sandwich panels when the measured radiation loss factor values near coincidence are used instead of the theoretical values for single-layer panels. The proposed boundary element analysis model provides more accurate predictions of sound transmission loss for the thick foam-filled honeycomb sandwich panels than either the wave impedance analysis model or the statistical energy analysis model.

Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

Imaging utilization has significantly increased over the last two decades, and is only recently showing signs of moderating. To help healthcare providers identify patients at risk for high imaging utilization, we developed a prediction model to recognize high imaging utilizers based on their initial imaging reports. The prediction model uses a machine learning text classification framework. In this study, we used radiology reports from 18,384 patients with at least one abdomen computed tomography study in their imaging record at Stanford Health Care as the training set. We modeled the radiology reports in a vector space and trained a support vector machine classifier for this prediction task. We evaluated our model on a separate test set of 4791 patients. In addition to high prediction accuracy, in our method, we aimed at achieving high specificity to identify patients at high risk for high imaging utilization. Our results (accuracy: 94.0%, sensitivity: 74.4%, specificity: 97.9%, positive predictive value: 87.3%, negative predictive value: 95.1%) show that a prediction model can enable healthcare providers to identify in advance patients who are likely to be high utilizers of imaging services. Machine learning classifiers developed from narrative radiology reports are feasible methods to predict imaging utilization. Such systems can be used to identify high utilizers, inform future image ordering behavior, and encourage judicious use of imaging. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

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 ...

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.

Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation

PubMed Central

Morita, Kenji

2016-01-01

It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of ‘Go’ or ‘No-Go’ selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of ‘Go’ values towards a goal, and (2) value-contrasts between ‘Go’ and ‘No-Go’ are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems for value-learning are active even though learning has apparently converged, the systems might be in a state of dynamic equilibrium, where learning and forgetting are balanced. PMID:27736881

Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

PubMed

Kato, Ayaka; Morita, Kenji

2016-10-01

It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2) value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems for value-learning are active even though learning has apparently converged, the systems might be in a state of dynamic equilibrium, where learning and forgetting are balanced.

Operating characteristics of depression and anxiety disorder phenotype dimensions and trait neuroticism: a theoretical examination of the fear and distress disorders from the Netherlands study of depression and anxiety.

PubMed

Tully, Phillip J; Wardenaar, Klaas J; Penninx, Brenda W J H

2015-03-15

The receiver operating characteristics (ROC) of anhedonic depression and anxious arousal to detect the distress- (major depression, dysthymia, generalized anxiety disorder) and fear-disorder clusters (i.e. panic disorder, agoraphobia, social phobia) have not been reported in a large sample. A sample of 2981 persons underwent structured psychiatric interview; n=652 were without lifetime depression and anxiety disorder history. Participants also completed a neuroticism scale (Revised NEO Five Factor Inventory [NEO-FFI]), and the 30-item short adaptation of the Mood and Anxiety Symptoms Questionnaire (MASQ-D30) measuring anhedonic depression, anxious arousal and general distress. Maximal sensitivity and specificity was determined by the Youden Index and the area-under-the-curve (AUC) in ROC analysis. A total of 2624 completed all measures (age M=42.4 years±13.1, 1760 females [67.1%]), including 1060 (40.4%) persons who met criteria for a distress-disorder, and 973 (37.1%) who met criteria for a fear-disorder. The general distress dimension provided the highest ROC values in the detection of the distress-disorders (AUC=.814, sensitivity=71.95%, specificity=76.34%, positive predictive value=67.33, negative predictive value=80.07). None of the measures provided suitable operating characteristics in the detection of the fear-disorders with specificity values <75%. Over sampling of depression and anxiety disorders may lead to inflated positive- and negative predictive values. The MASQ-D30 general distress dimension showed clinically suitable operating characteristics in the detection of distress-disorders. Neither neuroticism nor the MASQ-D30 dimensions provided suitable operating characteristics in the detection of the fear-disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn

2014-01-01

We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892

Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

PubMed Central

Angelantonio, Emanuele Di; Gao, Pei; Khan, Hassan; Butterworth, Adam S.; Wormser, David; Kaptoge, Stephen; Kondapally Seshasai, Sreenivasa Rao; Thompson, Alex; Sarwar, Nadeem; Willeit, Peter; Ridker, Paul M; Barr, Elizabeth L.M.; Khaw, Kay-Tee; Psaty, Bruce M.; Brenner, Hermann; Balkau, Beverley; Dekker, Jacqueline M.; Lawlor, Debbie A.; Daimon, Makoto; Willeit, Johann; Njølstad, Inger; Nissinen, Aulikki; Brunner, Eric J.; Kuller, Lewis H.; Price, Jackie F.; Sundström, Johan; Knuiman, Matthew W.; Feskens, Edith J. M.; Verschuren, W. M. M.; Wald, Nicholas; Bakker, Stephan J. L.; Whincup, Peter H.; Ford, Ian; Goldbourt, Uri; Gómez-de-la-Cámara, Agustín; Gallacher, John; Simons, Leon A.; Rosengren, Annika; Sutherland, Susan E.; Björkelund, Cecilia; Blazer, Dan G.; Wassertheil-Smoller, Sylvia; Onat, Altan; Marín Ibañez, Alejandro; Casiglia, Edoardo; Jukema, J. Wouter; Simpson, Lara M.; Giampaoli, Simona; Nordestgaard, Børge G.; Selmer, Randi; Wennberg, Patrik; Kauhanen, Jussi; Salonen, Jukka T.; Dankner, Rachel; Barrett-Connor, Elizabeth; Kavousi, Maryam; Gudnason, Vilmundur; Evans, Denis; Wallace, Robert B.; Cushman, Mary; D’Agostino, Ralph B.; Umans, Jason G.; Kiyohara, Yutaka; Nakagawa, Hidaeki; Sato, Shinichi; Gillum, Richard F.; Folsom, Aaron R.; van der Schouw, Yvonne T.; Moons, Karel G.; Griffin, Simon J.; Sattar, Naveed; Wareham, Nicholas J.; Selvin, Elizabeth; Thompson, Simon G.; Danesh, John

2015-01-01

IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. OBJECTIVE To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. DESIGN, SETTING, AND PARTICIPANTS Analysis of individual-participant data available from 73 prospective studies involving 294 998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. MAIN OUTCOMES AND MEASURES Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5%to <7.5%), and high (≥7.5%) risk. RESULTS During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20 840 incident fatal and nonfatal CVD outcomes (13 237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (−0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. CONCLUSIONS AND RELEVANCE In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk. PMID:24668104

A New Interpretation of Augmented Subscores and Their Added Value in Terms of Parallel Forms

ERIC Educational Resources Information Center

Sinharay, Sandip

2018-01-01

The value-added method of Haberman is arguably one of the most popular methods to evaluate the quality of subscores. The method is based on the classical test theory and deems a subscore to be of added value if the subscore predicts the corresponding true subscore better than does the total score. Sinharay provided an interpretation of the added…

[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.

Lateral habenula neurons signal errors in the prediction of reward information

PubMed Central

Bromberg-Martin, Ethan S.; Hikosaka, Okihide

2011-01-01

Humans and animals have a remarkable ability to predict future events, which they achieve by persistently searching their environment for sources of predictive information. Yet little is known about the neural systems that motivate this behavior. We hypothesized that information-seeking is assigned value by the same circuits that support reward-seeking, so that neural signals encoding conventional “reward prediction errors” include analogous “information prediction errors”. To test this we recorded from neurons in the lateral habenula, a nucleus which encodes reward prediction errors, while monkeys chose between cues that provided different amounts of information about upcoming rewards. We found that a subpopulation of lateral habenula neurons transmitted signals resembling information prediction errors, responding when reward information was unexpectedly cued, delivered, or denied. Their signals evaluated information sources reliably even when the animal’s decisions did not. These neurons could provide a common instructive signal for reward-seeking and information-seeking behavior. PMID:21857659

A pilot study of river flow prediction in urban area based on phase space reconstruction

NASA Astrophysics Data System (ADS)

Adenan, Nur Hamiza; Hamid, Nor Zila Abd; Mohamed, Zulkifley; Noorani, Mohd Salmi Md

2017-08-01

River flow prediction is significantly related to urban hydrology impact which can provide information to solve any problems such as flood in urban area. The daily river flow of Klang River, Malaysia was chosen to be forecasted in this pilot study which based on phase space reconstruction. The reconstruction of phase space involves a single variable of river flow data to m-dimensional phase space in which the dimension (m) is based on the optimal values of Cao method. The results from the reconstruction of phase space have been used in the forecasting process using local linear approximation method. From our investigation, river flow at Klang River is chaotic based on the analysis from Cao method. The overall results provide good value of correlation coefficient. The value of correlation coefficient is acceptable since the area of the case study is influence by a lot of factors. Therefore, this pilot study may be proposed to forecast daily river flow data with the purpose of providing information about the flow of the river system in urban area.

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

PubMed

Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

2014-12-01

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

IL-10 combined with procalcitonin improves early prediction of complications of febrile neutropenia in hematological patients.

PubMed

Vänskä, Matti; Koivula, Irma; Jantunen, Esa; Hämäläinen, Sari; Purhonen, Anna-Kaisa; Pulkki, Kari; Juutilainen, Auni

2012-12-01

Early diagnosis of complicated course in febrile neutropenia is cumbersome due to the non-specificity of clinical and laboratory signs of severe infection. This prospective study included 100 adult hematological patients with febrile neutropenia after intensive chemotherapy at the onset of fever (d0) and for 3 days (d1-d3) thereafter. The study aim was to find early predictors for complicated course of febrile neutropenia, defined as bacteremia or septic shock. Interleukin 6 (IL-6), interleukin 10 (IL-10), procalcitonin (PCT) and C-reactive protein (CRP) all predicted complicated course of febrile neutropenia on d0, but only PCT was predictive throughout the study period. For IL-10 on d0-1 with cut-off 37 ng/L, sensitivity was 0.71, specificity 0.82, positive predictive value 0.52 and negative predictive value 0.92. For PCT on d0-1 with cut-off 0.13 μg/L, the respective measures were 0.95, 0.53, 0.36, and 0.98. For the combination of IL-10 and PCT on d0-1 with the same cut-offs, specificity improved to 0.85 and positive predictive value to 0.56. In conclusion, the present study confirms the high negative predictive value of PCT and provides new evidence for IL-10 as an early predictor for complicated course of febrile neutropenia in hematological patients. Combining IL-10 with PCT improves the early prediction for complicated course of febrile neutropenia. Copyright © 2012 Elsevier Ltd. All rights reserved.

A life prediction methodology for encapsulated solar cells

NASA Technical Reports Server (NTRS)

Coulbert, C. D.

1978-01-01

This paper presents an approach to the development of a life prediction methodology for encapsulated solar cells which are intended to operate for twenty years or more in a terrestrial environment. Such a methodology, or solar cell life prediction model, requires the development of quantitative intermediate relationships between local environmental stress parameters and the basic chemical mechanisms of encapsulant aging leading to solar cell failures. The use of accelerated/abbreviated testing to develop these intermediate relationships and in revealing failure modes is discussed. Current field and demonstration tests of solar cell arrays and the present laboratory tests to qualify solar module designs provide very little data applicable to predicting the long-term performance of encapsulated solar cells. An approach to enhancing the value of such field tests to provide data for life prediction is described.

Evaluation of puberty by verifying spontaneous and stimulated gonadotropin values in girls.

PubMed

Chin, Vivian L; Cai, Ziyong; Lam, Leslie; Shah, Bina; Zhou, Ping

2015-03-01

Changes in pharmacological agents and advancements in laboratory assays have changed the gonadotropin-releasing hormone analog stimulation test. To determine the best predictive model for detecting puberty in girls. Thirty-five girls, aged 2 years 7 months to 9 years 3 months, with central precocious puberty (CPP) (n=20) or premature thelarche/premature adrenarche (n=15). Diagnoses were based on clinical information, baseline hormones, bone age, and pelvic sonogram. Gonadotropins and E2 were analyzed using immunochemiluminometric assay. Logistic regression for CPP was performed. The best predictor of CPP is the E2-change model based on 3- to 24-h values, providing 80% sensitivity and 87% specificity. Three-hour luteinizing hormone (LH) provided 75% sensitivity and 87% specificity. Basal LH lowered sensitivity to 65% and specificity to 53%. The E2-change model provided the best predictive power; however, 3-h LH was more practical and convenient when evaluating puberty in girls.

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

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.

QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

NASA Astrophysics Data System (ADS)

Zhi, Hui; Zheng, Junxia; Chang, Yiqun; Li, Qingguo; Liao, Guochao; Wang, Qi; Sun, Pinghua

2015-10-01

Forty-six sodium-dependent glucose cotransporters-2 (SGLT-2) inhibitors with hypoglycemic activity were selected to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set of 39 compounds were used to build up the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 7 compounds was used for the external validation. The CoMFA model predicted a q2 value of 0.792 and an r2 value of 0.985. The best CoMSIA model predicted a q2 value of 0.633 and an r2 value of 0.895 based on a combination of steric, electrostatic, hydrophobic and hydrogen-bond acceptor effects. The predictive correlation coefficients (rpred2) of CoMFA and CoMSIA models were 0.872 and 0.839, respectively. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active sglt inhibitors, and on the basis of the models 8 new sglt inhibitors were designed and predicted.

The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.

PubMed

Burden, Natalie; Maynard, Samuel K; Weltje, Lennart; Wheeler, James R

2016-10-01

The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of this non-testing approach, it can be concluded there is a strong data driven rationale for the applicability of QSAR models in the metabolite assessment scheme recommended by EFSA. As such there is value in further refining this approach, to improve the method and enable its future incorporation into regulatory guidance and practice. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The prognostic value of standardized reference values for speckle-tracking global longitudinal strain in hypertrophic cardiomyopathy.

PubMed

Hartlage, Gregory R; Kim, Jonathan H; Strickland, Patrick T; Cheng, Alan C; Ghasemzadeh, Nima; Pernetz, Maria A; Clements, Stephen D; Williams, B Robinson

2015-03-01

Speckle-tracking left ventricular global longitudinal strain (GLS) assessment may provide substantial prognostic information for hypertrophic cardiomyopathy (HCM) patients. Reference values for GLS have been recently published. We aimed to evaluate the prognostic value of standardized reference values for GLS in HCM patients. An analysis of HCM clinic patients who underwent GLS was performed. GLS was defined as normal (more negative or equal to -16%) and abnormal (less negative than -16%) based on recently published reference values. Patients were followed for a composite of events including heart failure hospitalization, sustained ventricular arrhythmia, and all-cause death. The power of GLS to predict outcomes was assessed relative to traditional clinical and echocardiographic variables present in HCM. 79 HCM patients were followed for a median of 22 months (interquartile range 9-30 months) after imaging. During follow-up, 15 patients (19%) met the primary outcome. Abnormal GLS was the only echocardiographic variable independently predictive of the primary outcome [multivariate Hazard ratio 5.05 (95% confidence interval 1.09-23.4, p = 0.038)]. When combined with traditional clinical variables, abnormal GLS remained independently predictive of the primary outcome [multivariate Hazard ratio 5.31 (95 % confidence interval 1.18-24, p = 0.030)]. In a model including the strongest clinical and echocardiographic predictors of the primary outcome, abnormal GLS demonstrated significant incremental benefit for risk stratification [net reclassification improvement 0.75 (95 % confidence interval 0.21-1.23, p < 0.0001)]. Abnormal GLS is an independent predictor of adverse outcomes in HCM patients. Standardized use of GLS may provide significant incremental value over traditional variables for risk stratification.

Uncertainty Analysis of Thermal Comfort Parameters

NASA Astrophysics Data System (ADS)

Ribeiro, A. Silva; Alves e Sousa, J.; Cox, Maurice G.; Forbes, Alistair B.; Matias, L. Cordeiro; Martins, L. Lages

2015-08-01

International Standard ISO 7730:2005 defines thermal comfort as that condition of mind that expresses the degree of satisfaction with the thermal environment. Although this definition is inevitably subjective, the Standard gives formulae for two thermal comfort indices, predicted mean vote ( PMV) and predicted percentage dissatisfied ( PPD). The PMV formula is based on principles of heat balance and experimental data collected in a controlled climate chamber under steady-state conditions. The PPD formula depends only on PMV. Although these formulae are widely recognized and adopted, little has been done to establish measurement uncertainties associated with their use, bearing in mind that the formulae depend on measured values and tabulated values given to limited numerical accuracy. Knowledge of these uncertainties are invaluable when values provided by the formulae are used in making decisions in various health and civil engineering situations. This paper examines these formulae, giving a general mechanism for evaluating the uncertainties associated with values of the quantities on which the formulae depend. Further, consideration is given to the propagation of these uncertainties through the formulae to provide uncertainties associated with the values obtained for the indices. Current international guidance on uncertainty evaluation is utilized.

Predicting Teacher Value-Added Results in Non-Tested Subjects Based on Confounding Variables: A Multinomial Logistic Regression

ERIC Educational Resources Information Center

Street, Nathan Lee

2017-01-01

Teacher value-added measures (VAM) are designed to provide information regarding teachers' causal impact on the academic growth of students while controlling for exogenous variables. While some researchers contend VAMs successfully and authentically measure teacher causality on learning, others suggest VAMs cannot adequately control for exogenous…

Curvature Constraints from the Causal Entropic Principle

NASA Astrophysics Data System (ADS)

Bozek, Brandon

2010-01-01

Current cosmological observations indicate a preference for a cosmological constant that is drastically smaller than what can be explained by conventional particle physics. The Causal Entropic Principle (Bousso, et al.) provides an alternative approach to anthropic attempts to predict our observed value of the cosmological constant by calculating the entropy created within a causal diamond. We have extended this work to use the Causal Entropic Principle to predict the preferred curvature within the "multiverse." We have found that values larger than ρk = 40*ρm are disfavored by more than 99.99% and a peak value at ρΛ = 7.9*10-123 and ρk =4.3*ρm for open universes. For universes that allow only positive curvature or both positive and negative curvature, we find a correlation between curvature and dark energy that leads to an extended region of preferred values. Our universe is found to be disfavored to an extent depending on the priors on curvature. We also provide a comparison to previous anthropic constraints on open universes and discuss future directions for this work.

Curvature constraints from the causal entropic principle

NASA Astrophysics Data System (ADS)

Bozek, Brandon; Albrecht, Andreas; Phillips, Daniel

2009-07-01

Current cosmological observations indicate a preference for a cosmological constant that is drastically smaller than what can be explained by conventional particle physics. The causal entropic principle (Bousso et al.) provides an alternative approach to anthropic attempts to predict our observed value of the cosmological constant by calculating the entropy created within a causal diamond. We have extended this work to use the causal entropic principle to predict the preferred curvature within the “multiverse.” We have found that values larger than ρk=40ρm are disfavored by more than 99.99% peak value at ρΛ=7.9×10-123 and ρk=4.3ρm for open universes. For universes that allow only positive curvature or both positive and negative curvature, we find a correlation between curvature and dark energy that leads to an extended region of preferred values. Our universe is found to be disfavored to an extent depending on the priors on curvature. We also provide a comparison to previous anthropic constraints on open universes and discuss future directions for this work.

Curvature constraints from the causal entropic principle

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

Bozek, Brandon; Albrecht, Andreas; Phillips, Daniel

2009-07-15

Current cosmological observations indicate a preference for a cosmological constant that is drastically smaller than what can be explained by conventional particle physics. The causal entropic principle (Bousso et al.) provides an alternative approach to anthropic attempts to predict our observed value of the cosmological constant by calculating the entropy created within a causal diamond. We have extended this work to use the causal entropic principle to predict the preferred curvature within the 'multiverse'. We have found that values larger than {rho}{sub k}=40{rho}{sub m} are disfavored by more than 99.99% peak value at {rho}{sub {lambda}}=7.9x10{sup -123} and {rho}{sub k}=4.3{rho}{sub m}more » for open universes. For universes that allow only positive curvature or both positive and negative curvature, we find a correlation between curvature and dark energy that leads to an extended region of preferred values. Our universe is found to be disfavored to an extent depending on the priors on curvature. We also provide a comparison to previous anthropic constraints on open universes and discuss future directions for this work.« less

Assessing cutoff values for increased exercise blood pressure to predict incident hypertension in a general population.

PubMed

Lorbeer, Roberto; Ittermann, Till; Völzke, Henry; Gläser, Sven; Ewert, Ralf; Felix, Stephan B; Dörr, Marcus

2015-07-01

Cutoff values for increased exercise blood pressure (BP) are not established in hypertension guidelines. The aim of the study was to assess optimal cutoff values for increased exercise BP to predict incident hypertension. Data of 661 normotensive participants (386 women) aged 25-77 years from the Study of Health in Pomerania (SHIP-1) with a 5-year follow-up were used. Exercise BP was measured at a submaximal level of 100 W and at maximum level of a symptom-limited cycle ergometry test. Cutoff values for increased exercise BP were defined at the maximum sum of sensitivity and specificity for the prediction of incident hypertension. The area under the receiver-operating characteristic curve (AUC) and net reclassification index (NRI) were calculated to investigate whether increased exercise BP adds predictive value for incident hypertension beyond established cardiovascular risk factors. In men, values of 160  mmHg (100  W level; AUC = 0.7837; NRI = 0.534, P < 0.001) and 210  mmHg (maximum level; AUC = 0.7677; NRI = 0.340, P = 0.003) were detected as optimal cutoff values for the definition of increased exercise SBP. A value of 190  mmHg (AUC = 0.8347; NRI = 0.519, P < 0.001) showed relevance for the definition of increased exercise SBP in women at the maximum level. According to our analyses, 190 and 210  mmHg are clinically relevant cutoff values for increased exercise SBP at the maximum exercise level of cycle ergometry test for women and men, respectively. In addition, for men, our analyses provided a cutoff value of 160  mmHg for increased exercise SBP at the 100  W level.

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.

An equation for the prediction of human skin permeability of neutral molecules, ions and ionic species.

PubMed

Zhang, Keda; Abraham, Michael H; Liu, Xiangli

2017-04-15

Experimental values of permeability coefficients, as log K p , of chemical compounds across human skin were collected by carefully screening the literature, and adjusted to 37°C for the effect of temperature. The values of log K p for partially ionized acids and bases were separated into those for their neutral and ionic species, forming a total data set of 247 compounds and species (including 35 ionic species). The obtained log K p values have been regressed against Abraham solute descriptors to yield a correlation equation with R 2 =0.866 and SD=0.432 log units. The equation can provide valid predictions for log K p of neutral molecules, ions and ionic species, with predictive R 2 =0.858 and predictive SD=0.445 log units calculated by the leave-one-out statistics. The predicted log K p values for Na + and Et 4 N + are in good agreement with the observed values. We calculated the values of log K p of ketoprofen as a function of the pH of the donor solution, and found that log K p markedly varies only when ketoprofen is largely ionized. This explains why models that neglect ionization of permeants still yield reasonable statistical results. The effect of skin thickness on log K p was investigated by inclusion of two indicator variables, one for intermediate thickness skin and one for full thickness skin, into the above equation. The newly obtained equations were found to be statistically very close to the above equation. Therefore, the thickness of human skin used makes little difference to the experimental values of log K p . Copyright © 2017 Elsevier B.V. All rights reserved.

Waist circumference shows the highest predictive value for metabolic syndrome, and waist-to-hip ratio for its components, in Spanish adolescents.

PubMed

Perona, Javier S; Schmidt-RioValle, Jacqueline; Rueda-Medina, Blanca; Correa-Rodríguez, María; González-Jiménez, Emilio

2017-09-01

Both waist circumference (WC) and waist-to-hip ratio (WHR) have been proposed as predictors of metabolic syndrome (MetS) in adolescents, but no consensus has been reached to date. This study hypothesizes that WC provides a greater predictive value for MetS in Spanish adolescents than WHR. A cross-sectional study was performed on 1001 adolescents (13.2 ± 1.2 years) randomly recruited from schools in southeast Spain. Anthropometric measures were correlated with the components of MetS (triglycerides, glucose, blood pressure, and high-density lipoprotein cholesterol) as well as inflammation markers (interleukin-6 and tumor necrosis factor-alpha , C-reactive protein, and ceruloplasmin). Receiver-operator curves were created to determine the predictive value of these variables for MetS. Boys had higher values of all anthropometric parameters compared with girls, but the prevalence of MetS was significantly higher in girls. WHR was the only parameter that correlated significantly with all biochemical and inflammatory variables in boys. In girls, WHR, body mass index, waist-to-height ratio, WC, and body fat percentage correlated only with plasma insulin levels, systolic and diastolic pressures, and ceruloplasmin. In both groups, all anthropometric measures were able to predict MetS (area under the curve > 0.94). In particular, WC was able to predict MetS with area under the curve = 1.00. However, WHR was able to predict a higher number of components of MetS. WHR was the anthropometric index that showed the highest predictive value for MetS components, whereas WC was the one that best predicted the MetS among the population of adolescents studied. These findings justify the need to incorporate WHR and WC determinations into daily clinical practice to predict the MetS. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Continuous noninvasive monitoring in the neonatal ICU.

PubMed

Sahni, Rakesh

2017-04-01

Standard hemodynamic monitoring such as heart rate and systemic blood pressure may only provide a crude estimation of organ perfusion during neonatal intensive care. Pulse oximetry monitoring allows for continuous noninvasive monitoring of hemoglobin oxygenation and thus provides estimation of end-organ oxygenation. This review aims to provide an overview of pulse oximetry and discuss its current and potential clinical use during neonatal intensive care. Technological advances in continuous assessment of dynamic changes in systemic oxygenation with pulse oximetry during transition to extrauterine life and beyond provide additional details about physiological interactions among the key hemodynamic factors regulating systemic blood flow distribution along with the subtle changes that are frequently transient and undetectable with standard monitoring. Noninvasive real-time continuous systemic oxygen monitoring has the potential to serve as biomarkers for early-organ dysfunction, to predict adverse short-term and long-term outcomes in critically ill neonates, and to optimize outcomes. Further studies are needed to establish values predicting adverse outcomes and to validate targeted interventions to normalize abnormal values to improve outcomes.

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.

Evaluating Prospective Teachers: Testing the Predictive Validity of the EdTPA

ERIC Educational Resources Information Center

Goldhaber, Dan; Cowan, James; Theobald, Roddy

2017-01-01

We use longitudinal data from Washington State to provide estimates of the extent to which performance on the edTPA, a performance-based, subject-specific assessment of teacher candidates, is predictive of the likelihood of employment in the teacher workforce and value-added measures of teacher effectiveness. While edTPA scores are highly…

Evaluating Prospective Teachers: Testing the Predictive Validity of the edTPA. Working Paper 157

ERIC Educational Resources Information Center

Goldhaber, Dan; Cowan, James; Theobald, Roddy

2016-01-01

We use longitudinal data from Washington State to provide estimates of the extent to which performance on the edTPA, a performance-based, subject-specific assessment of teacher candidates, is predictive of the likelihood of employment in the teacher workforce and value-added measures of teacher effectiveness. While edTPA scores are highly…

Trajectories of Anxiety during Elementary-School Years and the Prediction of High School Noncompletion

ERIC Educational Resources Information Center

Duchesne, Stephane; Vitaro, Frank; Larose, Simon; Tremblay, Richard E.

2008-01-01

Previous research has provided mixed results regarding the effect of anxiety on academic achievement. Building on this body of research, the present longitudinal study pursued two goals. The first goal was to describe trajectories of anxiety during elementary-school years. The second goal was to determine the predictive value of these trajectories…

Predictive values of thermal and electrical dental pulp tests: a clinical study.

PubMed

Villa-Chávez, Carlos E; Patiño-Marín, Nuria; Loyola-Rodríguez, Juan P; Zavala-Alonso, Norma V; Martínez-Castañón, Gabriel A; Medina-Solís, Carlo E

2013-08-01

For a diagnostic test to be useful, it is necessary to determine the probability that the test will provide the correct diagnosis. Therefore, it is necessary to calculate the predictive value of diagnostics. The aim of the present study was to identify the sensitivity, specificity, positive and negative predictive values, accuracy, and reproducibility of thermal and electrical tests of pulp sensitivity. The thermal tests studied were the 1, 1, 1, 2-tetrafluoroethane (cold) and hot gutta-percha (hot) tests. For the electrical test, the Analytic Technology Pulp Tester (Analytic Technology, Redmond, WA) was used. A total of 110 teeth were tested: 60 teeth with vital pulp and 50 teeth with necrotic pulps (disease prevalence of 45%). The ideal standard was established by direct pulp inspection. The sensitivities of the diagnostic tests were 0.88 for the cold test, 0.86 for the heat test, and 0.76 for the electrical test, and the specificity was 1.0 for all 3 tests. The negative predictive value was 0.90 for the cold test, 0.89 for the heat test, and 0.83 for the electrical test, and the positive predictive value was 1.0 for all 3 tests. The highest accuracy (0.94) and reproducibility (0.88) were observed for the cold test. The cold test was the most accurate method for diagnostic testing. Copyright © 2013 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Evidence from bond lengths and bond angles for enneacovalence of cobalt, rhodium, iridium, iron, ruthenium, and osmium in compounds with elements of medium electronegativity.

PubMed

Pauling, L

1984-03-01

Enneacovalence of neutral atoms can be achieved for Co, Rh, and Ir by promoting some electrons from the nd orbital to the (n + 1)s and (n + 1)p orbitals and for Fe, Ru, and Os by a similar promotion together with the addition of an electron, which may be provided by an electron pair from a singly bonded carbonyl group or other group. The bond lengths and bond angles are predicted by the theory of enneacovalence to be significantly different for the different transition metals. Recently reported experimental values are shown to be in good agreement with the predicted values, providing support for the theory of enneacovalence and the theory of hybrid sp(3)d(5) bond orbitals.

Acute toxicity prediction to threatened and endangered ...

EPA Pesticide Factsheets

Evaluating contaminant sensitivity of threatened and endangered (listed) species and protectiveness of chemical regulations often depends on toxicity data for commonly tested surrogate species. The U.S. EPA’s Internet application Web-ICE is a suite of Interspecies Correlation Estimation (ICE) models that can extrapolate species sensitivity to listed taxa using least-squares regressions of the sensitivity of a surrogate species and a predicted taxon (species, genus, or family). Web-ICE was expanded with new models that can predict toxicity to over 250 listed species. A case study was used to assess protectiveness of genus and family model estimates derived from either geometric mean or minimum taxa toxicity values for listed species. Models developed from the most sensitive value for each chemical were generally protective of the most sensitive species within predicted taxa, including listed species, and were more protective than geometric means models. ICE model estimates were compared to HC5 values derived from Species Sensitivity Distributions for the case study chemicals to assess protectiveness of the two approaches. ICE models provide robust toxicity predictions and can generate protective toxicity estimates for assessing contaminant risk to listed species. Reporting on the development and optimization of ICE models for listed species toxicity estimation

Acute Toxicity Prediction to Threatened and Endangered Species Using Interspecies Correlation Estimation (ICE) Models.

PubMed

Willming, Morgan M; Lilavois, Crystal R; Barron, Mace G; Raimondo, Sandy

2016-10-04

Evaluating contaminant sensitivity of threatened and endangered (listed) species and protectiveness of chemical regulations often depends on toxicity data for commonly tested surrogate species. The U.S. EPA's Internet application Web-ICE is a suite of Interspecies Correlation Estimation (ICE) models that can extrapolate species sensitivity to listed taxa using least-squares regressions of the sensitivity of a surrogate species and a predicted taxon (species, genus, or family). Web-ICE was expanded with new models that can predict toxicity to over 250 listed species. A case study was used to assess protectiveness of genus and family model estimates derived from either geometric mean or minimum taxa toxicity values for listed species. Models developed from the most sensitive value for each chemical were generally protective of the most sensitive species within predicted taxa, including listed species, and were more protective than geometric means models. ICE model estimates were compared to HC5 values derived from Species Sensitivity Distributions for the case study chemicals to assess protectiveness of the two approaches. ICE models provide robust toxicity predictions and can generate protective toxicity estimates for assessing contaminant risk to listed species.

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

PubMed

Xianfang, Wang; Junmei, Wang; Xiaolei, Wang; Yue, Zhang

2017-01-01

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

PubMed Central

Xiaolei, Wang

2017-01-01

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server. PMID:28497044

Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data.

PubMed

Yılmaz Isıkhan, Selen; Karabulut, Erdem; Alpar, Celal Reha

2016-01-01

Background/Aim . Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods . Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results . The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion . Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n = 25 as a cutoff point for RT bagging to outperform a single RT.

pKa prediction from an ab initio bond length: part 3--benzoic acids and anilines.

PubMed

Harding, A P; Popelier, P L A

2011-06-21

The prediction of pK(a) from a single ab initio bond length has been extended to provide equations for benzoic acids and anilines. The HF/6-31G(d) level of theory is used for all geometry optimisations. Similarly to phenols (Part 2 of this series of publications), the meta-/para-substituted benzoic acids can be predicted from a single model constructed from one bond length. This model had an impressive RMSEP of 0.13 pK(a) units. The prediction of ortho-substituted benzoic acids required the identification of high-correlation subsets, where the compounds in the same subset have at least one of the same (e.g. halogens, hydroxy) ortho substituent. Two pK(a) equations are provided for o-halogen benzoic acids and o-hydroxybenzoic acids, where the RMSEP values are 0.19 and 0.15 pK(a) units, respectively. Interestingly, the bond length that provided the best model differed between these two high-correlation subsets. This demonstrates the importance of investigating the most predictive bond length, which is not necessarily the bond involving the acid hydrogen. Three high-correlation subsets were identified for the ortho-substituted anilines. These were o-halogen, o-nitro and o-alkyl-substituted aniline high-correlation subsets, where the RMSEP ranged from 0.23 to 0.44 pK(a) units. The RMSEP for the meta-/para-substituted aniline model was 0.54 pK(a) units. This value exceeded our threshold of 0.50 pK(a) units and was higher than both the m-/p-benzoic acids in this work and the m-/p-phenols (RMSEP = 0.43) of Part 2. Constructing two separate models for the meta- and para- substituted anilines, where RMSEP values of 0.63 and 0.33 pK(a) units were obtained respectively, revealed it was the meta-substituted anilines that caused the large RMSEP value. For unknown reasons the RMSEP value increased with the addition of a further twenty meta-substituted anilines to this model. The C-N bond always produced the best correlations with pK(a) for all the high-correlation subsets. A higher level of theory and an ammonia probe improved the statistics only marginally for the hydroxybenzoic acid high-correlation subsets.

Computation of Standard Errors

PubMed Central

Dowd, Bryan E; Greene, William H; Norton, Edward C

2014-01-01

Objectives We discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages. Study Design We show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the dependent variable for an individual subject and average effect for a sample of subjects. Empirical Application Using a publicly available dataset, we explain three different methods of computing standard errors: the delta method, Krinsky–Robb, and bootstrapping. We provide computer code for Stata 12 and LIMDEP 10/NLOGIT 5. Conclusions In most applications, choice of the computational method for standard errors of functions of estimated parameters is a matter of convenience. However, when computing standard errors of the sample average of functions that involve both estimated parameters and nonstochastic explanatory variables, it is important to consider the sources of variation in the function's values. PMID:24800304

Accuracy of genomic breeding values in multibreed beef cattle populations derived from deregressed breeding values and phenotypes.

PubMed

Weber, K L; Thallman, R M; Keele, J W; Snelling, W M; Bennett, G L; Smith, T P L; McDaneld, T G; Allan, M F; Van Eenennaam, A L; Kuehn, L A

2012-12-01

Genomic selection involves the assessment of genetic merit through prediction equations that allocate genetic variation with dense marker genotypes. It has the potential to provide accurate breeding values for selection candidates at an early age and facilitate selection for expensive or difficult to measure traits. Accurate across-breed prediction would allow genomic selection to be applied on a larger scale in the beef industry, but the limited availability of large populations for the development of prediction equations has delayed researchers from providing genomic predictions that are accurate across multiple beef breeds. In this study, the accuracy of genomic predictions for 6 growth and carcass traits were derived and evaluated using 2 multibreed beef cattle populations: 3,358 crossbred cattle of the U.S. Meat Animal Research Center Germplasm Evaluation Program (USMARC_GPE) and 1,834 high accuracy bull sires of the 2,000 Bull Project (2000_BULL) representing influential breeds in the U.S. beef cattle industry. The 2000_BULL EPD were deregressed, scaled, and weighted to adjust for between- and within-breed heterogeneous variance before use in training and validation. Molecular breeding values (MBV) trained in each multibreed population and in Angus and Hereford purebred sires of 2000_BULL were derived using the GenSel BayesCπ function (Fernando and Garrick, 2009) and cross-validated. Less than 10% of large effect loci were shared between prediction equations trained on (USMARC_GPE) relative to 2000_BULL although locus effects were moderately to highly correlated for most traits and the traits themselves were highly correlated between populations. Prediction of MBV accuracy was low and variable between populations. For growth traits, MBV accounted for up to 18% of genetic variation in a pooled, multibreed analysis and up to 28% in single breeds. For carcass traits, MBV explained up to 8% of genetic variation in a pooled, multibreed analysis and up to 42% in single breeds. Prediction equations trained in multibreed populations were more accurate for Angus and Hereford subpopulations because those were the breeds most highly represented in the training populations. Accuracies were less for prediction equations trained in a single breed due to the smaller number of records derived from a single breed in the training populations.

Theoretical prediction of the ionization energies of the C4H7 radicals: 1-methylallyl, 2-methylallyl, cyclopropylmethyl, and cyclobutyl radicals.

PubMed

Lau, Kai-Chung; Zheng, Wenxu; Wong, Ning-Bew; Li, Wai-Kee

2007-10-21

The ionization energies (IEs) for the 1-methylallyl, 2-methylallyl, cyclopropylmethyl, and cyclobutyl radicals have been calculated by the wave function based ab initio CCSD(T)/CBS approach, which involves the approximation to the complete basis set (CBS) limit at the coupled cluster level with single and double excitations plus quasiperturbative triple excitation [CCSD(T)]. The zero-point vibrational energy correction, the core-valence electronic correction, and the scalar relativistic effect correction are included in these calculations. The present CCSD(T)/CBS results are then compared with the IEs determined in the photoelectron experiment by Schultz et al. [J. Am. Chem. Soc. 106, 7336 (1984)] The predicted IE value (7.881 eV) of 2-methylallyl radical is found to compare very favorably with the experimental value of 7.90+/-0.02 eV. Two ionization transitions for cis-1-methylallyl and trans-1-methylallyl radicals have been considered here. The comparison between the predicted IE values and the previous measurements shows that the photoelectron peak observed by Schultz et al. likely corresponds to the adiabatic ionization transition for the trans-1-methylallyl radical to form trans-1-methylallyl cation. Although a precise IE value for the cyclopropylmethyl radical has not been directly determined, the experimental value deduced indirectly using other known energetic data is found to be in good accord with the present CCSD(T)/CBS prediction. We expect that the Franck-Condon factor for ionization transition of c-C4H7-->bicyclobutonium is much less favorable than that for ionization transition of c-C4H7-->planar-C4H7+, and the observed IE in the previous photoelectron experiment is likely due to the ionization transition for c-C4H7-->planar-C4H7+. Based on our CCSD(T)/CBS prediction, the ionization transition of c-C4H7-->bicyclobutonium with an IE value around 6.92 eV should be taken as the adiabatic ionization transition for the cyclobutyl radical. The present study provides support for the conclusion that the CCSD(T)/CBS approach with high-level energetic corrections can be used to provide reliable IE predictions for C4 hydrocarbon radicals with an uncertainty of +/-22 meV. The CCSD(T)/CBS predictions to the heats of formation for the aforementioned radicals and cations are also presented.

Predicting the solubility and lability of Zn, Cd, and Pb in soils from a minespoil-contaminated catchment by stable isotopic exchange

NASA Astrophysics Data System (ADS)

Marzouk, E. R.; Chenery, S. R.; Young, S. D.

2013-12-01

The Rookhope catchment of Weardale, England, has a diverse legacy of contaminated soils due to extensive lead mining activity over four centuries. We measured the isotopically exchangeable content of Pb, Cd and Zn (E-values) in a large representative subset of the catchment soils (n = 246) using stable isotope dilution. All three metals displayed a wide range of %E-values (c. 1-100%) but relative lability followed the sequence Cd > Pb > Zn. A refinement of the stable isotope dilution approach also enabled detection of non-reactive metal contained within suspended sub-micron (<0.22 μm) colloidal particles (SCP-metal). For most soils, the presence of non-labile SCP-metal caused only minor over-estimation of E-values (<2%) but the effect was greater for soils with particularly large humus or carbonate contents. Approximately 80%, 53% and 66% of the variability in Zn, Cd and Pb %E-values (respectively) could be explained by pH, loss on ignition and total metal content. E-values were affected by the presence of ore minerals at high metal contents leading to an inconsistent trend in the relationship between %E-value and soil metal concentration. Metal solubility, in the soil suspensions used to measure E-values, was predicted using the WHAM geochemical speciation model (versions VI and VII). The use of total and isotopically exchangeable metal as alternative input variables was compared; the latter provided significantly better predictions of solubility, especially in the case of Zn. Lead solubility was less well predicted by either version of WHAM, with over-prediction at low pH and under-prediction at high soil pH values. Quantify the isotopically exchangeable fractions of Zn, Cd and Pb (E-values), and assess their local and regional variability, using multi-element stable isotope dilution, in a diverse range of soil ecosystems within the catchment of an old Pb/Zn mining area. Assess the controlling influences of soil properties on metal lability and develop predictive algorithms for metal lability in the contaminated catchment based on simple soil properties (such as pH, organic matter (LOI), and total metal content). Examine the incidence of non-isotopically-exchangeable metal held within suspended colloidal particles (SCP-metal) in filtered soil solutions (<0.22 μm) by comparing E-values from isotopic abundance in solutions equilibrated with soil and in a resin phase equilibrated with the separated solution. Assess the ability of a geochemical speciation model, WHAM(VII), to predict metal solubility using isotopically exchangeable metal as an input variable.

Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Toliyat, Hamid A.

2005-01-01

An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

Recent Results on "Approximations to Optimal Alarm Systems for Anomaly Detection"

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2009-01-01

An optimal alarm system and its approximations may use Kalman filtering for univariate linear dynamic systems driven by Gaussian noise to provide a layer of predictive capability. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. An optimal alarm system can be designed to elicit the fewest false alarms for a fixed detection probability in this particular scenario.

Scores for post-myocardial infarction risk stratification in the community.

PubMed

Singh, Mandeep; Reeder, Guy S; Jacobsen, Steven J; Weston, Susan; Killian, Jill; Roger, Véronique L

2002-10-29

Several scores, most of which were derived from clinical trials, have been proposed for stratifying risk after myocardial infarctions (MIs). Little is known about their generalizability to the community, their respective advantages, and whether the ejection fraction (EF) adds prognostic information to the scores. The purpose of this study is to evaluate the Thrombolysis in Myocardial Infarction (TIMI) and Predicting Risk of Death in Cardiac Disease Tool (PREDICT) scores in a geographically defined MI cohort and determine the incremental value of EF for risk stratification. MIs occurring in Olmsted County were validated with the use of standardized criteria and stratified with the ECG into ST-segment elevation (STEMI) and non-ST-segment elevation (NSTEMI) MI. Logistic regression examined the discriminant accuracy of the TIMI and PREDICT scores to predict death and recurrent MI and assessed the incremental value of the EF. After 6.3+/-4.7 years, survival was similar for the 562 STEMIs and 717 NSTEMIs. The discriminant accuracy of the TIMI score was good in STEMI but only fair in NSTEMI. Across time and end points, irrespective of reperfusion therapy, the discriminant accuracy of the PREDICT score was consistently superior to that of the TIMI scores, largely because PREDICT includes comorbidity; EF provided incremental information over that provided by the scores and comorbidity. In the community, comorbidity and EF convey important prognostic information and should be included in approaches for stratifying risk after MI.

Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations.

PubMed

Wang, D; Salah El-Basyoni, I; Stephen Baenziger, P; Crossa, J; Eskridge, K M; Dweikat, I

2012-11-01

Though epistasis has long been postulated to have a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed least absolute shrinkage and selection operator (LASSO). The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than onefold in some cases as measured by cross-validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.

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.

NWP model forecast skill optimization via closure parameter variations

NASA Astrophysics Data System (ADS)

Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

2012-04-01

We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) project: a summary

NASA Astrophysics Data System (ADS)

Hawkins, Ed; Day, Jonny; Tietsche, Steffen

2016-04-01

Recent years have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. We describe a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual TimEscales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we provide a summary and update of the project's results which include: (1) quantifying the predictability of Arctic climate, especially sea ice; (2) the state-dependence of this predictability, finding that extreme years are potentially more predictable than neutral years; (3) analysing a spring 'predictability barrier' to skillful forecasts; (4) initial sea ice thickness information provides much of the skill for summer forecasts; (5) quantifying the sources of error growth and uncertainty in Arctic predictions. The dataset is now publicly available.

Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24 827 people: systematic review and meta-analysis

PubMed Central

Singh, Jay P; Doll, Helen; Grann, Martin

2012-01-01

Objective To investigate the predictive validity of tools commonly used to assess the risk of violence, sexual, and criminal behaviour. Design Systematic review and tabular meta-analysis of replication studies following PRISMA guidelines. Data sources PsycINFO, Embase, Medline, and United States Criminal Justice Reference Service Abstracts. Review methods We included replication studies from 1 January 1995 to 1 January 2011 if they provided contingency data for the offending outcome that the tools were designed to predict. We calculated the diagnostic odds ratio, sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, the number needed to detain to prevent one offence, as well as a novel performance indicator—the number safely discharged. We investigated potential sources of heterogeneity using metaregression and subgroup analyses. Results Risk assessments were conducted on 73 samples comprising 24 847 participants from 13 countries, of whom 5879 (23.7%) offended over an average of 49.6 months. When used to predict violent offending, risk assessment tools produced low to moderate positive predictive values (median 41%, interquartile range 27-60%) and higher negative predictive values (91%, 81-95%), and a corresponding median number needed to detain of 2 (2-4) and number safely discharged of 10 (4-18). Instruments designed to predict violent offending performed better than those aimed at predicting sexual or general crime. Conclusions Although risk assessment tools are widely used in clinical and criminal justice settings, their predictive accuracy varies depending on how they are used. They seem to identify low risk individuals with high levels of accuracy, but their use as sole determinants of detention, sentencing, and release is not supported by the current evidence. Further research is needed to examine their contribution to treatment and management. PMID:22833604

Correlations between the 1H NMR chemical shieldings and the pKa values of organic acids and amines.

PubMed

Lu, Juanfeng; Lu, Tingting; Zhao, Xinyun; Chen, Xi; Zhan, Chang-Guo

2018-06-01

The acid dissociation constants and 1 H NMR chemical shieldings of organic compounds are important properties that have attracted much research interest. However, few studies have explored the relationship between these two properties. In this work, we theoretically studied the NMR chemical shifts of a series of carboxylic acids and amines in the gas phase and in aqueous solution. It was found that the negative logarithms of the experimental acid dissociation constants (i.e., the pK a values) of the organic acids and amines in aqueous solution correlate almost linearly with the corresponding calculated NMR chemical shieldings. Key factors that affect the theoretically predicted pK a values are discussed in this paper. The present work provides a new way to predict the pK a values of organic/biochemical compounds. Graphical abstract The chemical shielding values of organic acids and amines correlate near linearly with their corresponding pK a values.

The habenula governs the attribution of incentive salience to reward predictive cues

PubMed Central

Danna, Carey L.; Shepard, Paul D.; Elmer, Greg I.

2013-01-01

The attribution of incentive salience to reward associated cues is critical for motivation and the pursuit of rewards. Disruptions in the integrity of the neural systems controlling these processes can lead to avolition and anhedonia, symptoms that cross the diagnostic boundaries of many neuropsychiatric illnesses. Here, we consider whether the habenula (Hb), a region recently demonstrated to encode negatively valenced events, also modulates the attribution of incentive salience to a neutral cue predicting a food reward. The Pavlovian autoshaping paradigm was used in the rat as an investigative tool to dissociate Pavlovian learning processes imparting strictly predictive value from learning that attributes incentive motivational value. Electrolytic lesions of the fasciculus retroflexus (fr), the sole pathway through which descending Hb efferents are conveyed, significantly increased incentive salience as measured by conditioned approaches to a cue light predictive of reward. Conversely, generation of a fictive Hb signal via fr stimulation during CS+ presentation significantly decreased the incentive salience of the predictive cue. Neither manipulation altered the reward predictive value of the cue as measured by conditioned approach to the food. Our results provide new evidence supporting a significant role for the Hb in governing the attribution of incentive motivational salience to reward predictive cues and further imply that pathological changes in Hb activity could contribute to the aberrant pursuit of debilitating goals or avolition and depression-like symptoms. PMID:24368898

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

PubMed

Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

2017-12-04

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

Evaluating Prospective Teachers: Testing the Predictive Validity of the edTPA. CEDR Working Paper. WP #2016-2.2

ERIC Educational Resources Information Center

Goldhaber, Dan; Cowan, James; Theobald, Roddy

2016-01-01

We use longitudinal data from Washington State to provide estimates of the extent to which performance on the edTPA, a performance-based, subject-specific assessment of teacher candidates, is predictive of the likelihood of employment in the teacher workforce and value-added measures of teacher effectiveness. While edTPA scores are highly…

Evaluating Prospective Teachers: Testing the Predictive Validity of the edTPA. CEDR Working Paper. WP #2016-7

ERIC Educational Resources Information Center

Goldhaber, Dan; Cowan, James; Theobald, Roddy

2016-01-01

We use longitudinal data from Washington State to provide estimates of the extent to which performance on the edTPA, a performance-based, subject-specific assessment of teacher candidates, is predictive of the likelihood of employment in the teacher workforce and value-added measures of teacher effectiveness. While edTPA scores are highly…

The physics behind Van der Burgh's empirical equation, providing a new predictive equation for salinity intrusion in estuaries

NASA Astrophysics Data System (ADS)

Zhang, Zhilin; Savenije, Hubert H. G.

2017-07-01

The practical value of the surprisingly simple Van der Burgh equation in predicting saline water intrusion in alluvial estuaries is well documented, but the physical foundation of the equation is still weak. In this paper we provide a connection between the empirical equation and the theoretical literature, leading to a theoretical range of Van der Burgh's coefficient of 1/2 < K < 2/3 for density-driven mixing which falls within the feasible range of 0 < K < 1. In addition, we developed a one-dimensional predictive equation for the dispersion of salinity as a function of local hydraulic parameters that can vary along the estuary axis, including mixing due to tide-driven residual circulation. This type of mixing is relevant in the wider part of alluvial estuaries where preferential ebb and flood channels appear. Subsequently, this dispersion equation is combined with the salt balance equation to obtain a new predictive analytical equation for the longitudinal salinity distribution. Finally, the new equation was tested and applied to a large database of observations in alluvial estuaries, whereby the calibrated K values appeared to correspond well to the theoretical range.

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.

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

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.

GEMAS: prediction of solid-solution phase partitioning coefficients (Kd) for oxoanions and boric acid in soils using mid-infrared diffuse reflectance spectroscopy.

PubMed

Janik, Leslie J; Forrester, Sean T; Soriano-Disla, José M; Kirby, Jason K; McLaughlin, Michael J; Reimann, Clemens

2015-02-01

The authors' aim was to develop rapid and inexpensive regression models for the prediction of partitioning coefficients (Kd), defined as the ratio of the total or surface-bound metal/metalloid concentration of the solid phase to the total concentration in the solution phase. Values of Kd were measured for boric acid (B[OH]3(0)) and selected added soluble oxoanions: molybdate (MoO4(2-)), antimonate (Sb[OH](6-)), selenate (SeO4(2-)), tellurate (TeO4(2-)) and vanadate (VO4(3-)). Models were developed using approximately 500 spectrally representative soils of the Geochemical Mapping of Agricultural Soils of Europe (GEMAS) program. These calibration soils represented the major properties of the entire 4813 soils of the GEMAS project. Multiple linear regression (MLR) from soil properties, partial least-squares regression (PLSR) using mid-infrared diffuse reflectance Fourier-transformed (DRIFT) spectra, and models using DRIFT spectra plus analytical pH values (DRIFT + pH), were compared with predicted log K(d + 1) values. Apart from selenate (R(2)  = 0.43), the DRIFT + pH calibrations resulted in marginally better models to predict log K(d + 1) values (R(2)  = 0.62-0.79), compared with those from PSLR-DRIFT (R(2)  = 0.61-0.72) and MLR (R(2)  = 0.54-0.79). The DRIFT + pH calibrations were applied to the prediction of log K(d + 1) values in the remaining 4313 soils. An example map of predicted log K(d + 1) values for added soluble MoO4(2-) in soils across Europe is presented. The DRIFT + pH PLSR models provided a rapid and inexpensive tool to assess the risk of mobility and potential availability of boric acid and selected oxoanions in European soils. For these models to be used in the prediction of log K(d + 1) values in soils globally, additional research will be needed to determine if soil variability is accounted on the calibration. © 2014 SETAC.

Effect of bulk modulus on deformation of the brain under rotational accelerations

NASA Astrophysics Data System (ADS)

Ganpule, S.; Daphalapurkar, N. P.; Cetingul, M. P.; Ramesh, K. T.

2018-01-01

Traumatic brain injury such as that developed as a consequence of blast is a complex injury with a broad range of symptoms and disabilities. Computational models of brain biomechanics hold promise for illuminating the mechanics of traumatic brain injury and for developing preventive devices. However, reliable material parameters are needed for models to be predictive. Unfortunately, the properties of human brain tissue are difficult to measure, and the bulk modulus of brain tissue in particular is not well characterized. Thus, a wide range of bulk modulus values are used in computational models of brain biomechanics, spanning up to three orders of magnitude in the differences between values. However, the sensitivity of these variations on computational predictions is not known. In this work, we study the sensitivity of a 3D computational human head model to various bulk modulus values. A subject-specific human head model was constructed from T1-weighted MRI images at 2-mm3 voxel resolution. Diffusion tensor imaging provided data on spatial distribution and orientation of axonal fiber bundles for modeling white matter anisotropy. Non-injurious, full-field brain deformations in a human volunteer were used to assess the simulated predictions. The comparison suggests that a bulk modulus value on the order of GPa gives the best agreement with experimentally measured in vivo deformations in the human brain. Further, simulations of injurious loading suggest that bulk modulus values on the order of GPa provide the closest match with the clinical findings in terms of predicated injured regions and extent of injury.

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

Reference values of inspiratory spirometry for Finnish adults.

PubMed

Kainu, Annette; Timonen, Kirsi L; Vanninen, Esko; Sovijärvi, Anssi R

2018-03-07

Inspiratory spirometry is used in evaluation of upper airway disorders e.g. fixed or variable obstruction. There are, however, very few published data on normal values for inspiratory spirometry. The main aim of this study was to produce reference values for inspiratory spirometry for healthy Finnish adults. Inspiratory spirometry was preplanned to a sample of the Finnish spirometry reference values sample. Data was successfully retrieved from 368 healthy nonsmoking adults (132 males) between 19 and 83 years of age. Reference equations were produced for forced inspiratory vital capacity (FIVC), forced inspiratory volume in one second (FIV1), FIV1/FIVC, peak inspiratory flow (PIF) and the ratios of FIV1/forced expiratory volume in one second and PIF/peak expiratory flow. The present values were compared to PIF values from previously used Finnish study of Viljanen et al. (1982) reference values and Norwegian values for FIV1, FIVC and FIV1/FIVC presented by Gulsvik et al. (2001). The predicted values from the Gulsvik et al. (2001), provided a good fit for FIVC, but smaller values for FIV1 with mean 108.3 and 109.1% of predicted values for males and females, respectively. PIF values were 87.4 and 91.2% of Viljanen et al. (1982) predicted values in males and females, respectively. Differences in measurement methods and selection of results may contribute to the observed differences. Inspiratory spirometry is technically more demanding and needs repeatability criteria to improve validity. New reference values are suggested to clinical use in Finland when assessing inspiratory spirometry. Utility of inspiratory to expiratory values indices in assessment of airway collapse need further study.

Integrating GLL-Weibull Distribution Within a Bayesian Framework for Life Prediction of Shape Memory Alloy Spring Undergoing Thermo-mechanical Fatigue

NASA Astrophysics Data System (ADS)

Kundu, Pradeep; Nath, Tameshwer; Palani, I. A.; Lad, Bhupesh K.

2018-06-01

The present paper tackles an important but unmapped problem of the reliability estimations of smart materials. First, an experimental setup is developed for accelerated life testing of the shape memory alloy (SMA) springs. Generalized log-linear Weibull (GLL-Weibull) distribution-based novel approach is then developed for SMA spring life estimation. Applied stimulus (voltage), elongation and cycles of operation are used as inputs for the life prediction model. The values of the parameter coefficients of the model provide better interpretability compared to artificial intelligence based life prediction approaches. In addition, the model also considers the effect of operating conditions, making it generic for a range of the operating conditions. Moreover, a Bayesian framework is used to continuously update the prediction with the actual degradation value of the springs, thereby reducing the uncertainty in the data and improving the prediction accuracy. In addition, the deterioration of material with number of cycles is also investigated using thermogravimetric analysis and scanning electron microscopy.

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.

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.

Some considerations on the use of ecological models to predict species' geographic distributions

USGS Publications Warehouse

Peterjohn, B.G.

2001-01-01

Peterson (2001) used Genetic Algorithm for Rule-set Prediction (GARP) models to predict distribution patterns from Breeding Bird Survey (BBS) data. Evaluations of these models should consider inherent limitations of BBS data: (1) BBS methods may not sample species and habitats equally; (2) using BBS data for both model development and testing may overlook poor fit of some models; and (3) BBS data may not provide the desired spatial resolution or capture temporal changes in species distributions. The predictive value of GARP models requires additional study, especially comparisons with distribution patterns from independent data sets. When employed at appropriate temporal and geographic scales, GARP models show considerable promise for conservation biology applications but provide limited inferences concerning processes responsible for the observed patterns.

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.

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

Universality, Limits and Predictability of Gold-Medal Performances at the Olympic Games

PubMed Central

Radicchi, Filippo

2012-01-01

Inspired by the Games held in ancient Greece, modern Olympics represent the world’s largest pageant of athletic skill and competitive spirit. Performances of athletes at the Olympic Games mirror, since 1896, human potentialities in sports, and thus provide an optimal source of information for studying the evolution of sport achievements and predicting the limits that athletes can reach. Unfortunately, the models introduced so far for the description of athlete performances at the Olympics are either sophisticated or unrealistic, and more importantly, do not provide a unified theory for sport performances. Here, we address this issue by showing that relative performance improvements of medal winners at the Olympics are normally distributed, implying that the evolution of performance values can be described in good approximation as an exponential approach to an a priori unknown limiting performance value. This law holds for all specialties in athletics–including running, jumping, and throwing–and swimming. We present a self-consistent method, based on normality hypothesis testing, able to predict limiting performance values in all specialties. We further quantify the most likely years in which athletes will breach challenging performance walls in running, jumping, throwing, and swimming events, as well as the probability that new world records will be established at the next edition of the Olympic Games. PMID:22808137

Predicting critical micelle concentration and micelle molecular weight of polysorbate 80 using compendial methods.

PubMed

Braun, Alexandra C; Ilko, David; Merget, Benjamin; Gieseler, Henning; Germershaus, Oliver; Holzgrabe, Ulrike; Meinel, Lorenz

2015-08-01

This manuscript addresses the capability of compendial methods in controlling polysorbate 80 (PS80) functionality. Based on the analysis of sixteen batches, functionality related characteristics (FRC) including critical micelle concentration (CMC), cloud point, hydrophilic-lipophilic balance (HLB) value and micelle molecular weight were correlated to chemical composition including fatty acids before and after hydrolysis, content of non-esterified polyethylene glycols and sorbitan polyethoxylates, sorbitan- and isosorbide polyethoxylate fatty acid mono- and diesters, polyoxyethylene diesters, and peroxide values. Batches from some suppliers had a high variability in functionality related characteristic (FRC), questioning the ability of the current monograph in controlling these. Interestingly, the combined use of the input parameters oleic acid content and peroxide value - both of which being monographed methods - resulted in a model adequately predicting CMC. Confining the batches to those complying with specifications for peroxide value proved oleic acid content alone as being predictive for CMC. Similarly, a four parameter model based on chemical analyses alone was instrumental in predicting the molecular weight of PS80 micelles. Improved models based on analytical outcome from fingerprint analyses are also presented. A road map controlling PS80 batches with respect to FRC and based on chemical analyses alone is provided for the formulator. Copyright © 2014 Elsevier B.V. All rights reserved.

Evidence from bond lengths and bond angles for enneacovalence of cobalt, rhodium, iridium, iron, ruthenium, and osmium in compounds with elements of medium electronegativity

PubMed Central

Pauling, Linus

1984-01-01

Enneacovalence of neutral atoms can be achieved for Co, Rh, and Ir by promoting some electrons from the nd orbital to the (n + 1)s and (n + 1)p orbitals and for Fe, Ru, and Os by a similar promotion together with the addition of an electron, which may be provided by an electron pair from a singly bonded carbonyl group or other group. The bond lengths and bond angles are predicted by the theory of enneacovalence to be significantly different for the different transition metals. Recently reported experimental values are shown to be in good agreement with the predicted values, providing support for the theory of enneacovalence and the theory of hybrid sp3d5 bond orbitals. PMID:16593439

[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.

A Prospective Study on the Predictive Value of Plasma BK Virus-DNA Load for Hemorrhagic Cystitis in Pediatric Patients After Stem Cell Transplantation.

PubMed

Cesaro, Simone; Tridello, Gloria; Pillon, Marta; Calore, Elisabetta; Abate, Davide; Tumino, Manuela; Carucci, Nicolina; Varotto, Stefania; Cannata, Elisa; Pegoraro, Anna; Barzon, Luisa; Palù, Giorgio; Messina, Chiara

2015-06-01

In hematopoietic stem cell transplantation (HSCT), late hemorrhagic cystitis (HC) has been associated with BK virus (BKV) infection. We assessed the value of plasma BKV load in predicting HC. Plasma and urine BKV-DNA load were assessed prospectively in 107 pediatric patients. Twenty patients developed grade II and III HC, with 100-day cumulative incidence of 18.8%. At diagnosis of HC, the median load of BKV DNA was 2.3 × 10(3) copies/mL. A plasma BKV-DNA load of 10(3) copies/mL had a sensitivity of 100% and a specificity of 86% with a negative predictive value (NPV) of 100% and a positive predictive value (PPV) of 39% for HC. A urine BKV-DNA load of >10(7) copies/mL had a sensitivity of 86% and a specificity of 60% with a NPV of 98% and a PPV of 14% for HC. A BKV load of 10(3) copies/mL on plasma was significantly associated with HC in multivariate analysis (hazard ratio [HR], 6.1; P = .0006). Patients with HC had a significantly higher risk of mortality than patients who did not have HC (HR, 2.6; P = .018). The above values were used to monitor plasma BKV-DNA load, and they provided a better prediction of patients at risk of HC than urine BKV-DNA load. © The Author 2014. Published by Oxford University Press on behalf of the Pediatric Infectious Diseases Society. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

PubMed

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

2018-03-01

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

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.

Predicting the digestible energy of corn determined with growing swine from nutrient composition and cross-species measurements.

PubMed

Smith, B; Hassen, A; Hinds, M; Rice, D; Jones, D; Sauber, T; Iiams, C; Sevenich, D; Allen, R; Owens, F; McNaughton, J; Parsons, C

2015-03-01

The DE values of corn grain for pigs will differ among corn sources. More accurate prediction of DE may improve diet formulation and reduce diet cost. Corn grain sources ( = 83) were assayed with growing swine (20 kg) in DE experiments with total collection of feces, with 3-wk-old broiler chick in nitrogen-corrected apparent ME (AME) trials and with cecectomized adult roosters in nitrogen-corrected true ME (TME) studies. Additional AME data for the corn grain source set was generated based on an existing near-infrared transmittance prediction model (near-infrared transmittance-predicted AME [NIT-AME]). Corn source nutrient composition was determined by wet chemistry methods. These data were then used to 1) test the accuracy of predicting swine DE of individual corn sources based on available literature equations and nutrient composition and 2) develop models for predicting DE of sources from nutrient composition and the cross-species information gathered above (AME, NIT-AME, and TME). The overall measured DE, AME, NIT-AME, and TME values were 4,105 ± 11, 4,006 ± 10, 4,004 ± 10, and 4,086 ± 12 kcal/kg DM, respectively. Prediction models were developed using 80% of the corn grain sources; the remaining 20% was reserved for validation of the developed prediction equation. Literature equations based on nutrient composition proved imprecise for predicting corn DE; the root mean square error of prediction ranged from 105 to 331 kcal/kg, an equivalent of 2.6 to 8.8% error. Yet among the corn composition traits, 4-variable models developed in the current study provided adequate prediction of DE (model ranging from 0.76 to 0.79 and root mean square error [RMSE] of 50 kcal/kg). When prediction equations were tested using the validation set, these models had a 1 to 1.2% error of prediction. Simple linear equations from AME, NIT-AME, or TME provided an accurate prediction of DE for individual sources ( ranged from 0.65 to 0.73 and RMSE ranged from 50 to 61 kcal/kg). Percentage error of prediction based on the validation data set was greater (1.4%) for the TME model than for the NIT-AME or AME models (1 and 1.2%, respectively), indicating that swine DE values could be accurately predicted by using AME or NIT-AME. In conclusion, regression equations developed from broiler measurements or from analyzed nutrient composition proved adequate to reliably predict the DE of commercially available corn hybrids for growing pigs.

Assessing temporally and spatially resolved PM 2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements

NASA Astrophysics Data System (ADS)

Kloog, Itai; Koutrakis, Petros; Coull, Brent A.; Lee, Hyung Joo; Schwartz, Joel

2011-11-01

Land use regression (LUR) models provide good estimates of spatially resolved long-term exposures, but are poor at capturing short term exposures. Satellite-derived Aerosol Optical Depth (AOD) measurements have the potential to provide spatio-temporally resolved predictions of both long and short term exposures, but previous studies have generally showed relatively low predictive power. Our objective was to extend our previous work on day-specific calibrations of AOD data using ground PM 2.5 measurements by incorporating commonly used LUR variables and meteorological variables, thus benefiting from both the spatial resolution from the LUR models and the spatio-temporal resolution from the satellite models. Later we use spatial smoothing to predict PM 2.5 concentrations for day/locations with missing AOD measures. We used mixed models with random slopes for day to calibrate AOD data for 2000-2008 across New-England with monitored PM 2.5 measurements. We then used a generalized additive mixed model with spatial smoothing to estimate PM 2.5 in location-day pairs with missing AOD, using regional measured PM 2.5, AOD values in neighboring cells, and land use. Finally, local (100 m) land use terms were used to model the difference between grid cell prediction and monitored value to capture very local traffic particles. Out-of-sample ten-fold cross-validation was used to quantify the accuracy of our predictions. For days with available AOD data we found high out-of-sample R2 (mean out-of-sample R2 = 0.830, year to year variation 0.725-0.904). For days without AOD values, our model performance was also excellent (mean out-of-sample R2 = 0.810, year to year variation 0.692-0.887). Importantly, these R2 are for daily, rather than monthly or yearly, values. Our model allows one to assess short term and long-term human exposures in order to investigate both the acute and chronic effects of ambient particles, respectively.

Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans

PubMed Central

Thomas, Julie; Vanni-Mercier, Giovanna; Dreher, Jean-Claude

2013-01-01

Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error) are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs) arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies. PMID:24302894

An investigation of new toxicity test method performance in validation studies: 1. Toxicity test methods that have predictive capacity no greater than chance.

PubMed

Bruner, L H; Carr, G J; Harbell, J W; Curren, R D

2002-06-01

An approach commonly used to measure new toxicity test method (NTM) performance in validation studies is to divide toxicity results into positive and negative classifications, and the identify true positive (TP), true negative (TN), false positive (FP) and false negative (FN) results. After this step is completed, the contingent probability statistics (CPS), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are calculated. Although these statistics are widely used and often the only statistics used to assess the performance of toxicity test methods, there is little specific guidance in the validation literature on what values for these statistics indicate adequate performance. The purpose of this study was to begin developing data-based answers to this question by characterizing the CPS obtained from an NTM whose data have a completely random association with a reference test method (RTM). Determining the CPS of this worst-case scenario is useful because it provides a lower baseline from which the performance of an NTM can be judged in future validation studies. It also provides an indication of relationships in the CPS that help identify random or near-random relationships in the data. The results from this study of randomly associated tests show that the values obtained for the statistics vary significantly depending on the cut-offs chosen, that high values can be obtained for individual statistics, and that the different measures cannot be considered independently when evaluating the performance of an NTM. When the association between results of an NTM and RTM is random the sum of the complementary pairs of statistics (sensitivity + specificity, NPV + PPV) is approximately 1, and the prevalence (i.e., the proportion of toxic chemicals in the population of chemicals) and PPV are equal. Given that combinations of high sensitivity-low specificity or low specificity-high sensitivity (i.e., the sum of the sensitivity and specificity equal to approximately 1) indicate lack of predictive capacity, an NTM having these performance characteristics should be considered no better for predicting toxicity than by chance alone.

Subsonic Longitudinal Performance Coefficient Extraction from Shuttle Flight Data: an Accuracy Assessment for Determination of Data Base Updates

NASA Technical Reports Server (NTRS)

Findlay, J. T.; Kelly, G. M.; Mcconnell, J. G.; Compton, H. R.

1983-01-01

Longitudinal performance comparisons between flight derived and predicted values are presented for the first five NASA Space Shuttle Columbia flights. Though subsonic comparisons are emphasized, comparisons during the transonic and low supersonic regions of flight are included. Computed air data information based on the remotely sensed atmospheric measurements as well as in situ Orbiter Air Data System (ADS) measurements were incorporated. Each air data source provides for comparisons versus the predicted values from the LaRC data base. Principally, L/D, C sub L, and C sub D, comparisons are presented, though some pitching moment results are included. Similarities in flight conditions and spacecraft configuration during the first five flights are discussed. Contributions from the various elements of the data base are presented and the overall differences observed between the flight and predicted values are discussed in terms of expected variations. A discussion on potential data base updates is presented based on the results from the five flights to date.

Models of Affective Decision Making

PubMed Central

Charpentier, Caroline J.; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P.; Sharot, Tali

2016-01-01

Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. PMID:27071751

Bringing modeling to the masses: A web based system to predict potential species distributions

USGS Publications Warehouse

Graham, Jim; Newman, Greg; Kumar, Sunil; Jarnevich, Catherine S.; Young, Nick; Crall, Alycia W.; Stohlgren, Thomas J.; Evangelista, Paul

2010-01-01

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Retooling Predictive Relations for non-volatile PM by Comparison to Measurements

NASA Astrophysics Data System (ADS)

Vander Wal, R. L.; Abrahamson, J. P.

2015-12-01

Non-volatile particulate matter (nvPM) emissions from jet aircraft at cruise altitude are of particular interest for climate and atmospheric processes but are difficult to measure and are normally approximated. To provide such inventory estimates the present approach is to use measured, ground-based values with scaling to cruise (engine operating) conditions. Several points are raised by this approach. First is what ground based values to use. Empirical and semi-empirical approaches, such as the revised first order approximation (FOA3) and formation-oxidation (FOX) methods, each with embedded assumptions are available to calculate a ground-based black carbon concentration, CBC. Second is the scaling relation that can depend upon the ratios of fuel-air equivalence, pressure, and combustor flame temperature. We are using measured ground-based values to evaluate the accuracy of present methods towards developing alternative methods for CBCby smoke number or via a semi-empirical kinetic method for the specific engine, CFM56-2C, representative of a rich-dome style combustor, and as one of the most prevalent engine families in commercial use. Applying scaling relations to measured ground based values and comparison to measurements at cruise evaluates the accuracy of current scaling formalism. In partnership with GE Aviation, performing engine cycle deck calculations enables critical comparison between estimated or predicted thermodynamic parameters and true (engine) operational values for the CFM56-2C engine. Such specific comparisons allow tracing differences between predictive estimates for, and measurements of nvPM to their origin - as either divergence of input parameters or in the functional form of the predictive relations. Such insights will lead to development of new predictive tools for jet aircraft nvPM emissions. Such validated relations can then be extended to alternative fuels with confidence in operational thermodynamic values and functional form. Comparisons will then be made between these new predictive relationships and measurements of nvPM from alternative fuels using ground and cruise data - as collected during NASA-led AAFEX and ACCESS field campaigns, respectively.

Visible and near-infrared spectroscopic analysis of raw milk for cow health monitoring: reflectance or transmittance?

PubMed

Aernouts, B; Polshin, E; Lammertyn, J; Saeys, W

2011-11-01

The composition of produced milk has great value for the dairy farmer. It determines the economic value of the milk and provides valuable information about the metabolism of the corresponding cow. Therefore, online measurement of milk components during milking 2 or more times per day would provide knowledge about the current health and nutritional status of each cow individually. This information provides a solid basis for optimizing cow management. The potential of visible and near-infrared (Vis/NIR) spectroscopy for predicting the fat, crude protein, lactose, and urea content of raw milk online during milking was, therefore, investigated in this study. Two measurement modes (reflectance and transmittance) and different wavelength ranges for Vis/NIR spectroscopy were evaluated and their ability to measure the milk composition online was compared. The Vis/NIR reflectance measurements allowed for very accurate monitoring of the fat and crude protein content in raw milk (R(2)>0.95), but resulted in poor lactose predictions (R(2)<0.75). In contrast, Vis/NIR transmittance spectra of the milk samples gave accurate fat and crude protein predictions (R(2)>0.90) and useful lactose predictions (R(2)=0.88). Neither Vis/NIR reflectance nor transmittance spectroscopy lead to an acceptable prediction of the milk urea content. Transmittance spectroscopy can thus be used to predict the 3 major milk components, but with lower accuracy for fat and crude protein than the reflectance mode. Moreover, the small sample thickness (1mm) required for NIR transmittance measurement considerably complicates its online use. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Sensitivity, Specificity, PPV, and NPV for Predictive Biomarkers.

PubMed

Simon, Richard

2015-08-01

Molecularly targeted cancer drugs are often developed with companion diagnostics that attempt to identify which patients will have better outcome on the new drug than the control regimen. Such predictive biomarkers are playing an increasingly important role in precision oncology. For diagnostic tests, sensitivity, specificity, positive predictive value, and negative predictive are usually used as performance measures. This paper discusses these indices for predictive biomarkers, provides methods for their calculation with survival or response endpoints, and describes assumptions involved in their use. Published by Oxford University Press 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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.

Graphical assessment of incremental value of novel markers in prediction models: From statistical to decision analytical perspectives.

PubMed

Steyerberg, Ewout W; Vedder, Moniek M; Leening, Maarten J G; Postmus, Douwe; D'Agostino, Ralph B; Van Calster, Ben; Pencina, Michael J

2015-07-01

New markers may improve prediction of diagnostic and prognostic outcomes. We aimed to review options for graphical display and summary measures to assess the predictive value of markers over standard, readily available predictors. We illustrated various approaches using previously published data on 3264 participants from the Framingham Heart Study, where 183 developed coronary heart disease (10-year risk 5.6%). We considered performance measures for the incremental value of adding HDL cholesterol to a prediction model. An initial assessment may consider statistical significance (HR = 0.65, 95% confidence interval 0.53 to 0.80; likelihood ratio p < 0.001), and distributions of predicted risks (densities or box plots) with various summary measures. A range of decision thresholds is considered in predictiveness and receiver operating characteristic curves, where the area under the curve (AUC) increased from 0.762 to 0.774 by adding HDL. We can furthermore focus on reclassification of participants with and without an event in a reclassification graph, with the continuous net reclassification improvement (NRI) as a summary measure. When we focus on one particular decision threshold, the changes in sensitivity and specificity are central. We propose a net reclassification risk graph, which allows us to focus on the number of reclassified persons and their event rates. Summary measures include the binary AUC, the two-category NRI, and decision analytic variants such as the net benefit (NB). Various graphs and summary measures can be used to assess the incremental predictive value of a marker. Important insights for impact on decision making are provided by a simple graph for the net reclassification risk. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Simulation of nutrient and sediment concentrations and loads in the Delaware inland bays watershed: Extension of the hydrologic and water-quality model to ungaged segments

USGS Publications Warehouse

Gutierrez-Magness, Angelica L.

2006-01-01

Rapid population increases, agriculture, and industrial practices have been identified as important sources of excessive nutrients and sediments in the Delaware Inland Bays watershed. The amount and effect of excessive nutrients and sediments in the Inland Bays watershed have been well documented by the Delaware Geological Survey, the Delaware Department of Natural Resources and Environmental Control, the U.S. Environmental Protection Agency's National Estuary Program, the Delaware Center for Inland Bays, the University of Delaware, and other agencies. This documentation and data previously were used to develop a hydrologic and water-quality model of the Delaware Inland Bays watershed to simulate nutrients and sediment concentrations and loads, and to calibrate the model by comparing concentrations and streamflow data at six stations in the watershed over a limited period of time (October 1998 through April 2000). Although the model predictions of nutrient and sediment concentrations for the calibrated segments were fairly accurate, the predictions for the 28 ungaged segments located near tidal areas, where stream data were not available, were above the range of values measured in the area. The cooperative study established in 2000 by the Delaware Department of Natural Resources and Environmental Control, the Delaware Geological Survey, and the U.S. Geological Survey was extended to evaluate the model predictions in ungaged segments and to ensure that the model, developed as a planning and management tool, could accurately predict nutrient and sediment concentrations within the measured range of values in the area. The evaluation of the predictions was limited to the period of calibration (1999) of the 2003 model. To develop estimates on ungaged watersheds, parameter values from calibrated segments are transferred to the ungaged segments; however, accurate predictions are unlikely where parameter transference is subject to error. The unexpected nutrient and sediment concentrations simulated with the 2003 model were likely the result of inappropriate criteria for the transference of parameter values. From a model-simulation perspective, it is a common practice to transfer parameter values based on the similarity of soils or the similarity of land-use proportions between segments. For the Inland Bays model, the similarity of soils between segments was used as the basis to transfer parameter values. An alternative approach, which is documented in this report, is based on the similarity of the spatial distribution of the land use between segments and the similarity of land-use proportions, as these can be important factors for the transference of parameter values in lumped models. Previous work determined that the difference in the variation of runoff due to various spatial distributions of land use within a watershed can cause substantialloss of accuracy in the model predictions. The incorporation of the spatial distribution of land use to transfer parameter values from calibrated to uncalibrated segments provided more consistent and rational predictions of flow, especially during the summer, and consequently, predictions of lower nutrient concentrations during the same period. For the segments where the similarity of spatial distribution of land use was not clearly established with a calibrated segment, the similarity of the location of the most impervious areas was also used as a criterion for the transference of parameter values. The model predictions from the 28 ungaged segments were verified through comparison with measured in-stream concentrations from local and nearby streams provided by the Delaware Department of Natural Resources and Environmental Control. Model results indicated that the predicted edge-of-stream total suspended solids loads in the Inland Bays watershed were low in comparison to loads reported for the Eastern Shore of Maryland from the Chesapeake Bay watershed model. The flatness of the ter

On the predictions of the 11B solid state NMR parameters

NASA Astrophysics Data System (ADS)

Czernek, Jiří; Brus, Jiří

2016-07-01

The set of boron containing compounds has been subject to the prediction of the 11B solid state NMR spectral parameters using DFT-GIPAW methods properly treating the solid phase effects. The quantification of the differences between measured and theoretical values has been presented, which is directly applicable in structural studies involving 11B nuclei. In particular, a simple scheme has been proposed, which is expected to provide for an estimate of the 11B chemical shift within ±2.0 ppm from the experimental value. The computer program, INFOR, enabling the visualization of concomitant Euler rotations related to the tensorial transformations has been presented.

Streamlining the Evaluation of Low Back Pain in Children

PubMed Central

Auerbach, Joshua D.; Ahn, Jaimo; Zgonis, Miltiadis H.; Reddy, Sudheer C.; Ecker, Malcolm L.

2008-01-01

The workup of low back pain in children often results in overimaging so as not to miss organic back pain. The primary goal of this study was to identify which combination of imaging modalities provides the most sensitive and specific screening protocol for children with low back pain. Medical records from 100 consecutive patients between 2 and 18 years of age presenting with low back pain, without night pain or constitutional symptoms, were evaluated. A hyperextension test combined with a radiograph showed a negative predictive value of 0.81 and sensitivity of 0.90. The addition of a bone scan was highly effective in achieving good negative predictive value and sensitivity. Bone scans had perfect negative predictive value and sensitivity when symptom duration was less than 6 weeks. We identified a set of factors that is highly predictive for distinguishing organic back pain from mechanical back pain. Painless hyperextension combined with negative anteroposterior, lateral, and oblique lumbar radiographs and magnetic resonance images predicts mechanical back pain. For patients with nonneurologic back pain of less than 6 weeks duration, bone scan is the most useful screening test because it is accurate, accessible, inexpensive, and unlikely to require sedation. Level of Evidence: Level III, diagnostic study. See the Guidelines for Authors for a complete description of levels of evidence. PMID:18553213

Technical Note: On the calculation of stopping-power ratio for stoichiometric calibration in proton therapy

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

Ödén, Jakob; Zimmerman, Jens; Nowik, Patrik

2015-09-15

Purpose: The quantitative effects of assumptions made in the calculation of stopping-power ratios (SPRs) are investigated, for stoichiometric CT calibration in proton therapy. The assumptions investigated include the use of the Bethe formula without correction terms, Bragg additivity, the choice of I-value for water, and the data source for elemental I-values. Methods: The predictions of the Bethe formula for SPR (no correction terms) were validated against more sophisticated calculations using the SRIM software package for 72 human tissues. A stoichiometric calibration was then performed at our hospital. SPR was calculated for the human tissues using either the assumption of simplemore » Bragg additivity or the Seltzer-Berger rule (as used in ICRU Reports 37 and 49). In each case, the calculation was performed twice: First, by assuming the I-value of water was an experimentally based value of 78 eV (value proposed in Errata and Addenda for ICRU Report 73) and second, by recalculating the I-value theoretically. The discrepancy between predictions using ICRU elemental I-values and the commonly used tables of Janni was also investigated. Results: Errors due to neglecting the correction terms to the Bethe formula were calculated at less than 0.1% for biological tissues. Discrepancies greater than 1%, however, were estimated due to departures from simple Bragg additivity when a fixed I-value for water was imposed. When the I-value for water was calculated in a consistent manner to that for tissue, this disagreement was substantially reduced. The difference between SPR predictions when using Janni’s or ICRU tables for I-values was up to 1.6%. Experimental data used for materials of relevance to proton therapy suggest that the ICRU-derived values provide somewhat more accurate results (root-mean-square-error: 0.8% versus 1.6%). Conclusions: The conclusions from this study are that (1) the Bethe formula can be safely used for SPR calculations without correction terms; (2) simple Bragg additivity can be reasonably assumed for compound materials; (3) if simple Bragg additivity is assumed, then the I-value for water should be calculated in a consistent manner to that of the tissue of interest (rather than using an experimentally derived value); (4) the ICRU Report 37 I-values may provide a better agreement with experiment than Janni’s tables.« less

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.

Analysis of the unusual wavelength dependence of the first hyperpolarizability of porphyrin derivatives

NASA Astrophysics Data System (ADS)

De Mey, K.; Clays, K.; Therien, Michael J.; Beratan, David N.; Asselberghs, Inge

2010-08-01

Successfully predicting the frequency dispersion of electronic hyperpolarizabilities is an unresolved challenge in materials science and electronic structure theory. It has been shown1 that the generalized Thomas-Kuhn sum rules combined with linear absorption data and measured hyperpolarizabilities at one or two frequencies, may be used to predict the entire frequency-dependent electronic hyperpolarizability spectrum. This treatment includes two- and threelevel contributions that arise from the lowest two or three excited state manifolds, enabling us to describe the unusual observed frequency dispersion of the dynamic hyperpolarizability in high oscillator strength M-PZn chromophores, where (porphinato)zinc(II) (PZn) and metal(II)polypyridyl (M) units are connected via an ethyne unit that aligns the high oscillator strength transition dipoles of these components in a head-to-tail arrangement. Importantly, this approach provides a quantitative scheme to use linear optical absorption spectra and very few individual hyperpolarizability values to predict the entire frequency-dependent nonlinear optical response. In addition we provide here experimental dynamic hyperpolarizability values determined by hyper-Rayleigh scattering that underscore the validity of our approach.

Influenza forecasting with Google Flu Trends.

PubMed

Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E

2013-01-01

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

The potential predictability of fire danger provided by ECMWF forecast

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca

2017-04-01

The European Forest Fire Information System (EFFIS), is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. The system provides timely information to civil protection authorities in 38 nations across Europe and mostly concentrates on flagging regions which might be at high danger of spontaneous ignition due to persistent drought. The daily predictions of fire danger conditions are based on the US Forest Service National Fire Danger Rating System (NFDRS), the Canadian forest service Fire Weather Index Rating System (FWI) and the Australian McArthur (MARK-5) rating systems. Weather forcings are provided in real time by the European Centre for Medium range Weather Forecasts (ECMWF) forecasting system. The global system's potential predictability is assessed using re-analysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 years of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire and low values correspond to non observed events. A more quantitative skill evaluation was performed using the Extremal Dependency Index which is a skill score specifically designed for rare events. It revealed that the three indices were more skilful on a global scale than the random forecast to detect large fires. The performance peaks in the boreal forests, in the Mediterranean, the Amazon rain-forests and southeast Asia. The skill-scores were then aggregated at country level to reveal which nations could potentiallty benefit from the system information in aid of decision making and fire control support. Overall we found that fire danger modelling based on weather forecasts, can provide reasonable predictability over large parts of the global landmass.

Prediction equations for maximal respiratory pressures of Brazilian adolescents.

PubMed

Mendes, Raquel E F; Campos, Tania F; Macêdo, Thalita M F; Borja, Raíssa O; Parreira, Verônica F; Mendonça, Karla M P P

2013-01-01

The literature emphasizes the need for studies to provide reference values and equations able to predict respiratory muscle strength of Brazilian subjects at different ages and from different regions of Brazil. To develop prediction equations for maximal respiratory pressures (MRP) of Brazilian adolescents. In total, 182 healthy adolescents (98 boys and 84 girls) aged between 12 and 18 years, enrolled in public and private schools in the city of Natal-RN, were evaluated using an MVD300 digital manometer (Globalmed®) according to a standardized protocol. Statistical analysis was performed using SPSS Statistics 17.0 software, with a significance level of 5%. Data normality was verified using the Kolmogorov-Smirnov test, and descriptive analysis results were expressed as the mean and standard deviation. To verify the correlation between the MRP and the independent variables (age, weight, height and sex), the Pearson correlation test was used. To obtain the prediction equations, stepwise multiple linear regression was used. The variables height, weight and sex were correlated to MRP. However, weight and sex explained part of the variability of MRP, and the regression analysis in this study indicated that these variables contributed significantly in predicting maximal inspiratory pressure, and only sex contributed significantly to maximal expiratory pressure. This study provides reference values and two models of prediction equations for maximal inspiratory and expiratory pressures and sets the necessary normal lower limits for the assessment of the respiratory muscle strength of Brazilian adolescents.

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

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.

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world

PubMed Central

McDannald, Michael A.; Takahashi, Yuji K.; Lopatina, Nina; Pietras, Brad W.; Jones, Josh L.; Schoenbaum, Geoffrey

2012-01-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. PMID:22487030

Domains of acculturation and their effects on substance use and sexual behavior in recent Hispanic immigrant adolescents.

PubMed

Schwartz, Seth J; Unger, Jennifer B; Des Rosiers, Sabrina E; Lorenzo-Blanco, Elma I; Zamboanga, Byron L; Huang, Shi; Baezconde-Garbanati, Lourdes; Villamar, Juan A; Soto, Daniel W; Pattarroyo, Monica; Szapocznik, José

2014-06-01

This study evaluated the immigrant paradox by ascertaining the effects of multiple components of acculturation on substance use and sexual behavior among recently immigrated Hispanic adolescents primarily from Mexico (35 %) and Cuba (31 %). A sample of 302 adolescents (53 % boys; mean age 14.51 years) from Miami (n = 152) and Los Angeles (n = 150) provided data on Hispanic and US cultural practices, values, and identifications at baseline and provided reports of cigarette use, alcohol use, sexual activity, and unprotected sex approximately 1 year later. Results indicated strong gender differences, with the majority of significant findings emerging for boys. Supporting the immigrant paradox (i.e., that becoming oriented toward US culture is predictive of increased health risks), individualist values predicted greater numbers of oral sex partners and unprotected sex occasions for boys. However, contrary to the immigrant paradox, for boys, both US practices and US identification predicted less heavy drinking, fewer oral and vaginal/anal sex partners, and less unprotected vaginal/anal sex. Ethnic identity (identification with one's heritage culture) predicted greater numbers of sexual partners but negatively predicted unprotected sex. Results indicate a need for multidimensional, multi-domain models of acculturation and suggest that more work is needed to determine the most effective ways to culturally inform prevention programs.

Domains of Acculturation and their Effects on Substance Use and Sexual Behavior in Recent Hispanic Immigrant Adolescents

PubMed Central

Schwartz, Seth J.; Unger, Jennifer B.; Des Rosiers, Sabrina E.; Lorenzo-Blanco, Elma I.; Zamboanga, Byron L.; Huang, Shi; Baezconde-Garbanati, Lourdes; Villamar, Juan A.; Soto, Daniel W.; Pattarroyo, Monica; Szapocznik, José

2013-01-01

This study evaluated the immigrant paradox by ascertaining the effects of multiple components of acculturation on substance use and sexual behavior among recently immigrated Hispanic adolescents primarily from Mexico (35%) and Cuba (31%). A sample of 302 adolescents (53% boys; mean age 14.51 years) from Miami (n = 152) and Los Angeles (n = 150) provided data on Hispanic and U.S. cultural practices, values, and identifications at baseline and provided reports of cigarette use, alcohol use, sexual activity, and unprotected sex approximately one year later. Results indicated strong gender differences, with the majority of significant findings emerging for boys. Supporting the immigrant paradox (i.e., that becoming oriented toward U.S. culture is predictive of increased health risks), individualist values predicted greater numbers of oral sex partners and unprotected sex occasions for boys. However, contrary to the immigrant paradox, for boys, both U.S. practices and U.S. identification predicted less heavy drinking, fewer oral and vaginal/anal sex partners, and less unprotected vaginal/anal sex. Ethnic identity (identification with one’s heritage culture) predicted greater numbers of sexual partners but negatively predicted unprotected sex. Results indicate a need for multidimensional, multi-domain models of acculturation and suggest that more work is needed to determine the most effective ways to culturally inform prevention programs. PMID:23828449

Breast cancer mammographic diagnosis performance in a public health institution: a retrospective cohort study.

PubMed

Mello, Juliana M R B; Bittelbrunn, Fernando P; Rockenbach, Marcio A B C; May, Guilherme G; Vedolin, Leonardo M; Kruger, Marilia S; Soldatelli, Matheus D; Zwetsch, Guilherme; de Miranda, Gabriel T F; Teixeira, Saone I P; Arruda, Bruna S

2017-12-01

To evaluate the quality assurance of mammography results at a reference institution for the diagnosis and treatment of breast cancer in southern Brazil, based on the BIRADS (Breast Imaging Reporting and Data System) 5th edition recommendations for auditing purposes. Retrospective cohort and cross-sectional study with 4502 patients (9668 mammographies)) who underwent at least one or both breast mammographies throughout 2013 at a regional public hospital, linked to a federal public university. The results were followed until 31 December 2014, including true positives (TPs), true negatives (TNs), false positives (FPs), false negatives (FNs), positive predictive values (PPVs), negative predictive value (NPV), sensitivity and specificity, with a confidence interval of 95%. The study showed high quality assurance, particularly regarding sensitivity (90.22%) and specificity (92.31%). The overall positive predictive value (PPV) was 65.35%, and the negative predictive value (NPV) was 98.32%. The abnormal interpretation rate (recall rate) was 12.26%. The results are appropriate when compared to the values proposed by the BIRADS 5th edition. Additionally, the study provided self-reflection considering our radiological practice, which is essential for improvements and collaboration regarding breast cancer detection. It may stimulate better radiological practice performance and continuing education, despite possible infrastructure and facility limitations. • Accurate quality performance rates are possible despite financial and governmental limitations. • Low-income institutions should develop standardised teamwork to improve radiological practice. • Regular mammography audits may help to increase the quality of public health systems.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

NASA Astrophysics Data System (ADS)

Liang, Zhongmin; Li, Yujie; Hu, Yiming; Li, Binquan; Wang, Jun

2017-06-01

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Probable flood predictions in ungauged coastal basins of El Salvador

USGS Publications Warehouse

Friedel, M.J.; Smith, M.E.; Chica, A.M.E.; Litke, D.

2008-01-01

A regionalization procedure is presented and used to predict probable flooding in four ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. The flood-prediction problem is sequentially solved for two regions: upstream mountains and downstream alluvial plains. In the upstream mountains, a set of rainfall-runoff parameter values and recurrent peak-flow discharge hydrographs are simultaneously estimated for 20 tributary-basin models. Application of dissimilarity equations among tributary basins (soft prior information) permitted development of a parsimonious parameter structure subject to information content in the recurrent peak-flow discharge values derived using regression equations based on measurements recorded outside the ungauged study basins. The estimated joint set of parameter values formed the basis from which probable minimum and maximum peak-flow discharge limits were then estimated revealing that prediction uncertainty increases with basin size. In the downstream alluvial plain, model application of the estimated minimum and maximum peak-flow hydrographs facilitated simulation of probable 100-year flood-flow depths in confined canyons and across unconfined coastal alluvial plains. The regionalization procedure provides a tool for hydrologic risk assessment and flood protection planning that is not restricted to the case presented herein. ?? 2008 ASCE.

Fragment based group QSAR and molecular dynamics mechanistic studies on arylthioindole derivatives targeting the α-β interfacial site of human tubulin

PubMed Central

2014-01-01

Background A number of microtubule disassembly blocking agents and inhibitors of tubulin polymerization have been elements of great interest in anti-cancer therapy, some of them even entering into the clinical trials. One such class of tubulin assembly inhibitors is of arylthioindole derivatives which results in effective microtubule disorganization responsible for cell apoptosis by interacting with the colchicine binding site of the β-unit of tubulin close to the interface with the α unit. We modelled the human tubulin β unit (chain D) protein and performed docking studies to elucidate the detailed binding mode of actions associated with their inhibition. The activity enhancing structural aspects were evaluated using a fragment-based Group QSAR (G-QSAR) model and was validated statistically to determine its robustness. A combinatorial library was generated keeping the arylthioindole moiety as the template and their activities were predicted. Results The G-QSAR model obtained was statistically significant with r2 value of 0.85, cross validated correlation coefficient q2 value of 0.71 and pred_r2 (r2 value for test set) value of 0.89. A high F test value of 65.76 suggests robustness of the model. Screening of the combinatorial library on the basis of predicted activity values yielded two compounds HPI (predicted pIC50 = 6.042) and MSI (predicted pIC50 = 6.001) whose interactions with the D chain of modelled human tubulin protein were evaluated in detail. A toxicity evaluation resulted in MSI being less toxic in comparison to HPI. Conclusions The study provides an insight into the crucial structural requirements and the necessary chemical substitutions required for the arylthioindole moiety to exhibit enhanced inhibitory activity against human tubulin. The two reported compounds HPI and MSI showed promising anti cancer activities and thus can be considered as potent leads against cancer. The toxicity evaluation of these compounds suggests that MSI is a promising therapeutic candidate. This study provided another stepping stone in the direction of evaluating tubulin inhibition and microtubule disassembly degeneration as viable targets for development of novel therapeutics against cancer. PMID:25521775

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets.

PubMed

Sun, Jiangming; Carlsson, Lars; Ahlberg, Ernst; Norinder, Ulf; Engkvist, Ola; Chen, Hongming

2017-07-24

Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.

A Comprehensive Model of the Meteoroids Environment Around Mercury

NASA Astrophysics Data System (ADS)

Pokorny, P.; Sarantos, M.; Janches, D.

2018-05-01

We present a comprehensive dynamical model for the meteoroid environment around Mercury comprised of meteoroids originating in asteroids, short and long period comets. Our model is fully calibrated and provides predictions for different values of TAA.

A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data

DOE PAGES

Yue, Meng; Toto, Tami; Jensen, Michael P.; ...

2017-05-18

Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This study developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models (FRMs) for different grid components. Such models can provide an estimation or prediction of the outagemore » numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction (BOP) algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this study.« less

A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data

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

Yue, Meng; Toto, Tami; Jensen, Michael P.

Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This study developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models (FRMs) for different grid components. Such models can provide an estimation or prediction of the outagemore » numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction (BOP) algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this study.« less

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 Dual-Carbon-and-Nitrogen Stable Isotope Ratio Model Is Not Superior to a Single-Carbon Stable Isotope Ratio Model for Predicting Added Sugar Intake in Southwest Virginian Adults12

PubMed Central

Hedrick, Valisa E; Zoellner, Jamie M; Jahren, A Hope; Woodford, Natalie A; Bostic, Joshua N; Davy, Brenda M

2015-01-01

Background: An objective measure of added sugar (AS) and sugar-sweetened beverage (SSB) intake is needed. The δ13C value of finger-stick blood is a novel validated biomarker of AS/SSB intake; however, nonsweetener corn products and animal protein also carry a δ13C value similar to AS sources, which may affect blood δ13C values. The δ15N value of blood has been proposed as a “correction factor” for animal protein intake. Objectives: The objectives were to 1) identify foods associated with δ13C and δ15N blood values, 2) determine the contribution of nonsweetener corn to the diet relative to AS intake, and 3) determine if the dual-isotope model (δ13C and δ15N) is a better predictor of AS/SSB intake than δ13C alone. Methods: A cross-sectional sample of southwest Virginian adults (n = 257; aged 42 ± 15 y; 74% overweight/obese) underwent dietary intake assessments and provided finger-stick blood samples, which were analyzed for δ13C and δ15N values by using natural abundance stable isotope mass spectrometry. Statistical analyses included ANOVAs, paired-samples t tests, and multiple linear regressions. Results: The mean ± SD daily AS intake was 88 ± 59 g and nonsweetener corn intake was 13 ± 13 g. The mean δ13C value was −19.1 ± 0.9‰, which was significantly correlated with AS and SSB intakes (r = 0.32 and 0.39, respectively; P ≤ 0.01). The δ13C value and nonsweetener corn intake and the δ15N value and animal protein intake were not correlated. AS intake was significantly greater than nonsweetener corn intake (mean difference = 76.2 ± 57.2 g; P ≤ 0.001). The δ13C value was predictive of AS/SSB intake (β range: 0.28–0.35; P ≤ 0.01); however, δ15N was not predictive and minimal increases in R2 values were observed when the δ15N value was added to the model. Conclusions: The data do not provide evidence that the dual-isotope method is superior for predicting AS/SSB intakes within a southwest Virginian population. Our results support the potential of the δ13C value of finger-stick blood to serve as an objective measure of AS/SSB intake. This trial was registered at clinicaltrials.gov as NCT02193009. PMID:25855120

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.

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

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

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

Endometrial Receptivity and its Predictive Value for IVF/ICSI-Outcome

PubMed Central

Heger, A.; Sator, M.; Pietrowski, D.

2012-01-01

Endometrial receptivity plays a crucial role in the establishment of a healthy pregnancy in cycles of assisted reproduction. The endometrium as a key factor during reproduction can be assessed in multiple ways, most commonly through transvaginal grey-scale or 3-D ultrasound. It has been shown that controlled ovarian hyperstimulation has a great impact on the uterine lining, which leads to different study results for the predictive value of endometrial factors measured on different cycle days. There is no clear consensus on whether endometrial factors are appropriate to predict treatment outcome and if so, which one is suited best. The aim of this review is to summarize recent findings of studies about the influence of endometrial thickness, volume and pattern on IVF- and ICSI-treatment outcome and provide an overview of future developments in the field. PMID:25258462

Endometrial Receptivity and its Predictive Value for IVF/ICSI-Outcome.

PubMed

Heger, A; Sator, M; Pietrowski, D

2012-08-01

Endometrial receptivity plays a crucial role in the establishment of a healthy pregnancy in cycles of assisted reproduction. The endometrium as a key factor during reproduction can be assessed in multiple ways, most commonly through transvaginal grey-scale or 3-D ultrasound. It has been shown that controlled ovarian hyperstimulation has a great impact on the uterine lining, which leads to different study results for the predictive value of endometrial factors measured on different cycle days. There is no clear consensus on whether endometrial factors are appropriate to predict treatment outcome and if so, which one is suited best. The aim of this review is to summarize recent findings of studies about the influence of endometrial thickness, volume and pattern on IVF- and ICSI-treatment outcome and provide an overview of future developments in the field.

Climate change and predicting soil loss from rainfall

NASA Astrophysics Data System (ADS)

Kinnell, Peter

2017-04-01

Conceptually, rainfall has a certain capacity to cause soil loss from an eroding area while soil surfaces have a certain resistance to being eroded by rainfall. The terms "rainfall erosivity' and "soil erodibility" are frequently used to encapsulate the concept and in the Revised Universal Soil Loss Equation (RUSLE), the most widely used soil loss prediction equation in the world, average annual values of the R "erosivity" factor and the K "erodibility" factor provide a basis for accounting for variation in rainfall erosion associated with geographic variations of climate and soils. In many applications of RUSLE, R and K are considered to be independent but in reality they are not. In RUSLE2, provision has been made to take account of the fact that K values determined using soil physical factors have to be adjusted for variations in climate because runoff is not directly included as a factor in determining R. Also, the USLE event erosivity index EI30 is better related to accounting for event sediment concentration than event soil loss. While the USLE-M, a modification of the USLE which includes runoff as a factor in determining the event erosivity index provides better estimates of event soil loss when event runoff is known, runoff prediction provides a challenge to modelling event soil loss as climate changes

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

2016-04-01

Traditionally, the simulation of regional ocean, wave and atmosphere components of the Earth System have been considered separately, with some information on other components provided by means of boundary or forcing conditions. More recently, the potential value of a more integrated approach, as required for global climate and Earth System prediction, for regional short-term applications has begun to gain increasing research effort. In the UK, this activity is motivated by an understanding that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system, including a new eddy-permitting resolution ocean component, and discuss progress and initial results from further development to integrate wave interactions in this relatively high resolution system. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

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.

A Comparison of Hyperelastic Warping of PET Images with Tagged MRI for the Analysis of Cardiac Deformation

DOE PAGES

Veress, Alexander I.; Klein, Gregory; Gullberg, Grant T.

2013-01-01

Tmore » he objectives of the following research were to evaluate the utility of a deformable image registration technique known as hyperelastic warping for the measurement of local strains in the left ventricle through the analysis of clinical, gated PE image datasets. wo normal human male subjects were sequentially imaged with PE and tagged MRI imaging. Strain predictions were made for systolic contraction using warping analyses of the PE images and HARP based strain analyses of the MRI images. Coefficient of determination R 2 values were computed for the comparison of circumferential and radial strain predictions produced by each methodology. here was good correspondence between the methodologies, with R 2 values of 0.78 for the radial strains of both hearts and from an R 2 = 0.81 and R 2 = 0.83 for the circumferential strains. he strain predictions were not statistically different ( P ≤ 0.01 ) . A series of sensitivity results indicated that the methodology was relatively insensitive to alterations in image intensity, random image noise, and alterations in fiber structure. his study demonstrated that warping was able to provide strain predictions of systolic contraction of the LV consistent with those provided by tagged MRI Warping.« less

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.

Stimulus-Response-Outcome Coding in the Pigeon Nidopallium Caudolaterale

PubMed Central

Starosta, Sarah; Güntürkün, Onur; Stüttgen, Maik C.

2013-01-01

A prerequisite for adaptive goal-directed behavior is that animals constantly evaluate action outcomes and relate them to both their antecedent behavior and to stimuli predictive of reward or non-reward. Here, we investigate whether single neurons in the avian nidopallium caudolaterale (NCL), a multimodal associative forebrain structure and a presumed analogue of mammalian prefrontal cortex, represent information useful for goal-directed behavior. We subjected pigeons to a go-nogo task, in which responding to one visual stimulus (S+) was partially reinforced, responding to another stimulus (S–) was punished, and responding to test stimuli from the same physical dimension (spatial frequency) was inconsequential. The birds responded most intensely to S+, and their response rates decreased monotonically as stimuli became progressively dissimilar to S+; thereby, response rates provided a behavioral index of reward expectancy. We found that many NCL neurons' responses were modulated in the stimulus discrimination phase, the outcome phase, or both. A substantial fraction of neurons increased firing for cues predicting non-reward or decreased firing for cues predicting reward. Interestingly, the same neurons also responded when reward was expected but not delivered, and could thus provide a negative reward prediction error or, alternatively, signal negative value. In addition, many cells showed motor-related response modulation. In summary, NCL neurons represent information about the reward value of specific stimuli, instrumental actions as well as action outcomes, and therefore provide signals useful for adaptive behavior in dynamically changing environments. PMID:23437383

Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects.

PubMed

Guntupalli, Kalpalatha K; Alapat, Philip M; Bandi, Venkata D; Kushnir, Igal

2008-12-01

Computerized lung-sound analysis is a sensitive and quantitative method to identify wheezing by its typical pattern on spectral analysis. We evaluated the accuracy of the VRI, a multi-sensor, computer-based device with an automated technique of wheeze detection. The method was validated in 100 sound files from seven subjects with asthma or chronic obstructive pulmonary disease and seven healthy subjects by comparison of auscultation findings, examination of audio files, and computer detection of wheezes. Three blinded physicians identified 40 sound files with wheezes and 60 sound files without wheezes. Sensitivity and specificity were 83% and 85%, respectively. Negative predictive value and positive predictive value were 89% and 79%, respectively. Overall inter-rater agreement was 84%. False positive cases were found to contain sounds that simulate wheezes, such as background noises with high frequencies or strong noises from the throat that could be heard and identified without a stethoscope. The present findings demonstrate that the wheeze detection algorithm has good accuracy, sensitivity, specificity, negative predictive value and positive predictive value for wheeze detection in regional analyses with a single sensor and multiple sensors. Results are similar to those reported in the literature. The device is user-friendly, requires minimal patient effort, and, distinct from other devices, it provides a dynamic image of breath sound distribution with wheeze detection output in less than 1 minute.

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.

Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer.

PubMed

Beukinga, Roelof J; Hulshoff, Jan Binne; Mul, Véronique E M; Noordzij, Walter; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Plukker, John T M

2018-06-01

Purpose To assess the value of baseline and restaging fluorine 18 ( 18 F) fluorodeoxyglucose (FDG) positron emission tomography (PET) radiomics in predicting pathologic complete response to neoadjuvant chemotherapy and radiation therapy (NCRT) in patients with locally advanced esophageal cancer. Materials and Methods In this retrospective study, 73 patients with histologic analysis-confirmed T1/N1-3/M0 or T2-4a/N0-3/M0 esophageal cancer were treated with NCRT followed by surgery (Chemoradiotherapy for Esophageal Cancer followed by Surgery Study regimen) between October 2014 and August 2017. Clinical variables and radiomic features from baseline and restaging 18 F-FDG PET were selected by univariable logistic regression and least absolute shrinkage and selection operator. The selected variables were used to fit a multivariable logistic regression model, which was internally validated by using bootstrap resampling with 20 000 replicates. The performance of this model was compared with reference prediction models composed of maximum standardized uptake value metrics, clinical variables, and maximum standardized uptake value at baseline NCRT radiomic features. Outcome was defined as complete versus incomplete pathologic response (tumor regression grade 1 vs 2-5 according to the Mandard classification). Results Pathologic response was complete in 16 patients (21.9%) and incomplete in 57 patients (78.1%). A prediction model combining clinical T-stage and restaging NCRT (post-NCRT) joint maximum (quantifying image orderliness) yielded an optimism-corrected area under the receiver operating characteristics curve of 0.81. Post-NCRT joint maximum was replaceable with five other redundant post-NCRT radiomic features that provided equal model performance. All reference prediction models exhibited substantially lower discriminatory accuracy. Conclusion The combination of clinical T-staging and quantitative assessment of post-NCRT 18 F-FDG PET orderliness (joint maximum) provided high discriminatory accuracy in predicting pathologic complete response in patients with esophageal cancer. © RSNA, 2018 Online supplemental material is available for this article.

High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity.

PubMed

De Wolf, Hans; Cougnaud, Laure; Van Hoorde, Kirsten; De Bondt, An; Wegner, Joerg K; Ceulemans, Hugo; Göhlmann, Hinrich

2018-04-01

By adding biological information, beyond the chemical properties and desired effect of a compound, uncharted compound areas and connections can be explored. In this study, we add transcriptional information for 31K compounds of Janssen's primary screening deck, using the HT L1000 platform and assess (a) the transcriptional connection score for generating compound similarities, (b) machine learning algorithms for generating target activity predictions, and (c) the scaffold hopping potential of the resulting hits. We demonstrate that the transcriptional connection score is best computed from the significant genes only and should be interpreted within its confidence interval for which we provide the stats. These guidelines help to reduce noise, increase reproducibility, and enable the separation of specific and promiscuous compounds. The added value of machine learning is demonstrated for the NR3C1 and HSP90 targets. Support Vector Machine models yielded balanced accuracy values ≥80% when the expression values from DDIT4 & SERPINE1 and TMEM97 & SPR were used to predict the NR3C1 and HSP90 activity, respectively. Combining both models resulted in 22 new and confirmed HSP90-independent NR3C1 inhibitors, providing two scaffolds (i.e., pyrimidine and pyrazolo-pyrimidine), which could potentially be of interest in the treatment of depression (i.e., inhibiting the glucocorticoid receptor (i.e., NR3C1), while leaving its chaperone, HSP90, unaffected). As such, the initial hit rate increased by a factor 300, as less, but more specific chemistry could be screened, based on the upfront computed activity predictions.

Reassessment of the positive predictive value and specificity of Xpert MTB/RIF: a diagnostic accuracy study in the context of community-wide screening for tuberculosis.

PubMed

Ho, Jennifer; Nguyen, Phuong Thi Bich; Nguyen, Thu Anh; Tran, Khoa Hien; Van Nguyen, Son; Nguyen, Nhung Viet; Nguyen, Hoa Binh; Luu, Khanh Boi; Fox, Greg J; Marks, Guy B

2016-09-01

Community-wide screening for tuberculosis with Xpert MTB/RIF as a primary screening tool overcomes some of the limitations of conventional screening. However, concerns exist about the low positive predictive value of this test in screening settings. We did a cross-sectional assessment of this diagnostic test to directly estimate the actual positive predictive value of Xpert MTB/RIF when used in the setting of community-wide screening for tuberculosis, and to draw an inference about the specificity of the test for tuberculosis detection. Field staff visited households in 60 randomly selected villages in Ca Mau province, Vietnam. We included people aged 15 years or older who provided written informed consent and were able to produce 0·5 mL or more of sputum, irrespective of reported symptoms. Participants were tested with Xpert MTB/RIF, then those with positive results had two further sputum samples tested for smear microscopy and culture, and underwent chest radiography at the provincial TB Health Center. The positive predictive value of Xpert MTB/RIF was compared against two reference standards for tuberculosis diagnosis-a positive sputum culture for Mycobacterium tuberculosis, and a positive sputum culture or a chest radiograph consistent with active pulmonary tuberculosis. We then calculated the specificity of Xpert MTB/RIF for tuberculosis detection on the basis of these positive predictive values and disease prevalence in this setting. 43 435 adults consented to screening with Xpert MTB/RIF. Sputum samples of 0·5 mL or greater were collected from 23 202 participants, producing 22 673 valid results. 169 participants had positive Xpert MTB/RIF results (0·39% of those screened and 0·75% of those with valid sputum results). The positive predictive value of Xpert MTB/RIF was 61·0% (95% CI 52·8-68·7) when compared against a positive sputum culture and 83·9% (76·8-89·2) when compared against a positive sputum culture or chest radiograph consistent with active tuberculosis. On the basis of these positive predictive values, the specificity of Xpert MTB/RIF was determined to be between 99·78% (95% CI 99·71-99·84) and 99·93% (99·88-99·96). The positive predictive value and specificity of Xpert MTB/RIF in the context of community-wide screening for tuberculosis is substantially higher than that predicted in previous studies. Our findings support the potential role of Xpert MTB/RIF as a primary screening tool to detect prevalent cases of tuberculosis in the community. Australian National Health and Medical Research Council. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Reconsidering the use of rankings in the valuation of health states: a model for estimating cardinal values from ordinal data

PubMed Central

Salomon, Joshua A

2003-01-01

Background In survey studies on health-state valuations, ordinal ranking exercises often are used as precursors to other elicitation methods such as the time trade-off (TTO) or standard gamble, but the ranking data have not been used in deriving cardinal valuations. This study reconsiders the role of ordinal ranks in valuing health and introduces a new approach to estimate interval-scaled valuations based on aggregate ranking data. Methods Analyses were undertaken on data from a previously published general population survey study in the United Kingdom that included rankings and TTO values for hypothetical states described using the EQ-5D classification system. The EQ-5D includes five domains (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) with three possible levels on each. Rank data were analysed using a random utility model, operationalized through conditional logit regression. In the statistical model, probabilities of observed rankings were related to the latent utilities of different health states, modeled as a linear function of EQ-5D domain scores, as in previously reported EQ-5D valuation functions. Predicted valuations based on the conditional logit model were compared to observed TTO values for the 42 states in the study and to predictions based on a model estimated directly from the TTO values. Models were evaluated using the intraclass correlation coefficient (ICC) between predictions and mean observations, and the root mean squared error of predictions at the individual level. Results Agreement between predicted valuations from the rank model and observed TTO values was very high, with an ICC of 0.97, only marginally lower than for predictions based on the model estimated directly from TTO values (ICC = 0.99). Individual-level errors were also comparable in the two models, with root mean squared errors of 0.503 and 0.496 for the rank-based and TTO-based predictions, respectively. Conclusions Modeling health-state valuations based on ordinal ranks can provide results that are similar to those obtained from more widely analyzed valuation techniques such as the TTO. The information content in aggregate ranking data is not currently exploited to full advantage. The possibility of estimating cardinal valuations from ordinal ranks could also simplify future data collection dramatically and facilitate wider empirical study of health-state valuations in diverse settings and population groups. PMID:14687419

Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS

USGS Publications Warehouse

Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.

2011-01-01

The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.

Age and Environmental Concern: A Multivariate Analysis.

ERIC Educational Resources Information Center

Buttel, Frederick H.

1979-01-01

This paper provides detailed evidence on the relationships among age, education, and environmental values. The relative strengths of association of age and education in predicting environmental attitudes are evaluated. Present and future generational politics of environmentalism are discussed. (Author/EB)

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.

Post-injury personality in the prediction of outcome following severe acquired brain injury.

PubMed

Cattran, Charlotte Jane; Oddy, Michael; Wood, Rodger Llewellyn; Moir, Jane Frances

2011-01-01

The aim of the study was to examine the utility of five measures of non-cognitive neurobehavioural (NCNB) changes that often occur following acquired brain injury, in predicting outcome (measured in terms of participation and social adaptation) at 1-year follow-up. The study employed a longitudinal, correlational design. Multiple regression was employed to investigate the value of five new NCNB measures of social perception, emotional regulation, motivation, impulsivity and disinhibition in the prediction of outcome as measured by the Mayo-Portland Adaptability Inventory (MPAI). Two NCNB measures (motivation and emotional regulation) were found to significantly predict outcome at 1-year follow-up, accounting for 53% of the variance in MPAI total scores. These measures provide a method of quantifying the extent of NCNB changes following brain injury. The predictive value of the measures indicates that they may represent a useful tool which could aid clinicians in identifying early-on those whose symptoms are likely to persist and who may require ongoing intervention. This could facilitate the planning of rehabilitation programmes.

Motivational state controls the prediction error in Pavlovian appetitive-aversive interactions.

PubMed

Laurent, Vincent; Balleine, Bernard W; Westbrook, R Frederick

2018-01-01

Contemporary theories of learning emphasize the role of a prediction error signal in driving learning, but the nature of this signal remains hotly debated. Here, we used Pavlovian conditioning in rats to investigate whether primary motivational and emotional states interact to control prediction error. We initially generated cues that positively or negatively predicted an appetitive food outcome. We then assessed how these cues modulated aversive conditioning when a novel cue was paired with a foot shock. We found that a positive predictor of food enhances, whereas a negative predictor of that same food impairs, aversive conditioning. Critically, we also showed that the enhancement produced by the positive predictor is removed by reducing the value of its associated food. In contrast, the impairment triggered by the negative predictor remains insensitive to devaluation of its associated food. These findings provide compelling evidence that the motivational value attributed to a predicted food outcome can directly control appetitive-aversive interactions and, therefore, that motivational processes can modulate emotional processes to generate the final error term on which subsequent learning is based. Copyright © 2017 Elsevier Inc. All rights reserved.

Curiosity and reward: Valence predicts choice and information prediction errors enhance learning.

PubMed

Marvin, Caroline B; Shohamy, Daphna

2016-03-01

Curiosity drives many of our daily pursuits and interactions; yet, we know surprisingly little about how it works. Here, we harness an idea implied in many conceptualizations of curiosity: that information has value in and of itself. Reframing curiosity as the motivation to obtain reward-where the reward is information-allows one to leverage major advances in theoretical and computational mechanisms of reward-motivated learning. We provide new evidence supporting 2 predictions that emerge from this framework. First, we find an asymmetric effect of positive versus negative information, with positive information enhancing both curiosity and long-term memory for information. Second, we find that it is not the absolute value of information that drives learning but, rather, the gap between the reward expected and reward received, an "information prediction error." These results support the idea that information functions as a reward, much like money or food, guiding choices and driving learning in systematic ways. (c) 2016 APA, all rights reserved).

QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls.

PubMed

Yu, S; Gao, S; Gan, Y; Zhang, Y; Ruan, X; Wang, Y; Yang, L; Shi, J

2016-04-01

Quantitative structure-property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol-water (KOW) and organic carbon-water (KOC) partition coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure-activity relationships, were employed to predict suspended particulate matter (SPM) derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of 209 PCBs. The predictive ability of the derived models was validated using a test set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of PCBs.

[Research of prevalence of schistosomiasis in Hunan province, 1984-2015].

PubMed

Li, F Y; Tan, H Z; Ren, G H; Jiang, Q; Wang, H L

2017-03-10

Objective: To analyze the prevalence of schistosomiasis in Hunan province, and provide scientific evidence for the control and elimination of schistosomiasis. Methods: The changes of infection rates of Schistosoma ( S .) japonicum among residents and cattle in Hunan from 1984 to 2015 were analyzed by using dynamic trend diagram; and the time regression model was used to fit the infection rates of S. japonicum , and predict the recent infection rate. Results: The overall infection rates of S. japonicum in Hunan from 1984 to 2015 showed downward trend (95.29% in residents and 95.16% in cattle). By using the linear regression model, the actual values of infection rates in residents and cattle were all in the 95% confidence intervals of the value predicted; and the prediction showed that the infection rates in the residents and cattle would continue to decrease from 2016 to 2020. Conclusion: The prevalence of schistosomiasis was in decline in Hunan. The regression model has a good effect in the short-term prediction of schistosomiasis prevalence.

Geographical distribution of reference value of aging people's left ventricular end systolic diameter based on the support vector regression.

PubMed

Han, Xiao; Ge, Miao; Dong, Jie; Xue, Ranying; Wang, Zixuan; He, Jinwei

2014-09-01

The aim of this paper is to analyze the geographical distribution of reference value of aging people's left ventricular end systolic diameter (LVDs), and to provide a scientific basis for clinical examination. The study is focus on the relationship between reference value of left ventricular end systolic diameter of aging people and 14 geographical factors, selecting 2495 samples of left ventricular end systolic diameter (LVDs) of aging people in 71 units of China, in which including 1620 men and 875 women. By using the Moran's I index to make sure the relationship between the reference values and spatial geographical factors, extracting 5 geographical factors which have significant correlation with left ventricular end systolic diameter for building the support vector regression, detecting by the method of paired sample t test to make sure the consistency between predicted and measured values, finally, makes the distribution map through the disjunctive kriging interpolation method and fits the three-dimensional trend of normal reference value. It is found that the correlation between the extracted geographical factors and the reference value of left ventricular end systolic diameter is quite significant, the 5 indexes respectively are latitude, annual mean air temperature, annual mean relative humidity, annual precipitation amount, annual range of air temperature, the predicted values and the observed ones are in good conformity, there is no significant difference at 95% degree of confidence. The overall trend of predicted values increases from west to east, increases first and then decreases from north to south. If geographical values are obtained in one region, the reference value of left ventricular end systolic diameter of aging people in this region can be obtained by using the support vector regression model. It could be more scientific to formulate the different distributions on the basis of synthesizing the physiological and the geographical factors. -Use Moran's index to analyze the spatial correlation. -Choose support vector machine to build model that overcome complexity of variables. -Test normal distribution of predicted data to guarantee the interpolation results. -Through trend analysis to explain the changes of reference value clearly. Copyright © 2014 Elsevier Inc. All rights reserved.

Model for estimating enteric methane emissions from United States dairy and feedlot cattle.

PubMed

Kebreab, E; Johnson, K A; Archibeque, S L; Pape, D; Wirth, T

2008-10-01

Methane production from enteric fermentation in cattle is one of the major sources of anthropogenic greenhouse gas emission in the United States and worldwide. National estimates of methane emissions rely on mathematical models such as the one recommended by the Intergovernmental Panel for Climate Change (IPCC). Models used for prediction of methane emissions from cattle range from empirical to mechanistic with varying input requirements. Two empirical and 2 mechanistic models (COWPOLL and MOLLY) were evaluated for their prediction ability using individual cattle measurements. Model selection was based on mean square prediction error (MSPE), concordance correlation coefficient, and residuals vs. predicted values analyses. In dairy cattle, COWPOLL had the lowest root MSPE and greatest accuracy and precision of predicting methane emissions (correlation coefficient estimate = 0.75). The model simulated differences in diet more accurately than the other models, and the residuals vs. predicted value analysis showed no mean bias (P = 0.71). In feedlot cattle, MOLLY had the lowest root MSPE with almost all errors from random sources (correlation coefficient estimate = 0.69). The IPCC model also had good agreement with observed values, and no significant mean (P = 0.74) or linear bias (P = 0.11) was detected when residuals were plotted against predicted values. A fixed methane conversion factor (Ym) might be an easier alternative to diet-dependent variable Ym. Based on the results, the 2 mechanistic models were used to simulate methane emissions from representative US diets and were compared with the IPCC model. The average Ym in dairy cows was 5.63% of GE (range 3.78 to 7.43%) compared with 6.5% +/- 1% recommended by IPCC. In feedlot cattle, the average Ym was 3.88% (range 3.36 to 4.56%) compared with 3% +/- 1% recommended by IPCC. Based on our simulations, using IPCC values can result in an overestimate of about 12.5% and underestimate of emissions by about 9.8% for dairy and feedlot cattle, respectively. In addition to providing improved estimates of emissions based on diets, mechanistic models can be used to assess mitigation options such as changing source of carbohydrate or addition of fat to decrease methane, which is not possible with empirical models. We recommend national inventories use diet-specific Ym values predicted by mechanistic models to estimate methane emissions from cattle.

Sensitivity and Bias in Decision-Making under Risk: Evaluating the Perception of Reward, Its Probability and Value

PubMed Central

Sharp, Madeleine E.; Viswanathan, Jayalakshmi; Lanyon, Linda J.; Barton, Jason J. S.

2012-01-01

Background There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. Objective We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. Design/Methods Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. Results Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a ‘risk premium’ of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. Conclusions This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia. PMID:22493669

Petrophysics of low-permeability medina sandstone, northwestern Pennsylvania, Appalachian Basin

USGS Publications Warehouse

Castle, J.W.; Byrnes, A.P.

1998-01-01

Petrophysical core testing combined with geophysical log analysis of low-permeability, Lower Silurian sandstones of the Appalachian basin provides guidelines and equations for predicting gas producibility. Permeability values are predictable from the borehole logs by applying empirically derived equations based on correlation between in-situ porosity and in-situ effective gas permeability. An Archie-form equation provides reasonable accuracy of log-derived water saturations because of saturated brine salinities and low clay content in the sands. Although measured porosity and permeability average less than 6% and 0.1 mD, infrequent values as high as 18% and 1,048 mD occur. Values of effective gas permeability at irreducible water saturation (Swi) range from 60% to 99% of routine values for the highest permeability rocks to several orders of magnitude less for the lowest permeability rocks. Sandstones having porosity greater than 6% and effective gas permeability greater than 0.01 mD exhibit Swi less than 20%. With decreasing porosity, Swi sharply increases to values near 40% at 3 porosity%. Analysis of cumulative storage and flow capacity indicates zones with porosity greater than 6% generally contain over 90% of flow capacity and hold a major portion of storage capacity. For rocks with Swi < 20%, gas relative permeabilities exceed 45%. Gas relative permeability and hydrocarbon volume decrease rapidly with increasing Swi as porosity drops below 6%. At Swi above 40%, gas relative permeabilities are less than approximately 10%.

Sensitivity and bias in decision-making under risk: evaluating the perception of reward, its probability and value.

PubMed

Sharp, Madeleine E; Viswanathan, Jayalakshmi; Lanyon, Linda J; Barton, Jason J S

2012-01-01

There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a 'risk premium' of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia.

Prediction of Tibial Rotation Pathologies Using Particle Swarm Optimization and K-Means Algorithms.

PubMed

Sari, Murat; Tuna, Can; Akogul, Serkan

2018-03-28

The aim of this article is to investigate pathological subjects from a population through different physical factors. To achieve this, particle swarm optimization (PSO) and K-means (KM) clustering algorithms have been combined (PSO-KM). Datasets provided by the literature were divided into three clusters based on age and weight parameters and each one of right tibial external rotation (RTER), right tibial internal rotation (RTIR), left tibial external rotation (LTER), and left tibial internal rotation (LTIR) values were divided into three types as Type 1, Type 2 and Type 3 (Type 2 is non-pathological (normal) and the other two types are pathological (abnormal)), respectively. The rotation values of every subject in any cluster were noted. Then the algorithm was run and the produced values were also considered. The values of the produced algorithm, the PSO-KM, have been compared with the real values. The hybrid PSO-KM algorithm has been very successful on the optimal clustering of the tibial rotation types through the physical criteria. In this investigation, Type 2 (pathological subjects) is of especially high predictability and the PSO-KM algorithm has been very successful as an operation system for clustering and optimizing the tibial motion data assessments. These research findings are expected to be very useful for health providers, such as physiotherapists, orthopedists, and so on, in which this consequence may help clinicians to appropriately designing proper treatment schedules for patients.

Artificial neural network prediction of ischemic tissue fate in acute stroke imaging

PubMed Central

Huang, Shiliang; Shen, Qiang; Duong, Timothy Q

2010-01-01

Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin–spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis. PMID:20424631

CFD Modeling of Launch Vehicle Aerodynamic Heating

NASA Technical Reports Server (NTRS)

Tashakkor, Scott B.; Canabal, Francisco; Mishtawy, Jason E.

2011-01-01

The Loci-CHEM 3.2 Computational Fluid Dynamics (CFD) code is being used to predict Ares-I launch vehicle aerodynamic heating. CFD has been used to predict both ascent and stage reentry environments and has been validated against wind tunnel tests and the Ares I-X developmental flight test. Most of the CFD predictions agreed with measurements. On regions where mismatches occurred, the CFD predictions tended to be higher than measured data. These higher predictions usually occurred in complex regions, where the CFD models (mainly turbulence) contain less accurate approximations. In some instances, the errors causing the over-predictions would cause locations downstream to be affected even though the physics were still being modeled properly by CHEM. This is easily seen when comparing to the 103-AH data. In the areas where predictions were low, higher grid resolution often brought the results closer to the data. Other disagreements are attributed to Ares I-X hardware not being present in the grid, as a result of computational resources limitations. The satisfactory predictions from CHEM provide confidence that future designs and predictions from the CFD code will provide an accurate approximation of the correct values for use in design and other applications

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.

Information filtering based on transferring similarity.

PubMed

Sun, Duo; Zhou, Tao; Liu, Jian-Guo; Liu, Run-Ran; Jia, Chun-Xiao; Wang, Bing-Hong

2009-07-01

In this Brief Report, we propose an index of user similarity, namely, the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarkably higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach its optimal value when the parameter, contained in the definition of transferring similarity, gets close to its critical value, before which the series expansion of transferring similarity is convergent and after which it is divergent. Our study is complementary to the one reported in [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E 73, 026120 (2006)], and is relevant to the missing link prediction problem.

Appropriate clinical use of human leukocyte antigen typing for coeliac disease: an Australasian perspective

PubMed Central

Tye-Din, J A; Cameron, D J S; Daveson, A J; Day, A S; Dellsperger, P; Hogan, C; Newnham, E D; Shepherd, S J; Steele, R H; Wienholt, L; Varney, M D

2015-01-01

The past decade has seen human leukocyte antigen (HLA) typing emerge as a remarkably popular test for the diagnostic work-up of coeliac disease with high patient acceptance. Although limited in its positive predictive value for coeliac disease, the strong disease association with specific HLA genes imparts exceptional negative predictive value to HLA typing, enabling a negative result to exclude coeliac disease confidently. In response to mounting evidence that the clinical use and interpretation of HLA typing often deviates from best practice, this article outlines an evidence-based approach to guide clinically appropriate use of HLA typing, and establishes a reporting template for pathology providers to improve communication of results. PMID:25827511

Calculations of reliability predictions for the Apollo spacecraft

NASA Technical Reports Server (NTRS)

Amstadter, B. L.

1966-01-01

A new method of reliability prediction for complex systems is defined. Calculation of both upper and lower bounds are involved, and a procedure for combining the two to yield an approximately true prediction value is presented. Both mission success and crew safety predictions can be calculated, and success probabilities can be obtained for individual mission phases or subsystems. Primary consideration is given to evaluating cases involving zero or one failure per subsystem, and the results of these evaluations are then used for analyzing multiple failure cases. Extensive development is provided for the overall mission success and crew safety equations for both the upper and lower bounds.

Comparisons of AEROX computer program predictions of lift and induced drag with flight test data

NASA Technical Reports Server (NTRS)

Axelson, J.; Hill, G. C.

1981-01-01

The AEROX aerodynamic computer program which provides accurate predictions of induced drag and trim drag for the full angle of attack range and for Mach numbers from 0.4 to 3.0 is described. This capability is demonstrated comparing flight test data and AEROX predictions for 17 different tactical aircraft. Values of minimum (skin friction, pressure, and zero lift wave) drag coefficients and lift coefficient offset due to camber (when required) were input from the flight test data to produce total lift and drag curves. The comparisons of trimmed lift drag polars show excellent agreement between the AEROX predictions and the in flight measurements.

Empirical prediction of peak pressure levels in anthropogenic impulsive noise. Part I: Airgun arrays signals.

PubMed

Galindo-Romero, Marta; Lippert, Tristan; Gavrilov, Alexander

2015-12-01

This paper presents an empirical linear equation to predict peak pressure level of anthropogenic impulsive signals based on its correlation with the sound exposure level. The regression coefficients are shown to be weakly dependent on the environmental characteristics but governed by the source type and parameters. The equation can be applied to values of the sound exposure level predicted with a numerical model, which provides a significant improvement in the prediction of the peak pressure level. Part I presents the analysis for airgun arrays signals, and Part II considers the application of the empirical equation to offshore impact piling noise.

Statistical distribution of mechanical properties for three graphite-epoxy material systems

NASA Technical Reports Server (NTRS)

Reese, C.; Sorem, J., Jr.

1981-01-01

Graphite-epoxy composites are playing an increasing role as viable alternative materials in structural applications necessitating thorough investigation into the predictability and reproducibility of their material strength properties. This investigation was concerned with tension, compression, and short beam shear coupon testing of large samples from three different material suppliers to determine their statistical strength behavior. Statistical results indicate that a two Parameter Weibull distribution model provides better overall characterization of material behavior for the graphite-epoxy systems tested than does the standard Normal distribution model that is employed for most design work. While either a Weibull or Normal distribution model provides adequate predictions for average strength values, the Weibull model provides better characterization in the lower tail region where the predictions are of maximum design interest. The two sets of the same material were found to have essentially the same material properties, and indicate that repeatability can be achieved.

Prediction and typicality in multiverse cosmology

NASA Astrophysics Data System (ADS)

Azhar, Feraz

2014-02-01

In the absence of a fundamental theory that precisely predicts values for observable parameters, anthropic reasoning attempts to constrain probability distributions over those parameters in order to facilitate the extraction of testable predictions. The utility of this approach has been vigorously debated of late, particularly in light of theories that claim we live in a multiverse, where parameters may take differing values in regions lying outside our observable horizon. Within this cosmological framework, we investigate the efficacy of top-down anthropic reasoning based on the weak anthropic principle. We argue contrary to recent claims that it is not clear one can either dispense with notions of typicality altogether or presume typicality, in comparing resulting probability distributions with observations. We show in a concrete, top-down setting related to dark matter, that assumptions about typicality can dramatically affect predictions, thereby providing a guide to how errors in reasoning regarding typicality translate to errors in the assessment of predictive power. We conjecture that this dependence on typicality is an integral feature of anthropic reasoning in broader cosmological contexts, and argue in favour of the explicit inclusion of measures of typicality in schemes invoking anthropic reasoning, with a view to extracting predictions from multiverse scenarios.

Positive predictive value estimates for cell-free noninvasive prenatal screening from data of a large referral genetic diagnostic laboratory.

PubMed

Petersen, Andrea K; Cheung, Sau Wai; Smith, Janice L; Bi, Weimin; Ward, Patricia A; Peacock, Sandra; Braxton, Alicia; Van Den Veyver, Ignatia B; Breman, Amy M

2017-12-01

Since its debut in 2011, cell-free fetal DNA screening has undergone rapid expansion with respect to both utilization and coverage. However, conclusive data regarding the clinical validity and utility of this screening tool, both for the originally included common autosomal and sex-chromosomal aneuploidies as well as the more recently added chromosomal microdeletion syndromes, have lagged behind. Thus, there is a continued need to educate clinicians and patients about the current benefits and limitations of this screening tool to inform pre- and posttest counseling, pre/perinatal decision making, and medical risk assessment/management. The objective of this study was to determine the positive predictive value and false-positive rates for different chromosomal abnormalities identified by cell-free fetal DNA screening using a large data set of diagnostic testing results on invasive samples submitted to the laboratory for confirmatory studies. We tested 712 patient samples sent to our laboratory to confirm a cell-free fetal DNA screening result, indicating high risk for a chromosome abnormality. We compiled data from all cases in which the indication for confirmatory testing was a positive cell-free fetal DNA screen, including the common trisomies, sex chromosomal aneuploidies, microdeletion syndromes, and other large genome-wide copy number abnormalities. Testing modalities included fluorescence in situ hybridization, G-banded karyotype, and/or chromosomal microarray analysis performed on chorionic villus samples, amniotic fluid, or postnatally obtained blood samples. Positive predictive values and false-positive rates were calculated from tabulated data. The positive predictive values for trisomy 13, 18, and 21 were consistent with previous reports at 45%, 76%, and 84%, respectively. For the microdeletion syndrome regions, positive predictive values ranged from 0% for detection of Cri-du-Chat syndrome and Prader-Willi/Angelman syndrome to 14% for 1p36 deletion syndrome and 21% for 22q11.2 deletion syndrome. Detection of sex chromosomal aneuploidies had positive predictive values of 26% for monosomy X, 50% for 47,XXX, and 86% for 47,XXY. The positive predictive values for detection of common autosomal and sex chromosomal aneuploidies by cell-free fetal DNA screening were comparable with other studies. Identification of microdeletions was associated with lower positive predictive values and higher false-positive rates, likely because of the low prevalence of the individual targeted microdeletion syndromes in the general population. Although the obtained positive predictive values compare favorably with those seen in traditional screening approaches for common aneuploidies, they highlight the importance of educating clinicians and patients on the limitations of cell-free fetal DNA screening tests. Improvement of the cell-free fetal DNA screening technology and continued monitoring of its performance after introduction into clinical practice will be important to fully establish its clinical utility. Nonetheless, our data provide valuable information that may aid result interpretation, patient counseling, and clinical decision making/management. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Comparison between fine needle aspiration cytology (FNAC) and core needle biopsy (CNB) in the diagnosis of breast lesions.

PubMed

Moschetta, M; Telegrafo, M; Carluccio, D A; Jablonska, J P; Rella, L; Serio, Gabriella; Carrozzo, M; Stabile Ianora, A A; Angelelli, G

2014-01-01

To compare the diagnostic accuracy of fine-needle aspiration cytology (FNAC) and core needle biopsy (CNB) in patients with USdetected breast lesions. Between September 2011 and May 2013, 3469 consecutive breast US examinations were performed. 400 breast nodules were detected in 398 patients. 210 FNACs and 190 CNBs were performed. 183 out of 400 (46%) lesions were surgically removed within 30 days form diagnosis; in the remaining cases, a six month follow up US examination was performed. Sensitivity, specificity, diagnostic accuracy, positive predictive (PPV) and negative predictive (NPV) values were calculated for FNAC and CNB. 174 out of 400 (43%) malignant lesions were found while the remaining 226 resulted to be benign lesions. 166 out of 210 (79%) FNACs and 154 out of 190 (81%) CNBs provided diagnostic specimens. Sensitivity, specificity, diagnostic accuracy, PPV and NPV of 97%, 94%, 95%, 91% and 98% were found for FNAC, and values of 92%, 82%, 89%, 92% and 82% were obtained for CNB. Sensitivity, specificity, diagnostic accuracy, PPV and NPV of 97%, 96%, 96%, 97% and 96% were found for FNAC, and values of 97%, 96%, 96%, 97% and 96% were obtained for CNB. FNAC and CNB provide similar values of diagnostic accuracy.

Serum Protein Electrophoresis in the Evaluation of Lytic Bone Lesions

PubMed Central

Nystrom, Lukas M.; Buckwalter, Joseph A.; Syrbu, Sergei; Miller, Benjamin J.

2013-01-01

Serum protein electrophoresis (SPEP) is often obtained at the initial evaluation of a radiolucent bone lesion of unknown etiology. The results are considered convincing evidence of the presence or absence of a plasma cell neoplasm. The sensitivity and specificity of the SPEP have not been reported in this clinical scenario. Our purpose is to assess the diagnostic value of the SPEP in the initial work-up of the radiolucent bone lesion. We identified 182 patients undergoing evaluation of a radiolucent bone lesion that included tissue biopsy and an SPEP value. We then calculated the sen-sitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SPEP as a diagnostic test for a plasma cell neo-plasm in this clinical scenario. Forty-six of 182 (25.3%) patients in our series were diagnosed with a plasma cell neo-plasm by histopathologic analysis. The sensitivity of SPEP was 71% and the specificity was 83%. PPV was 47% and NPV was 94%. When analyzing only those presenting with multiple lesions, the percentage of patients diag-nosed with multiple myeloma increased to 44.7% (34 of 76 patients). The SPEP, however, did not have a substantially increased diagnostic accuracy with sensitivity of 71%, specificity 79%, PPV 40% and NPV 93%. SPEP lacks sensitivity and positive predictive value to provide a definitive diagnosis of myeloma in radiolucent bone lesions, but has a high negative predictive value which may make it useful in ruling out the disease. We recommend that this test either be performed in conjunction with urine electrophoresis, immunofixation electro-phoresis and free light chain assay, or after biopsy confirming the diagnosis of myeloma. PMID:24027470

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.

The clinical value of adipokines in predicting the severity and outcome of acute pancreatitis.

PubMed

Karpavicius, Andrius; Dambrauskas, Zilvinas; Gradauskas, Audrius; Samuilis, Arturas; Zviniene, Kristina; Kupcinskas, Juozas; Brimas, Gintautas; Meckovski, Artur; Sileikis, Audrius; Strupas, Kestutis

2016-08-22

Recent data shows that patients with severe acute pancreatic might benefit from early intensive therapy, enteral nutrition and timely transfer to specialized centers. The early prophylactic use of antibiotics in AP remains controversial. The role and need for new markers in stratification of acute pancreatitis is also uncertain. This study aims to evaluate the prognostic usefulness of adipokines in prediction of the severity and outcome of acute pancreatitis (AP). Prospective study was conducted in four clinical centers. The diagnosis and severity assessment of AP was established according to the revised 2012 Atlanta classification. Adipokines, IL-6 and CRP levels were measured at admission and on 3rd day of hospital stay and compared with the control group. The predictive accuracy of each marker was measured by area under the receiver operating curve. Forty healthy controls and 102 patients were enrolled in to the study. Twenty seven (26.5 %) patients had mild, 55 (53.9 %) - moderate and 20 (19.6 %) - severe AP. Only resistin (cut-off value 13.7 ng/ml) and IL-6 (cut-off value 473.4 pg/ml) were reliable early markers of SAP. IL-6 with cut-off value of 157.0 pg/ml was a predictor of necrosis. The peripancreatic necrosis volume of 112.5 ml was a marker of SAP and 433.0 ml cut-off value could be used to predict the need of interventions. The prognostic value of adipokines in AP is limited. Only admission resistin levels could serve as an early predictor for SAP. The Lithuanian Regional Ethics Committee approved the study protocol (permission No. L-12-02/1/2/3/4) and all the patients and the control group provided written informed consent.

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

Retrospective Review of Treponema pallidum PCR and Serology Results: Are Both Tests Necessary?

PubMed

Brischetto, Anna; Gassiep, Ian; Whiley, David; Norton, Robert

2018-05-01

There has been a resurgence of syphilis diagnoses in Australia. We investigated whether our Treponema pallidum PCR test provides any additional diagnostic information over syphilis serology (chemiluminescence immunoassay [CMIA], Treponema pallidum particle agglutination [TPPA] assay, and the rapid plasma reagin [RPR] flocculation test). A retrospective audit of all T. pallidum PCR requests that came through our laboratory from January 2010 to June 2017 was conducted; data collected included age, gender, site of swab, and results from T. pallidum PCR, syphilis serology, and herpes simplex virus 1 (HSV-1) and HSV-2 PCRs. A total of 441 T. pallidum PCR tests were performed; on average, 3 T. pallidum PCRs per month were requested in 2011, and this rate increased to 17.2 requests per month in 2017. A total of 323 patients had both T. pallidum PCR and syphilis serology performed, with 67% of swabs taken from the genitals. T. pallidum PCR gave positive results for 61/323 (19%) patients; of these 61 patients, 59 (97%) also had positive syphilis serology results ( T. pallidum PCR sensitivity, 68%; specificity, 99%; positive predictive value, 97%; negative predictive value, 89%). Syphilis serology was positive for 91/323 patients (28%); of these 91 patients, 61 (66%) were also T. pallidum PCR positive (syphilis serology sensitivity, 97%; specificity, 88%; positive predictive value, 60%; negative predictive value, 99%). The Cohen's kappa value was 0.74, indicating substantial agreement between the two tests. Our results show that most patients with positive T. pallidum PCR results also had positive syphilis serology. Therefore, T. pallidum PCR adds little clinical value over serology for the diagnosis of syphilis in certain clinical settings. Copyright © 2018 American Society for Microbiology.

Absolute Measurements of Macrophage Migration Inhibitory Factor and Interleukin-1-β mRNA Levels Accurately Predict Treatment Response in Depressed Patients.

PubMed

Cattaneo, Annamaria; Ferrari, Clarissa; Uher, Rudolf; Bocchio-Chiavetto, Luisella; Riva, Marco Andrea; Pariante, Carmine M

2016-10-01

Increased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms. Here we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins. We identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration. We believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more assertive antidepressant strategies, including the addition of other antidepressants or antiinflammatory drugs. © The Author 2016. Published by Oxford University Press on behalf of CINP.

Evaluation of the impact of computed high b-value diffusion-weighted imaging on prostate cancer detection.

PubMed

Verma, Sadhna; Sarkar, Saradwata; Young, Jason; Venkataraman, Rajesh; Yang, Xu; Bhavsar, Anil; Patil, Nilesh; Donovan, James; Gaitonde, Krishnanath

2016-05-01

The purpose of this study was to compare high b-value (b = 2000 s/mm(2)) acquired diffusion-weighted imaging (aDWI) with computed DWI (cDWI) obtained using four diffusion models-mono-exponential (ME), intra-voxel incoherent motion (IVIM), stretched exponential (SE), and diffusional kurtosis (DK)-with respect to lesion visibility, conspicuity, contrast, and ability to predict significant prostate cancer (PCa). Ninety four patients underwent 3 T MRI including acquisition of b = 2000 s/mm(2) aDWI and low b-value DWI. High b = 2000 s/mm(2) cDWI was obtained using ME, IVIM, SE, and DK models. All images were scored on quality independently by three radiologists. Lesions were identified on all images and graded for lesion conspicuity. For a subset of lesions for which pathological truth was established, lesion-to-background contrast ratios (LBCRs) were computed and binomial generalized linear mixed model analysis was conducted to compare clinically significant PCa predictive capabilities of all DWI. For all readers and all models, cDWI demonstrated higher ratings for image quality and lesion conspicuity than aDWI except DK (p < 0.001). The LBCRs of ME, IVIM, and SE were significantly higher than LBCR of aDWI (p < 0.001). Receiver Operating Characteristic curves obtained from binomial generalized linear mixed model analysis demonstrated higher Area Under the Curves for ME, SE, IVIM, and aDWI compared to DK or PSAD alone in predicting significant PCa. High b-value cDWI using ME, IVIM, and SE diffusion models provide better image quality, lesion conspicuity, and increased LBCR than high b-value aDWI. Using cDWI can potentially provide comparable sensitivity and specificity for detecting significant PCa as high b-value aDWI without increased scan times and image degradation artifacts.

PREDICTING ESTUARINE SEDIMENT METAL CONCENTRATIONS AND INFERRED ECOLOGICAL CONDITIONS: AN INFORMATION THEORETIC APPROACH

EPA Science Inventory

Empirically derived values associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. However, resources limit the number, magnitude, and frequency of monitoring programs to gather these data. As su...

Vicarious reinforcement learning signals when instructing others.

PubMed

Apps, Matthew A J; Lesage, Elise; Ramnani, Narender

2015-02-18

Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.

The DynaMine webserver: predicting protein dynamics from sequence.

PubMed

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim F

2014-07-01

Protein dynamics are important for understanding protein function. Unfortunately, accurate protein dynamics information is difficult to obtain: here we present the DynaMine webserver, which provides predictions for the fast backbone movements of proteins directly from their amino-acid sequence. DynaMine rapidly produces a profile describing the statistical potential for such movements at residue-level resolution. The predicted values have meaning on an absolute scale and go beyond the traditional binary classification of residues as ordered or disordered, thus allowing for direct dynamics comparisons between protein regions. Through this webserver, we provide molecular biologists with an efficient and easy to use tool for predicting the dynamical characteristics of any protein of interest, even in the absence of experimental observations. The prediction results are visualized and can be directly downloaded. The DynaMine webserver, including instructive examples describing the meaning of the profiles, is available at http://dynamine.ibsquare.be. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Heat Capacity Anomaly Near the Lower Critical Consolute Point of Triethylamine-Water

NASA Technical Reports Server (NTRS)

Flewelling, Anne C.; DeFonseka, Rohan J.; Khaleeli, Nikfar; Partee, J.; Jacobs, D. T.

1996-01-01

The heat capacity of the binary liquid mixture triethylamine-water has been measured near its lower critical consolute point using a scanning, adiabatic calorimeter. Two data runs are analyzed to provide heat capacity and enthalpy data that are fitted by equations with background terms and a critical term that includes correction to scaling. The critical exponent a was determined to be 0.107 +/- 0.006, consistent with theoretical predictions. When alpha was fixed at 0.11 to determine various amplitudes consistently, our values of A(+) and A(-) agreed with a previous heat capacity measurement, but the value of A(-) was inconsistent with values determined by density or refractive index measurements. While our value for the amplitude ratio A(+)/ A(-) = 0.56 +/- 0.02 was consistent with other recent experimental determinations in binary liquid mixtures, it was slightly larger than either theoretical predictions or recent experimental values in liquid-vapor systems. The correction to scaling amplitude ratio D(+)/D(-) = 0.5 +/- 0.1 was half of that predicted. As a result of several more precise theoretical calculations and experimental determinations, the two-scale-factor universality ratio X, which we found to be 0.019 +/- 0.003, now is consistent among experiments and theories. A new 'universal' amplitude ratio R(sup +/-)(sub Bcr) involving the amplitudes for the specific heat was tested. Our determination of R(sup +/-)(sub Bcr) = -0.5 +/- 0.1 and R(sup -)(sub Bcr) = 1.1 +/- 0.1 is smaller in magnitude than predicted and is the first such determination in a binary fluid mixture.

Accuracy of 'My Gut Feeling:' Comparing System 1 to System 2 Decision-Making for Acuity Prediction, Disposition and Diagnosis in an Academic Emergency Department.

PubMed

Cabrera, Daniel; Thomas, Jonathan F; Wiswell, Jeffrey L; Walston, James M; Anderson, Joel R; Hess, Erik P; Bellolio, M Fernanda

2015-09-01

Current cognitive sciences describe decision-making using the dual-process theory, where a System 1 is intuitive and a System 2 decision is hypothetico-deductive. We aim to compare the performance of these systems in determining patient acuity, disposition and diagnosis. Prospective observational study of emergency physicians assessing patients in the emergency department of an academic center. Physicians were provided the patient's chief complaint and vital signs and allowed to observe the patient briefly. They were then asked to predict acuity, final disposition (home, intensive care unit (ICU), non-ICU bed) and diagnosis. A patient was classified as sick by the investigators using previously published objective criteria. We obtained 662 observations from 289 patients. For acuity, the observers had a sensitivity of 73.9% (95% CI [67.7-79.5%]), specificity 83.3% (95% CI [79.5-86.7%]), positive predictive value 70.3% (95% CI [64.1-75.9%]) and negative predictive value 85.7% (95% CI [82.0-88.9%]). For final disposition, the observers made a correct prediction in 80.8% (95% CI [76.1-85.0%]) of the cases. For ICU admission, emergency physicians had a sensitivity of 33.9% (95% CI [22.1-47.4%]) and a specificity of 96.9% (95% CI [94.0-98.7%]). The correct diagnosis was made 54% of the time with the limited data available. System 1 decision-making based on limited information had a sensitivity close to 80% for acuity and disposition prediction, but the performance was lower for predicting ICU admission and diagnosis. System 1 decision-making appears insufficient for final decisions in these domains but likely provides a cognitive framework for System 2 decision-making.

Anisotropic yield function capable of predicting eight ears

NASA Astrophysics Data System (ADS)

Yoon, J. H.; Cazacu, O.

2011-08-01

Deep drawing of a cylindrical cup from a rolled sheet is one of the typical forming operations where the effect of this anisotropy is most evident. Indeed, it is well documented in the literature that the number of ears and the shape of the earing pattern correlate with the r-values profile. For the strongly textured aluminum alloy AA 5042 (Numisheet Benchmark 2011), the experimental r-value distribution has two minima between the rolling and transverse direction data provided for this show that the r-value along the transverse direction (TD) is five times larger than the value corresponding to the rolling direction. Therefore, it is expected that there are more that the earing profile has more than four ears. The main objective of this paper is to assess whether a new form of CPB06ex2 yield function (Plunkett et al. (2008)) tailored for metals with no tension-compression asymmetry is capable of predicting more than four ears for this material.

Method and apparatus for sensor fusion

NASA Technical Reports Server (NTRS)

Krishen, Kumar (Inventor); Shaw, Scott (Inventor); Defigueiredo, Rui J. P. (Inventor)

1991-01-01

Method and apparatus for fusion of data from optical and radar sensors by error minimization procedure is presented. The method was applied to the problem of shape reconstruction of an unknown surface at a distance. The method involves deriving an incomplete surface model from an optical sensor. The unknown characteristics of the surface are represented by some parameter. The correct value of the parameter is computed by iteratively generating theoretical predictions of the radar cross sections (RCS) of the surface, comparing the predicted and the observed values for the RCS, and improving the surface model from results of the comparison. Theoretical RCS may be computed from the surface model in several ways. One RCS prediction technique is the method of moments. The method of moments can be applied to an unknown surface only if some shape information is available from an independent source. The optical image provides the independent information.

Evaluation of a method of estimating low-flow frequencies from base-flow measurements at Indiana streams

USGS Publications Warehouse

Wilson, John Thomas

2000-01-01

A mathematical technique of estimating low-flow frequencies from base-flow measurements was evaluated by using data for streams in Indiana. Low-flow frequencies at low- flow partial-record stations were estimated by relating base-flow measurements to concurrent daily flows at nearby streamflow-gaging stations (index stations) for which low-flowfrequency curves had been developed. A network of long-term streamflow-gaging stations in Indiana provided a sample of sites with observed low-flow frequencies. Observed values of 7-day, 10-year low flow and 7-day, 2-year low flow were compared to predicted values to evaluate the accuracy of the method. Five test cases were used to evaluate the method under a variety of conditions in which the location of the index station and its drainage area varied relative to the partial-record station. A total of 141 pairs of streamflow-gaging stations were used in the five test cases. Four of the test cases used one index station, the fifth test case used two index stations. The number of base-flow measurements was varied for each test case to see if the accuracy of the method was affected by the number of measurements used. The most accurate and least variable results were produced when two index stations on the same stream or tributaries of the partial-record station were used. All but one value of the predicted 7-day, 10-year low flow were within 15 percent of the values observed for the long-term continuous record, and all of the predicted values of the 7-day, 2-year lowflow were within 15 percent of the observed values. This apparent accuracy, to some extent, may be a result of the small sample set of 15. Of the four test cases that used one index station, the most accurate and least variable results were produced in the test case where the index station and partial-record station were on the same stream or on streams tributary to each other and where the index station had a larger drainage area than the partial-record station. In that test case, the method tended to over predict, based on the median relative error. In 23 of 28 test pairs, the predicted 7-day, 10-year low flow was within 15 percent of the observed value; in 26 of 28 test pairs, the predicted 7-day, 2-year low flow was within 15 percent of the observed value. When the index station and partial-record station were on the same stream or streams tributary to each other and the index station had a smaller drainage area than the partial-record station, the method tended to under predict the low-flow frequencies. Nineteen of 28 predicted values of the 7-day, 10-year low flow were within 15 percent of the observed values. Twenty-five of 28 predicted values of the 7-day, 2-year low flow were within 15 percent of the observed values. When the index station and the partial-record station were on different streams, the method tended to under predict regardless of whether the index station had a larger or smaller drainage area than that of the partial-record station. Also, the variability of the relative error of estimate was greatest for the test cases that used index stations and partial-record stations from different streams. This variability, in part, may be caused by using more streamflow-gaging stations with small low-flow frequencies in these test cases. A small difference in the predicted and observed values can equate to a large relative error when dealing with stations that have small low-flow frequencies. In the test cases that used one index station, the method tended to predict smaller low-flow frequencies as the number of base-flow measurements was reduced from 20 to 5. Overall, the average relative error of estimate and the variability of the predicted values increased as the number of base-flow measurements was reduced.

Analysis of the regional MiKlip decadal prediction system over Europe: skill, added value of regionalization, and ensemble size dependeny

NASA Astrophysics Data System (ADS)

Reyers, Mark; Moemken, Julia; Pinto, Joaquim; Feldmann, Hendrik; Kottmeier, Christoph; MiKlip Module-C Team

2017-04-01

Decadal climate predictions can provide a useful basis for decision making support systems for the public and private sectors. Several generations of decadal hindcasts and predictions have been generated throughout the German research program MiKlip. Together with the global climate predictions computed with MPI-ESM, the regional climate model (RCM) COSMO-CLM is used for regional downscaling by MiKlip Module-C. The RCMs provide climate information on spatial and temporal scales closer to the needs of potential users. In this study, two downscaled hindcast generations are analysed (named b0 and b1). The respective global generations are both initialized by nudging them towards different reanalysis anomaly fields. An ensemble of five starting years (1961, 1971, 1981, 1991, and 2001), each comprising ten ensemble members, is used for both generations in order to quantify the regional decadal prediction skill for precipitation and near-surface temperature and wind speed over Europe. All datasets (including hindcasts, observations, reanalysis, and historical MPI-ESM runs) are pre-processed in an analogue manner by (i) removing the long-term trend and (ii) re-gridding to a common grid. Our analysis shows that there is potential for skillful decadal predictions over Europe in the regional MiKlip ensemble, but the skill is not systematic and depends on the PRUDENCE region and the variable. Further, the differences between the two hindcast generations are mostly small. As we used detrended time series, the predictive skill found in our study can probably attributed to reasonable predictions of anomalies which are associated with the natural climate variability. In a sensitivity study, it is shown that the results may strongly change when the long-term trend is kept in the datasets, as here the skill of predicting the long-term trend (e.g. for temperature) also plays a major role. The regionalization of the global ensemble provides an added value for decadal predictions for some complex regions like the Mediterranean and Iberian Peninsula, while for other regions no systematic improvement is found. A clear dependence of the performance of the regional MiKlip system on the ensemble size is detected. For all variables in both hindcast generations, the skill increases when the ensemble is enlarged. The results indicate that a number of ten members is an appropriate ensemble size for decadal predictions over Europe.

Developing predictive systems models to address complexity and relevance for ecological risk assessment.

PubMed

Forbes, Valery E; Calow, Peter

2013-07-01

Ecological risk assessments (ERAs) are not used as well as they could be in risk management. Part of the problem is that they often lack ecological relevance; that is, they fail to grasp necessary ecological complexities. Adding realism and complexity can be difficult and costly. We argue that predictive systems models (PSMs) can provide a way of capturing complexity and ecological relevance cost-effectively. However, addressing complexity and ecological relevance is only part of the problem. Ecological risk assessments often fail to meet the needs of risk managers by not providing assessments that relate to protection goals and by expressing risk in ratios that cannot be weighed against the costs of interventions. Once more, PSMs can be designed to provide outputs in terms of value-relevant effects that are modulated against exposure and that can provide a better basis for decision making than arbitrary ratios or threshold values. Recent developments in the modeling and its potential for implementation by risk assessors and risk managers are beginning to demonstrate how PSMs can be practically applied in risk assessment and the advantages that doing so could have. Copyright © 2013 SETAC.

Predictive value of stroke discharge diagnoses in the Danish National Patient Register.

PubMed

Lühdorf, Pernille; Overvad, Kim; Schmidt, Erik B; Johnsen, Søren P; Bach, Flemming W

2017-08-01

To determine the positive predictive values for stroke discharge diagnoses, including subarachnoidal haemorrhage, intracerebral haemorrhage and cerebral infarction in the Danish National Patient Register. Participants in the Danish cohort study Diet, Cancer and Health with a stroke discharge diagnosis in the National Patient Register between 1993 and 2009 were identified and their medical records were retrieved for validation of the diagnoses. A total of 3326 records of possible cases of stroke were reviewed. The overall positive predictive value for stroke was 69.3% (95% confidence interval (CI) 67.8-70.9%). The predictive values differed according to hospital characteristics, with the highest predictive value of 87.8% (95% CI 85.5-90.1%) found in departments of neurology and the lowest predictive value of 43.0% (95% CI 37.6-48.5%) found in outpatient clinics. The overall stroke diagnosis in the Danish National Patient Register had a limited predictive value. We therefore recommend the critical use of non-validated register data for research on stroke. The possibility of optimising the predictive values based on more advanced algorithms should be considered.

Predictive displays for a process-control schematic interface.

PubMed

Yin, Shanqing; Wickens, Christopher D; Helander, Martin; Laberge, Jason C

2015-02-01

Our objective was to examine the extent to which increasing precision of predictive (rate of change) information in process control will improve performance on a simulated process-control task. Predictive displays have been found to be useful in process control (as well as aviation and maritime industries). However, authors of prior research have not examined the extent to which predictive value is increased by increasing predictor resolution, nor has such research tied potential improvements to changes in process control strategy. Fifty nonprofessional participants each controlled a simulated chemical mixture process (honey mixer simulation) that simulated the operations found in process control. Participants in each of five groups controlled with either no predictor or a predictor ranging in the resolution of prediction of the process. Increasing detail resolution generally increased the benefit of prediction over the control condition although not monotonically so. The best overall performance, combining quality and predictive ability, was obtained by the display of intermediate resolution. The two displays with the lowest resolution were clearly inferior. Predictors with higher resolution are of value but may trade off enhanced sensitivity to variable change (lower-resolution discrete state predictor) with smoother control action (higher-resolution continuous predictors). The research provides guidelines to the process-control industry regarding displays that can most improve operator performance.

Translation, adaptation, and validation of the Sunderland Scale and the Cubbin & Jackson Revised Scale in Portuguese

PubMed Central

Sousa, Bruno

2013-01-01

Objective To translate into Portuguese and evaluate the measuring properties of the Sunderland Scale and the Cubbin & Jackson Revised Scale, which are instruments for evaluating the risk of developing pressure ulcers during intensive care. Methods This study included the process of translation and adaptation of the scales to the Portuguese language, as well as the validation of these tools. To assess the reliability, Cronbach alpha values of 0.702 to 0.708 were identified for the Sunderland Scale and the Cubbin & Jackson Revised Scale, respectively. The validation criteria (predictive) were performed comparatively with the Braden Scale (gold standard), and the main measurements evaluated were sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve, which were calculated based on cutoff points. Results The Sunderland Scale exhibited 60% sensitivity, 86.7% specificity, 47.4% positive predictive value, 91.5% negative predictive value, and 0.86 for the area under the curve. The Cubbin & Jackson Revised Scale exhibited 73.3% sensitivity, 86.7% specificity, 52.4% positive predictive value, 94.2% negative predictive value, and 0.91 for the area under the curve. The Braden scale exhibited 100% sensitivity, 5.3% specificity, 17.4% positive predictive value, 100% negative predictive value, and 0.72 for the area under the curve. Conclusions Both tools demonstrated reliability and validity for this sample. The Cubbin & Jackson Revised Scale yielded better predictive values for the development of pressure ulcers during intensive care. PMID:23917975

The value of mainstreaming human rights into health impact assessment.

PubMed

MacNaughton, Gillian; Forman, Lisa

2014-09-26

Health impact assessment (HIA) is increasingly being used to predict the health and social impacts of domestic and global laws, policies and programs. In a comprehensive review of HIA practice in 2012, the authors indicated that, given the diverse range of HIA practice, there is an immediate need to reconsider the governing values and standards for HIA implementation [1]. This article responds to this call for governing values and standards for HIA. It proposes that international human rights standards be integrated into HIA to provide a universal value system backed up by international and domestic laws and mechanisms of accountability. The idea of mainstreaming human rights into HIA is illustrated with the example of impact assessments that have been carried out to predict the potential effects of intellectual property rights in international trade agreements on the availability and affordability of medicines. The article concludes by recommending international human rights standards as a legal and ethical framework for HIA that will enhance the universal values of nondiscrimination, participation, transparency and accountability and bring legitimacy and coherence to HIA practice as well.

The Value of Mainstreaming Human Rights into Health Impact Assessment

PubMed Central

MacNaughton, Gillian; Forman, Lisa

2014-01-01

Health impact assessment (HIA) is increasingly being used to predict the health and social impacts of domestic and global laws, policies and programs. In a comprehensive review of HIA practice in 2012, the authors indicated that, given the diverse range of HIA practice, there is an immediate need to reconsider the governing values and standards for HIA implementation [1]. This article responds to this call for governing values and standards for HIA. It proposes that international human rights standards be integrated into HIA to provide a universal value system backed up by international and domestic laws and mechanisms of accountability. The idea of mainstreaming human rights into HIA is illustrated with the example of impact assessments that have been carried out to predict the potential effects of intellectual property rights in international trade agreements on the availability and affordability of medicines. The article concludes by recommending international human rights standards as a legal and ethical framework for HIA that will enhance the universal values of nondiscrimination, participation, transparency and accountability and bring legitimacy and coherence to HIA practice as well. PMID:25264683

Research on strength attenuation law of concrete in freezing - thawing environment

NASA Astrophysics Data System (ADS)

Xiao, qianhui; Cao, zhiyuan; Li, qiang

2018-03-01

By rapid freezing and thawing method, the experiments of concrete have been 300 freeze-thaw cycles specimens in the water. The cubic compression strength value under different freeze-thaw cycles was measured. By analyzing the test results, the water-binder ratio of the concrete under freeze-thaw environments, fly ash and air entraining agent is selected dosage recommendations. The exponential attenuation prediction model and life prediction model of compression strength of concrete under freezing-thawing cycles considering the factors of water-binder ratio, fly ash content and air-entraining agent dosage were established. The model provides the basis for predicting the durability life of concrete under freezing-thawing environment. It also provides experimental basis and references for further research on concrete structures with antifreeze requirements.

The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer.

PubMed

van Rossum, Peter S N; Fried, David V; Zhang, Lifei; Hofstetter, Wayne L; van Vulpen, Marco; Meijer, Gert J; Court, Laurence E; Lin, Steven H

2016-05-01

A reliable prediction of a pathologic complete response (pathCR) to chemoradiotherapy before surgery for esophageal cancer would enable investigators to study the feasibility and outcome of an organ-preserving strategy after chemoradiotherapy. So far no clinical parameters or diagnostic studies are able to accurately predict which patients will achieve a pathCR. The aim of this study was to determine whether subjective and quantitative assessment of baseline and postchemoradiation (18)F-FDG PET can improve the accuracy of predicting pathCR to preoperative chemoradiotherapy in esophageal cancer beyond clinical predictors. This retrospective study was approved by the institutional review board, and the need for written informed consent was waived. Clinical parameters along with subjective and quantitative parameters from baseline and postchemoradiation (18)F-FDG PET were derived from 217 esophageal adenocarcinoma patients who underwent chemoradiotherapy followed by surgery. The associations between these parameters and pathCR were studied in univariable and multivariable logistic regression analysis. Four prediction models were constructed and internally validated using bootstrapping to study the incremental predictive values of subjective assessment of (18)F-FDG PET, conventional quantitative metabolic features, and comprehensive (18)F-FDG PET texture/geometry features, respectively. The clinical benefit of (18)F-FDG PET was determined using decision-curve analysis. A pathCR was found in 59 (27%) patients. A clinical prediction model (corrected c-index, 0.67) was improved by adding (18)F-FDG PET-based subjective assessment of response (corrected c-index, 0.72). This latter model was slightly improved by the addition of 1 conventional quantitative metabolic feature only (i.e., postchemoradiation total lesion glycolysis; corrected c-index, 0.73), and even more by subsequently adding 4 comprehensive (18)F-FDG PET texture/geometry features (corrected c-index, 0.77). However, at a decision threshold of 0.9 or higher, representing a clinically relevant predictive value for pathCR at which one may be willing to omit surgery, there was no clear incremental value. Subjective and quantitative assessment of (18)F-FDG PET provides statistical incremental value for predicting pathCR after preoperative chemoradiotherapy in esophageal cancer. However, the discriminatory improvement beyond clinical predictors does not translate into a clinically relevant benefit that could change decision making. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Dark matter and MOND dynamical models of the massive spiral galaxy NGC 2841

NASA Astrophysics Data System (ADS)

Samurović, S.; Vudragović, A.; Jovanović, M.

2015-08-01

We study dynamical models of the massive spiral galaxy NGC 2841 using both the Newtonian models with Navarro-Frenk-White (NFW) and isothermal dark haloes, as well as various MOND (MOdified Newtonian Dynamics) models. We use the observations coming from several publicly available data bases: we use radio data, near-infrared photometry as well as spectroscopic observations. In our models, we find that both tested Newtonian dark matter approaches can successfully fit the observed rotational curve of NGC 2841. The three tested MOND models (standard, simple and, for the first time applied to another spiral galaxy than the Milky Way, Bekenstein's toy model) provide fits of the observed rotational curve with various degrees of success: the best result was obtained with the standard MOND model. For both approaches, Newtonian and MOND, the values of the mass-to-light ratios of the bulge are consistent with the predictions from the stellar population synthesis (SPS) based on the Salpeter initial mass function (IMF). Also, for Newtonian and simple and standard MOND models, the estimated stellar mass-to-light ratios of the disc agree with the predictions from the SPS models based on the Kroupa IMF, whereas the toy MOND model provides too low a value of the stellar mass-to-light ratio, incompatible with the predictions of the tested SPS models. In all our MOND models, we vary the distance to NGC 2841, and our best-fitting standard and toy models use the values higher than the Cepheid-based distance to the galaxy NGC 2841, and the best-fitting simple MOND model is based on the lower value of the distance. The best-fitting NFW model is inconsistent with the predictions of the Λ cold dark matter cosmology, because the inferred concentration index is too high for the established virial mass.

Predicting who will major in a science discipline: Expectancy-value theory as part of an ecological model for studying academic communities

NASA Astrophysics Data System (ADS)

Sullins, Ellen S.; Hernandez, Delia; Fuller, Carol; Shiro Tashiro, Jay

Research on factors that shape recruitment and retention in undergraduate science majors currently is highly fragmented and in need of an integrative research framework. Such a framework should incorporate analyses of the various levels of organization that characterize academic communities (i.e., the broad institutional level, the departmental level, and the student level), and should also provide ways to study the interactions occurring within and between these structural levels. We propose that academic communities are analogous to ecosystems, and that the research paradigms of modern community ecology can provide the necessary framework, as well as new and innovative approaches to a very complex area. This article also presents the results of a pilot study that demonstrates the promise of this approach at the student level. We administered a questionnaire based on expectancy-value theory to undergraduates enrolled in introductory biology courses. Itself an integrative approach, expectancy-value theory views achievement-related behavior as a joint function of the person's expectancy of success in the behavior and the subjective value placed on such success. Our results indicated: (a) significant gender differences in the underlying factor structures of expectations and values related to the discipline of biology, (b) expectancy-value factors significantly distinguished biology majors from nonmajors, and (c) expectancy-value factors significantly predicted students' intent to enroll in future biology courses. We explore the expectancy-value framework as an operationally integrative framework in our ecological model for studying academic communities, especially in the context of assessing the underrepresentation of women and minorities in the sciences. Future research directions as well as practical implications are also discussed.

[Research progress on the clinical value of Ki-67 in breast cancer and its cut-off definition].

PubMed

Chen, Qing; Wu, Kejin

2015-08-01

Ki-67 has an important application value in clinical practice. However, it is still a little tough in clinical application because of the debate on the cut-off definition of Ki-67 index. This review summarizes most studies on the prognostic and predictive value of Ki-67, analyzes the reasons for the discrepancies among the studies cited, and presents the necessity and clinical significance of scientifically defining the cut-off of Ki-67 index, providing a theoretical basis for Ki-67 in clinical application.

SU-F-E-19: A Novel Method for TrueBeam Jaw Calibration

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

Corns, R; Zhao, Y; Huang, V

2016-06-15

Purpose: A simple jaw calibration method is proposed for Varian TrueBeam using an EPID-Encoder combination that gives accurate fields sizes and a homogeneous junction dose. This benefits clinical applications such as mono-isocentric half-beam block breast cancer or head and neck cancer treatment with junction/field matching. Methods: We use EPID imager with pixel size 0.392 mm × 0.392 mm to determine the radiation jaw position as measured from radio-opaque markers aligned with the crosshair. We acquire two images with different symmetric field sizes and record each individual jaw encoder values. A linear relationship between each jaw’s position and its encoder valuemore » is established, from which we predict the encoder values that produce the jaw positions required by TrueBeam’s calibration procedure. During TrueBeam’s jaw calibration procedure, we move the jaw with the pendant to set the jaw into position using the predicted encoder value. The overall accuracy is under 0.1 mm. Results: Our in-house software analyses images and provides sub-pixel accuracy to determine field centre and radiation edges (50% dose of the profile). We verified the TrueBeam encoder provides a reliable linear relationship for each individual jaw position (R{sup 2}>0.9999) from which the encoder values necessary to set jaw calibration points (1 cm and 19 cm) are predicted. Junction matching dose inhomogeneities were improved from >±20% to <±6% using this new calibration protocol. However, one technical challenge exists for junction matching, if the collimator walkout is large. Conclusion: Our new TrueBeam jaw calibration method can systematically calibrate the jaws to crosshair within sub-pixel accuracy and provides both good junction doses and field sizes. This method does not compensate for a larger collimator walkout, but can be used as the underlying foundation for addressing the walkout issue.« less

Diagnostic value of "dysphagia limit" for neurogenic dysphagia: 17 years of experience in 1278 adults.

PubMed

Aydogdu, Ibrahim; Kiylioglu, Nefati; Tarlaci, Sultan; Tanriverdi, Zeynep; Alpaydin, Sezin; Acarer, Ahmet; Baysal, Leyla; Arpaci, Esra; Yuceyar, Nur; Secil, Yaprak; Ozdemirkiran, Tolga; Ertekin, Cumhur

2015-03-01

Neurogenic dysphagia (ND) is a prevalent condition that accounts for significant mortality and morbidity worldwide. Screening and follow-up are critical for early diagnosis and management which can mitigate its complications and be cost-saving. The aims of this study are to provide a comprehensive investigation of the dysphagia limit (DL) in a large diverse cohort and to provide a longitudinal assessment of dysphagia in a subset of subjects. We developed a quantitative and noninvasive method for objective assessment of dysphagia by using laryngeal sensor and submental electromyography. DL is the volume at which second or more swallows become necessary to swallow the whole amount of bolus. This study represents 17 years experience with the DL approach in assessing ND in a cohort of 1278 adult subjects consisting of 292 healthy controls, 784 patients with dysphagia, and 202 patients without dysphagia. A total of 192 of all patients were also reevaluated longitudinally over a period of 1-19 months. DL has 92% sensitivity, 91% specificity, 94% positive predictive value, and 88% negative predictive value with an accuracy of 0.92. Patients with ALS, stroke, and movement disorders have the highest sensitivity (85-97%) and positive predictive value (90-99%). The clinical severity of dysphagia has significant negative correlation with DL (r=-0.67, p<0.0001). We propose the DL as a reliable, quick, noninvasive, quantitative test to detect and follow both clinical and subclinical dysphagia and it can be performed in an EMG laboratory. Our study provides specific quantitative features of DL test that can be readily utilized by the neurologic community and nominates DL as an objective and robust method to evaluate dysphagia in a wide range of neurologic conditions. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Quantifying confidence in density functional theory predictions of magnetic ground states

NASA Astrophysics Data System (ADS)

Houchins, Gregory; Viswanathan, Venkatasubramanian

2017-10-01

Density functional theory (DFT) simulations, at the generalized gradient approximation (GGA) level, are being routinely used for material discovery based on high-throughput descriptor-based searches. The success of descriptor-based material design relies on eliminating bad candidates and keeping good candidates for further investigation. While DFT has been widely successfully for the former, oftentimes good candidates are lost due to the uncertainty associated with the DFT-predicted material properties. Uncertainty associated with DFT predictions has gained prominence and has led to the development of exchange correlation functionals that have built-in error estimation capability. In this work, we demonstrate the use of built-in error estimation capabilities within the BEEF-vdW exchange correlation functional for quantifying the uncertainty associated with the magnetic ground state of solids. We demonstrate this approach by calculating the uncertainty estimate for the energy difference between the different magnetic states of solids and compare them against a range of GGA exchange correlation functionals as is done in many first-principles calculations of materials. We show that this estimate reasonably bounds the range of values obtained with the different GGA functionals. The estimate is determined as a postprocessing step and thus provides a computationally robust and systematic approach to estimating uncertainty associated with predictions of magnetic ground states. We define a confidence value (c-value) that incorporates all calculated magnetic states in order to quantify the concurrence of the prediction at the GGA level and argue that predictions of magnetic ground states from GGA level DFT is incomplete without an accompanying c-value. We demonstrate the utility of this method using a case study of Li-ion and Na-ion cathode materials and the c-value metric correctly identifies that GGA-level DFT will have low predictability for NaFePO4F . Further, there needs to be a systematic test of a collection of plausible magnetic states, especially in identifying antiferromagnetic (AFM) ground states. We believe that our approach of estimating uncertainty can be readily incorporated into all high-throughput computational material discovery efforts and this will lead to a dramatic increase in the likelihood of finding good candidate materials.

Task value profiles across subjects and aspirations to physical and IT-related sciences in the United States and Finland.

PubMed

Chow, Angela; Eccles, Jacquelynne S; Salmela-Aro, Katariina

2012-11-01

Two independent studies were conducted to extend previous research by examining the associations between task value priority patterns across school subjects and aspirations toward the physical and information technology- (IT-) related sciences. Study 1 measured task values of a sample of 10th graders in the United States (N = 249) across (a) physics and chemistry, (b) math, and (c) English. Study 2 measured task values of a sample of students in the second year of high school in Finland (N = 351) across (a) math and science, (b) Finnish, and (c) the arts and physical education. In both studies, students were classified into groups according to how they ranked math and science in relation to the other subjects. Regression analyses indicated that task value group membership significantly predicted subsequent aspirations toward physical and IT-related sciences measured 1-2 years later. The task value groups who placed the highest priority on math and science were significantly more likely to aspire to physical and IT-related sciences than were the other groups. These findings provide support for the theoretical assumption regarding the predictive role of intraindividual hierarchical patterns of task values for subsequent preferences and choices suggested by the Eccles [Parsons] (1983) expectancy-value model.

External validation of the NUn score for predicting anastomotic leakage after oesophageal resection.

PubMed

Paireder, Matthias; Jomrich, Gerd; Asari, Reza; Kristo, Ivan; Gleiss, Andreas; Preusser, Matthias; Schoppmann, Sebastian F

2017-08-29

Early detection of anastomotic leakage (AL) after oesophageal resection for malignancy is crucial. This retrospective study validates a risk score, predicting AL, which includes C-reactive protein, albumin and white cell count in patients undergoing oesophageal resection between 2003 and 2014. For validation of the NUn score a receiver operating characteristic (ROC) curve is estimated. Area under the ROC curve (AUC) is reported with 95% confidence interval (CI). Among 258 patients (79.5% male) 32 patients showed signs of anastomotic leakage (12.4%). NUn score in our data has a median of 9.3 (range 6.2-17.6). The odds ratio for AL was 1.31 (CI 1.03-1.67; p = 0.028). AUC for AL was 0.59 (CI 0.47-0.72). Using the original cutoff value of 10, the sensitivity was 45.2% an the specificity was 73.8%. This results in a positive predictive value of 19.4% and a negative predictive value of 90.6%. The proportion of variation in AL occurrence, which is explained by the NUn score, was 2.5% (PEV = 0.025). This study provides evidence for an external validation of a simple risk score for AL after oesophageal resection. In this cohort, the NUn score is not useful due to its poor discrimination.

Spatial epidemiology of bovine tuberculosis in Mexico.

PubMed

Martínez, Horacio Zendejas; Suazo, Feliciano Milián; Cuador Gil, José Quintín; Bello, Gustavo Cruz; Anaya Escalera, Ana María; Márquez, Gabriel Huitrón; Casanova, Leticia García

2007-01-01

The purpose of this study was to use geographic information systems (GIS) and geo-statistical methods of ordinary kriging to predict the prevalence and distribution of bovine tuberculosis (TB) in Jalisco, Mexico. A random sample of 2 287 herds selected from a set of 48 766 was used for the analysis. Spatial location of herds was obtained by either a personal global positioning system (GPS), a database from the Instituto Nacional de Estadìstica Geografìa e Informàtica (INEGI) or Google Earth. Information on TB prevalence was provided by the Jalisco Commission for the Control and Eradication of Tuberculosis (COEETB). Prediction of TB was obtained using ordinary kriging in the geostatistical analyst module in ArcView8. A predicted high prevalence area of TB matching the distribution of dairy cattle was observed. This prediction was in agreement with the prevalence calculated on the total 48 766 herds. Validation was performed taking estimated values of TB prevalence at each municipality, extracted from the kriging surface and then compared with the real prevalence values using a correlation test, giving a value of 0.78, indicating that GIS and kriging are reliable tools for the estimation of TB distribution based on a random sample. This resulted in a significant savings of resources.

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

Analysis and prediction of operating vehicle load effects on Highway bridges under the weight charge policy

NASA Astrophysics Data System (ADS)

Huang, Haiyun; Zhang, Junping; Li, Yonghe

2018-05-01

Under the weight charge policy, the weigh in motion data at a toll station on the Jing-Zhu Expressway were collected. The statistic analysis of vehicle load data was carried out. For calculating the operating vehicle load effects on bridges, by Monte Carlo method used to generate random traffic flow and influence line loading method, the maximum bending moment effect of simple supported beams were obtained. The extreme value I distribution and normal distribution were used to simulate the distribution of the maximum bending moment effect. By the extrapolation of Rice formula and the extreme value I distribution, the predicted values of the maximum load effects were obtained. By comparing with vehicle load effect according to current specification, some references were provided for the management of the operating vehicles and the revision of the bridge specifications.

Anomalous electrical conductivity of nanoscale colloidal suspensions.

PubMed

Chakraborty, Suman; Padhy, Sourav

2008-10-28

The electrical conductivity of colloidal suspensions containing nanoscale conducting particles is nontrivially related to the particle volume fraction and the electrical double layer thickness. Classical electrochemical models, however, tend to grossly overpredict the pertinent effective electrical conductivity values, as compared to those obtained under experimental conditions. We attempt to address this discrepancy by appealing to the complex interconnection between the aggregation kinetics of the nanoscale particles and the electrodynamics within the double layer. In particular, we model the consequent alterations in the effective electrophoretic mobility values of the suspension by addressing the fundamentals of agglomeration-deagglomeration mechanisms through the pertinent variations in the effective particulate dimensions, solid fractions, as well as the equivalent suspension viscosity. The consequent alterations in the electrical conductivity values provide a substantially improved prediction of the corresponding experimental findings and explain the apparent anomalous behavior predicted by the classical theoretical postulates.

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.

Epigenome-wide cross-tissue predictive modeling and comparison of cord blood and placental methylation in a birth cohort

PubMed Central

De Carli, Margherita M; Baccarelli, Andrea A; Trevisi, Letizia; Pantic, Ivan; Brennan, Kasey JM; Hacker, Michele R; Loudon, Holly; Brunst, Kelly J; Wright, Robert O; Wright, Rosalind J; Just, Allan C

2017-01-01

Aim: We compared predictive modeling approaches to estimate placental methylation using cord blood methylation. Materials & methods: We performed locus-specific methylation prediction using both linear regression and support vector machine models with 174 matched pairs of 450k arrays. Results: At most CpG sites, both approaches gave poor predictions in spite of a misleading improvement in array-wide correlation. CpG islands and gene promoters, but not enhancers, were the genomic contexts where the correlation between measured and predicted placental methylation levels achieved higher values. We provide a list of 714 sites where both models achieved an R2 ≥0.75. Conclusion: The present study indicates the need for caution in interpreting cross-tissue predictions. Few methylation sites can be predicted between cord blood and placenta. PMID:28234020

The accuracy of the SenseWear Pro3 and the activPAL3 Micro devices for measurement of energy expenditure.

PubMed

Powell, Cormac; Carson, Brian P; Dowd, Kieran P; Donnelly, Alan E

2016-09-21

Activity monitors such as the SenseWear Pro3 (SWP3) and the activPAL3 Micro (aP 3 M) are regularly used by researchers and practitioners to provide estimates of the metabolic cost (METs) of activities in free-living settings. The purpose of this study is to examine the accuracy of the MET predictions from the SWP3 and the aP 3 M compared to the criterion standard MET values from indirect calorimetry. Fifty-six participants (mean age: 39.9 (±11.5), 25M/31F) performed eight activities (four daily living, three ambulatory and one cycling), while simultaneously wearing a SWP3, aP 3 M and the Cosmed K4B 2 (K4B 2 ) mobile metabolic unit. Paired samples T-tests were used to examine differences between device predicted METs and criterion METs. Bland-Altman plots were constructed to examine the mean bias and limits of agreement for predicted METs compared to criterion METs. SWP3 predicted MET values were significantly different from the K4B 2 for each activity (p  ⩽  0.004), excluding sweeping (p  =  0.122). aP 3 M predicted MET values were significantly different (p  <  0.001) from the K4B 2 for each activity. When examining the activities collectively, both devices underestimated activity intensity (0.20 METs (SWP3), 0.95 METs (aP 3 M)). The greatest mean bias for the SWP3 was for cycling (-3.25 METs), with jogging (-5.16 METs) producing the greatest mean bias for the aP 3 M. All of the activities (excluding SWP3 sweeping) were significantly different from the criterion measure. Although the SWP3 predicted METs are more accurate than their aP 3 M equivalent, the predicted MET values from both devices are significantly different from the criterion measure for the majority of activities.

Predicting effects of environmental change on river inflows to Tillamook Bay, Oregon

EPA Science Inventory

Estuarine river watersheds provide valued ecosystem services to their surrounding communities including drinking water, fish habitat, and regulation of estuarine water quality. However, the provisioning of these services can be affected by changes in the quantity and quality of ...

Overview of T.E.S.T. (Toxicity Estimation Software Tool)

EPA Science Inventory

This talk provides an overview of T.E.S.T. (Toxicity Estimation Software Tool). T.E.S.T. predicts toxicity values and physical properties using a variety of different QSAR (quantitative structure activity relationship) approaches including hierarchical clustering, group contribut...

77 FR 71191 - 2012 Recreational Water Quality Criteria

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-29

... Criteria AGENCY: Environmental Protection Agency (EPA). ACTION: Notice of availability of the 2012... for beach monitoring, quantitative polymerase chain reaction (qPCR), for the detection of enterococci... managing recreational waters, such as predictive modeling; the EPA is providing a beach action value for...

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate

PubMed Central

2013-01-01

Background The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Methods and findings Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China. The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Conclusions Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships. PMID:23497145

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate.

PubMed

Yang, Qingsheng; Mwenda, Kevin M; Ge, Miao

2013-03-12

The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China.The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships.

Electronic alerts and clinician turnover: the influence of user acceptance.

PubMed

Hysong, Sylvia J; Spitzmuller, Christiane; Espadas, Donna; Sittig, Dean F; Singh, Hardeep

2014-11-01

Use of certain components of electronic health records (EHRs), such as EHR-based alerting systems (EASs), might reduce provider satisfaction, a strong precursor to turnover. We examined the impact of factors likely to influence providers' acceptance of an alerting system, designed to facilitate electronic communication in outpatient settings, on provider satisfaction, intentions to quit, and turnover. We conducted a cross-sectional Web-based survey of EAS-related practices from a nationwide sample of primary care providers (PCPs) practicing at Department of Veterans Affairs (VA) medical facilities. Of 5001 invited VA PCPs, 2590 completed the survey. We relied on Venkatesh's Unified Theory of Acceptance and Use of Technology to create survey measures of 4 factors likely to impact user acceptance of EAS: supportive norms, monitoring/ feedback, training, and providers' perceptions of the value (PPOV) of EASs to provider effectiveness. Facility-level PCP turnover was measured via the VA's Service Support Center Human Resources Cube. Hypotheses were tested using structural equation modeling. After accounting for intercorrelations among predictors, monitoring/feedback regarding EASs significantly predicted intention to quit (b = 0.30, P < .01), and PPOV of EASs predicted both overall provider satisfaction (b = 0.58, P < .01) and facility-level provider turnover levels (b = -0.19, P < .05), all without relying on any intervening mechanisms. Design, implementation, and use of EASs might impact provider satisfaction and retention. Institutions should consider strategies to help providers perceive greater value in these clinical tools.

Comparison of Primary Models to Predict Microbial Growth by the Plate Count and Absorbance Methods.

PubMed

Pla, María-Leonor; Oltra, Sandra; Esteban, María-Dolores; Andreu, Santiago; Palop, Alfredo

2015-01-01

The selection of a primary model to describe microbial growth in predictive food microbiology often appears to be subjective. The objective of this research was to check the performance of different mathematical models in predicting growth parameters, both by absorbance and plate count methods. For this purpose, growth curves of three different microorganisms (Bacillus cereus, Listeria monocytogenes, and Escherichia coli) grown under the same conditions, but with different initial concentrations each, were analysed. When measuring the microbial growth of each microorganism by optical density, almost all models provided quite high goodness of fit (r(2) > 0.93) for all growth curves. The growth rate remained approximately constant for all growth curves of each microorganism, when considering one growth model, but differences were found among models. Three-phase linear model provided the lowest variation for growth rate values for all three microorganisms. Baranyi model gave a variation marginally higher, despite a much better overall fitting. When measuring the microbial growth by plate count, similar results were obtained. These results provide insight into predictive microbiology and will help food microbiologists and researchers to choose the proper primary growth predictive model.

18O/16O in CO2 evolved from goethite during some unusually rapid solid state α-FeOOH to α-Fe2O3 phase transitions: Test of an exchange model for possible use in oxygen isotope analyses of goethite

NASA Astrophysics Data System (ADS)

Yapp, Crayton J.

2015-12-01

The initial ∼60% of an isothermal vacuum dehydration of goethite can commonly be approximated by first order kinetics. Also, natural goethites contain small amounts of an Fe(CO3)OH component in apparent solid solution. The 18O/16O of CO2 evolved from the Fe(CO3)OH during isothermal vacuum dehydrations is related to the 18O/16O of the goethite by an apparent fractionation factor (αapp) that is, in turn, correlated with a first order rate constant, |m|. A kinetic exchange model predicts that αapp should decrease as |m| increases for a range of |m| that corresponds to relatively slow rates of dehydration. This pattern has been observed in published results. In contrast, for rapid rates of dehydration, αapp is predicted to increase with increasing |m|. Isothermal vacuum dehydrations of two natural goethites had unusually large values of |m| and provided serendipitous tests of this rapid-rate prediction. For these experiments, the measured values of αapp were consistent with patterns of variation predicted by the model. This allowed an estimate of the activation energy (E2) of a model parameter, K2, which is the rate constant for oxygen isotope exchange between CO2 and H2O during the solid-state goethite to hematite phase transition. The estimated value of E2 is only ∼9 kJ/mol. Heterogeneous catalysis tends to decrease the activation energies of gas reactions. Consequently, the inferred value of E2 suggests that goethite and/or hematite catalyze oxygen isotope exchange between CO2 and H2O during the solid-state phase change. Yield, δ13C, and δ18O values are routinely measured for increments of CO2 evolved from the Fe(CO3)OH component during isothermal vacuum dehydration of goethite. Model-predicted values of αapp can be combined with plateau δ18O values of the evolved CO2 to estimate the δ18O of the goethite with a less than optimal, but potentially useful, precision of about ±0.8‰. Therefore, a single analytical procedure (incremental dehydration) applied to one aliquot of a sample could provide not only δ13C and mole fractions (X) of the Fe(CO3)OH component, but also hydrogen yield, δD, and the approximate δ18O value of the goethite. Recovery of multiple types of geochemical data from a single aliquot would be particularly useful if the amount of sample available for analysis were limited. Also, the method could be used to estimate the δ18O value of goethite in mixtures of minerals not amenable to selective dissolution - e.g., goethite admixed with hematite.

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

Magnitude Estimation for Large Earthquakes from Borehole Recordings

NASA Astrophysics Data System (ADS)

Eshaghi, A.; Tiampo, K. F.; Ghofrani, H.; Atkinson, G.

2012-12-01

We present a simple and fast method for magnitude determination technique for earthquake and tsunami early warning systems based on strong ground motion prediction equations (GMPEs) in Japan. This method incorporates borehole strong motion records provided by the Kiban Kyoshin network (KiK-net) stations. We analyzed strong ground motion data from large magnitude earthquakes (5.0 ≤ M ≤ 8.1) with focal depths < 50 km and epicentral distances of up to 400 km from 1996 to 2010. Using both peak ground acceleration (PGA) and peak ground velocity (PGV) we derived GMPEs in Japan. These GMPEs are used as the basis for regional magnitude determination. Predicted magnitudes from PGA values (Mpga) and predicted magnitudes from PGV values (Mpgv) were defined. Mpga and Mpgv strongly correlate with the moment magnitude of the event, provided sufficient records for each event are available. The results show that Mpgv has a smaller standard deviation in comparison to Mpga when compared with the estimated magnitudes and provides a more accurate early assessment of earthquake magnitude. We test this new method to estimate the magnitude of the 2011 Tohoku earthquake and we present the results of this estimation. PGA and PGV from borehole recordings allow us to estimate the magnitude of this event 156 s and 105 s after the earthquake onset, respectively. We demonstrate that the incorporation of borehole strong ground-motion records immediately available after the occurrence of large earthquakes significantly increases the accuracy of earthquake magnitude estimation and the associated improvement in earthquake and tsunami early warning systems performance. Moment magnitude versus predicted magnitude (Mpga and Mpgv).

Predicting posttraumatic stress disorder in children and parents following accidental child injury: evaluation of the Screening Tool for Early Predictors of Posttraumatic Stress Disorder (STEPP).

PubMed

van Meijel, Els P M; Gigengack, Maj R; Verlinden, Eva; Opmeer, Brent C; Heij, Hugo A; Goslings, J Carel; Bloemers, Frank W; Luitse, Jan S K; Boer, Frits; Grootenhuis, Martha A; Lindauer, Ramón J L

2015-05-12

Children and their parents are at risk of posttraumatic stress disorder (PTSD) following injury due to pediatric accidental trauma. Screening could help predict those at greatest risk and provide an opportunity for monitoring so that early intervention may be provided. The purpose of this study was to evaluate the Screening Tool for Early Predictors of Posttraumatic Stress Disorder (STEPP) in a mixed-trauma sample in a non-English speaking country (the Netherlands). Children aged 8-18 and one of their parents were recruited in two academic level I trauma centers. The STEPP was assessed in 161 children (mean age 13.9 years) and 156 parents within one week of the accident. Three months later, clinical diagnoses and symptoms of PTSD were assessed in 147 children and 135 parents. We used the Anxiety Disorders Interview Schedule for DSM-IV - Child and Parent version, the Children's Revised Impact of Event Scale and the Impact of Event Scale-Revised. Receiver Operating Characteristic analyses were performed to estimate the Areas Under the Curve as a measure of performance and to determine the optimal cut-off score in our sample. Sensitivity, specificity, positive and negative predictive values were calculated. The aim was to maximize both sensitivity and negative predictive values. PTSD was diagnosed in 12% of the children; 10% of their parents scored above the cut-off point for PTSD. At the originally recommended cut-off scores (4 for children, 3 for parents), the sensitivity in our sample was 41% for children and 54% for parents. Negative predictive values were 92% for both groups. Adjusting the cut-off scores to 2 improved sensitivity to 82% for children and 92% for parents, with negative predictive values of 92% and 96%, respectively. With adjusted cut-off scores, the STEPP performed well: 82% of the children and 92% of the parents with a subsequent positive diagnosis were identified correctly. Special attention in the screening procedure is required because of a high rate of false positives. The STEPP appears to be a valid and useful instrument that can be used in the Netherlands as a first screening method in stepped psychotrauma care following accidents.

Spectrum sensitivity, energy yield, and revenue prediction of PV and CPV modules

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

Kinsey, Geoffrey S., E-mail: Geoffrey.kinsey@ee.doe.gov

2015-09-28

Impact on module performance of spectral irradiance variation has been determined for III-V multijunctions compared against the four most common flat-plate module types (cadmium telluride, multicrystalline silicon, copper indium gallium selenide, and monocrystalline silicon. Hour-by-hour representative spectra were generated using atmospheric variables for Albuquerque, New Mexico, USA. Convolution with published values for external quantum efficiency gave the predicted current output. When combined with specifications of commercial PV modules, energy yield and revenue were predicted. This approach provides a means for optimizing PV module design based on various site-specific temporal variables.

Spectrum sensitivity, energy yield, and revenue prediction of PV and CPV modules

NASA Astrophysics Data System (ADS)

Kinsey, Geoffrey S.

2015-09-01

Impact on module performance of spectral irradiance variation has been determined for III-V multijunctions compared against the four most common flat-plate module types (cadmium telluride, multicrystalline silicon, copper indium gallium selenide, and monocrystalline silicon. Hour-by-hour representative spectra were generated using atmospheric variables for Albuquerque, New Mexico, USA. Convolution with published values for external quantum efficiency gave the predicted current output. When combined with specifications of commercial PV modules, energy yield and revenue were predicted. This approach provides a means for optimizing PV module design based on various site-specific temporal variables.

A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

NASA Astrophysics Data System (ADS)

Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

2018-01-01

This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

PubMed Central

Cheng, Ningtao; Wu, Leihong; Cheng, Yiyu

2013-01-01

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability. PMID:23861920

Prediction of frozen food properties during freezing using product composition.

PubMed

Boonsupthip, W; Heldman, D R

2007-06-01

Frozen water fraction (FWF), as a function of temperature, is an important parameter for use in the design of food freezing processes. An FWF-prediction model, based on concentrations and molecular weights of specific product components, has been developed. Published food composition data were used to determine the identity and composition of key components. The model proposed in this investigation had been verified using published experimental FWF data and initial freezing temperature data, and by comparison to outputs from previously published models. It was found that specific food components with significant influence on freezing temperature depression of food products included low molecular weight water-soluble compounds with molality of 50 micromol per 100 g food or higher. Based on an analysis of 200 high-moisture food products, nearly 45% of the experimental initial freezing temperature data were within an absolute difference (AD) of +/- 0.15 degrees C and standard error (SE) of +/- 0.65 degrees C when compared to values predicted by the proposed model. The predicted relationship between temperature and FWF for all analyzed food products provided close agreements with experimental data (+/- 0.06 SE). The proposed model provided similar prediction capability for high- and intermediate-moisture food products. In addition, the proposed model provided statistically better prediction of initial freezing temperature and FWF than previous published models.

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.

Neuronal Reward and Decision Signals: From Theories to Data

PubMed Central

Schultz, Wolfram

2015-01-01

Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act. PMID:26109341

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis.

PubMed

Lee, Byeong-Ju; Kim, Hye-Youn; Lim, Sa Rang; Huang, Linfang; Choi, Hyung-Kyoon

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis

PubMed Central

Lim, Sa Rang; Huang, Linfang

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values. PMID:29049369

Conjunctivitis and Total IgE in Lacrimal Fluid: Lacrytest Screening

PubMed Central

Monzón, Susana; Arrondo, Elena; Bartra, Joan; Torres, Ferran; Basagaña, María; San Miguel, M. del Mar; Alonso, Rosario; Cisteró-Bahima, Anna

2009-01-01

Total tear IgE has been considered to play an important role in allergic conjunctivitis, and measurement has been considered useful for diagnosis. The aim of this study was to ascertain whether Lacrytest®, a new commercialised method to detect IgE levels in lacrimal fluid, could constitute a screening test for the diagnosis of allergic conjunctivitis. This was a cross-sectional study. Patients with seasonal and perennial allergic conjunctivitis, vernal keratoconjunctivitis and a control group were included. Clinical history, ophthalmic examination, skin prick test and conjunctival provocation test were obtained. Lacrytest® was later performed in all groups. Fifty-four patients were enrolled: thirty with IgE-mediated conjunctivitis and, nine with vernal keratoconjunctivitis and fifteen controls. Lacrytest® was negative in all controls, positive in 20% of the IgE-mediated conjunctivitis group and in 88.9% of the vernal keratoconjunctivitis group. Global statistically-significant differences were found among the three groups (P = .003). Sensitivity of the test in the IgE-mediated conjunctivitis group was 20%, specificity 100%, positive predictive value 100%, and negative predictive value 38.46%, while in VKC sensitivity was 88.88%, specificity 100%, positive predictive value 100%, and negative predictive value 93.75%. Our data confirm that this test is not useful for screening allergic conjunctivitis. Lacrytest®, while not providing any useful information to an allergist, could be helpful for ophthalmologists to confirm an IgE-mediated or VKC conjunctivitis. PMID:20975798

The probability of being identified as an outlier with commonly used funnel plot control limits for the standardised mortality ratio.

PubMed

Seaton, Sarah E; Manktelow, Bradley N

2012-07-16

Emphasis is increasingly being placed on the monitoring of clinical outcomes for health care providers. Funnel plots have become an increasingly popular graphical methodology used to identify potential outliers. It is assumed that a provider only displaying expected random variation (i.e. 'in-control') will fall outside a control limit with a known probability. In reality, the discrete count nature of these data, and the differing methods, can lead to true probabilities quite different from the nominal value. This paper investigates the true probability of an 'in control' provider falling outside control limits for the Standardised Mortality Ratio (SMR). The true probabilities of an 'in control' provider falling outside control limits for the SMR were calculated and compared for three commonly used limits: Wald confidence interval; 'exact' confidence interval; probability-based prediction interval. The probability of falling above the upper limit, or below the lower limit, often varied greatly from the nominal value. This was particularly apparent when there were a small number of expected events: for expected events ≤ 50 the median probability of an 'in-control' provider falling above the upper 95% limit was 0.0301 (Wald), 0.0121 ('exact'), 0.0201 (prediction). It is important to understand the properties and probability of being identified as an outlier by each of these different methods to aid the correct identification of poorly performing health care providers. The limits obtained using probability-based prediction limits have the most intuitive interpretation and their properties can be defined a priori. Funnel plot control limits for the SMR should not be based on confidence intervals.

Neural-network-based state of health diagnostics for an automated radioxenon sampler/analyzer

NASA Astrophysics Data System (ADS)

Keller, Paul E.; Kangas, Lars J.; Hayes, James C.; Schrom, Brian T.; Suarez, Reynold; Hubbard, Charles W.; Heimbigner, Tom R.; McIntyre, Justin I.

2009-05-01

Artificial neural networks (ANNs) are used to determine the state-of-health (SOH) of the Automated Radioxenon Analyzer/Sampler (ARSA). ARSA is a gas collection and analysis system used for non-proliferation monitoring in detecting radioxenon released during nuclear tests. SOH diagnostics are important for automated, unmanned sensing systems so that remote detection and identification of problems can be made without onsite staff. Both recurrent and feed-forward ANNs are presented. The recurrent ANN is trained to predict sensor values based on current valve states, which control air flow, so that with only valve states the normal SOH sensor values can be predicted. Deviation between modeled value and actual is an indication of a potential problem. The feed-forward ANN acts as a nonlinear version of principal components analysis (PCA) and is trained to replicate the normal SOH sensor values. Because of ARSA's complexity, this nonlinear PCA is better able to capture the relationships among the sensors than standard linear PCA and is applicable to both sensor validation and recognizing off-normal operating conditions. Both models provide valuable information to detect impending malfunctions before they occur to avoid unscheduled shutdown. Finally, the ability of ANN methods to predict the system state is presented.

Theoretical Study of pKa Values for Trivalent Rare-Earth Metal Cations in Aqueous Solution.

PubMed

Yu, Donghai; Du, Ruobing; Xiao, Ji-Chang; Xu, Shengming; Rong, Chunying; Liu, Shubin

2018-01-18

Molecular acidity of trivalent rare-earth metal cations in aqueous solution is an important factor dedicated to the efficiency of their extraction and separation processes. In this work, the aqueous acidity of these metal ions has been quantitatively investigated using a few theoretical approaches. Our computational results expressed in terms of pK a values agree well with the tetrad effect of trivalent rare-earth ions extensively reported in the extraction and separation of these elements. Strong linear relationships have been observed between the acidity and quantum electronic descriptors such as the molecular electrostatic potential on the acidic nucleus and the sum of the valence natural atomic orbitals energies of the dissociating proton. Making use of the predicted pK a values, we have also predicted the major ionic forms of these species in the aqueous environment with different pH values, which can be employed to rationalize the behavior difference of different rare-earth metal cations during the extraction process. Our present results should provide needed insights not only for the qualitatively understanding about the extraction and separation between yttrium and lanthanide elements but also for the prediction of novel and more efficient rare-earth metal extractants in the future.

Assessment of Forest Conservation Value Using a Species Distribution Model and Object-based Image Analysis

NASA Astrophysics Data System (ADS)

Jin, Y.; Lee, D. K.; Jeong, S. G.

2015-12-01

The ecological and social values of forests have recently been highlighted. Assessments of the biodiversity of forests, as well as their other ecological values, play an important role in regional and national conservation planning. The preservation of habitats is linked to the protection of biodiversity. For mapping habitats, species distribution model (SDM) is used for predicting suitable habitat of significant species, and such distribution modeling is increasingly being used in conservation science. However, the pixel-based analysis does not contain contextual or topological information. In order to provide more accurate habitats predictions, a continuous field view that assumes the real world is required. Here we analyze and compare at different scales, habitats of the Yellow Marten's(Martes Flavigula), which is a top predator and also an umbrella species in South Korea. The object-scale, which is a group of pixels that have similar spatial and spectral characteristics, and pixel-scale were used for SDM. Our analysis using the SDM at different scales suggests that object-scale analysis provides a superior representation of continuous habitat, and thus will be useful in forest conservation planning as well as for species habitat monitoring.

The changing psychology of culture in German-speaking countries: A Google Ngram study.

PubMed

Younes, Nadja; Reips, Ulf-Dietrich

2017-05-05

This article provides evidence for the long-term affiliation between ecological and cultural changes in German-speaking countries, based on the assumptions derived from social change and human development theory. Based on this theory, the increase in urbanisation, as a measure of ecological change, is associated with significant cultural changes of psychology. Whereas urbanisation is linked to greater individualistic values and materialistic attitudes, rural environments are strongly associated with collectivistic values like allegiance, prevalence of religion, and feelings of belonging and benevolence. Due to an increase in the German urbanisation rate over time, our study investigates whether Germany and the German-speaking countries around show the presumed changes in psychology. By using Google Books Ngram Viewer, we find that word frequencies, signifying individualistic (collectivistic) values, are positively (negatively) related to the urbanisation rate of Germany. Our results indicate that predictions about implications of an urbanising population for the psychology of culture hold true, supporting international universality of the social change and human development theory. Furthermore, we provide evidence for a predicted reversal for the time during and after World War II, reflecting Nazi propaganda and influence. © 2017 International Union of Psychological Science.

A "TNM" classification system for cancer pain: the Edmonton Classification System for Cancer Pain (ECS-CP).

PubMed

Fainsinger, Robin L; Nekolaichuk, Cheryl L

2008-06-01

The purpose of this paper is to provide an overview of the development of a "TNM" cancer pain classification system for advanced cancer patients, the Edmonton Classification System for Cancer Pain (ECS-CP). Until we have a common international language to discuss cancer pain, understanding differences in clinical and research experience in opioid rotation and use remains problematic. The complexity of the cancer pain experience presents unique challenges for the classification of pain. To date, no universally accepted pain classification measure can accurately predict the complexity of pain management, particularly for patients with cancer pain that is difficult to treat. In response to this gap in clinical assessment, the Edmonton Staging System (ESS), a classification system for cancer pain, was developed. Difficulties in definitions and interpretation of some aspects of the ESS restricted acceptance and widespread use. Construct, inter-rater reliability, and predictive validity evidence have contributed to the development of the ECS-CP. The five features of the ECS-CP--Pain Mechanism, Incident Pain, Psychological Distress, Addictive Behavior and Cognitive Function--have demonstrated value in predicting pain management complexity. The development of a standardized classification system that is comprehensive, prognostic and simple to use could provide a common language for clinical management and research of cancer pain. An international study to assess the inter-rater reliability and predictive value of the ECS-CP is currently in progress.

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

PubMed

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

2018-05-31

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

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

PubMed Central

2014-01-01

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ∼1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units. PMID:24564264

Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis

PubMed Central

Hueber, Wolfgang; Tomooka, Beren H; Batliwalla, Franak; Li, Wentian; Monach, Paul A; Tibshirani, Robert J; Van Vollenhoven, Ronald F; Lampa, Jon; Saito, Kazuyoshi; Tanaka, Yoshiya; Genovese, Mark C; Klareskog, Lars; Gregersen, Peter K; Robinson, William H

2009-01-01

Introduction Anti-TNF therapies have revolutionized the treatment of rheumatoid arthritis (RA), a common systemic autoimmune disease involving destruction of the synovial joints. However, in the practice of rheumatology approximately one-third of patients demonstrate no clinical improvement in response to treatment with anti-TNF therapies, while another third demonstrate a partial response, and one-third an excellent and sustained response. Since no clinical or laboratory tests are available to predict response to anti-TNF therapies, great need exists for predictive biomarkers. Methods Here we present a multi-step proteomics approach using arthritis antigen arrays, a multiplex cytokine assay, and conventional ELISA, with the objective to identify a biomarker signature in three ethnically diverse cohorts of RA patients treated with the anti-TNF therapy etanercept. Results We identified a 24-biomarker signature that enabled prediction of a positive clinical response to etanercept in all three cohorts (positive predictive values 58 to 72%; negative predictive values 63 to 78%). Conclusions We identified a multi-parameter protein biomarker that enables pretreatment classification and prediction of etanercept responders, and tested this biomarker using three independent cohorts of RA patients. Although further validation in prospective and larger cohorts is needed, our observations demonstrate that multiplex characterization of autoantibodies and cytokines provides clinical utility for predicting response to the anti-TNF therapy etanercept in RA patients. PMID:19460157

An Early Model for Value and Sustainability in Health Information Exchanges: Qualitative Study

PubMed Central

2018-01-01

Background The primary value relative to health information exchange has been seen in terms of cost savings relative to laboratory and radiology testing, emergency department expenditures, and admissions. However, models are needed to statistically quantify value and sustainability and better understand the dependent and mediating factors that contribute to value and sustainability. Objective The purpose of this study was to provide a basis for early model development for health information exchange value and sustainability. Methods A qualitative study was conducted with 21 interviews of eHealth Exchange participants across 10 organizations. Using a grounded theory approach and 3.0 as a relative frequency threshold, 5 main categories and 16 subcategories emerged. Results This study identifies 3 core current perceived value factors and 5 potential perceived value factors—how interviewees predict health information exchanges may evolve as there are more participants. These value factors were used as the foundation for early model development for sustainability of health information exchange. Conclusions Using the value factors from the interviews, the study provides the basis for early model development for health information exchange value and sustainability. This basis includes factors from the research: fostering consumer engagement; establishing a provider directory; quantifying use, cost, and clinical outcomes; ensuring data integrity through patient matching; and increasing awareness, usefulness, interoperability, and sustainability of eHealth Exchange. PMID:29712623

An Early Model for Value and Sustainability in Health Information Exchanges: Qualitative Study.

PubMed

Feldman, Sue S

2018-04-30

The primary value relative to health information exchange has been seen in terms of cost savings relative to laboratory and radiology testing, emergency department expenditures, and admissions. However, models are needed to statistically quantify value and sustainability and better understand the dependent and mediating factors that contribute to value and sustainability. The purpose of this study was to provide a basis for early model development for health information exchange value and sustainability. A qualitative study was conducted with 21 interviews of eHealth Exchange participants across 10 organizations. Using a grounded theory approach and 3.0 as a relative frequency threshold, 5 main categories and 16 subcategories emerged. This study identifies 3 core current perceived value factors and 5 potential perceived value factors-how interviewees predict health information exchanges may evolve as there are more participants. These value factors were used as the foundation for early model development for sustainability of health information exchange. Using the value factors from the interviews, the study provides the basis for early model development for health information exchange value and sustainability. This basis includes factors from the research: fostering consumer engagement; establishing a provider directory; quantifying use, cost, and clinical outcomes; ensuring data integrity through patient matching; and increasing awareness, usefulness, interoperability, and sustainability of eHealth Exchange. ©Sue S Feldman. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 30.04.2018.

Space Shuttle Main Engine performance analysis

NASA Technical Reports Server (NTRS)

Santi, L. Michael

1993-01-01

For a number of years, NASA has relied primarily upon periodically updated versions of Rocketdyne's power balance model (PBM) to provide space shuttle main engine (SSME) steady-state performance prediction. A recent computational study indicated that PBM predictions do not satisfy fundamental energy conservation principles. More recently, SSME test results provided by the Technology Test Bed (TTB) program have indicated significant discrepancies between PBM flow and temperature predictions and TTB observations. Results of these investigations have diminished confidence in the predictions provided by PBM, and motivated the development of new computational tools for supporting SSME performance analysis. A multivariate least squares regression algorithm was developed and implemented during this effort in order to efficiently characterize TTB data. This procedure, called the 'gains model,' was used to approximate the variation of SSME performance parameters such as flow rate, pressure, temperature, speed, and assorted hardware characteristics in terms of six assumed independent influences. These six influences were engine power level, mixture ratio, fuel inlet pressure and temperature, and oxidizer inlet pressure and temperature. A BFGS optimization algorithm provided the base procedure for determining regression coefficients for both linear and full quadratic approximations of parameter variation. Statistical information relative to data deviation from regression derived relations was also computed. A new strategy for integrating test data with theoretical performance prediction was also investigated. The current integration procedure employed by PBM treats test data as pristine and adjusts hardware characteristics in a heuristic manner to achieve engine balance. Within PBM, this integration procedure is called 'data reduction.' By contrast, the new data integration procedure, termed 'reconciliation,' uses mathematical optimization techniques, and requires both measurement and balance uncertainty estimates. The reconciler attempts to select operational parameters that minimize the difference between theoretical prediction and observation. Selected values are further constrained to fall within measurement uncertainty limits and to satisfy fundamental physical relations (mass conservation, energy conservation, pressure drop relations, etc.) within uncertainty estimates for all SSME subsystems. The parameter selection problem described above is a traditional nonlinear programming problem. The reconciler employs a mixed penalty method to determine optimum values of SSME operating parameters associated with this problem formulation.

Exploring the potential uses of value-added metrics in the context of postgraduate medical education.

PubMed

Gregory, Simon; Patterson, Fiona; Baron, Helen; Knight, Alec; Walsh, Kieran; Irish, Bill; Thomas, Sally

2016-10-01

Increasing pressure is being placed on external accountability and cost efficiency in medical education and training internationally. We present an illustrative data analysis of the value-added of postgraduate medical education. We analysed historical selection (entry) and licensure (exit) examination results for trainees sitting the UK Membership of the Royal College of General Practitioners (MRCGP) licensing examination (N = 2291). Selection data comprised: a clinical problem solving test (CPST); a situational judgement test (SJT); and a selection centre (SC). Exit data was an applied knowledge test (AKT) from MRCGP. Ordinary least squares (OLS) regression analyses were used to model differences in attainment in the AKT based on performance at selection (the value-added score). Results were aggregated to the regional level for comparisons. We discovered significant differences in the value-added score between regional training providers. Whilst three training providers confer significant value-added, one training provider was significantly lower than would be predicted based on the attainment of trainees at selection. Value-added analysis in postgraduate medical education potentially offers useful information, although the methodology is complex, controversial, and has significant limitations. Developing models further could offer important insights to support continuous improvement in medical education in future.

Predictive value and construct validity of the work functioning screener-healthcare (WFS-H).

PubMed

Boezeman, Edwin J; Nieuwenhuijsen, Karen; Sluiter, Judith K

2016-05-25

To test the predictive value and convergent construct validity of a 6-item work functioning screener (WFS-H). Healthcare workers (249 nurses) completed a questionnaire containing the work functioning screener (WFS-H) and a work functioning instrument (NWFQ) measuring the following: cognitive aspects of task execution and general incidents, avoidance behavior, conflicts and irritation with colleagues, impaired contact with patients and their family, and level of energy and motivation. Productivity and mental health were also measured. Negative and positive predictive values, AUC values, and sensitivity and specificity were calculated to examine the predictive value of the screener. Correlation analysis was used to examine the construct validity. The screener had good predictive value, since the results showed that a negative screener score is a strong indicator of work functioning not hindered by mental health problems (negative predictive values: 94%-98%; positive predictive values: 21%-36%; AUC:.64-.82; sensitivity: 42%-76%; and specificity 85%-87%). The screener has good construct validity due to moderate, but significant (p<.001), associations with productivity (r=.51), mental health (r=.48), and distress (r=.47). The screener (WFS-H) had good predictive value and good construct validity. Its score offers occupational health professionals a helpful preliminary insight into the work functioning of healthcare workers.

Evaluation of missing value methods for predicting ambient BTEX concentrations in two neighbouring cities in Southwestern Ontario Canada

NASA Astrophysics Data System (ADS)

Miller, Lindsay; Xu, Xiaohong; Wheeler, Amanda; Zhang, Tianchu; Hamadani, Mariam; Ejaz, Unam

2018-05-01

High density air monitoring campaigns provide spatial patterns of pollutant concentrations which are integral in exposure assessment. Such analysis can assist with the determination of links between air quality and health outcomes, however, problems due to missing data can threaten to compromise these studies. This research evaluates four methods; mean value imputation, inverse distance weighting (IDW), inter-species ratios, and regression, to address missing spatial concentration data ranging from one missing data point up to 50% missing data. BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations were measured in Windsor and Sarnia, Ontario in the fall of 2005. Concentrations and inter-species ratios were generally similar between the two cities. Benzene (B) was observed to be higher in Sarnia, whereas toluene (T) and the T/B ratios were higher in Windsor. Using these urban, industrialized cities as case studies, this research demonstrates that using inter-species ratios or regression of the data for which there is complete information, along with one measured concentration (i.e. benzene) to predict for missing concentrations (i.e. TEX) results in good agreement between predicted and measured values. In both cities, the general trend remains that best agreement is observed for the leave-one-out scenario, followed by 10% and 25% missing, and the least agreement for the 50% missing cases. In the absence of any known concentrations IDW can provide reasonable agreement between observed and estimated concentrations for the BTEX species, and was superior over mean value imputation which was not able to preserve the spatial trend. The proposed methods can be used to fill in missing data, while preserving the general characteristics and rank order of the data which are sufficient for epidemiologic studies.

Comparison of in situ uranium KD values with a laboratory determined surface complexation model

USGS Publications Warehouse

Curtis, G.P.; Fox, P.; Kohler, M.; Davis, J.A.

2004-01-01

Reactive solute transport simulations in groundwater require a large number of parameters to describe hydrologic and chemical reaction processes. Appropriate methods for determining chemical reaction parameters required for reactive solute transport simulations are still under investigation. This work compares U(VI) distribution coefficients (i.e. KD values) measured under field conditions with KD values calculated from a surface complexation model developed in the laboratory. Field studies were conducted in an alluvial aquifer at a former U mill tailings site near the town of Naturita, CO, USA, by suspending approximately 10 g samples of Naturita aquifer background sediments (NABS) in 17-5.1-cm diameter wells for periods of 3 to 15 months. Adsorbed U(VI) on these samples was determined by extraction with a pH 9.45 NaHCO3/Na2CO3 solution. In wells where the chemical conditions in groundwater were nearly constant, adsorbed U concentrations for samples taken after 3 months of exposure to groundwater were indistinguishable from samples taken after 15 months. Measured in situ K D values calculated from the measurements of adsorbed and dissolved U(VI) ranged from 0.50 to 10.6 mL/g and the KD values decreased with increasing groundwater alkalinity, consistent with increased formation of soluble U(VI)-carbonate complexes at higher alkalinities. The in situ K D values were compared with KD values predicted from a surface complexation model (SCM) developed under laboratory conditions in a separate study. A good agreement between the predicted and measured in situ KD values was observed. The demonstration that the laboratory derived SCM can predict U(VI) adsorption in the field provides a critical independent test of a submodel used in a reactive transport model. ?? 2004 Elsevier Ltd. All rights reserved.

Effective heating of magnetic nanoparticle aggregates for in vivo nano-theranostic hyperthermia.

PubMed

Wang, Chencai; Hsu, Chao-Hsiung; Li, Zhao; Hwang, Lian-Pin; Lin, Ying-Chih; Chou, Pi-Tai; Lin, Yung-Ya

2017-01-01

Magnetic resonance (MR) nano-theranostic hyperthermia uses magnetic nanoparticles to target and accumulate at the lesions and generate heat to kill lesion cells directly through hyperthermia or indirectly through thermal activation and control releasing of drugs. Preclinical and translational applications of MR nano-theranostic hyperthermia are currently limited by a few major theoretical difficulties and experimental challenges in in vivo conditions. For example, conventional models for estimating the heat generated and the optimal magnetic nanoparticle sizes for hyperthermia do not accurately reproduce reported in vivo experimental results. In this work, a revised cluster-based model was proposed to predict the specific loss power (SLP) by explicitly considering magnetic nanoparticle aggregation in in vivo conditions. By comparing with the reported experimental results of magnetite Fe 3 O 4 and cobalt ferrite CoFe 2 O 4 magnetic nanoparticles, it is shown that the revised cluster-based model provides a more accurate prediction of the experimental values than the conventional models that assume magnetic nanoparticles act as single units. It also provides a clear physical picture: the aggregation of magnetic nanoparticles increases the cluster magnetic anisotropy while reducing both the cluster domain magnetization and the average magnetic moment, which, in turn, shift the predicted SLP toward a smaller magnetic nanoparticle diameter with lower peak values. As a result, the heating efficiency and the SLP values are decreased. The improvement in the prediction accuracy in in vivo conditions is particularly pronounced when the magnetic nanoparticle diameter is in the range of ~10-20 nm. This happens to be an important size range for MR cancer nano-theranostics, as it exhibits the highest efficacy against both primary and metastatic tumors in vivo. Our studies show that a relatively 20%-25% smaller magnetic nanoparticle diameter should be chosen to reach the maximal heating efficiency in comparison with the optimal size predicted by previous models.

Effective heating of magnetic nanoparticle aggregates for in vivo nano-theranostic hyperthermia

PubMed Central

Wang, Chencai; Hsu, Chao-Hsiung; Li, Zhao; Hwang, Lian-Pin; Lin, Ying-Chih; Chou, Pi-Tai; Lin, Yung-Ya

2017-01-01

Magnetic resonance (MR) nano-theranostic hyperthermia uses magnetic nanoparticles to target and accumulate at the lesions and generate heat to kill lesion cells directly through hyperthermia or indirectly through thermal activation and control releasing of drugs. Preclinical and translational applications of MR nano-theranostic hyperthermia are currently limited by a few major theoretical difficulties and experimental challenges in in vivo conditions. For example, conventional models for estimating the heat generated and the optimal magnetic nanoparticle sizes for hyperthermia do not accurately reproduce reported in vivo experimental results. In this work, a revised cluster-based model was proposed to predict the specific loss power (SLP) by explicitly considering magnetic nanoparticle aggregation in in vivo conditions. By comparing with the reported experimental results of magnetite Fe3O4 and cobalt ferrite CoFe2O4 magnetic nanoparticles, it is shown that the revised cluster-based model provides a more accurate prediction of the experimental values than the conventional models that assume magnetic nanoparticles act as single units. It also provides a clear physical picture: the aggregation of magnetic nanoparticles increases the cluster magnetic anisotropy while reducing both the cluster domain magnetization and the average magnetic moment, which, in turn, shift the predicted SLP toward a smaller magnetic nanoparticle diameter with lower peak values. As a result, the heating efficiency and the SLP values are decreased. The improvement in the prediction accuracy in in vivo conditions is particularly pronounced when the magnetic nanoparticle diameter is in the range of ~10–20 nm. This happens to be an important size range for MR cancer nano-theranostics, as it exhibits the highest efficacy against both primary and metastatic tumors in vivo. Our studies show that a relatively 20%–25% smaller magnetic nanoparticle diameter should be chosen to reach the maximal heating efficiency in comparison with the optimal size predicted by previous models. PMID:28894366

Relationships among values, achievement orientations, and attitudes in youth sport.

PubMed

Lee, Martin J; Whitehead, Jean; Ntoumanis, Nikos; Hatzigeorgiadis, Antonis

2008-10-01

This research examines the value-expressive function of attitudes and achievement goal theory in predicting moral attitudes. In Study 1, the Youth Sport Values Questionnaire (YSVQ; Lee, Whitehead, & Balchin, 2000) was modified to measure moral, competence, and status values. In Study 2, structural equation modeling on data from 549 competitors (317 males, 232 females) aged 12-15 years showed that moral and competence values predicted prosocial attitudes, whereas moral (negatively) and status values (positively) predicted antisocial attitudes. Competence and status values predicted task and ego orientation, respectively, and task and ego orientation partially mediated the effect of competence values on prosocial attitudes and of status values on antisocial attitudes, respectively. The role of sport values is discussed, and new research directions are proposed.

Practical implications for genetic modeling in the genomics era

USDA-ARS?s Scientific Manuscript database

Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, incl...

Cardiothoracic ratio for prediction of left ventricular dilation: a systematic review and pooled analysis.

PubMed

Loomba, Rohit S; Shah, Parinda H; Nijhawan, Karan; Aggarwal, Saurabh; Arora, Rohit

2015-03-01

Increased cardiothoracic ratio noted on chest radiographs often prompts concern and further evaluation with additional imaging. This study pools available data assessing the utility of cardiothoracic ratio in predicting left ventricular dilation. A systematic review of the literature was conducted to identify studies comparing cardiothoracic ratio by chest x-ray to left ventricular dilation by echocardiography. Electronic databases were used to identify studies which were then assessed for quality and bias, with those with adequate quality and minimal bias ultimately being included in the pooled analysis. The pooled data were used to determine the sensitivity, specificity, positive predictive value and negative predictive value of cardiomegaly in predicting left ventricular dilation. A total of six studies consisting of 466 patients were included in this analysis. Cardiothoracic ratio had 83.3% sensitivity, 45.4% specificity, 43.5% positive predictive value and 82.7% negative predictive value. When a secondary analysis was conducted with a pediatric study excluded, a total of five studies consisting of 371 patients were included. Cardiothoracic ratio had 86.2% sensitivity, 25.2% specificity, 42.5% positive predictive value and 74.0% negative predictive value. Cardiothoracic ratio as determined by chest radiograph is sensitive but not specific for identifying left ventricular dilation. Cardiothoracic ratio also has a strong negative predictive value for identifying left ventricular dilation.

Current nonclinical testing paradigm enables safe entry to First-In-Human clinical trials: The IQ consortium nonclinical to clinical translational database.

PubMed

Monticello, Thomas M; Jones, Thomas W; Dambach, Donna M; Potter, David M; Bolt, Michael W; Liu, Maggie; Keller, Douglas A; Hart, Timothy K; Kadambi, Vivek J

2017-11-01

The contribution of animal testing in drug development has been widely debated and challenged. An industry-wide nonclinical to clinical translational database was created to determine how safety assessments in animal models translate to First-In-Human clinical risk. The blinded database was composed of 182 molecules and contained animal toxicology data coupled with clinical observations from phase I human studies. Animal and clinical data were categorized by organ system and correlations determined. The 2×2 contingency table (true positive, false positive, true negative, false negative) was used for statistical analysis. Sensitivity was 48% with a 43% positive predictive value (PPV). The nonhuman primate had the strongest performance in predicting adverse effects, especially for gastrointestinal and nervous system categories. When the same target organ was identified in both the rodent and nonrodent, the PPV increased. Specificity was 84% with an 86% negative predictive value (NPV). The beagle dog had the strongest performance in predicting an absence of clinical adverse effects. If no target organ toxicity was observed in either test species, the NPV increased. While nonclinical studies can demonstrate great value in the PPV for certain species and organ categories, the NPV was the stronger predictive performance measure across test species and target organs indicating that an absence of toxicity in animal studies strongly predicts a similar outcome in the clinic. These results support the current regulatory paradigm of animal testing in supporting safe entry to clinical trials and provide context for emerging alternate models. Copyright © 2017 Elsevier Inc. All rights reserved.

[Predictive value of preoperative tests in estimating difficult intubation in patients who underwent direct laryngoscopy in ear, nose, and throat surgery].

PubMed

Karakus, Osman; Kaya, Cengiz; Ustun, Faik Emre; Koksal, Ersin; Ustun, Yasemin Burcu

2015-01-01

Predictive value of preoperative tests in estimating difficult intubation may differ in the laryngeal pathologies. Patients who had undergone direct laryngoscopy (DL) were reviewed, and predictive value of preoperative tests in estimating difficult intubation was investigated. Preoperative, and intraoperative anesthesia record forms, and computerized system of the hospital were screened. A total of 2611 patients were assessed. In 7.4% of the patients, difficult intubations were detected. Difficult intubations were encountered in some of the patients with Mallampati scoring (MS) system Class 4 (50%), Cormack-Lehane classification (CLS) Grade 4 (95.7%), previous knowledge of difficult airway (86.2%), restricted neck movements (cervical ROM) (75.8%), short thyromental distance (TMD) (81.6%), vocal cord mass (49.5%) as indicated in parentheses (p<0.0001). MS had a low sensitivity, while restricted cervical ROM, presence of a vocal cord mass, short thyromental distance, and MS each had a relatively higher positive predictive value. Incidence of difficult intubations increased 6.159 and 1.736-fold with each level of increase in CLS grade and MS class, respectively. When all tests were considered in combination difficult intubation could be classified accurately in 96.3% of the cases. Test results predicting difficult intubations in cases with DL had observedly overlapped with the results provided in the literature for the patient populations in general. Differences in some test results when compared with those of the general population might stem from the concomitant underlying laryngeal pathological conditions in patient populations with difficult intubation. Copyright © 2014 Sociedade Brasileira de Anestesiologia. Publicado por Elsevier Editora Ltda. All rights reserved.

Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index.

PubMed

Dodd, Hayley; Williams, Sheila; Brown, Rachel; Venn, Bernard

2011-10-01

Glycemic index (GI) testing is normally based on individual foods, whereas GIs for meals or diets are based on a formula using a weighted sum of the constituents. The accuracy with which the formula can predict a meal or diet GI is questionable. Our objective was to compare the GI of meals, obtained by using the formula and by using both measured food GI and published values, with directly measured meal GIs. The GIs of 7 foods were tested in 30 healthy people. The foods were combined into 3 meals, each of which provided 50 g available carbohydrate, including a staple (potato, rice, or spaghetti), vegetables, sauce, and pan-fried chicken. The mean (95% CI) meal GIs determined from individual food GI values and by direct measurement were as follows: potato meal [predicted, 63 (56, 70); measured, 53 (46, 62)], rice meal [predicted, 51 (45, 56); measured, 38 (33, 45)], and spaghetti meal [predicted, 54 (49, 60); measured, 38 (33, 44)]. The predicted meal GIs were all higher than the measured GIs (P < 0.001). The extent of the overestimation depended on the particular food, ie, 12, 15, and 19 GI units (or 22%, 40%, and 50%) for the potato, rice, and spaghetti meals, respectively. The formula overestimated the GI of the meals by between 22% and 50%. The use of published food values also overestimated the measured meal GIs. Investigators using the formula to calculate a meal or diet GI should be aware of limitations in the method. This trial is registered with the Australian and New Zealand Clinical Trials Registry as ACTRN12611000210976.

T2 Relaxometry MRI Predicts Cerebral Palsy in Preterm Infants.

PubMed

Chen, L-W; Wang, S-T; Huang, C-C; Tu, Y-F; Tsai, Y-S

2018-01-18

T2-relaxometry brain MR imaging enables objective measurement of brain maturation based on the water-macromolecule ratio in white matter, but the outcome correlation is not established in preterm infants. Our study aimed to predict neurodevelopment with T2-relaxation values of brain MR imaging among preterm infants. From January 1, 2012, to May 31, 2015, preterm infants who underwent both T2-relaxometry brain MR imaging and neurodevelopmental follow-up were retrospectively reviewed. T2-relaxation values were measured over the periventricular white matter, including sections through the frontal horns, midbody of the lateral ventricles, and centrum semiovale. Periventricular T2 relaxometry in relation to corrected age was analyzed with restricted cubic spline regression. Prediction of cerebral palsy was examined with the receiver operating characteristic curve. Thirty-eight preterm infants were enrolled for analysis. Twenty patients (52.6%) had neurodevelopmental abnormalities, including 8 (21%) with developmental delay without cerebral palsy and 12 (31.6%) with cerebral palsy. The periventricular T2-relaxation values in relation to age were curvilinear in preterm infants with normal development, linear in those with developmental delay without cerebral palsy, and flat in those with cerebral palsy. When MR imaging was performed at >1 month corrected age, cerebral palsy could be predicted with T2 relaxometry of the periventricular white matter on sections through the midbody of the lateral ventricles (area under the receiver operating characteristic curve = 0.738; cutoff value of >217.4 with 63.6% sensitivity and 100.0% specificity). T2-relaxometry brain MR imaging could provide prognostic prediction of neurodevelopmental outcomes in premature infants. Age-dependent and area-selective interpretation in preterm brains should be emphasized. © 2018 by American Journal of Neuroradiology.

Detection of colorectal neoplasia: Combination of eight blood-based, cancer-associated protein biomarkers.

PubMed

Wilhelmsen, Michael; Christensen, Ib J; Rasmussen, Louise; Jørgensen, Lars N; Madsen, Mogens R; Vilandt, Jesper; Hillig, Thore; Klaerke, Michael; Nielsen, Knud T; Laurberg, Søren; Brünner, Nils; Gawel, Susan; Yang, Xiaoqing; Davis, Gerard; Heijboer, Annemieke; Martens, Frans; Nielsen, Hans J

2017-03-15

Serological biomarkers may be an option for early detection of colorectal cancer (CRC). The present study assessed eight cancer-associated protein biomarkers in plasma from subjects undergoing first time ever colonoscopy due to symptoms attributable to colorectal neoplasia. Plasma AFP, CA19-9, CEA, hs-CRP, CyFra21-1, Ferritin, Galectin-3 and TIMP-1 were determined in EDTA-plasma using the Abbott ARCHITECT® automated immunoassay platform. Primary endpoints were detection of (i) CRC and high-risk adenoma and (ii) CRC. Logistic regression was performed. Final reduced models were constructed selecting the four biomarkers with the highest likelihood scores. Subjects (N = 4,698) were consecutively included during 2010-2012. Colonoscopy detected 512 CRC patients, 319 colonic cancer and 193 rectal cancer. Extra colonic malignancies were detected in 177 patients, 689 had adenomas of which 399 were high-risk, 1,342 had nonneoplastic bowell disease and 1,978 subjects had 'clean' colorectum. Univariable analysis demonstrated that all biomarkers were statistically significant. Multivariate logistic regression demonstrated that the blood-based biomarkers in combination significantly predicted the endpoints. The reduced model resulted in the selection of CEA, hs-CRP, CyFra21-1 and Ferritin for the two endpoints; AUCs were 0.76 and 0.84, respectively. The postive predictive value at 90% sensitivity was 25% for endpoint 1 and the negative predictive value was 93%. For endpoint 2, the postive predictive value was 18% and the negative predictive value was 97%. Combinations of serological protein biomarkers provided a significant identification of subjects with high risk of the presence of colorectal neoplasia. The present set of biomarkers could become important adjunct in early detection of CRC. © 2016 UICC.

Prediction of metabolism-induced hepatotoxicity on three-dimensional hepatic cell culture and enzyme microarrays.

PubMed

Yu, Kyeong-Nam; Nadanaciva, Sashi; Rana, Payal; Lee, Dong Woo; Ku, Bosung; Roth, Alexander D; Dordick, Jonathan S; Will, Yvonne; Lee, Moo-Yeal

2018-03-01

Human liver contains various oxidative and conjugative enzymes that can convert nontoxic parent compounds to toxic metabolites or, conversely, toxic parent compounds to nontoxic metabolites. Unlike primary hepatocytes, which contain myriad drug-metabolizing enzymes (DMEs), but are difficult to culture and maintain physiological levels of DMEs, immortalized hepatic cell lines used in predictive toxicity assays are easy to culture, but lack the ability to metabolize compounds. To address this limitation and predict metabolism-induced hepatotoxicity in high-throughput, we developed an advanced miniaturized three-dimensional (3D) cell culture array (DataChip 2.0) and an advanced metabolizing enzyme microarray (MetaChip 2.0). The DataChip is a functionalized micropillar chip that supports the Hep3B human hepatoma cell line in a 3D microarray format. The MetaChip is a microwell chip containing immobilized DMEs found in the human liver. As a proof of concept for generating compound metabolites in situ on the chip and rapidly assessing their toxicity, 22 model compounds were dispensed into the MetaChip and sandwiched with the DataChip. The IC 50 values obtained from the chip platform were correlated with rat LD 50 values, human C max values, and drug-induced liver injury categories to predict adverse drug reactions in vivo. As a result, the platform had 100% sensitivity, 86% specificity, and 93% overall predictivity at optimum cutoffs of IC 50 and C max values. Therefore, the DataChip/MetaChip platform could be used as a high-throughput, early stage, microscale alternative to conventional in vitro multi-well plate platforms and provide a rapid and inexpensive assessment of metabolism-induced toxicity at early phases of drug development.

Early Changes in QRS Frequency Following Cardiac Resynchronization Predict Hemodynamic Response in Left Bundle Branch Block Patients.

PubMed

Niebauer, Mark J; Rickard, John; Tchou, Patrick J; Varma, Niraj

2016-05-01

QRS characteristics are the cornerstone of patient selection in cardiac resynchronization therapy (CRT) and the presence of left bundle branch block (LBBB) and baseline QRS ≥150 milliseconds portends a good outcome. We previously showed that baseline QRS frequency analysis adds predictive value to LBBB alone and have hypothesized that a change in frequency characteristics following CRT may produce additional predictive value. We examined the QRS frequency characteristics of 182 LBBB patients before and soon after CRT. Patients were assigned to responder and nonresponder groups. Responders were defined by a decrease in left ventricular end-systolic volume (LVESV) ≥15% following CRT. We analyzed the QRS in ECG leads I, AVF, and V3 before and soon after CRT using the discrete Fourier transform algorithm. The percentage of total QRS power within discrete frequency intervals before and after CRT was calculated. The reduction in lead V3 power <10 Hz was the best indicator of response. Baseline QRS width was similar between the responders and nonresponders (162.2 ± 17.2 milliseconds vs. 158 ± 22.1 milliseconds, respectively; P = 0.180). Responders exhibited a greater reduction in QRS power <10 Hz (-17.0 ± 11.9% vs. -6.6 ± 12.5%; P < 0.001) and a significant AUC (0.743; P < 0.001). A ≥8% decline in QRS power <10 Hz produced the best predictive values (PPV = 84%, NPV = 59%). Importantly, when patients with baseline QRS <150 milliseconds were compared, the AUC improved (0.892, P < 0.001). Successful CRT produces a significant reduction in QRS power below 10 Hz, particularly when baseline QRS <150 milliseconds. These results indicate that QRS frequency changes after CRT provide additional predictive value to QRS alone. © 2016 Wiley Periodicals, Inc.

Prediction of Short-Distance Aerial Movement of Phakopsora pachyrhizi Urediniospores Using Machine Learning.

PubMed

Wen, L; Bowen, C R; Hartman, G L

2017-10-01

Dispersal of urediniospores by wind is the primary means of spread for Phakopsora pachyrhizi, the cause of soybean rust. Our research focused on the short-distance movement of urediniospores from within the soybean canopy and up to 61 m from field-grown rust-infected soybean plants. Environmental variables were used to develop and compare models including the least absolute shrinkage and selection operator regression, zero-inflated Poisson/regular Poisson regression, random forest, and neural network to describe deposition of urediniospores collected in passive and active traps. All four models identified distance of trap from source, humidity, temperature, wind direction, and wind speed as the five most important variables influencing short-distance movement of urediniospores. The random forest model provided the best predictions, explaining 76.1 and 86.8% of the total variation in the passive- and active-trap datasets, respectively. The prediction accuracy based on the correlation coefficient (r) between predicted values and the true values were 0.83 (P < 0.0001) and 0.94 (P < 0.0001) for the passive and active trap datasets, respectively. Overall, multiple machine learning techniques identified the most important variables to make the most accurate predictions of movement of P. pachyrhizi urediniospores short-distance.

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.

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

PubMed Central

Cruz-Cano, Raul; Chew, David S.H.; Kwok-Pui, Choi; Ming-Ying, Leung

2010-01-01

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications. PMID:20729987

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction.

PubMed

Cruz-Cano, Raul; Chew, David S H; Kwok-Pui, Choi; Ming-Ying, Leung

2010-06-01

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications.

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

PubMed

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

2011-12-01

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

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

Developmental Change in Social Responsibility during Adolescence: An Ecological Perspective

PubMed Central

Wray-Lake, Laura; Syvertsen, Amy K.; Flanagan, Constance A.

2015-01-01

Social responsibility can be defined as a set of prosocial values representing personal commitments to contribute to community and society. Little is known about developmental change – and predictors of that change – in social responsibility during adolescence. The present study used an accelerated longitudinal research design to investigate the developmental trajectory of social responsibility values and ecological assets across family, school, community, and peer settings that predict these values. Data come from a three-year study of 3,683 U.S. adolescents enrolled in upper-level elementary, middle school, and high schools in rural, semi-urban, and urban communities. Social responsibility values significantly decreased from age 9 to 16 before leveling off in later adolescence. Family compassion messages and democratic climate, school solidarity, community connectedness, and trusted friendship positively predicted within-person change in adolescents’ social responsibility values. These findings held after accounting for other individual-level and demographic factors and provide support for the role of ecological assets in adolescents’ social responsibility development. In addition, fair society beliefs and volunteer experience had positive between- and within-person associations with social responsibility values. The manuscript discusses theoretical and practical implications of the conclusion that declines in ecological assets may partly explain age-related declines in social responsibility values. PMID:26619322

Developmental change in social responsibility during adolescence: An ecological perspective.

PubMed

Wray-Lake, Laura; Syvertsen, Amy K; Flanagan, Constance A

2016-01-01

Social responsibility can be defined as a set of prosocial values representing personal commitments to contribute to community and society. Little is known about developmental change-and predictors of that change-in social responsibility during adolescence. The present study used an accelerated longitudinal research design to investigate the developmental trajectory of social responsibility values and ecological assets across family, school, community, and peer settings that predict these values. Data come from a 3-year study of 3,683 U.S. adolescents enrolled in upper-level elementary, middle, and high schools in rural, semiurban, and urban communities. Social responsibility values significantly decreased from age 9 to 16 before leveling off in later adolescence. Family compassion messages and democratic climate, school solidarity, community connectedness, and trusted friendship, positively predicted within-person change in adolescents' social responsibility values. These findings held after accounting for other individual-level and demographic factors and provide support for the role of ecological assets in adolescents' social responsibility development. In addition, fair society beliefs and volunteer experience had positive between- and within-person associations with social responsibility values. The manuscript discusses theoretical and practical implications of the conclusion that declines in ecological assets may partly explain age-related declines in social responsibility values. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

[Detection of the main quality indicators in red wine with infrared spectroscopy based on FastICA and neural network].

PubMed

Fang, Li-Min; Lin, Min

2009-08-01

For the rapid detection of the ethanol, pH and rest sugar in red wine, infrared (IR) spectra of 44 wine samples were analyzed. The algorithm of fast independent component analysis (FastICA) was used to decompose the data of IR spectra, and their independent components and the mixing matrix were obtained. Then, the ICA-NNR calibration model with three-level artificial neural network (ANN) structure was built by using back-propagation (BP) algorithm. The models were used to estimate the contents of ethanol, pH and rest sugar in red wine samples for both in calibration set and predicted set. Correlation coefficient (r) of prediction and root mean square error of prediction (RMSEP) were used as the evaluation indexes. The results indicate that the r and RMSEP for the prediction of ethanol content, pH and rest sugar content are 0.953, 0.983 and 0.994, and 0.161, 0.017 and 0.181, respectively. The maximum relative deviations between the ICA-NNR method predicted value and referenced value of the 22 samples in predicted set are less than 4%. The results of this paper provide a foundation for the application and further development of IR on-line red wine analyzer.

FLOCK cluster analysis of mast cell event clustering by high-sensitivity flow cytometry predicts systemic mastocytosis.

PubMed

Dorfman, David M; LaPlante, Charlotte D; Pozdnyakova, Olga; Li, Betty

2015-11-01

In our high-sensitivity flow cytometric approach for systemic mastocytosis (SM), we identified mast cell event clustering as a new diagnostic criterion for the disease. To objectively characterize mast cell gated event distributions, we performed cluster analysis using FLOCK, a computational approach to identify cell subsets in multidimensional flow cytometry data in an unbiased, automated fashion. FLOCK identified discrete mast cell populations in most cases of SM (56/75 [75%]) but only a minority of non-SM cases (17/124 [14%]). FLOCK-identified mast cell populations accounted for 2.46% of total cells on average in SM cases and 0.09% of total cells on average in non-SM cases (P < .0001) and were predictive of SM, with a sensitivity of 75%, a specificity of 86%, a positive predictive value of 76%, and a negative predictive value of 85%. FLOCK analysis provides useful diagnostic information for evaluating patients with suspected SM, and may be useful for the analysis of other hematopoietic neoplasms. Copyright© by the American Society for Clinical Pathology.

Determination of the Spatial Distribution in Hydraulic Conductivity Using Genetic Algorithm Optimization

NASA Astrophysics Data System (ADS)

Aksoy, A.; Lee, J. H.; Kitanidis, P. K.

2016-12-01

Heterogeneity in hydraulic conductivity (K) impacts the transport and fate of contaminants in subsurface as well as design and operation of managed aquifer recharge (MAR) systems. Recently, improvements in computational resources and availability of big data through electrical resistivity tomography (ERT) and remote sensing have provided opportunities to better characterize the subsurface. Yet, there is need to improve prediction and evaluation methods in order to obtain information from field measurements for better field characterization. In this study, genetic algorithm optimization, which has been widely used in optimal aquifer remediation designs, was used to determine the spatial distribution of K. A hypothetical 2 km by 2 km aquifer was considered. A genetic algorithm library, PGAPack, was linked with a fast Fourier transform based random field generator as well as a groundwater flow and contaminant transport simulation model (BIO2D-KE). The objective of the optimization model was to minimize the total squared error between measured and predicted field values. It was assumed measured K values were available through ERT. Performance of genetic algorithm in predicting the distribution of K was tested for different cases. In the first one, it was assumed that observed K values were evaluated using the random field generator only as the forward model. In the second case, as well as K-values obtained through ERT, measured head values were incorporated into evaluation in which BIO2D-KE and random field generator were used as the forward models. Lastly, tracer concentrations were used as additional information in the optimization model. Initial results indicated enhanced performance when random field generator and BIO2D-KE are used in combination in predicting the spatial distribution in K.

[Effect of vitamin D deficiency on hypocalcaemia after total thyroidectomy due to benign goitre].

PubMed

Díez, Manuel; Vera, Cristina; Ratia, Tomás; Diego, Lucía; Mendoza, Fernando; Guillamot, Paloma; San Román, Rosario; Mugüerza, José M; Rodríguez, Angel; Medina, Carlos; Gómez, Beatriz; Granell, Javier

2013-04-01

The purpose of this study was to analyse the relationship between preoperative serum levels of vitamin D and postoperative hypocalcaemia after total thyroidectomy. A prospective observational study was conducted on 113 patients treated by total thyroidectomy due to benign disease. Preoperative vitamin D serum levels and postoperative albumin-corrected calcium and parathormone (PTH) levels were determined. Sensitivity, specificity, positive predictive value and negative predictive value of vitamin D and PTH levels, respectively, in the diagnosis of postoperative hypocalcaemia were calculated. Hypocalcaemia was diagnosed in 44 (38.9%) patients. Vitamin D levels were significantly higher in the group of patients with normal postoperative calcium (median: 25.4pg/mL; range: 4-60), compared to those who developed hypocalcaemia (median: 16.4pg/mL; range: 6.3-46.9) (P=.001). Postoperative hypocalcaemia was more frequent in patients with vitamin D < 30ng/mL (39/78) (50%), than among those with normal levels (5/35) (14.2%) (P=.001). Sensitivity, specificity, positive predictive value and negative predictive value were 88% and 68%, 43% and 82%, 50% and 71%, and 85% and 80% for vitamin D and PTH, respectively. Vitamin D and PTH showed independent prognostic values on the risk of hypocalcaemia. The OR associated with vitamin D < 30ng/mL was 4.25 (95% CI: 1.31-13.78) (P=.016), and the OR of PTH<13pg/mL was 15.4 (95% CI: 4.83-49.1) (P<.001). Vitamin D deficiency is a risk factor of hypocalcaemia after total thyroidectomy for benign goitre. The vitamin D level provides independent prognostic information, which is complementary to that given by PTH. Copyright © 2012 AEC. Published by Elsevier Espana. All rights reserved.

Trending in Probability of Collision Measurements via a Bayesian Zero-Inflated Beta Mixed Model

NASA Technical Reports Server (NTRS)

Vallejo, Jonathon; Hejduk, Matt; Stamey, James

2015-01-01

We investigate the performance of a generalized linear mixed model in predicting the Probabilities of Collision (Pc) for conjunction events. Specifically, we apply this model to the log(sub 10) transformation of these probabilities and argue that this transformation yields values that can be considered bounded in practice. Additionally, this bounded random variable, after scaling, is zero-inflated. Consequently, we model these values using the zero-inflated Beta distribution, and utilize the Bayesian paradigm and the mixed model framework to borrow information from past and current events. This provides a natural way to model the data and provides a basis for answering questions of interest, such as what is the likelihood of observing a probability of collision equal to the effective value of zero on a subsequent observation.

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.

Diffusion of isolated DNA molecules: dependence on length and topology.

PubMed

Robertson, Rae M; Laib, Stephan; Smith, Douglas E

2006-05-09

The conformation and dynamics of circular polymers is a subject of considerable theoretical and experimental interest. DNA is an important example because it occurs naturally in different topological states, including linear, relaxed circular, and supercoiled circular forms. A fundamental question is how the diffusion coefficients of isolated polymers scale with molecular length and how they vary for different topologies. Here, diffusion coefficients D for relaxed circular, supercoiled, and linear DNA molecules of length L ranging from approximately 6 to 290 kbp were measured by tracking the Brownian motion of single molecules. A topology-independent scaling law D approximately L(-nu) was observed with nu(L) = 0.571 +/- 0.014, nu(C) = 0.589 +/- 0.018, and nu(S) = 0.571 +/- 0.057 for linear, relaxed circular, and supercoiled DNA, respectively, in good agreement with the scaling exponent of nu congruent with 0.588 predicted by renormalization group theory for polymers with significant excluded volume interactions. Our findings thus provide evidence in support of several theories that predict an effective diameter of DNA much greater than the Debye screening length. In addition, the measured ratio D(Circular)/D(Linear) = 1.32 +/- 0.014 was closer to the value of 1.45 predicted by using renormalization group theory than the value of 1.18 predicted by classical Kirkwood hydrodynamic theory and agreed well with a value of 1.31 predicted when incorporating a recently proposed expression for the radius of gyration of circular polymers into the Zimm model.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

Human Resources Data in Weapon System Design: An Initial Plan for Development of a Unified Data Base.

DTIC Science & Technology

1980-11-01

Dela Bnrted) Item 19 Continued: system design design handbooks maintenance manpower simulation de’ision options cost estimating relationships prediction...determine the extent to which human resources data (HRD) are used in early system design. The third was to assess the availability and ade - quacy of...relationships, regression analysis, comparability analysis, expected value techniques) to provide initial data values in the very early stages of weapon system

Single versus triple daily activation of the distractor: no significant effects of frequency of distraction on bone regenerate quantity and architecture.

PubMed

Djasim, Urville Mardijanto; Wolvius, Eppo Bonne; Van Neck, Johan Wilhelm; Van Wamel, Annemieke; Weinans, Harrie; Van Der Wal, Karel George Hendrik

2008-04-01

To study the effect of two different frequencies of distraction on the quantity and architecture of bone regenerate using micro-computed tomography, and to determine whether radiographic and ultrasonographic bone-fill scores provide reliable predictive value for the amount of new bone in the distraction area. Twenty-six skeletally mature rabbits underwent three full days of latency, after which midface distraction was started. Low-frequency group (n=12): a distraction rate of 0.9 mm/d achieved by one daily activation for 11 days to create a 10mm distraction gap. High-frequency group (n=12): idem, but three daily activations were used instead of one. Control group (n=2) underwent no distraction. After 21 days of consolidation, bone-fill in the distraction area was assessed by means of ultrasonography and radiography. Micro-computed tomography was used to quantify new bone formation and bone architecture. Relative bone volume (BV/TV) showed a tendency towards a difference (P=0.09) between the low and high-frequency groups. No significant differences were found for bone architecture. No significant correlation between BV/TV values and bone-fill scores was found. An increase in rhythm from one to three activations daily does not create significantly more bone. Bone-fill score values provided no reliable predictive value for the amount of new bone formation.

Genotyping by sequencing for genomic prediction in a soybean breeding population.

PubMed

Jarquín, Diego; Kocak, Kyle; Posadas, Luis; Hyma, Katie; Jedlicka, Joseph; Graef, George; Lorenz, Aaron

2014-08-29

Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations. Genotyping by sequencing scored 16,502 single nucleotide polymorphisms (SNPs) with minor-allele frequency (MAF) > 0.05 and percentage of missing values ≤ 5% on 301 elite soybean breeding lines. When SNPs with up to 80% missing values were included, 52,349 SNPs were scored. Prediction accuracy for grain yield, assessed using cross validation, was estimated to be 0.64, indicating good potential for using genomic selection for grain yield in soybean. Filtering SNPs based on missing data percentage had little to no effect on prediction accuracy, especially when random forest imputation was used to impute missing values. The highest accuracies were observed when random forest imputation was used on all SNPs, but differences were not significant. A standard additive G-BLUP model was robust; modeling additive-by-additive epistasis did not provide any improvement in prediction accuracy. The effect of training population size on accuracy began to plateau around 100, but accuracy steadily climbed until the largest possible size was used in this analysis. Including only SNPs with MAF > 0.30 provided higher accuracies when training populations were smaller. Using GBS for genomic prediction in soybean holds good potential to expedite genetic gain. Our results suggest that standard additive G-BLUP models can be used on unfiltered, imputed GBS data without loss in accuracy.

Effects of Barometric Fluctuations on Well Water-Level Measurements and Aquifer Test Data

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

Spane, Frank A.

1999-12-16

This report examines the effects of barometric fluctuations on well water-level measurements and evaluates adjustment and removal methods for determining areal aquifer head conditions and aquifer test analysis. Two examples of Hanford Site unconfined aquifer tests are examined that demonstrate baro-metric response analysis and illustrate the predictive/removal capabilities of various methods for well water-level and aquifer total head values. Good predictive/removal characteristics were demonstrated with best corrective results provided by multiple-regression deconvolution methods.

Establishing a Real-Money Prediction Market for Climate on Decadal Horizons

NASA Astrophysics Data System (ADS)

Roulston, M. S.; Hand, D. J.; Harding, D. W.

2016-12-01

A plan to establish a not-for-profit prediction market that will allow participants to bet on the value of selected climate variables decades into the future will be presented. It is hoped that this market will provide an objective measure of the consensus view on climate change, including information concerning the uncertainty of climate projections. The proposed design of the market and the definition of the climate variables underlying the contracts will be discussed, as well as relevant regulatory and legal issues.

Entropy of gaseous boron monobromide

NASA Astrophysics Data System (ADS)

Wang, Jian-Feng; Peng, Xiao-Long; Zhang, Lie-Hui; Wang, Chao-Wen; Jia, Chun-Sheng

2017-10-01

We present an explicit representation of molar entropy for gaseous boron monobromide in terms of experimental values of only three molecular constants. Fortunately, through comparison of theoretically calculated results and experimental data, we find that the molar entropy of gaseous boron monobromide can be well predicted by employing the improved Manning-Rosen oscillator to describe the internal vibration of boron monobromide molecule. The present approach provides also opportunities for theoretical predictions of molar entropy for other gases with no use of large amounts of experimental spectroscopy data.

Predicting toxic effects of copper on aquatic biota in mineralized areas by using the Biotic Ligand Model

USGS Publications Warehouse

Smith, Kathleen S.; Ranville, James F.; Adams, M.; Choate, LaDonna M.; Church, Stan E.; Fey, David L.; Wanty, Richard B.; Crock, James G.

2006-01-01

The chemical speciation of metals influences their biological effects. The Biotic Ligand Model (BLM) is a computational approach to predict chemical speciation and acute toxicological effects of metals on aquatic biota. Recently, the U.S. Environmental Protection Agency incorporated the BLM into their regulatory water-quality criteria for copper. Results from three different laboratory copper toxicity tests were compared with BLM predictions for simulated test-waters. This was done to evaluate the ability of the BLM to accurately predict the effects of hardness and concentrations of dissolved organic carbon (DOC) and iron on aquatic toxicity. In addition, we evaluated whether the BLM and the three toxicity tests provide consistent results. Comparison of BLM predictions with two types of Ceriodaphnia dubia toxicity tests shows that there is fairly good agreement between predicted LC50 values computed by the BLM and LC50 values determined from the two toxicity tests. Specifically, the effect of increasing calcium concentration (and hardness) on copper toxicity appears to be minimal. Also, there is fairly good agreement between the BLM and the two toxicity tests for test solutions containing elevated DOC, for which the LC50 is 3-to-5 times greater (less toxic) than the LC50 for the lower-DOC test water. This illustrates the protective effects of DOC on copper toxicity and demonstrates the ability of the BLM to predict these protective effects. In contrast, for test solutions with added iron there is a decrease in LC50 values (increase in toxicity) in results from the two C. dubia toxicity tests, and the agreement between BLM LC50 predictions and results from these toxicity tests is poor. The inability of the BLM to account for competitive iron binding to DOC or DOC fractionation may be a significant shortcoming of the BLM for predicting site- specific water-quality criteria in streams affected by iron-rich acidic drainage in mined and mineralized areas.

21 CFR 868.1890 - Predictive pulmonary-function value calculator.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Predictive pulmonary-function value calculator. 868.1890 Section 868.1890 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... pulmonary-function value calculator. (a) Identification. A predictive pulmonary-function value calculator is...

21 CFR 868.1890 - Predictive pulmonary-function value calculator.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Predictive pulmonary-function value calculator. 868.1890 Section 868.1890 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... pulmonary-function value calculator. (a) Identification. A predictive pulmonary-function value calculator is...

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world.

PubMed

McDannald, Michael A; Takahashi, Yuji K; Lopatina, Nina; Pietras, Brad W; Jones, Josh L; Schoenbaum, Geoffrey

2012-04-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Solar radio proxies for improved satellite orbit prediction

NASA Astrophysics Data System (ADS)

Yaya, Philippe; Hecker, Louis; Dudok de Wit, Thierry; Fèvre, Clémence Le; Bruinsma, Sean

2017-12-01

Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10.7 flux.

Radiative PQ breaking and the Higgs boson mass

NASA Astrophysics Data System (ADS)

D'Eramo, Francesco; Hall, Lawrence J.; Pappadopulo, Duccio

2015-06-01

The small and negative value of the Standard Model Higgs quartic coupling at high scales can be understood in terms of anthropic selection on a landscape where large and negative values are favored: most universes have a very short-lived electroweak vacuum and typical observers are in universes close to the corresponding metastability boundary. We provide a simple example of such a landscape with a Peccei-Quinn symmetry breaking scale generated through dimensional transmutation and supersymmetry softly broken at an intermediate scale. Large and negative contributions to the Higgs quartic are typically generated on integrating out the saxion field. Cancellations among these contributions are forced by the anthropic requirement of a sufficiently long-lived electroweak vacuum, determining the multiverse distribution for the Higgs quartic in a similar way to that of the cosmological constant. This leads to a statistical prediction of the Higgs boson mass that, for a wide range of parameters, yields the observed value within the 1σ statistical uncertainty of ˜ 5 GeV originating from the multiverse distribution. The strong CP problem is solved and single-component axion dark matter is predicted, with an abundance that can be understood from environmental selection. A more general setting for the Higgs mass prediction is discussed.

Functional Connectivity of Child and Adolescent Attention Deficit Hyperactivity Disorder Patients: Correlation with IQ.

PubMed

Park, Bo-Yong; Hong, Jisu; Lee, Seung-Hak; Park, Hyunjin

2016-01-01

Attention deficit hyperactivity disorder (ADHD) is a pervasive neuropsychological disorder that affects both children and adolescents. Child and adolescent ADHD patients exhibit different behavioral symptoms such as hyperactivity and impulsivity, but not much connectivity research exists to help explain these differences. We analyzed openly accessible resting-state functional magnetic resonance imaging (rs-fMRI) data on 112 patients (28 child ADHD, 28 adolescent ADHD, 28 child normal control (NC), and 28 adolescent NC). We used group independent component analysis (ICA) and weighted degree values to identify interaction effects of age (child and adolescent) and symptom (ADHD and NC) in brain networks. The frontoparietal network showed significant interaction effects ( p = 0.0068). The frontoparietal network is known to be related to hyperactive and impulsive behaviors. Intelligence quotient (IQ) is an important factor in ADHD, and we predicted IQ scores using the results of our connectivity analysis. IQ was predicted using degree centrality values of networks with significant interaction effects of age and symptom. Actual and predicted IQ scores demonstrated significant correlation values, with an error of about 10%. Our study might provide imaging biomarkers for future ADHD and intelligence studies.

Use of Munsell color charts to measure skin tone objectively in nursing home residents at risk for pressure ulcer development.

PubMed

McCreath, Heather E; Bates-Jensen, Barbara M; Nakagami, Gojiro; Patlan, Anabel; Booth, Howard; Connolly, Dana; Truong, Cyndi; Woldai, Agazi

2016-09-01

To assess the feasibility of classifying skin tone using Munsell color chart values and to compare Munsell-based skin tone categories to ethnicity/race to predict pressure ulcer risk. Pressure ulcer classification uses level of visible tissue damage, including skin discoloration over bony prominences. Prevention begins with early detection of damage. Skin discoloration in those with dark skin tones can be difficult to observe, hindering early detection. Observational cohort of 417 nursing home residents from 19 nursing homes collected between 2009-2014, with weekly skin assessments for up to 16 weeks. Assessment included forearm and buttocks skin tone based on Munsell values (Dark, Medium, Light) at three time points, ethnicity/race medical record documentation, and weekly skin assessment on trunk and heels. Inter-rater reliability was high for forearm and buttock values and skin tone. Mean Munsell buttocks values differed significantly by ethnicity/race. Across ethnicity/race, Munsell value ranges overlapped, with the greatest range among African Americans. Trunk pressure ulcer incidence varied by skin tone, regardless of ethnicity/race. In multinomial regression, skin tone was more predictive of skin damage than ethnicity/race for trunk locations but ethnicity/race was more predictive for heels. Given the overlap of Munsell values across ethnicity/race, color charts provide more objective measurement of skin tone than demographic categories. An objective measure of skin tone can improve pressure ulcer risk assessment among patients for whom current clinical guidelines are less effective. © 2016 John Wiley & Sons Ltd.

HotRegion: a database of predicted hot spot clusters.

PubMed

Cukuroglu, Engin; Gursoy, Attila; Keskin, Ozlem

2012-01-01

Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion.

Computational tools for fitting the Hill equation to dose-response curves.

PubMed

Gadagkar, Sudhindra R; Call, Gerald B

2015-01-01

Many biological response curves commonly assume a sigmoidal shape that can be approximated well by means of the 4-parameter nonlinear logistic equation, also called the Hill equation. However, estimation of the Hill equation parameters requires access to commercial software or the ability to write computer code. Here we present two user-friendly and freely available computer programs to fit the Hill equation - a Solver-based Microsoft Excel template and a stand-alone GUI-based "point and click" program, called HEPB. Both computer programs use the iterative method to estimate two of the Hill equation parameters (EC50 and the Hill slope), while constraining the values of the other two parameters (the minimum and maximum asymptotes of the response variable) to fit the Hill equation to the data. In addition, HEPB draws the prediction band at a user-defined confidence level, and determines the EC50 value for each of the limits of this band to give boundary values that help objectively delineate sensitive, normal and resistant responses to the drug being tested. Both programs were tested by analyzing twelve datasets that varied widely in data values, sample size and slope, and were found to yield estimates of the Hill equation parameters that were essentially identical to those provided by commercial software such as GraphPad Prism and nls, the statistical package in the programming language R. The Excel template provides a means to estimate the parameters of the Hill equation and plot the regression line in a familiar Microsoft Office environment. HEPB, in addition to providing the above results, also computes the prediction band for the data at a user-defined level of confidence, and determines objective cut-off values to distinguish among response types (sensitive, normal and resistant). Both programs are found to yield estimated values that are essentially the same as those from standard software such as GraphPad Prism and the R-based nls. Furthermore, HEPB also has the option to simulate 500 response values based on the range of values of the dose variable in the original data and the fit of the Hill equation to that data. Copyright © 2014. Published by Elsevier Inc.

Advanced non-contrasted computed tomography post-processing by CT-Calculometry (CT-CM) outperforms established predictors for the outcome of shock wave lithotripsy.

PubMed

Langenauer, J; Betschart, P; Hechelhammer, L; Güsewell, S; Schmid, H P; Engeler, D S; Abt, D; Zumstein, V

2018-05-29

To evaluate the predictive value of advanced non-contrasted computed tomography (NCCT) post-processing using novel CT-calculometry (CT-CM) parameters compared to established predictors of success of shock wave lithotripsy (SWL) for urinary calculi. NCCT post-processing was retrospectively performed in 312 patients suffering from upper tract urinary calculi who were treated by SWL. Established predictors such as skin to stone distance, body mass index, stone diameter or mean stone attenuation values were assessed. Precise stone size and shape metrics, 3-D greyscale measurements and homogeneity parameters such as skewness and kurtosis, were analysed using CT-CM. Predictive values for SWL outcome were analysed using logistic regression and receiver operating characteristics (ROC) statistics. Overall success rate (stone disintegration and no re-intervention needed) of SWL was 59% (184 patients). CT-CM metrics mainly outperformed established predictors. According to ROC analyses, stone volume and surface area performed better than established stone diameter, mean 3D attenuation value was a stronger predictor than established mean attenuation value, and parameters skewness and kurtosis performed better than recently emerged variation coefficient of stone density. Moreover, prediction of SWL outcome with 80% probability to be correct would be possible in a clearly higher number of patients (up to fivefold) using CT-CM-derived parameters. Advanced NCCT post-processing by CT-CM provides novel parameters that seem to outperform established predictors of SWL response. Implementation of these parameters into clinical routine might reduce SWL failure rates.

Conditional power and predictive power based on right censored data with supplementary auxiliary information.

PubMed

Sun, Libo; Wan, Ying

2018-04-22

Conditional power and predictive power provide estimates of the probability of success at the end of the trial based on the information from the interim analysis. The observed value of the time to event endpoint at the interim analysis could be biased for the true treatment effect due to early censoring, leading to a biased estimate of conditional power and predictive power. In such cases, the estimates and inference for this right censored primary endpoint are enhanced by incorporating a fully observed auxiliary variable. We assume a bivariate normal distribution of the transformed primary variable and a correlated auxiliary variable. Simulation studies are conducted that not only shows enhanced conditional power and predictive power but also can provide the framework for a more efficient futility interim analysis in terms of an improved accuracy in estimator, a smaller inflation in type II error and an optimal timing for such analysis. We also illustrated the new approach by a real clinical trial example. Copyright © 2018 John Wiley & Sons, Ltd.

Predicting the size of individual and group differences on speeded cognitive tasks.

PubMed

Chen, Jing; Hale, Sandra; Myerson, Joel

2007-06-01

An a priori test of the difference engine model (Myerson, Hale, Zheng, Jenkins, & Widaman, 2003) was conducted using a large, diverse sample of individuals who performed three speeded verbal tasks and three speeded visuospatial tasks. Results demonstrated that, as predicted by the model, the group standard deviation (SD) on any task was proportional to the amount of processing required by that task. Both individual performances as well as those of fast and slow subgroups could be accurately predicted by the model using no free parameters, just an individual or subgroup's mean z-score and the values of theoretical constructs estimated from fits to the group SDs. Taken together, these results are consistent with post hoc analyses reported by Myerson et al. and provide even stronger supporting evidence. In particular, the ability to make quantitative predictions without using any free parameters provides the clearest demonstration to date of the power of an analytic approach on the basis of the difference engine.

A simple lead dust fall method predicts children's blood lead level: New evidence from Australia.

PubMed

Gulson, Brian; Taylor, Alan

2017-11-01

We have measured dust fall accumulation in petri dishes (PDD) collected 6 monthly from inside residences in Sydney urban area, New South Wales, Australia as part of a 5-year longitudinal study to determine environmental associations, including soil. with blood lead (PbB) levels. The Pb loading in the dishes (n = 706) had geometric means (GM) of 24µg/m 2 /30d, a median value of 22µg/m 2 /30d with a range from 0.2 to 11,390µg/m 2 /30d. Observed geometric mean PbB was 2.4µg/dL at ages 2-3 years. Regression analyses showed a statistically significant relationship between predicted PbB and PDD. The predicted PbB values from dust in our study are consistent with similar analyses from the US in which floor dust was collected by wipes. Predicted PbB values from PDD indicate that an increase in PDD of about 100µg/m 2 /30d would increase PbB by about 1.5µg/dL or a doubling PbB at the low levels currently observed in many countries. Predicted PbB values from soil indicate that a change from 0 to 1000mg Pb/kg results in an increase of 1.7µg/dL in PbB, consistent with earlier investigations. Blood Pb levels can be predicted from dust fall accumulation (and soil) in cases where blood sampling is not always possible, especially in young children. Petri dish loading data could provide an alternative or complementary "action level" at about 100µg Pb/m 2 /30 days, similar to the suggested level of about 110µg Pb/m 2 for surface wipes, for use in monitoring activities such as housing rehabilitation, demolition or soil resuspension. Copyright © 2017 Elsevier Inc. All rights reserved.

2D dynamic studies combined with the surface curvature analysis to predict Arias Intensity amplification

NASA Astrophysics Data System (ADS)

Torgoev, Almaz; Havenith, Hans-Balder

2016-07-01

A 2D elasto-dynamic modelling of the pure topographic seismic response is performed for six models with a total length of around 23.0 km. These models are reconstructed from the real topographic settings of the landslide-prone slopes situated in the Mailuu-Suu River Valley, Southern Kyrgyzstan. The main studied parameter is the Arias Intensity (Ia, m/sec), which is applied in the GIS-based Newmark method to regionally map the seismically-induced landslide susceptibility. This method maps the Ia values via empirical attenuation laws and our studies investigate a potential to include topographic input into them. Numerical studies analyse several signals with varying shape and changing central frequency values. All tests demonstrate that the spectral amplification patterns directly affect the amplification of the Ia values. These results let to link the 2D distribution of the topographically amplified Ia values with the parameter called as smoothed curvature. The amplification values for the low-frequency signals are better correlated with the curvature smoothed over larger spatial extent, while those values for the high-frequency signals are more linked to the curvature with smaller smoothing extent. The best predictions are provided by the curvature smoothed over the extent calculated according to Geli's law. The sample equations predicting the Ia amplification based on the smoothed curvature are presented for the sinusoid-shape input signals. These laws cannot be directly implemented in the regional Newmark method, as 3D amplification of the Ia values addresses more problem complexities which are not studied here. Nevertheless, our 2D results prepare the theoretical framework which can potentially be applied to the 3D domain and, therefore, represent a robust basis for these future research targets.

Managing Uncertainty and Risk in Public-sector Investments

DTIC Science & Technology

2007-04-30

parameters with the most predictive power. There are, of course , methods other than market-based Capital Asset Pricing for determining asset...harness new information as it becomes available. For private-sector firms, prices provide two important types of information: 1. The rate at which...based valuation, thus providing a basis for testing the validity of using the internal efficiencies to derive a “synthetic price” for the value of a

Medications Development for the Treatment of Alcohol Use Disorder: Insights into the Predictive Value of Animal and Human Laboratory Models

PubMed Central

Yardley, Megan M.; Ray, Lara A.

2016-01-01

Development of effective treatments for alcohol use disorder (AUD) represents an important public health goal. This review provides a summary of completed preclinical and clinical studies testing pharmacotherapies for treatment of AUD. We discuss opportunities for improving the translation from preclinical findings to clinical trial outcomes, focusing on the validity and predictive value of animal and human laboratory models of AUD. Specifically, while preclinical studies of medications development have offered important insights into the neurobiology of the disorder and alcohol's molecular targets, limitations include the lack of standardized methods and streamlined processes whereby animal studies can readily inform human studies. Behavioral pharmacology studies provide a less expensive and valuable opportunity to assess the feasibility of a pharmacotherapy prior to initiating larger scale clinical trials by providing insights into the mechanism of the drug, which can then inform recruitment, analyses, and assessments. Summary tables are provided to illustrate the wide range of preclinical, human laboratory, and clinical studies of medications development for alcoholism. Taken together, this review highlights the challenges associated with animal paradigms, human laboratory studies and clinical trials with the overarching goal of advancing treatment development and highlighting opportunities to bridge the gap between preclinical and clinical research. PMID:26833803

Methods for using groundwater model predictions to guide hydrogeologic data collection, with application to the Death Valley regional groundwater flow system

USGS Publications Warehouse

Tiedeman, C.R.; Hill, M.C.; D'Agnese, F. A.; Faunt, C.C.

2003-01-01

Calibrated models of groundwater systems can provide substantial information for guiding data collection. This work considers using such models to guide hydrogeologic data collection for improving model predictions by identifying model parameters that are most important to the predictions. Identification of these important parameters can help guide collection of field data about parameter values and associated flow system features and can lead to improved predictions. Methods for identifying parameters important to predictions include prediction scaled sensitivities (PSS), which account for uncertainty on individual parameters as well as prediction sensitivity to parameters, and a new "value of improved information" (VOII) method presented here, which includes the effects of parameter correlation in addition to individual parameter uncertainty and prediction sensitivity. In this work, the PSS and VOII methods are demonstrated and evaluated using a model of the Death Valley regional groundwater flow system. The predictions of interest are advective transport paths originating at sites of past underground nuclear testing. Results show that for two paths evaluated the most important parameters include a subset of five or six of the 23 defined model parameters. Some of the parameters identified as most important are associated with flow system attributes that do not lie in the immediate vicinity of the paths. Results also indicate that the PSS and VOII methods can identify different important parameters. Because the methods emphasize somewhat different criteria for parameter importance, it is suggested that parameters identified by both methods be carefully considered in subsequent data collection efforts aimed at improving model predictions.

An analysis of the use of dogs in predicting human toxicology and drug safety.

PubMed

Bailey, Jarrod; Thew, Michelle; Balls, Michael

2013-11-01

Dogs remain the main non-rodent species in preclinical drug development. Despite the current dearth of new drug approvals and meagre pipelines, this continues, with little supportive evidence of its value or necessity. To estimate the evidential weight provided by canine data to the probability that a new drug may be toxic to humans, we have calculated Likelihood Ratios (LRs) for an extensive dataset of 2,366 drugs with both animal and human data, including tissue-level effects and Medical Dictionary for Regulatory Activities (MedDRA) Level 1-4 biomedical observations. The resulting LRs show that the absence of toxicity in dogs provides virtually no evidence that adverse drug reactions (ADRs) will also be absent in humans. While the LRs suggest that the presence of toxic effects in dogs can provide considerable evidential weight for a risk of potential ADRs in humans, this is highly inconsistent, varying by over two orders of magnitude for different classes of compounds and their effects. Our results therefore have important implications for the value of the dog in predicting human toxicity, and suggest that alternative methods are urgently required. 2013 FRAME.

Effects of correcting in situ ruminal microbial colonization of feed particles on the relationship between ruminally undegraded and intestinally digested crude protein in concentrate feeds.

PubMed

González, Javier; Mouhbi, Rabiaa; Guevara-González, Jesús Alberto; Arroyo, José María

2018-02-01

In situ estimates of ruminally undegraded protein (RUP) and intestinally digested protein (IDP) of ten concentrates, uncorrected or corrected for the ruminal microbial colonization, were used to examine the effects of this correction on the relationship between IDP and RUP values. Both variables were established for three rumen and duodenum cannulated wethers using 15 N labeling-techniques and considering measured rates of ruminal particle comminution (k c ) and outflow (k p ). A covariance analysis showed that the close relationship found between both variables (IDP = -0.0132 ± 0.00679 + 0.776 ± 0.0002 RUP; n = 60; P < 0.001; r = 0.960) is not affected by correcting for microbial colonization (P = 0.682). The IDP content in concentrates and industrial by-products can be predicted from RUP values, thus avoiding the laborious and complex procedure of determining intestinal digestibility; however, a larger sample of feeds is necessary to achieve more accurate predictions. The lack of influence of the correction for microbial contamination on the prediction observed in the present study increases the data available for this prediction. However, only the use of corrected values may provide an accurate evaluation. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Measurement of photoemission and secondary emission from laboratory dust grains

NASA Technical Reports Server (NTRS)

Hazelton, Robert C.; Yadlowsky, Edward J.; Settersten, Thomas B.; Spanjers, Gregory G.; Moschella, John J.

1995-01-01

The overall goal of this project is experimentally determine the emission properties of dust grains in order to provide theorists and modelers with an accurate data base to use in codes that predict the charging of grains in various plasma environments encountered in the magnetospheres of the planets. In general these modelers use values which have been measured on planar, bulk samples of the materials in question. The large enhancements expected due to the small size of grains can have a dramatic impact upon the predictions and the ultimate utility of these predictions. The first experimental measurement of energy resolved profiles of the secondary electron emission coefficient, 6, of sub-micron diameter particles has been accomplished. Bismuth particles in the size range of .022 to .165 micrometers were generated in a moderate pressure vacuum oven (average size is a function of oven temperature and pressure) and introduced into a high vacuum chamber where they interacted with a high energy electron beam (0.4 to 20 keV). Large enhancements in emission were observed with a peak value, delta(sub max) = 4. 5 measured for the ensemble of particles with a mean size of .022 micrometers. This is in contrast to the published value, delta(sub max) = 1.2, for bulk bismuth. The observed profiles are in general agreement with recent theoretical predictions made by Chow et al. at UCSD.

New Mexico Educator Equity Plan

ERIC Educational Resources Information Center

New Mexico Public Education Department, 2015

2015-01-01

Both the U.S. Department of Education and the New Mexico Public Education Department (PED) believe that equal opportunity is a core American value. Equal access to excellent education provides meaningful opportunities for students to achieve their goals. Recognizing that family income and race often predicts a student's ability to access excellent…

Practical implications for genetic modeling in the genomics era for the dairy industry

USDA-ARS?s Scientific Manuscript database

Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, incl...

Predictors of Incomes. AIR Forum 1981 Paper.

ERIC Educational Resources Information Center

Witmer, David R.

Income predictions that provide some indication of the potential value of attending college are considered. Standard multiple regression analysis of data describing the income experiences of men 25 years old and older were used to determine differences in incomes of high school and college graduates. Information on the gross national product was…

Subjective Expected Utility: A Model of Decision-Making.

ERIC Educational Resources Information Center

Fischoff, Baruch; And Others

1981-01-01

Outlines a model of decision making known to researchers in the field of behavioral decision theory (BDT) as subjective expected utility (SEU). The descriptive and predictive validity of the SEU model, probability and values assessment using SEU, and decision contexts are examined, and a 54-item reference list is provided. (JL)

Thermometric convection coefficients for rocket meteorological sensors (tables)

NASA Technical Reports Server (NTRS)

Staffanson, F. L.

1974-01-01

Values of the convective heat transfer coefficient h, and the recovery factor r, for miniature beads, fine wires, and films in rarefied air flow are shown. Data provide a standard reference for computing consistent operational corrections to rocket meteorological measurements, and for predicting the performance of existing and proposed sensor systems.

Ultrasound as a screening test for genitourinary anomalies in children with UTI.

PubMed

Nelson, Caleb P; Johnson, Emilie K; Logvinenko, Tanya; Chow, Jeanne S

2014-03-01

The 2011 American Academy of Pediatrics guidelines state that renal and bladder ultrasound (RBUS) should be performed after initial febrile urinary tract infection (UTI) in a young child, with voiding cystourethrogram (VCUG) performed only if RBUS shows abnormalities. We sought to determine test characteristics and predictive values of RBUS for VCUG findings in this setting. We analyzed 3995 clinical encounters from January 1, 2006 to December 31, 2010 during which VCUG and RBUS were performed for history of UTI. Patients who had previous postnatal genitourinary imaging or history of prenatal hydronephrosis were excluded. Sensitivity, specificity, and predictive values of RBUS for VCUG abnormalities were determined. We identified 2259 patients age <60 months who had UTI as the indication for imaging. RBUS was reported as "normal" in 75%. On VCUG, any vesicoureteral reflux (VUR) was identified in 41.7%, VUR grade >II in 20.9%, and VUR grade >III in 2.8%. Sensitivity of RBUS for any abnormal findings on VCUG ranged from 5% (specificity: 97%) to 28% (specificity: 77%). Sensitivity for VUR grade >III ranged from 18% (specificity: 97%) to 55% (specificity: 77%). Among the 1203 children aged 2 to 24 months imaged after a first febrile UTI, positive predictive value of RBUS was 37% to 47% for VUR grade >II (13% to 24% for VUR grade >III); negative predictive value was 72% to 74% for VUR grade >II (95% to 96% for VUR grade >III). RBUS is a poor screening test for genitourinary abnormalities. RBUS and VCUG should be considered complementary as they provide important, but different, information.

Added prognostic value of ischaemic threshold in radionuclide myocardial perfusion imaging: a common-sense integration of exercise tolerance and ischaemia severity.

PubMed

Marini, Cecilia; Acampa, Wanda; Bauckneht, Matteo; Daniele, Stefania; Capitanio, Selene; Cantoni, Valeria; Fiz, Francesco; Zampella, Emilia; Dib, Bassam; Assante, Roberta; Bruzzi, Paolo; Sambuceti, Gianmario; Cuocolo, Alberto

2015-04-01

Reversible ischaemia at radionuclide myocardial perfusion imaging (MPI) accurately predicts risk of cardiac death and nonfatal myocardial infarction (major adverse cardiac events, MACE). This prognostic penetrance might be empowered by accounting for exercise tolerance as an indirect index of ischaemia severity. The present study aimed to verify this hypothesis integrating imaging assessment of ischaemia severity with exercise maximal rate pressure product (RPP) in a large cohort of patients with suspected or known coronary artery disease (CAD). We analysed 1,502 consecutive patients (1,014 men aged 59 ± 10 years) submitted to exercise stress/rest MPI. To account for exercise tolerance, the summed difference score (SDS) was divided by RPP at tracer injection providing a clinical prognostic index (CPI). Reversible ischaemia was documented in 357 patients (24 %) and was classified by SDS as mild (SDS 2-4) in 180, moderate (SDS 5-7) in 118 and severe (SDS >7) in 59. CPI values of ischaemic patients were clustered into tertiles with lowest and highest values indicating low and high risk, respectively. CPI modified SDS risk prediction in 119/357 (33 %) patients. During a 60-month follow-up, MACE occurred in 68 patients. Kaplan-Meier analysis revealed that CPI significantly improved predictive power for MACE incidence with respect to SDS alone. Multivariate Cox analysis confirmed the additive independent value of CPI-derived information. Integration of ischaemic threshold and ischaemia extension and severity can improve accuracy of exercise MPI in predicting long-term outcome in a large cohort of patients with suspected or known CAD.

Ultrasound as a Screening Test for Genitourinary Anomalies in Children With UTI

PubMed Central

Johnson, Emilie K.; Logvinenko, Tanya; Chow, Jeanne S.

2014-01-01

BACKGROUND: The 2011 American Academy of Pediatrics guidelines state that renal and bladder ultrasound (RBUS) should be performed after initial febrile urinary tract infection (UTI) in a young child, with voiding cystourethrogram (VCUG) performed only if RBUS shows abnormalities. We sought to determine test characteristics and predictive values of RBUS for VCUG findings in this setting. METHODS: We analyzed 3995 clinical encounters from January 1, 2006 to December 31, 2010 during which VCUG and RBUS were performed for history of UTI. Patients who had previous postnatal genitourinary imaging or history of prenatal hydronephrosis were excluded. Sensitivity, specificity, and predictive values of RBUS for VCUG abnormalities were determined. RESULTS: We identified 2259 patients age <60 months who had UTI as the indication for imaging. RBUS was reported as “normal” in 75%. On VCUG, any vesicoureteral reflux (VUR) was identified in 41.7%, VUR grade >II in 20.9%, and VUR grade >III in 2.8%. Sensitivity of RBUS for any abnormal findings on VCUG ranged from 5% (specificity: 97%) to 28% (specificity: 77%). Sensitivity for VUR grade >III ranged from 18% (specificity: 97%) to 55% (specificity: 77%). Among the 1203 children aged 2 to 24 months imaged after a first febrile UTI, positive predictive value of RBUS was 37% to 47% for VUR grade >II (13% to 24% for VUR grade >III); negative predictive value was 72% to 74% for VUR grade >II (95% to 96% for VUR grade >III). CONCLUSIONS: RBUS is a poor screening test for genitourinary abnormalities. RBUS and VCUG should be considered complementary as they provide important, but different, information. PMID:24515519

Assessment of internal mammary artery and saphenous vein graft patency and flow reserve using transthoracic Doppler echocardiography

NASA Technical Reports Server (NTRS)

Chirillo, F.; Bruni, A.; Balestra, G.; Cavallini, C.; Olivari, Z.; Thomas, J. D.; Stritoni, P.

2001-01-01

OBJECTIVE: To investigate transthoracic Doppler echocardiography in the identification of coronary artery bypass graft (CABG) flow for assessing graft patency. DESIGN: The initial study group comprised 45 consecutive patients with previous CABG undergoing elective cardiac catheterisation for recurrent ischaemia. The Doppler variables best correlated with angiographic graft patency were then tested prospectively in a further 84 patients (test group). SETTING: Three tertiary referral centres. INTERVENTIONS: Flow velocities in grafts were recorded at rest and during hyperaemia induced by dipyridamole (0.56 mg/kg/4 min), under the guidance of transthoracic colour Doppler flow mapping. Findings on transthoracic Doppler were compared with angiography. MAIN OUTCOME MEASURES: Feasibility of identifying open grafts by Doppler and diagnostic accuracy for Doppler detection of significant (>/= 70%) graft stenosis. RESULTS: In the test group the identification rate for mammary artery grafts was 100%, for saphenous vein grafts to left anterior descending coronary artery 91%, for vein grafts to right coronary artery 96%, and for vein grafts to circumflex artery 90%. Coronary flow reserve (the ratio between peak diastolic velocity under hyperaemia and at baseline) of < 1.9 (95% confidence interval 1.83 to 2.08) had 100% sensitivity, 98% specificity, 87.5% positive predictive value, and 100% negative predictive value for mammary artery graft stenosis. Coronary flow reserve of < 1.6 (95% CI 1.51 to 1.73) had 91% sensitivity, 87% specificity, 85.4% positive predictive value, and 92.3% negative predictive value for significant vein graft stenosis. CONCLUSIONS: Transthoracic Doppler can provide non-invasive assessment of CABG patency.

Right hemisphere structures predict poststroke speech fluency.

PubMed

Pani, Ethan; Zheng, Xin; Wang, Jasmine; Norton, Andrea; Schlaug, Gottfried

2016-04-26

We sought to determine via a cross-sectional study the contribution of (1) the right hemisphere's speech-relevant white matter regions and (2) interhemispheric connectivity to speech fluency in the chronic phase of left hemisphere stroke with aphasia. Fractional anisotropy (FA) of white matter regions underlying the right middle temporal gyrus (MTG), precentral gyrus (PreCG), pars opercularis (IFGop) and triangularis (IFGtri) of the inferior frontal gyrus, and the corpus callosum (CC) was correlated with speech fluency measures. A region within the superior parietal lobule (SPL) was examined as a control. FA values of regions that significantly predicted speech measures were compared with FA values from healthy age- and sex-matched controls. FA values for the right MTG, PreCG, and IFGop significantly predicted speech fluency, but FA values of the IFGtri and SPL did not. A multiple regression showed that combining FA of the significant right hemisphere regions with the lesion load of the left arcuate fasciculus-a previously identified biomarker of poststroke speech fluency-provided the best model for predicting speech fluency. FA of CC fibers connecting left and right supplementary motor areas (SMA) was also correlated with speech fluency. FA of the right IFGop and PreCG was significantly higher in patients than controls, while FA of a whole CC region of interest (ROI) and the CC-SMA ROI was significantly lower in patients. Right hemisphere white matter integrity is related to speech fluency measures in patients with chronic aphasia. This may indicate premorbid anatomical variability beneficial for recovery or be the result of poststroke remodeling. © 2016 American Academy of Neurology.

Right hemisphere structures predict poststroke speech fluency

PubMed Central

Pani, Ethan; Zheng, Xin; Wang, Jasmine; Norton, Andrea

2016-01-01

Objective: We sought to determine via a cross-sectional study the contribution of (1) the right hemisphere's speech-relevant white matter regions and (2) interhemispheric connectivity to speech fluency in the chronic phase of left hemisphere stroke with aphasia. Methods: Fractional anisotropy (FA) of white matter regions underlying the right middle temporal gyrus (MTG), precentral gyrus (PreCG), pars opercularis (IFGop) and triangularis (IFGtri) of the inferior frontal gyrus, and the corpus callosum (CC) was correlated with speech fluency measures. A region within the superior parietal lobule (SPL) was examined as a control. FA values of regions that significantly predicted speech measures were compared with FA values from healthy age- and sex-matched controls. Results: FA values for the right MTG, PreCG, and IFGop significantly predicted speech fluency, but FA values of the IFGtri and SPL did not. A multiple regression showed that combining FA of the significant right hemisphere regions with the lesion load of the left arcuate fasciculus—a previously identified biomarker of poststroke speech fluency—provided the best model for predicting speech fluency. FA of CC fibers connecting left and right supplementary motor areas (SMA) was also correlated with speech fluency. FA of the right IFGop and PreCG was significantly higher in patients than controls, while FA of a whole CC region of interest (ROI) and the CC-SMA ROI was significantly lower in patients. Conclusions: Right hemisphere white matter integrity is related to speech fluency measures in patients with chronic aphasia. This may indicate premorbid anatomical variability beneficial for recovery or be the result of poststroke remodeling. PMID:27029627

Preprocedural C-Reactive Protein Predicts Outcomes after Primary Percutaneous Coronary Intervention in Patients with ST-elevation Myocardial Infarction a systematic meta-analysis

NASA Astrophysics Data System (ADS)

Mincu, Raluca-Ileana; Jánosi, Rolf Alexander; Vinereanu, Dragos; Rassaf, Tienush; Totzeck, Matthias

2017-01-01

Risk assessment in patients with acute coronary syndromes (ACS) is critical in order to provide adequate treatment. We performed a systematic meta-analysis to assess the predictive role of serum C-reactive protein (CRP) in patients with ST-segment elevation myocardial infarction (STEMI), treated with primary percutaneous coronary intervention (PPCI). We included 7 studies, out of 1,033 studies, with a total of 6,993 patients with STEMI undergoing PPCI, which were divided in the high or low CRP group, according to the validated cut-off values provided by the corresponding CRP assay. High CRP values were associated with increased in-hospital and follow-up all-cause mortality, in-hospital and follow-up major adverse cardiac events (MACE), and recurrent myocardial infarction (MI). The pre-procedural CRP predicted in-hospital target vessel revascularization (TVR), but was not associated with acute/subacute and follow-up in-stent restenosis (ISR), and follow-up TVR. Thus, pre-procedural serum CRP could be a valuable predictor of global cardiovascular risk, rather than a predictor of stent-related complications in patients with STEMI undergoing PPCI. This biomarker might have the potential to improve the management of these high-risk patients.

Use of thermodynamic coupling between antibody-antigen binding and phospholipid acyl chain phase transition energetics to predict immunoliposome targeting affinity.

PubMed

Klegerman, Melvin E; Zou, Yuejiao; Golunski, Eva; Peng, Tao; Huang, Shao-Ling; McPherson, David D

2014-09-01

Thermodynamic analysis of ligand-target binding has been a useful tool for dissecting the nature of the binding mechanism and, therefore, potentially can provide valuable information regarding the utility of targeted formulations. Based on a consistent coupling of antibody-antigen binding and gel-liquid crystal transition energetics observed for antibody-phosphatidylethanolamine (Ab-PE) conjugates, we hypothesized that the thermodynamic parameters and the affinity for antigen of the Ab-PE conjugates could be effectively predicted once the corresponding information for the unconjugated antibody is determined. This hypothesis has now been tested in nine different antibody-targeted echogenic liposome (ELIP) preparations, where antibody is conjugated to dipalmitoylphosphatidylethanolamine (DPPE) head groups through a thioether linkage. Predictions were satisfactory (affinity not significantly different from the population of values found) in five cases (55.6%), but the affinity of the unconjugated antibody was not significantly different from the population of values found in six cases (66.7%), indicating that the affinities of the conjugated antibody tended not to deviate appreciably from those of the free antibody. While knowledge of the affinities of free antibodies may be sufficient to judge their suitability as targeting agents, thermodynamic analysis may still provide valuable information regarding their usefulness for specific applications.

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

PubMed

Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

2015-01-01

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

PubMed Central

Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

2015-01-01

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it. PMID:26241496

Forecasting impact injuries of unrestrained occupants in railway vehicle passenger compartments.

PubMed

Xie, Suchao; Zhou, Hui

2014-01-01

In order to predict the injury parameters of the occupants corresponding to different experimental parameters and to determine impact injury indices conveniently and efficiently, a model forecasting occupant impact injury was established in this work. The work was based on finite experimental observation values obtained by numerical simulation. First, the various factors influencing the impact injuries caused by the interaction between unrestrained occupants and the compartment's internal structures were collated and the most vulnerable regions of the occupant's body were analyzed. Then, the forecast model was set up based on a genetic algorithm-back propagation (GA-BP) hybrid algorithm, which unified the individual characteristics of the back propagation-artificial neural network (BP-ANN) model and the genetic algorithm (GA). The model was well suited to studies of occupant impact injuries and allowed multiple-parameter forecasts of the occupant impact injuries to be realized assuming values for various influencing factors. Finally, the forecast results for three types of secondary collision were analyzed using forecasting accuracy evaluation methods. All of the results showed the ideal accuracy of the forecast model. When an occupant faced a table, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.0 percent and the average relative error (ARE) values did not exceed 3.0 percent. When an occupant faced a seat, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 5.2 percent and the ARE values did not exceed 3.1 percent. When the occupant faced another occupant, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.3 percent and the ARE values did not exceed 3.8 percent. The injury forecast model established in this article reduced repeat experiment times and improved the design efficiency of the internal compartment's structure parameters, and it provided a new way for assessing the safety performance of the interior structural parameters in existing, and newly designed, railway vehicle compartments.

An interdisciplinary study of the estaurine and coastal oceanography of Block Island Sound and adjacent New York coastal waters

NASA Technical Reports Server (NTRS)

Yost, E.; Hollman, R.; Alexander, J.; Nuzzi, R.

1974-01-01

ERTS-1 photographic data products have been analyzed using additive color viewing and electronic image analysis techniques. Satellite data were compared to water sample data collected simultaneously with the data of ERTS-1 coverage in New York Bight. Prediction of the absolute value of total suspended particles can be made using composites of positives of MSS bands 5 and 6 which have been precisely made using the step wedge supplied on the imagery. Predictions of the relative value of the extinction coefficient can be made using bands 4 and 5. Thematic charts of total suspended particles (particles per litre) and extinction coefficient provide scientists conducting state and federal water sampling programs in New York Bight with data which improves the performance of these programs.

Measuring self-esteem in context: the importance of stability of self-esteem in psychological functioning.

PubMed

Kernis, Michael H

2005-12-01

In this article, I report on a research program that has focused on the joint roles of stability and level of self-esteem in various aspects of psychological functioning. Stability of self-esteem refers to the magnitude of short-term fluctuations that people experience in their current, contextually based feelings of self-worth. In contrast, level of self-esteem refers to representations of people's general, or typical, feelings of self-worth. A considerable amount of research reveals that self-esteem stability has predictive value beyond the predictive value of self-esteem level. Moreover, considering self-esteem stability provides one way to distinguish fragile from secure forms of high self-esteem. Results from a number of studies are presented and theoretical implications are discussed.

Filter Tuning Using the Chi-Squared Statistic

NASA Technical Reports Server (NTRS)

Lilly-Salkowski, Tyler B.

2017-01-01

This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The goal of the process is to characterize the filter performance in the metric of covariance realism. The Chi-squared statistic is the value calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance. The process of tuning an Extended Kalman Filter (EKF) for Aqua and Aura support is described, including examination of the measurement errors of available observation types, and methods of dealing with potentially volatile atmospheric drag modeling. Predictive accuracy and the distribution of the Chi-squared statistic, calculated from EKF solutions, are assessed.

Method and system for monitoring and displaying engine performance parameters

NASA Technical Reports Server (NTRS)

Abbott, Terence S. (Inventor); Person, Lee H., Jr. (Inventor)

1988-01-01

The invention is believed a major improvement that will have a broad application in governmental and commercial aviation. It provides a dynamic method and system for monitoring and simultaneously displaying in easily scanned form the available, predicted, and actual thrust of a jet aircraft engine under actual operating conditions. The available and predicted thrusts are based on the performance of a functional model of the aircraft engine under the same operating conditions. Other critical performance parameters of the aircraft engine and functional model are generated and compared, the differences in value being simultaneously displayed in conjunction with the displayed thrust values. Thus, the displayed information permits the pilot to make power adjustments directly while keeping him aware of total performance at a glance of a single display panel.

Index of prolonged air leak score validation in case of video-assisted thoracoscopic surgery anatomical lung resection: results of a nationwide study based on the French national thoracic database, EPITHOR.

PubMed

Orsini, Bastien; Baste, Jean Marc; Gossot, Dominique; Berthet, Jean Philippe; Assouad, Jalal; Dahan, Marcel; Bernard, Alain; Thomas, Pascal Alexandre

2015-10-01

The incidence rate of prolonged air leak (PAL) after lobectomy, defined as any air leak prolonged beyond 7 days, can be estimated to be in between 6 and 15%. In 2011, the Epithor group elaborated an accurate predictive score for PAL after open lung resections, so-called IPAL (index of prolonged air leak), from a nation-based surgical cohort constituted between 2004 and 2008. Since 2008, video-assisted thoracic surgery (VATS) has become popular in France among the thoracic surgical community, reaching almost 14% of lobectomies performed with this method in 2012. This minimally invasive approach was reported as a means to reduce the duration of chest tube drainage. The aim of our study was thus to validate the IPAL scoring system in patients having received VATS anatomical lung resections. We collected all anatomical VATS lung resections (lobectomy and segmentectomy) registered in the French national general thoracic surgery database (EPITHOR) between 2009 and 2012. The area under the receiver operating characteristic (ROC) curve estimated the discriminating value of the IPAL score. The slope value described the relation between the predicted and observed incidences of PALs. The Hosmer-Lemeshow test was also used to estimate the quality of adequacy between predicted and observed values. A total of 1233 patients were included: 1037 (84%) lobectomies and 196 (16%) segmentectomies. In 1099 cases (89.1%), the resection was performed for a malignant disease. Ninety-six patients (7.7%) presented with a PAL. The IPAL score provided a satisfactory predictive value with an area under the ROC curve of 0.72 (0.67-0.77). The value of the slope, 1.25 (0.9-1.58), and the Hosmer-Lemeshow test (χ(2) = 11, P = 0.35) showed that predicted and observed values were adequate. The IPAL score is valid for the estimation of the predictive risk of PAL after VATS lung resections. It may thus a priori be used to characterize any surgical population submitted to potential preventive measures. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network.

PubMed

Liu, Ruixin; Zhang, Xiaodong; Zhang, Lu; Gao, Xiaojie; Li, Huiling; Shi, Junhan; Li, Xuelin

2014-06-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network

PubMed Central

LIU, RUIXIN; ZHANG, XIAODONG; ZHANG, LU; GAO, XIAOJIE; LI, HUILING; SHI, JUNHAN; LI, XUELIN

2014-01-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue. PMID:24926369

Shuttle STS-2 mission communication systems RF coverage and performance predictions. Volume 1: Ascent

NASA Technical Reports Server (NTRS)

Porter, J. A.; Gibson, J. S.; Kroll, Q. D.; Loh, Y. C.

1981-01-01

The RF communications capabilities and nominally expected performance for the ascent phase of the second orbital flight of the shuttle are provided. Predicted performance is given mainly in the form of plots of signal strength versus elapsed mission time for the STDN (downlink) and shuttle orbiter (uplink) receivers for the S-band PM and FM, and UHF systems. Performance of the NAV and landing RF systems is treated for RTLS abort, since in this case the spacecraft will loop around and return to the launch site. NAV and landing RF systems include TACAN, MSBLS, and C-band altimeter. Signal strength plots were produced by a computer program which combines the spacecraft trajectory, antenna patterns, transmit and receive performance characteristics, and system mathematical models. When available, measured spacecraft parameters were used in the predictions; otherwise, specified values were used. Specified ground station parameter values were also used. Thresholds and other criteria on the graphs are explained.

A Systematic Review of Biomarkers and Risk of Incident Type 2 Diabetes: An Overview of Epidemiological, Prediction and Aetiological Research Literature

PubMed Central

Sahlqvist, Anna-Stina; Lotta, Luca; Brosnan, Julia M.; Vollenweider, Peter; Giabbanelli, Philippe; Nunez, Derek J.; Waterworth, Dawn; Scott, Robert A.; Langenberg, Claudia; Wareham, Nicholas J.

2016-01-01

Background Blood-based or urinary biomarkers may play a role in quantifying the future risk of type 2 diabetes (T2D) and in understanding possible aetiological pathways to disease. However, no systematic review has been conducted that has identified and provided an overview of available biomarkers for incident T2D. We aimed to systematically review the associations of biomarkers with risk of developing T2D and to highlight evidence gaps in the existing literature regarding the predictive and aetiological value of these biomarkers and to direct future research in this field. Methods and Findings We systematically searched PubMed MEDLINE (January 2000 until March 2015) and Embase (until January 2016) databases for observational studies of biomarkers and incident T2D according to the 2009 PRISMA guidelines. We also searched availability of meta-analyses, Mendelian randomisation and prediction research for the identified biomarkers. We reviewed 3910 titles (705 abstracts) and 164 full papers and included 139 papers from 69 cohort studies that described the prospective relationships between 167 blood-based or urinary biomarkers and incident T2D. Only 35 biomarkers were reported in large scale studies with more than 1000 T2D cases, and thus the evidence for association was inconclusive for the majority of biomarkers. Fourteen biomarkers have been investigated using Mendelian randomisation approaches. Only for one biomarker was there strong observational evidence of association and evidence from genetic association studies that was compatible with an underlying causal association. In additional search for T2D prediction, we found only half of biomarkers were examined with formal evidence of predictive value for a minority of these biomarkers. Most biomarkers did not enhance the strength of prediction, but the strongest evidence for prediction was for biomarkers that quantify measures of glycaemia. Conclusions This study presents an extensive review of the current state of the literature to inform the strategy for future interrogation of existing and newly described biomarkers for T2D. Many biomarkers have been reported to be associated with the risk of developing T2D. The evidence of their value in adding to understanding of causal pathways to disease is very limited so far. The utility of most biomarkers remains largely unknown in clinical prediction. Future research should focus on providing good genetic instruments across consortia for possible biomarkers in Mendelian randomisation, prioritising biomarkers for measurement in large-scale cohort studies and examining predictive utility of biomarkers for a given context. PMID:27788146

SU-D-207B-03: A PET-CT Radiomics Comparison to Predict Distant Metastasis in Lung Adenocarcinoma

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

Coroller, T; Yip, S; Lee, S

2016-06-15

Purpose: Early prediction of distant metastasis may provide crucial information for adaptive therapy, subsequently improving patient survival. Radiomic features that extracted from PET and CT images have been used for assessing tumor phenotype and predicting clinical outcomes. This study investigates the values of radiomic features in predicting distant metastasis (DM) in non-small cell lung cancer (NSCLC). Methods: A total of 108 patients with stage II–III lung adenocarcinoma were included in this retrospective study. Twenty radiomic features were selected (10 from CT and 10 from PET). Conventional features (metabolic tumor volume, SUV, volume and diameter) were included for comparison. Concordance indexmore » (CI) was used to evaluate features prognostic value. Noether test was used to compute p-value to consider CI significance from random (CI = 0.5) and were adjusted for multiple testing using false rate discovery (FDR). Results: A total of 70 patients had DM (64.8%) with a median time to event of 8.8 months. The median delivered dose was 60 Gy (range 33–68 Gy). None of the conventional features from PET (CI ranged from 0.51 to 0.56) or CT (CI ranged from 0.57 to 0.58) were significant from random. Five radiomics features were significantly prognostic from random for DM (p-values < 0.05). Four were extracted from CT (CI = 0.61 to 0.63, p-value <0.01) and one from PET which was also the most prognostic (CI = 0.64, p-value <0.001). Conclusion: This study demonstrated significant association between radiomic features and DM for patients with locally advanced lung adenocarcinoma. Moreover, conventional (clinically utilized) metrics were not significantly associated with DM. Radiomics can potentially help classify patients at higher risk of DM, allowing clinicians to individualize treatment, such as intensification of chemotherapy) to reduce the risk of DM and improve survival. R.M. has consulting interests with Amgen.« less

Good health checks according to the general public; expectations and criteria: a focus group study.

PubMed

Stol, Yrrah H; Asscher, Eva C A; Schermer, Maartje H N

2018-06-22

Health checks or health screenings identify (risk factors for) disease in people without a specific medical indication. So far, the perspective of (potential) health check users has remained underexposed in discussions about the ethics and regulation of health checks. In 2017, we conducted a qualitative study with lay people from the Netherlands (four focus groups). We asked what participants consider characteristics of good and bad health checks, and whether they saw a role for the Dutch government. Participants consider a good predictive value the most important characteristic of a good health check. Information before, during and after the test, knowledgeable and reliable providers, tests for treatable (risk factors for) disease, respect for privacy, no unnecessary health risks and accessibility are also mentioned as criteria for good health checks. Participants make many assumptions about health check offers. They assume health checks provide certainty about the presence or absence of disease, that health checks offer opportunities for health benefits and that the privacy of health check data is guaranteed. In their choice for provider and test they tend to rely more on heuristics than information. Participants trust physicians to put the interest of potential health check users first and expect the Dutch government to intervene if providers other than physicians failed to do so by offering tests with a low predictive value, or tests that may harm people, or by infringing the privacy of users. Assumptions of participants are not always justified, but they may influence the choice to participate. This is problematic because choices for checks with a low predictive value that do not provide health benefits may create uncertainty and may cause harm to health; an outcome diametrically opposite to the one intended. Also, this may impair the relationship of trust with physicians and the Dutch government. To further and protect autonomous choice and to maintain trust, we recommend the following measures to timely adjust false expectations: advertisements that give an accurate impression of health check offers, and the installation of a quality mark.

NOVA-2 -- A Digital Computer Program for Analyzing Nuclear Overpressure Effects on Aircraft. Part 1. Theory

DTIC Science & Technology

1976-08-01

extensive areas of good agreement with measured loadings where the prediction is based on acoustic theory. Acoustic theory as applied to thin airfoils...Acoustic thaory ha« baen demonstrated by references 12 through 18 to provide fairly good agreeaent with measured airloads due to blast and shock... ia found to riae to large values near the leading edge. Higher observed values of Ac further rearward of the leading edge ere found to compel

ROCK I Has More Accurate Prognostic Value than MET in Predicting Patient Survival in Colorectal Cancer.

PubMed

Li, Jian; Bharadwaj, Shruthi S; Guzman, Grace; Vishnubhotla, Ramana; Glover, Sarah C

2015-06-01

Colorectal cancer remains the second leading cause of death in the United States despite improvements in incidence rates and advancements in screening. The present study evaluated the prognostic value of two tumor markers, MET and ROCK I, which have been noted in other cancers to provide more accurate prognoses of patient outcomes than tumor staging alone. We constructed a tissue microarray from surgical specimens of adenocarcinomas from 108 colorectal cancer patients. Using immunohistochemistry, we examined the expression levels of tumor markers MET and ROCK I, with a pathologist blinded to patient identities and clinical outcomes providing the scoring of MET and ROCK I expression. We then used retrospective analysis of patients' survival data to provide correlations with expression levels of MET and ROCK I. Both MET and ROCK I were significantly over-expressed in colorectal cancer tissues, relative to the unaffected adjacent mucosa. Kaplan-Meier survival analysis revealed that patients' 5-year survival was inversely correlated with levels of expression of ROCK I. In contrast, MET was less strongly correlated with five-year survival. ROCK I provides better efficacy in predicting patient outcomes, compared to either tumor staging or MET expression. As a result, ROCK I may provide a less invasive method of assessing patient prognoses and directing therapeutic interventions. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Implementation of machine learning for high-volume manufacturing metrology challenges (Conference Presentation)

NASA Astrophysics Data System (ADS)

Timoney, Padraig; Kagalwala, Taher; Reis, Edward; Lazkani, Houssam; Hurley, Jonathan; Liu, Haibo; Kang, Charles; Isbester, Paul; Yellai, Naren; Shifrin, Michael; Etzioni, Yoav

2018-03-01

In recent years, the combination of device scaling, complex 3D device architecture and tightening process tolerances have strained the capabilities of optical metrology tools to meet process needs. Two main categories of approaches have been taken to address the evolving process needs. In the first category, new hardware configurations are developed to provide more spectral sensitivity. Most of this category of work will enable next generation optical metrology tools to try to maintain pace with next generation process needs. In the second category, new innovative algorithms have been pursued to increase the value of the existing measurement signal. These algorithms aim to boost sensitivity to the measurement parameter of interest, while reducing the impact of other factors that contribute to signal variability but are not influenced by the process of interest. This paper will evaluate the suitability of machine learning to address high volume manufacturing metrology requirements in both front end of line (FEOL) and back end of line (BEOL) sectors from advanced technology nodes. In the FEOL sector, initial feasibility has been demonstrated to predict the fin CD values from an inline measurement using machine learning. In this study, OCD spectra were acquired after an etch process that occurs earlier in the process flow than where the inline CD is measured. The fin hard mask etch process is known to impact the downstream inline CD value. Figure 1 shows the correlation of predicted CD vs downstream inline CD measurement obtained after the training of the machine learning algorithm. For BEOL, machine learning is shown to provide an additional source of information in prediction of electrical resistance from structures that are not compatible for direct copper height measurement. Figure 2 compares the trench height correlation to electrical resistance (Rs) and the correlation of predicted Rs to the e-test Rs value for a far back end of line (FBEOL) metallization level across 3 products. In the case of product C, it is found that the predicted Rs correlation to the e-test value is significantly improved utilizing spectra acquired at the e-test structure. This paper will explore the considerations required to enable use of machine learning derived metrology output to enable improved process monitoring and control. Further results from the FEOL and BEOL sectors will be presented, together with further discussion on future proliferation of machine learning based metrology solutions in high volume manufacturing.

On the Distribution of Protein Refractive Index Increments

PubMed Central

Zhao, Huaying; Brown, Patrick H.; Schuck, Peter

2011-01-01

The protein refractive index increment, dn/dc, is an important parameter underlying the concentration determination and the biophysical characterization of proteins and protein complexes in many techniques. In this study, we examine the widely used assumption that most proteins have dn/dc values in a very narrow range, and reappraise the prediction of dn/dc of unmodified proteins based on their amino acid composition. Applying this approach in large scale to the entire set of known and predicted human proteins, we obtain, for the first time, to our knowledge, an estimate of the full distribution of protein dn/dc values. The distribution is close to Gaussian with a mean of 0.190 ml/g (for unmodified proteins at 589 nm) and a standard deviation of 0.003 ml/g. However, small proteins <10 kDa exhibit a larger spread, and almost 3000 proteins have values deviating by more than two standard deviations from the mean. Due to the widespread availability of protein sequences and the potential for outliers, the compositional prediction should be convenient and provide greater accuracy than an average consensus value for all proteins. We discuss how this approach should be particularly valuable for certain protein classes where a high dn/dc is coincidental to structural features, or may be functionally relevant such as in proteins of the eye. PMID:21539801

On the distribution of protein refractive index increments.

PubMed

Zhao, Huaying; Brown, Patrick H; Schuck, Peter

2011-05-04

The protein refractive index increment, dn/dc, is an important parameter underlying the concentration determination and the biophysical characterization of proteins and protein complexes in many techniques. In this study, we examine the widely used assumption that most proteins have dn/dc values in a very narrow range, and reappraise the prediction of dn/dc of unmodified proteins based on their amino acid composition. Applying this approach in large scale to the entire set of known and predicted human proteins, we obtain, for the first time, to our knowledge, an estimate of the full distribution of protein dn/dc values. The distribution is close to Gaussian with a mean of 0.190 ml/g (for unmodified proteins at 589 nm) and a standard deviation of 0.003 ml/g. However, small proteins <10 kDa exhibit a larger spread, and almost 3000 proteins have values deviating by more than two standard deviations from the mean. Due to the widespread availability of protein sequences and the potential for outliers, the compositional prediction should be convenient and provide greater accuracy than an average consensus value for all proteins. We discuss how this approach should be particularly valuable for certain protein classes where a high dn/dc is coincidental to structural features, or may be functionally relevant such as in proteins of the eye. Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Prognostic and diagnostic value of EEG signal coupling measures in coma.

PubMed

Zubler, Frederic; Koenig, Christa; Steimer, Andreas; Jakob, Stephan M; Schindler, Kaspar A; Gast, Heidemarie

2016-08-01

Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Familism Values, Family Time, and Mexican-Origin Young Adults’ Depressive Symptoms

PubMed Central

Zeiders, Katharine H.; Updegraff, Kimberly A.; Umaña-Taylor, Adriana J.; McHale, Susan M.; Padilla, Jenny

2015-01-01

Using longitudinal data across eight years, this study examined how parents’ familism values in early adolescence predicted youths’ depressive symptoms in young adulthood via youths’ familism values and family time. We examined these processes among 246 Mexican-origin families using interview and phone-diary data. Findings revealed that fathers’ familism values predicted male and female youths’ familism values in middle adolescence. For female youth only, fathers’ familism values also predicted youths’ family time in late adolescence. The link between family time and young adults’ depressive symptoms depended on parental acceptance and adolescent gender: Among female and male youth, family time predicted fewer depressive symptoms, but only when paternal acceptance was high. For female adolescents only, family time predicted fewer depressive symptoms when maternal acceptance was high but more depressive symptoms when maternal acceptance was low. Findings highlight family dynamics as the mechanisms through which familism values have implications for youths’ adjustment. PMID:26778855

Non-animal approaches for toxicokinetics in risk evaluations of food chemicals.

PubMed

Punt, Ans; Peijnenburg, Ad A C M; Hoogenboom, Ron L A P; Bouwmeester, Hans

2017-01-01

The objective of the present work was to review the availability and predictive value of non-animal toxicokinetic approaches and to evaluate their current use in European risk evaluations of food contaminants, additives and food contact materials, as well as pesticides and medicines. Results revealed little use of quantitative animal or human kinetic data in risk evaluations of food chemicals, compared with pesticides and medicines. Risk evaluations of medicines provided sufficient in vivo kinetic data from different species to evaluate the predictive value of animal kinetic data for humans. These data showed a relatively poor correlation between the in vivo bioavailability in rats and dogs versus that in humans. In contrast, in vitro (human) kinetic data have been demonstrated to provide adequate predictions of the fate of compounds in humans, using appropriate in vitro-in vivo scalers and by integration of in vitro kinetic data with in silico kinetic modelling. Even though in vitro kinetic data were found to be occasionally included within risk evaluations of food chemicals, particularly results from Caco-2 absorption experiments and in vitro data on gut-microbial conversions, only minor use of in vitro methods for metabolism and quantitative in vitro-in vivo extrapolation methods was identified. Yet, such quantitative predictions are essential in the development of alternatives to animal testing as well as to increase human relevance of toxicological risk evaluations. Future research should aim at further improving and validating quantitative alternative methods for kinetics, thereby increasing regulatory acceptance of non-animal kinetic data.

Interleukin-27 is a novel candidate diagnostic biomarker for bacterial infection in critically ill children.

PubMed

Wong, Hector R; Cvijanovich, Natalie Z; Hall, Mark; Allen, Geoffrey L; Thomas, Neal J; Freishtat, Robert J; Anas, Nick; Meyer, Keith; Checchia, Paul A; Lin, Richard; Bigham, Michael T; Sen, Anita; Nowak, Jeffrey; Quasney, Michael; Henricksen, Jared W; Chopra, Arun; Banschbach, Sharon; Beckman, Eileen; Harmon, Kelli; Lahni, Patrick; Shanley, Thomas P

2012-10-29

Differentiating between sterile inflammation and bacterial infection in critically ill patients with fever and other signs of the systemic inflammatory response syndrome (SIRS) remains a clinical challenge. The objective of our study was to mine an existing genome-wide expression database for the discovery of candidate diagnostic biomarkers to predict the presence of bacterial infection in critically ill children. Genome-wide expression data were compared between patients with SIRS having negative bacterial cultures (n = 21) and patients with sepsis having positive bacterial cultures (n = 60). Differentially expressed genes were subjected to a leave-one-out cross-validation (LOOCV) procedure to predict SIRS or sepsis classes. Serum concentrations of interleukin-27 (IL-27) and procalcitonin (PCT) were compared between 101 patients with SIRS and 130 patients with sepsis. All data represent the first 24 hours of meeting criteria for either SIRS or sepsis. Two hundred twenty one gene probes were differentially regulated between patients with SIRS and patients with sepsis. The LOOCV procedure correctly predicted 86% of the SIRS and sepsis classes, and Epstein-Barr virus-induced gene 3 (EBI3) had the highest predictive strength. Computer-assisted image analyses of gene-expression mosaics were able to predict infection with a specificity of 90% and a positive predictive value of 94%. Because EBI3 is a subunit of the heterodimeric cytokine, IL-27, we tested the ability of serum IL-27 protein concentrations to predict infection. At a cut-point value of ≥5 ng/ml, serum IL-27 protein concentrations predicted infection with a specificity and a positive predictive value of >90%, and the overall performance of IL-27 was generally better than that of PCT. A decision tree combining IL-27 and PCT improved overall predictive capacity compared with that of either biomarker alone. Genome-wide expression analysis has provided the foundation for the identification of IL-27 as a novel candidate diagnostic biomarker for predicting bacterial infection in critically ill children. Additional studies will be required to test further the diagnostic performance of IL-27. The microarray data reported in this article have been deposited in the Gene Expression Omnibus under accession number GSE4607.

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.

Predicting lake trophic state by relating Secchi-disk transparency measurements to Landsat-satellite imagery for Michigan inland lakes, 2003-05 and 2007-08

USGS Publications Warehouse

Fuller, L.M.; Jodoin, R.S.; Minnerick, R.J.

2011-01-01

Inland lakes are an important economic and environmental resource for Michigan. The U.S. Geological Survey and the Michigan Department of Natural Resources and Environment have been cooperatively monitoring the quality of selected lakes in Michigan through the Lake Water Quality Assessment program. Sampling for this program began in 2001; by 2010, 730 of Michigan’s 11,000 inland lakes are expected to have been sampled once. Volunteers coordinated by the Michigan Department of Natural Resources and Environment began sampling lakes in 1974 and continue to sample (in 2010) approximately 250 inland lakes each year through the Michigan Cooperative Lakes Monitoring Program. Despite these sampling efforts, it still is impossible to physically collect measurements for all Michigan inland lakes; however, Landsat-satellite imagery has been used successfully in Minnesota, Wisconsin, Michigan, and elsewhere to predict the trophic state of unsampled inland lakes greater than 20 acres by producing regression equations relating in-place Secchi-disk measurements to Landsat bands. This study tested three alternatives to methods previously used in Michigan to improve results for predicted statewide Trophic State Index (TSI) computed from Secchi-disk transparency (TSI (SDT)). The alternative methods were used on 14 Landsat-satellite scenes with statewide TSI (SDT) for two time periods (2003– 05 and 2007–08). Specifically, the methods were (1) satellitedata processing techniques to remove areas affected by clouds, cloud shadows, haze, shoreline, and dense vegetation for inland lakes greater than 20 acres in Michigan; (2) comparison of the previous method for producing a single open-water predicted TSI (SDT) value (which was based on an area of interest (AOI) and lake-average approach) to an alternative Gethist method for identifying open-water areas in inland lakes (which follows the initial satellite-data processing and targets the darkest pixels, representing the deepest water, before regression equations are created); and (3) checking to see whether the predicted TSI (SDT) values compared well between two regression equations, one previously used in Michigan and an alternative equation from the hydrologic literature. The combination of improved satellite-data processing techniques and the Gethist method to identify open-water areas in inland lakes during 2003–05 and 2007–08 provided a stronger relation and statistical significance between predicted TSI (SDT) and measured TSI than did the AOI lake-average method; differences in results for the two methods were significant at the 99-percent confidence level. With regard to the comparison of the regression equations, there were no statistically significant differences at the 95-percent confidence level between results from the two equations. The previously used equation, in combination with the Gethist method, yielded coefficient of determination (R2) values of 0.71 and 0.77 for the periods 2003–05 and 2007–08, respectively. The alternative equation, in combination with the Gethist method, yielded R2 values of 0.74 and 0.75 for 2003–05 and 2007–08, respectively. Predicted TSI (SDT) and measured TSI (SDT) values for lakes used in the regression equations compared well, with R2 values of 0.95 and 0.96 for predicted TSI (SDT) for 2003–05 and 2007–08, respectively. The R2 values for statewide predicted TSI (SDT) for all inland lakes with available open-water areas for 2003–05 and 2007–08 were 0.91 and 0.93, respectively. Although the two equations predicted similar trophic-state classes, the alternative equation is planned to be used for future prediction of TSI (SDT) values for Michigan inland lakes, to promote consistency in comparing predicted values between States and for potential use in trend analysis.

Data assimilation for groundwater flow modelling using Unbiased Ensemble Square Root Filter: Case study in Guantao, North China Plain

NASA Astrophysics Data System (ADS)

Li, N.; Kinzelbach, W.; Li, H.; Li, W.; Chen, F.; Wang, L.

2017-12-01

Data assimilation techniques are widely used in hydrology to improve the reliability of hydrological models and to reduce model predictive uncertainties. This provides critical information for decision makers in water resources management. This study aims to evaluate a data assimilation system for the Guantao groundwater flow model coupled with a one-dimensional soil column simulation (Hydrus 1D) using an Unbiased Ensemble Square Root Filter (UnEnSRF) originating from the Ensemble Kalman Filter (EnKF) to update parameters and states, separately or simultaneously. To simplify the coupling between unsaturated and saturated zone, a linear relationship obtained from analyzing inputs to and outputs from Hydrus 1D is applied in the data assimilation process. Unlike EnKF, the UnEnSRF updates parameter ensemble mean and ensemble perturbations separately. In order to keep the ensemble filter working well during the data assimilation, two factors are introduced in the study. One is called damping factor to dampen the update amplitude of the posterior ensemble mean to avoid nonrealistic values. The other is called inflation factor to relax the posterior ensemble perturbations close to prior to avoid filter inbreeding problems. The sensitivities of the two factors are studied and their favorable values for the Guantao model are determined. The appropriate observation error and ensemble size were also determined to facilitate the further analysis. This study demonstrated that the data assimilation of both model parameters and states gives a smaller model prediction error but with larger uncertainty while the data assimilation of only model states provides a smaller predictive uncertainty but with a larger model prediction error. Data assimilation in a groundwater flow model will improve model prediction and at the same time make the model converge to the true parameters, which provides a successful base for applications in real time modelling or real time controlling strategies in groundwater resources management.

An approach to value-based simulator selection: The creation and evaluation of the simulator value index tool.

PubMed

Rooney, Deborah M; Hananel, David M; Covington, Benjamin J; Dionise, Patrick L; Nykamp, Michael T; Pederson, Melvin; Sahloul, Jamal M; Vasquez, Rachael; Seagull, F Jacob; Pinsky, Harold M; Sweier, Domenica G; Cooke, James M

2018-04-01

Currently there is no reliable, standardized mechanism to support health care professionals during the evaluation of and procurement processes for simulators. A tool founded on best practices could facilitate simulator purchase processes. In a 3-phase process, we identified top factors considered during the simulator purchase process through expert consensus (n = 127), created the Simulator Value Index (SVI) tool, evaluated targeted validity evidence, and evaluated the practical value of this SVI. A web-based survey was sent to simulation professionals. Participants (n = 79) used the SVI and provided feedback. We evaluated the practical value of 4 tool variations by calculating their sensitivity to predict a preferred simulator. Seventeen top factors were identified and ranked. The top 2 were technical stability/reliability of the simulator and customer service, with no practical differences in rank across institution or stakeholder role. Full SVI variations predicted successfully the preferred simulator with good (87%) sensitivity, whereas the sensitivity of variations in cost and customer service and cost and technical stability decreased (≤54%). The majority (73%) of participants agreed that the SVI was helpful at guiding simulator purchase decisions, and 88% agreed the SVI tool would help facilitate discussion with peers and leadership. Our findings indicate the SVI supports the process of simulator purchase using a standardized framework. Sensitivity of the tool improved when factors extend beyond traditionally targeted factors. We propose the tool will facilitate discussion amongst simulation professionals dealing with simulation, provide essential information for finance and procurement professionals, and improve the long-term value of simulation solutions. Limitations and application of the tool are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction model of dissolved oxygen in ponds based on ELM neural network

NASA Astrophysics Data System (ADS)

Li, Xinfei; Ai, Jiaoyan; Lin, Chunhuan; Guan, Haibin

2018-02-01

Dissolved oxygen in ponds is affected by many factors, and its distribution is unbalanced. In this paper, in order to improve the imbalance of dissolved oxygen distribution more effectively, the dissolved oxygen prediction model of Extreme Learning Machine (ELM) intelligent algorithm is established, based on the method of improving dissolved oxygen distribution by artificial push flow. Select the Lake Jing of Guangxi University as the experimental area. Using the model to predict the dissolved oxygen concentration of different voltage pumps, the results show that the ELM prediction accuracy is higher than the BP algorithm, and its mean square error is MSEELM=0.0394, the correlation coefficient RELM=0.9823. The prediction results of the 24V voltage pump push flow show that the discrete prediction curve can approximate the measured values well. The model can provide the basis for the artificial improvement of the dissolved oxygen distribution decision.

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China

PubMed Central

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R2 = 0.86–0.93 for 72 data sets collected in the industrial river and R2 = 0.60–0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals’ concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density. PMID:27028017

Predicting obstructive coronary artery disease using carotid ultrasound parameters: a nomogram from a large real-world clinical data.

PubMed

Wu, Na; Chen, Xinghua; Li, Mingyang; Qu, Xiaolong; Li, Yueli; Xie, Weijia; Wu, Long; Xiang, Ying; Li, Yafei; Zhong, Li

2018-05-21

Carotid ultrasound is a non-invasive tool for risk assessment of coronary artery disease (CAD). There is no consensus on which carotid ultrasound parameter constitutes the best measurement of atherosclerosis. We investigated which model of carotid ultrasound parameters and clinical risk factors (CRF) have the highest predictive value for CAD. We enrolled 2431 consecutive patients who have suspected CAD and underwent coronary angiography and carotid ultrasound with measurements of carotid intima-media thickness (CIMT), total number of plaques and areas of different types of plaques classified by echogenicity. Total number of plaques demonstrated the highest incremental prediction ability to predict CAD over CRF (area under the curve [AUC] 0.752 vs 0.701, net reclassification index [NRI] = 0.514, P < 0.001), followed by area of maximum mixed and soft plaques. CIMT had no significant incremental value over CRF (AUC 0.704 vs 0.701, P = 0.241; NRI = 0.062, P = 0.168). The model comprising total number of plaques, areas of maximum soft, hard and mixed plaques plus CRF had the highest discriminatory (AUC = 0.757) and reclassification value (NRI = 0.567) for CAD. A nomogram based on this model was developed to predict CAD. For subjects at low and intermediate risk, the model comprising total number of plaques plus CRF was the best. Total number of plaques, area of maximum soft, hard and mixed plaques showed significantly incremental prediction ability over CRF. A nomogram based on these factors provided an intuitive and practical method in detecting CAD. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China.

PubMed

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R(2) = 0.86-0.93 for 72 data sets collected in the industrial river and R(2) = 0.60-0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals' concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density.

Can blood and semen presepsin levels in males predict pregnancy in couples undergoing intra-cytoplasmic sperm injection?

PubMed

Ovayolu, Ali; Arslanbuğa, Cansev Yilmaz; Gun, Ismet; Devranoglu, Belgin; Ozdemir, Arman; Cakar, Sule Eren

2016-01-01

To determine whether semen and plasma presepsin values measured in men with normozoospermia and oligoasthenospermia undergoing invitro-fertilization would be helpful in predicting ongoing pregnancy and live birth. Group-I was defined as patients who had pregnancy after treatment and Group-II comprised those with no pregnancy. Semen and blood presepsin values were subsequently compared between the groups. Parametric comparisons were performed using Student's t-test, and non-parametric comparisons were conducted using the Mann-Whitney U test. There were 42 patients in Group-I and 72 in Group-II. In the context of successful pregnancy and live birth, semen presepsin values were statistically significantly higher in Group-I than in Group-II (p= 0.004 and p= 0.037, respectively). The most appropriate semen presepsin cut-off value for predicting both ongoing pregnancy and live birth was calculated as 199 pg/mL. Accordingly, their sensitivity was 64.5% to 59.3%, their specificity was 57.0% to 54.2%, and their positive predictive value was 37.0% to 29.6%, respectively; their negative predictive value was 80.4% in both instances. Semen presepsin values could be a new marker that may enable the prediction of successful pregnancy and/or live birth. Its negative predictive values are especially high.

Translation and validation of the Canadian diabetes risk assessment questionnaire in China.

PubMed

Guo, Jia; Shi, Zhengkun; Chen, Jyu-Lin; Dixon, Jane K; Wiley, James; Parry, Monica

2018-01-01

To adapt the Canadian Diabetes Risk Assessment Questionnaire for the Chinese population and to evaluate its psychometric properties. A cross-sectional study was conducted with a convenience sample of 194 individuals aged 35-74 years from October 2014 to April 2015. The Canadian Diabetes Risk Assessment Questionnaire was adapted and translated for the Chinese population. Test-retest reliability was conducted to measure stability. Criterion and convergent validity of the adapted questionnaire were assessed using 2-hr 75 g oral glucose tolerance tests and the Finnish Diabetes Risk Scores, respectively. Sensitivity and specificity were evaluated to establish its predictive validity. The test-retest reliability was 0.988. Adequate validity of the adapted questionnaire was demonstrated by positive correlations found between the scores and 2-hr 75 g oral glucose tolerance tests (r = .343, p < .001) and with the Finnish Diabetes Risk Scores (r = .738, p < .001). The area under receiver operating characteristic curve was 0.705 (95% CI .632, .778), demonstrating moderate diagnostic value at a cutoff score of 30. The sensitivity was 73%, with a positive predictive value of 57% and negative predictive value of 78%. Our results provided evidence supporting the translation consistency, content validity, convergent validity, criterion validity, sensitivity, and specificity of the translated Canadian Diabetes Risk Assessment Questionnaire with minor modifications. This paper provides clinical, practical, and methodological information on how to adapt a diabetes risk calculator between cultures for public health nurses. © 2017 Wiley Periodicals, Inc.

Weather Forecasting Systems and Methods

NASA Technical Reports Server (NTRS)

Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

2014-01-01

A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

Animal models of addiction

PubMed Central

Spanagel, Rainer

2017-01-01

In recent years, animal models in psychiatric research have been criticized for their limited translational value to the clinical situation. Failures in clinical trials have thus often been attributed to the lack of predictive power of preclinical animal models. Here, I argue that animal models of voluntary drug intake—under nonoperant and operant conditions—and addiction models based on the Diagnostic and Statistical Manual of Mental Disorders are crucial and informative tools for the identification of pathological mechanisms, target identification, and drug development. These models provide excellent face validity, and it is assumed that the neurochemical and neuroanatomical substrates involved in drug-intake behavior are similar in laboratory rodents and humans. Consequently, animal models of drug consumption and addiction provide predictive validity. This predictive power is best illustrated in alcohol research, in which three approved medications—acamprosate, naltrexone, and nalmefene—were developed by means of animal models and then successfully translated into the clinical situation. PMID:29302222

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

Deur, Alexandre; Shen, Jian -Ming; Wu, Xing -Gang

The Principle of Maximum Conformality (PMC) provides scale-fixed perturbative QCD predictions which are independent of the choice of the renormalization scheme, as well as the choice of the initial renormalization scale. In this article, we will test the PMC by comparing its predictions for the strong couplingmore » $$\\alpha^s_{g_1}(Q)$$, defined from the Bjorken sum rule, with predictions using conventional pQCD scale-setting. The two results are found to be compatible with each other and with the available experimental data. However, the PMC provides a significantly more precise determination, although its domain of applicability ($$Q \\gtrsim 1.5$$ GeV) does not extend to as small values of momentum transfer as that of a conventional pQCD analysis ($$Q \\gtrsim 1$$ GeV). In conclusion, we suggest that the PMC range of applicability could be improved by a modified intermediate scheme choice or using a single effective PMC scale.« less

Methods of developing core collections based on the predicted genotypic value of rice ( Oryza sativa L.).

PubMed

Li, C T; Shi, C H; Wu, J G; Xu, H M; Zhang, H Z; Ren, Y L

2004-04-01

The selection of an appropriate sampling strategy and a clustering method is important in the construction of core collections based on predicted genotypic values in order to retain the greatest degree of genetic diversity of the initial collection. In this study, methods of developing rice core collections were evaluated based on the predicted genotypic values for 992 rice varieties with 13 quantitative traits. The genotypic values of the traits were predicted by the adjusted unbiased prediction (AUP) method. Based on the predicted genotypic values, Mahalanobis distances were calculated and employed to measure the genetic similarities among the rice varieties. Six hierarchical clustering methods, including the single linkage, median linkage, centroid, unweighted pair-group average, weighted pair-group average and flexible-beta methods, were combined with random, preferred and deviation sampling to develop 18 core collections of rice germplasm. The results show that the deviation sampling strategy in combination with the unweighted pair-group average method of hierarchical clustering retains the greatest degree of genetic diversities of the initial collection. The core collections sampled using predicted genotypic values had more genetic diversity than those based on phenotypic values.

Assessing the predictive value of the American Board of Family Practice In-training Examination.

PubMed

Replogle, William H; Johnson, William D

2004-03-01

The American Board of Family Practice In-training Examination (ABFP ITE) is a cognitive examination similar in content to the ABFP Certification Examination (CE). The ABFP ITE is widely used in family medicine residency programs. It was originally developed and intended to be used for assessment of groups of residents. Despite lack of empirical support, however, some residency programs are using ABFP ITE scores as individual resident performance indicators. This study's objective was to estimate the positive predictive value of the ABFP ITE for identifying residents at risk for poor performance on the ABFP CE or a subsequent ABFP ITE. We used a normal distribution model for correlated test scores and Monte Carlo simulation to investigate the effect of test reliability (measurement errors) on the positive predictive value of the ABFP ITE. The positive predictive value of the composite score was .72. The positive predictive value of the eight specialty subscales ranged from .26 to .57. Only the composite score of the ABFP ITE has acceptable positive predictive value to be used as part of a comprehension resident evaluation system. The ABFP ITE specialty subscales do not have sufficient positive predictive value or reliability to warrant use as performance indicators.

Acidity of the amidoxime functional group in aqueous solution. A combined experimental and computational study

DOE PAGES

Mehio, Nada; Lashely, Mark A.; Nugent, Joseph W.; ...

2015-01-26

Poly(acrylamidoxime) adsorbents are often invoked in discussions of mining uranium from seawater. It has been demonstrated repeatedly in the literature that the success of these materials is due to the amidoxime functional group. While the amidoxime-uranyl chelation mode has been established, a number of essential binding constants remain unclear. This is largely due to the wide range of conflicting pK a values that have been reported for the amidoxime functional group in the literature. To resolve this existing controversy we investigated the pK a values of the amidoxime functional group using a combination of experimental and computational methods. Experimentally, wemore » used spectroscopic titrations to measure the pK a values of representative amidoximes, acetamidoxime and benzamidoxime. Computationally, we report on the performance of several protocols for predicting the pK a values of aqueous oxoacids. Calculations carried out at the MP2 or M06-2X levels of theory combined with solvent effects calculated using the SMD model provide the best overall performance with a mean absolute error of 0.33 pK a units and 0.35 pK a units, respectively, and a root mean square deviation of 0.46 pK a units and 0.45 pK a units, respectively. Finally, we employ our two best methods to predict the pK a values of promising, uncharacterized amidoxime ligands. Hence, our study provides a convenient means for screening suitable amidoxime monomers for future generations of poly(acrylamidoxime) adsorbents used to mine uranium from seawater.« less

A physically-based method for predicting peak discharge of floods caused by failure of natural and constructed earthen dams

USGS Publications Warehouse

Walder, J.S.; O'Connor, J. E.; Costa, J.E.; ,

1997-01-01

We analyse a simple, physically-based model of breach formation in natural and constructed earthen dams to elucidate the principal factors controlling the flood hydrograph at the breach. Formation of the breach, which is assumed trapezoidal in cross-section, is parameterized by the mean rate of downcutting, k, the value of which is constrained by observations. A dimensionless formulation of the model leads to the prediction that the breach hydrograph depends upon lake shape, the ratio r of breach width to depth, the side slope ?? of the breach, and the parameter ?? = (V.D3)(k/???gD), where V = lake volume, D = lake depth, and g is the acceleration due to gravity. Calculations show that peak discharge Qp depends weakly on lake shape r and ??, but strongly on ??, which is the product of a dimensionless lake volume and a dimensionless erosion rate. Qp(??) takes asymptotically distinct forms depending on whether < ??? 1 or < ??? 1. Theoretical predictions agree well with data from dam failures for which k could be reasonably estimated. The analysis provides a rapid and in many cases graphical way to estimate plausible values of Qp at the breach.We analyze a simple, physically-based model of breach formation in natural and constructed earthen dams to elucidate the principal factors controlling the flood hydrograph at the breach. Formation of the breach, which is assumed trapezoidal in cross-section, is parameterized by the mean rate of downcutting, k, the value of which is constrained by observations. A dimensionless formulation of the model leads to the prediction that the breach hydrograph depends upon lake shape, the ratio r of breach width to depth, the side slope ?? of the breach, and the parameter ?? = (V/D3)(k/???gD), where V = lake volume, D = lake depth, and g is the acceleration due to gravity. Calculations show that peak discharge Qp depends weakly on lake shape r and ??, but strongly on ??, which is the product of a dimensionless lake volume and a dimensionless erosion rate. Qp(??) takes asymptotically distinct forms depending on whether ?????1 or ?????1. Theoretical predictions agree well with data from dam failures for which k could be reasonably estimated. The analysis provides a rapid and in many cases graphical way to estimate plausible values of Qp at the breach.

Near-infrared Raman spectroscopy to detect anti-Toxoplasma gondii antibody in blood sera of domestic cats: quantitative analysis based on partial least-squares multivariate statistics

NASA Astrophysics Data System (ADS)

Duarte, Janaína; Pacheco, Marcos T. T.; Villaverde, Antonio Balbin; Machado, Rosangela Z.; Zângaro, Renato A.; Silveira, Landulfo

2010-07-01

Toxoplasmosis is an important zoonosis in public health because domestic cats are the main agents responsible for the transmission of this disease in Brazil. We investigate a method for diagnosing toxoplasmosis based on Raman spectroscopy. Dispersive near-infrared Raman spectra are used to quantify anti-Toxoplasma gondii (IgG) antibodies in blood sera from domestic cats. An 830-nm laser is used for sample excitation, and a dispersive spectrometer is used to detect the Raman scattering. A serological test is performed in all serum samples by the enzyme-linked immunosorbent assay (ELISA) for validation. Raman spectra are taken from 59 blood serum samples and a quantification model is implemented based on partial least squares (PLS) to quantify the sample's serology by Raman spectra compared to the results provided by the ELISA test. Based on the serological values provided by the Raman/PLS model, diagnostic parameters such as sensitivity, specificity, accuracy, positive prediction values, and negative prediction values are calculated to discriminate negative from positive samples, obtaining 100, 80, 90, 83.3, and 100%, respectively. Raman spectroscopy, associated with the PLS, is promising as a serological assay for toxoplasmosis, enabling fast and sensitive diagnosis.

Gap-filling methods to impute eddy covariance flux data by preserving variance.

NASA Astrophysics Data System (ADS)

Kunwor, S.; Staudhammer, C. L.; Starr, G.; Loescher, H. W.

2015-12-01

To represent carbon dynamics, in terms of exchange of CO2 between the terrestrial ecosystem and the atmosphere, eddy covariance (EC) data has been collected using eddy flux towers from various sites across globe for more than two decades. However, measurements from EC data are missing for various reasons: precipitation, routine maintenance, or lack of vertical turbulence. In order to have estimates of net ecosystem exchange of carbon dioxide (NEE) with high precision and accuracy, robust gap-filling methods to impute missing data are required. While the methods used so far have provided robust estimates of the mean value of NEE, little attention has been paid to preserving the variance structures embodied by the flux data. Preserving the variance of these data will provide unbiased and precise estimates of NEE over time, which mimic natural fluctuations. We used a non-linear regression approach with moving windows of different lengths (15, 30, and 60-days) to estimate non-linear regression parameters for one year of flux data from a long-leaf pine site at the Joseph Jones Ecological Research Center. We used as our base the Michaelis-Menten and Van't Hoff functions. We assessed the potential physiological drivers of these parameters with linear models using micrometeorological predictors. We then used a parameter prediction approach to refine the non-linear gap-filling equations based on micrometeorological conditions. This provides us an opportunity to incorporate additional variables, such as vapor pressure deficit (VPD) and volumetric water content (VWC) into the equations. Our preliminary results indicate that improvements in gap-filling can be gained with a 30-day moving window with additional micrometeorological predictors (as indicated by lower root mean square error (RMSE) of the predicted values of NEE). Our next steps are to use these parameter predictions from moving windows to gap-fill the data with and without incorporation of potential driver variables of the parameters traditionally used. Then, comparisons of the predicted values from these methods and 'traditional' gap-filling methods (using 12 fixed monthly windows) will be assessed to show the scale of preserving variance. Further, this method will be applied to impute artificially created gaps for analyzing if variance is preserved.

Spacecraft Communications System Verification Using On-Axis Near Field Measurement Techniques

NASA Technical Reports Server (NTRS)

Keating, Thomas; Baugh, Mark; Gosselin, R. B.; Lecha, Maria C.; Krebs, Carolyn A. (Technical Monitor)

2000-01-01

Determination of the readiness of a spacecraft for launch is a critical requirement. The final assembly of all subsystems must be verified. Testing of a communications system can mostly be done using closed-circuits (cabling to/from test ports), but the final connections to the antenna require radiation tests. The Tropical Rainfall Measuring Mission (TRMM) Project used a readily available 'near-fleld on-axis' equation to predict the values to be used for comparison with those obtained in a test program. Tests were performed in a 'clean room' environment at both Goddard Space Flight Center (GSFC) and in Japan at the Tanegashima Space Center (TnSC) launch facilities. Most of the measured values agreed with the predicted values to within 0.5 dB. This demonstrates that sometimes you can use relatively simple techniques to make antenna performance measurements when use of the 'far field ranges, anechoic chambers, or precision near-field ranges' are neither available nor practical. Test data and photographs are provided.

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.

Evaluation of antimicrobial resistance phenotypes for predicting multidrug-resistant Salmonella recovered from retail meats and humans in the United States.

PubMed

Whichard, Jean M; Medalla, Felicita; Hoekstra, Robert M; McDermott, Patrick F; Joyce, Kevin; Chiller, Tom; Barrett, Timothy J; White, David G

2010-03-01

Although multidrug-resistant (MDR) non-Typhi Salmonella (NTS) strains are a concern in food production, determining resistance to multiple antimicrobial agents at slaughter or processing may be impractical. Single antimicrobial resistance results for predicting multidrug resistance are desirable. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value were used to determine each antimicrobial agent's ability to predict MDR phenotypes of human health significance: ACSSuT (resistance to at least ampicillin, chloramphenicol, streptomycin, sulfamethoxazole, tetracycline) in NTS isolates, and MDR-AmpC-SN (resistance to ACSSuT, additional resistance to amoxicillin-clavulanate and to ceftiofur, and decreased susceptibility [MIC >= 2 microg/ml] to ceftriaxone) in NTS serotype Newport. The U.S. National Antimicrobial Resistance Monitoring System determined MICs to 15 or more antimicrobial agents for 9,955 NTS isolates from humans from 1999 to 2004 and 689 NTS isolates from retail meat from 2002 to 2004. A total of 847 (8.5%) human and 26 (3.8%) retail NTS isolates were ACSSuT; 995 (10.0%) human and 16 (2.3%) retail isolates were serotype Newport. Among Salmonella Newport, 204 (20.5%) human and 9 (56.3%) retail isolates were MDR-AmpC-SN. Chloramphenicol resistance provided the highest PPVs for ACSSuT among human (90.5%; 95% confidence interval, 88.4 to 92.3) and retail NTS isolates (96.3%; 95% confidence interval, 81.0 to 99.9). Resistance to ceftiofur and to amoxicillin-clavulanate and decreased susceptibility to ceftriaxone provided the highest PPVs (97.1, 98.1, and 98.6%, respectively) for MDR-AmpC-SN from humans. High PPVs for these agents applied to retail meat MDR-AmpC-SN, but isolate numbers were lower. Variations in MIC results may complicate ceftriaxone's predictive utility. Selecting specific antimicrobial resistance offers practical alternatives for predicting MDR phenotypes. Chloramphenicol resistance works best for ACSSuT-NTS, and resistance to ceftiofur, amoxicillin-clavulanate, or chloramphenicol works best for MDR-AmpC-SN.

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.

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

PubMed

Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

2018-05-15

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

Prospective evaluation of a screening protocol to exclude deep vein thrombosis on the basis of a combination of quantitative D-dimer testing and pretest clinical probability score.

PubMed

Yamaki, Takashi; Nozaki, Motohiro; Sakurai, Hiroyuki; Takeuchi, Masaki; Soejima, Kazutaka; Kono, Taro

2005-11-01

Clinical signs and symptoms such as swelling, pain, and redness are unreliable markers of deep vein thrombosis (DVT). Because of this venous duplex scanning (VDS) has been heavily used in DVT detection. The purpose of this study was to determine if a combination of D-dimer testing and pretest clinical score could reduce the use of VDS in symptomatic patients with suspected DVT. One hundred seventy-four consecutive patients with suspected DVT were prospectively evaluated using pretest clinical probability (PCP) score and D-dimer testing before VDS. After calculating clinical probability scores developed by Wells and associates, patients were divided into low risk (or=3 points) PCP. One hundred fifty-eight patients were enrolled. The prevalence of DVT in this study was 37%. Thirty-eight patients (24%) were classified as low risk, 64 (41%) as moderate risk, and 56 (35%) as high risk PCP. DVT was identified in only one patient (2.6%) with low risk PCP. In contrast, DVT was found in 22 (34%) with moderate risk, and 35 (63%) with high risk PCP. In the high and moderate risk PCP groups, positive scan patients had a markedly higher value of D-dimer assay than negative scan patients (p=0.0001 and p=0.0057, respectively). In the low risk PCP patients, D-dimer testing provided 100% sensitivity, 46% specificity, 4.8% positive predictive value, and 100% negative predictive value in the diagnosis of DVT. Similarly, in the moderate risk PCP, the D-dimer testing showed 100% sensitivity, 45% specificity, 49% positive predictive value, and 100% negative predictive value. In the high risk group, D-dimer testing achieved 100% sensitivity, 57% specificity, 80% positive predictive value, and 100% negative predictive value in the diagnosis of DVT. These results suggested that 36 of 158 patients who had a non-high PCP (low and moderate PCP) and a normal D-dimer concentration were considered to have no additional investigation, so VDS could have been reduced by 23% (36/158). A combination of D-dimer testing and clinical probability score may be effective in avoiding unnecessary VDS in suspected symptomatic DVT in the low and moderate PCP patients. The need for VDS could be reduced by 23% despite a relatively high prevalence of DVT.

Passenger Flow Forecasting Research for Airport Terminal Based on SARIMA Time Series Model

NASA Astrophysics Data System (ADS)

Li, Ziyu; Bi, Jun; Li, Zhiyin

2017-12-01

Based on the data of practical operating of Kunming Changshui International Airport during2016, this paper proposes Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict the passenger flow. This article not only considers the non-stationary and autocorrelation of the sequence, but also considers the daily periodicity of the sequence. The prediction results can accurately describe the change trend of airport passenger flow and provide scientific decision support for the optimal allocation of airport resources and optimization of departure process. The result shows that this model is applicable to the short-term prediction of airport terminal departure passenger traffic and the average error ranges from 1% to 3%. The difference between the predicted and the true values of passenger traffic flow is quite small, which indicates that the model has fairly good passenger traffic flow prediction ability.

Predictive Value of Matrix Metalloproteinases and Their Inhibitors for Mortality in Septic Patients: A Cohort Study.

PubMed

Serrano-Gomez, Sergio; Burgos-Angulo, Gabriel; Niño-Vargas, Daniela Camila; Niño, María Eugenia; Cárdenas, María Eugenia; Chacón-Valenzuela, Estephania; McCosham, Diana Margarita; Peinado-Acevedo, Juan Sebastián; Lopez, M Marcos; Cunha, Fernando; Pazin-Filho, Antonio; Ilarraza, Ramses; Schulz, Richard; Torres-Dueñas, Diego

2017-01-01

Over 170 biomarkers are being investigated regarding their prognostic and diagnostic accuracy in sepsis in order to find new tools to reduce morbidity and mortality. Matrix metalloproteinases (MMPs) and their inhibitors have been recently studied as promising new prognostic biomarkers in patients with sepsis. This study is aimed at determining the utility of several cutoff points of these biomarkers to predict mortality in patients with sepsis. A multicenter, prospective, analytic cohort study was performed in the metropolitan area of Bucaramanga, Colombia. A total of 289 patients with sepsis and septic shock were included. MMP-9, MMP-2, tissue inhibitor of metalloproteinase 1 (TIMP-1), TIMP-2, TIMP-1/MMP-9 ratio, and TIMP-2/MMP-2 ratio were determined in blood samples. Value ranges were correlated with mortality to estimate sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. Sensitivity ranged from 33.3% (MMP-9/TIMP-1 ratio) to 60.6% (TIMP-1) and specificity varied from 38.8% (MMP-2/TIMP-2 ratio) to 58.5% (TIMP-1). As for predictive values, positive predictive value range was from 17.5% (MMP-9/TIMP-1 ratio) to 70.4% (MMP-2/TIMP-2 ratio), whereas negative predictive values were between 23.2% (MMP-2/TIMP-2 ratio) and 80.9% (TIMP-1). Finally, area under the curve scores ranged from 0.31 (MMP-9/TIMP-1 ratio) to 0.623 (TIMP-1). Although TIMP-1 showed higher sensitivity, specificity, and negative predictive value, with a representative population sample, we conclude that none of the evaluated biomarkers had significant predictive value for mortality.

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

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

NASA Astrophysics Data System (ADS)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

Development of a Greek solar map based on solar model estimations

NASA Astrophysics Data System (ADS)

Kambezidis, H. D.; Psiloglou, B. E.; Kavadias, K. A.; Paliatsos, A. G.; Bartzokas, A.

2016-05-01

The realization of Renewable Energy Sources (RES) for power generation as the only environmentally friendly solution, moved solar systems to the forefront of the energy market in the last decade. The capacity of the solar power doubles almost every two years in many European countries, including Greece. This rise has brought the need for reliable predictions of meteorological data that can easily be utilized for proper RES-site allocation. The absence of solar measurements has, therefore, raised the demand for deploying a suitable model in order to create a solar map. The generation of a solar map for Greece, could provide solid foundations on the prediction of the energy production of a solar power plant that is installed in the area, by providing an estimation of the solar energy acquired at each longitude and latitude of the map. In the present work, the well-known Meteorological Radiation Model (MRM), a broadband solar radiation model, is engaged. This model utilizes common meteorological data, such as air temperature, relative humidity, barometric pressure and sunshine duration, in order to calculate solar radiation through MRM for areas where such data are not available. Hourly values of the above meteorological parameters are acquired from 39 meteorological stations, evenly dispersed around Greece; hourly values of solar radiation are calculated from MRM. Then, by using an integrated spatial interpolation method, a Greek solar energy map is generated, providing annual solar energy values all over Greece.

Anti-inflammatory drugs and prediction of new structures by comparative analysis.

PubMed

Bartzatt, Ronald

2012-01-01

Nonsteroidal anti-inflammatory drugs (NSAIDs) are a group of agents important for their analgesic, anti-inflammatory, and antipyretic properties. This study presents several approaches to predict and elucidate new molecular structures of NSAIDs based on 36 known and proven anti-inflammatory compounds. Based on 36 known NSAIDs the mean value of Log P is found to be 3.338 (standard deviation= 1.237), mean value of polar surface area is 63.176 Angstroms2 (standard deviation = 20.951 A2), and the mean value of molecular weight is 292.665 (standard deviation = 55.627). Nine molecular properties are determined for these 36 NSAID agents, including Log P, number of -OH and -NHn, violations of Rule of 5, number of rotatable bonds, and number of oxygens and nitrogens. Statistical analysis of these nine molecular properties provides numerical parameters to conform to in the design of novel NSAID drug candidates. Multiple regression analysis is accomplished using these properties of 36 agents followed with examples of predicted molecular weight based on minimum and maximum property values. Hierarchical cluster analysis indicated that licofelone, tolfenamic acid, meclofenamic acid, droxicam, and aspirin are substantially distinct from all remaining NSAIDs. Analysis of similarity (ANOSIM) produced R = 0.4947, which indicates low to moderate level of dissimilarity between these 36 NSAIDs. Non-hierarchical K-means cluster analysis separated the 36 NSAIDs into four groups having members of greatest similarity. Likewise, discriminant analysis divided the 36 agents into two groups indicating the greatest level of distinction (discrimination) based on nine properties. These two multivariate methods together provide investigators a means to compare and elucidate novel drug designs to 36 proven compounds and ascertain to which of those are most analogous in pharmacodynamics. In addition, artificial neural network modeling is demonstrated as an approach to predict numerous molecular properties of new drug designs that is based on neural training from 36 proven NSAIDs. Comprehensive and effective approaches are presented in this study for the design of new NSAID type agents which are so very important for inhibition of COX-2 and COX-1 isoenzymes.

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.

The RIPASA score is sensitive and specific for the diagnosis of acute appendicitis in a western population.

PubMed

Malik, Muhammad Usman; Connelly, Tara M; Awan, Faisal; Pretorius, Frederik; Fiuza-Castineira, Constantino; El Faedy, Osama; Balfe, Paul

2017-04-01

The definitive diagnosis of acute appendicitis (AA) requires histopathological examination. Various clinical diagnostic scoring systems attempt to reduce negative appendectomy rates. The most commonly used in Western Europe and the USA is the Alvarado score. The Raja Isteri Pengiran Anak Saleha appendicitis (RIPASA) score achieves better sensitivity and specificity in Asian and Middle Eastern populations. We aimed to determine the diagnostic accuracy of the RIPASA score in Irish patients with AA. All patients who presented to our institution with right iliac fossa pain and clinically suspected AA between January 1 and December 31, 2015, were indentified from our hospital inpatient enquiry database and retrospectively studied. Operating theatre records and histology reports confirmed those who underwent a non-elective operative procedure and the presence or absence of AA. SPSS version 22 was used for statistical analysis. Standard deviation is provided where appropriate. Two hundred eight patients were included in the study (106/51% male, mean age 22.7 ± 9.2 years). One hundred thirty-five (64.9%) had histologically confirmed AA (mean symptom duration = 36.19 ± 15.90 h). At a score ≥7.5, the previously determined score most likely associated with AA in Eastern populations, the RIPASA scoring system demonstrated a sensitivity of 85.39%, specificity of 69.86%, positive predictive value of 84.06%, negative predictive value of 72.86% and diagnostic accuracy of 80% in our cohort. The RIPASA score is a useful tool to aid in the diagnosis of acute appendicitis in the Irish population. A score of ≥7.5 provides sensitivity and specificity exceeding that previously documented for the Alvarado score in Western populations. WHAT DOES THIS PAPER ADD TO THE LITERATURE?: This is the first study evaluating the utility of the RIPASA score in predicting acute appendicitis in a Western population. At a value of 7.5, a cut-off score suggestive of appendicitis in the Eastern population, RIPASA demonstrated a high-sensitivity, specificity, positive predictive value and diagnostic accuracy in our cohort and was more accurate than the commonly used Alvarado score.

Development of a predictive model for lead, cadmium and fluorine soil-water partition coefficients using sparse multiple linear regression analysis.

PubMed

Nakamura, Kengo; Yasutaka, Tetsuo; Kuwatani, Tatsu; Komai, Takeshi

2017-11-01

In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters. The SMLR analysis indicated that fluorine adsorption to soil may be associated with organic matter, whereas cadmium or lead adsorption to soil is more likely to be influenced by soil pH, IL. We found that an accurate K value can be predicted from more than three soil parameters for most soils. Approximately 65% of the predicted values were between 33 and 300% of their measured values for the K value; 76% of the predicted values were within ±30% of their measured values for the n value. Our findings suggest that adsorption properties of lead, cadmium and fluorine to soil can be predicted from the soil physical and chemical properties using the presented models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Office and 24-h ambulatory blood pressure control by treatment in general practice: the 'Monitoraggio della pressione ARteriosa nella medicina TErritoriale' study.

PubMed

Zaninelli, Augusto; Parati, Gianfranco; Cricelli, Claudio; Bignamini, Angelo A; Modesti, Pietro A; Pamparana, Franco; Bilo, Grzegorz; Mancia, Giuseppe; Gensini, Gian F

2010-05-01

Guidelines recommend that blood pressure (BP) should be lowered in hypertensive patients to prevent cardiovascular accidents. Management of antihypertensive treatment by general practitioners is usually based on office measurements, which may not allow an assessment of BP control over 24 h, which requires ambulatory BP monitoring (ABPM) to be implemented. This is rarely done in general practice, and limited information is available on the consistency between the evaluations of the response to treatment provided by office measurement and by ABPM in this setting. To assess concordance between office BP measurements and ABPM-based estimates of hypertension control in a general practice setting. Prospective, comparative between techniques. General practice. Seventy-eight general practices, representative of all Italian regions, participated in this study by recruiting sequential hypertensive adults on stabilized treatment, who were subdivided into even groups with office BP, respectively, controlled or noncontrolled by treatment. In each individual, ABPM was applied by the general practitioner after appropriate training, and 24-h ABP values were defined as controlled or not according to current guidelines. Concordance between office and ABPM evaluation of BP control was assessed with kappa statistics. Positive and negative predictive values of office measurement versus ABPM were estimated. Between July 2005 and November 2006, 190 general practitioners recruited 2059 hypertensive patients based on office BP measurements; in 1728 patients, a 24-h ABPM was performed, yielding 1524 recordings considered as valid for further analysis. The agreement between the assessment of BP control by office measurement and by ABPM was poor (kappa = 0.120), with office measurements showing a satisfactory positive predictive value (0.842) and a poor negative predictive value (0.278); the situation was worse in patients with three or more among the following features: male sex, age of at least 65 years, alcohol consumption, diabetes, and obesity (negative predictive value = 0.149). In general practice, the agreement between assessment of BP control by treatment provided by office and ambulatory BP measurements is better in patients of 'uncontrolled' office BP than in 'controlled' office BP patients. This emphasizes the need for the larger use of out-of-office BP monitoring in a general practice setting, in particular, in patients considered as 'controlled' during consultation.

Evaluation of the WinROP system for identifying retinopathy of prematurity in Czech preterm infants.

PubMed

Timkovic, Juraj; Pokryvkova, Martina; Janurova, Katerina; Barinova, Denisa; Polackova, Renata; Masek, Petr

2017-03-01

Retinopathy of Prematurity (ROP) is a potentially serious condition that can afflict preterm infants. Timely and correct identification of individuals at risk of developing a serious form of ROP is therefore of paramount importance. WinROP is an online system for predicting ROP based on birth weight and weight increments. However, the results vary significantly for various populations. It has not been evaluated in the Czech population. This study evaluates the test characteristics (specificity, sensitivity, positive and negative predictive values) of the WinROP system in Czech preterm infants. Data on 445 prematurely born infants included in the ROP screening program at the University Hospital Ostrava, Czech Republic, were retrospectively entered into the WinROP system and the outcomes of the WinROP and regular screening were compared. All 24 infants who developed high-risk (Type 1 or Type 2) ROP were correctly identified by the system. The sensitivity and negative predictive values for this group were 100%. However, the specificity and positive predictive values were substantially lower, resulting in a large number of false positives. Extending the analysis to low risk ROP, the system did not provide such reliable results. The system is a valuable tool for identifying infants who are not likely to develop high-risk ROP and this could help to substantially reduce the number of preterm infants in need of regular ROP screening. It is not suitable for predicting the development of less serious forms of ROP which is however in accordance with the declared aims of the WinROP system.

Validation of the Oxford classification of IgA nephropathy in cohorts with different presentations and treatments

PubMed Central

Coppo, Rosanna; Troyanov, Stéphan; Bellur, Shubha; Cattran, Daniel; Cook, H Terence; Feehally, John; Roberts, Ian S D; Morando, Laura; Camilla, Roberta; Tesar, Vladimir; Lunberg, Sigrid; Gesualdo, Loreto; Emma, Francesco; Rollino, Cristiana; Amore, Alessandro; Praga, Manuel; Feriozzi, Sandro; Segoloni, Giuseppe; Pani, Antonello; Cancarini, Giovanni; Durlik, Magalena; Moggia, Elisabetta; Mazzucco, Gianna; Giannakakis, Costantinos; Honsova, Eva; Sundelin, B Brigitta; Di Palma, Anna Maria; Ferrario, Franco; Gutierrez, Eduardo; Asunis, Anna Maria; Barratt, Jonathan; Tardanico, Regina; Perkowska-Ptasinska, Agnieszka

2014-01-01

The Oxford Classification of IgA Nephropathy (IgAN) identified mesangial hypercellularity (M), endocapillary proliferation (E), segmental glomerulosclerosis (S), and tubular atrophy/interstitial fibrosis (T) as independent predictors of outcome. Whether it applies to individuals excluded from the original study and how therapy influences the predictive value of pathology remain uncertain. The VALIGA study examined 1147 patients from 13 European countries that encompassed the whole spectrum of IgAN. Over a median follow-up of 4.7 years, 86% received renin–angiotensin system blockade and 42% glucocorticoid/immunosuppressive drugs. M, S, and T lesions independently predicted the loss of estimated glomerular filtration rate (eGFR) and a lower renal survival. Their value was also assessed in patients not represented in the Oxford cohort. In individuals with eGFR less than 30 ml/min per 1.73 m2, the M and T lesions independently predicted a poor survival. In those with proteinuria under 0.5 g/day, both M and E lesions were associated with a rise in proteinuria to 1 or 2 g/day or more. The addition of M, S, and T lesions to clinical variables significantly enhanced the ability to predict progression only in those who did not receive immunosuppression (net reclassification index 11.5%). The VALIGA study provides a validation of the Oxford classification in a large European cohort of IgAN patients across the whole spectrum of the disease. The independent predictive value of pathology MEST score is reduced by glucocorticoid/immunosuppressive therapy. PMID:24694989

Prediction of Mass Spectral Response Factors from Predicted Chemometric Data for Druglike Molecules

NASA Astrophysics Data System (ADS)

Cramer, Christopher J.; Johnson, Joshua L.; Kamel, Amin M.

2017-02-01

A method is developed for the prediction of mass spectral ion counts of drug-like molecules using in silico calculated chemometric data. Various chemometric data, including polar and molecular surface areas, aqueous solvation free energies, and gas-phase and aqueous proton affinities were computed, and a statistically significant relationship between measured mass spectral ion counts and the combination of aqueous proton affinity and total molecular surface area was identified. In particular, through multilinear regression of ion counts on predicted chemometric data, we find that log10(MS ion counts) = -4.824 + c 1•PA + c 2•SA, where PA is the aqueous proton affinity of the molecule computed at the SMD(aq)/M06-L/MIDI!//M06-L/MIDI! level of electronic structure theory, SA is the total surface area of the molecule in its conjugate base form, and c 1 and c 2 have values of -3.912 × 10-2 mol kcal-1 and 3.682 × 10-3 Å-2. On a 66-molecule training set, this regression exhibits a multiple R value of 0.791 with p values for the intercept, c 1, and c 2 of 1.4 × 10-3, 4.3 × 10-10, and 2.5 × 10-6, respectively. Application of this regression to an 11-molecule test set provides a good correlation of prediction with experiment ( R = 0.905) albeit with a systematic underestimation of about 0.2 log units. This method may prove useful for semiquantitative analysis of drug metabolites for which MS response factors or authentic standards are not readily available.

Coupled molecular dynamics and continuum electrostatic method to compute the ionization pKa's of proteins as a function of pH. Test on a large set of proteins.

PubMed

Vorobjev, Yury N; Scheraga, Harold A; Vila, Jorge A

2018-02-01

A computational method, to predict the pKa values of the ionizable residues Asp, Glu, His, Tyr, and Lys of proteins, is presented here. Calculation of the electrostatic free-energy of the proteins is based on an efficient version of a continuum dielectric electrostatic model. The conformational flexibility of the protein is taken into account by carrying out molecular dynamics simulations of 10 ns in implicit water. The accuracy of the proposed method of calculation of pKa values is estimated from a test set of experimental pKa data for 297 ionizable residues from 34 proteins. The pKa-prediction test shows that, on average, 57, 86, and 95% of all predictions have an error lower than 0.5, 1.0, and 1.5 pKa units, respectively. This work contributes to our general understanding of the importance of protein flexibility for an accurate computation of pKa, providing critical insight about the significance of the multiple neutral states of acid and histidine residues for pKa-prediction, and may spur significant progress in our effort to develop a fast and accurate electrostatic-based method for pKa-predictions of proteins as a function of pH.

Verification of the directivity index and other measures of directivity in predicting directional benefit

NASA Astrophysics Data System (ADS)

Dittberner, Andrew; Bentler, Ruth

2005-09-01

The relationship between various directivity measures and subject performance with directional microphone hearing aids was determined. Test devices included first- and second-order directional microphones. Recordings of sentences and noise (Hearing in Noise Test, HINT) were made through each test device in simple, complex, and anisotropic background noise conditions. Twenty-six subjects, with normal hearing, were administered the HINT test recordings, and directional benefit was computed. These measures were correlated to theoretical, free-field, and KEMAR DI values, as well as front-to-back ratios, in situ SNRs, and a newly proposed Db-SNR, wherein a predictive value of the SNR improvement is calculated as a function of the noise source incidence. The different predictive scores showed high correlation to the measured directional benefit scores in the complex (diffuse-like) background noise condition (r=0.89-0.97, p<0.05) but not across all background noise conditions (r=0.45-0.97, p<0.05). The Db-SNR approach and the in situ SNR measures provided excellent prediction of subject performance in all background noise conditions (0.85-0.97, p<0.05) None of the predictive measures could account for the effects of reverberation on the speech signal (r=0.35-0.40, p<0.05).

Markovian prediction of future values for food grains in the economic survey

NASA Astrophysics Data System (ADS)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

Now-a-days prediction and forecasting are plays a vital role in research. For prediction, regression is useful to predict the future value and current value on production process. In this paper, we assume food grain production exhibit Markov chain dependency and time homogeneity. The economic generative performance evaluation the balance time artificial fertilization different level in Estrusdetection using a daily Markov chain model. Finally, Markov process prediction gives better performance compare with Regression model.

Comparison of predictability for human pharmacokinetics parameters among monkeys, rats, and chimeric mice with humanised liver.

PubMed

Miyamoto, Maki; Iwasaki, Shinji; Chisaki, Ikumi; Nakagawa, Sayaka; Amano, Nobuyuki; Hirabayashi, Hideki

2017-12-01

1. The aim of the present study was to evaluate the usefulness of chimeric mice with humanised liver (PXB mice) for the prediction of clearance (CL t ) and volume of distribution at steady state (Vd ss ), in comparison with monkeys, which have been reported as a reliable model for human pharmacokinetics (PK) prediction, and with rats, as a conventional PK model. 2. CL t and Vd ss values in PXB mice, monkeys and rats were determined following intravenous administration of 30 compounds known to be mainly eliminated in humans via the hepatic metabolism by various drug-metabolising enzymes. Using single-species allometric scaling, human CL t and Vd ss values were predicted from the three animal models. 3. Predicted CL t values from PXB mice exhibited the highest predictability: 25 for PXB mice, 21 for monkeys and 14 for rats were predicted within a three-fold range of actual values among 30 compounds. For predicted human Vd ss values, the number of compounds falling within a three-fold range was 23 for PXB mice, 24 for monkeys, and 16 for rats among 29 compounds. PXB mice indicated a higher predictability for CL t and Vd ss values than the other animal models. 4. These results demonstrate the utility of PXB mice in predicting human PK parameters.

Predicting driving performance in older adults: we are not there yet!

PubMed

Bédard, Michel; Weaver, Bruce; Darzins, Peteris; Porter, Michelle M

2008-08-01

We set up this study to determine the predictive value of approaches for which a statistical association with driving performance has been documented. We determined the statistical association (magnitude of association and probability of occurrence by chance alone) between four different predictors (the Mini-Mental State Examination, Trails A test, Useful Field of View [UFOV], and a composite measure of past driving incidents) and driving performance. We then explored the predictive value of these measures with receiver operating characteristic (ROC) curves and various cutoff values. We identified associations between the predictors and driving performance well beyond the play of chance (p < .01). Nonetheless, the predictors had limited predictive value with areas under the curve ranging from .51 to .82. Statistical associations are not sufficient to infer adequate predictive value, especially when crucial decisions such as whether one can continue driving are at stake. The predictors we examined have limited predictive value if used as stand-alone screening tests.

Evaluating the validity of using unverified indices of body condition

USGS Publications Warehouse

Schamber, J.L.; Esler, Daniel N.; Flint, Paul L.

2009-01-01

Condition indices are commonly used in an attempt to link body condition of birds to ecological variables of interest, including demographic attributes such as survival and reproduction. Most indices are based on body mass adjusted for structural body size, calculated as simple ratios or residuals from regressions. However, condition indices are often applied without confirming their predictive value (i.e., without being validated against measured values of fat and protein), which we term ‘unverified’ use. We evaluated the ability of a number of unverified indices frequently found in the literature to predict absolute and proportional levels of fat and protein across five species of waterfowl. Among indices we considered, those accounting for body size never predicted absolute protein more precisely than body mass, however, some indices improved predictability of fat, although the form of the best index varied by species. Further, the gain in precision by using a condition index to predict either absolute or percent fat was minimal (rise in r2≤0.13), and in many cases model fit was actually reduced. Our data agrees with previous assertions that the assumption that indices provide more precise indicators of body condition than body mass alone is often invalid. We strongly discourage the use of unverified indices, because subjectively selecting indices likely does little to improve precision and might in fact decrease predictability relative to using body mass alone.

Validation of a prediction model that allows direct comparison of the Oxford Knee Score and American Knee Society clinical rating system.

PubMed

Maempel, J F; Clement, N D; Brenkel, I J; Walmsley, P J

2015-04-01

This study demonstrates a significant correlation between the American Knee Society (AKS) Clinical Rating System and the Oxford Knee Score (OKS) and provides a validated prediction tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed five years after TKR and completed AKS assessments and an OKS questionnaire. Multivariate regression analysis demonstrated significant correlations between OKS and the AKS knee and function scores but a stronger correlation (r = 0.68, p < 0.001) when using the sum of the AKS knee and function scores. Addition of body mass index and age (other statistically significant predictors of OKS) to the algorithm did not significantly increase the predictive value. The simple regression model was used to predict the OKS in a group of 236 patients who were clinically assessed nine to ten years after TKR using the AKS system. The predicted OKS was compared with actual OKS in the second group. Intra-class correlation demonstrated excellent reliability (r = 0.81, 95% confidence intervals 0.75 to 0.85) for the combined knee and function score when used to predict OKS. Our findings will facilitate comparison of outcome data from studies and registries using either the OKS or the AKS scores and may also be of value for those undertaking meta-analyses and systematic reviews. ©2015 The British Editorial Society of Bone & Joint Surgery.