Outcome of temporal lobe epilepsy surgery predicted by statistical parametric PET imaging.

PubMed

Wong, C Y; Geller, E B; Chen, E Q; MacIntyre, W J; Morris, H H; Raja, S; Saha, G B; Lüders, H O; Cook, S A; Go, R T

1996-07-01

PET is useful in the presurgical evaluation of temporal lobe epilepsy. The purpose of this retrospective study is to assess the clinical use of statistical parametric imaging in predicting surgical outcome. Interictal 18FDG-PET scans in 17 patients with surgically-treated temporal lobe epilepsy (Group A-13 seizure-free, group B = 4 not seizure-free at 6 mo) were transformed into statistical parametric imaging, with each pixel representing a z-score value by using the mean and s.d. of count distribution in each individual patient, for both visual and quantitative analysis. Mean z-scores were significantly more negative in anterolateral (AL) and mesial (M) regions on the operated side than the nonoperated side in group A (AL: p < 0.00005, M: p = 0.0097), but not in group B (AL: p = 0.46, M: p = 0.08). Statistical parametric imaging correctly lateralized 16 out of 17 patients. Only the AL region, however, was significant in predicting surgical outcome (F = 29.03, p < 0.00005). Using a cut-off z-score value of -1.5, statistical parametric imaging correctly classified 92% of temporal lobes from group A and 88% of those from Group B. The preliminary results indicate that statistical parametric imaging provides both clinically useful information for lateralization in temporal lobe epilepsy and a reliable predictive indicator of clinical outcome following surgical treatment.

Statistical validation of predictive TRANSP simulations of baseline discharges in preparation for extrapolation to JET D-T

NASA Astrophysics Data System (ADS)

Kim, Hyun-Tae; Romanelli, M.; Yuan, X.; Kaye, S.; Sips, A. C. C.; Frassinetti, L.; Buchanan, J.; Contributors, JET

2017-06-01

This paper presents for the first time a statistical validation of predictive TRANSP simulations of plasma temperature using two transport models, GLF23 and TGLF, over a database of 80 baseline H-mode discharges in JET-ILW. While the accuracy of the predicted T e with TRANSP-GLF23 is affected by plasma collisionality, the dependency of predictions on collisionality is less significant when using TRANSP-TGLF, indicating that the latter model has a broader applicability across plasma regimes. TRANSP-TGLF also shows a good matching of predicted T i with experimental measurements allowing for a more accurate prediction of the neutron yields. The impact of input data and assumptions prescribed in the simulations are also investigated in this paper. The statistical validation and the assessment of uncertainty level in predictive TRANSP simulations for JET-ILW-DD will constitute the basis for the extrapolation to JET-ILW-DT experiments.

Statistical Models for Predicting Automobile Driving Postures for Men and Women Including Effects of Age.

PubMed

Park, Jangwoon; Ebert, Sheila M; Reed, Matthew P; Hallman, Jason J

2016-03-01

Previously published statistical models of driving posture have been effective for vehicle design but have not taken into account the effects of age. The present study developed new statistical models for predicting driving posture. Driving postures of 90 U.S. drivers with a wide range of age and body size were measured in laboratory mockup in nine package conditions. Posture-prediction models for female and male drivers were separately developed by employing a stepwise regression technique using age, body dimensions, vehicle package conditions, and two-way interactions, among other variables. Driving posture was significantly associated with age, and the effects of other variables depended on age. A set of posture-prediction models is presented for women and men. The results are compared with a previously developed model. The present study is the first study of driver posture to include a large cohort of older drivers and the first to report a significant effect of age. The posture-prediction models can be used to position computational human models or crash-test dummies for vehicle design and assessment. © 2015, Human Factors and Ergonomics Society.

Predicting Success in Psychological Statistics Courses.

PubMed

Lester, David

2016-06-01

Many students perform poorly in courses on psychological statistics, and it is useful to be able to predict which students will have difficulties. In a study of 93 undergraduates enrolled in Statistical Methods (18 men, 75 women; M age = 22.0 years, SD = 5.1), performance was significantly associated with sex (female students performed better) and proficiency in algebra in a linear regression analysis. Anxiety about statistics was not associated with course performance, indicating that basic mathematical skills are the best correlate for performance in statistics courses and can usefully be used to stream students into classes by ability. © The Author(s) 2016.

Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds.

PubMed

Cuyabano, B C D; Su, G; Rosa, G J M; Lund, M S; Gianola, D

2015-10-01

This study compared the accuracy of genome-enabled prediction models using individual single nucleotide polymorphisms (SNP) or haplotype blocks as covariates when using either a single breed or a combined population of Nordic Red cattle. The main objective was to compare predictions of breeding values of complex traits using a combined training population with haplotype blocks, with predictions using a single breed as training population and individual SNP as predictors. To compare the prediction reliabilities, bootstrap samples were taken from the test data set. With the bootstrapped samples of prediction reliabilities, we built and graphed confidence ellipses to allow comparisons. Finally, measures of statistical distances were used to calculate the gain in predictive ability. Our analyses are innovative in the context of assessment of predictive models, allowing a better understanding of prediction reliabilities and providing a statistical basis to effectively calibrate whether one prediction scenario is indeed more accurate than another. An ANOVA indicated that use of haplotype blocks produced significant gains mainly when Bayesian mixture models were used but not when Bayesian BLUP was fitted to the data. Furthermore, when haplotype blocks were used to train prediction models in a combined Nordic Red cattle population, we obtained up to a statistically significant 5.5% average gain in prediction accuracy, over predictions using individual SNP and training the model with a single breed. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Comparing and combining process-based crop models and statistical models with some implications for climate change

NASA Astrophysics Data System (ADS)

Roberts, Michael J.; Braun, Noah O.; Sinclair, Thomas R.; Lobell, David B.; Schlenker, Wolfram

2017-09-01

We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.

Human Deception Detection from Whole Body Motion Analysis

DTIC Science & Technology

2015-12-01

9.3.2. Prediction Probability The output reports from SPSS detail the stepwise procedures for each series of analyses using Wald statistic values for... statistical significance in determining replication, but instead used a combination of significance and direction of means to determine partial or...and the independents need not be unbound. All data were analyzed utilizing the Statistical Package for Social Sciences ( SPSS , v.19.0, Chicago, IL

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist's assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.

Risk prediction model: Statistical and artificial neural network approach

NASA Astrophysics Data System (ADS)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach.

PubMed

Hu, Yuh-Jyh; Ku, Tien-Hsiung; Yang, Yu-Hung; Shen, Jia-Ying

2018-01-01

Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.

A statistical model including age to predict passenger postures in the rear seats of automobiles.

PubMed

Park, Jangwoon; Ebert, Sheila M; Reed, Matthew P; Hallman, Jason J

2016-06-01

Few statistical models of rear seat passenger posture have been published, and none has taken into account the effects of occupant age. This study developed new statistical models for predicting passenger postures in the rear seats of automobiles. Postures of 89 adults with a wide range of age and body size were measured in a laboratory mock-up in seven seat configurations. Posture-prediction models for female and male passengers were separately developed by stepwise regression using age, body dimensions, seat configurations and two-way interactions as potential predictors. Passenger posture was significantly associated with age and the effects of other two-way interaction variables depended on age. A set of posture-prediction models are presented for women and men, and the prediction results are compared with previously published models. This study is the first study of passenger posture to include a large cohort of older passengers and the first to report a significant effect of age for adults. The presented models can be used to position computational and physical human models for vehicle design and assessment. Practitioner Summary: The significant effects of age, body dimensions and seat configuration on rear seat passenger posture were identified. The models can be used to accurately position computational human models or crash test dummies for older passengers in known rear seat configurations.

Epipolis-laser in situ keratomileusis versus photorefractive keratectomy for the correction of myopia: a meta-analysis.

PubMed

Wu, Wenjing; Wang, Yan; Xu, Lulu

2015-10-01

It is unclear whether epipolis-laser in situ keratomileusis (Epi-LASIK) has any significant advantage over photorefractive keratectomy (PRK) for correcting myopia. We undertook this meta-analysis of randomized controlled trials and cohort studies to examine possible differences in efficacy, predictability, and side effects between Epi-LASIK and PRK for correcting myopia. A system literature review was conducted in the PubMed, Cochrane Library EMBASE. The statistical analysis was performed by RevMan 5.0 software. The results included efficacy outcomes (percentage of eyes with 20/20 uncorrected visual acuity post-treatment), predictability (proportion of eyes within ±0.5 D of the target refraction), epithelial healing time, and the incidence of significant haze and pain scores after surgery. There are seven articles with total 987 eyes suitable for the meta-analysis. There is no statistical significance in the predictability between Epi-LASIK and PRK, the risk ratio (RR) is 1.03, 95% confidence interval (CI) [0.92, 1.16], p = 0.18; with respect to efficacy, the odds ratio is 1.43, 95% CI = [0.85, 2.40], p = 0.56 between Epi-LASIK and PRK, there is no statistical significance either. The epithelial cell layer healing time and the pain scores and the incidence of significant haze showed no significance between these two techniques although more pains can be found in Epi-LASIK than PRK at the early-stage post-operation. According to the above analysis, Epi-LASIK has good efficacy and predictability as PRK. In addition, both techniques have low pain scores and low incidence of significant haze.

Testing earthquake prediction algorithms: Statistically significant advance prediction of the largest earthquakes in the Circum-Pacific, 1992-1997

USGS Publications Warehouse

Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.

1999-01-01

Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier Science B.V. All rights reserved.

The Real World Significance of Performance Prediction

ERIC Educational Resources Information Center

Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu

2012-01-01

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…

Humidity-corrected Arrhenius equation: The reference condition approach.

PubMed

Naveršnik, Klemen; Jurečič, Rok

2016-03-16

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

K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong

2016-03-01

Gears are the most commonly used components in mechanical transmission systems. Their failures may cause transmission system breakdown and result in economic loss. Identification of different gear crack levels is important to prevent any unexpected gear failure because gear cracks lead to gear tooth breakage. Signal processing based methods mainly require expertize to explain gear fault signatures which is usually not easy to be achieved by ordinary users. In order to automatically identify different gear crack levels, intelligent gear crack identification methods should be developed. The previous case studies experimentally proved that K-nearest neighbors based methods exhibit high prediction accuracies for identification of 3 different gear crack levels under different motor speeds and loads. In this short communication, to further enhance prediction accuracies of existing K-nearest neighbors based methods and extend identification of 3 different gear crack levels to identification of 5 different gear crack levels, redundant statistical features are constructed by using Daubechies 44 (db44) binary wavelet packet transform at different wavelet decomposition levels, prior to the use of a K-nearest neighbors method. The dimensionality of redundant statistical features is 620, which provides richer gear fault signatures. Since many of these statistical features are redundant and highly correlated with each other, dimensionality reduction of redundant statistical features is conducted to obtain new significant statistical features. At last, the K-nearest neighbors method is used to identify 5 different gear crack levels under different motor speeds and loads. A case study including 3 experiments is investigated to demonstrate that the developed method provides higher prediction accuracies than the existing K-nearest neighbors based methods for recognizing different gear crack levels under different motor speeds and loads. Based on the new significant statistical features, some other popular statistical models including linear discriminant analysis, quadratic discriminant analysis, classification and regression tree and naive Bayes classifier, are compared with the developed method. The results show that the developed method has the highest prediction accuracies among these statistical models. Additionally, selection of the number of new significant features and parameter selection of K-nearest neighbors are thoroughly investigated.

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

Joint probability of statistical success of multiple phase III trials.

PubMed

Zhang, Jianliang; Zhang, Jenny J

2013-01-01

In drug development, after completion of phase II proof-of-concept trials, the sponsor needs to make a go/no-go decision to start expensive phase III trials. The probability of statistical success (PoSS) of the phase III trials based on data from earlier studies is an important factor in that decision-making process. Instead of statistical power, the predictive power of a phase III trial, which takes into account the uncertainty in the estimation of treatment effect from earlier studies, has been proposed to evaluate the PoSS of a single trial. However, regulatory authorities generally require statistical significance in two (or more) trials for marketing licensure. We show that the predictive statistics of two future trials are statistically correlated through use of the common observed data from earlier studies. Thus, the joint predictive power should not be evaluated as a simplistic product of the predictive powers of the individual trials. We develop the relevant formulae for the appropriate evaluation of the joint predictive power and provide numerical examples. Our methodology is further extended to the more complex phase III development scenario comprising more than two (K > 2) trials, that is, the evaluation of the PoSS of at least k₀ (k₀≤ K) trials from a program of K total trials. Copyright © 2013 John Wiley & Sons, Ltd.

Prediction of seizure-onset laterality by using Wada memory asymmetries in pediatric epilepsy surgery candidates.

PubMed

Lee, Gregory P; Park, Yong D; Hempel, Ann; Westerveld, Michael; Loring, David W

2002-09-01

Because the capacity of intracarotid amobarbital (Wada) memory assessment to predict seizure-onset laterality in children has not been thoroughly investigated, three comprehensive epilepsy surgery centers pooled their data and examined Wada memory asymmetries to predict side of seizure onset in children being considered for epilepsy surgery. One hundred fifty-two children with intractable epilepsy underwent Wada testing. Although the type and number of memory stimuli and methods varied at each institution, all children were presented with six to 10 items soon after amobarbital injection. After return to neurologic baseline, recognition memory for the stimuli was assessed. Seizure onset was determined by simultaneous video-EEG recordings of multiple seizures. In children with unilateral temporal lobe seizures (n = 87), Wada memory asymmetries accurately predicted seizure laterality to a statistically significant degree. Wada memory asymmetries also correctly predicted side of seizure onset in children with extra-temporal lobe seizures (n = 65). Although individual patient prediction accuracy was statistically significant in temporal lobe cases, onset laterality was incorrectly predicted in < or =52% of children with left temporal lobe seizure onset, depending on the methods and asymmetry criterion used. There also were significant differences between Wada prediction accuracy across the three epilepsy centers. Results suggest that Wada memory assessment is useful in predicting side of seizure onset in many children. However, Wada memory asymmetries should be interpreted more cautiously in children than in adults.

Prediction of rainfall anomalies during the dry to wet transition season over the Southern Amazonia using machine learning tools

NASA Astrophysics Data System (ADS)

Shan, X.; Zhang, K.; Zhuang, Y.; Fu, R.; Hong, Y.

2017-12-01

Seasonal prediction of rainfall during the dry-to-wet transition season in austral spring (September-November) over southern Amazonia is central for improving planting crops and fire mitigation in that region. Previous studies have identified the key large-scale atmospheric dynamic and thermodynamics pre-conditions during the dry season (June-August) that influence the rainfall anomalies during the dry to wet transition season over Southern Amazonia. Based on these key pre-conditions during dry season, we have evaluated several statistical models and developed a Neural Network based statistical prediction system to predict rainfall during the dry to wet transition for Southern Amazonia (5-15°S, 50-70°W). Multivariate Empirical Orthogonal Function (EOF) Analysis is applied to the following four fields during JJA from the ECMWF Reanalysis (ERA-Interim) spanning from year 1979 to 2015: geopotential height at 200 hPa, surface relative humidity, convective inhibition energy (CIN) index and convective available potential energy (CAPE), to filter out noise and highlight the most coherent spatial and temporal variations. The first 10 EOF modes are retained for inputs to the statistical models, accounting for at least 70% of the total variance in the predictor fields. We have tested several linear and non-linear statistical methods. While the regularized Ridge Regression and Lasso Regression can generally capture the spatial pattern and magnitude of rainfall anomalies, we found that that Neural Network performs best with an accuracy greater than 80%, as expected from the non-linear dependence of the rainfall on the large-scale atmospheric thermodynamic conditions and circulation. Further tests of various prediction skill metrics and hindcasts also suggest this Neural Network prediction approach can significantly improve seasonal prediction skill than the dynamic predictions and regression based statistical predictions. Thus, this statistical prediction system could have shown potential to improve real-time seasonal rainfall predictions in the future.

Neutrophil-to-lymphocyte ratio as a predictor of preoperative tumor staging in testicular germ cell tumors.

PubMed

Jankovich, M; Jankovichova, T; Ondrus, D; Breza, J

2017-01-01

The aim of our study was to evaluate associations of elevated preoperative neutrophil-to-lymphocyte ratio (NLR) with testicular germ cell tumors (GCT) characteristics other than cancer specific survival (CSS) and progression free survival (PFS). NLR was recently presented as a widely available and inexpensive marker of poor prognosis in several types of solid tumors. Previous study showed no predictive value of NLR for CSS and PFS in testicular GCT. Association of high NLR with histological type of tumor, presence of metastatic disease preoperatively and worse than T1 stadium in TNM classification preoperatively was analyzed in 103 patients who underwent radical orchiectomy for testicular GCT. No statistically significant difference in the prevalence of seminomas and non-seminomas neither in the group with NLR≥4 (p=0.6698) nor in the group with NLR<4 (p=0.9115) was detected. Similarly, no statistically significant difference in the prevalence of metastatic and non-metastatic disease in the group with NLR≥4 (p=0.2008), however statistically significant higher prevalence of non-metastatic disease in the group with NLR<4 (p=0.0001) was found. There was a statistically significant higher number of patients with worse than T1 stadium in patients with NLR≥4 (p=0.0105), but not significant difference in the group with NLR<4 (p=0.0956). The results of our study showed that NLR lower than 4 predicts non-metastatic disease and NLR higher or equal 4 predicts worse than T1 stadium (Tab. 3, Ref. 12).

Prediction of local concentration statistics in variably saturated soils: Influence of observation scale and comparison with field data

NASA Astrophysics Data System (ADS)

Graham, Wendy; Destouni, Georgia; Demmy, George; Foussereau, Xavier

1998-07-01

The methodology developed in Destouni and Graham [Destouni, G., Graham, W.D., 1997. The influence of observation method on local concentration statistics in the subsurface. Water Resour. Res. 33 (4) 663-676.] for predicting locally measured concentration statistics for solute transport in heterogeneous porous media under saturated flow conditions is applied to the prediction of conservative nonreactive solute transport in the vadose zone where observations are obtained by soil coring. Exact analytical solutions are developed for both the mean and variance of solute concentrations measured in discrete soil cores using a simplified physical model for vadose-zone flow and solute transport. Theoretical results show that while the ensemble mean concentration is relatively insensitive to the length-scale of the measurement, predictions of the concentration variance are significantly impacted by the sampling interval. Results also show that accounting for vertical heterogeneity in the soil profile results in significantly less spreading in the mean and variance of the measured solute breakthrough curves, indicating that it is important to account for vertical heterogeneity even for relatively small travel distances. Model predictions for both the mean and variance of locally measured solute concentration, based on independently estimated model parameters, agree well with data from a field tracer test conducted in Manatee County, Florida.

A multibody knee model with discrete cartilage prediction of tibio-femoral contact mechanics.

PubMed

Guess, Trent M; Liu, Hongzeng; Bhashyam, Sampath; Thiagarajan, Ganesh

2013-01-01

Combining musculoskeletal simulations with anatomical joint models capable of predicting cartilage contact mechanics would provide a valuable tool for studying the relationships between muscle force and cartilage loading. As a step towards producing multibody musculoskeletal models that include representation of cartilage tissue mechanics, this research developed a subject-specific multibody knee model that represented the tibia plateau cartilage as discrete rigid bodies that interacted with the femur through deformable contacts. Parameters for the compliant contact law were derived using three methods: (1) simplified Hertzian contact theory, (2) simplified elastic foundation contact theory and (3) parameter optimisation from a finite element (FE) solution. The contact parameters and contact friction were evaluated during a simulated walk in a virtual dynamic knee simulator, and the resulting kinematics were compared with measured in vitro kinematics. The effects on predicted contact pressures and cartilage-bone interface shear forces during the simulated walk were also evaluated. The compliant contact stiffness parameters had a statistically significant effect on predicted contact pressures as well as all tibio-femoral motions except flexion-extension. The contact friction was not statistically significant to contact pressures, but was statistically significant to medial-lateral translation and all rotations except flexion-extension. The magnitude of kinematic differences between model formulations was relatively small, but contact pressure predictions were sensitive to model formulation. The developed multibody knee model was computationally efficient and had a computation time 283 times faster than a FE simulation using the same geometries and boundary conditions.

[How reliable is the monitoring for doping?].

PubMed

Hüsler, J

1990-12-01

The reliability of the dope control, of the chemical analysis of the urine probes in the accredited laboratories and their decisions, is discussed using probabilistic and statistical methods. Basically, we evaluated and estimated the positive predictive value which means the probability that an urine probe contains prohibited dope substances given a positive test decision. Since there are not statistical data and evidence for some important quantities in relation to the predictive value, an exact evaluation is not possible, only conservative, lower bounds can be given. We found that the predictive value is at least 90% or 95% with respect to the analysis and decision based on the A-probe only, and at least 99% with respect to both A- and B-probes. A more realistic observation, but without sufficient statistical confidence, points to the fact that the true predictive value is significantly larger than these lower estimates.

Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

PubMed

Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

2017-06-01

To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (P<0.05). In addition, a comparison of the area under receiver operating characteristic curves of the two models showed a statistically significant difference (P<0.05). The RBF ANNs model is more likely to predict the occurrence of PVT induced by AP than logistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.

Seasonal drought predictability in Portugal using statistical-dynamical techniques

NASA Astrophysics Data System (ADS)

Ribeiro, A. F. S.; Pires, C. A. L.

2016-08-01

Atmospheric forecasting and predictability are important to promote adaption and mitigation measures in order to minimize drought impacts. This study estimates hybrid (statistical-dynamical) long-range forecasts of the regional drought index SPI (3-months) over homogeneous regions from mainland Portugal, based on forecasts from the UKMO operational forecasting system, with lead-times up to 6 months. ERA-Interim reanalysis data is used for the purpose of building a set of SPI predictors integrating recent past information prior to the forecast launching. Then, the advantage of combining predictors with both dynamical and statistical background in the prediction of drought conditions at different lags is evaluated. A two-step hybridization procedure is performed, in which both forecasted and observed 500 hPa geopotential height fields are subjected to a PCA in order to use forecasted PCs and persistent PCs as predictors. A second hybridization step consists on a statistical/hybrid downscaling to the regional SPI, based on regression techniques, after the pre-selection of the statistically significant predictors. The SPI forecasts and the added value of combining dynamical and statistical methods are evaluated in cross-validation mode, using the R2 and binary event scores. Results are obtained for the four seasons and it was found that winter is the most predictable season, and that most of the predictive power is on the large-scale fields from past observations. The hybridization improves the downscaling based on the forecasted PCs, since they provide complementary information (though modest) beyond that of persistent PCs. These findings provide clues about the predictability of the SPI, particularly in Portugal, and may contribute to the predictability of crops yields and to some guidance on users (such as farmers) decision making process.

An injury mortality prediction based on the anatomic injury scale

PubMed Central

Wang, Muding; Wu, Dan; Qiu, Wusi; Wang, Weimi; Zeng, Yunji; Shen, Yi

2017-01-01

Abstract To determine whether the injury mortality prediction (IMP) statistically outperforms the trauma mortality prediction model (TMPM) as a predictor of mortality. The TMPM is currently the best trauma score method, which is based on the anatomic injury. Its ability of mortality prediction is superior to the injury severity score (ISS) and to the new injury severity score (NISS). However, despite its statistical significance, the predictive power of TMPM needs to be further improved. Retrospective cohort study is based on the data of 1,148,359 injured patients in the National Trauma Data Bank hospitalized from 2010 to 2011. Sixty percent of the data was used to derive an empiric measure of severity of different Abbreviated Injury Scale predot codes by taking the weighted average death probabilities of trauma patients. Twenty percent of the data was used to create computing method of the IMP model. The remaining 20% of the data was used to evaluate the statistical performance of IMP and then be compared with the TMPM and the single worst injury by examining area under the receiver operating characteristic curve (ROC), the Hosmer–Lemeshow (HL) statistic, and the Akaike information criterion. IMP exhibits significantly both better discrimination (ROC-IMP, 0.903 [0.899–0.907] and ROC-TMPM, 0.890 [0.886–0.895]) and calibration (HL-IMP, 9.9 [4.4–14.7] and HL-TMPM, 197 [143–248]) compared with TMPM. All models show slight changes after the extension of age, gender, and mechanism of injury, but the extended IMP still dominated TMPM in every performance. The IMP has slight improvement in discrimination and calibration compared with the TMPM and can accurately predict mortality. Therefore, we consider it as a new feasible scoring method in trauma research. PMID:28858124

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

Using prediction markets to estimate the reproducibility of scientific research.

PubMed

Dreber, Anna; Pfeiffer, Thomas; Almenberg, Johan; Isaksson, Siri; Wilson, Brad; Chen, Yiling; Nosek, Brian A; Johannesson, Magnus

2015-12-15

Concerns about a lack of reproducibility of statistically significant results have recently been raised in many fields, and it has been argued that this lack comes at substantial economic costs. We here report the results from prediction markets set up to quantify the reproducibility of 44 studies published in prominent psychology journals and replicated in the Reproducibility Project: Psychology. The prediction markets predict the outcomes of the replications well and outperform a survey of market participants' individual forecasts. This shows that prediction markets are a promising tool for assessing the reproducibility of published scientific results. The prediction markets also allow us to estimate probabilities for the hypotheses being true at different testing stages, which provides valuable information regarding the temporal dynamics of scientific discovery. We find that the hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%) and that a "statistically significant" finding needs to be confirmed in a well-powered replication to have a high probability of being true. We argue that prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications.

Prediction, Diagnosis, and Casual Thinking in Forecasting.

DTIC Science & Technology

1981-09-03

diagnostic process. However, a significant feature of causal/diagnostic thinking is the remarkable speed and fluency which people seem to have for generating...The cement of the universe: A study of causation. Oxford: Clarendon Press. Meehl, Paul E., (1954), Clinical versus statistical prediction: A

Impulse oscillometry at preschool age is a strong predictor of lung function by flow-volume spirometry in adolescence.

PubMed

Lauhkonen, Eero; Riikonen, Riikka; Törmänen, Sari; Koponen, Petri; Nuolivirta, Kirsi; Helminen, Merja; Toikka, Jyri; Korppi, Matti

2018-05-01

The transition from early childhood wheezing to persistent asthma is linked to lung function impairment over time. Little is known how the methods used to study lung function at different ages correlate longitudinally. Sixty-four children with a history of hospitalization for bronchiolitis before 6 months of age were prospectively studied with impulse oscillometry (IOS) at the mean age of 6.3 years and these preschool IOS results were compared with flow-volume spirometry (FVS) measurements at mean age of 11.4 years. The baseline respiratory system resistance at 5 Hz (Rrs5) showed a modest statistically significant correlation with all baseline FVS parameters except FVC. The post-bronchodilator (post-BD) Rrs5 showed a modest statistically significant correlation with post-BD FEV 1 and FEV 1 /FVC. The bronchodilator-induced decrease in Rrs5 showed a modest statistically significant correlation with the percent increase in FEV 1 . Baseline and post-BD respiratory reactance at 5 Hz (Xrs5) showed a modest statistically significant correlation with baseline and post-BD FVS parameters except post-BD FEV 1 /FVC, respectively, and post-BD Xrs5 showed a strong correlation with post-BD FVC (ρ = 0.61) and post-BD FEV 1 (ρ = 0.59). In adjusted linear regression, preschool Xrs5 remained as a statistically significant independent predictor of FVS parameters in adolescence; the one-unit decrease in the Z-score of preschool post-BD Xrs5 predicted 9.6% lower post-BD FEV 1 , 9.3% lower post-BD FVC, and 9.7% lower post-BD MEF 50 when expressed as %-predicted parameters. Persistent post-BD small airway impairment in children with a history of bronchiolitis detected with IOS at preschool age predicted FVS results measured in early adolescence. © 2018 Wiley Periodicals, Inc.

Robust multiscale prediction of Po River discharge using a twofold AR-NN approach

NASA Astrophysics Data System (ADS)

Alessio, Silvia; Taricco, Carla; Rubinetti, Sara; Zanchettin, Davide; Rubino, Angelo; Mancuso, Salvatore

2017-04-01

The Mediterranean area is among the regions most exposed to hydroclimatic changes, with a likely increase of frequency and duration of droughts in the last decades and potentially substantial future drying according to climate projections. However, significant decadal variability is often superposed or even dominates these long-term hydrological trend as observed, for instance, in North Italian precipitation and river discharge records. The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. In order to forecast short and noisy hydroclimatic time series, we apply a twofold statistical approach that we improved with respect to previous works [1]. Our prediction strategy consists in the application of two independent methods that use autoregressive models and feed-forward neural networks. Since all prediction methods work better on clean signals, the predictions are not performed directly on the series, but rather on each significant variability components extracted with Singular Spectrum Analysis (SSA). In this contribution, we will illustrate the multiscale prediction approach and its application to the case of decadal prediction of annual-average Po River discharges (Italy). The discharge record is available for the last 209 years and allows to work with both interannual and decadal time-scale components. Fifteen-year forecasts obtained with both methods robustly indicate a prominent dry period in the second half of the 2020s. We will discuss advantages and limitations of the proposed statistical approach in the light of the current capabilities of decadal climate prediction systems based on numerical climate models, toward an integrated dynamical and statistical approach for the interannual-to-decadal prediction of hydroclimate variability in medium-size river basins. [1] Alessio et. al., Natural variability and anthropogenic effects in a Central Mediterranean core, Clim. of the Past, 8, 831-839, 2012.

Prediction of late disease recurrence and extended adjuvant letrozole benefit by the HOXB13/IL17BR biomarker.

PubMed

Sgroi, Dennis C; Carney, Erin; Zarrella, Elizabeth; Steffel, Lauren; Binns, Shemeica N; Finkelstein, Dianne M; Szymonifka, Jackie; Bhan, Atul K; Shepherd, Lois E; Zhang, Yi; Schnabel, Catherine A; Erlander, Mark G; Ingle, James N; Porter, Peggy; Muss, Hyman B; Pritchard, Katherine I; Tu, Dongsheng; Rimm, David L; Goss, Paul E

2013-07-17

Biomarkers to optimize extended adjuvant endocrine therapy for women with estrogen receptor (ER)-positive breast cancer are limited. The HOXB13/IL17BR (H/I) biomarker predicts recurrence risk in ER-positive, lymph node-negative breast cancer patients. H/I was evaluated in MA.17 trial for prognostic performance for late recurrence and treatment benefit from extended adjuvant letrozole. A prospective-retrospective, nested case-control design of 83 recurrences matched to 166 nonrecurrences from letrozole- and placebo-treated patients within MA.17 was conducted. Expression of H/I within primary tumors was determined by reverse-transcription polymerase chain reaction with a prespecified cutpoint. The predictive ability of H/I for ascertaining benefit from letrozole was determined using multivariable conditional logistic regression including standard clinicopathological factors as covariates. All statistical tests were two-sided. High H/I was statistically significantly associated with a decrease in late recurrence in patients receiving extended letrozole therapy (odds ratio [OR] = 0.35; 95% confidence interval [CI] = 0.16 to 0.75; P = .007). In an adjusted model with standard clinicopathological factors, high H/I remained statistically significantly associated with patient benefit from letrozole (OR = 0.33; 95% CI = 0.15 to 0.73; P = .006). Reduction in the absolute risk of recurrence at 5 years was 16.5% for patients with high H/I (P = .007). The interaction between H/I and letrozole treatment was statistically significant (P = .03). In the absence of extended letrozole therapy, high H/I identifies a subgroup of ER-positive patients disease-free after 5 years of tamoxifen who are at risk for late recurrence. When extended endocrine therapy with letrozole is prescribed, high H/I predicts benefit from therapy and a decreased probability of late disease recurrence.

Timely Antecedent CT or MRI Can Help Predict Hemorrhage Site of Posttreatment Head and Neck Cancer, With Digital Subtraction Angiography Used as the Reference Standard.

PubMed

Ku, Yi-Kang; Wong, Yon-Cheong; Fu, Chen-Ju; Tseng, Hsiao-Jung; Wang, Li-Jen; Wang, Chao-Jan; Chin, Shy-Chyi

2016-04-01

We investigated the timing of CT and MRI performed before digital subtraction angiography (DSA) in the prediction of hemorrhage sites in patients with head and neck cancers who present with acute oral or neck bleeding after receiving treatment. A total of 123 DSA examinations that evaluated 123 oral or neck bleeding events in 85 patients were analyzed. The last CT or MRI examinations performed within a time frame of 0-337 days before transarterial embolization were reviewed retrospectively, with three findings (pseudoaneurysm, air-containing necrotic tissue, and residual tumor) used to predict hemorrhage sites. DSA findings of pseudoaneurysm or active contrast extravasation were used as a reference standard. The sensitivity of CT and MRI for correctly predicting hemorrhage sites was used to determine the optimal timing of CT or MRI examinations performed before DSA. A total of 8.9% of the DSA examinations (11/123) had equivocal findings but were followed by another bleeding event for which DSA findings were positive. CT or MRI was statistically significantly better at predicting hemorrhage sites in patients with bleeding events associated with nonhypopharyngeal cancers (p = 0.019) than in those with bleeding events associated with hypopharyngeal cancers. The sensitivity of CT or MRI in the prediction of hemorrhage sites was statistically significantly higher for the common carotid artery and the internal carotid artery when CT or MRI was performed less than 30 days before bleeding events occurred. Prediction of hemorrhagic sites was better with the use of CT angiography than with the use of enhanced CT or MRI, although it was not statistically significant. DSA findings can temporarily be equivocal. CT or MRI examinations performed within 30 days of bleeding events can predict the site of hemorrhage. If no CT or MRI findings from the past 30 days are available, we suggest performing emergent CT angiography for the sake of obtaining better arterial detail.

Learning Predictive Statistics: Strategies and Brain Mechanisms.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-08-30

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to changes in the environment's statistics. We provide evidence for an alternate route for learning complex temporal statistics: extracting the most probable outcome in a given context is implemented by interactions between executive and motor corticostriatal mechanisms compared with visual corticostriatal circuits (including hippocampal cortex) that support learning of the exact temporal statistics. Copyright © 2017 Wang et al.

Three Strategies for the Critical Use of Statistical Methods in Psychological Research

ERIC Educational Resources Information Center

Campitelli, Guillermo; Macbeth, Guillermo; Ospina, Raydonal; Marmolejo-Ramos, Fernando

2017-01-01

We present three strategies to replace the null hypothesis statistical significance testing approach in psychological research: (1) visual representation of cognitive processes and predictions, (2) visual representation of data distributions and choice of the appropriate distribution for analysis, and (3) model comparison. The three strategies…

Addendum to the article: Misuse of null hypothesis significance testing: Would estimation of positive and negative predictive values improve certainty of chemical risk assessment?

PubMed

Bundschuh, Mirco; Newman, Michael C; Zubrod, Jochen P; Seitz, Frank; Rosenfeldt, Ricki R; Schulz, Ralf

2015-03-01

We argued recently that the positive predictive value (PPV) and the negative predictive value (NPV) are valuable metrics to include during null hypothesis significance testing: They inform the researcher about the probability of statistically significant and non-significant test outcomes actually being true. Although commonly misunderstood, a reported p value estimates only the probability of obtaining the results or more extreme results if the null hypothesis of no effect was true. Calculations of the more informative PPV and NPV require a priori estimate of the probability (R). The present document discusses challenges of estimating R.

Three-dimensional virtual planning in orthognathic surgery enhances the accuracy of soft tissue prediction.

PubMed

Van Hemelen, Geert; Van Genechten, Maarten; Renier, Lieven; Desmedt, Maria; Verbruggen, Elric; Nadjmi, Nasser

2015-07-01

Throughout the history of computing, shortening the gap between the physical and digital world behind the screen has always been strived for. Recent advances in three-dimensional (3D) virtual surgery programs have reduced this gap significantly. Although 3D assisted surgery is now widely available for orthognathic surgery, one might still argue whether a 3D virtual planning approach is a better alternative to a conventional two-dimensional (2D) planning technique. The purpose of this study was to compare the accuracy of a traditional 2D technique and a 3D computer-aided prediction method. A double blind randomised prospective study was performed to compare the prediction accuracy of a traditional 2D planning technique versus a 3D computer-aided planning approach. The accuracy of the hard and soft tissue profile predictions using both planning methods was investigated. There was a statistically significant difference between 2D and 3D soft tissue planning (p < 0.05). The statistically significant difference found between 2D and 3D planning and the actual soft tissue outcome was not confirmed by a statistically significant difference between methods. The 3D planning approach provides more accurate soft tissue planning. However, the 2D orthognathic planning is comparable to 3D planning when it comes to hard tissue planning. This study provides relevant results for choosing between 3D and 2D planning in clinical practice. Copyright © 2015 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Statistical Analysis of the AIAA Drag Prediction Workshop CFD Solutions

NASA Technical Reports Server (NTRS)

Morrison, Joseph H.; Hemsch, Michael J.

2007-01-01

The first AIAA Drag Prediction Workshop (DPW), held in June 2001, evaluated the results from an extensive N-version test of a collection of Reynolds-Averaged Navier-Stokes CFD codes. The code-to-code scatter was more than an order of magnitude larger than desired for design and experimental validation of cruise conditions for a subsonic transport configuration. The second AIAA Drag Prediction Workshop, held in June 2003, emphasized the determination of installed pylon-nacelle drag increments and grid refinement studies. The code-to-code scatter was significantly reduced compared to the first DPW, but still larger than desired. However, grid refinement studies showed no significant improvement in code-to-code scatter with increasing grid refinement. The third AIAA Drag Prediction Workshop, held in June 2006, focused on the determination of installed side-of-body fairing drag increments and grid refinement studies for clean attached flow on wing alone configurations and for separated flow on the DLR-F6 subsonic transport model. This report compares the transonic cruise prediction results of the second and third workshops using statistical analysis.

Progress of statistical analysis in biomedical research through the historical review of the development of the Framingham score.

PubMed

Ignjatović, Aleksandra; Stojanović, Miodrag; Milošević, Zoran; Anđelković Apostolović, Marija

2017-12-02

The interest in developing risk models in medicine not only is appealing, but also associated with many obstacles in different aspects of predictive model development. Initially, the association of biomarkers or the association of more markers with the specific outcome was proven by statistical significance, but novel and demanding questions required the development of new and more complex statistical techniques. Progress of statistical analysis in biomedical research can be observed the best through the history of the Framingham study and development of the Framingham score. Evaluation of predictive models comes from a combination of the facts which are results of several metrics. Using logistic regression and Cox proportional hazards regression analysis, the calibration test, and the ROC curve analysis should be mandatory and eliminatory, and the central place should be taken by some new statistical techniques. In order to obtain complete information related to the new marker in the model, recently, there is a recommendation to use the reclassification tables by calculating the net reclassification index and the integrated discrimination improvement. Decision curve analysis is a novel method for evaluating the clinical usefulness of a predictive model. It may be noted that customizing and fine-tuning of the Framingham risk score initiated the development of statistical analysis. Clinically applicable predictive model should be a trade-off between all abovementioned statistical metrics, a trade-off between calibration and discrimination, accuracy and decision-making, costs and benefits, and quality and quantity of patient's life.

Functional differences between statistical learning with and without explicit training

PubMed Central

Reber, Paul J.; Paller, Ken A.

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly. PMID:26472644

Predictive capacity of a non-radioisotopic local lymph node assay using flow cytometry, LLNA:BrdU-FCM: Comparison of a cutoff approach and inferential statistics.

PubMed

Kim, Da-Eun; Yang, Hyeri; Jang, Won-Hee; Jung, Kyoung-Mi; Park, Miyoung; Choi, Jin Kyu; Jung, Mi-Sook; Jeon, Eun-Young; Heo, Yong; Yeo, Kyung-Wook; Jo, Ji-Hoon; Park, Jung Eun; Sohn, Soo Jung; Kim, Tae Sung; Ahn, Il Young; Jeong, Tae-Cheon; Lim, Kyung-Min; Bae, SeungJin

2016-01-01

In order for a novel test method to be applied for regulatory purposes, its reliability and relevance, i.e., reproducibility and predictive capacity, must be demonstrated. Here, we examine the predictive capacity of a novel non-radioisotopic local lymph node assay, LLNA:BrdU-FCM (5-bromo-2'-deoxyuridine-flow cytometry), with a cutoff approach and inferential statistics as a prediction model. 22 reference substances in OECD TG429 were tested with a concurrent positive control, hexylcinnamaldehyde 25%(PC), and the stimulation index (SI) representing the fold increase in lymph node cells over the vehicle control was obtained. The optimal cutoff SI (2.7≤cutoff <3.5), with respect to predictive capacity, was obtained by a receiver operating characteristic curve, which produced 90.9% accuracy for the 22 substances. To address the inter-test variability in responsiveness, SI values standardized with PC were employed to obtain the optimal percentage cutoff (42.6≤cutoff <57.3% of PC), which produced 86.4% accuracy. A test substance may be diagnosed as a sensitizer if a statistically significant increase in SI is elicited. The parametric one-sided t-test and non-parametric Wilcoxon rank-sum test produced 77.3% accuracy. Similarly, a test substance could be defined as a sensitizer if the SI means of the vehicle control, and of the low, middle, and high concentrations were statistically significantly different, which was tested using ANOVA or Kruskal-Wallis, with post hoc analysis, Dunnett, or DSCF (Dwass-Steel-Critchlow-Fligner), respectively, depending on the equal variance test, producing 81.8% accuracy. The absolute SI-based cutoff approach produced the best predictive capacity, however the discordant decisions between prediction models need to be examined further. Copyright © 2015 Elsevier Inc. All rights reserved.

Association of serum KL-6 levels with interstitial lung disease in patients with connective tissue disease: a cross-sectional study.

PubMed

Oguz, Ekin Oktay; Kucuksahin, Orhan; Turgay, Murat; Yildizgoren, Mustafa Turgut; Ates, Askin; Demir, Nalan; Kumbasar, Ozlem Ozdemir; Kinikli, Gulay; Duzgun, Nursen

2016-03-01

It was aimed to evaluate KL-6 glycoprotein levels to determine if it may be a diagnostic marker for the connective tissue diseases (CTDs) predicting CTD-related interstitial lung diseases (ILDs) (CTD-ILD) development and to examine if there was a difference between patients and healthy controls. The study included 113 patients with CTD (45 CTD without lung involvement, 68 CTD-ILD) and 45 healthy control subjects. KL-6 glycoprotein levels were analyzed with ELISA in patients and the control group. The relationship between KL-6 glycoprotein levels and CTD-ILD was assessed. In the comparison of all the groups in the study, significantly higher levels of KL-6 were determined in the CTD-ILD group than in either the CTD without pulmonary involvement group or the healthy control group (p < 0.008 and p < 0.001, respectively). There was no statistically significant difference between the KL-6 levels in the healthy control group and the CTD without pulmonary involvement group (p = 0.289). The KL-6 levels did not differ significantly according to the connective tissue diseases in the diagnostic groups (systemic lupus erythematosus, Sjögren's syndrome, rheumatoid arthritis, mixed connective tissue disease, scleroderma, polymyositis/ dermatomyositis). In the healthy control group, there was a statistically significant difference between KL-6 levels in smokers and non-smokers. Smokers had significantly higher serum KL-6 levels compared with non-smokers (p < 0.05). There was no statistically significant difference between smoking status (pack-year) and serum KL-6 levels. There was no statistically significant correlation between serum KL-6 levels and time since diagnosis of CTD and CTD-ILD. The level of KL-6 as a predictive factor could be used to identify the clinical development of ILD before it is detected on imaging modality. Further prospective clinical studies are needed to define whether levels of KL-6 might have prognostic value or might predict progressive ILD.

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

Wang, S.; Ancell, B. C.; Huang, G. H.; Baetz, B. W.

2018-03-01

Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions.

Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features

PubMed Central

Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.

2009-01-01

In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409

Towards good practice for health statistics: lessons from the Millennium Development Goal health indicators.

PubMed

Murray, Christopher J L

2007-03-10

Health statistics are at the centre of an increasing number of worldwide health controversies. Several factors are sharpening the tension between the supply and demand for high quality health information, and the health-related Millennium Development Goals (MDGs) provide a high-profile example. With thousands of indicators recommended but few measured well, the worldwide health community needs to focus its efforts on improving measurement of a small set of priority areas. Priority indicators should be selected on the basis of public-health significance and several dimensions of measurability. Health statistics can be divided into three types: crude, corrected, and predicted. Health statistics are necessary inputs to planning and strategic decision making, programme implementation, monitoring progress towards targets, and assessment of what works and what does not. Crude statistics that are biased have no role in any of these steps; corrected statistics are preferred. For strategic decision making, when corrected statistics are unavailable, predicted statistics can play an important part. For monitoring progress towards agreed targets and assessment of what works and what does not, however, predicted statistics should not be used. Perhaps the most effective method to decrease controversy over health statistics and to encourage better primary data collection and the development of better analytical methods is a strong commitment to provision of an explicit data audit trail. This initiative would make available the primary data, all post-data collection adjustments, models including covariates used for farcasting and forecasting, and necessary documentation to the public.

Correlation of RNA secondary structure statistics with thermodynamic stability and applications to folding.

PubMed

Wu, Johnny C; Gardner, David P; Ozer, Stuart; Gutell, Robin R; Ren, Pengyu

2009-08-28

The accurate prediction of the secondary and tertiary structure of an RNA with different folding algorithms is dependent on several factors, including the energy functions. However, an RNA higher-order structure cannot be predicted accurately from its sequence based on a limited set of energy parameters. The inter- and intramolecular forces between this RNA and other small molecules and macromolecules, in addition to other factors in the cell such as pH, ionic strength, and temperature, influence the complex dynamics associated with transition of a single stranded RNA to its secondary and tertiary structure. Since all of the factors that affect the formation of an RNAs 3D structure cannot be determined experimentally, statistically derived potential energy has been used in the prediction of protein structure. In the current work, we evaluate the statistical free energy of various secondary structure motifs, including base-pair stacks, hairpin loops, and internal loops, using their statistical frequency obtained from the comparative analysis of more than 50,000 RNA sequences stored in the RNA Comparative Analysis Database (rCAD) at the Comparative RNA Web (CRW) Site. Statistical energy was computed from the structural statistics for several datasets. While the statistical energy for a base-pair stack correlates with experimentally derived free energy values, suggesting a Boltzmann-like distribution, variation is observed between different molecules and their location on the phylogenetic tree of life. Our statistical energy values calculated for several structural elements were utilized in the Mfold RNA-folding algorithm. The combined statistical energy values for base-pair stacks, hairpins and internal loop flanks result in a significant improvement in the accuracy of secondary structure prediction; the hairpin flanks contribute the most.

Quantification of uncertainty in the prediction of railway induced ground vibration due to the use of statistical track unevenness data

NASA Astrophysics Data System (ADS)

Lombaert, G.; Galvín, P.; François, S.; Degrande, G.

2014-09-01

Environmental vibrations due to railway traffic are predominantly due to dynamic axle loads caused by wheel and track unevenness and impact excitation by rail joints and wheel flats. Because of its irregular character, track unevenness is commonly processed statistically and represented by its power spectral density function or its root mean square (RMS) value in one-third octave bands. This statistical description does not uniquely define the track unevenness at a given site, however, and different track unevenness profiles matching the statistical description will lead to different predictions of dynamic axle loads and resulting ground vibration. This paper presents a methodology that allows quantifying the corresponding variability in ground vibration predictions. The procedure is derived assuming the geometry of the track and soil to be homogeneous along the track. The procedure is verified by means of Monte Carlo simulations and its usefulness for assessing the mismatch between predicted and measured ground vibrations is demonstrated in a case study. The results show that the response in time domain and its narrow band spectrum exhibit significant variability which is reduced when the running RMS value or the one-third octave band spectrum of the response is considered.

Significant-Loophole-Free Test of Bell's Theorem with Entangled Photons.

PubMed

Giustina, Marissa; Versteegh, Marijn A M; Wengerowsky, Sören; Handsteiner, Johannes; Hochrainer, Armin; Phelan, Kevin; Steinlechner, Fabian; Kofler, Johannes; Larsson, Jan-Åke; Abellán, Carlos; Amaya, Waldimar; Pruneri, Valerio; Mitchell, Morgan W; Beyer, Jörn; Gerrits, Thomas; Lita, Adriana E; Shalm, Lynden K; Nam, Sae Woo; Scheidl, Thomas; Ursin, Rupert; Wittmann, Bernhard; Zeilinger, Anton

2015-12-18

Local realism is the worldview in which physical properties of objects exist independently of measurement and where physical influences cannot travel faster than the speed of light. Bell's theorem states that this worldview is incompatible with the predictions of quantum mechanics, as is expressed in Bell's inequalities. Previous experiments convincingly supported the quantum predictions. Yet, every experiment requires assumptions that provide loopholes for a local realist explanation. Here, we report a Bell test that closes the most significant of these loopholes simultaneously. Using a well-optimized source of entangled photons, rapid setting generation, and highly efficient superconducting detectors, we observe a violation of a Bell inequality with high statistical significance. The purely statistical probability of our results to occur under local realism does not exceed 3.74×10^{-31}, corresponding to an 11.5 standard deviation effect.

Test anxiety and academic performance in chiropractic students.

PubMed

Zhang, Niu; Henderson, Charles N R

2014-01-01

Objective : We assessed the level of students' test anxiety, and the relationship between test anxiety and academic performance. Methods : We recruited 166 third-quarter students. The Test Anxiety Inventory (TAI) was administered to all participants. Total scores from written examinations and objective structured clinical examinations (OSCEs) were used as response variables. Results : Multiple regression analysis shows that there was a modest, but statistically significant negative correlation between TAI scores and written exam scores, but not OSCE scores. Worry and emotionality were the best predictive models for written exam scores. Mean total anxiety and emotionality scores for females were significantly higher than those for males, but not worry scores. Conclusion : Moderate-to-high test anxiety was observed in 85% of the chiropractic students examined. However, total test anxiety, as measured by the TAI score, was a very weak predictive model for written exam performance. Multiple regression analysis demonstrated that replacing total anxiety (TAI) with worry and emotionality (TAI subscales) produces a much more effective predictive model of written exam performance. Sex, age, highest current academic degree, and ethnicity contributed little additional predictive power in either regression model. Moreover, TAI scores were not found to be statistically significant predictors of physical exam skill performance, as measured by OSCEs.

Do statistical segmentation abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?

PubMed Central

Mainela-Arnold, Elina; Evans, Julia L.

2014-01-01

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included 40 children (ages 8;5–12;3), 20 children with SLI and 20 with typical development. Children completed Saffran’s statistical word segmentation task, a lexical-phonological access task (gating task), and a word definition task. Poor statistical learners were also poor at managing lexical-phonological competition during the gating task. However, statistical learning was not a significant predictor of semantic richness in word definitions. The ability to track statistical sequential regularities may be important for learning the inherently sequential structure of lexical-phonology, but not as important for learning lexical-semantic knowledge. Consistent with the procedural/declarative memory distinction, the brain networks associated with the two types of lexical learning are likely to have different learning properties. PMID:23425593

Stationary statistical theory of two-surface multipactor regarding all impacts for efficient threshold analysis

NASA Astrophysics Data System (ADS)

Lin, Shu; Wang, Rui; Xia, Ning; Li, Yongdong; Liu, Chunliang

2018-01-01

Statistical multipactor theories are critical prediction approaches for multipactor breakdown determination. However, these approaches still require a negotiation between the calculation efficiency and accuracy. This paper presents an improved stationary statistical theory for efficient threshold analysis of two-surface multipactor. A general integral equation over the distribution function of the electron emission phase with both the single-sided and double-sided impacts considered is formulated. The modeling results indicate that the improved stationary statistical theory can not only obtain equally good accuracy of multipactor threshold calculation as the nonstationary statistical theory, but also achieve high calculation efficiency concurrently. By using this improved stationary statistical theory, the total time consumption in calculating full multipactor susceptibility zones of parallel plates can be decreased by as much as a factor of four relative to the nonstationary statistical theory. It also shows that the effect of single-sided impacts is indispensable for accurate multipactor prediction of coaxial lines and also more significant for the high order multipactor. Finally, the influence of secondary emission yield (SEY) properties on the multipactor threshold is further investigated. It is observed that the first cross energy and the energy range between the first cross and the SEY maximum both play a significant role in determining the multipactor threshold, which agrees with the numerical simulation results in the literature.

Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.

PubMed

Ye, Jiang-Feng; Zhao, Yu-Xin; Ju, Jian; Wang, Wei

2017-10-01

To discuss the value of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Modified Early Warning Score (MEWS), serum Ca2+, similarly hereinafter, and red cell distribution width (RDW) for predicting the severity grade of acute pancreatitis and to develop and verify a more accurate scoring system to predict the severity of AP. In 302 patients with AP, we calculated BISAP and MEWS scores and conducted regression analyses on the relationships of BISAP scoring, RDW, MEWS, and serum Ca2+ with the severity of AP using single-factor logistics. The variables with statistical significance in the single-factor logistic regression were used in a multi-factor logistic regression model; forward stepwise regression was used to screen variables and build a multi-factor prediction model. A receiver operating characteristic curve (ROC curve) was constructed, and the significance of multi- and single-factor prediction models in predicting the severity of AP using the area under the ROC curve (AUC) was evaluated. The internal validity of the model was verified through bootstrapping. Among 302 patients with AP, 209 had mild acute pancreatitis (MAP) and 93 had severe acute pancreatitis (SAP). According to single-factor logistic regression analysis, we found that BISAP, MEWS and serum Ca2+ are prediction indexes of the severity of AP (P-value<0.001), whereas RDW is not a prediction index of AP severity (P-value>0.05). The multi-factor logistic regression analysis showed that BISAP and serum Ca2+ are independent prediction indexes of AP severity (P-value<0.001), and MEWS is not an independent prediction index of AP severity (P-value>0.05); BISAP is negatively related to serum Ca2+ (r=-0.330, P-value<0.001). The constructed model is as follows: ln()=7.306+1.151*BISAP-4.516*serum Ca2+. The predictive ability of each model for SAP follows the order of the combined BISAP and serum Ca2+ prediction model>Ca2+>BISAP. There is no statistical significance for the predictive ability of BISAP and serum Ca2+ (P-value>0.05); however, there is remarkable statistical significance for the predictive ability using the newly built prediction model as well as BISAP and serum Ca2+ individually (P-value<0.01). Verification of the internal validity of the models by bootstrapping is favorable. BISAP and serum Ca2+ have high predictive value for the severity of AP. However, the model built by combining BISAP and serum Ca2+ is remarkably superior to those of BISAP and serum Ca2+ individually. Furthermore, this model is simple, practical and appropriate for clinical use. Copyright © 2016. Published by Elsevier Masson SAS.

Can Eosinophil Count, Platelet Count, and Mean Platelet Volume Be a Positive Predictive Factor in Penile Arteriogenic Erectile Dysfunction Etiopathogenesis?

PubMed Central

Sönmez, Mehmet Giray; Göğer, Yunus Emre; Sönmez, Leyla Öztürk; Aydın, Arif; Balasar, Mehmet; Kara, Cengiz

2016-01-01

Blood count parameters of patients referring with erectile dysfunction (ED) were examined in this study and it was investigated whether eosinophil count (EC), platelet count (PC), and mean platelet volume values among the suspected predictive parameters which may play a role in especially penile arteriogenic ED etiopathogenesis had a contribution on pathogenesis. Patients referring with ED complaint were evaluated. Depending on the medical story, ED degree was determined by measuring International Index of Erectile Function. Penile Doppler ultrasonography was taken in patients suspected to have vasculogenic ED. According to penile Doppler ultrasonography result, patients with arterial deficiency were included in the penile arteriogenic ED group and the patients with normal results were included in the nonvasculogenic ED group. A total of 36 patients participated in the study from the penile arteriogenic ED group and 32 patients from the nonvasculogenic ED group. Compared with the nonvasculogenic ED group, the penile arteriogenic ED group’s low International Index of Erectile Function score, high EC, mean platelet volume and PC values were detected to be statistically significant (p < .001, p = .021, p = .018, p = .034, respectively). No statistically significant difference was observed among the two groups when age, white blood cells, red blood cells, and hemoglobin values were considered. Pansystolic volume velocities were detected as statistically significantly low compared with the nonvasculogenic ED group in the measurements made in 5th, 10th, 15th, and 20th minutes on the right and left sides in the penile arteriogenic ED group. High MPV value and PC is a significant predictive factor for penile arteriogenic ED and vasculogenic ED and high EC is specifically predictive of arteriogenic ED. PMID:27895254

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

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

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

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

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

DOE PAGES

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

2016-12-28

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

Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

PubMed Central

2013-01-01

Background The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. Methods We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0. Conclusion ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population. PMID:23902963

A significant-loophole-free test of Bell's theorem with entangled photons

NASA Astrophysics Data System (ADS)

Giustina, Marissa; Versteegh, Marijn A. M.; Wengerowsky, Sören; Handsteiner, Johannes; Hochrainer, Armin; Phelan, Kevin; Steinlechner, Fabian; Kofler, Johannes; Larsson, Jan-Åke; Abellán, Carlos; Amaya, Waldimar; Mitchell, Morgan W.; Beyer, Jörn; Gerrits, Thomas; Lita, Adriana E.; Shalm, Lynden K.; Nam, Sae Woo; Scheidl, Thomas; Ursin, Rupert; Wittmann, Bernhard; Zeilinger, Anton

2017-10-01

John Bell's theorem of 1964 states that local elements of physical reality, existing independent of measurement, are inconsistent with the predictions of quantum mechanics (Bell, J. S. (1964), Physics (College. Park. Md). Specifically, correlations between measurement results from distant entangled systems would be smaller than predicted by quantum physics. This is expressed in Bell's inequalities. Employing modifications of Bell's inequalities, many experiments have been performed that convincingly support the quantum predictions. Yet, all experiments rely on assumptions, which provide loopholes for a local realist explanation of the measurement. Here we report an experiment with polarization-entangled photons that simultaneously closes the most significant of these loopholes. We use a highly efficient source of entangled photons, distributed these over a distance of 58.5 meters, and implemented rapid random setting generation and high-efficiency detection to observe a violation of a Bell inequality with high statistical significance. The merely statistical probability of our results to occur under local realism is less than 3.74×10-31, corresponding to an 11.5 standard deviation effect.

A cross-national analysis of how economic inequality predicts biodiversity loss.

PubMed

Holland, Tim G; Peterson, Garry D; Gonzalez, Andrew

2009-10-01

We used socioeconomic models that included economic inequality to predict biodiversity loss, measured as the proportion of threatened plant and vertebrate species, across 50 countries. Our main goal was to evaluate whether economic inequality, measured as the Gini index of income distribution, improved the explanatory power of our statistical models. We compared four models that included the following: only population density, economic footprint (i.e., the size of the economy relative to the country area), economic footprint and income inequality (Gini index), and an index of environmental governance. We also tested the environmental Kuznets curve hypothesis, but it was not supported by the data. Statistical comparisons of the models revealed that the model including both economic footprint and inequality was the best predictor of threatened species. It significantly outperformed population density alone and the environmental governance model according to the Akaike information criterion. Inequality was a significant predictor of biodiversity loss and significantly improved the fit of our models. These results confirm that socioeconomic inequality is an important factor to consider when predicting rates of anthropogenic biodiversity loss.

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.

Prediction of Growth and Mortality of Oregon White Oak in the Pacific Northwest

Treesearch

Peter J. Gould; David D. X. Marshall; Constance A. Harrington

2008-01-01

We developed new equations to predict Oregon white oak (Quercus garryano Dougl. ex Hook.) development within ORGANON, a stand-development model that is widely used in the Pacific Northwest. Tree size, competitive status, crown ratio, and site productivity were statistically significant predictors of growth and mortality. Three...

Prediction of growth and mortality of Oregon White Oak in the Pacific Northwest.

Treesearch

Peter J. Gould; David D. Marshall; Constance A. Harrington

2008-01-01

We developed new equations to predict Oregon white oak (Quercus garryana Dougl. ex Hook.) development with ORGANON, a stand-development model that is widely used in the Pacific Northwest. Tree size, competitive status, crown ratio, and site productivity were statistically significant predictors of growth and mortality. Three scenarios were...

SNP-based heritability estimates of the personality dimensions and polygenic prediction of both neuroticism and major depression: findings from CONVERGE.

PubMed

Docherty, A R; Moscati, A; Peterson, R; Edwards, A C; Adkins, D E; Bacanu, S A; Bigdeli, T B; Webb, B T; Flint, J; Kendler, K S

2016-10-25

Biometrical genetic studies suggest that the personality dimensions, including neuroticism, are moderately heritable (~0.4 to 0.6). Quantitative analyses that aggregate the effects of many common variants have recently further informed genetic research on European samples. However, there has been limited research to date on non-European populations. This study examined the personality dimensions in a large sample of Han Chinese descent (N=10 064) from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology study, aimed at identifying genetic risk factors for recurrent major depression among a rigorously ascertained cohort. Heritability of neuroticism as measured by the Eysenck Personality Questionnaire (EPQ) was estimated to be low but statistically significant at 10% (s.e.=0.03, P=0.0001). In addition to EPQ, neuroticism based on a three-factor model, data for the Big Five (BF) personality dimensions (neuroticism, openness, conscientiousness, extraversion and agreeableness) measured by the Big Five Inventory were available for controls (n=5596). Heritability estimates of the BF were not statistically significant despite high power (>0.85) to detect heritabilities of 0.10. Polygenic risk scores constructed by best linear unbiased prediction weights applied to split-half samples failed to significantly predict any of the personality traits, but polygenic risk for neuroticism, calculated with LDpred and based on predictive variants previously identified from European populations (N=171 911), significantly predicted major depressive disorder case-control status (P=0.0004) after false discovery rate correction. The scores also significantly predicted EPQ neuroticism (P=6.3 × 10 -6 ). Factor analytic results of the measures indicated that any differences in heritabilities across samples may be due to genetic variation or variation in haplotype structure between samples, rather than measurement non-invariance. Findings demonstrate that neuroticism can be significantly predicted across ancestry, and highlight the importance of studying polygenic contributions to personality in non-European populations.

Preoperative Planning and Intraoperative Technique for Accurate Translation of a Distal First Metatarsal Osteotomy.

PubMed

Wynes, Jacob; Lamm, Bradley M; Andrade, Bijan J; Malay, D Scot

2016-01-01

We used preoperative radiographic and intraoperative anatomic measurements to predict and achieve, respectively, the precise amount of capital fragment lateral translation required to restore anatomic balance to the first metatarsophalangeal joint. Correlation was used to relate the amount of capital fragment translation and operative reduction of the first intermetatarsal angle (IMA), hallux abductus angle (HAA), tibial sesamoid position (TSP), metatarsus adductus angle, and first metatarsal length. The mean capital fragment lateral translation was 5.54 ± 1.64 mm, and the mean radiographic reductions included a first IMA of 5.04° ± 2.85°, an HAA of 9.39° ± 8.38°, and a TSP of 1.38 ± 0.9. These changes were statistically (p < .001) and clinically (≥32.55%) significant. The mean reduction of the metatarsus adductus angle was 0.66° ± 4.44° and that for the first metatarsal length was 0.33 ± 7.27 mm, and neither of these were statistically (p = .5876 and 0.1247, respectively) or clinically (≤3.5%) significant. Pairwise correlations between the amount of lateral translation of the capital fragment and the first IMA, HAA, and TSP values were moderately positive and statistically significant (r = 0.4412, p = .0166; r = 0.5391, p = .0025; and r = 0.3729, p = .0463; respectively). In contrast, the correlation with metatarsus adductus and the first metatarsal shortening were weak and not statistically significant (r = 0.2296, p = .2308 and r = -0.2394, p = .2109, respectively). The results of our study indicate that predicted preoperative and executed intraoperative lateral translation of the capital fragment correlates with statistically and clinically significant reductions in the first IMA, HAA, and TSP. Copyright © 2016 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Early PET/CT scan is more effective than RECIST in predicting outcome of patients with liver metastases from colorectal cancer treated with preoperative chemotherapy plus bevacizumab.

PubMed

Lastoria, Secondo; Piccirillo, Maria Carmela; Caracò, Corradina; Nasti, Guglielmo; Aloj, Luigi; Arrichiello, Cecilia; de Lutio di Castelguidone, Elisabetta; Tatangelo, Fabiana; Ottaiano, Alessandro; Iaffaioli, Rosario Vincenzo; Izzo, Francesco; Romano, Giovanni; Giordano, Pasqualina; Signoriello, Simona; Gallo, Ciro; Perrone, Francesco

2013-12-01

Markers predictive of treatment effect might be useful to improve the treatment of patients with metastatic solid tumors. Particularly, early changes in tumor metabolism measured by PET/CT with (18)F-FDG could predict the efficacy of treatment better than standard dimensional Response Evaluation Criteria In Solid Tumors (RECIST) response. We performed PET/CT evaluation before and after 1 cycle of treatment in patients with resectable liver metastases from colorectal cancer, within a phase 2 trial of preoperative FOLFIRI plus bevacizumab. For each lesion, the maximum standardized uptake value (SUV) and the total lesion glycolysis (TLG) were determined. On the basis of previous studies, a ≤ -50% change from baseline was used as a threshold for significant metabolic response for maximum SUV and, exploratively, for TLG. Standard RECIST response was assessed with CT after 3 mo of treatment. Pathologic response was assessed in patients undergoing resection. The association between metabolic and CT/RECIST and pathologic response was tested with the McNemar test; the ability to predict progression-free survival (PFS) and overall survival (OS) was tested with the Log-rank test and a multivariable Cox model. Thirty-three patients were analyzed. After treatment, there was a notable decrease of all the parameters measured by PET/CT. Early metabolic PET/CT response (either SUV- or TLG-based) had a stronger, independent and statistically significant predictive value for PFS and OS than both CT/RECIST and pathologic response at multivariate analysis, although with different degrees of statistical significance. The predictive value of CT/RECIST response was not significant at multivariate analysis. PET/CT response was significantly predictive of long-term outcomes during preoperative treatment of patients with liver metastases from colorectal cancer, and its predictive ability was higher than that of CT/RECIST response after 3 mo of treatment. Such findings need to be confirmed by larger prospective trials.

Meteorological models for estimating phenology of corn

NASA Technical Reports Server (NTRS)

Daughtry, C. S. T.; Cochran, J. C.; Hollinger, S. E.

1984-01-01

Knowledge of when critical crop stages occur and how the environment affects them should provide useful information for crop management decisions and crop production models. Two sources of data were evaluated for predicting dates of silking and physiological maturity of corn (Zea mays L.). Initial evaluations were conducted using data of an adapted corn hybrid grown on a Typic Agriaquoll at the Purdue University Agronomy Farm. The second phase extended the analyses to large areas using data acquired by the Statistical Reporting Service of USDA for crop reporting districts (CRD) in Indiana and Iowa. Several thermal models were compared to calendar days for predicting dates of silking and physiological maturity. Mixed models which used a combination of thermal units to predict silking and days after silking to predict physiological maturity were also evaluated. At the Agronomy Farm the models were calibrated and tested on the same data. The thermal models were significantly less biased and more accurate than calendar days for predicting dates of silking. Differences among the thermal models were small. Significant improvements in both bias and accuracy were observed when the mixed models were used to predict dates of physiological maturity. The results indicate that statistical data for CRD can be used to evaluate models developed at agricultural experiment stations.

Novel Nomogram That Predicts Aggressive Disease and Treatment Failure Among African-American Men with Prostate Cancer

DTIC Science & Technology

2015-12-01

analyzed including TMPRSS2-ERG, AMACR, PSMA , RB, c- Myc, and AR. We observed statistically significant differences in biomarker expression between EA...biomarkers have been analyzed so far including TMPRSS2-ERG, AMACR, PSMA , RB, c-Myc, and AR. In this cohort we observed statistically significant...markers or markers that give relative intensity - p53, p63, Ki67, PSMA , AR - Other 12 markers will be read as present/absent/indeterminate etc

Predictive validity of the UK clinical aptitude test in the final years of medical school: a prospective cohort study.

PubMed

Husbands, Adrian; Mathieson, Alistair; Dowell, Jonathan; Cleland, Jennifer; MacKenzie, Rhoda

2014-04-23

The UK Clinical Aptitude Test (UKCAT) was designed to address issues identified with traditional methods of selection. This study aims to examine the predictive validity of the UKCAT and compare this to traditional selection methods in the senior years of medical school. This was a follow-up study of two cohorts of students from two medical schools who had previously taken part in a study examining the predictive validity of the UKCAT in first year. The sample consisted of 4th and 5th Year students who commenced their studies at the University of Aberdeen or University of Dundee medical schools in 2007. Data collected were: demographics (gender and age group), UKCAT scores; Universities and Colleges Admissions Service (UCAS) form scores; admission interview scores; Year 4 and 5 degree examination scores. Pearson's correlations were used to examine the relationships between admissions variables, examination scores, gender and age group, and to select variables for multiple linear regression analysis to predict examination scores. Ninety-nine and 89 students at Aberdeen medical school from Years 4 and 5 respectively, and 51 Year 4 students in Dundee, were included in the analysis. Neither UCAS form nor interview scores were statistically significant predictors of examination performance. Conversely, the UKCAT yielded statistically significant validity coefficients between .24 and .36 in four of five assessments investigated. Multiple regression analysis showed the UKCAT made a statistically significant unique contribution to variance in examination performance in the senior years. Results suggest the UKCAT appears to predict performance better in the later years of medical school compared to earlier years and provides modest supportive evidence for the UKCAT's role in student selection within these institutions. Further research is needed to assess the predictive validity of the UKCAT against professional and behavioural outcomes as the cohort commences working life.

Predictive validity of the UK clinical aptitude test in the final years of medical school: a prospective cohort study

PubMed Central

2014-01-01

Background The UK Clinical Aptitude Test (UKCAT) was designed to address issues identified with traditional methods of selection. This study aims to examine the predictive validity of the UKCAT and compare this to traditional selection methods in the senior years of medical school. This was a follow-up study of two cohorts of students from two medical schools who had previously taken part in a study examining the predictive validity of the UKCAT in first year. Methods The sample consisted of 4th and 5th Year students who commenced their studies at the University of Aberdeen or University of Dundee medical schools in 2007. Data collected were: demographics (gender and age group), UKCAT scores; Universities and Colleges Admissions Service (UCAS) form scores; admission interview scores; Year 4 and 5 degree examination scores. Pearson’s correlations were used to examine the relationships between admissions variables, examination scores, gender and age group, and to select variables for multiple linear regression analysis to predict examination scores. Results Ninety-nine and 89 students at Aberdeen medical school from Years 4 and 5 respectively, and 51 Year 4 students in Dundee, were included in the analysis. Neither UCAS form nor interview scores were statistically significant predictors of examination performance. Conversely, the UKCAT yielded statistically significant validity coefficients between .24 and .36 in four of five assessments investigated. Multiple regression analysis showed the UKCAT made a statistically significant unique contribution to variance in examination performance in the senior years. Conclusions Results suggest the UKCAT appears to predict performance better in the later years of medical school compared to earlier years and provides modest supportive evidence for the UKCAT’s role in student selection within these institutions. Further research is needed to assess the predictive validity of the UKCAT against professional and behavioural outcomes as the cohort commences working life. PMID:24762134

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

USGS Publications Warehouse

Thompson, Ronald E.; Hoffman, Scott A.

2006-01-01

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

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

PubMed

Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P

2010-06-01

The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it was possible to interchange certain descriptors (i.e. molecular weight and melting point) without incurring a loss of model quality. Such synergy suggested that a model constructed from discrete terms in an equation may not be the most appropriate way of representing mechanistic understandings of skin absorption.

QSAR study of curcumine derivatives as HIV-1 integrase inhibitors.

PubMed

Gupta, Pawan; Sharma, Anju; Garg, Prabha; Roy, Nilanjan

2013-03-01

A QSAR study was performed on curcumine derivatives as HIV-1 integrase inhibitors using multiple linear regression. The statistically significant model was developed with squared correlation coefficients (r(2)) 0.891 and cross validated r(2) (r(2) cv) 0.825. The developed model revealed that electronic, shape, size, geometry, substitution's information and hydrophilicity were important atomic properties for determining the inhibitory activity of these molecules. The model was also tested successfully for external validation (r(2) pred = 0.849) as well as Tropsha's test for model predictability. Furthermore, the domain analysis was carried out to evaluate the prediction reliability of external set molecules. The model was statistically robust and had good predictive power which can be successfully utilized for screening of new molecules.

Rolling Bearing Life Prediction-Past, Present, and Future

NASA Technical Reports Server (NTRS)

Zaretsky, E V; Poplawski, J. V.; Miller, C. R.

2000-01-01

Comparisons were made between the life prediction formulas of Lundberg and Palmgren, Ioannides and Harris, and Zaretsky and full-scale ball and roller bearing life data. The effect of Weibull slope on bearing life prediction was determined. Life factors are proposed to adjust the respective life formulas to the normalized statistical life distribution of each bearing type. The Lundberg-Palmgren method resulted in the most conservative life predictions compared to Ioannides and Harris, and Zaretsky methods which produced statistically similar results. Roller profile can have significant effects on bearing life prediction results. Roller edge loading can reduce life by as much as 98 percent. The resultant predicted life not only depends on the life equation used but on the Weibull slope assumed, the least variation occurring with the Zaretsky equation. The load-life exponent p of 10/3 used in the American National Standards Institute (ANSI)/American Bearing Manufacturers Association (ABMA)/International Organization for Standardization (ISO) standards is inconsistent with the majority roller bearings designed and used today.

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.

Use of statistical and neural net approaches in predicting toxicity of chemicals.

PubMed

Basak, S C; Grunwald, G D; Gute, B D; Balasubramanian, K; Opitz, D

2000-01-01

Hierarchical quantitative structure-activity relationships (H-QSAR) have been developed as a new approach in constructing models for estimating physicochemical, biomedicinal, and toxicological properties of interest. This approach uses increasingly more complex molecular descriptors in a graduated approach to model building. In this study, statistical and neural network methods have been applied to the development of H-QSAR models for estimating the acute aquatic toxicity (LC50) of 69 benzene derivatives to Pimephales promelas (fathead minnow). Topostructural, topochemical, geometrical, and quantum chemical indices were used as the four levels of the hierarchical method. It is clear from both the statistical and neural network models that topostructural indices alone cannot adequately model this set of congeneric chemicals. Not surprisingly, topochemical indices greatly increase the predictive power of both statistical and neural network models. Quantum chemical indices also add significantly to the modeling of this set of acute aquatic toxicity data.

What can 35 years and over 700,000 measurements tell us about noise exposure in the mining industry?

PubMed

Roberts, Benjamin; Sun, Kan; Neitzel, Richard L

2017-01-01

To analyse over 700,000 cross-sectional measurements from the Mine Safety and Health Administration (MHSA) and develop statistical models to predict noise exposure for a worker. Descriptive statistics were used to summarise the data. Two linear regression models were used to predict noise exposure based on MSHA-permissible exposure limit (PEL) and action level (AL), respectively. Twofold cross validation was used to compare the exposure estimates from the models to actual measurement. The mean difference and t-statistic was calculated for each job title to determine whether the model predictions were significantly different from the actual data. Measurements were acquired from MSHA through a Freedom of Information Act request. From 1979 to 2014, noise exposure has decreased. Measurements taken before the implementation of MSHA's revised noise regulation in 2000 were on average 4.5 dBA higher than after the law was implemented. Both models produced exposure predictions that were less than 1 dBA different than the holdout data. Overall noise levels in mines have been decreasing. However, this decrease has not been uniform across all mining sectors. The exposure predictions from the model will be useful to help predict hearing loss in workers in the mining industry.

Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

NASA Astrophysics Data System (ADS)

Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

2015-03-01

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

Respiratory effects of diesel exhaust in salt miners

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

Gamble, J.F.; Jones, W.G.

1983-09-01

The respiratory health of 259 white males working at 5 salt (NaCl) mines was assessed by questionnaire, chest radiographs, and air and He-O/sup 2/ spirometry. Response variables were symptoms, pneumoconiosis, and spirometry. Predictor variables included age, height, smoking, mine, and tenure in diesel-exposed jobs. The purpose was to assess the association of response measures of respiratory health with exposure to diesel exhaust. There were only 2 cases of Grade 1 pneumoconiosis, so no further analysis was done. Comparisons within the study population showed a statistically significant dose-related association of phlegm and diesel exposure. There was a nonsignificant trend for coughmore » and dyspnea, and no association with spirometry. Age- and smoking-adjusted rates of cough, phlegm, and dyspnea were 145, 159, and 93% of an external comparison population. Percent predicted flow rates showed statistically significant reductions, but the reductions were small and there were no dose-response relations. Percent predicted FEV1 and FVC were about 96% of predicted.« less

Do sophisticated epistemic beliefs predict meaningful learning? Findings from a structural equation model of undergraduate biology learning

NASA Astrophysics Data System (ADS)

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-10-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely 'multiple-source,' 'uncertainty,' 'development,' and 'justification.' COLB is further divided into 'constructivist' and 'reproductive' conceptions, while SLB represents deep strategies and surface learning strategies. Questionnaire responses were gathered from 303 college students. The results of the confirmatory factor analysis and structural equation modelling showed acceptable model fits. Mediation testing further revealed two paths with complete mediation. In sum, students' epistemic beliefs of 'uncertainty' and 'justification' in biology were statistically significant in explaining the constructivist and reproductive COLB, respectively; and 'uncertainty' was statistically significant in explaining the deep SLB as well. The results of mediation testing further revealed that 'uncertainty' predicted surface strategies through the mediation of 'reproductive' conceptions; and the relationship between 'justification' and deep strategies was mediated by 'constructivist' COLB. This study provides evidence for the essential roles some epistemic beliefs play in predicting students' learning.

A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Huang, W.; Fan, Y. R.; Li, Z.

2015-10-01

In this study, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space. The proposed methodology is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability, as well as its capability of revealing complex and dynamic parameter interactions. A set of reduced polynomial chaos expansions (PCEs) only with statistically significant terms can be obtained based on the results of factorial analysis of variance (ANOVA), achieving a reduction of uncertainty in hydrologic predictions. The predictive performance of reduced PCEs is verified by comparing against standard PCEs and the Monte Carlo with Latin hypercube sampling (MC-LHS) method in terms of reliability, sharpness, and Nash-Sutcliffe efficiency (NSE). Results reveal that the reduced PCEs are able to capture hydrologic behaviors of the Xiangxi River watershed, and they are efficient functional representations for propagating uncertainties in hydrologic predictions.

The Predictive Validity of a Computer-Adaptive Assessment of Kindergarten and First-Grade Reading Skills

ERIC Educational Resources Information Center

Clemens, Nathan H.; Hagan-Burke, Shanna; Luo, Wen; Cerda, Carissa; Blakely, Alane; Frosch, Jennifer; Gamez-Patience, Brenda; Jones, Meredith

2015-01-01

This study examined the predictive validity of a computer-adaptive assessment for measuring kindergarten reading skills using the STAR Early Literacy (SEL) test. The findings showed that the results of SEL assessments administered during the fall, winter, and spring of kindergarten were moderate and statistically significant predictors of year-end…

Performance of bed-load transport equations relative to geomorphic significance: Predicting effective discharge and its transport rate

Treesearch

Jeffrey J. Barry; John M. Buffington; Peter Goodwin; John .G. King; William W. Emmett

2008-01-01

Previous studies assessing the accuracy of bed-load transport equations have considered equation performance statistically based on paired observations of measured and predicted bed-load transport rates. However, transport measurements were typically taken during low flows, biasing the assessment of equation performance toward low discharges, and because equation...

Augmented Cognition - Phase 4 Cognitive Assessment and Task Management (CAT-M)

DTIC Science & Technology

2008-12-01

Angle Brake Pedal Force Accelerator Pedal ...Wheel Angle • Brake Pedal Force • Accelerator Pedal Deflection Note that we are using the controls as input to the prediction system. This means... Angle . At time >2.5 seconds, the Accelerator Pedal and Brake Pedal become statistically significantly easier to predict than Steering Wheel Angle .

Frequency, probability, and prediction: easy solutions to cognitive illusions?

PubMed

Griffin, D; Buehler, R

1999-02-01

Many errors in probabilistic judgment have been attributed to people's inability to think in statistical terms when faced with information about a single case. Prior theoretical analyses and empirical results imply that the errors associated with case-specific reasoning may be reduced when people make frequentistic predictions about a set of cases. In studies of three previously identified cognitive biases, we find that frequency-based predictions are different from-but no better than-case-specific judgments of probability. First, in studies of the "planning fallacy, " we compare the accuracy of aggregate frequency and case-specific probability judgments in predictions of students' real-life projects. When aggregate and single-case predictions are collected from different respondents, there is little difference between the two: Both are overly optimistic and show little predictive validity. However, in within-subject comparisons, the aggregate judgments are significantly more conservative than the single-case predictions, though still optimistically biased. Results from studies of overconfidence in general knowledge and base rate neglect in categorical prediction underline a general conclusion. Frequentistic predictions made for sets of events are no more statistically sophisticated, nor more accurate, than predictions made for individual events using subjective probability. Copyright 1999 Academic Press.

Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

NASA Astrophysics Data System (ADS)

Chung, K. K.; Do, D. Q.

2010-09-01

In order to model relationships between chemical structures and biological effects in quantitative structure-activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data.

Lymph node ratio may predict relapse free survival and overall survival in patients with stage II & III colorectal carcinoma.

PubMed

Zekri, Jamal; Ahmad, Imran; Fawzy, Ehab; Elkhodary, Tawfik R; Al-Gahmi, Aboelkhair; Hassouna, Ashraf; El Sayed, Mohamed E; Ur Rehman, Jalil; Karim, Syed M; Bin Sadiq, Bakr

2015-01-01

Lymph node ratio (LNR) defined as the number of lymph nodes (LNs) involved with metastases divided by number of LNs examined, has been shown to be an independent prognostic factor in breast, stomach and various other solid tumors. Its significance as a prognostic determinant in colorectal cancer (CRC) is still under investigation. This study investigated the prognostic value of LNR in patients with resected CRC. We retrospectively ex- amined 145 patients with stage II & III CRC diagnosed and treated at a single institution during 9 years pe- riod. Patients were grouped according to LNR in three groups. Group 1; LNR < 0.05, Group 2; LNR = 0.05-0.19 & Group 3 > 0.19. Chi square, life table analysis and multivariate Cox regression were used for statistical analysis. On multivariate analysis, number of involved LNs (NILN) (HR = 1.15, 95% CI 1.055-1.245; P = 0.001) and pathological T stage (P = 0.002) were statistically significant predictors of relapse free survival (RFS). LNR as a continuous variable (but not as a categorical variable) was statistically significant predictor of RFS (P = 0.02). LNR was also a statistically significant predictor of overall survival (OS) (P = 0.02). LNR may predict RFS and OS in patients with resected stage II & III CRC. Studies with larger cohorts and longer follow up are needed to further examine and validate theprognostic value of LNR.

Predictors of surgeons' efficiency in the operating rooms.

PubMed

Nakata, Yoshinori; Watanabe, Yuichi; Narimatsu, Hiroto; Yoshimura, Tatsuya; Otake, Hiroshi; Sawa, Tomohiro

2017-02-01

The sustainability of the Japanese healthcare system is questionable because of a huge fiscal debt. One of the solutions is to improve the efficiency of healthcare. The purpose of this study is to determine what factors are predictive of surgeons' efficiency scores. The authors collected data from all the surgical procedures performed at Teikyo University Hospital from April 1 through September 30 in 2013-2015. Output-oriented Charnes-Cooper-Rhodes model of data envelopment analysis was employed to calculate each surgeon's efficiency score. Seven independent variables that may predict their efficiency scores were selected: experience, medical school, surgical volume, gender, academic rank, surgical specialty, and the surgical fee schedule. Multiple regression analysis using random-effects Tobit model was used for our panel data. The data from total 8722 surgical cases were obtained in 18-month study period. The authors analyzed 134 surgeons. The only statistically significant coefficients were surgical specialty and surgical fee schedule (p = 0.000 and p = 0.016, respectively). Experience had some positive association with efficiency scores but did not reach statistical significance (p = 0.062). The other coefficients were not statistically significant. These results demonstrated that the surgical reimbursement system, not surgeons' personal characteristics, is a significant predictor of surgeons' efficiency.

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

PubMed

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

2015-09-01

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

Prediction of phenotypes of missense mutations in human proteins from biological assemblies.

PubMed

Wei, Qiong; Xu, Qifang; Dunbrack, Roland L

2013-02-01

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence-based and structure-based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure-based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X-ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease-associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e-5). When adding this information to sequence-based features such as the difference between wildtype and mutant position-specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence-based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease-associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins. Copyright © 2012 Wiley Periodicals, Inc.

Evidence-based pathology in its second decade: toward probabilistic cognitive computing.

PubMed

Marchevsky, Alberto M; Walts, Ann E; Wick, Mark R

2017-03-01

Evidence-based pathology advocates using a combination of best available data ("evidence") from the literature and personal experience for the diagnosis, estimation of prognosis, and assessment of other variables that impact individual patient care. Evidence-based pathology relies on systematic reviews of the literature, evaluation of the quality of evidence as categorized by evidence levels and statistical tools such as meta-analyses, estimates of probabilities and odds, and others. However, it is well known that previously "statistically significant" information usually does not accurately forecast the future for individual patients. There is great interest in "cognitive computing" in which "data mining" is combined with "predictive analytics" designed to forecast future events and estimate the strength of those predictions. This study demonstrates the use of IBM Watson Analytics software to evaluate and predict the prognosis of 101 patients with typical and atypical pulmonary carcinoid tumors in which Ki-67 indices have been determined. The results obtained with this system are compared with those previously reported using "routine" statistical software and the help of a professional statistician. IBM Watson Analytics interactively provides statistical results that are comparable to those obtained with routine statistical tools but much more rapidly, with considerably less effort and with interactive graphics that are intuitively easy to apply. It also enables analysis of natural language variables and yields detailed survival predictions for patient subgroups selected by the user. Potential applications of this tool and basic concepts of cognitive computing are discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

ICSI Outcome in Infertile Couples with Different Causes of Infertility: A Cross-Sectional Study.

PubMed

Ashrafi, Mahnaz; Jahanian Sadatmahalleh, Shahideh; Akhoond, Mohammad Reza; Ghaffari, Firouzeh; Zolfaghari, Zahra

2013-07-01

Different success rate of Intracytoplasmic Sperm injection (ICSI) has been observed in various causes of infertility. In this study, we evaluated the relation between ICSI outcome and different causes of infertility. We also aimed to examine parameters that might predict the pregnancy success rate following ICSI. This cross sectional study included1492 infertile women referred to Infertility Center of Royan Institute between 2010 and 2011. We assigned two groups including pregnant (n=504) and non-pregnant (n=988), while all participants underwent ICSI cycles. All statistics were performed by SPSS program. Statistical Analysis was carried out using Chi-square and t test. Logistic regression was done to build a prediction model in ICSI cycles. The overall clinical pregnancy rate in our study was 33.9% (n=1492). There was a statistically significant difference in mean serum concentration on day 3 after application of luteinizing hormone (LH) between the pregnant and the non-pregnant groups (p<0.05). However, There were no significant differences between two groups in the serum concentrations on day 3 after application of the following hormones: folliclestimulating hormone (FSH), thyroid-stimulating hormone (TSH), and metoclopramidestimulated prolactin (PRL) . We found no association between different causes of infertility and clinical outcomes . The number of metaphase II (MII) oocytes, embryo transfer, number of good embryo (grade A, B, AB), total dose of gonadotropin, endometrial thickness, maternal age, number of previous cycle were statistically significant between two groups (p<0.05). Our results indicate that ICSI in an effective option in couples with different causes of infertility. These variables were integrated into a statistical model to allow the prediction for the chance of pregnancy following ICSI cycles. It is required that each infertility center gather enough information about the causes of infertility in order to provide more information and better assistance to patients. Therefore, we suggest that physicians prepare adequate training and required information regarding these procedures for infertile couples in order to improve their knowledge.

Four hundred or more participants needed for stable contingency table estimates of clinical prediction rule performance.

PubMed

Kent, Peter; Boyle, Eleanor; Keating, Jennifer L; Albert, Hanne B; Hartvigsen, Jan

2017-02-01

To quantify variability in the results of statistical analyses based on contingency tables and discuss the implications for the choice of sample size for studies that derive clinical prediction rules. An analysis of three pre-existing sets of large cohort data (n = 4,062-8,674) was performed. In each data set, repeated random sampling of various sample sizes, from n = 100 up to n = 2,000, was performed 100 times at each sample size and the variability in estimates of sensitivity, specificity, positive and negative likelihood ratios, posttest probabilities, odds ratios, and risk/prevalence ratios for each sample size was calculated. There were very wide, and statistically significant, differences in estimates derived from contingency tables from the same data set when calculated in sample sizes below 400 people, and typically, this variability stabilized in samples of 400-600 people. Although estimates of prevalence also varied significantly in samples below 600 people, that relationship only explains a small component of the variability in these statistical parameters. To reduce sample-specific variability, contingency tables should consist of 400 participants or more when used to derive clinical prediction rules or test their performance. Copyright © 2016 Elsevier Inc. All rights reserved.

The efficacy of the reverse contrast mode in digital radiography for the detection of proximal dentinal caries

PubMed Central

Miri, Shimasadat; Mehralizadeh, Sandra; Sadri, Donya; Motamedi, Mahmood Reza Kalantar

2015-01-01

Purpose This study evaluated the diagnostic accuracy of the reverse contrast mode in intraoral digital radiography for the detection of proximal dentinal caries, in comparison with the original digital radiographs. Materials and Methods Eighty extracted premolars with no clinically apparent caries were selected, and digital radiographs of them were taken separately in standard conditions. Four observers examined the original radiographs and the same radiographs in the reverse contrast mode with the goal of identifying proximal dentinal caries. Microscopic sections 5 µm in thickness were prepared from the teeth in the mesiodistal direction. Four slides prepared from each sample used as the diagnostic gold standard. The data were analyzed using SPSS (α=0.05). Results Our results showed that the original radiographs in order to identify proximal dentinal caries had the following values for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, respectively: 72.5%, 90%, 87.2%, 76.5%, and 80.9%. For the reverse contrast mode, however, the corresponding values were 63.1%, 89.4%, 87.1%, 73.5%, and 78.8%, respectively. The sensitivity of original digital radiograph for detecting proximal dentinal caries was significantly higher than that of reverse contrast mode (p<0.05). However, no statistically significant differences were found regarding specificity, positive predictive value, negative predictive value, or accuracy (p>0.05). Conclusion The sensitivity of the original digital radiograph for detecting proximal dentinal caries was significantly higher than that of the reversed contrast images. However, no statistically significant differences were found between these techniques regarding specificity, positive predictive value, negative predictive value, or accuracy. PMID:26389055

Drivers and seasonal predictability of extreme wind speeds in the ECMWF System 4 and a statistical model

NASA Astrophysics Data System (ADS)

Walz, M. A.; Donat, M.; Leckebusch, G. C.

2017-12-01

As extreme wind speeds are responsible for large socio-economic losses in Europe, a skillful prediction would be of great benefit for disaster prevention as well as for the actuarial community. Here we evaluate patterns of large-scale atmospheric variability and the seasonal predictability of extreme wind speeds (e.g. >95th percentile) in the European domain in the dynamical seasonal forecast system ECMWF System 4, and compare to the predictability based on a statistical prediction model. The dominant patterns of atmospheric variability show distinct differences between reanalysis and ECMWF System 4, with most patterns in System 4 extended downstream in comparison to ERA-Interim. The dissimilar manifestations of the patterns within the two models lead to substantially different drivers associated with the occurrence of extreme winds in the respective model. While the ECMWF System 4 is shown to provide some predictive power over Scandinavia and the eastern Atlantic, only very few grid cells in the European domain have significant correlations for extreme wind speeds in System 4 compared to ERA-Interim. In contrast, a statistical model predicts extreme wind speeds during boreal winter in better agreement with the observations. Our results suggest that System 4 does not seem to capture the potential predictability of extreme winds that exists in the real world, and therefore fails to provide reliable seasonal predictions for lead months 2-4. This is likely related to the unrealistic representation of large-scale patterns of atmospheric variability. Hence our study points to potential improvements of dynamical prediction skill by improving the simulation of large-scale atmospheric dynamics.

Impact of Damping Uncertainty on SEA Model Response Variance

NASA Technical Reports Server (NTRS)

Schiller, Noah; Cabell, Randolph; Grosveld, Ferdinand

2010-01-01

Statistical Energy Analysis (SEA) is commonly used to predict high-frequency vibroacoustic levels. This statistical approach provides the mean response over an ensemble of random subsystems that share the same gross system properties such as density, size, and damping. Recently, techniques have been developed to predict the ensemble variance as well as the mean response. However these techniques do not account for uncertainties in the system properties. In the present paper uncertainty in the damping loss factor is propagated through SEA to obtain more realistic prediction bounds that account for both ensemble and damping variance. The analysis is performed on a floor-equipped cylindrical test article that resembles an aircraft fuselage. Realistic bounds on the damping loss factor are determined from measurements acquired on the sidewall of the test article. The analysis demonstrates that uncertainties in damping have the potential to significantly impact the mean and variance of the predicted response.

Indoor NO2 air pollution and lung function of professional cooks.

PubMed

Arbex, M A; Martins, L C; Pereira, L A A; Negrini, F; Cardoso, A A; Melchert, W R; Arbex, R F; Saldiva, P H N; Zanobetti, A; Braga, A L F

2007-04-01

Studies of cooking-generated NO2 effects are rare in occupational epidemiology. In the present study, we evaluated the lung function of professional cooks exposed to NO2 in hospital kitchens. We performed spirometry in 37 cooks working in four hospital kitchens and estimated the predicted FVC, FEV1 and FEF(25-75), based on age, sex, race, weight, and height, according to Knudson standards. NO2 measurements were obtained for 4 consecutive days during 4 different periods at 20-day intervals in each kitchen. Measurements were performed inside and outside the kitchens, simultaneously using Palm diffusion tubes. A time/exposure indicator was defined as representative of the cumulative exposure of each cook. No statistically significant effect of NO2 exposure on FVC was found. Each year of work as a cook corresponded to a decrease in predicted FEV1 of 2.5% (P = 0.046) for the group as a whole. When smoking status and asthma were included in the analysis the effect of time/exposure decreased about 10% and lost statistical significance. On predicted FEF(25-75), a decrease of 3.5% (P = 0.035) was observed for the same group and the inclusion of controllers for smoking status and asthma did not affect the effects of time/exposure on pulmonary function parameter. After a 10-year period of work as cooks the participants of the study may present decreases in both predicted FEV1 and FEF(25-75) that can reach 20 and 30%, respectively. The present study showed small but statistically significant adverse effects of gas stove exposure on the lung function of professional cooks.

Generalized Joint Hypermobility Is Predictive of Hip Capsular Thickness.

PubMed

Devitt, Brian M; Smith, Bjorn N; Stapf, Robert; Tacey, Mark; O'Donnell, John M

2017-04-01

The pathomechanics of hip microinstability are not clearly defined but are thought to involve anatomical abnormalities, repetitive forces across the hip, and ligamentous laxity. The purpose of this study was to explore the relationship between generalized joint hypermobility (GJH) and hip capsular thickness. The hypothesis was that GJH would be predictive of a thin hip capsule. Cross-sectional study; Level of evidence, 3. A prospective study was performed on 100 consecutive patients undergoing primary hip arthroscopy for the treatment of hip pain. A Beighton test score (BTS) was obtained prior to each procedure. The maximum score was 9, and a score of ≥4 was defined as hypermobile. Capsular thickness at the level of the anterior portal, corresponding to the location of the iliofemoral ligament, was measured arthroscopically using a calibrated probe. The presence of ligamentum teres (LT) pathology was also recorded. Fifty-five women and 45 men were included in the study. The mean age was 32 years (range, 18-45 years). The median hip capsule thickness was statistically greater in men than women (12.5 and 7.5 mm, respectively). The median BTS for men was 1 compared with 4 for women ( P < .001). A statistically significant association was found between BTS and capsular thickness; a BTS of <4 is strongly predictive of having a capsular thickness of ≥10 mm, while a BTS ≥4 correlates with a capsular thickness of <10 mm. There was a statistically greater incidence of LT tears in patients with a capsular thickness of ≤7.5 mm and a BTS of ≥4 ( P < .001). Measurement of the GJH is highly predictive of hip capsular thickness. A BTS of <4 correlates significantly with a capsular thickness of ≥10 mm, while a BTS ≥4 correlates significantly with a thickness of <10 mm.

Risk of mortality associated to chronic kidney disease in patients with type 2 diabetes mellitus: a 13-year follow-up.

PubMed

Gimeno-Orna, José Antonio; Blasco-Lamarca, Yolanda; Campos-Gutierrez, Belén; Molinero-Herguedas, Edmundo; Lou-Arnal, Luis Miguel; García-García, Blanca

2015-01-01

Our aim was to assess the usefulness of glomerular filtration rate (GFR) and urinary albumin excretion (UAE) to predict the risk of mortality in patients with type 2 diabetes mellitus. This is a prospective cohort study in patients with type 2 diabetes mellitus. Clinical end-point was mortality rate. GFR was measured in ml/min/1.73 m2 and stratified in 3 categories (≥60; 45-59; <45); UAE was measured in mg/24hours and was also stratified in 3 categories (<30; 30-300; >300). Mortality rates were reported per 1000 patient-years. Cox regression models were used to predict mortality risk associated with combined GFR and UAE. The predictive power was estimated with C-Harrell statistic. A total of 453 patients (39.3% males), aged 64.9 (SD 9.3) years were included; mean diabetes duration was 10.4 (SD 7.5) years. Median follow-up was 13 years. Total mortality rate was 39.5/1000. The progressive increase in mortality in the successive categories of GFR and UAE was statistically significant (P<.001). In a multivariable analysis, UAE (HR30-300=1.02 and HR>300=2.83; X2=11.6; P =.003) and GFR (HR45-59=1.34 and HR<45=1.84; X2=6.4; P =.041) were independent predictors for mortality, with no significant interaction. Simultaneous inclusion of GFR and UAE improved the predictive power of models (C-Harrell 0.741 vs. 0.726; P =.045). GFR and UAE are independent predictors for mortality in type 2 diabetic patients and do not show a statistically significant interaction. Copyright © 2015 The Authors. Published by Elsevier España, S.L.U. All rights reserved.

Tract-Based Spatial Statistics in Preterm-Born Neonates Predicts Cognitive and Motor Outcomes at 18 Months.

PubMed

Duerden, E G; Foong, J; Chau, V; Branson, H; Poskitt, K J; Grunau, R E; Synnes, A; Zwicker, J G; Miller, S P

2015-08-01

Adverse neurodevelopmental outcome is common in children born preterm. Early sensitive predictors of neurodevelopmental outcome such as MR imaging are needed. Tract-based spatial statistics, a diffusion MR imaging analysis method, performed at term-equivalent age (40 weeks) is a promising predictor of neurodevelopmental outcomes in children born very preterm. We sought to determine the association of tract-based spatial statistics findings before term-equivalent age with neurodevelopmental outcome at 18-months corrected age. Of 180 neonates (born at 24-32-weeks' gestation) enrolled, 153 had DTI acquired early at 32 weeks' postmenstrual age and 105 had DTI acquired later at 39.6 weeks' postmenstrual age. Voxelwise statistics were calculated by performing tract-based spatial statistics on DTI that was aligned to age-appropriate templates. At 18-month corrected age, 166 neonates underwent neurodevelopmental assessment by using the Bayley Scales of Infant Development, 3rd ed, and the Peabody Developmental Motor Scales, 2nd ed. Tract-based spatial statistics analysis applied to early-acquired scans (postmenstrual age of 30-33 weeks) indicated a limited significant positive association between motor skills and axial diffusivity and radial diffusivity values in the corpus callosum, internal and external/extreme capsules, and midbrain (P < .05, corrected). In contrast, for term scans (postmenstrual age of 37-41 weeks), tract-based spatial statistics analysis showed a significant relationship between both motor and cognitive scores with fractional anisotropy in the corpus callosum and corticospinal tracts (P < .05, corrected). Tract-based spatial statistics in a limited subset of neonates (n = 22) scanned at <30 weeks did not significantly predict neurodevelopmental outcomes. The strength of the association between fractional anisotropy values and neurodevelopmental outcome scores increased from early-to-late-acquired scans in preterm-born neonates, consistent with brain dysmaturation in this population. © 2015 by American Journal of Neuroradiology.

Quality assessment of butter cookies applying multispectral imaging

PubMed Central

Andresen, Mette S; Dissing, Bjørn S; Løje, Hanne

2013-01-01

A method for characterization of butter cookie quality by assessing the surface browning and water content using multispectral images is presented. Based on evaluations of the browning of butter cookies, cookies were manually divided into groups. From this categorization, reference values were calculated for a statistical prediction model correlating multispectral images with a browning score. The browning score is calculated as a function of oven temperature and baking time. It is presented as a quadratic response surface. The investigated process window was the intervals 4–16 min and 160–200°C in a forced convection electrically heated oven. In addition to the browning score, a model for predicting the average water content based on the same images is presented. This shows how multispectral images of butter cookies may be used for the assessment of different quality parameters. Statistical analysis showed that the most significant wavelengths for browning predictions were in the interval 400–700 nm and the wavelengths significant for water prediction were primarily located in the near-infrared spectrum. The water prediction model was found to correctly estimate the average water content with an absolute error of 0.22%. From the images it was also possible to follow the browning and drying propagation from the cookie edge toward the center. PMID:24804036

Visualization of the significance of Receiver Operating Characteristics based on confidence ellipses

NASA Astrophysics Data System (ADS)

Sarlis, Nicholas V.; Christopoulos, Stavros-Richard G.

2014-03-01

The Receiver Operating Characteristics (ROC) is used for the evaluation of prediction methods in various disciplines like meteorology, geophysics, complex system physics, medicine etc. The estimation of the significance of a binary prediction method, however, remains a cumbersome task and is usually done by repeating the calculations by Monte Carlo. The FORTRAN code provided here simplifies this problem by evaluating the significance of binary predictions for a family of ellipses which are based on confidence ellipses and cover the whole ROC space. Catalogue identifier: AERY_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AERY_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 11511 No. of bytes in distributed program, including test data, etc.: 72906 Distribution format: tar.gz Programming language: FORTRAN. Computer: Any computer supporting a GNU FORTRAN compiler. Operating system: Linux, MacOS, Windows. RAM: 1Mbyte Classification: 4.13, 9, 14. Nature of problem: The Receiver Operating Characteristics (ROC) is used for the evaluation of prediction methods in various disciplines like meteorology, geophysics, complex system physics, medicine etc. The estimation of the significance of a binary prediction method, however, remains a cumbersome task and is usually done by repeating the calculations by Monte Carlo. The FORTRAN code provided here simplifies this problem by evaluating the significance of binary predictions for a family of ellipses which are based on confidence ellipses and cover the whole ROC space. Solution method: Using the statistics of random binary predictions for a given value of the predictor threshold ɛt, one can construct the corresponding confidence ellipses. The envelope of these corresponding confidence ellipses is estimated when ɛt varies from 0 to 1. This way a new family of ellipses is obtained, named k-ellipses, which covers the whole ROC plane and leads to a well defined Area Under the Curve (AUC). For the latter quantity, Mason and Graham [1] have shown that it follows the Mann-Whitney U-statistics [2] which can be applied [3] for the estimation of the statistical significance of each k-ellipse. As the transformation is invertible, any point on the ROC plane corresponds to a unique value of k, thus to a unique p-value to obtain this point by chance. The present FORTRAN code provides this p-value field on the ROC plane as well as the k-ellipses corresponding to the (p=)10%, 5% and 1% significance levels using as input the number of the positive (P) and negative (Q) cases to be predicted. Unusual features: In some machines, the compiler directive -O2 or -O3 should be used to avoid NaN’s in some points of the p-field along the diagonal. Running time: Depending on the application, e.g., 4s for an Intel(R) Core(TM)2 CPU E7600 at 3.06 GHz with 2 GB RAM for the examples presented here References: [1] S.J. Mason, N.E. Graham, Quart. J. Roy. Meteor. Soc. 128 (2002) 2145. [2] H.B. Mann, D.R. Whitney, Ann. Math. Statist. 18 (1947) 50. [3] L.C. Dinneen, B.C. Blakesley, J. Roy. Stat. Soc. Ser. C Appl. Stat. 22 (1973) 269.

Prediction of high-frequency vibration transmission across coupled, periodic ribbed plates by incorporating tunneling mechanisms.

PubMed

Yin, Jianfei; Hopkins, Carl

2013-04-01

Prediction of structure-borne sound transmission on built-up structures at audio frequencies is well-suited to Statistical Energy Analysis (SEA) although the inclusion of periodic ribbed plates presents challenges. This paper considers an approach using Advanced SEA (ASEA) that can incorporate tunneling mechanisms within a statistical approach. The coupled plates used for the investigation form an L-junction comprising a periodic ribbed plate with symmetric ribs and an isotropic homogeneous plate. Experimental SEA (ESEA) is carried out with input data from Finite Element Methods (FEM). This indicates that indirect coupling is significant at high frequencies where bays on the periodic ribbed plate can be treated as individual subsystems. SEA using coupling loss factors from wave theory leads to significant underestimates in the energy of the bays when the isotropic homogeneous plate is excited. This is due to the absence of tunneling mechanisms in the SEA model. In contrast, ASEA shows close agreement with FEM and laboratory measurements. The errors incurred with SEA rapidly increase as the bays become more distant from the source subsystem. ASEA provides significantly more accurate predictions by accounting for the spatial filtering that leads to non-diffuse vibration fields on these more distant bays.

Relations Between Environmental and Water-Quality Variables and Escherichia coli in the Cuyahoga River With Emphasis on Turbidity as a Predictor of Recreational Water Quality, Cuyahoga Valley National Park, Ohio, 2008

USGS Publications Warehouse

Brady, Amie M.G.; Plona, Meg B.

2009-01-01

During the recreational season of 2008 (May through August), a regression model relating turbidity to concentrations of Escherichia coli (E. coli) was used to predict recreational water quality in the Cuyahoga River at the historical community of Jaite, within the present city of Brecksville, Ohio, a site centrally located within Cuyahoga Valley National Park. Samples were collected three days per week at Jaite and at three other sites on the river. Concentrations of E. coli were determined and compared to environmental and water-quality measures and to concentrations predicted with a regression model. Linear relations between E. coli concentrations and turbidity, gage height, and rainfall were statistically significant for Jaite. Relations between E. coli concentrations and turbidity were statistically significant for the three additional sites, and relations between E. coli concentrations and gage height were significant at the two sites where gage-height data were available. The turbidity model correctly predicted concentrations of E. coli above or below Ohio's single-sample standard for primary-contact recreation for 77 percent of samples collected at Jaite.

Comparing Weighted and Unweighted Grade Point Averages in Predicting College Success of Diverse and Low-Income College Students

ERIC Educational Resources Information Center

Warne, Russell T.; Nagaishi, Chanel; Slade, Michael K.; Hermesmeyer, Paul; Peck, Elizabeth Kimberli

2014-01-01

While research has shown the statistical significance of high school grade point averages (HSGPAs) in predicting future academic outcomes, the systems with which HSGPAs are calculated vary drastically across schools. Some schools employ unweighted grades that carry the same point value regardless of the course in which they are earned; other…

Skillful prediction of hot temperature extremes over the source region of ancient Silk Road.

PubMed

Zhang, Jingyong; Yang, Zhanmei; Wu, Lingyun

2018-04-27

The source region of ancient Silk Road (SRASR) in China, a region of around 150 million people, faces a rapidly increased risk of extreme heat in summer. In this study, we develop statistical models to predict summer hot temperature extremes over the SRASR based on a timescale decomposition approach. Results show that after removing the linear trends, the inter-annual components of summer hot days and heatwaves over the SRASR are significantly related with those of spring soil temperature over Central Asia and sea surface temperature over Northwest Atlantic while their inter-decadal components are closely linked to those of spring East Pacific/North Pacific pattern and Atlantic Multidecadal Oscillation for 1979-2016. The physical processes involved are also discussed. Leave-one-out cross-validation for detrended 1979-2016 time series indicates that the statistical models based on identified spring predictors can predict 47% and 57% of the total variances of summer hot days and heatwaves averaged over the SRASR, respectively. When the linear trends are put back, the prediction skills increase substantially to 64% and 70%. Hindcast experiments for 2012-2016 show high skills in predicting spatial patterns of hot temperature extremes over the SRASR. The statistical models proposed herein can be easily applied to operational seasonal forecasting.

Cross-validation of Peak Oxygen Consumption Prediction Models From OMNI Perceived Exertion.

PubMed

Mays, R J; Goss, F L; Nagle, E F; Gallagher, M; Haile, L; Schafer, M A; Kim, K H; Robertson, R J

2016-09-01

This study cross-validated statistical models for prediction of peak oxygen consumption using ratings of perceived exertion from the Adult OMNI Cycle Scale of Perceived Exertion. 74 participants (men: n=36; women: n=38) completed a graded cycle exercise test. Ratings of perceived exertion for the overall body, legs, and chest/breathing were recorded each test stage and entered into previously developed 3-stage peak oxygen consumption prediction models. There were no significant differences (p>0.05) between measured and predicted peak oxygen consumption from ratings of perceived exertion for the overall body, legs, and chest/breathing within men (mean±standard deviation: 3.16±0.52 vs. 2.92±0.33 vs. 2.90±0.29 vs. 2.90±0.26 L·min(-1)) and women (2.17±0.29 vs. 2.02±0.22 vs. 2.03±0.19 vs. 2.01±0.19 L·min(-1)) participants. Previously developed statistical models for prediction of peak oxygen consumption based on subpeak OMNI ratings of perceived exertion responses were similar to measured peak oxygen consumption in a separate group of participants. These findings provide practical implications for the use of the original statistical models in standard health-fitness settings. © Georg Thieme Verlag KG Stuttgart · New York.

Predicting Esophagitis After Chemoradiation Therapy for Non-Small Cell Lung Cancer: An Individual Patient Data Meta-Analysis

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

Palma, David A., E-mail: david.palma@uwo.ca; Senan, Suresh; Oberije, Cary

Purpose: Concurrent chemoradiation therapy (CCRT) improves survival compared with sequential treatment for locally advanced non-small cell lung cancer, but it increases toxicity, particularly radiation esophagitis (RE). Validated predictors of RE for clinical use are lacking. We performed an individual-patient-data meta-analysis to determine factors predictive of clinically significant RE. Methods and Materials: After a systematic review of the literature, data were obtained on 1082 patients who underwent CCRT, including patients from Europe, North America, Asia, and Australia. Patients were randomly divided into training and validation sets (2/3 vs 1/3 of patients). Factors predictive of RE (grade ≥2 and grade ≥3) weremore » assessed using logistic modeling, with the concordance statistic (c statistic) used to evaluate the performance of each model. Results: The median radiation therapy dose delivered was 65 Gy, and the median follow-up time was 2.1 years. Most patients (91%) received platinum-containing CCRT regimens. The development of RE was common, scored as grade 2 in 348 patients (32.2%), grade 3 in 185 (17.1%), and grade 4 in 10 (0.9%). There were no RE-related deaths. On univariable analysis using the training set, several baseline factors were statistically predictive of RE (P<.05), but only dosimetric factors had good discrimination scores (c > .60). On multivariable analysis, the esophageal volume receiving ≥60 Gy (V60) alone emerged as the best predictor of grade ≥2 and grade ≥3 RE, with good calibration and discrimination. Recursive partitioning identified 3 risk groups: low (V60 <0.07%), intermediate (V60 0.07% to 16.99%), and high (V60 ≥17%). With use of the validation set, the predictive model performed inferiorly for the grade ≥2 endpoint (c = .58) but performed well for the grade ≥3 endpoint (c = .66). Conclusions: Clinically significant RE is common, but life-threatening complications occur in <1% of patients. Although several factors are statistically predictive of RE, the V60 alone provides the best predictive ability. Efforts to reduce the V60 should be prioritized, with further research needed to identify and validate new predictive factors.« less

Prediction of Primary Care Depression Outcomes at Six Months: Validation of DOC-6 ©.

PubMed

Angstman, Kurt B; Garrison, Gregory M; Gonzalez, Cesar A; Cozine, Daniel W; Cozine, Elizabeth W; Katzelnick, David J

2017-01-01

The goal of this study was to develop and validate an assessment tool for adult primary care patients diagnosed with depression to determine predictive probability of clinical outcomes at 6 months. We retrospectively reviewed 3096 adult patients enrolled in collaborative care management (CCM) for depression. Patients enrolled on or before December 31, 2013, served as the training set (n = 2525), whereas those enrolled after that date served as the preliminary validation set (n = 571). Six variables (2 demographic and 4 clinical) were statistically significant in determining clinical outcomes. Using the validation data set, the remission classifier produced the receiver operating characteristics (ROC) curve with a c-statistic or area under the curve (AUC) of 0.62 with predicted probabilities than ranged from 14.5% to 79.1%, with a median of 50.6%. The persistent depressive symptoms (PDS) classifier produced an ROC curve with a c-statistic or AUC of 0.67 and predicted probabilities that ranged from 5.5% to 73.1%, with a median of 23.5%. We were able to identify readily available variables and then validated these in the prediction of depression remission and PDS at 6 months. The DOC-6 tool may be used to predict which patients may be at risk for worse outcomes. © Copyright 2017 by the American Board of Family Medicine.

What can 35 years and over 700,000 measurements tell us about noise exposure in the mining industry?

PubMed Central

Roberts, Benjamin; Sun, Kan; Neitzel, Richard L.

2017-01-01

Objective To analyze over 700,000 cross-sectional measurements from the Mine Safety and Health Administration (MHSA) and develop statistical models to predict noise exposure for a worker. Design Descriptive statistics were used to summarize the data. Two linear regression models were used to predict noise exposure based on MSHA permissible exposure limit (PEL) and action level (AL) respectively. Two-fold cross validation was used to compare the exposure estimates from the models to actual measurements in the hold out data. The mean difference and t-statistic was calculated for each job title to determine if the model exposure predictions were significantly different from the actual data. Study Sample Measurements were acquired from MSHA through a Freedom of Information Act request. Results From 1979 to 2014 the average noise measurement has decreased. Measurements taken before the implementation of MSHA’s revised noise regulation in 2000 were on average 4.5 dBA higher than after the law came in to effect. Both models produced mean exposure predictions that were less than 1 dBA different compared to the holdout data. Conclusion Overall noise levels in mines have been decreasing. However, this decrease has not been uniform across all mining sectors. The exposure predictions from the model will be useful to help predict hearing loss in workers from the mining industry. PMID:27871188

Investigating the cognitive structure of stereotypes: Generic beliefs about groups predict social judgments better than statistical beliefs.

PubMed

Hammond, Matthew D; Cimpian, Andrei

2017-05-01

Stereotypes are typically defined as beliefs about groups, but this definition is underspecified. Beliefs about groups can be generic or statistical. Generic beliefs attribute features to entire groups (e.g., men are strong), whereas statistical beliefs encode the perceived prevalence of features (e.g., how common it is for men to be strong). In the present research, we sought to determine which beliefs-generic or statistical-are more central to the cognitive structure of stereotypes. Specifically, we tested whether generic or statistical beliefs are more influential in people's social judgments, on the assumption that greater functional importance indicates greater centrality in stereotype structure. Relative to statistical beliefs, generic beliefs about social groups were significantly stronger predictors of expectations (Studies 1-3) and explanations (Study 4) for unfamiliar individuals' traits. In addition, consistent with prior evidence that generic beliefs are cognitively simpler than statistical beliefs, generic beliefs were particularly predictive of social judgments for participants with more intuitive (vs. analytic) cognitive styles and for participants higher (vs. lower) in authoritarianism, who tend to view outgroups in simplistic, all-or-none terms. The present studies suggest that generic beliefs about groups are more central than statistical beliefs to the cognitive structure of stereotypes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predicting dire outcomes of patients with community acquired pneumonia.

PubMed

Cooper, Gregory F; Abraham, Vijoy; Aliferis, Constantin F; Aronis, John M; Buchanan, Bruce G; Caruana, Richard; Fine, Michael J; Janosky, Janine E; Livingston, Gary; Mitchell, Tom; Monti, Stefano; Spirtes, Peter

2005-10-01

Community-acquired pneumonia (CAP) is an important clinical condition with regard to patient mortality, patient morbidity, and healthcare resource utilization. The assessment of the likely clinical course of a CAP patient can significantly influence decision making about whether to treat the patient as an inpatient or as an outpatient. That decision can in turn influence resource utilization, as well as patient well being. Predicting dire outcomes, such as mortality or severe clinical complications, is a particularly important component in assessing the clinical course of patients. We used a training set of 1601 CAP patient cases to construct 11 statistical and machine-learning models that predict dire outcomes. We evaluated the resulting models on 686 additional CAP-patient cases. The primary goal was not to compare these learning algorithms as a study end point; rather, it was to develop the best model possible to predict dire outcomes. A special version of an artificial neural network (NN) model predicted dire outcomes the best. Using the 686 test cases, we estimated the expected healthcare quality and cost impact of applying the NN model in practice. The particular, quantitative results of this analysis are based on a number of assumptions that we make explicit; they will require further study and validation. Nonetheless, the general implication of the analysis seems robust, namely, that even small improvements in predictive performance for prevalent and costly diseases, such as CAP, are likely to result in significant improvements in the quality and efficiency of healthcare delivery. Therefore, seeking models with the highest possible level of predictive performance is important. Consequently, seeking ever better machine-learning and statistical modeling methods is of great practical significance.

Watershed regressions for pesticides (warp) models for predicting atrazine concentrations in Corn Belt streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.

2012-01-01

Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region-specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. The WARP-CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model-development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model-development sites. The WARP-CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine-use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP-CB models. The WARP-CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine-use intensities of 17 kg/km2 of watershed area or greater.

Evaluating concentration estimation errors in ELISA microarray experiments

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

Daly, Don S.; White, Amanda M.; Varnum, Susan M.

Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to predict a protein concentration in a sample. Deploying ELISA in a microarray format permits simultaneous prediction of the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Evaluating prediction error is critical to interpreting biological significance and improving the ELISA microarray process. Evaluating prediction error must be automated to realize a reliable high-throughput ELISA microarray system. Methods: In this paper, we present a statistical method based on propagation of error to evaluate prediction errors in the ELISA microarray process. Althoughmore » propagation of error is central to this method, it is effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization and statistical diagnostics when evaluating ELISA microarray prediction errors. We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of prediction errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.« less

Random glucose is useful for individual prediction of type 2 diabetes: results of the Study of Health in Pomerania (SHIP).

PubMed

Kowall, Bernd; Rathmann, Wolfgang; Giani, Guido; Schipf, Sabine; Baumeister, Sebastian; Wallaschofski, Henri; Nauck, Matthias; Völzke, Henry

2013-04-01

Random glucose is widely used in routine clinical practice. We investigated whether this non-standardized glycemic measure is useful for individual diabetes prediction. The Study of Health in Pomerania (SHIP), a population-based cohort study in north-east Germany, included 3107 diabetes-free persons aged 31-81 years at baseline in 1997-2001. 2475 persons participated at 5-year follow-up and gave self-reports of incident diabetes. For the total sample and for subjects aged ≥50 years, statistical properties of prediction models with and without random glucose were compared. A basic model (including age, sex, diabetes of parents, hypertension and waist circumference) and a comprehensive model (additionally including various lifestyle variables and blood parameters, but not HbA1c) performed statistically significantly better after adding random glucose (e.g., the area under the receiver-operating curve (AROC) increased from 0.824 to 0.856 after adding random glucose to the comprehensive model in the total sample). Likewise, adding random glucose to prediction models which included HbA1c led to significant improvements of predictive ability (e.g., for subjects ≥50 years, AROC increased from 0.824 to 0.849 after adding random glucose to the comprehensive model+HbA1c). Random glucose is useful for individual diabetes prediction, and improves prediction models including HbA1c. Copyright © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Validating Variational Bayes Linear Regression Method With Multi-Central Datasets.

PubMed

Murata, Hiroshi; Zangwill, Linda M; Fujino, Yuri; Matsuura, Masato; Miki, Atsuya; Hirasawa, Kazunori; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Shoji, Nobuyuki; Asaoka, Ryo

2018-04-01

To validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset. The training dataset consisted of 7268 eyes of 4278 subjects from the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2-4) to 2nd to 10th VF (VF2-10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic. OLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2-4 to VF2-10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2-2 to VF2-10) (P > 0.05). VBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings.

Sex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos.

PubMed

Shah, Neomi; Hanna, David B; Teng, Yanping; Sotres-Alvarez, Daniela; Hall, Martica; Loredo, Jose S; Zee, Phyllis; Kim, Mimi; Yaggi, H Klar; Redline, Susan; Kaplan, Robert C

2016-06-01

We developed and validated the first-ever sleep apnea (SA) risk calculator in a large population-based cohort of Hispanic/Latino subjects. Cross-sectional data on adults from the Hispanic Community Health Study/Study of Latinos (2008-2011) were analyzed. Subjective and objective sleep measurements were obtained. Clinically significant SA was defined as an apnea-hypopnea index ≥ 15 events per hour. Using logistic regression, four prediction models were created: three sex-specific models (female-only, male-only, and a sex × covariate interaction model to allow differential predictor effects), and one overall model with sex included as a main effect only. Models underwent 10-fold cross-validation and were assessed by using the C statistic. SA and its predictive variables; a total of 17 variables were considered. A total of 12,158 participants had complete sleep data available; 7,363 (61%) were women. The population-weighted prevalence of SA (apnea-hypopnea index ≥ 15 events per hour) was 6.1% in female subjects and 13.5% in male subjects. Male-only (C statistic, 0.808) and female-only (C statistic, 0.836) prediction models had the same predictor variables (ie, age, BMI, self-reported snoring). The sex-interaction model (C statistic, 0.836) contained sex, age, age × sex, BMI, BMI × sex, and self-reported snoring. The final overall model (C statistic, 0.832) contained age, BMI, snoring, and sex. We developed two websites for our SA risk calculator: one in English (https://www.montefiore.org/sleepapneariskcalc.html) and another in Spanish (http://www.montefiore.org/sleepapneariskcalc-es.html). We created an internally validated, highly discriminating, well-calibrated, and parsimonious prediction model for SA. Contrary to the study hypothesis, the variables did not have different predictive magnitudes in male and female subjects. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Impact of database quality in knowledge-based treatment planning for prostate cancer.

PubMed

Wall, Phillip D H; Carver, Robert L; Fontenot, Jonas D

2018-03-13

This article investigates dose-volume prediction improvements in a common knowledge-based planning (KBP) method using a Pareto plan database compared with using a conventional, clinical plan database. Two plan databases were created using retrospective, anonymized data of 124 volumetric modulated arc therapy (VMAT) prostate cancer patients. The clinical plan database (CPD) contained planning data from each patient's clinically treated VMAT plan, which were manually optimized by various planners. The multicriteria optimization database (MCOD) contained Pareto-optimal plan data from VMAT plans created using a standardized multicriteria optimization protocol. Overlap volume histograms, incorporating fractional organ at risk volumes only within the treatment fields, were computed for each patient and used to match new patient anatomy to similar database patients. For each database patient, CPD and MCOD KBP predictions were generated for D 10 , D 30 , D 50 , D 65 , and D 80 of the bladder and rectum in a leave-one-out manner. Prediction achievability was evaluated through a replanning study on a subset of 31 randomly selected database patients using the best KBP predictions, regardless of plan database origin, as planning goals. MCOD predictions were significantly lower than CPD predictions for all 5 bladder dose-volumes and rectum D 50 (P = .004) and D 65 (P < .001), whereas CPD predictions for rectum D 10 (P = .005) and D 30 (P < .001) were significantly less than MCOD predictions. KBP predictions were statistically achievable in the replans for all predicted dose-volumes, excluding D 10 of bladder (P = .03) and rectum (P = .04). Compared with clinical plans, replans showed significant average reductions in D mean for bladder (7.8 Gy; P < .001) and rectum (9.4 Gy; P < .001), while maintaining statistically similar planning target volume, femoral head, and penile bulb dose. KBP dose-volume predictions derived from Pareto plans were more optimal overall than those resulting from manually optimized clinical plans, which significantly improved KBP-assisted plan quality. This work investigates how the plan quality of knowledge databases affects the performance and achievability of dose-volume predictions from a common knowledge-based planning approach for prostate cancer. Bladder and rectum dose-volume predictions derived from a database of standardized Pareto-optimal plans were compared with those derived from clinical plans manually designed by various planners. Dose-volume predictions from the Pareto plan database were significantly lower overall than those from the clinical plan database, without compromising achievability. Copyright © 2018 Elsevier Inc. All rights reserved.

Prevalence of upper airway obstruction in patients with apparently asymptomatic euthyroid multi nodular goitre

PubMed Central

Menon, Sunil K.; Jagtap, Varsha S.; Sarathi, Vijaya; Lila, Anurag R.; Bandgar, Tushar R.; Menon, Padmavathy S; Shah, Nalini S.

2011-01-01

Aims: To study the prevalence of upper airway obstruction (UAO) in “apparently asymptomatic” patients with euthyroid multinodular goitre (MNG) and find correlation between clinical features, UAO on pulmonary function test (PFT) and tracheal narrowing on computerised tomography (CT). Materials and Methods: Consecutive patients with apparently asymptomatic euthyroid MNG attending thyroid clinic in a tertiary centre underwent clinical examination to elicit features of UAO, PFT, and CT of neck and chest. Statistical Analysis Used: Statistical analysis was done with SPSS version 11.5 using paired t-test, Chi square test, and Fisher's exact test. P value of <0.05 was considered to be significant. Results: Fifty-six patients (52 females and four males) were studied. The prevalence of UAO (PFT) and significant tracheal narrowing (CT) was 14.3%. and 9.3%, respectively. Clinical features failed to predict UAO or significant tracheal narrowing. Tracheal narrowing (CT) did not correlate with UAO (PFT). Volume of goitre significantly correlated with degree of tracheal narrowing. Conclusions: Clinical features do not predict UAO on PFT or tracheal narrowing on CT in apparently asymptomatic patients with euthyroid MNG. PMID:21966649

Alternative metrics for real-ear-to-coupler difference average values in children.

PubMed

Blumsack, Judith T; Clark-Lewis, Sandra; Watts, Kelli M; Wilson, Martha W; Ross, Margaret E; Soles, Lindsey; Ennis, Cydney

2014-10-01

Ideally, individual real-ear-to-coupler difference (RECD) measurements are obtained for pediatric hearing instrument-fitting purposes. When RECD measurements cannot be obtained, age-related average RECDs based on typically developing North American children are used. Evidence suggests that these values may not be appropriate for populations of children with retarded growth patterns. The purpose of this study was to determine if another metric, such as head circumference, height, or weight, can be used for prediction of RECDs in children. Design was a correlational study. For all participants, RECD values in both ears, head circumference, height, and weight were measured. The sample consisted of 68 North American children (ages 3-11 yr). Height, weight, head circumference, and RECDs were measured and were analyzed for both ears at 500, 750, 1000, 1500, 2000, 3000, 4000, and 6000 Hz. A backward elimination multiple-regression analysis was used to determine if age, height, weight, and/or head circumference are significant predictors of RECDs. For the left ear, head circumference was retained as the only statistically significant variable in the final model. For the right ear, head circumference was retained as the only statistically significant independent variable at all frequencies except at 2000 and 4000 Hz. At these latter frequencies, weight was retained as the only statistically significant independent variable after all other variables were eliminated. Head circumference can be considered as a metric for RECD prediction in children when individual measurements cannot be obtained. In developing countries where equipment is often unavailable and stunted growth can reduce the value of using age as a metric, head circumference can be considered as an alternative metric in the prediction of RECDs. American Academy of Audiology.

Exposure to negative acts and risk of turnover: a study of a register-based outcome among employees in three occupational groups.

PubMed

Clausen, Thomas; Hansen, Jørgen V; Hogh, Annie; Garde, Anne Helene; Persson, Roger; Conway, Paul Maurice; Grynderup, Matias; Hansen, Åse Marie; Rugulies, Reiner

2016-11-01

To investigate whether self-reported exposure to negative acts in the workplace (bullying and threats of violence) predicted turnover in three occupational groups (human service and sales workers, office workers and manual workers). Survey data on 2766 respondents were combined with data from a national labour force register to assess turnover. Mixed effects logistic regression analysis was used to examine the association between self-reported exposure to negative acts at baseline and risk of turnover after a 1-year follow-up. We found no significant associations between exposure to negative acts (bullying and threats of violence) and risk of turnover. When participants were stratified by occupational group and analyses were adjusted for age, gender, tenure and psychosocial working conditions, we found that exposure to bullying predicted risk of turnover in office workers (OR 2.03, 95 % CI 1.05-3.90), but neither in human service and sales workers, nor in manual workers. The association in office workers lost statistical significance when additionally adjusted for depressive symptoms (OR 1.77, 95 % CI 0.90-3.49). However, in a sensitivity analysis in which we used a 2-year (instead of a 1-year) follow-up period the association between bullying and turnover remained statistically significant in office workers even after adjusting for depressive symptoms (OR 2.10, 95 % CI 1.17-3.76). We found no statistically significant associations between threats of violence and risk of turnover in the stratified analyses. Exposure to bullying predicted risk of turnover among office workers but not among human service and sales workers and among manual workers. Threats of violence were not associated with turnover in any occupational group.

Development and validation of a risk calculator predicting exercise-induced ventricular arrhythmia in patients with cardiovascular disease.

PubMed

Hermes, Ilarraza-Lomelí; Marianna, García-Saldivia; Jessica, Rojano-Castillo; Carlos, Barrera-Ramírez; Rafael, Chávez-Domínguez; María Dolores, Rius-Suárez; Pedro, Iturralde

2016-10-01

Mortality due to cardiovascular disease is often associated with ventricular arrhythmias. Nowadays, patients with cardiovascular disease are more encouraged to take part in physical training programs. Nevertheless, high-intensity exercise is associated to a higher risk for sudden death, even in apparently healthy people. During an exercise testing (ET), health care professionals provide patients, in a controlled scenario, an intense physiological stimulus that could precipitate cardiac arrhythmia in high risk individuals. There is still no clinical or statistical tool to predict this incidence. The aim of this study was to develop a statistical model to predict the incidence of exercise-induced potentially life-threatening ventricular arrhythmia (PLVA) during high intensity exercise. 6415 patients underwent a symptom-limited ET with a Balke ramp protocol. A multivariate logistic regression model where the primary outcome was PLVA was performed. Incidence of PLVA was 548 cases (8.5%). After a bivariate model, thirty one clinical or ergometric variables were statistically associated with PLVA and were included in the regression model. In the multivariate model, 13 of these variables were found to be statistically significant. A regression model (G) with a X(2) of 283.987 and a p<0.001, was constructed. Significant variables included: heart failure, antiarrhythmic drugs, myocardial lower-VD, age and use of digoxin, nitrates, among others. This study allows clinicians to identify patients at risk of ventricular tachycardia or couplets during exercise, and to take preventive measures or appropriate supervision. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Cross-modality PET/CT and contrast-enhanced CT imaging for pancreatic cancer

PubMed Central

Zhang, Jian; Zuo, Chang-Jing; Jia, Ning-Yang; Wang, Jian-Hua; Hu, Sheng-Ping; Yu, Zhong-Fei; Zheng, Yuan; Zhang, An-Yu; Feng, Xiao-Yuan

2015-01-01

AIM: To explore the diagnostic value of the cross-modality fusion images provided by positron emission tomography/computed tomography (PET/CT) and contrast-enhanced CT (CECT) for pancreatic cancer (PC). METHODS: Data from 70 patients with pancreatic lesions who underwent CECT and PET/CT examinations at our hospital from August 2010 to October 2012 were analyzed. PET/CECT for the cross-modality image fusion was obtained using TureD software. The diagnostic efficiencies of PET/CT, CECT and PET/CECT were calculated and compared with each other using a χ2 test. P < 0.05 was considered to indicate statistical significance. RESULTS: Of the total 70 patients, 50 had PC and 20 had benign lesions. The differences in the sensitivity, negative predictive value (NPV), and accuracy between CECT and PET/CECT in detecting PC were statistically significant (P < 0.05 for each). In 15 of the 31 patients with PC who underwent a surgical operation, peripancreatic vessel invasion was verified. The differences in the sensitivity, positive predictive value, NPV, and accuracy of CECT vs PET/CT and PET/CECT vs PET/CT in diagnosing peripancreatic vessel invasion were statistically significant (P < 0.05 for each). In 19 of the 31 patients with PC who underwent a surgical operation, regional lymph node metastasis was verified by postsurgical histology. There was no statistically significant difference among the three methods in detecting regional lymph node metastasis (P > 0.05 for each). In 17 of the 50 patients with PC confirmed by histology or clinical follow-up, distant metastasis was confirmed. The differences in the sensitivity and NPV between CECT and PET/CECT in detecting distant metastasis were statistically significant (P < 0.05 for each). CONCLUSION: Cross-modality image fusion of PET/CT and CECT is a convenient and effective method that can be used to diagnose and stage PC, compensating for the defects of PET/CT and CECT when they are conducted individually. PMID:25780297

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

PubMed

Loha, Eskindir; Lindtjørn, Bernt

2010-06-16

Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors.

Prediction of new-onset atrial fibrillation after first-ever ischemic stroke: A comparison of CHADS2, CHA2DS2-VASc and HATCH scores and the added value of stroke severity.

PubMed

Hsieh, Cheng-Yang; Lee, Cheng-Han; Wu, Darren Philbert; Sung, Sheng-Feng

2018-05-01

Early detection of atrial fibrillation after stroke is important for secondary prevention in stroke patients without known atrial fibrillation (AF). We aimed to compare the performance of CHADS 2 , CHA 2 DS 2 -VASc and HATCH scores in predicting AF detected after stroke (AFDAS) and to test whether adding stroke severity to the risk scores improves predictive performance. Adult patients with first ischemic stroke event but without a prior history of AF were retrieved from a nationwide population-based database. We compared C-statistics of CHADS 2 , CHA 2 DS 2 -VASc and HATCH scores for predicting the occurrence of AFDAS during stroke admission (cohort I) and during follow-up after hospital discharge (cohort II). The added value of stroke severity to prediction models was evaluated using C-statistics, net reclassification improvement, and integrated discrimination improvement. Cohort I comprised 13,878 patients and cohort II comprised 12,567 patients. Among them, 806 (5.8%) and 657 (5.2%) were diagnosed with AF, respectively. The CHADS 2 score had the lowest C-statistics (0.558 in cohort I and 0.597 in cohort II), whereas the CHA 2 DS 2 -VASc score had comparable C-statistics (0.603 and 0.644) to the HATCH score (0.612 and 0.653) in predicting AFDAS. Adding stroke severity to each of the three risk scores significantly increased the model performance. In stroke patients without known AF, all three risk scores predicted AFDAS during admission and follow-up, but with suboptimal discrimination. Adding stroke severity improved their predictive abilities. These risk scores, when combined with stroke severity, may help prioritize patients for continuous cardiac monitoring in daily practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Statistics of contractive cracking patterns. [frozen soil-water rheology

NASA Technical Reports Server (NTRS)

Noever, David A.

1991-01-01

The statistics of convective soil patterns are analyzed using statistical crystallography. An underlying hierarchy of order is found to span four orders of magnitude in characteristic pattern length. Strict mathematical requirements determine the two-dimensional (2D) topology, such that random partitioning of space yields a predictable statistical geometry for polygons. For all lengths, Aboav's and Lewis's laws are verified; this result is consistent both with the need to fill 2D space and most significantly with energy carried not by the patterns' interior, but by the boundaries. Together, this suggests a common mechanism of formation for both micro- and macro-freezing patterns.

Using prediction markets to estimate the reproducibility of scientific research

PubMed Central

Dreber, Anna; Pfeiffer, Thomas; Almenberg, Johan; Isaksson, Siri; Wilson, Brad; Chen, Yiling; Nosek, Brian A.; Johannesson, Magnus

2015-01-01

Concerns about a lack of reproducibility of statistically significant results have recently been raised in many fields, and it has been argued that this lack comes at substantial economic costs. We here report the results from prediction markets set up to quantify the reproducibility of 44 studies published in prominent psychology journals and replicated in the Reproducibility Project: Psychology. The prediction markets predict the outcomes of the replications well and outperform a survey of market participants’ individual forecasts. This shows that prediction markets are a promising tool for assessing the reproducibility of published scientific results. The prediction markets also allow us to estimate probabilities for the hypotheses being true at different testing stages, which provides valuable information regarding the temporal dynamics of scientific discovery. We find that the hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%) and that a “statistically significant” finding needs to be confirmed in a well-powered replication to have a high probability of being true. We argue that prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications. PMID:26553988

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.

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

Improving the Validity of Activity of Daily Living Dependency Risk Assessment

PubMed Central

Clark, Daniel O.; Stump, Timothy E.; Tu, Wanzhu; Miller, Douglas K.

2015-01-01

Objectives Efforts to prevent activity of daily living (ADL) dependency may be improved through models that assess older adults’ dependency risk. We evaluated whether cognition and gait speed measures improve the predictive validity of interview-based models. Method Participants were 8,095 self-respondents in the 2006 Health and Retirement Survey who were aged 65 years or over and independent in five ADLs. Incident ADL dependency was determined from the 2008 interview. Models were developed using random 2/3rd cohorts and validated in the remaining 1/3rd. Results Compared to a c-statistic of 0.79 in the best interview model, the model including cognitive measures had c-statistics of 0.82 and 0.80 while the best fitting gait speed model had c-statistics of 0.83 and 0.79 in the development and validation cohorts, respectively. Conclusion Two relatively brief models, one that requires an in-person assessment and one that does not, had excellent validity for predicting incident ADL dependency but did not significantly improve the predictive validity of the best fitting interview-based models. PMID:24652867

Grain-Size Based Additivity Models for Scaling Multi-rate Uranyl Surface Complexation in Subsurface Sediments

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

Zhang, Xiaoying; Liu, Chongxuan; Hu, Bill X.

This study statistically analyzed a grain-size based additivity model that has been proposed to scale reaction rates and parameters from laboratory to field. The additivity model assumed that reaction properties in a sediment including surface area, reactive site concentration, reaction rate, and extent can be predicted from field-scale grain size distribution by linearly adding reaction properties for individual grain size fractions. This study focused on the statistical analysis of the additivity model with respect to reaction rate constants using multi-rate uranyl (U(VI)) surface complexation reactions in a contaminated sediment as an example. Experimental data of rate-limited U(VI) desorption in amore » stirred flow-cell reactor were used to estimate the statistical properties of multi-rate parameters for individual grain size fractions. The statistical properties of the rate constants for the individual grain size fractions were then used to analyze the statistical properties of the additivity model to predict rate-limited U(VI) desorption in the composite sediment, and to evaluate the relative importance of individual grain size fractions to the overall U(VI) desorption. The result indicated that the additivity model provided a good prediction of the U(VI) desorption in the composite sediment. However, the rate constants were not directly scalable using the additivity model, and U(VI) desorption in individual grain size fractions have to be simulated in order to apply the additivity model. An approximate additivity model for directly scaling rate constants was subsequently proposed and evaluated. The result found that the approximate model provided a good prediction of the experimental results within statistical uncertainty. This study also found that a gravel size fraction (2-8mm), which is often ignored in modeling U(VI) sorption and desorption, is statistically significant to the U(VI) desorption in the sediment.« less

What Is the Ideal Tumor Regression Grading System in Rectal Cancer Patients after Preoperative Chemoradiotherapy?

PubMed Central

Kim, Soo Hee; Chang, Hee Jin; Kim, Dae Yong; Park, Ji Won; Baek, Ji Yeon; Kim, Sun Young; Park, Sung Chan; Oh, Jae Hwan; Yu, Ami; Nam, Byung-Ho

2016-01-01

Purpose Tumor regression grade (TRG) is predictive of therapeutic response in rectal cancer patients after chemoradiotherapy (CRT) followed by curative resection. However, various TRG systems have been suggested, with subjective categorization, resulting in interobserver variability. This study compared the prognostic validity of four different TRG systems in order to identify the most ideal TRG system. Materials and Methods This study included 933 patients who underwent preoperative CRT and curative resection. Primary tumors alone were graded according to the American Joint Committee on Cancer (AJCC), Dworak, and Ryan TRG systems, and both primary tumors and regional lymph nodes were graded according to a modified Dworak TRG system. The ability of each TRG system to predict recurrence-free survival (RFS) and overall survival (OS) was analyzed using chi-square and C statistics. Results All four TRG systems were significantly predictive of both RFS and OS (p < 0.001 each), however none was a better predictor of prognosis than ypStage. Among the four TRGs, the mDworak TRG system was a better predictor of RFS and OS than the AJCC, Dworak, and Ryan TRG systems, and both the chi-square and C statistics were higher for the former, although the differences were not statistically significant. The combination of ypStage and the modified Dworak TRG better predicted RFS and OS than ypStage alone. Conclusion The modified Dworak TRG system for evaluation of entire tumors including regional lymph nodes is a better predictor of survival than current TRG systems for evaluation of the primary tumor alone. PMID:26511803

Maternal factors predicting cognitive and behavioral characteristics of children with fetal alcohol spectrum disorders.

PubMed

May, Philip A; Tabachnick, Barbara G; Gossage, J Phillip; Kalberg, Wendy O; Marais, Anna-Susan; Robinson, Luther K; Manning, Melanie A; Blankenship, Jason; Buckley, David; Hoyme, H Eugene; Adnams, Colleen M

2013-06-01

To provide an analysis of multiple predictors of cognitive and behavioral traits for children with fetal alcohol spectrum disorders (FASDs). Multivariate correlation techniques were used with maternal and child data from epidemiologic studies in a community in South Africa. Data on 561 first-grade children with fetal alcohol syndrome (FAS), partial FAS (PFAS), and not FASD and their mothers were analyzed by grouping 19 maternal variables into categories (physical, demographic, childbearing, and drinking) and used in structural equation models (SEMs) to assess correlates of child intelligence (verbal and nonverbal) and behavior. A first SEM using only 7 maternal alcohol use variables to predict cognitive/behavioral traits was statistically significant (B = 3.10, p < .05) but explained only 17.3% of the variance. The second model incorporated multiple maternal variables and was statistically significant explaining 55.3% of the variance. Significantly correlated with low intelligence and problem behavior were demographic (B = 3.83, p < .05) (low maternal education, low socioeconomic status [SES], and rural residence) and maternal physical characteristics (B = 2.70, p < .05) (short stature, small head circumference, and low weight). Childbearing history and alcohol use composites were not statistically significant in the final complex model and were overpowered by SES and maternal physical traits. Although other analytic techniques have amply demonstrated the negative effects of maternal drinking on intelligence and behavior, this highly controlled analysis of multiple maternal influences reveals that maternal demographics and physical traits make a significant enabling or disabling contribution to child functioning in FASD.

Biological availability and in vitro dissolution of oxytetracycline dihydrate tablets

PubMed Central

Barber, H. E.; Calvey, T. N.; Muir, K.; Hart, A.

1974-01-01

1 The concentration of oxytetracycline in plasma was studied by microbiological assay after oral administration of four different preparations of oxytetracycline dihydrate tablets. 2 There were statistically significant differences in biological availability between the four preparations, as assessed by the peak plasma level, the area under the plasma concentration-time curve, or the cumulative fraction of the dose excreted in urine at 405 minutes. In contrast, differences between the subjects were not statistically significant. 3 The differences in biological availability were not predictably related to the in vitro dissolution of the tablets. PMID:22454918

US Intergroup Anal Carcinoma Trial: Tumor Diameter Predicts for Colostomy

PubMed Central

Ajani, Jaffer A.; Winter, Kathryn A.; Gunderson, Leonard L.; Pedersen, John; Benson, Al B.; Thomas, Charles R.; Mayer, Robert J.; Haddock, Michael G.; Rich, Tyvin A.; Willett, Christopher G.

2009-01-01

Purpose The US Gastrointestinal Intergroup Radiation Therapy Oncology Group 98-11 anal carcinoma trial showed that cisplatin-based concurrent chemoradiotherapy resulted in a significantly higher rate of colostomy compared with mitomycin-based therapy. Established prognostic variables for patients with anal carcinoma include tumor diameter, clinical nodal status, and sex, but pretreatment variables that would predict the likelihood of colostomy are unknown. Methods A secondary analysis was performed by combining patients in the two treatment arms to evaluate whether new predictive and prognostic variables would emerge. Univariate and multivariate analyses were carried out to correlate overall survival (OS), disease-free survival, and time to colostomy (TTC) with pretreatment and treatment variables. Results Of 682 patients enrolled, 644 patients were assessable and analyzed. In the multivariate analysis, tumor-related prognosticators for poorer OS included node-positive cancer (P ≤ .0001), large (> 5 cm) tumor diameter (P = .01), and male sex (P = .016). In the treatment-related categories, cisplatin-based therapy was statistically significantly associated with a higher rate of colostomy (P = .03) than was mitomycin-based therapy. In the pretreatment variables category, only large tumor diameter independently predicted for TTC (P = .008). Similarly, the cumulative 5-year colostomy rate was statistically significantly higher for large tumor diameter than for small tumor diameter (Gray's test; P = .0074). Clinical nodal status and sex were not predictive of TTC. Conclusion The combined analysis of the two arms of RTOG 98-11, representing the largest prospective database, reveals that tumor diameter (irrespective of the nodal status) is the only independent pretreatment variable that predicts TTC and 5-year colostomy rate in patients with anal carcinoma. PMID:19139424

Review and statistical analysis of the use of ultrasonic velocity for estimating the porosity fraction in polycrystalline materials

NASA Technical Reports Server (NTRS)

Roth, D. J.; Swickard, S. M.; Stang, D. B.; Deguire, M. R.

1991-01-01

A review and statistical analysis of the ultrasonic velocity method for estimating the porosity fraction in polycrystalline materials is presented. Initially, a semiempirical model is developed showing the origin of the linear relationship between ultrasonic velocity and porosity fraction. Then, from a compilation of data produced by many researchers, scatter plots of velocity versus percent porosity data are shown for Al2O3, MgO, porcelain-based ceramics, PZT, SiC, Si3N4, steel, tungsten, UO2,(U0.30Pu0.70)C, and YBa2Cu3O(7-x). Linear regression analysis produces predicted slope, intercept, correlation coefficient, level of significance, and confidence interval statistics for the data. Velocity values predicted from regression analysis of fully-dense materials are in good agreement with those calculated from elastic properties.

Artificial neural network study on organ-targeting peptides

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Kim, Junhyoung; Choi, Seung-Hoon; Kim, Minkyoung; Rhee, Hokyoung; Shin, Jae-Min; Choi, Kihang; Kang, Sang-Kee; Lee, Nam Kyung; Choi, Yun-Jaie; Jung, Dong Hyun

2010-01-01

We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

Review and statistical analysis of the ultrasonic velocity method for estimating the porosity fraction in polycrystalline materials

NASA Technical Reports Server (NTRS)

Roth, D. J.; Swickard, S. M.; Stang, D. B.; Deguire, M. R.

1990-01-01

A review and statistical analysis of the ultrasonic velocity method for estimating the porosity fraction in polycrystalline materials is presented. Initially, a semi-empirical model is developed showing the origin of the linear relationship between ultrasonic velocity and porosity fraction. Then, from a compilation of data produced by many researchers, scatter plots of velocity versus percent porosity data are shown for Al2O3, MgO, porcelain-based ceramics, PZT, SiC, Si3N4, steel, tungsten, UO2,(U0.30Pu0.70)C, and YBa2Cu3O(7-x). Linear regression analysis produced predicted slope, intercept, correlation coefficient, level of significance, and confidence interval statistics for the data. Velocity values predicted from regression analysis for fully-dense materials are in good agreement with those calculated from elastic properties.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

PubMed

Mainali, Kumar P; Bewick, Sharon; Thielen, Peter; Mehoke, Thomas; Breitwieser, Florian P; Paudel, Shishir; Adhikari, Arjun; Wolfe, Joshua; Slud, Eric V; Karig, David; Fagan, William F

2017-01-01

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

CURB-65 Score is Equal to NEWS for Identifying Mortality Risk of Pneumonia Patients: An Observational Study.

PubMed

Brabrand, Mikkel; Henriksen, Daniel Pilsgaard

2018-06-01

The CURB-65 score is widely implemented as a prediction tool for identifying patients with community-acquired pneumonia (cap) at increased risk of 30-day mortality. However, since most ingredients of CURB-65 are used as general prediction tools, it is likely that other prediction tools, e.g. the British National Early Warning Score (NEWS), could be as good as CURB-65 at predicting the fate of CAP patients. To determine whether NEWS is better than CURB-65 at predicting 30-day mortality of CAP patients. This was a single-centre, 6-month observational study using patients' vital signs and demographic information registered upon admission, survival status extracted from the Danish Civil Registration System after discharge and blood test results extracted from a local database. The study was conducted in the medical admission unit (MAU) at the Hospital of South West Jutland, a regional teaching hospital in Denmark. The participants consisted of 570 CAP patients, 291 female and 279 male, median age 74 (20-102) years. The CURB-65 score had a discriminatory power of 0.728 (0.667-0.789) and NEWS 0.710 (0.645-0.775), both with good calibration and no statistical significant difference. CURB-65 was not demonstrated to be significantly statistically better than NEWS at identifying CAP patients at risk of 30-day mortality.

Ergonomics study on mobile phones for thumb physiology discomfort

NASA Astrophysics Data System (ADS)

Bendero, J. M. S.; Doon, M. E. R.; Quiogue, K. C. A.; Soneja, L. C.; Ong, N. R.; Sauli, Z.; Vairavan, R.

2017-09-01

The study was conducted on Filipino undergraduate college students and aimed to find out about the significant factors associated with mobile phone usage and its effect on thumb pain.A correlation-prediction analysisand Multiple Linear Regression was adopted and used as the main tool in determining the significant factors and coming up with predictive models on thumb related pain. With the use of the software Statistical Package for the Social Sciences or SPSS in conducting linear regression, 2 significant factors on thumb-related pain (percentage of time using portrait as screen orientation when text messaging, amount of time playing games using one hand in a day) were found.

Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy.

PubMed

Yamanouchi, Masayuki; Hoshino, Junichi; Ubara, Yoshifumi; Takaichi, Kenmei; Kinowaki, Keiichi; Fujii, Takeshi; Ohashi, Kenichi; Mise, Koki; Toyama, Tadashi; Hara, Akinori; Kitagawa, Kiyoki; Shimizu, Miho; Furuichi, Kengo; Wada, Takashi

2018-01-01

There have been a limited number of biopsy-based studies on diabetic nephropathy, and therefore the clinical importance of renal biopsy in patients with diabetes in late-stage chronic kidney disease (CKD) is still debated. We aimed to clarify the renal prognostic value of pathological information to clinical information in patients with diabetes and advanced CKD. We retrospectively assessed 493 type 2 diabetics with biopsy-proven diabetic nephropathy in four centers in Japan. 296 patients with stage 3-5 CKD at the time of biopsy were identified and assigned two risk prediction scores for end-stage renal disease (ESRD): the Kidney Failure Risk Equation (KFRE, a score composed of clinical parameters) and the Diabetic Nephropathy Score (D-score, a score integrated pathological parameters of the Diabetic Nephropathy Classification by the Renal Pathology Society (RPS DN Classification)). They were randomized 2:1 to development and validation cohort. Hazard Ratios (HR) of incident ESRD were reported with 95% confidence interval (CI) of the KFRE, D-score and KFRE+D-score in Cox regression model. Improvement of risk prediction with the addition of D-score to the KFRE was assessed using c-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). During median follow-up of 1.9 years, 194 patients developed ESRD. The cox regression analysis showed that the KFRE,D-score and KFRE+D-score were significant predictors of ESRD both in the development cohort and in the validation cohort. The c-statistics of the D-score was 0.67. The c-statistics of the KFRE was good, but its predictive value was weaker than that in the miscellaneous CKD cohort originally reported (c-statistics, 0.78 vs. 0.90) and was not significantly improved by adding the D-score (0.78 vs. 0.79, p = 0.83). Only continuous NRI was positive after adding the D-score to the KFRE (0.4%; CI: 0.0-0.8%). We found that the predict values of the KFRE and the D-score were not as good as reported, and combining the D-score with the KFRE did not significantly improve prediction of the risk of ESRD in advanced diabetic nephropathy. To improve prediction of renal prognosis for advanced diabetic nephropathy may require different approaches with combining clinical and pathological parameters that were not measured in the KFRE and the RPS DN Classification.

Complex networks as a unified framework for descriptive analysis and predictive modeling in climate

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

Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further,more » we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.« less

Introduction to statistical modelling 2: categorical variables and interactions in linear regression.

PubMed

Lunt, Mark

2015-07-01

In the first article in this series we explored the use of linear regression to predict an outcome variable from a number of predictive factors. It assumed that the predictive factors were measured on an interval scale. However, this article shows how categorical variables can also be included in a linear regression model, enabling predictions to be made separately for different groups and allowing for testing the hypothesis that the outcome differs between groups. The use of interaction terms to measure whether the effect of a particular predictor variable differs between groups is also explained. An alternative approach to testing the difference between groups of the effect of a given predictor, which consists of measuring the effect in each group separately and seeing whether the statistical significance differs between the groups, is shown to be misleading. © The Author 2013. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Validation of statistical predictive models meant to select melanoma patients for sentinel lymph node biopsy.

PubMed

Sabel, Michael S; Rice, John D; Griffith, Kent A; Lowe, Lori; Wong, Sandra L; Chang, Alfred E; Johnson, Timothy M; Taylor, Jeremy M G

2012-01-01

To identify melanoma patients at sufficiently low risk of nodal metastases who could avoid sentinel lymph node biopsy (SLNB), several statistical models have been proposed based upon patient/tumor characteristics, including logistic regression, classification trees, random forests, and support vector machines. We sought to validate recently published models meant to predict sentinel node status. We queried our comprehensive, prospectively collected melanoma database for consecutive melanoma patients undergoing SLNB. Prediction values were estimated based upon four published models, calculating the same reported metrics: negative predictive value (NPV), rate of negative predictions (RNP), and false-negative rate (FNR). Logistic regression performed comparably with our data when considering NPV (89.4 versus 93.6%); however, the model's specificity was not high enough to significantly reduce the rate of biopsies (SLN reduction rate of 2.9%). When applied to our data, the classification tree produced NPV and reduction in biopsy rates that were lower (87.7 versus 94.1 and 29.8 versus 14.3, respectively). Two published models could not be applied to our data due to model complexity and the use of proprietary software. Published models meant to reduce the SLNB rate among patients with melanoma either underperformed when applied to our larger dataset, or could not be validated. Differences in selection criteria and histopathologic interpretation likely resulted in underperformance. Statistical predictive models must be developed in a clinically applicable manner to allow for both validation and ultimately clinical utility.

Validation of Statistical Predictive Models Meant to Select Melanoma Patients for Sentinel Lymph Node Biopsy

PubMed Central

Sabel, Michael S.; Rice, John D.; Griffith, Kent A.; Lowe, Lori; Wong, Sandra L.; Chang, Alfred E.; Johnson, Timothy M.; Taylor, Jeremy M.G.

2013-01-01

Introduction To identify melanoma patients at sufficiently low risk of nodal metastases who could avoid SLN biopsy (SLNB). Several statistical models have been proposed based upon patient/tumor characteristics, including logistic regression, classification trees, random forests and support vector machines. We sought to validate recently published models meant to predict sentinel node status. Methods We queried our comprehensive, prospectively-collected melanoma database for consecutive melanoma patients undergoing SLNB. Prediction values were estimated based upon 4 published models, calculating the same reported metrics: negative predictive value (NPV), rate of negative predictions (RNP), and false negative rate (FNR). Results Logistic regression performed comparably with our data when considering NPV (89.4% vs. 93.6%); however the model’s specificity was not high enough to significantly reduce the rate of biopsies (SLN reduction rate of 2.9%). When applied to our data, the classification tree produced NPV and reduction in biopsies rates that were lower 87.7% vs. 94.1% and 29.8% vs. 14.3%, respectively. Two published models could not be applied to our data due to model complexity and the use of proprietary software. Conclusions Published models meant to reduce the SLNB rate among patients with melanoma either underperformed when applied to our larger dataset, or could not be validated. Differences in selection criteria and histopathologic interpretation likely resulted in underperformance. Development of statistical predictive models must be created in a clinically applicable manner to allow for both validation and ultimately clinical utility. PMID:21822550

The extension of total gain (TG) statistic in survival models: properties and applications.

PubMed

Choodari-Oskooei, Babak; Royston, Patrick; Parmar, Mahesh K B

2015-07-01

The results of multivariable regression models are usually summarized in the form of parameter estimates for the covariates, goodness-of-fit statistics, and the relevant p-values. These statistics do not inform us about whether covariate information will lead to any substantial improvement in prediction. Predictive ability measures can be used for this purpose since they provide important information about the practical significance of prognostic factors. R (2)-type indices are the most familiar forms of such measures in survival models, but they all have limitations and none is widely used. In this paper, we extend the total gain (TG) measure, proposed for a logistic regression model, to survival models and explore its properties using simulations and real data. TG is based on the binary regression quantile plot, otherwise known as the predictiveness curve. Standardised TG ranges from 0 (no explanatory power) to 1 ('perfect' explanatory power). The results of our simulations show that unlike many of the other R (2)-type predictive ability measures, TG is independent of random censoring. It increases as the effect of a covariate increases and can be applied to different types of survival models, including models with time-dependent covariate effects. We also apply TG to quantify the predictive ability of multivariable prognostic models developed in several disease areas. Overall, TG performs well in our simulation studies and can be recommended as a measure to quantify the predictive ability in survival models.

Commentary on "Predicted plasma 25-hydroxyvitamin D and risk of renal cell cancer." Joh HK, Giovannucci EL, Bertrand KA, Lim S, Cho E, Department of Medicine, Seoul National University College of Medicine, Seoul, South Korea. J Natl Cancer Inst 2013; 105(10):726-32. [Epub 2013 Apr 8]. doi: 10.1093/jnci/djt082.

PubMed

Boorjian, Stephen

2014-08-01

Although the kidney is a primary organ for vitamin D metabolism, the association between vitamin D and renal cell cancer (RCC) remains unclear. We prospectively evaluated the association between predicted plasma 25-hydroxyvitamin D [25(OH)D] and RCC risk among 72,051 women and 46,380 men in the period from 1986 to 2008. Predicted plasma 25(OH)D scores were computed using validated regression models that included major determinants of vitamin D status (race, ultraviolet B flux, physical activity, body mass index, estimated vitamin D intake, alcohol consumption, and postmenopausal hormone use in women). Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards models. All statistical tests were two-sided. During 22 years of follow-up, we documented 201 cases of incident RCC in women and 207 cases in men. The multivariable hazard ratios between extreme quintiles of predicted 25(OH)D score were 0.50 (95% CI = 0.32 to 0.80) in women, 0.59 (95% CI = 0.37 to 0.94) in men, and 0.54 (95% CI = 0.39 to 0.75; P trend<.001) in the pooled cohorts. An increment of 10 ng/mL in predicted 25(OH)D score was associated with a 44% lower incidence of RCC (pooled HR = 0.56, 95% CI = 0.42 to 0.74). We found no statistically significant association between vitamin D intake estimated from food-frequency questionnaires and RCC incidence. Higher predicted plasma 25(OH)D levels were associated with a statistically significantly lower risk of RCC in men and women. Our findings need to be confirmed by other prospective studies using valid markers of long-term vitamin D status. Copyright © 2014 Elsevier Inc. All rights reserved.

Cluster analysis as a prediction tool for pregnancy outcomes.

PubMed

Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

2015-03-01

Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.

A test to evaluate the earthquake prediction algorithm, M8

USGS Publications Warehouse

Healy, John H.; Kossobokov, Vladimir G.; Dewey, James W.

1992-01-01

A test of the algorithm M8 is described. The test is constructed to meet four rules, which we propose to be applicable to the test of any method for earthquake prediction:  1. An earthquake prediction technique should be presented as a well documented, logical algorithm that can be used by  investigators without restrictions. 2. The algorithm should be coded in a common programming language and implementable on widely available computer systems. 3. A test of the earthquake prediction technique should involve future predictions with a black box version of the algorithm in which potentially adjustable parameters are fixed in advance. The source of the input data must be defined and ambiguities in these data must be resolved automatically by the algorithm. 4. At least one reasonable null hypothesis should be stated in advance of testing the earthquake prediction method, and it should be stated how this null hypothesis will be used to estimate the statistical significance of the earthquake predictions. The M8 algorithm has successfully predicted several destructive earthquakes, in the sense that the earthquakes occurred inside regions with linear dimensions from 384 to 854 km that the algorithm had identified as being in times of increased probability for strong earthquakes. In addition, M8 has successfully "post predicted" high percentages of strong earthquakes in regions to which it has been applied in retroactive studies. The statistical significance of previous predictions has not been established, however, and post-prediction studies in general are notoriously subject to success-enhancement through hindsight. Nor has it been determined how much more precise an M8 prediction might be than forecasts and probability-of-occurrence estimates made by other techniques. We view our test of M8 both as a means to better determine the effectiveness of M8 and as an experimental structure within which to make observations that might lead to improvements in the algorithm or conceivably lead to a radically different approach to earthquake prediction.

Complex Adaptive System Models and the Genetic Analysis of Plasma HDL-Cholesterol Concentration

PubMed Central

Rea, Thomas J.; Brown, Christine M.; Sing, Charles F.

2006-01-01

Despite remarkable advances in diagnosis and therapy, ischemic heart disease (IHD) remains a leading cause of morbidity and mortality in industrialized countries. Recent efforts to estimate the influence of genetic variation on IHD risk have focused on predicting individual plasma high-density lipoprotein cholesterol (HDL-C) concentration. Plasma HDL-C concentration (mg/dl), a quantitative risk factor for IHD, has a complex multifactorial etiology that involves the actions of many genes. Single gene variations may be necessary but are not individually sufficient to predict a statistically significant increase in risk of disease. The complexity of phenotype-genotype-environment relationships involved in determining plasma HDL-C concentration has challenged commonly held assumptions about genetic causation and has led to the question of which combination of variations, in which subset of genes, in which environmental strata of a particular population significantly improves our ability to predict high or low risk phenotypes. We document the limitations of inferences from genetic research based on commonly accepted biological models, consider how evidence for real-world dynamical interactions between HDL-C determinants challenges the simplifying assumptions implicit in traditional linear statistical genetic models, and conclude by considering research options for evaluating the utility of genetic information in predicting traits with complex etiologies. PMID:17146134

Impacts of pesticide mixtures in European rivers as predicted by the Species Sensitivity Distribution (SSD) models and SPEAR bioindication

NASA Astrophysics Data System (ADS)

Jesenska, Sona; Liess, Mathias; Schäfer, Ralf; Beketov, Mikhail; Blaha, Ludek

2013-04-01

Species sensitivity distribution (SSD) is statistical method broadly used in the ecotoxicological risk assessment of chemicals. Originally it has been used for prospective risk assessment of single substances but nowadays it is becoming more important also in the retrospective risk assessment of mixtures, including the catchment scale. In the present work, SSD predictions (impacts of mixtures consisting of 25 pesticides; data from several catchments in Germany, France and Finland) were compared with SPEAR-pesticides, which a bioindicator index based on biological traits responsive to the effects of pesticides and post-contamination recovery. The results showed statistically significant correlations (Pearson's R, p<0.01) between SSD (predicted msPAF values) and values of SPEAR-pesticides (based on field biomonitoring observations). Comparisons of the thresholds established for the SSD and SPEAR approaches (SPEAR-pesticides=45%, i.e. LOEC level, and msPAF = 0.05 for SSD, i.e. HC5) showed that use of chronic toxicity data significantly improved the agreement between the two methods but the SPEAR-pesticides index was still more sensitive. Taken together, the validation study shows good potential of SSD models in predicting the real impacts of micropollutant mixtures on natural communities of aquatic biota.

Comparison of classical statistical methods and artificial neural network in traffic noise prediction

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

Nedic, Vladimir, E-mail: vnedic@kg.ac.rs; Despotovic, Danijela, E-mail: ddespotovic@kg.ac.rs; Cvetanovic, Slobodan, E-mail: slobodan.cvetanovic@eknfak.ni.ac.rs

2014-11-15

Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. Themore » output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.« less

PSYCHOLOGY. Estimating the reproducibility of psychological science.

PubMed

2015-08-28

Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams. Copyright © 2015, American Association for the Advancement of Science.

Macroscopic anomalies before the September 2010 M = 7.1 earthquake in Christchurch, New Zealand

NASA Astrophysics Data System (ADS)

Whitehead, N. E.; Ulusoy, Ü.

2013-01-01

Previous published work after the Kobe and İzmit earthquakes (1995 and 1999, respectively) demonstrated some reported meteorological and animal behaviour precursors were valid. Predictions were freshly tested for the Christchurch earthquake (M = 7.1, 4 September 2010). An internet survey with nearly 400 valid replies showed relative numbers of reports in precursor categories the day before the quake, were statistically significantly different from those in the preceding three days (excess meteorological events and animal behaviour). The day before the quake, there was also altered relative precursor class occurrence within 56 km compared with further away. Both these confirmed the earlier published work. Owners were woken up by unique pet behaviour 12 times as often in the hour before the quake compared with other hours immediately before (statistically highly significant). Lost and Found pet reports were double normal the week before, and 4.5 times normal both the day before the quake, and 9 days before. (Results were again statistically significant). Unique animal behaviour before the quake was often repeated before the numerous aftershocks. These pet owners claimed an approximate 80% prediction reliability. However, a preliminary telephone survey suggested that animals showing any precursor response are a minority. Some precursors seem real, but usefulness seemed mostly restricted to 7 cases where owners were in, or near, a place of safety through disruptive pet behaviour, and one in which owners were diverted by a pet from being struck by falling fixtures. For a later 22 February 2011 M = 6.3 quake no reports of escape through warning by pets were recorded, which raises serious questions whether such prediction is practically useful, because lives claimed saved are extremely low compared with fatalities. It is shown the lost-pet statistics dates, correspond to ionospheric anomalies recorded using the GPS satellite system and geomagnetic disturbance data, and claimed as precursory. The latter more objective measurements may be the way of the future, but improved statistical treatment should include observations over longer periods of time without earthquakes.

Arsenic exposure and bladder cancer: quantitative assessment of studies in human populations to detect risks at low doses.

PubMed

Tsuji, Joyce S; Alexander, Dominik D; Perez, Vanessa; Mink, Pamela J

2014-03-20

While exposures to high levels of arsenic in drinking water are associated with excess cancer risk (e.g., skin, bladder, and lung), exposures at lower levels (e.g., <100-200 µg/L) generally are not. Lack of significant associations may result from methodological issues (e.g., inadequate statistical power, exposure misclassification), or a different dose-response relationship at low exposures, possibly associated with a toxicological mode of action that requires a sufficient dose for increased tumor formation. The extent to which bladder cancer risk for low-level arsenic exposure can be statistically measured by epidemiological studies was examined using an updated meta-analysis of bladder cancer risk with data from two new publications. The summary relative risk estimate (SRRE) for all nine studies was elevated slightly, but not significantly (1.07; 95% confidence interval [CI]: 0.95-1.21, p-Heterogeneity [p-H]=0.543). The SRRE among never smokers was 0.85 (95% CI: 0.66-1.08, p-H=0.915), whereas the SRRE was positive and more heterogeneous among ever smokers (1.18; 95% CI: 0.97-1.44, p-H=0.034). The SRRE was statistically significantly lower than relative risks predicted for never smokers in the United States based on linear extrapolation of risks from higher doses in southwest Taiwan to arsenic water exposures >10 µg/L for more than one-third of a lifetime. By contrast, for all study subjects, relative risks predicted for one-half of lifetime exposure to 50 µg/L were just above the upper 95% CI on the SRRE. Thus, results from low-exposure studies, particularly for never smokers, were statistically inconsistent with predicted risk based on high-dose extrapolation. Additional studies that better characterize tobacco use and stratify analyses of arsenic and bladder cancer by smoking status are necessary to further examine risks of arsenic exposure for smokers. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Heritability of and mortality prediction with a longevity phenotype: the healthy aging index.

PubMed

Sanders, Jason L; Minster, Ryan L; Barmada, M Michael; Matteini, Amy M; Boudreau, Robert M; Christensen, Kaare; Mayeux, Richard; Borecki, Ingrid B; Zhang, Qunyuan; Perls, Thomas; Newman, Anne B

2014-04-01

Longevity-associated genes may modulate risk for age-related diseases and survival. The Healthy Aging Index (HAI) may be a subphenotype of longevity, which can be constructed in many studies for genetic analysis. We investigated the HAI's association with survival in the Cardiovascular Health Study and heritability in the Long Life Family Study. The HAI includes systolic blood pressure, pulmonary vital capacity, creatinine, fasting glucose, and Modified Mini-Mental Status Examination score, each scored 0, 1, or 2 using approximate tertiles and summed from 0 (healthy) to 10 (unhealthy). In Cardiovascular Health Study, the association with mortality and accuracy predicting death were determined with Cox proportional hazards analysis and c-statistics, respectively. In Long Life Family Study, heritability was determined with a variance component-based family analysis using a polygenic model. Cardiovascular Health Study participants with unhealthier index scores (7-10) had 2.62-fold (95% confidence interval: 2.22, 3.10) greater mortality than participants with healthier scores (0-2). The HAI alone predicted death moderately well (c-statistic = 0.643, 95% confidence interval: 0.626, 0.661, p < .0001) and slightly worse than age alone (c-statistic = 0.700, 95% confidence interval: 0.684, 0.717, p < .0001; p < .0001 for comparison of c-statistics). Prediction increased significantly with adjustment for demographics, health behaviors, and clinical comorbidities (c-statistic = 0.780, 95% confidence interval: 0.765, 0.794, p < .0001). In Long Life Family Study, the heritability of the HAI was 0.295 (p < .0001) overall, 0.387 (p < .0001) in probands, and 0.238 (p = .0004) in offspring. The HAI should be investigated further as a candidate phenotype for uncovering longevity-associated genes in humans.

Heritability of and Mortality Prediction With a Longevity Phenotype: The Healthy Aging Index

PubMed Central

2014-01-01

Background. Longevity-associated genes may modulate risk for age-related diseases and survival. The Healthy Aging Index (HAI) may be a subphenotype of longevity, which can be constructed in many studies for genetic analysis. We investigated the HAI’s association with survival in the Cardiovascular Health Study and heritability in the Long Life Family Study. Methods. The HAI includes systolic blood pressure, pulmonary vital capacity, creatinine, fasting glucose, and Modified Mini-Mental Status Examination score, each scored 0, 1, or 2 using approximate tertiles and summed from 0 (healthy) to 10 (unhealthy). In Cardiovascular Health Study, the association with mortality and accuracy predicting death were determined with Cox proportional hazards analysis and c-statistics, respectively. In Long Life Family Study, heritability was determined with a variance component–based family analysis using a polygenic model. Results. Cardiovascular Health Study participants with unhealthier index scores (7–10) had 2.62-fold (95% confidence interval: 2.22, 3.10) greater mortality than participants with healthier scores (0–2). The HAI alone predicted death moderately well (c-statistic = 0.643, 95% confidence interval: 0.626, 0.661, p < .0001) and slightly worse than age alone (c-statistic = 0.700, 95% confidence interval: 0.684, 0.717, p < .0001; p < .0001 for comparison of c-statistics). Prediction increased significantly with adjustment for demographics, health behaviors, and clinical comorbidities (c-statistic = 0.780, 95% confidence interval: 0.765, 0.794, p < .0001). In Long Life Family Study, the heritability of the HAI was 0.295 (p < .0001) overall, 0.387 (p < .0001) in probands, and 0.238 (p = .0004) in offspring. Conclusion. The HAI should be investigated further as a candidate phenotype for uncovering longevity-associated genes in humans. PMID:23913930

Large eddy simulation of orientation and rotation of ellipsoidal particles in isotropic turbulent flows

NASA Astrophysics Data System (ADS)

Chen, Jincai; Jin, Guodong; Zhang, Jian

2016-03-01

The rotational motion and orientational distribution of ellipsoidal particles in turbulent flows are of significance in environmental and engineering applications. Whereas the translational motion of an ellipsoidal particle is controlled by the turbulent motions at large scales, its rotational motion is determined by the fluid velocity gradient tensor at small scales, which raises a challenge when predicting the rotational dispersion of ellipsoidal particles using large eddy simulation (LES) method due to the lack of subgrid scale (SGS) fluid motions. We report the effects of the SGS fluid motions on the orientational and rotational statistics, such as the alignment between the long axis of ellipsoidal particles and the vorticity, the mean rotational energy at various aspect ratios against those obtained with direct numerical simulation (DNS) and filtered DNS. The performances of a stochastic differential equation (SDE) model for the SGS velocity gradient seen by the particles and the approximate deconvolution method (ADM) for LES are investigated. It is found that the missing SGS fluid motions in LES flow fields have significant effects on the rotational statistics of ellipsoidal particles. Alignment between the particles and the vorticity is weakened; and the rotational energy of the particles is reduced in LES. The SGS-SDE model leads to a large error in predicting the alignment between the particles and the vorticity and over-predicts the rotational energy of rod-like particles. The ADM significantly improves the rotational energy prediction of particles in LES.

A weighted generalized score statistic for comparison of predictive values of diagnostic tests.

PubMed

Kosinski, Andrzej S

2013-03-15

Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations that are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we presented, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic that incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, always reduces to the score statistic in the independent samples situation, and preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe that the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the WGS test statistic in a general GEE setting. Copyright © 2012 John Wiley & Sons, Ltd.

A weighted generalized score statistic for comparison of predictive values of diagnostic tests

PubMed Central

Kosinski, Andrzej S.

2013-01-01

Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations which are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we present, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic which incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, it always reduces to the score statistic in the independent samples situation, and it preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the weighted generalized score test statistic in a general GEE setting. PMID:22912343

Prognostic significance of electrical alternans versus signal averaged electrocardiography in predicting the outcome of electrophysiological testing and arrhythmia-free survival

NASA Technical Reports Server (NTRS)

Armoundas, A. A.; Rosenbaum, D. S.; Ruskin, J. N.; Garan, H.; Cohen, R. J.

1998-01-01

OBJECTIVE: To investigate the accuracy of signal averaged electrocardiography (SAECG) and measurement of microvolt level T wave alternans as predictors of susceptibility to ventricular arrhythmias. DESIGN: Analysis of new data from a previously published prospective investigation. SETTING: Electrophysiology laboratory of a major referral hospital. PATIENTS AND INTERVENTIONS: 43 patients, not on class I or class III antiarrhythmic drug treatment, undergoing invasive electrophysiological testing had SAECG and T wave alternans measurements. The SAECG was considered positive in the presence of one (SAECG-I) or two (SAECG-II) of three standard criteria. T wave alternans was considered positive if the alternans ratio exceeded 3.0. MAIN OUTCOME MEASURES: Inducibility of sustained ventricular tachycardia or fibrillation during electrophysiological testing, and 20 month arrhythmia-free survival. RESULTS: The accuracy of T wave alternans in predicting the outcome of electrophysiological testing was 84% (p < 0.0001). Neither SAECG-I (accuracy 60%; p < 0.29) nor SAECG-II (accuracy 71%; p < 0.10) was a statistically significant predictor of electrophysiological testing. SAECG, T wave alternans, electrophysiological testing, and follow up data were available in 36 patients while not on class I or III antiarrhythmic agents. The accuracy of T wave alternans in predicting the outcome of arrhythmia-free survival was 86% (p < 0.030). Neither SAECG-I (accuracy 65%; p < 0.21) nor SAECG-II (accuracy 71%; p < 0.48) was a statistically significant predictor of arrhythmia-free survival. CONCLUSIONS: T wave alternans was a highly significant predictor of the outcome of electrophysiological testing and arrhythmia-free survival, while SAECG was not a statistically significant predictor. Although these results need to be confirmed in prospective clinical studies, they suggest that T wave alternans may serve as a non-invasive probe for screening high risk populations for malignant ventricular arrhythmias.

A statistical approach to evaluate the performance of cardiac biomarkers in predicting death due to acute myocardial infarction: time-dependent ROC curve

PubMed

Karaismailoğlu, Eda; Dikmen, Zeliha Günnur; Akbıyık, Filiz; Karaağaoğlu, Ahmet Ergun

2018-04-30

Background/aim: Myoglobin, cardiac troponin T, B-type natriuretic peptide (BNP), and creatine kinase isoenzyme MB (CK-MB) are frequently used biomarkers for evaluating risk of patients admitted to an emergency department with chest pain. Recently, time- dependent receiver operating characteristic (ROC) analysis has been used to evaluate the predictive power of biomarkers where disease status can change over time. We aimed to determine the best set of biomarkers that estimate cardiac death during follow-up time. We also obtained optimal cut-off values of these biomarkers, which differentiates between patients with and without risk of death. A web tool was developed to estimate time intervals in risk. Materials and methods: A total of 410 patients admitted to the emergency department with chest pain and shortness of breath were included. Cox regression analysis was used to determine an optimal set of biomarkers that can be used for estimating cardiac death and to combine the significant biomarkers. Time-dependent ROC analysis was performed for evaluating performances of significant biomarkers and a combined biomarker during 240 h. The bootstrap method was used to compare statistical significance and the Youden index was used to determine optimal cut-off values. Results : Myoglobin and BNP were significant by multivariate Cox regression analysis. Areas under the time-dependent ROC curves of myoglobin and BNP were about 0.80 during 240 h, and that of the combined biomarker (myoglobin + BNP) increased to 0.90 during the first 180 h. Conclusion: Although myoglobin is not clinically specific to a cardiac event, in our study both myoglobin and BNP were found to be statistically significant for estimating cardiac death. Using this combined biomarker may increase the power of prediction. Our web tool can be useful for evaluating the risk status of new patients and helping clinicians in making decisions.

No-Impact Threshold Values for NRAP's Reduced Order Models

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

Last, George V.; Murray, Christopher J.; Brown, Christopher F.

2013-02-01

The purpose of this study was to develop methodologies for establishing baseline datasets and statistical protocols for determining statistically significant changes between background concentrations and predicted concentrations that would be used to represent a contamination plume in the Gen II models being developed by NRAP’s Groundwater Protection team. The initial effort examined selected portions of two aquifer systems; the urban shallow-unconfined aquifer system of the Edwards-Trinity Aquifer System (being used to develop the ROM for carbon-rock aquifers, and the a portion of the High Plains Aquifer (an unconsolidated and semi-consolidated sand and gravel aquifer, being used to development the ROMmore » for sandstone aquifers). Threshold values were determined for Cd, Pb, As, pH, and TDS that could be used to identify contamination due to predicted impacts from carbon sequestration storage reservoirs, based on recommendations found in the EPA’s ''Unified Guidance for Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities'' (US Environmental Protection Agency 2009). Results from this effort can be used to inform a ''no change'' scenario with respect to groundwater impacts, rather than the use of an MCL that could be significantly higher than existing concentrations in the aquifer.« less

SU-F-T-386: Analysis of Three QA Methods for Predicting Dose Deviation Pass Percentage for Lung SBRT VMAT Plans

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

Hardin, M; To, D; Giaddui, T

2016-06-15

Purpose: To investigate the significance of using pinpoint ionization chambers (IC) and RadCalc (RC) in determining the quality of lung SBRT VMAT plans with low dose deviation pass percentage (DDPP) as reported by ScandiDos Delta4 (D4). To quantify the relationship between DDPP and point dose deviations determined by IC (ICDD), RadCalc (RCDD), and median dose deviation reported by D4 (D4DD). Methods: Point dose deviations and D4 DDPP were compiled for 45 SBRT VMAT plans. Eighteen patients were treated on Varian Truebeam linear accelerators (linacs); the remaining 27 were treated on Elekta Synergy linacs with Agility collimators. A one-way analysis ofmore » variance (ANOVA) was performed to determine if there were any statistically significant differences between D4DD, ICDD, and RCDD. Tukey’s test was used to determine which pair of means was statistically different from each other. Multiple regression analysis was performed to determine if D4DD, ICDD, or RCDD are statistically significant predictors of DDPP. Results: Median DDPP, D4DD, ICDD, and RCDD were 80.5% (47.6%–99.2%), −0.3% (−2.0%–1.6%), 0.2% (−7.5%–6.3%), and 2.9% (−4.0%–19.7%), respectively. The ANOVA showed a statistically significant difference between D4DD, ICDD, and RCDD for a 95% confidence interval (p < 0.001). Tukey’s test revealed a statistically significant difference between two pairs of groups, RCDD-D4DD and RCDD-ICDD (p < 0.001), but no difference between ICDD-D4DD (p = 0.485). Multiple regression analysis revealed that ICDD (p = 0.04) and D4DD (p = 0.03) are statistically significant predictors of DDPP with an adjusted r{sup 2} of 0.115. Conclusion: This study shows ICDD predicts trends in D4 DDPP; however this trend is highly variable as shown by our low r{sup 2}. This work suggests that ICDD can be used as a method to verify DDPP in delivery of lung SBRT VMAT plans. RCDD may not validate low DDPP discovered in D4 QA for small field SBRT treatments.« less

Fragility of Results in Ophthalmology Randomized Controlled Trials: A Systematic Review.

PubMed

Shen, Carl; Shamsudeen, Isabel; Farrokhyar, Forough; Sabri, Kourosh

2018-05-01

Evidence-based medicine is guided by our interpretation of randomized controlled trials (RCTs) that address important clinical questions. Evaluation of the robustness of statistically significant outcomes adds a crucial element to the global assessment of trial findings. The purpose of this systematic review was to determine the robustness of ophthalmology RCTs through application of the Fragility Index (FI), a novel metric of the robustness of statistically significant outcomes. Systematic review. A literature search (MEDLINE) was performed for all RCTs published in top ophthalmology journals and ophthalmology-related RCTs published in high-impact journals in the past 10 years. Two reviewers independently screened 1811 identified articles for inclusion if they (1) were a human ophthalmology-related trial, (2) had a 1:1 prospective study design, and (3) reported a statistically significant dichotomous outcome in the abstract. All relevant data, including outcome, P value, number of patients in each group, number of events in each group, number of patients lost to follow-up, and trial characteristics, were extracted. The FI of each RCT was calculated and multivariate regression applied to determine predictive factors. The 156 trials had a median sample size of 91.5 (range, 13-2593) patients/eyes, and a median of 28 (range, 4-2217) events. The median FI of the included trials was 2 (range, 0-48), meaning that if 2 non-events were switched to events in the treatment group, the result would lose its statistical significance. A quarter of all trials had an FI of 1 or less, and 75% of trials had an FI of 6 or less. The FI was less than the number of missing data points in 52.6% of trials. Predictive factors for FI by multivariate regression included smaller P value (P < 0.001), larger sample size (P = 0.001), larger number of events (P = 0.011), and journal impact factor (P = 0.029). In ophthalmology trials, statistically significant dichotomous results are often fragile, meaning that a difference of only a couple of events can change the statistical significance. An application of the FI in RCTs may aid in the interpretation of results and assessment of quality of evidence. Copyright © 2017 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Vegetation management with fire modifies peatland soil thermal regime.

PubMed

Brown, Lee E; Palmer, Sheila M; Johnston, Kerrylyn; Holden, Joseph

2015-05-01

Vegetation removal with fire can alter the thermal regime of the land surface, leading to significant changes in biogeochemistry (e.g. carbon cycling) and soil hydrology. In the UK, large expanses of carbon-rich upland environments are managed to encourage increased abundance of red grouse (Lagopus lagopus scotica) by rotational burning of shrub vegetation. To date, though, there has not been any consideration of whether prescribed vegetation burning on peatlands modifies the thermal regime of the soil mass in the years after fire. In this study thermal regime was monitored across 12 burned peatland soil plots over an 18-month period, with the aim of (i) quantifying thermal dynamics between burned plots of different ages (from <2 to 15 + years post burning), and (ii) developing statistical models to determine the magnitude of thermal change caused by vegetation management. Compared to plots burned 15 + years previously, plots recently burned (<2-4 years) showed higher mean, maximum and range of soil temperatures, and lower minima. Statistical models (generalised least square regression) were developed to predict daily mean and maximum soil temperature in plots burned 15 + years prior to the study. These models were then applied to predict temperatures of plots burned 2, 4 and 7 years previously, with significant deviations from predicted temperatures illustrating the magnitude of burn management effects. Temperatures measured in soil plots burned <2 years previously showed significant statistical disturbances from model predictions, reaching +6.2 °C for daily mean temperatures and +19.6 °C for daily maxima. Soil temperatures in plots burnt 7 years previously were most similar to plots burned 15 + years ago indicating the potential for soil temperatures to recover as vegetation regrows. Our findings that prescribed peatland vegetation burning alters soil thermal regime should provide an impetus for further research to understand the consequences of thermal regime change for carbon processing and release, and hydrological processes, in these peatlands. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Generalized Joint Hypermobility Is Predictive of Hip Capsular Thickness

PubMed Central

Devitt, Brian M.; Smith, Bjorn N.; Stapf, Robert; Tacey, Mark; O’Donnell, John M.

2017-01-01

Background: The pathomechanics of hip microinstability are not clearly defined but are thought to involve anatomical abnormalities, repetitive forces across the hip, and ligamentous laxity. Purpose/Hypothesis: The purpose of this study was to explore the relationship between generalized joint hypermobility (GJH) and hip capsular thickness. The hypothesis was that GJH would be predictive of a thin hip capsule. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A prospective study was performed on 100 consecutive patients undergoing primary hip arthroscopy for the treatment of hip pain. A Beighton test score (BTS) was obtained prior to each procedure. The maximum score was 9, and a score of ≥4 was defined as hypermobile. Capsular thickness at the level of the anterior portal, corresponding to the location of the iliofemoral ligament, was measured arthroscopically using a calibrated probe. The presence of ligamentum teres (LT) pathology was also recorded. Results: Fifty-five women and 45 men were included in the study. The mean age was 32 years (range, 18-45 years). The median hip capsule thickness was statistically greater in men than women (12.5 and 7.5 mm, respectively). The median BTS for men was 1 compared with 4 for women (P < .001). A statistically significant association was found between BTS and capsular thickness; a BTS of <4 is strongly predictive of having a capsular thickness of ≥10 mm, while a BTS ≥4 correlates with a capsular thickness of <10 mm. There was a statistically greater incidence of LT tears in patients with a capsular thickness of ≤7.5 mm and a BTS of ≥4 (P < .001). Conclusion: Measurement of the GJH is highly predictive of hip capsular thickness. A BTS of <4 correlates significantly with a capsular thickness of ≥10 mm, while a BTS ≥4 correlates significantly with a thickness of <10 mm. PMID:28451620

Development of a Microsimulation Model to Predict Stroke and Long-Term Mortality in Adherent and Nonadherent Medically Managed and Surgically Treated Octogenarians with Asymptomatic Significant Carotid Artery Stenosis.

PubMed

Luebke, Thomas; Brunkwall, Jan

2016-08-01

The primary study objective was to develop a microsimulation model to predict preventable first-ever and recurrent strokes and mortality for a population of medically or surgically managed octogenarians with substantial (>60%) asymptomatic carotid artery stenosis and comparing an adherent with a real-world nonadherent best medical treatment (BMT) regimen subjected to sex. A Monte Carlo microsimulation model was constructed with a 14-year time horizon and with 10,000 patients. Probabilities and values for clinical outcomes were obtained from the current literature. The stratification of the microsimulation estimates by treatment strategy within the female group of octogenarians showed a statistically significant lower stroke rate during follow-up for carotid endarterectomy (CEA) compared with nonadherent BMT (P < 0.0001) as well as compared with adherent BMT (P < 0.0001). In male octogenarians, the CEA strategy was also associated with statistically significant lower stroke rates compared with adherent and nonadherent BMT (P < 0.0001 and P < 0.0001, respectively). For each treatment strategy, female octogenarians had a statistically significant longer overall long-term survival compared with male octogenarians (P < 0.0001, respectively). In terms of stratification by sex, in octogenarian men and women, long-term survival was significantly better for adherent BMT compared with nonadherent BMT, and CEA was associated with a significant better long-term survival compared with nonadherent BMT. In the present microsimulation, in real-world drug adherence, it was likely that a strategy of early endarterectomy was beneficial in octogenarians with significant asymptomatic carotid artery disease compared with BMT alone. Copyright © 2016 Elsevier Inc. All rights reserved.

Towards Principled Experimental Study of Autonomous Mobile Robots

NASA Technical Reports Server (NTRS)

Gat, Erann

1995-01-01

We review the current state of research in autonomous mobile robots and conclude that there is an inadequate basis for predicting the reliability and behavior of robots operating in unengineered environments. We present a new approach to the study of autonomous mobile robot performance based on formal statistical analysis of independently reproducible experiments conducted on real robots. Simulators serve as models rather than experimental surrogates. We demonstrate three new results: 1) Two commonly used performance metrics (time and distance) are not as well correlated as is often tacitly assumed. 2) The probability distributions of these performance metrics are exponential rather than normal, and 3) a modular, object-oriented simulation accurately predicts the behavior of the real robot in a statistically significant manner.

Restoration of the Patient-Specific Anatomy of the Proximal and Distal Parts of the Humerus: Statistical Shape Modeling Versus Contralateral Registration Method.

PubMed

Vlachopoulos, Lazaros; Lüthi, Marcel; Carrillo, Fabio; Gerber, Christian; Székely, Gábor; Fürnstahl, Philipp

2018-04-18

In computer-assisted reconstructive surgeries, the contralateral anatomy is established as the best available reconstruction template. However, existing intra-individual bilateral differences or a pathological, contralateral humerus may limit the applicability of the method. The aim of the study was to evaluate whether a statistical shape model (SSM) has the potential to predict accurately the pretraumatic anatomy of the humerus from the posttraumatic condition. Three-dimensional (3D) triangular surface models were extracted from the computed tomographic data of 100 paired cadaveric humeri without a pathological condition. An SSM was constructed, encoding the characteristic shape variations among the individuals. To predict the patient-specific anatomy of the proximal (or distal) part of the humerus with the SSM, we generated segments of the humerus of predefined length excluding the part to predict. The proximal and distal humeral prediction (p-HP and d-HP) errors, defined as the deviation of the predicted (bone) model from the original (bone) model, were evaluated. For comparison with the state-of-the-art technique, i.e., the contralateral registration method, we used the same segments of the humerus to evaluate whether the SSM or the contralateral anatomy yields a more accurate reconstruction template. The p-HP error (mean and standard deviation, 3.8° ± 1.9°) using 85% of the distal end of the humerus to predict the proximal humeral anatomy was significantly smaller (p = 0.001) compared with the contralateral registration method. The difference between the d-HP error (mean, 5.5° ± 2.9°), using 85% of the proximal part of the humerus to predict the distal humeral anatomy, and the contralateral registration method was not significant (p = 0.61). The restoration of the humeral length was not significantly different between the SSM and the contralateral registration method. SSMs accurately predict the patient-specific anatomy of the proximal and distal aspects of the humerus. The prediction errors of the SSM depend on the size of the healthy part of the humerus. The prediction of the patient-specific anatomy of the humerus is of fundamental importance for computer-assisted reconstructive surgeries.

Forecasting runout of rock and debris avalanches

USGS Publications Warehouse

Iverson, Richard M.; Evans, S.G.; Mugnozza, G.S.; Strom, A.; Hermanns, R.L.

2006-01-01

Physically based mathematical models and statistically based empirical equations each may provide useful means of forecasting runout of rock and debris avalanches. This paper compares the foundations, strengths, and limitations of a physically based model and a statistically based forecasting method, both of which were developed to predict runout across three-dimensional topography. The chief advantage of the physically based model results from its ties to physical conservation laws and well-tested axioms of soil and rock mechanics, such as the Coulomb friction rule and effective-stress principle. The output of this model provides detailed information about the dynamics of avalanche runout, at the expense of high demands for accurate input data, numerical computation, and experimental testing. In comparison, the statistical method requires relatively modest computation and no input data except identification of prospective avalanche source areas and a range of postulated avalanche volumes. Like the physically based model, the statistical method yields maps of predicted runout, but it provides no information on runout dynamics. Although the two methods differ significantly in their structure and objectives, insights gained from one method can aid refinement of the other.

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

A local basal area adjustment for crown width prediction

Treesearch

Don C. Bragg

2001-01-01

Nonlinear crown width regressive equations were developed for 24 species common to the upper Lake States of Michigan, Minnesota, and Wisconsin. Of the species surveyed, 15 produced statistically significant (P 0.05) local basal area effect coefficients showing a reduction in crown...

Neurodegenerative cerebrospinal fluid biomarkers tau and amyloid beta predict functional, quality of life, and neuropsychological outcomes after aneurysmal subarachnoid hemorrhage.

PubMed

Joswig, Holger; Korte, Wolfgang; Früh, Severin; Epprecht, Lorenz; Hildebrandt, Gerhard; Fournier, Jean-Yves; Stienen, Martin Nikolaus

2018-04-01

Cerebrospinal fluid (CSF) biomarkers might be useful in predicting outcome after aneurysmal subarachnoid hemorrhage (aSAH). It was the aim to determine whether tau and amyloid beta CSF concentrations predict functional, health-related quality of life (hrQoL), and neuropsychological outcomes after aSAH. Ventricular CSF was obtained from n = 24 aSAH patients at admission (D0), day 2 (D2), and day 6 (D6). CSF total (t)Tau, phosphorylated (p)Tau (181P) , and amyloid beta (1-40 and 1-42) (Aβ40/Aβ42) levels were compared between patients with favorable and unfavorable functional (modified Rankin Scale (mRS)), hrQoL (Euro-Qol (EQ-5D)), and neuropsychological outcomes at 3 (3 m) and 12 months (12 m). Patients with unfavorable functional (mRS 4-6) and hrQoL outcome (EQ-5D z-score ≤ - 1.0) at 3 and 12 m had higher CSF tTau/pTau and lower Aβ40/Aβ42 at D0, D2, and D6 with varying degrees of statistical significance. In terms of predicting neuropsychological outcome, CSF pTau showed a statistically significant correlation with the z-scores of executive function (r = - 0.7486, p = 0.008), verbal memory (r = - 0.8101, p = 0.002), attention (r = - 0.6498, p = 0.030), and visuospatial functioning (r = - 0.6944, p = 0.017) at 3 m. At 12 m, CSF pTau had statistically significant correlations with the z-scores of verbal memory (r = - 0.7473, p = 0.008) and visuospatial functioning (r = - 0.6678, p = 0.024). In conclusion, higher tTau/pTau and lower Aβ40/Aβ42 CSF levels predict unfavorable long-term functional and hrQoL outcomes. Neuropsychological deficits correlate with increased CSF tTau and pTau concentrations.

First trimester prediction of maternal glycemic status.

PubMed

Gabbay-Benziv, Rinat; Doyle, Lauren E; Blitzer, Miriam; Baschat, Ahmet A

2015-05-01

To predict gestational diabetes mellitus (GDM) or normoglycemic status using first trimester maternal characteristics. We used data from a prospective cohort study. First trimester maternal characteristics were compared between women with and without GDM. Association of these variables with sugar values at glucose challenge test (GCT) and subsequent GDM was tested to identify key parameters. A predictive algorithm for GDM was developed and receiver operating characteristics (ROC) statistics was used to derive the optimal risk score. We defined normoglycemic state, when GCT and all four sugar values at oral glucose tolerance test, whenever obtained, were normal. Using same statistical approach, we developed an algorithm to predict the normoglycemic state. Maternal age, race, prior GDM, first trimester BMI, and systolic blood pressure (SBP) were all significantly associated with GDM. Age, BMI, and SBP were also associated with GCT values. The logistic regression analysis constructed equation and the calculated risk score yielded sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 62%, 13.8%, and 98.3% for a cut-off value of 0.042, respectively (ROC-AUC - area under the curve 0.819, CI - confidence interval 0.769-0.868). The model constructed for normoglycemia prediction demonstrated lower performance (ROC-AUC 0.707, CI 0.668-0.746). GDM prediction can be achieved during the first trimester encounter by integration of maternal characteristics and basic measurements while normoglycemic status prediction is less effective.

The evaluation of different forest structural indices to predict the stand aboveground biomass of even-aged Scotch pine (Pinus sylvestris L.) forests in Kunduz, Northern Turkey.

PubMed

Ercanli, İlker; Kahriman, Aydın

2015-03-01

We assessed the effect of stand structural diversity, including the Shannon, improved Shannon, Simpson, McIntosh, Margelef, and Berger-Parker indices, on stand aboveground biomass (AGB) and developed statistical prediction models for the stand AGB values, including stand structural diversity indices and some stand attributes. The AGB prediction model, including only stand attributes, accounted for 85 % of the total variance in AGB (R (2)) with an Akaike's information criterion (AIC) of 807.2407, Bayesian information criterion (BIC) of 809.5397, Schwarz Bayesian criterion (SBC) of 818.0426, and root mean square error (RMSE) of 38.529 Mg. After inclusion of the stand structural diversity into the model structure, considerable improvement was observed in statistical accuracy, including 97.5 % of the total variance in AGB, with an AIC of 614.1819, BIC of 617.1242, SBC of 633.0853, and RMSE of 15.8153 Mg. The predictive fitting results indicate that some indices describing the stand structural diversity can be employed as significant independent variables to predict the AGB production of the Scotch pine stand. Further, including the stand diversity indices in the AGB prediction model with the stand attributes provided important predictive contributions in estimating the total variance in AGB.

Echocardiography and risk prediction in advanced heart failure: incremental value over clinical markers.

PubMed

Agha, Syed A; Kalogeropoulos, Andreas P; Shih, Jeffrey; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Anarado, Perry; Mangalat, Deepa; Hussain, Imad; Book, Wendy; Laskar, Sonjoy; Smith, Andrew L; Martin, Randolph; Butler, Javed

2009-09-01

Incremental value of echocardiography over clinical parameters for outcome prediction in advanced heart failure (HF) is not well established. We evaluated 223 patients with advanced HF receiving optimal therapy (91.9% angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 92.8% beta-blockers, 71.8% biventricular pacemaker, and/or defibrillator use). The Seattle Heart Failure Model (SHFM) was used as the reference clinical risk prediction scheme. The incremental value of echocardiographic parameters for event prediction (death or urgent heart transplantation) was measured by the improvement in fit and discrimination achieved by addition of standard echocardiographic parameters to the SHFM. After a median follow-up of 2.4 years, there were 38 (17.0%) events (35 deaths; 3 urgent transplants). The SHFM had likelihood ratio (LR) chi(2) 32.0 and C statistic 0.756 for event prediction. Left ventricular end-systolic volume, stroke volume, and severe tricuspid regurgitation were independent echocardiographic predictors of events. The addition of these parameters to SHFM improved LR chi(2) to 72.0 and C statistic to 0.866 (P < .001 and P=.019, respectively). Reclassifying the SHFM-predicted risk with use of the echocardiography-added model resulted in improved prognostic separation. Addition of standard echocardiographic variables to the SHFM results in significant improvement in risk prediction for patients with advanced HF.

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

PubMed Central

Wang, Chih-Wei; Liu, Yi-Jui; Lee, Yi-Hsiung; Hueng, Dueng-Yuan; Fan, Hueng-Chuen; Yang, Fu-Chi; Hsueh, Chun-Jen; Kao, Hung-Wen; Juan, Chun-Jung; Hsu, Hsian-He

2014-01-01

Purpose To investigate the performance of hematoma shape, hematoma size, Glasgow coma scale (GCS) score, and intracerebral hematoma (ICH) score in predicting the 30-day mortality for ICH patients. To examine the influence of the estimation error of hematoma size on the prediction of 30-day mortality. Materials and Methods This retrospective study, approved by a local institutional review board with written informed consent waived, recruited 106 patients diagnosed as ICH by non-enhanced computed tomography study. The hemorrhagic shape, hematoma size measured by computer-assisted volumetric analysis (CAVA) and estimated by ABC/2 formula, ICH score and GCS score was examined. The predicting performance of 30-day mortality of the aforementioned variables was evaluated. Statistical analysis was performed using Kolmogorov-Smirnov tests, paired t test, nonparametric test, linear regression analysis, and binary logistic regression. The receiver operating characteristics curves were plotted and areas under curve (AUC) were calculated for 30-day mortality. A P value less than 0.05 was considered as statistically significant. Results The overall 30-day mortality rate was 15.1% of ICH patients. The hematoma shape, hematoma size, ICH score, and GCS score all significantly predict the 30-day mortality for ICH patients, with an AUC of 0.692 (P = 0.0018), 0.715 (P = 0.0008) (by ABC/2) to 0.738 (P = 0.0002) (by CAVA), 0.877 (P<0.0001) (by ABC/2) to 0.882 (P<0.0001) (by CAVA), and 0.912 (P<0.0001), respectively. Conclusion Our study shows that hematoma shape, hematoma size, ICH scores and GCS score all significantly predict the 30-day mortality in an increasing order of AUC. The effect of overestimation of hematoma size by ABC/2 formula in predicting the 30-day mortality could be remedied by using ICH score. PMID:25029592

Dose-distance metric that predicts late rectal bleeding in patients receiving radical prostate external-beam radiotherapy

NASA Astrophysics Data System (ADS)

Lee, Richard; Chan, Elisa K.; Kosztyla, Robert; Liu, Mitchell; Moiseenko, Vitali

2012-12-01

The relationship between rectal dose distribution and the incidence of late rectal complications following external-beam radiotherapy has been previously studied using dose-volume histograms or dose-surface histograms. However, they do not account for the spatial dose distribution. This study proposes a metric based on both surface dose and distance that can predict the incidence of rectal bleeding in prostate cancer patients treated with radical radiotherapy. One hundred and forty-four patients treated with radical radiotherapy for prostate cancer were prospectively followed to record the incidence of grade ≥2 rectal bleeding. Radiotherapy plans were used to evaluate a dose-distance metric that accounts for the dose and its spatial distribution on the rectal surface, characterized by a logistic weighting function with slope a and inflection point d0. This was compared to the effective dose obtained from dose-surface histograms, characterized by the parameter n which describes sensitivity to hot spots. The log-rank test was used to determine statistically significant (p < 0.05) cut-off values for the dose-distance metric and effective dose that predict for the occurrence of rectal bleeding. For the dose-distance metric, only d0 = 25 and 30 mm combined with a > 5 led to statistical significant cut-offs. For the effective dose metric, only values of n in the range 0.07-0.35 led to statistically significant cut-offs. The proposed dose-distance metric is a predictor of rectal bleeding in prostate cancer patients treated with radiotherapy. Both the dose-distance metric and the effective dose metric indicate that the incidence of grade ≥2 rectal bleeding is sensitive to localized damage to the rectal surface.

The Perception of Being a Burden in Acute and Chronic Pain Patients Is Associated with Affirmation of Different Types of Suicidality.

PubMed

Fishbain, David A; Bruns, Daniel; Bruns, Alexander; Gao, Jinrun; Lewis, John E; Meyer, Laura J; Disorbio, John Mark

2016-03-01

The perception of being a burden or self-perceived burden (SPB) is associated with suicide ideation in chronic pain patients (CPPs). The objective of this study was to determine if SPB is associated with five types of suicidality (wish to die, active suicide ideation, presence of suicide plan, history of suicide attempts, and preference for death over being disabled) in CPPs and acute pain patients (APPs). Affirmation of SPB was statistically compared between community nonpatients without pain (CNPWP), APPs, and CPPs. APPs and CPPs who had affirmed any of the five types of suicidality were compared statistically for affirmation of SPB. Hierarchical regression analysis was utilized to determine the significance of SPB in predicting each of the five types of suicidality in APPs and CPPs controlling for age, gender, race, education status, and two types of measures of depression (current depression and vegetative depression). APPs and CPPs were statistically more likely to affirm SPB than CNPWPs and CPPs were more likely than APPs to do so. There were no differences between APPs and CPPs in affirming SPB in APPs and CPPs who had affirmed any of the five types of suicidality. In CPPs, SPB predicted each type of suicidality in a significant fashion utilizing both types of depression measures. For APPs, SPB predicted each type of suicidality in a significant fashion except for history of suicide attempt controlling for vegetative depression. SPB is associated with the vast majority of different types of suicidality in APPs and CPPs. © 2015 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Groundwater nitrate contamination: Factors and indicators

PubMed Central

Wick, Katharina; Heumesser, Christine; Schmid, Erwin

2012-01-01

Identifying significant determinants of groundwater nitrate contamination is critical in order to define sensible agri-environmental indicators that support the design, enforcement, and monitoring of regulatory policies. We use data from approximately 1200 Austrian municipalities to provide a detailed statistical analysis of (1) the factors influencing groundwater nitrate contamination and (2) the predictive capacity of the Gross Nitrogen Balance, one of the most commonly used agri-environmental indicators. We find that the percentage of cropland in a given region correlates positively with nitrate concentration in groundwater. Additionally, environmental characteristics such as temperature and precipitation are important co-factors. Higher average temperatures result in lower nitrate contamination of groundwater, possibly due to increased evapotranspiration. Higher average precipitation dilutes nitrates in the soil, further reducing groundwater nitrate concentration. Finally, we assess whether the Gross Nitrogen Balance is a valid predictor of groundwater nitrate contamination. Our regression analysis reveals that the Gross Nitrogen Balance is a statistically significant predictor for nitrate contamination. We also show that its predictive power can be improved if we account for average regional precipitation. The Gross Nitrogen Balance predicts nitrate contamination in groundwater more precisely in regions with higher average precipitation. PMID:22906701

Comparative evaluation of guided tissue regeneration with use of collagen-based barrier freeze-dried dura mater allograft for mandibular class 2 furcation defects (a comparative controlled clinical study).

PubMed

Patel, Sandeep; Kubavat, Ajay; Ruparelia, Brijesh; Agarwal, Arvind; Panda, Anup

2012-01-01

The aim of periodontal surgery is complete regeneration. The present study was designed to evaluate and compare clinically soft tissue changes in form of probing pocket depth, gingival shrinkage, attachment level and hard tissue changes in form of horizontal and vertical bone level using resorbable membranes. Twelve subjects with bilateral class 2 furcation defects were selected. After initial phase one treatment, open debridement was performed in control site while freezedried dura mater allograft was used in experimental site. Soft and hard tissue parameters were registered intrasurgically. Nine months reentry ensured better understanding and evaluation of the final outcome of the study. Guided tissue regeneration is a predictable treatment modality for class 2 furcation defect. There was statistically significant reduction in pocket depth as compared to control (p < 0.01). There is statistically significant increase in periodontal attachment level within control and experimental sites showed better results (p < 0.01). For hard tissue parameter, significant defect fill resulted in experimental group, while in control group, less significant defect fill was found in horizontal direction and nonsignificant defect fill was found in vertical direction. The results showed statistically significant improvement in soft and hard tissue parameters and less gingival shrinkage in experimental sites compared to control site. The use of FDDMA in furcation defects helps us to achieve predictable results. This cross-linked collagen membrane has better handling properties and ease of procurement as well as economic viability making it a logical material to be used in regenerative surgeries.

Maternal Factors Predicting Cognitive and Behavioral Characteristics of Children with Fetal Alcohol Spectrum Disorders

PubMed Central

May, Philip A.; Tabachnick, Barbara G.; Gossage, J. Phillip; Kalberg, Wendy O.; Marais, Anna-Susan; Robinson, Luther K.; Manning, Melanie A.; Blankenship, Jason; Buckley, David; Hoyme, H. Eugene; Adnams, Colleen M.

2013-01-01

Objective To provide an analysis of multiple predictors of cognitive and behavioral traits for children with fetal alcohol spectrum disorders (FASD). Method Multivariate correlation techniques were employed with maternal and child data from epidemiologic studies in a community in South Africa. Data on 561 first grade children with fetal alcohol syndrome (FAS), partial FAS (PFAS), and not FASD and their mothers were analyzed by grouping 19 maternal variables into categories (physical, demographic, childbearing, and drinking) and employed in structural equation models (SEM) to assess correlates of child intelligence (verbal and non-verbal) and behavior. Results A first SEM utilizing only seven maternal alcohol use variables to predict cognitive/behavioral traits was statistically significant (B = 3.10, p < .05), but explained only 17.3% of the variance. The second model incorporated multiple maternal variables and was statistically significant explaining 55.3% of the variance. Significantly correlated with low intelligence and problem behavior were demographic (B = 3.83, p < .05) (low maternal education, low socioeconomic status (SES), and rural residence) and maternal physical characteristics (B = 2.70, p < .05) (short stature, small head circumference, and low weight). Childbearing history and alcohol use composites were not statistically significant in the final complex model, and were overpowered by SES and maternal physical traits. Conclusions While other analytic techniques have amply demonstrated the negative effects of maternal drinking on intelligence and behavior, this highly-controlled analysis of multiple maternal influences reveals that maternal demographics and physical traits make a significant enabling or disabling contribution to child functioning in FASD. PMID:23751886

Differential diagnosis of cardiovascular diseases and T-wave alternans

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Varadan, Vijay K.

2016-04-01

T wave alternans (TWA) is the variation of the T-wave in electrocardiogram that is observed between periodic beats. TWA is one of the important precursors used to diagnose sudden cardiac death (SCD). Several clinical studies have tried to determine the significance of using TWA analysis to detect abnormalities that may lead to Ventricular Arrhythmias, as well as establish metrics to perform risk stratification for cardiovascular patients with prior cardiac episodes. The statistical significance of TWA in predicting ventricular arrhythmias has been established in patients across several diagnoses. Studies have also shown the significance of the predictive value of TWA analysis in post myocardial infarction patients, risk of SCD, congestive heart failure, ischemic cardiomyopathy, and Chagas disease.

Probabilistic Analysis of Aircraft Gas Turbine Disk Life and Reliability

NASA Technical Reports Server (NTRS)

Melis, Matthew E.; Zaretsky, Erwin V.; August, Richard

1999-01-01

Two series of low cycle fatigue (LCF) test data for two groups of different aircraft gas turbine engine compressor disk geometries were reanalyzed and compared using Weibull statistics. Both groups of disks were manufactured from titanium (Ti-6Al-4V) alloy. A NASA Glenn Research Center developed probabilistic computer code Probable Cause was used to predict disk life and reliability. A material-life factor A was determined for titanium (Ti-6Al-4V) alloy based upon fatigue disk data and successfully applied to predict the life of the disks as a function of speed. A comparison was made with the currently used life prediction method based upon crack growth rate. Applying an endurance limit to the computer code did not significantly affect the predicted lives under engine operating conditions. Failure location prediction correlates with those experimentally observed in the LCF tests. A reasonable correlation was obtained between the predicted disk lives using the Probable Cause code and a modified crack growth method for life prediction. Both methods slightly overpredict life for one disk group and significantly under predict it for the other.

Ensemble-based prediction of RNA secondary structures.

PubMed

Aghaeepour, Nima; Hoos, Holger H

2013-04-24

Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA.

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Model identification using stochastic differential equation grey-box models in diabetes.

PubMed

Duun-Henriksen, Anne Katrine; Schmidt, Signe; Røge, Rikke Meldgaard; Møller, Jonas Bech; Nørgaard, Kirsten; Jørgensen, John Bagterp; Madsen, Henrik

2013-03-01

The acceptance of virtual preclinical testing of control algorithms is growing and thus also the need for robust and reliable models. Models based on ordinary differential equations (ODEs) can rarely be validated with standard statistical tools. Stochastic differential equations (SDEs) offer the possibility of building models that can be validated statistically and that are capable of predicting not only a realistic trajectory, but also the uncertainty of the prediction. In an SDE, the prediction error is split into two noise terms. This separation ensures that the errors are uncorrelated and provides the possibility to pinpoint model deficiencies. An identifiable model of the glucoregulatory system in a type 1 diabetes mellitus (T1DM) patient is used as the basis for development of a stochastic-differential-equation-based grey-box model (SDE-GB). The parameters are estimated on clinical data from four T1DM patients. The optimal SDE-GB is determined from likelihood-ratio tests. Finally, parameter tracking is used to track the variation in the "time to peak of meal response" parameter. We found that the transformation of the ODE model into an SDE-GB resulted in a significant improvement in the prediction and uncorrelated errors. Tracking of the "peak time of meal absorption" parameter showed that the absorption rate varied according to meal type. This study shows the potential of using SDE-GBs in diabetes modeling. Improved model predictions were obtained due to the separation of the prediction error. SDE-GBs offer a solid framework for using statistical tools for model validation and model development. © 2013 Diabetes Technology Society.

Predictive value of Tokuhashi scoring systems in spinal metastases, focusing on various primary tumor groups: evaluation of 448 patients in the Aarhus spinal metastases database.

PubMed

Wang, Miao; Bünger, Cody Eric; Li, Haisheng; Wu, Chunsen; Høy, Kristian; Niedermann, Bent; Helmig, Peter; Wang, Yu; Jensen, Anders Bonde; Schättiger, Katrin; Hansen, Ebbe Stender

2012-04-01

We conducted a prospective cohort study of 448 patients with spinal metastases from a variety of cancer groups. To determine the specific predictive value of the Tokuhashi scoring system (T12) and its revised version (T15) in spinal metastases of various primary tumors. The life expectancy of patients with spinal metastases is one of the most important factors in selecting the treatment modality. Tokuhashi et al formulated a prognostic scoring system with a total sum of 12 points for preoperative prediction of life expectancy in 1990 and revised it in 2005 to a total sum of 15 points. There is a lack of knowledge about the specific predictive value of those scoring systems in patients with spinal metastases from a variety of cancer groups. We included 448 patients with vertebral metastases who underwent surgical treatment during November 1992 to November 2009 in Aarhus University Hospital NBG. Data were retrieved from Aarhus Metastases Database. Scores based on T12 and T15 were calculated prospectively for each patient. We divided all the patients into different groups dictated by the site of their primary tumor. Predictive value and accuracy rate of the 2 scoring systems were compared in each cancer group. Both the T12 and T15 scoring systems showed statistically significant predictive value when the 448 patients were analyzed in total (T12, P < 0.0001; T15, P < 0.0001). The accuracy rate was significantly higher in T15 (P < 0.0001) than in T12. The further analyses by primary cancer groups showed that the predictive value of T12 and T15 was primarily determined by the prostate (P = 0.0003) and breast group (P = 0.0385). Only T12 displayed predictive value in the colon group (P = 0.0011). Neither of the scoring systems showed significant predictive value in the lung (P > 0.05), renal (P > 0.05), or miscellaneous primary tumor groups (P > 0.05). The accuracy rate of prognosis in T15 was significantly improved in the prostate (P = 0.0032) and breast group (P < 0.0001). Both T12 and T15 showed significant predictive value in patients with spinal metastases. T15 has a statistically higher accuracy rate than T12. Among the various cancer groups, the 2 scoring systems are especially reliable in prostate and breast metastases groups. T15 is recommended as superior to T12 because of its higher accuracy rate.

Governance and Regional Variation of Homicide Rates: Evidence From Cross-National Data.

PubMed

Cao, Liqun; Zhang, Yan

2017-01-01

Criminological theories of cross-national studies of homicide have underestimated the effects of quality governance of liberal democracy and region. Data sets from several sources are combined and a comprehensive model of homicide is proposed. Results of the spatial regression model, which controls for the effect of spatial autocorrelation, show that quality governance, human development, economic inequality, and ethnic heterogeneity are statistically significant in predicting homicide. In addition, regions of Latin America and non-Muslim Sub-Saharan Africa have significantly higher rates of homicides ceteris paribus while the effects of East Asian countries and Islamic societies are not statistically significant. These findings are consistent with the expectation of the new modernization and regional theories. © The Author(s) 2015.

Comparative Evaluation of Four Risk Scores for Predicting Mortality in Patients With Implantable Cardioverter-defibrillator for Primary Prevention.

PubMed

Rodríguez-Mañero, Moisés; Abu Assi, Emad; Sánchez-Gómez, Juan Miguel; Fernández-Armenta, Juan; Díaz-Infante, Ernesto; García-Bolao, Ignacio; Benezet-Mazuecos, Juan; Andrés Lahuerta, Ana; Expósito-García, Víctor; Bertomeu-González, Vicente; Arce-León, Álvaro; Barrio-López, María Teresa; Peinado, Rafael; Martínez-Sande, Luis; Arias, Miguel A

2016-11-01

Several clinical risk scores have been developed to identify patients at high risk of all-cause mortality despite implantation of an implantable cardioverter-defibrillator. We aimed to examine and compare the predictive capacity of 4 simple scoring systems (MADIT-II, FADES, PACE and SHOCKED) for predicting mortality after defibrillator implantation for primary prevention of sudden cardiac death in a Mediterranean country. A multicenter retrospective study was performed in 15 Spanish hospitals. Consecutive patients referred for defibrillator implantation between January 2010 and December 2011 were included. A total of 916 patients with ischemic and nonischemic heart disease were included (mean age, 62 ± 11 years, 81.4% male). Over 33.4 ± 12.9 months, 113 (12.3%) patients died (cardiovascular origin in 86 [9.4%] patients). At 12, 24, 36, and 48 months, mortality rates were 4.5%, 7.6%, 10.8%, and 12.3% respectively. All the risk scores showed a stepwise increase in the risk of death throughout the scoring system of each of the scores and all 4 scores identified patients at greater risk of mortality. The scores were significantly associated with all-cause mortality throughout the follow-up period. PACE displayed the lowest c-index value regardless of whether the population had heart disease of ischemic (c-statistic = 0.61) or nonischemic origin (c-statistic = 0.61), whereas MADIT-II (c-statistic = 0.67 and 0.65 in ischemic and nonischemic cardiomyopathy, respectively), SHOCKED (c-statistic = 0.68 and 0.66, respectively), and FADES (c-statistic = 0.66 and 0.60) provided similar c-statistic values (P ≥ .09). In this nontrial-based cohort of Mediterranean patients, the 4 evaluated risk scores showed a significant stepwise increase in the risk of death. Among the currently available risk scores, MADIT-II, FADES, and SHOCKED provide slightly better performance than PACE. Copyright © 2016 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

Estimation of trabecular bone parameters in children from multisequence MRI using texture-based regression

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

Lekadir, Karim, E-mail: karim.lekadir@upf.edu; Hoogendoorn, Corné; Armitage, Paul

Purpose: This paper presents a statistical approach for the prediction of trabecular bone parameters from low-resolution multisequence magnetic resonance imaging (MRI) in children, thus addressing the limitations of high-resolution modalities such as HR-pQCT, including the significant exposure of young patients to radiation and the limited applicability of such modalities to peripheral bones in vivo. Methods: A statistical predictive model is constructed from a database of MRI and HR-pQCT datasets, to relate the low-resolution MRI appearance in the cancellous bone to the trabecular parameters extracted from the high-resolution images. The description of the MRI appearance is achieved between subjects by usingmore » a collection of feature descriptors, which describe the texture properties inside the cancellous bone, and which are invariant to the geometry and size of the trabecular areas. The predictive model is built by fitting to the training data a nonlinear partial least square regression between the input MRI features and the output trabecular parameters. Results: Detailed validation based on a sample of 96 datasets shows correlations >0.7 between the trabecular parameters predicted from low-resolution multisequence MRI based on the proposed statistical model and the values extracted from high-resolution HRp-QCT. Conclusions: The obtained results indicate the promise of the proposed predictive technique for the estimation of trabecular parameters in children from multisequence MRI, thus reducing the need for high-resolution radiation-based scans for a fragile population that is under development and growth.« less

Predictive validity of the UKCAT for medical school undergraduate performance: a national prospective cohort study.

PubMed

Tiffin, Paul A; Mwandigha, Lazaro M; Paton, Lewis W; Hesselgreaves, H; McLachlan, John C; Finn, Gabrielle M; Kasim, Adetayo S

2016-09-26

The UK Clinical Aptitude Test (UKCAT) has been shown to have a modest but statistically significant ability to predict aspects of academic performance throughout medical school. Previously, this ability has been shown to be incremental to conventional measures of educational performance for the first year of medical school. This study evaluates whether this predictive ability extends throughout the whole of undergraduate medical study and explores the potential impact of using the test as a selection screening tool. This was an observational prospective study, linking UKCAT scores, prior educational attainment and sociodemographic variables with subsequent academic outcomes during the 5 years of UK medical undergraduate training. The participants were 6812 entrants to UK medical schools in 2007-8 using the UKCAT. The main outcome was academic performance at each year of medical school. A receiver operating characteristic (ROC) curve analysis was also conducted, treating the UKCAT as a screening test for a negative academic outcome (failing at least 1 year at first attempt). All four of the UKCAT scale scores significantly predicted performance in theory- and skills-based exams. After adjustment for prior educational achievement, the UKCAT scale scores remained significantly predictive for most years. Findings from the ROC analysis suggested that, if used as a sole screening test, with the mean applicant UKCAT score as the cut-off, the test could be used to reject candidates at high risk of failing at least 1 year at first attempt. However, the 'number needed to reject' value would be high (at 1.18), with roughly one candidate who would have been likely to pass all years at first sitting being rejected for every higher risk candidate potentially declined entry on this basis. The UKCAT scores demonstrate a statistically significant but modest degree of incremental predictive validity throughout undergraduate training. Whilst the UKCAT could be considered a fairly crude screening tool for future academic performance, it may offer added value when used in conjunction with other selection measures. Future work should focus on the optimum role of such tests within the selection process and the prediction of post-graduate performance.

Seasonal prediction of hurricane activity reaching the coast of the United States.

PubMed

Saunders, Mark A; Lea, Adam S

2005-04-21

Much of the property damage from natural hazards in the United States is caused by landfalling hurricanes--strong tropical cyclones that reach the coast. For the southeastern Atlantic coast of the US, a statistical method for forecasting the occurrence of landfalling hurricanes for the season ahead has been reported, but the physical mechanisms linking the predictor variables to the frequency of hurricanes remain unclear. Here we present a statistical model that uses July wind anomalies between 1950 and 2003 to predict with significant and useful skill the wind energy of US landfalling hurricanes for the following main hurricane season (August to October). We have identified six regions over North America and over the east Pacific and North Atlantic oceans where July wind anomalies, averaged between heights of 925 and 400 mbar, exhibit a stationary and significant link to the energy of landfalling hurricanes during the subsequent hurricane season. The wind anomalies in these regions are indicative of atmospheric circulation patterns that either favour or hinder evolving hurricanes from reaching US shores.

Posttraumatic cognitions and posttraumatic stress symptoms among war-affected children: a cross-lagged analysis.

PubMed

Palosaari, Esa; Punamäki, Raija-Leena; Diab, Marwan; Qouta, Samir

2013-08-01

In a longitudinal study of war-affected children, we tested, first, whether posttraumatic cognitions (PTCs) mediated the relationship between initial and later posttraumatic stress symptoms (PTSSs). Second, we analyzed the relative strength of influences that PTCs and PTSSs have on each other in cross-lagged models of levels and latent change scores. The participants were 240 Palestinian children 10-12 years of age, reporting PTSSs and PTCs measures at 3, 5, and 11 months after a major war. Results show that PTCs did not mediate between initial and later PTSSs. The levels and changes in PTCs statistically significantly predicted later levels and changes in PTSSs, but PTSSs did not statistically significantly predict later PTCs. The results are consistent with the hypothesis that PTCs have a central role in the development and maintenance of PTSSs over time, but they do not support the hypothesis that initial PTSSs develop to chronic PTSSs through negative PTCs. PsycINFO Database Record (c) 2013 APA, all rights reserved.

A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China

NASA Astrophysics Data System (ADS)

Xu, Lei; Chen, Nengcheng; Zhang, Xiang

2018-02-01

Drought is an extreme natural disaster that can lead to huge socioeconomic losses. Drought prediction ahead of months is helpful for early drought warning and preparations. In this study, we developed a statistical model, two weighted dynamic models and a statistical-dynamic (hybrid) model for 1-6 month lead drought prediction in China. Specifically, statistical component refers to climate signals weighting by support vector regression (SVR), dynamic components consist of the ensemble mean (EM) and Bayesian model averaging (BMA) of the North American Multi-Model Ensemble (NMME) climatic models, and the hybrid part denotes a combination of statistical and dynamic components by assigning weights based on their historical performances. The results indicate that the statistical and hybrid models show better rainfall predictions than NMME-EM and NMME-BMA models, which have good predictability only in southern China. In the 2011 China winter-spring drought event, the statistical model well predicted the spatial extent and severity of drought nationwide, although the severity was underestimated in the mid-lower reaches of Yangtze River (MLRYR) region. The NMME-EM and NMME-BMA models largely overestimated rainfall in northern and western China in 2011 drought. In the 2013 China summer drought, the NMME-EM model forecasted the drought extent and severity in eastern China well, while the statistical and hybrid models falsely detected negative precipitation anomaly (NPA) in some areas. Model ensembles such as multiple statistical approaches, multiple dynamic models or multiple hybrid models for drought predictions were highlighted. These conclusions may be helpful for drought prediction and early drought warnings in China.

Neuropeptide Y2 Receptor (NPY2R) Expression in Saliva Predicts Feeding Immaturity in the Premature Neonate

PubMed Central

Maron, Jill L.; Johnson, Kirby L.; Dietz, Jessica A.; Chen, Minghua L.; Bianchi, Diana W.

2012-01-01

Background The current practice in newborn medicine is to subjectively assess when a premature infant is ready to feed by mouth. When the assessment is inaccurate, the resulting feeding morbidities may be significant, resulting in long-term health consequences and millions of health care dollars annually. We hypothesized that the developmental maturation of hypothalamic regulation of feeding behavior is a predictor of successful oral feeding in the premature infant. To test this hypothesis, we analyzed the gene expression of neuropeptide Y2 receptor (NPY2R), a known hypothalamic regulator of feeding behavior, in neonatal saliva to determine its role as a biomarker in predicting oral feeding success in the neonate. Methodology/Principal Findings Salivary samples (n = 116), were prospectively collected from 63 preterm and 13 term neonates (post-conceptual age (PCA) 26 4/7 to 41 4/7 weeks) from five predefined feeding stages. Expression of NPY2R in neonatal saliva was determined by multiplex RT-qPCR amplification. Expression results were retrospectively correlated with feeding status at time of sample collection. Statistical analysis revealed that expression of NPY2R had a 95% positive predictive value for feeding immaturity. NPY2R expression statistically significantly decreased with advancing PCA (Wilcoxon test p value<0.01), and was associated with feeding status (chi square p value  =  0.013). Conclusions/Significance Developmental maturation of hypothalamic regulation of feeding behavior is an essential component of oral feeding success in the newborn. NPY2R expression in neonatal saliva is predictive of an immature feeding pattern. It is a clinically relevant biomarker that may be monitored in saliva to improve clinical care and reduce significant feeding-associated morbidities that affect the premature neonate. PMID:22629465

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

PubMed Central

2010-01-01

Background Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors. PMID:20553590

Analysis of Physicochemical and Structural Properties Determining HIV-1 Coreceptor Usage

PubMed Central

Bozek, Katarzyna; Lengauer, Thomas; Sierra, Saleta; Kaiser, Rolf; Domingues, Francisco S.

2013-01-01

The relationship of HIV tropism with disease progression and the recent development of CCR5-blocking drugs underscore the importance of monitoring virus coreceptor usage. As an alternative to costly phenotypic assays, computational methods aim at predicting virus tropism based on the sequence and structure of the V3 loop of the virus gp120 protein. Here we present a numerical descriptor of the V3 loop encoding its physicochemical and structural properties. The descriptor allows for structure-based prediction of HIV tropism and identification of properties of the V3 loop that are crucial for coreceptor usage. Use of the proposed descriptor for prediction results in a statistically significant improvement over the prediction based solely on V3 sequence with 3 percentage points improvement in AUC and 7 percentage points in sensitivity at the specificity of the 11/25 rule (95%). We additionally assessed the predictive power of the new method on clinically derived ‘bulk’ sequence data and obtained a statistically significant improvement in AUC of 3 percentage points over sequence-based prediction. Furthermore, we demonstrated the capacity of our method to predict therapy outcome by applying it to 53 samples from patients undergoing Maraviroc therapy. The analysis of structural features of the loop informative of tropism indicates the importance of two loop regions and their physicochemical properties. The regions are located on opposite strands of the loop stem and the respective features are predominantly charge-, hydrophobicity- and structure-related. These regions are in close proximity in the bound conformation of the loop potentially forming a site determinant for the coreceptor binding. The method is available via server under http://structure.bioinf.mpi-inf.mpg.de/. PMID:23555214

Estimation of trends

NASA Technical Reports Server (NTRS)

1981-01-01

The application of statistical methods to recorded ozone measurements. The effects of a long term depletion of ozone at magnitudes predicted by the NAS is harmful to most forms of life. Empirical prewhitening filters the derivation of which is independent of the underlying physical mechanisms were analyzed. Statistical analysis performs a checks and balances effort. Time series filters variations into systematic and random parts, errors are uncorrelated, and significant phase lag dependencies are identified. The use of time series modeling to enhance the capability of detecting trends is discussed.

A nomogram to predict brain metastasis as the first relapse in curatively resected non-small cell lung cancer patients.

PubMed

Won, Young-Woong; Joo, Jungnam; Yun, Tak; Lee, Geon-Kook; Han, Ji-Youn; Kim, Heung Tae; Lee, Jin Soo; Kim, Moon Soo; Lee, Jong Mog; Lee, Hyun-Sung; Zo, Jae Ill; Kim, Sohee

2015-05-01

Development of brain metastasis results in a significant reduction in overall survival. However, there is no an effective tool to predict brain metastasis in non-small cell lung cancer (NSCLC) patients. We conducted this study to develop a feasible nomogram that can predict metastasis to the brain as the first relapse site in patients with curatively resected NSCLC. A retrospective review of NSCLC patients who had received curative surgery at National Cancer Center (Goyang, South Korea) between 2001 and 2008 was performed. We chose metastasis to the brain as the first relapse site after curative surgery as the primary endpoint of the study. A nomogram was modeled using logistic regression. Among 1218 patients, brain metastasis as the first relapse developed in 87 patients (7.14%) during the median follow-up of 43.6 months. Occurrence rates of brain metastasis were higher in patients with adenocarcinoma or those with a high pT and pN stage. Younger age appeared to be associated with brain metastasis, but this result was not statistically significant. The final prediction model included histology, smoking status, pT stage, and the interaction between adenocarcinoma and pN stage. The model showed fairly good discriminatory ability with a C-statistic of 69.3% and 69.8% for predicting brain metastasis within 2 years and 5 years, respectively. Internal validation using 2000 bootstrap samples resulted in C-statistics of 67.0% and 67.4% which still indicated good discriminatory performances. The nomogram presented here provides the individual risk estimate of developing metastasis to the brain as the first relapse site in patients with NSCLC who have undergone curative surgery. Surveillance programs or preventive treatment strategies for brain metastasis could be established based on this nomogram. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Should I Pack My Umbrella? Clinical versus Statistical Prediction of Mental Health Decisions

ERIC Educational Resources Information Center

Aegisdottir, Stefania; Spengler, Paul M.; White, Michael J.

2006-01-01

In this rejoinder, the authors respond to the insightful commentary of Strohmer and Arm, Chwalisz, and Hilton, Harris, and Rice about the meta-analysis on statistical versus clinical prediction techniques for mental health judgments. The authors address issues including the availability of statistical prediction techniques for real-life psychology…

78 FR 70303 - Announcement of Requirements and Registration for the Predict the Influenza Season Challenge

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-25

... public. Mathematical and statistical models can be useful in predicting the timing and impact of the... applying any mathematical, statistical, or other approach to predictive modeling. This challenge will... Services (HHS) region level(s) in the United States by developing mathematical and statistical models that...

Performance of Disease-Specific Scoring Models in Intensive Care Patients with Severe Liver Diseases.

PubMed

El-Ghannam, Maged T; Hassanien, Moataz H; El-Talkawy, Mohamed D; Saleem, Abdel Aziz A; Sabry, Amal I; Abu Taleb, Hoda M

2017-06-01

Egypt has the highest prevalence of Hepatitis C Virus (HCV) in the world, estimated nationally at 14.7%. HCV treatment consumes 20% ($80 million) of Egypt's annual health budget. Outcomes of cirrhotic patients admitted to the ICU may, in fact, largely depend on differences in the state of the disease, criteria and indications for admission, resource utilization, and intensity of treatment. The aim of the present study was to evaluate the efficacy of liver specific scoring models in predicting the outcome of critically ill cirrhotic patients in the ICU as it may help in prioritization of high risk patients and preservation of ICU resources. Over one year, a total of 777 patients with End Stage Liver Disease (ESLD) due to HCV infection were included in this retrospective non-randomized human study. All statistical analyses were performed by the statistical software SPSS version 22.0 (SPSS, Chicago, IL, USA). Child Turcotte Pugh (CTP) score, MELD score, MELD-Na, MESO, iMELD, Refit MELD and Refit MELD-Na were calculated on ICU admission. ICU admission was mainly due to Gastrointestinal (GI) bleeding and Hepatic Encephalopathy (HE). Overall mortality was 27%. Age and sex showed no statistical difference between survivors and non survivors. Significantly higher mean values were observed for all models among individuals who died compared to survivors. MELD-Na was the most specific compared to the other scores. MELD-Na was highly predictive of mortality at an optimized cut-off value of 20.4 (AURC=0.789±0.03-CI 95%=0.711-0.865) while original MELD was highly predictive of mortality at an optimized cut-off value of 17.4 (AURC=0.678±0.01-CI 95%=0.613-0.682) denoting the importance of adding serum sodium to the original MELD. INR, serum creatinine, bilirubin, white blood cells count and hyponatremia were significantly higher in non survivors compared to survivors, while hypoalbuminemia showed no statistical difference. The advent of Hepatorenal Syndrome (HRS) and Spontaneous Bacterial Peritonitis (SBP) carried worse prognosis. Hyponatremia and number of transfused blood bags were additional independent predictors of mortality. In cirrhosis of liver, due to HCV infection, patients who died during their ICU stay displayed significantly higher values on all prognostic scores at admission. The addition of sodium to MELD score greatly improves the predictive accuracy of mortality. MELD-Na showed the highest predictive value of all scores.

Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images

NASA Astrophysics Data System (ADS)

Ashraf, Ahmed B.; Lin, Lilie; Gavenonis, Sara C.; Mies, Carolyn; Xanthopoulos, Eric; Kontos, Despina

2012-03-01

The presence of axillary lymph node metastases is the most important prognostic factor in breast cancer and can influence the selection of adjuvant therapy, both chemotherapy and radiotherapy. In this work we present a set of kinetic statistics derived from DCE-MRI for predicting axillary node status. Breast DCE-MRI images from 69 women with known nodal status were analyzed retrospectively under HIPAA and IRB approval. Axillary lymph nodes were positive in 12 patients while 57 patients had no axillary lymph node involvement. Kinetic curves for each pixel were computed and a pixel-wise map of time-to-peak (TTP) was obtained. Pixels were first partitioned according to the similarity of their kinetic behavior, based on TTP values. For every kinetic curve, the following pixel-wise features were computed: peak enhancement (PE), wash-in-slope (WIS), wash-out-slope (WOS). Partition-wise statistics for every feature map were calculated, resulting in a total of 21 kinetic statistic features. ANOVA analysis was done to select features that differ significantly between node positive and node negative women. Using the computed kinetic statistic features a leave-one-out SVM classifier was learned that performs with AUC=0.77 under the ROC curve, outperforming the conventional kinetic measures, including maximum peak enhancement (MPE) and signal enhancement ratio (SER), (AUCs of 0.61 and 0.57 respectively). These findings suggest that our DCE-MRI kinetic statistic features can be used to improve the prediction of axillary node status in breast cancer patients. Such features could ultimately be used as imaging biomarkers to guide personalized treatment choices for women diagnosed with breast cancer.

Learning Temporal Statistics for Sensory Predictions in Aging.

PubMed

Luft, Caroline Di Bernardi; Baker, Rosalind; Goldstone, Aimee; Zhang, Yang; Kourtzi, Zoe

2016-03-01

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

Evaluation of neutrophil-lymphocyte ratio, platelet-lymphocyte ratio and red blood cell distribution width-platelet ratio as early predictor of acute pancreatitis in pregnancy.

PubMed

İlhan, Mehmet; İlhan, Gülşah; Gök, Ali Fuat Kaan; Bademler, Süleyman; Verit Atmaca, Fatma; Ertekin, Cemalettin

2016-01-01

Acute pancreatitis (AP) is a state of inflammation. It has been widely known that neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and red blood cell distribution width (RDW) to platelet ratio (RPR) reflect systemic inflammation. The aim of this study is to investigate whether these inflammatory markers could be used as reliable markers in early prediction of AP in pregnancy and if there is a relationship between disease severity and these markers. The study group consisted of 14 patients, who developed AP in ongoing pregnancy, and the control group consisted of 30 healthy pregnant women. NLR, PLR and RPR were calculated for both the groups. NLR was significantly elevated in the AP group when compared with the controls (p = 0.00), but there was no statistically significant difference in terms of PLR and RPR (p > 0.05). ROC curve analysis results for NLR showed that there was a significant prediction power of NLR for AP (R(2) = 0.842; p < 0.001). For NLR parameter, if cut-off value is chosen to be 4.1030, then sensitivity is 71.4% and specificity is 100.0%. There was statistically significant and positive correlation between C-reactive protein (CRP) and glucose with NLR (p = 0.001, p = 0.043). It was seen that Ranson was close to be significant (p = 0.051). NLR might be used as an early marker of AP and may have a role in prediction of disease severity.

The clinical utility of fibrin-related biomarkers in sepsis.

PubMed

Toh, Julien M H; Ken-Dror, Gie; Downey, Colin; Abrams, Simon T

2013-12-01

Sepsis is associated with systemic inflammatory responses and induction of intravascular fibrin formation. Our aim is to investigate whether three fibrin-related markers (FRM) reflect the extent of coagulation activation in vivo and evaluate their clinical usefulness in identifying as well as monitoring patients with sepsis. Fibrin-degradation products (FDP), D-dimer and soluble fibrin monomer assays were measured on plasma samples from patients in the ICU with sepsis (n = 37), systemic inflammatory response syndrome (SIRS) (n = 35) and healthy individuals (n = 15). The levels were correlated with each other and also with fibrinogen, prothrombin time, platelets and antithrombin III. Clinical correlation was also performed for the diagnosis of sepsis and longitudinal monitoring for survival or death.There was strong correlation between the three FRM (r = 0.38-0.93, P < 0.0001) with only fibrin monomer correlating significantly with prothrombin time, fibrinogen and platelet levels. Clinically, all three FRM could discriminate between patients with sepsis, SIRS and healthy individuals with FDP, and D-dimer showing statistical significance (P < 0.05). No FRM predicted outcome from a single measurement but FDP was significantly able to predict patient survival from serial samples [mean FDP (μg/ml) from 35.36 to 21.37 (first to third ICU-day), P < 0.05]. Fibrin monomer appears the most sensitive indicator of coagulation activation, whereas D-dimer and FDP levels can significantly differentiate ICU patients with sepsis from those without. In addition, FDP would be preferable for monitoring with its statistically significant time-dependent prediction of survival or death from sepsis.

Maslow and the motivation hierarchy: measuring satisfaction of the needs.

PubMed

Taormina, Robert J; Gao, Jennifer H

2013-01-01

For each of the 5 needs in Maslow's motivational hierarchy (physiological, safety-security, belongingness, esteem, and self-actualization), operational definitions were developed from Maslow's theory of motivation. New measures were created based on the operational definitions (1) to assess the satisfaction of each need, (2) to assess their expected correlations (a) with each of the other needs and (b) with four social and personality measures (i.e., family support, traditional values, anxiety/worry, and life satisfaction), and (3) to test the ability of the satisfaction level of each need to statistically predict the satisfaction level of the next higher-level need. Psychometric tests of the scales conducted on questionnaire results from 386 adult respondents from the general population lent strong support for the validity and reliability of all 5 needs measures. Significant positive correlations among the scales were also found; that is, the more each lower-level need was satisfied, the more the next higher-level need was satisfied. Additionally, as predicted, family support, traditional values, and life satisfaction had significant positive correlations with the satisfaction of all 5 needs, and the anxiety/worry facet of neuroticism had significant negative correlations with the satisfaction of all the needs. Multiple regression analyses revealed that the satisfaction of each higher-level need was statistically predicted by the satisfaction of the need immediately below it in the hierarchy, as expected from Maslow's theory.

Comparison of the performances of copeptin and multiple biomarkers in long-term prognosis of severe traumatic brain injury.

PubMed

Zhang, Zu-Yong; Zhang, Li-Xin; Dong, Xiao-Qiao; Yu, Wen-Hua; Du, Quan; Yang, Ding-Bo; Shen, Yong-Feng; Wang, Hao; Zhu, Qiang; Che, Zhi-Hao; Liu, Qun-Jie; Jiang, Li; Du, Yuan-Feng

2014-10-01

Enhanced blood levels of copeptin correlate with poor clinical outcomes after acute critical illness. This study aimed to compare the prognostic performances of plasma concentrations of copeptin and other biomarkers like myelin basic protein, glial fibrillary astrocyte protein, S100B, neuron-specific enolase, phosphorylated axonal neurofilament subunit H, Tau and ubiquitin carboxyl-terminal hydrolase L1 in severe traumatic brain injury. We recruited 102 healthy controls and 102 acute patients with severe traumatic brain injury. Plasma concentrations of these biomarkers were determined using enzyme-linked immunosorbent assay. Their prognostic predictive performances of 6-month mortality and unfavorable outcome (Glasgow Outcome Scale score of 1-3) were compared. Plasma concentrations of these biomarkers were statistically significantly higher in all patients than in healthy controls, in non-survivors than in survivors and in patients with unfavorable outcome than with favorable outcome. Areas under receiver operating characteristic curves of plasma concentrations of these biomarkers were similar to those of Glasgow Coma Scale score for prognostic prediction. Except plasma copeptin concentration, other biomarkers concentrations in plasma did not statistically significantly improve prognostic predictive value of Glasgow Coma Scale score. Copeptin levels may be a useful tool to predict long-term clinical outcomes after severe traumatic brain injury and have a potential to assist clinicians. Copyright © 2014 Elsevier Inc. All rights reserved.

Modeling and optimization of trihalomethanes formation potential of surface water (a drinking water source) using Box-Behnken design.

PubMed

Singh, Kunwar P; Rai, Premanjali; Pandey, Priyanka; Sinha, Sarita

2012-01-01

The present research aims to investigate the individual and interactive effects of chlorine dose/dissolved organic carbon ratio, pH, temperature, bromide concentration, and reaction time on trihalomethanes (THMs) formation in surface water (a drinking water source) during disinfection by chlorination in a prototype laboratory-scale simulation and to develop a model for the prediction and optimization of THMs levels in chlorinated water for their effective control. A five-factor Box-Behnken experimental design combined with response surface and optimization modeling was used for predicting the THMs levels in chlorinated water. The adequacy of the selected model and statistical significance of the regression coefficients, independent variables, and their interactions were tested by the analysis of variance and t test statistics. The THMs levels predicted by the model were very close to the experimental values (R(2) = 0.95). Optimization modeling predicted maximum (192 μg/l) TMHs formation (highest risk) level in water during chlorination was very close to the experimental value (186.8 ± 1.72 μg/l) determined in laboratory experiments. The pH of water followed by reaction time and temperature were the most significant factors that affect the THMs formation during chlorination. The developed model can be used to determine the optimum characteristics of raw water and chlorination conditions for maintaining the THMs levels within the safe limit.

Reevaluation of a walleye (Sander vitreus) bioenergetics model

USGS Publications Warehouse

Madenjian, Charles P.; Wang, Chunfang

2013-01-01

Walleye (Sander vitreus) is an important sport fish throughout much of North America, and walleye populations support valuable commercial fisheries in certain lakes as well. Using a corrected algorithm for balancing the energy budget, we reevaluated the performance of the Wisconsin bioenergetics model for walleye in the laboratory. Walleyes were fed rainbow smelt (Osmerus mordax) in four laboratory tanks each day during a 126-day experiment. Feeding rates ranged from 1.4 to 1.7 % of walleye body weight per day. Based on a statistical comparison of bioenergetics model predictions of monthly consumption with observed monthly consumption, we concluded that the bioenergetics model estimated food consumption by walleye without any significant bias. Similarly, based on a statistical comparison of bioenergetics model predictions of weight at the end of the monthly test period with observed weight, we concluded that the bioenergetics model predicted walleye growth without any detectable bias. In addition, the bioenergetics model predictions of cumulative consumption over the 126-day experiment differed fromobserved cumulative consumption by less than 10 %. Although additional laboratory and field testing will be needed to fully evaluate model performance, based on our laboratory results, the Wisconsin bioenergetics model for walleye appears to be providing unbiased predictions of food consumption.

Risk adjusted surgical audit in gynaecological oncology: P-POSSUM does not predict outcome.

PubMed

Das, N; Talaat, A S; Naik, R; Lopes, A D; Godfrey, K A; Hatem, M H; Edmondson, R J

2006-12-01

To assess the Physiological and Operative Severity Score for the enumeration of mortality and morbidity (POSSUM) and its validity for use in gynaecological oncology surgery. All patients undergoing gynaecological oncology surgery at the Northern Gynaecological Oncology Centre (NGOC) Gateshead, UK over a period of 12months (2002-2003) were assessed prospectively. Mortality and morbidity predictions using the Portsmouth modification of the POSSUM algorithm (P-POSSUM) were compared to the actual outcomes. Performance of the model was also evaluated using the Hosmer and Lemeshow Chi square statistic (testing the goodness of fit). During this period 468 patients were assessed. The P-POSSUM appeared to over predict mortality rates for our patients. It predicted a 7% mortality rate for our patients compared to an observed rate of 2% (35 predicted deaths in comparison to 10 observed deaths), a difference that was statistically significant (H&L chi(2)=542.9, d.f. 8, p<0.05). The P-POSSUM algorithm overestimates the risk of mortality for gynaecological oncology patients undergoing surgery. The P-POSSUM algorithm will require further adjustments prior to adoption for gynaecological cancer surgery as a risk adjusted surgical audit tool.

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

PubMed

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

Statistical optimization of polysaccharide production by submerged cultivation of Lingzhi or Reishi medicinal mushroom, Ganoderma lucidum (W.Curt.: Fr.) P. Karst. MTCC 1039 (Aphyllophoromycetideae).

PubMed

Baskar, Gurunathan; Sathya, Shree Rajesh K Lakshmi Jai; Jinnah, Riswana Begum; Sahadevan, Renganathan

2011-01-01

Response surface methodology was employed to optimize the concentration of four important cultivation media components such as cottonseed oil cake, glucose, NH4Cl, and MgSO4 for maximum medicinal polysaccharide yield by Lingzhi or Reishi medicinal mushroom, Ganoderma lucidum MTCC 1039 in submerged culture. The second-order polynomial model describing the relationship between media components and polysaccharide yield was fitted in coded units of the variables. The higher value of the coefficient of determination (R2 = 0.953) justified an excellent correlation between media components and polysaccharide yield, and the model fitted well with high statistical reliability and significance. The predicted optimum concentration of the media components was 3.0% cottonseed oil cake, 3.0% glucose, 0.15% NH4Cl, and 0.045% MgSO4, with the maximum predicted polysaccharide yield of 819.76 mg/L. The experimental polysaccharide yield at the predicted optimum media components was 854.29 mg/L, which was 4.22% higher than the predicted yield.

Predicting Fog in the Nocturnal Boundary Layer

NASA Astrophysics Data System (ADS)

Izett, Jonathan; van de Wiel, Bas; Baas, Peter; van der Linden, Steven; van Hooft, Antoon; Bosveld, Fred

2017-04-01

Fog is a global phenomenon that presents a hazard to navigation and human safety, resulting in significant economic impacts for air and shipping industries as well as causing numerous road traffic accidents. Accurate prediction of fog events, however, remains elusive both in terms of timing and occurrence itself. Statistical methods based on set threshold criteria for key variables such as wind speed have been developed, but high rates of correct prediction of fog events still lead to similarly high "false alarms" when the conditions appear favourable, but no fog forms. Using data from the CESAR meteorological observatory in the Netherlands, we analyze specific cases and perform statistical analyses of event climatology, in order to identify the necessary conditions for correct prediction of fog. We also identify potential "missing ingredients" in current analysis that could help to reduce the number of false alarms. New variables considered include the indicators of boundary layer stability, as well as the presence of aerosols conducive to droplet formation. The poster presents initial findings of new research as well as plans for continued research.

A Predictive Approach to Network Reverse-Engineering

NASA Astrophysics Data System (ADS)

Wiggins, Chris

2005-03-01

A central challenge of systems biology is the ``reverse engineering" of transcriptional networks: inferring which genes exert regulatory control over which other genes. Attempting such inference at the genomic scale has only recently become feasible, via data-intensive biological innovations such as DNA microrrays (``DNA chips") and the sequencing of whole genomes. In this talk we present a predictive approach to network reverse-engineering, in which we integrate DNA chip data and sequence data to build a model of the transcriptional network of the yeast S. cerevisiae capable of predicting the response of genes in unseen experiments. The technique can also be used to extract ``motifs,'' sequence elements which act as binding sites for regulatory proteins. We validate by a number of approaches and present comparison of theoretical prediction vs. experimental data, along with biological interpretations of the resulting model. En route, we will illustrate some basic notions in statistical learning theory (fitting vs. over-fitting; cross- validation; assessing statistical significance), highlighting ways in which physicists can make a unique contribution in data- driven approaches to reverse engineering.

Factorial analysis of trihalomethanes formation in drinking water.

PubMed

Chowdhury, Shakhawat; Champagne, Pascale; McLellan, P James

2010-06-01

Disinfection of drinking water reduces pathogenic infection, but may pose risks to human health through the formation of disinfection byproducts. The effects of different factors on the formation of trihalomethanes were investigated using a statistically designed experimental program, and a predictive model for trihalomethanes formation was developed. Synthetic water samples with different factor levels were produced, and trihalomethanes concentrations were measured. A replicated fractional factorial design with center points was performed, and significant factors were identified through statistical analysis. A second-order trihalomethanes formation model was developed from 92 experiments, and the statistical adequacy was assessed through appropriate diagnostics. This model was validated using additional data from the Drinking Water Surveillance Program database and was applied to the Smiths Falls water supply system in Ontario, Canada. The model predictions were correlated strongly to the measured trihalomethanes, with correlations of 0.95 and 0.91, respectively. The resulting model can assist in analyzing risk-cost tradeoffs in the design and operation of water supply systems.

A statistical study of global ionospheric map total electron content changes prior to occurrences of M ≥ 6.0 earthquakes during 2000-2014

NASA Astrophysics Data System (ADS)

Thomas, J. N.; Huard, J.; Masci, F.

2017-02-01

There are many reports on the occurrence of anomalous changes in the ionosphere prior to large earthquakes. However, whether or not these changes are reliable precursors that could be useful for earthquake prediction is controversial within the scientific community. To test a possible statistical relationship between ionospheric disturbances and earthquakes, we compare changes in the total electron content (TEC) of the ionosphere with occurrences of M ≥ 6.0 earthquakes globally for 2000-2014. We use TEC data from the global ionosphere map (GIM) and an earthquake list declustered for aftershocks. For each earthquake, we look for anomalous changes in GIM-TEC within 2.5° latitude and 5.0° longitude of the earthquake location (the spatial resolution of GIM-TEC). Our analysis has not found any statistically significant changes in GIM-TEC prior to earthquakes. Thus, we have found no evidence that would suggest that monitoring changes in GIM-TEC might be useful for predicting earthquakes.

The Thurgood Marshall School of Law Empirical Findings: A Report of the 2012 Friday Academy Attendance and Statistical Comparisons of 1L GPA (Predicted and Actual)

ERIC Educational Resources Information Center

Kadhi, T.; Rudley, D.; Holley, D.; Krishna, K.; Ogolla, C.; Rene, E.; Green, T.

2010-01-01

The following report of descriptive statistics addresses the attendance of the 2012 class and the average Actual and Predicted 1L Grade Point Averages (GPAs). Correlational and Inferential statistics are also run on the variables of Attendance (Y/N), Attendance Number of Times, Actual GPA, and Predictive GPA (Predictive GPA is defined as the Index…

Grain-Size Based Additivity Models for Scaling Multi-rate Uranyl Surface Complexation in Subsurface Sediments

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

Zhang, Xiaoying; Liu, Chongxuan; Hu, Bill X.

The additivity model assumed that field-scale reaction properties in a sediment including surface area, reactive site concentration, and reaction rate can be predicted from field-scale grain-size distribution by linearly adding reaction properties estimated in laboratory for individual grain-size fractions. This study evaluated the additivity model in scaling mass transfer-limited, multi-rate uranyl (U(VI)) surface complexation reactions in a contaminated sediment. Experimental data of rate-limited U(VI) desorption in a stirred flow-cell reactor were used to estimate the statistical properties of the rate constants for individual grain-size fractions, which were then used to predict rate-limited U(VI) desorption in the composite sediment. The resultmore » indicated that the additivity model with respect to the rate of U(VI) desorption provided a good prediction of U(VI) desorption in the composite sediment. However, the rate constants were not directly scalable using the additivity model. An approximate additivity model for directly scaling rate constants was subsequently proposed and evaluated. The result found that the approximate model provided a good prediction of the experimental results within statistical uncertainty. This study also found that a gravel-size fraction (2 to 8 mm), which is often ignored in modeling U(VI) sorption and desorption, is statistically significant to the U(VI) desorption in the sediment.« less

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

PubMed

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

2007-01-15

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

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Checefsky, Walter A.; Abidin, Anas Z.; Tsai, Halley; Wang, Xixi; Hobbs, Susan K.; Bauer, Jan S.; Baum, Thomas; Wismüller, Axel

2015-03-01

While the proximal femur is preferred for measuring bone mineral density (BMD) in fracture risk estimation, the introduction of volumetric quantitative computed tomography has revealed stronger associations between BMD and spinal fracture status. In this study, we propose to capture properties of trabecular bone structure in spinal vertebrae with advanced second-order statistical features for purposes of fracture risk assessment. For this purpose, axial multi-detector CT (MDCT) images were acquired from 28 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. A semi-automated method was used to annotate the trabecular compartment in the central vertebral slice with a circular region of interest (ROI) to exclude cortical bone; pixels within were converted to values indicative of BMD. Six second-order statistical features derived from gray-level co-occurrence matrices (GLCM) and the mean BMD within the ROI were then extracted and used in conjunction with a generalized radial basis functions (GRBF) neural network to predict the failure load of the specimens; true failure load was measured through biomechanical testing. Prediction performance was evaluated with a root-mean-square error (RMSE) metric. The best prediction performance was observed with GLCM feature `correlation' (RMSE = 1.02 ± 0.18), which significantly outperformed all other GLCM features (p < 0.01). GLCM feature correlation also significantly outperformed MDCTmeasured mean BMD (RMSE = 1.11 ± 0.17) (p< 10-4). These results suggest that biomechanical strength prediction in spinal vertebrae can be significantly improved through characterization of trabecular bone structure with GLCM-derived texture features.

Word recognition and phonetic structure acquisition: Possible relations

NASA Astrophysics Data System (ADS)

Morgan, James

2002-05-01

Several accounts of possible relations between the emergence of the mental lexicon and acquisition of native language phonological structure have been propounded. In one view, acquisition of word meanings guides infants' attention toward those contrasts that are linguistically significant in their language. In the opposing view, native language phonological categories may be acquired from statistical patterns of input speech, prior to and independent of learning at the lexical level. Here, a more interactive account will be presented, in which phonological structure is modeled as emerging consequentially from the self-organization of perceptual space underlying word recognition. A key prediction of this model is that early native language phonological categories will be highly context specific. Data bearing on this prediction will be presented which provide clues to the nature of infants' statistical analysis of input.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets

PubMed Central

Bewick, Sharon; Thielen, Peter; Mehoke, Thomas; Breitwieser, Florian P.; Paudel, Shishir; Adhikari, Arjun; Wolfe, Joshua; Slud, Eric V.; Karig, David; Fagan, William F.

2017-01-01

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson’s correlation coefficient (r) and Jaccard’s index (J)–two of the most common metrics for correlation analysis of presence-absence data–can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson’s correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard’s index of similarity (J) can yield improvements over Pearson’s correlation coefficient. However, the standard null model for Jaccard’s index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard’s index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa. PMID:29145425

Predicting Sargassum blooms in the Caribbean Sea from MODIS observations

NASA Astrophysics Data System (ADS)

Wang, Mengqiu; Hu, Chuanmin

2017-04-01

Recurrent and significant Sargassum beaching events in the Caribbean Sea (CS) have caused serious environmental and economic problems, calling for a long-term prediction capacity of Sargassum blooms. Here we present predictions based on a hindcast of 2000-2016 observations from Moderate Resolution Imaging Spectroradiometer (MODIS), which showed Sargassum abundance in the CS and the Central West Atlantic (CWA), as well as connectivity between the two regions with time lags. This information was used to derive bloom and nonbloom probability matrices for each 1° square in the CS for the months of May-August, predicted from bloom conditions in a hotspot region in the CWA in February. A suite of standard statistical measures were used to gauge the prediction accuracy, among which the user's accuracy and kappa statistics showed high fidelity of the probability maps in predicting both blooms and nonblooms in the eastern CS with several months of lead time, with overall accuracy often exceeding 80%. The bloom probability maps from this hindcast analysis will provide early warnings to better study Sargassum blooms and prepare for beaching events near the study region. This approach may also be extendable to many other regions around the world that face similar challenges and opportunities of macroalgal blooms and beaching events.

Effects and detection of raw material variability on the performance of near-infrared calibration models for pharmaceutical products.

PubMed

Igne, Benoit; Shi, Zhenqi; Drennen, James K; Anderson, Carl A

2014-02-01

The impact of raw material variability on the prediction ability of a near-infrared calibration model was studied. Calibrations, developed from a quaternary mixture design comprising theophylline anhydrous, lactose monohydrate, microcrystalline cellulose, and soluble starch, were challenged by intentional variation of raw material properties. A design with two theophylline physical forms, three lactose particle sizes, and two starch manufacturers was created to test model robustness. Further challenges to the models were accomplished through environmental conditions. Along with full-spectrum partial least squares (PLS) modeling, variable selection by dynamic backward PLS and genetic algorithms was utilized in an effort to mitigate the effects of raw material variability. In addition to evaluating models based on their prediction statistics, prediction residuals were analyzed by analyses of variance and model diagnostics (Hotelling's T(2) and Q residuals). Full-spectrum models were significantly affected by lactose particle size. Models developed by selecting variables gave lower prediction errors and proved to be a good approach to limit the effect of changing raw material characteristics. Hotelling's T(2) and Q residuals provided valuable information that was not detectable when studying only prediction trends. Diagnostic statistics were demonstrated to be critical in the appropriate interpretation of the prediction of quality parameters. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

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

PubMed

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

2017-04-01

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

Statistics of Statisticians: Critical Mass of Statistics and Operational Research Groups

NASA Astrophysics Data System (ADS)

Kenna, Ralph; Berche, Bertrand

Using a recently developed model, inspired by mean field theory in statistical physics, and data from the UK's Research Assessment Exercise, we analyse the relationship between the qualities of statistics and operational research groups and the quantities of researchers in them. Similar to other academic disciplines, we provide evidence for a linear dependency of quality on quantity up to an upper critical mass, which is interpreted as the average maximum number of colleagues with whom a researcher can communicate meaningfully within a research group. The model also predicts a lower critical mass, which research groups should strive to achieve to avoid extinction. For statistics and operational research, the lower critical mass is estimated to be 9 ± 3. The upper critical mass, beyond which research quality does not significantly depend on group size, is 17 ± 6.

Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes

NASA Astrophysics Data System (ADS)

Vallam, P.; Qin, X. S.

2017-10-01

Anthropogenic-driven climate change would affect the global ecosystem and is becoming a world-wide concern. Numerous studies have been undertaken to determine the future trends of meteorological variables at different scales. Despite these studies, there remains significant uncertainty in the prediction of future climates. To examine the uncertainty arising from using different schemes to downscale the meteorological variables for the future horizons, projections from different statistical downscaling schemes were examined. These schemes included statistical downscaling method (SDSM), change factor incorporated with LARS-WG, and bias corrected disaggregation (BCD) method. Global circulation models (GCMs) based on CMIP3 (HadCM3) and CMIP5 (CanESM2) were utilized to perturb the changes in the future climate. Five study sites (i.e., Alice Springs, Edmonton, Frankfurt, Miami, and Singapore) with diverse climatic conditions were chosen for examining the spatial variability of applying various statistical downscaling schemes. The study results indicated that the regions experiencing heavy precipitation intensities were most likely to demonstrate the divergence between the predictions from various statistical downscaling methods. Also, the variance computed in projecting the weather extremes indicated the uncertainty derived from selection of downscaling tools and climate models. This study could help gain an improved understanding about the features of different downscaling approaches and the overall downscaling uncertainty.

Radon-222 concentrations in ground water and soil gas on Indian reservations in Wisconsin

USGS Publications Warehouse

DeWild, John F.; Krohelski, James T.

1995-01-01

For sites with wells finished in the sand and gravel aquifer, the coefficient of determination (R2) of the regression of concentration of radon-222 in ground water as a function of well depth is 0.003 and the significance level is 0.32, which indicates that there is not a statistically significant relation between radon-222 concentrations in ground water and well depth. The coefficient of determination of the regression of radon-222 in ground water and soil gas is 0.19 and the root mean square error of the regression line is 271 picocuries per liter. Even though the significance level (0.036) indicates a statistical relation, the root mean square error of the regression is so large that the regression equation would not give reliable predictions. Because of an inadequate number of samples, similar statistical analyses could not be performed for sites with wells finished in the crystalline and sedimentary bedrock aquifers.

Optimization of fermentation medium for the production of atrazine degrading strain Acinetobacter sp. DNS(32) by statistical analysis system.

PubMed

Zhang, Ying; Wang, Yang; Wang, Zhi-Gang; Wang, Xi; Guo, Huo-Sheng; Meng, Dong-Fang; Wong, Po-Keung

2012-01-01

Statistical experimental designs provided by statistical analysis system (SAS) software were applied to optimize the fermentation medium composition for the production of atrazine-degrading Acinetobacter sp. DNS(32) in shake-flask cultures. A "Plackett-Burman Design" was employed to evaluate the effects of different components in the medium. The concentrations of corn flour, soybean flour, and K(2)HPO(4) were found to significantly influence Acinetobacter sp. DNS(32) production. The steepest ascent method was employed to determine the optimal regions of these three significant factors. Then, these three factors were optimized using central composite design of "response surface methodology." The optimized fermentation medium composition was composed as follows (g/L): corn flour 39.49, soybean flour 25.64, CaCO(3) 3, K(2)HPO(4) 3.27, MgSO(4)·7H(2)O 0.2, and NaCl 0.2. The predicted and verifiable values in the medium with optimized concentration of components in shake flasks experiments were 7.079 × 10(8) CFU/mL and 7.194 × 10(8) CFU/mL, respectively. The validated model can precisely predict the growth of atrazine-degraing bacterium, Acinetobacter sp. DNS(32).

Adaptation to local ultraviolet radiation conditions among neighbouring Daphnia populations

PubMed Central

Miner, Brooks E.; Kerr, Benjamin

2011-01-01

Understanding the historical processes that generated current patterns of phenotypic diversity in nature is particularly challenging in subdivided populations. Populations often exhibit heritable genetic differences that correlate with environmental variables, but the non-independence among neighbouring populations complicates statistical inference of adaptation. To understand the relative influence of adaptive and non-adaptive processes in generating phenotypes requires joint evaluation of genetic and phenotypic divergence in an integrated and statistically appropriate analysis. We investigated phenotypic divergence, population-genetic structure and potential fitness trade-offs in populations of Daphnia melanica inhabiting neighbouring subalpine ponds of widely differing transparency to ultraviolet radiation (UVR). Using a combination of experimental, population-genetic and statistical techniques, we separated the effects of shared population ancestry and environmental variables in predicting phenotypic divergence among populations. We found that native water transparency significantly predicted divergence in phenotypes among populations even after accounting for significant population structure. This result demonstrates that environmental factors such as UVR can at least partially account for phenotypic divergence. However, a lack of evidence for a hypothesized trade-off between UVR tolerance and growth rates in the absence of UVR prevents us from ruling out the possibility that non-adaptive processes are partially responsible for phenotypic differentiation in this system. PMID:20943691

Effect of crowd size on patient volume at a large, multipurpose, indoor stadium.

PubMed

De Lorenzo, R A; Gray, B C; Bennett, P C; Lamparella, V J

1989-01-01

A prediction of patient volume expected at "mass gatherings" is desirable in order to provide optimal on-site emergency medical care. While several methods of predicting patient loads have been suggested, a reliable technique has not been established. This study examines the frequency of medical emergencies at the Syracuse University Carrier Dome, a 50,500-seat indoor stadium. Patient volume and level of care at collegiate basketball and football games as well as rock concerts, over a 7-year period were examined and tabulated. This information was analyzed using simple regression and nonparametric statistical methods to determine level of correlation between crowd size and patient volume. These analyses demonstrated no statistically significant increase in patient volume for increasing crowd size for basketball and football events. There was a small but statistically significant increase in patient volume for increasing crowd size for concerts. A comparison of similar crowd size for each of the three events showed that patient frequency is greatest for concerts and smallest for basketball. The study suggests that crowd size alone has only a minor influence on patient volume at any given event. Structuring medical services based solely on expected crowd size and not considering other influences such as event type and duration may give poor results.

Predicting adverse neonatal outcomes in fetuses with abdominal wall defects using prenatal risk factors.

PubMed

Nicholas, Sara S; Stamilio, David M; Dicke, Jeffery M; Gray, Diana L; Macones, George A; Odibo, Anthony O

2009-10-01

The aim of this study was to determine whether prenatal variables can predict adverse neonatal outcomes in fetuses with abdominal wall defects. A retrospective cohort study that used ultrasound and neonatal records for all cases of gastroschisis and omphalocele seen over a 16-year period. Cases with adverse neonatal outcomes were compared with noncases for multiple candidate predictive factors. Univariable and multivariable statistical methods were used to develop the prediction models, and effectiveness was evaluated using the area under the receiver operating characteristic curve. Of 80 fetuses with gastroschisis, 29 (36%) had the composite adverse outcome, compared with 15 of 33 (47%) live neonates with omphalocele. Intrauterine growth restriction was the only significant variable in gastroschisis, whereas exteriorized liver was the only predictor in omphalocele. The areas under the curve for the prediction models with gastroschisis and omphalocele are 0.67 and 0.74, respectively. Intrauterine growth restriction and exteriorization of the liver are significant predictors of adverse neonatal outcome with gastroschisis and omphalocele.

Post-mortem prediction of primal and selected retail cut weights of New Zealand lamb from carcass and animal characteristics.

PubMed

Ngo, L; Ho, H; Hunter, P; Quinn, K; Thomson, A; Pearson, G

2016-02-01

Post-mortem measurements (cold weight, grade and external carcass linear dimensions) as well as live animal data (age, breed, sex) were used to predict ovine primal and retail cut weights for 792 lamb carcases. Significant levels of variance could be explained using these predictors. The predictive power of those measurements on primal and retail cut weights was studied by using the results from principal component analysis and the absolute value of the t-statistics of the linear regression model. High prediction accuracy for primal cut weight was achieved (adjusted R(2) up to 0.95), as well as moderate accuracy for key retail cut weight: tenderloins (adj-R(2)=0.60), loin (adj-R(2)=0.62), French rack (adj-R(2)=0.76) and rump (adj-R(2)=0.75). The carcass cold weight had the best predictive power, with the accuracy increasing by around 10% after including the next three most significant variables. Copyright © 2015 Elsevier Ltd. All rights reserved.

Testing statistical self-similarity in the topology of river networks

USGS Publications Warehouse

Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

2010-01-01

Recent work has demonstrated that the topological properties of real river networks deviate significantly from predictions of Shreve's random model. At the same time the property of mean self-similarity postulated by Tokunaga's model is well supported by data. Recently, a new class of network model called random self-similar networks (RSN) that combines self-similarity and randomness has been introduced to replicate important topological features observed in real river networks. We investigate if the hypothesis of statistical self-similarity in the RSN model is supported by data on a set of 30 basins located across the continental United States that encompass a wide range of hydroclimatic variability. We demonstrate that the generators of the RSN model obey a geometric distribution, and self-similarity holds in a statistical sense in 26 of these 30 basins. The parameters describing the distribution of interior and exterior generators are tested to be statistically different and the difference is shown to produce the well-known Hack's law. The inter-basin variability of RSN parameters is found to be statistically significant. We also test generator dependence on two climatic indices, mean annual precipitation and radiative index of dryness. Some indication of climatic influence on the generators is detected, but this influence is not statistically significant with the sample size available. Finally, two key applications of the RSN model to hydrology and geomorphology are briefly discussed.

Gambling score in earthquake prediction analysis

NASA Astrophysics Data System (ADS)

Molchan, G.; Romashkova, L.

2011-03-01

The number of successes and the space-time alarm rate are commonly used to characterize the strength of an earthquake prediction method and the significance of prediction results. It has been recently suggested to use a new characteristic to evaluate the forecaster's skill, the gambling score (GS), which incorporates the difficulty of guessing each target event by using different weights for different alarms. We expand parametrization of the GS and use the M8 prediction algorithm to illustrate difficulties of the new approach in the analysis of the prediction significance. We show that the level of significance strongly depends (1) on the choice of alarm weights, (2) on the partitioning of the entire alarm volume into component parts and (3) on the accuracy of the spatial rate measure of target events. These tools are at the disposal of the researcher and can affect the significance estimate. Formally, all reasonable GSs discussed here corroborate that the M8 method is non-trivial in the prediction of 8.0 ≤M < 8.5 events because the point estimates of the significance are in the range 0.5-5 per cent. However, the conservative estimate 3.7 per cent based on the number of successes seems preferable owing to two circumstances: (1) it is based on relative values of the spatial rate and hence is more stable and (2) the statistic of successes enables us to construct analytically an upper estimate of the significance taking into account the uncertainty of the spatial rate measure.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

PubMed

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

School Violence: The Role of Parental and Community Involvement

ERIC Educational Resources Information Center

Lesneskie, Eric; Block, Steven

2017-01-01

This study utilizes the School Survey on Crime and Safety to identify variables that predict lower levels of violence from four domains: school security, school climate, parental involvement, and community involvement. Negative binomial regression was performed and the findings indicate that statistically significant results come from all four…

Predicting Teacher Job Satisfaction Based on Principals' Instructional Supervision Behaviours: A Study of Turkish Teachers

ERIC Educational Resources Information Center

Ilgan, Abdurrahman; Parylo, Oksana; Sungu, Hilmi

2015-01-01

This quantitative research examined instructional supervision behaviours of school principals as a predictor of teacher job satisfaction through the analysis of Turkish teachers' perceptions of principals' instructional supervision behaviours. There was a statistically significant difference found between the teachers' job satisfaction level and…

Commentary: Evidential Validity Versus Predictive Validity--The Need for Both

ERIC Educational Resources Information Center

Montgomery, Alyssa; Dumont, Ron; Willis, John O.

2017-01-01

The articles presented in this Special Issue provide evidence for many statistically significant relationships among error scores obtained from the Kaufman Test of Educational Achievement, Third Edition (KTEA)-3 between various groups of students with and without disabilities. The data reinforce the importance of examiners looking beyond the…

The role of shear wave elastography in the assessment of placenta previa-accreta.

PubMed

Alıcı Davutoglu, Ebru; Ariöz Habibi, Hatice; Ozel, Ayşegül; Yuksel, Mehmet Aytac; Adaletli, Ibrahim; Madazlı, Riza

2018-06-01

To evaluate the value of shear wave elastography (SWE) in the prediction of morbidly adherent placenta. Forty-three women with normal placental location and 26 women with anteriorly localized placenta previa were recruited for this case-control study. Placental elasticity values in both the groups were determined by SWE imaging. SWE values were higher in the placenta previa group in all regions than in normal localized placentas (p < .01). However, there was no statistically significant difference between SWE values of placenta previa with and without morbidly adherent placenta (p > .05). Placental stiffness is significantly higher in placenta previa than normal localized placentas. However, we could not demonstrate any statistically significant difference in the elasticity values between the placenta previa with and without accreta.

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

USGS Publications Warehouse

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

2007-01-01

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

Comparison of the predictive validity of diagnosis-based risk adjusters for clinical outcomes.

PubMed

Petersen, Laura A; Pietz, Kenneth; Woodard, LeChauncy D; Byrne, Margaret

2005-01-01

Many possible methods of risk adjustment exist, but there is a dearth of comparative data on their performance. We compared the predictive validity of 2 widely used methods (Diagnostic Cost Groups [DCGs] and Adjusted Clinical Groups [ACGs]) for 2 clinical outcomes using a large national sample of patients. We studied all patients who used Veterans Health Administration (VA) medical services in fiscal year (FY) 2001 (n = 3,069,168) and assigned both a DCG and an ACG to each. We used logistic regression analyses to compare predictive ability for death or long-term care (LTC) hospitalization for age/gender models, DCG models, and ACG models. We also assessed the effect of adding age to the DCG and ACG models. Patients in the highest DCG categories, indicating higher severity of illness, were more likely to die or to require LTC hospitalization. Surprisingly, the age/gender model predicted death slightly more accurately than the ACG model (c-statistic of 0.710 versus 0.700, respectively). The addition of age to the ACG model improved the c-statistic to 0.768. The highest c-statistic for prediction of death was obtained with a DCG/age model (0.830). The lowest c-statistics were obtained for age/gender models for LTC hospitalization (c-statistic 0.593). The c-statistic for use of ACGs to predict LTC hospitalization was 0.783, and improved to 0.792 with the addition of age. The c-statistics for use of DCGs and DCG/age to predict LTC hospitalization were 0.885 and 0.890, respectively, indicating the best prediction. We found that risk adjusters based upon diagnoses predicted an increased likelihood of death or LTC hospitalization, exhibiting good predictive validity. In this comparative analysis using VA data, DCG models were generally superior to ACG models in predicting clinical outcomes, although ACG model performance was enhanced by the addition of age.

Sibling Competition & Growth Tradeoffs. Biological vs. Statistical Significance

PubMed Central

Kramer, Karen L.; Veile, Amanda; Otárola-Castillo, Erik

2016-01-01

Early childhood growth has many downstream effects on future health and reproduction and is an important measure of offspring quality. While a tradeoff between family size and child growth outcomes is theoretically predicted in high-fertility societies, empirical evidence is mixed. This is often attributed to phenotypic variation in parental condition. However, inconsistent study results may also arise because family size confounds the potentially differential effects that older and younger siblings can have on young children’s growth. Additionally, inconsistent results might reflect that the biological significance associated with different growth trajectories is poorly understood. This paper addresses these concerns by tracking children’s monthly gains in height and weight from weaning to age five in a high fertility Maya community. We predict that: 1) as an aggregate measure family size will not have a major impact on child growth during the post weaning period; 2) competition from young siblings will negatively impact child growth during the post weaning period; 3) however because of their economic value, older siblings will have a negligible effect on young children’s growth. Accounting for parental condition, we use linear mixed models to evaluate the effects that family size, younger and older siblings have on children’s growth. Congruent with our expectations, it is younger siblings who have the most detrimental effect on children’s growth. While we find statistical evidence of a quantity/quality tradeoff effect, the biological significance of these results is negligible in early childhood. Our findings help to resolve why quantity/quality studies have had inconsistent results by showing that sibling competition varies with sibling age composition, not just family size, and that biological significance is distinct from statistical significance. PMID:26938742

Sibling Competition & Growth Tradeoffs. Biological vs. Statistical Significance.

PubMed

Kramer, Karen L; Veile, Amanda; Otárola-Castillo, Erik

2016-01-01

Early childhood growth has many downstream effects on future health and reproduction and is an important measure of offspring quality. While a tradeoff between family size and child growth outcomes is theoretically predicted in high-fertility societies, empirical evidence is mixed. This is often attributed to phenotypic variation in parental condition. However, inconsistent study results may also arise because family size confounds the potentially differential effects that older and younger siblings can have on young children's growth. Additionally, inconsistent results might reflect that the biological significance associated with different growth trajectories is poorly understood. This paper addresses these concerns by tracking children's monthly gains in height and weight from weaning to age five in a high fertility Maya community. We predict that: 1) as an aggregate measure family size will not have a major impact on child growth during the post weaning period; 2) competition from young siblings will negatively impact child growth during the post weaning period; 3) however because of their economic value, older siblings will have a negligible effect on young children's growth. Accounting for parental condition, we use linear mixed models to evaluate the effects that family size, younger and older siblings have on children's growth. Congruent with our expectations, it is younger siblings who have the most detrimental effect on children's growth. While we find statistical evidence of a quantity/quality tradeoff effect, the biological significance of these results is negligible in early childhood. Our findings help to resolve why quantity/quality studies have had inconsistent results by showing that sibling competition varies with sibling age composition, not just family size, and that biological significance is distinct from statistical significance.

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

The predictive power of Japanese candlestick charting in Chinese stock market

NASA Astrophysics Data System (ADS)

Chen, Shi; Bao, Si; Zhou, Yu

2016-09-01

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

Reliability of the Watch-PAT 200 in detecting sleep apnea in highway bus drivers.

PubMed

Yuceege, Melike; Firat, Hikmet; Demir, Ahmet; Ardic, Sadik

2013-04-15

To predict the validity of Watch-PAT (WP) device for sleep disordered breathing (SDB) among highway bus drivers. A total number of 90 highway bus drivers have undergone polysomnography (PSG) and Watch-PAT test simultaneously. Routine blood tests and the routine ear-nose-throat (ENT) exams have been done as well. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 89.1%, 76.9%, 82% and 85.7% for RDI > 15, respectively. WRDI, WODI, W < 90% duration and Wmean SaO2 results were well correlated with the PSG results. In the sensitivity and specificity analysis, when diagnosis of sleep apnea was defined for different cut-off values of RDI of 5, 10 and 15, AUC (95%CI) were found as 0.84 (0.74-0.93), 0.87 (95%CI: 0.79-0.94) and 0.91 (95%CI: 0.85-0.97), respectively. There were no statistically significant differences between Stage1+2/Wlight and Stage REM/WREM. The percentage of Stage 3 sleep had difference significant statistically from the percentage of Wdeep. Total sleep times in PSG and WP showed no statistically important difference. Total NREM duration and total WNREM duration had no difference either. Watch-PAT device is helpful in detecting SDB with RDI > 15 in highway bus drivers, especially in drivers older than 45 years, but has limited value in drivers younger than 45 years old who have less risk for OSA. Therefore, WP can be used in the former group when PSG is not easily available.

Model for predicting the injury severity score.

PubMed

Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

2015-07-01

To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  < 0.05. To select objective variables, the stepwise method was used. A total of 122 patients were included in this study. The formula for predicting the injury severity score (ISS) was as follows: ISS = 13.252-0.078(mean blood pressure) + 0.12(fibrin degradation products). The P -value of this formula from analysis of variance was <0.001, and the multiple correlation coefficient (R) was 0.739 (R 2  = 0.546). The multiple correlation coefficient adjusted for the degrees of freedom was 0.538. The Durbin-Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

NASA Astrophysics Data System (ADS)

Jia, Huizhen; Sun, Quansen; Ji, Zexuan; Wang, Tonghan; Chen, Qiang

2014-11-01

The goal of no-reference/blind image quality assessment (NR-IQA) is to devise a perceptual model that can accurately predict the quality of a distorted image as human opinions, in which feature extraction is an important issue. However, the features used in the state-of-the-art "general purpose" NR-IQA algorithms are usually natural scene statistics (NSS) based or are perceptually relevant; therefore, the performance of these models is limited. To further improve the performance of NR-IQA, we propose a general purpose NR-IQA algorithm which combines NSS-based features with perceptually relevant features. The new method extracts features in both the spatial and gradient domains. In the spatial domain, we extract the point-wise statistics for single pixel values which are characterized by a generalized Gaussian distribution model to form the underlying features. In the gradient domain, statistical features based on neighboring gradient magnitude similarity are extracted. Then a mapping is learned to predict quality scores using a support vector regression. The experimental results on the benchmark image databases demonstrate that the proposed algorithm correlates highly with human judgments of quality and leads to significant performance improvements over state-of-the-art methods.

A Statistical Skull Geometry Model for Children 0-3 Years Old

PubMed Central

Li, Zhigang; Park, Byoung-Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P.; Rupp, Jonathan D.; Hoff, Carrie N.; Hu, Jingwen

2015-01-01

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0–3 YO population. In this study, head CT scans from fifty-six 0–3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models. PMID:25992998

A statistical skull geometry model for children 0-3 years old.

PubMed

Li, Zhigang; Park, Byoung-Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2015-01-01

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.

Statistics for the Relative Detectability of Chemicals in Weak Gaseous Plumes in LWIR Hyperspectral Imagery

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

Metoyer, Candace N.; Walsh, Stephen J.; Tardiff, Mark F.

2008-10-30

The detection and identification of weak gaseous plumes using thermal imaging data is complicated by many factors. These include variability due to atmosphere, ground and plume temperature, and background clutter. This paper presents an analysis of one formulation of the physics-based model that describes the at-sensor observed radiance. The motivating question for the analyses performed in this paper is as follows. Given a set of backgrounds, is there a way to predict the background over which the probability of detecting a given chemical will be the highest? Two statistics were developed to address this question. These statistics incorporate data frommore » the long-wave infrared band to predict the background over which chemical detectability will be the highest. These statistics can be computed prior to data collection. As a preliminary exploration into the predictive ability of these statistics, analyses were performed on synthetic hyperspectral images. Each image contained one chemical (either carbon tetrachloride or ammonia) spread across six distinct background types. The statistics were used to generate predictions for the background ranks. Then, the predicted ranks were compared to the empirical ranks obtained from the analyses of the synthetic images. For the simplified images under consideration, the predicted and empirical ranks showed a promising amount of agreement. One statistic accurately predicted the best and worst background for detection in all of the images. Future work may include explorations of more complicated plume ingredients, background types, and noise structures.« less

Solar-terrestrial predictions proceedings. Volume 4: Prediction of terrestrial effects of solar activity

NASA Technical Reports Server (NTRS)

Donnelly, R. E. (Editor)

1980-01-01

Papers about prediction of ionospheric and radio propagation conditions based primarily on empirical or statistical relations is discussed. Predictions of sporadic E, spread F, and scintillations generally involve statistical or empirical predictions. The correlation between solar-activity and terrestrial seismic activity and the possible relation between solar activity and biological effects is discussed.

Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

PubMed

Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A

2013-08-01

As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

Randomized trials are frequently fragmented in multiple secondary publications.

PubMed

Ebrahim, Shanil; Montoya, Luis; Kamal El Din, Mostafa; Sohani, Zahra N; Agarwal, Arnav; Bance, Sheena; Saquib, Juliann; Saquib, Nazmus; Ioannidis, John P A

2016-11-01

To assess the frequency and features of secondary publications of randomized controlled trials (RCTs). For 191 RCTs published in high-impact journals in 2009, we searched for secondary publications coauthored by at least one same author of the primary trial publication. We evaluated the probability of having secondary publications, characteristics of the primary trial publication that predict having secondary publications, types of secondary analyses conducted, and statistical significance of those analyses. Of 191 primary trials, 88 (46%) had a total of 475 secondary publications by 2/2014. Eight trials had >10 (up to 51) secondary publications each. In multivariable modeling, the risk of having subsequent secondary publications increased 1.32-fold (95% CI 1.05-1.68) per 10-fold increase in sample size, and 1.71-fold (95% CI 1.19-2.45) in the presence of a design article. In a sample of 197 secondary publications examined in depth, 193 tested different hypotheses than the primary publication. Of the 193, 43 tested differences between subgroups, 85 assessed predictive factors associated with an outcome of interest, 118 evaluated different outcomes than the original article, 71 had differences in eligibility criteria, and 21 assessed different durations of follow-up; 176 (91%) presented at least one analysis with statistically significant results. Approximately half of randomized trials in high-impact journals have secondary publications published with a few trials followed by numerous secondary publications. Almost all of these publications report some statistically significant results. Copyright © 2016 Elsevier Inc. All rights reserved.

An application of statistics to comparative metagenomics

PubMed Central

Rodriguez-Brito, Beltran; Rohwer, Forest; Edwards, Robert A

2006-01-01

Background Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments. Results Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified. Conclusion The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems. PMID:16549025

An application of statistics to comparative metagenomics.

PubMed

Rodriguez-Brito, Beltran; Rohwer, Forest; Edwards, Robert A

2006-03-20

Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments. Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified. The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems.

Underprotection of unpredictable statistical lives compared to predictable ones

PubMed Central

Evans, Nicholas G.; Cotton-Barratt, Owen

2016-01-01

Existing ethical discussion considers the differences in care for identified versus statistical lives. However there has been little attention to the different degrees of care that are taken for different kinds of statistical lives. Here we argue that for a given number of statistical lives at stake, there will sometimes be different, and usually greater care taken to protect predictable statistical lives, in which the number of lives that will be lost can be predicted fairly accurately, than for unpredictable statistical lives, where the lives are at stake because of a low-probability event, such that most likely no one will be affected by the decision but with low probability some lives will be at stake. One reason for this difference is the statistical challenge of estimating low probabilities, and in particular the tendency of common approaches to underestimate these probabilities. Another is the existence of rational incentives to treat unpredictable risks as if the probabilities were lower than they are. Some of these factors apply outside the pure economic context, to institutions, individuals, and governments. We argue that there is no ethical reason to treat unpredictable statistical lives differently from predictable statistical lives. Moreover, lives that are unpredictable from the perspective of an individual agent may become predictable when aggregated to the level of a societal decision. Underprotection of unpredictable statistical lives is a form of market failure that may need to be corrected by altering regulation, introducing compulsory liability insurance, or other social policies. PMID:27393181

Spatial statistical network models for stream and river temperature in New England, USA

NASA Astrophysics Data System (ADS)

Detenbeck, Naomi E.; Morrison, Alisa C.; Abele, Ralph W.; Kopp, Darin A.

2016-08-01

Watershed managers are challenged by the need for predictive temperature models with sufficient accuracy and geographic breadth for practical use. We described thermal regimes of New England rivers and streams based on a reduced set of metrics for the May-September growing season (July or August median temperature, diurnal rate of change, and magnitude and timing of growing season maximum) chosen through principal component analysis of 78 candidate metrics. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance along the flow network and Euclidean distance between points. Calculation of spatial autocorrelation based on travel or retention time in place of network distance yielded tighter-fitting Torgegrams with less scatter but did not improve overall model prediction accuracy. We predicted monthly median July or August stream temperatures as a function of median air temperature, estimated urban heat island effect, shaded solar radiation, main channel slope, watershed storage (percent lake and wetland area), percent coarse-grained surficial deposits, and presence or maximum depth of a lake immediately upstream, with an overall root-mean-square prediction error of 1.4 and 1.5°C, respectively. Growing season maximum water temperature varied as a function of air temperature, local channel slope, shaded August solar radiation, imperviousness, and watershed storage. Predictive models for July or August daily range, maximum daily rate of change, and timing of growing season maximum were statistically significant but explained a much lower proportion of variance than the above models (5-14% of total).

Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

NASA Astrophysics Data System (ADS)

Folkert, Michael R.; Setton, Jeremy; Apte, Aditya P.; Grkovski, Milan; Young, Robert J.; Schöder, Heiko; Thorstad, Wade L.; Lee, Nancy Y.; Deasy, Joseph O.; Oh, Jung Hun

2017-07-01

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC)  =  0.65 (p  =  0.004), 0.73 (p  =  0.026), and 0.66 (p  =  0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC  =  0.68 (p  =  0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC  =  0.60 (p  =  0.092) and 0.65 (p  =  0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.

A clinical prediction rule for detecting major depressive disorder in primary care: the PREDICT-NL study.

PubMed

Zuithoff, Nicolaas P A; Vergouwe, Yvonne; King, Michael; Nazareth, Irwin; Hak, Eelko; Moons, Karel G M; Geerlings, Mirjam I

2009-08-01

Major depressive disorder often remains unrecognized in primary care. Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. A total of 1046 subjects, aged 18-65 years, were included from seven large general practices in the center of The Netherlands. All subjects were recruited in the general practice waiting room, irrespective of their presenting complaint. Major depressive disorder according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Text Revision edition criteria was assessed with the Composite International Diagnostic Interview. Candidate predictors were gender, age, educational level, being single, number of presented complaints, presence of non-somatic complaints, whether a diagnosis was assigned, consultation rate in past 12 months, presentation of depressive complaints or prescription of antidepressants in past 12 months, number of life events in past 6 months and any history of depression. The first multivariable logistic regression model including only predictors that require no confronting depression-related questions had a reasonable degree of discrimination (area under the receiver operating characteristic curve or concordance-statistic (c-statistic) = 0.71; 95% Confidence Interval (CI): 0.67-0.76). Addition of three simple though more depression-related predictors, number of life events and history of depression, significantly increased the c-statistic to 0.80 (95% CI: 0.76-0.83). After transforming this second model to an easily to use risk score, the lowest risk category (sum score < 5) showed a 1% risk of depression, which increased to 49% in the highest category (sum score > or = 30). A clinical prediction rule allows GPs to identify patients-irrespective of their complaints-in whom diagnostic workup for major depressive disorder is indicated.

Immunohistochemistry predictive markers for primary colorectal cancer tumors: where are we and where are we going?

PubMed

Bărbălan, Alexandru; Nicolaescu, Andrei Cristian; Măgăran, Antoanela Valentina; Mercuţ, Răzvan; Bălăşoiu, Maria; Băncescu, Gabriela; Şerbănescu, Mircea Sebastian; Lazăr, Octavian Fulger; Săftoiu, Adrian

2018-01-01

The aim of our study is to highlight and organize the recently published immunohistochemistry (IHC) predictive biomarkers of primary colorectal cancers (CRCs) that could lead to practical implementation. We reviewed articles that examined CRC samples with significant statistic correlation between the IHC marker expression and disease progression over time, relationships with the available clinical features and those who detect the prognosis of drug effects. Our analysis showed that nine markers could correlate with medical treatment response of CRCs in different stages. When using better overall survival (OS) and better disease-free survival (DFS) as a grouping factor, there were 14 markers that could be used in assessing CRC prognosis. By using poor prognostic for the OS and the DFS as a grouping factor, we found 43 markers. Subgroup analysis was also performed based on the 32 markers recently confirmed to predict metastasis evolution or the recurrence risks. Venous invasion could be predictable for tumors, statistically significant metastasis susceptibility was observed for markers and also the capacity to evaluate recurrence. CRCs integrate a variety of localizations and there are proofs that distinguish the sites of tumors. The studies reporting data specifically for rectal cancer separating it from colon cancer contained seven IHC markers. In order to be able to implement a predictive biomarker in clinical practice, it must comply with certain criteria as clinical value and analytical proof. Unique biological signature of CRC can be distinguished by identifying biomarkers expression. Several markers have shown potential, but the majority still need to render clinical utility.

Decreased mean platelet volume is associated with the developing stage of fetoplacental unit in spontaneous abortion.

PubMed

Kaplanoglu, Mustafa; Yuce, Tuncay; Bulbul, Mehmet

2015-01-01

The aim was to evaluate the place of mean platelet volume (MPV) in predicting spontaneous miscarriage and to identify any differences in its values following miscarriage after biochemical and clinical pregnancy. We retrospectively evaluated the data of 305 spontaneous miscarriages and 168 control subjects. The miscarriage subjects were evaluated in two groups: miscarriage after biochemical pregnancy (n=79) (BA group) and miscarriage after clinical pregnancy (n=226) (CA group). Demographic and laboratory data of all subjects were statistically compared. No statistically significant difference was found between the miscarriage and control subjects in terms of demographic data and Hb, Htc, WBC, and Plt values. The mean platelet volume (MPV) value in the miscarriage group (8.99±1.47 fl) was statistically significantly lower than in the control group (9.66±1.64 fl) (P<0.001). A statistically significant difference was present between the BA, CA and control group, with the lowest MPV value in the BA group (8.64±1.34 fl, 9.11±1.49 fl, and 9.66±1.64 fl, respectively) (P<0.001). MPV was significantly lower in patients with miscarriage than the control group, and this was correlated with the gestational stage when the miscarriage occurred.

Statistical Prediction of Sea Ice Concentration over Arctic

NASA Astrophysics Data System (ADS)

Kim, Jongho; Jeong, Jee-Hoon; Kim, Baek-Min

2017-04-01

In this study, a statistical method that predict sea ice concentration (SIC) over the Arctic is developed. We first calculate the Season-reliant Empirical Orthogonal Functions (S-EOFs) of monthly Arctic SIC from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, which contain the seasonal cycles (12 months long) of dominant SIC anomaly patterns. Then, the current SIC state index is determined by projecting observed SIC anomalies for latest 12 months to the S-EOFs. Assuming the current SIC anomalies follow the spatio-temporal evolution in the S-EOFs, we project the future (upto 12 months) SIC anomalies by multiplying the SI and the corresponding S-EOF and then taking summation. The predictive skill is assessed by hindcast experiments initialized at all the months for 1980-2010. When comparing predictive skill of SIC predicted by statistical model and NCEP CFS v2, the statistical model shows a higher skill in predicting sea ice concentration and extent.

aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events

PubMed Central

Harrison, Thomas; Ruiz, Jaime; Sloan, Daniel B.; Ben-Hur, Asa; Boucher, Christina

2016-01-01

Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The P class contains tandem P-type motif sequences, and the PLS class contains alternating P, L and S type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a PLS-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the PLS class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for PLS-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve. PMID:27560805

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

PubMed

Haslacher, Helmuth; Ratzinger, Franz; Perkmann, Thomas; Batmyagmar, Delgerdalai; Nistler, Sonja; Scherzer, Thomas M; Ponocny-Seliger, Elisabeth; Pilger, Alexander; Gerner, Marlene; Scheichenberger, Vanessa; Kundi, Michael; Endler, Georg; Wagner, Oswald F; Winker, Robert

2017-01-01

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups.

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes

PubMed Central

Ratzinger, Franz; Perkmann, Thomas; Batmyagmar, Delgerdalai; Nistler, Sonja; Scherzer, Thomas M.; Ponocny-Seliger, Elisabeth; Pilger, Alexander; Gerner, Marlene; Scheichenberger, Vanessa; Kundi, Michael; Endler, Georg; Wagner, Oswald F.; Winker, Robert

2017-01-01

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups. PMID:28475643

Multivariate statistical analysis of a high rate biofilm process treating kraft mill bleach plant effluent.

PubMed

Goode, C; LeRoy, J; Allen, D G

2007-01-01

This study reports on a multivariate analysis of the moving bed biofilm reactor (MBBR) wastewater treatment system at a Canadian pulp mill. The modelling approach involved a data overview by principal component analysis (PCA) followed by partial least squares (PLS) modelling with the objective of explaining and predicting changes in the BOD output of the reactor. Over two years of data with 87 process measurements were used to build the models. Variables were collected from the MBBR control scheme as well as upstream in the bleach plant and in digestion. To account for process dynamics, a variable lagging approach was used for variables with significant temporal correlations. It was found that wood type pulped at the mill was a significant variable governing reactor performance. Other important variables included flow parameters, faults in the temperature or pH control of the reactor, and some potential indirect indicators of biomass activity (residual nitrogen and pH out). The most predictive model was found to have an RMSEP value of 606 kgBOD/d, representing a 14.5% average error. This was a good fit, given the measurement error of the BOD test. Overall, the statistical approach was effective in describing and predicting MBBR treatment performance.

Fractional viscoelasticity of soft elastomers and auxetic foams

NASA Astrophysics Data System (ADS)

Solheim, Hannah; Stanisauskis, Eugenia; Miles, Paul; Oates, William

2018-03-01

Dielectric elastomers are commonly implemented in adaptive structures due to their unique capabilities for real time control of a structure's shape, stiffness, and damping. These active polymers are often used in applications where actuator control or dynamic tunability are important, making an accurate understanding of the viscoelastic behavior critical. This challenge is complicated as these elastomers often operate over a broad range of deformation rates. Whereas research has demonstrated success in applying a nonlinear viscoelastic constitutive model to characterize the behavior of Very High Bond (VHB) 4910, robust predictions of the viscoelastic response over the entire range of time scales is still a significant challenge. An alternative formulation for viscoelastic modeling using fractional order calculus has shown significant improvement in predictive capabilities. While fractional calculus has been explored theoretically in the field of linear viscoelasticity, limited experimental validation and statistical evaluation of the underlying phenomena have been considered. In the present study, predictions across several orders of magnitude in deformation rates are validated against data using a single set of model parameters. Moreover, we illustrate the fractional order is material dependent by running complementary experiments and parameter estimation on the elastomer VHB 4949 as well as an auxetic foam. All results are statistically validated using Bayesian uncertainty methods to obtain posterior densities for the fractional order as well as the hyperelastic parameters.

Restraint status improves the predictive value of motor vehicle crash criteria for pediatric trauma team activation.

PubMed

Bozeman, Andrew P; Dassinger, Melvin S; Recicar, John F; Smith, Samuel D; Rettiganti, Mallikarjuna R; Nick, Todd G; Maxson, Robert T

2012-12-01

Most trauma centers incorporate mechanistic criteria (MC) into their algorithm for trauma team activation (TTA). We hypothesized that characteristics of the crash are less reliable than restraint status in predicting significant injury and the need for TTA. We identified 271 patients (age, <15 y) admitted with a diagnosis of motor vehicle crash. Mechanistic criteria and restraint status of each patient were recorded. Both MC and MC plus restraint status were evaluated as separate measures for appropriately predicting TTA based on treatment outcomes and injury scores. Improper restraint alone predicted a need for TTA with an odds ratios of 2.69 (P = .002). MC plus improper restraint predicted the need for TTA with an odds ratio of 2.52 (P = .002). In contrast, the odds ratio when using MC alone was 1.65 (P = .16). When the 5 MC were evaluated individually as predictive of TTA, ejection, death of occupant, and intrusion more than 18 inches were statistically significant. Improper restraint is an independent predictor of necessitating TTA in this single-institution study. Copyright © 2012 Elsevier Inc. All rights reserved.

Evaluation of tunnel seismic prediction (TSP) result using the Japanese highway rock mass classification system for Pahang-Selangor Raw Water Transfer Tunnel

NASA Astrophysics Data System (ADS)

Von, W. C.; Ismail, M. A. M.

2017-10-01

The knowing of geological profile ahead of tunnel face is significant to minimize the risk in tunnel excavation work and cost control in preventative measure. Due to mountainous area, site investigation with vertical boring is not recommended to obtain the geological profile for Pahang-Selangor Raw Water Transfer project. Hence, tunnel seismic prediction (TSP) method is adopted to predict the geological profile ahead of tunnel face. In order to evaluate the TSP results, IBM SPSS Statistic 22 is used to run artificial neural network (ANN) analysis to back calculate the predicted Rock Grade Points (JH) from actual Rock Grade Points (JH) using Vp, Vs and Vp/Vs from TSP. The results show good correlation between predicted Rock Grade points and actual Rock Grade Points (JH). In other words, TSP can provide geological profile prediction ahead of tunnel face significantly while allowing continuously TBM excavation works. Identifying weak zones or faults ahead of tunnel face is crucial for preventative measures to be carried out in advance for a safer tunnel excavation works.

Ultrasound assessment of bladder wall thickness as a screening test for detrusor instability.

PubMed

Abou-Gamrah, Amgad; Fawzy, Mounir; Sammour, Hazem; Tadros, Sherif

2014-05-01

The aim of the current study was to evaluate the diagnostic accuracy of transvaginal ultrasound measurement of bladder wall thickness (BWT) in diagnosis of over active bladder (OAB). The current prospective study was conducted at Ain Shams University Maternity Hospital over 2 years. Patients presented to the urogynecology outpatient clinic with symptoms of urinary frequency, urgency, nocturia and/or urge incontinence were included in this study. The allocated patients were divided into two groups; Group 1(study group): fifty (50) patients with urodynamic diagnosis of detrusor instability (OAB) were included. Group 2 (control): fifty (50) patients with urodynamic diagnosis of stress incontinence were included. Using a transvaginal probe, BWT was measured in three sites at the thickest part of (a) the dome of the bladder (b) the trigone, and (c) the anterior wall of the bladder. An average of the three measurements was considered as the mean bladder thickness. A total of 100 patients with lower urinary symptoms were finally analyzed. There were no statistical significant differences between both groups regarding age, parity and body mass index, while there was statistically longer disease duration in group 2. Excluding urgency, there was statistical significant difference (P < 0.001) regarding lower urinary tract symptoms namely frequency, urgency incontinence, coital incontinence and nocturia. Patients in group 1 were more positive to symptoms of frequency, urgency incontinence, and nocturia, while patients in group 2 were more positive regarding coital incontinence. The thickness of trigon, dome, anterior wall and mean BWT was significantly higher in group 1 when compared to group 2. Receiver operator characteristics curve was constructed for estimating the association between mean BWT and prediction of OAB in patients with lower urinary tract symptoms. Mean BWT at 4.78 mm was considered as best cut-off value for prediction of OAB with sensitivity of 90 % and specificity of 78 %. Mean BWT was significantly associated with OAB > 4.78 mm as denoted by the significantly large area under the curve [AUC], AUC was 0.905. In women with lower urinary tract symptom, transvaginal ultrasounds measured mean BWT seems to be an effective non invasive diagnostic tool for prediction of OAB.

The relationship between procrastination, learning strategies and statistics anxiety among Iranian college students: a canonical correlation analysis.

PubMed

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

PubMed

Rivas, Elena; Lang, Raymond; Eddy, Sean R

2012-02-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

Statistical downscaling of IPCC sea surface wind and wind energy predictions for U.S. east coastal ocean, Gulf of Mexico and Caribbean Sea

NASA Astrophysics Data System (ADS)

Yao, Zhigang; Xue, Zuo; He, Ruoying; Bao, Xianwen; Song, Jun

2016-08-01

A multivariate statistical downscaling method is developed to produce regional, high-resolution, coastal surface wind fields based on the IPCC global model predictions for the U.S. east coastal ocean, the Gulf of Mexico (GOM), and the Caribbean Sea. The statistical relationship is built upon linear regressions between the empirical orthogonal function (EOF) spaces of a cross- calibrated, multi-platform, multi-instrument ocean surface wind velocity dataset (predictand) and the global NCEP wind reanalysis (predictor) over a 10 year period from 2000 to 2009. The statistical relationship is validated before applications and its effectiveness is confirmed by the good agreement between downscaled wind fields based on the NCEP reanalysis and in-situ surface wind measured at 16 National Data Buoy Center (NDBC) buoys in the U.S. east coastal ocean and the GOM during 1992-1999. The predictand-predictor relationship is applied to IPCC GFDL model output (2.0°×2.5°) of downscaled coastal wind at 0.25°×0.25° resolution. The temporal and spatial variability of future predicted wind speeds and wind energy potential over the study region are further quantified. It is shown that wind speed and power would significantly be reduced in the high CO2 climate scenario offshore of the mid-Atlantic and northeast U.S., with the speed falling to one quarter of its original value.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

PubMed Central

Rivas, Elena; Lang, Raymond; Eddy, Sean R.

2012-01-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases. PMID:22194308

Relationship between perceptual learning in speech and statistical learning in younger and older adults

PubMed Central

Neger, Thordis M.; Rietveld, Toni; Janse, Esther

2014-01-01

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly. PMID:25225475

Relationship between perceptual learning in speech and statistical learning in younger and older adults.

PubMed

Neger, Thordis M; Rietveld, Toni; Janse, Esther

2014-01-01

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly.

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

PubMed

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

2017-01-01

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

Influence of valproate on language functions in children with epilepsy.

PubMed

Doo, Jin Woong; Kim, Soon Chul; Kim, Sun Jun

2018-01-01

The aim of the current study was to assess the influences of valproate (VPA) on the language functions in newly diagnosed pediatric patients with epilepsy. We reviewed medical records of 53 newly diagnosed patients with epilepsy, who were being treated with VPA monotherapy (n=53; 22 male patients and 31 female patients). The subjects underwent standardized language tests, at least twice, before and after the initiation of VPA. The standardized language tests used were The Test of Language Problem Solving Abilities, a Korean version of The Expressive/Receptive Language Function Test, and the Urimal Test of Articulation and Phonology. Since all the patients analyzed spoke Korean as their first language, we used Korean language tests to reduce the bias within the data. All the language parameters of the Test of Language Problem Solving Abilities slightly improved after the initiation of VPA in the 53 pediatric patients with epilepsy (mean age: 11.6±3.2years), but only "prediction" was statistically significant (determining cause, 14.9±5.1 to 15.5±4.3; making inference, 16.1±5.8 to 16.9±5.6; prediction, 11.1±4.9 to 11.9±4.2; total score of TOPS, 42.0±14.4 to 44.2±12.5). The patients treated with VPA also exhibited a small extension in mean length of utterance in words (MLU-w) when responding, but this was not statistically significant (determining cause, 5.4±2.0 to 5.7±1.6; making inference, 5.8±2.2 to 6.0±1.8; prediction, 5.9±2.5 to 5.9±2.1; total, 5.7±2.1 to 5.9±1.7). The administration of VPA led to a slight, but not statistically significant, improvement in the receptive language function (range: 144.7±41.1 to 148.2±39.7). Finally, there were no statistically significant changes in the percentage of articulation performance after taking VPA. Therefore, our data suggested that VPA did not have negative impact on the language function, but rather slightly improved problem-solving abilities. Copyright © 2017 Elsevier Inc. All rights reserved.

Deriving inertial wave characteristics from surface drifter velocities - Frequency variability in the tropical Pacific

NASA Technical Reports Server (NTRS)

Poulain, Pierre-Marie; Luther, Douglas S.; Patzert, William C.

1992-01-01

Two techniques were developed for estimating statistics of inertial oscillations from satellite-tracked drifters that overcome the difficulties inherent in estimating such statistics from data dependent upon space coordinates that are a function of time. Application of these techniques to tropical surface drifter data collected during the NORPAX, EPOCS, and TOGA programs reveals a latitude-dependent, statistically significant 'blue shift' of inertial wave frequency. The latitudinal dependence of the blue shift is similar to predictions based on 'global' internal-wave spectral models, with a superposition of frequency shifting due to modification of the effective local inertial frequency by the presence of strongly sheared zonal mean currents within 12 deg of the equator.

Vascular endothelial growth factor (VEGF) expression in locally advanced prostate cancer: secondary analysis of radiation therapy oncology group (RTOG) 8610.

PubMed

Pan, Larry; Baek, Seunghee; Edmonds, Pamela R; Roach, Mack; Wolkov, Harvey; Shah, Satish; Pollack, Alan; Hammond, M Elizabeth; Dicker, Adam P

2013-04-25

Angiogenesis is a key element in solid-tumor growth, invasion, and metastasis. VEGF is among the most potent angiogenic factor thus far detected. The aim of the present study is to explore the potential of VEGF (also known as VEGF-A) as a prognostic and predictive biomarker among men with locally advanced prostate cancer. The analysis was performed using patients enrolled on RTOG 8610, a phase III randomized control trial of radiation therapy alone (Arm 1) versus short-term neoadjuvant and concurrent androgen deprivation and radiation therapy (Arm 2) in men with locally advanced prostate carcinoma. Tissue samples were obtained from the RTOG tissue repository. Hematoxylin and eosin slides were reviewed, and paraffin blocks were immunohistochemically stained for VEGF expression and graded by Intensity score (0-3). Cox or Fine and Gray's proportional hazards models were used. Sufficient pathologic material was available from 103 (23%) of the 456 analyzable patients enrolled in the RTOG 8610 study. There were no statistically significant differences in the pre-treatment characteristics between the patient groups with and without VEGF intensity data. Median follow-up for all surviving patients with VEGF intensity data is 12.2 years. Univariate and multivariate analyses demonstrated no statistically significant correlation between the intensity of VEGF expression and overall survival, distant metastasis, local progression, disease-free survival, or biochemical failure. VEGF expression was also not statistically significantly associated with any of the endpoints when analyzed by treatment arm. This study revealed no statistically significant prognostic or predictive value of VEGF expression for locally advanced prostate cancer. This analysis is among one of the largest sample bases with long-term follow-up in a well-characterized patient population. There is an urgent need to establish multidisciplinary initiatives for coordinating further research in the area of human prostate cancer biomarkers.

Measuring post-secondary stem majors' engagement in sustainability: The creation, assessment, and validation of an instrument for sustainability curricula evaluation

NASA Astrophysics Data System (ADS)

Little, David L., II

Ongoing changes in values, pedagogy, and curriculum concerning sustainability education necessitate that strong curricular elements are identified in sustainability education. However, quantitative research in sustainability education is largely undeveloped or relies on outdated instruments. In part, this is because no widespread quantitative instrument for measuring related educational outcomes has been developed for the field, though their development is pivotal for future efforts in sustainability education related to STEM majors. This research study details the creation, evaluation, and validation of an instrument -- the STEM Sustainability Engagement Instrument (STEMSEI) -- designed to measure sustainability engagement in post-secondary STEM majors. The study was conducted in three phases, using qualitative methods in phase 1, a concurrent mixed methods design in phase 2, and a sequential mixed methods design in phase 3. The STEMSEI was able to successfully predict statistically significant differences in the sample (n= 1017) that were predicted by prior research in environmental education. The STEMSEI also revealed statistically significant differences between STEM majors' sustainability engagement with a large effect size (.203 ≤ eta2 ≤ .211). As hypothesized, statistically significant differences were found on the environmental scales across gender and present religion. With respect to gender, self-perceived measures of emotional engagement with environmental sustainability was higher with females while males had higher measures in cognitive engagement with respect to knowing information related to environmental sustainability. With respect to present religion, self-perceived measures of general engagement and emotional engagement in environmental sustainability were higher for non-Christians as compared to Christians. On the economic scales, statistically significant differences were found across gender. Specifically, measures of males' self-perceived cognitive engagement in knowing information related to economic sustainability were greater than those of females. Future research should establish the generalizability of these results and further test the validity of the STEMSEI.

Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

DOE PAGES

Lewis, John R.; Zhang, Adah; Anderson-Cook, Christine Michaela

2017-10-29

Forensic science seeks to predict source characteristics using measured observables. Statistically, this objective can be thought of as an inverse problem where interest is in the unknown source characteristics or factors ( X) of some underlying causal model producing the observables or responses (Y = g ( X) + error). Here, this paper reviews several statistical methods for use in inverse problems and demonstrates that comparing results from multiple methods can be used to assess predictive capability. Motivation for assessing inverse predictions comes from the desired application to historical and future experiments involving nuclear material production for forensics research inmore » which inverse predictions, along with an assessment of predictive capability, are desired.« less

Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

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

Lewis, John R.; Zhang, Adah; Anderson-Cook, Christine Michaela

Forensic science seeks to predict source characteristics using measured observables. Statistically, this objective can be thought of as an inverse problem where interest is in the unknown source characteristics or factors ( X) of some underlying causal model producing the observables or responses (Y = g ( X) + error). Here, this paper reviews several statistical methods for use in inverse problems and demonstrates that comparing results from multiple methods can be used to assess predictive capability. Motivation for assessing inverse predictions comes from the desired application to historical and future experiments involving nuclear material production for forensics research inmore » which inverse predictions, along with an assessment of predictive capability, are desired.« less

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.

A simplified donor risk index for predicting outcome after deceased donor kidney transplantation.

PubMed

Watson, Christopher J E; Johnson, Rachel J; Birch, Rhiannon; Collett, Dave; Bradley, J Andrew

2012-02-15

We sought to determine the deceased donor factors associated with outcome after kidney transplantation and to develop a clinically applicable Kidney Donor Risk Index. Data from the UK Transplant Registry on 7620 adult recipients of adult deceased donor kidney transplants between 2000 and 2007 inclusive were analyzed. Donor factors potentially influencing transplant outcome were investigated using Cox regression, adjusting for significant recipient and transplant factors. A United Kingdom Kidney Donor Risk Index was derived from the model and validated. Donor age was the most significant factor predicting poor transplant outcome (hazard ratio for 18-39 and 60+ years relative to 40-59 years was 0.78 and 1.49, respectively, P<0.001). A history of donor hypertension was also associated with increased risk (hazard ratio 1.30, P=0.001), and increased donor body weight, longer hospital stay before death, and use of adrenaline were also significantly associated with poorer outcomes up to 3 years posttransplant. Other donor factors including donation after circulatory death, history of cardiothoracic disease, diabetes history, and terminal creatinine were not significant. A donor risk index based on the five significant donor factors was derived and confirmed to be prognostic of outcome in a validation cohort (concordance statistic 0.62). An index developed in the United States by Rao et al., Transplantation 2009; 88: 231-236, included 15 factors and gave a concordance statistic of 0.63 in the UK context, suggesting that our much simpler model has equivalent predictive ability. A Kidney Donor Risk Index based on five donor variables provides a clinically useful tool that may help with organ allocation and informed consent.

When can social media lead financial markets?

PubMed

Zheludev, Ilya; Smith, Robert; Aste, Tomaso

2014-02-27

Social media analytics is showing promise for the prediction of financial markets. However, the true value of such data for trading is unclear due to a lack of consensus on which instruments can be predicted and how. Current approaches are based on the evaluation of message volumes and are typically assessed via retrospective (ex-post facto) evaluation of trading strategy returns. In this paper, we present instead a sentiment analysis methodology to quantify and statistically validate which assets could qualify for trading from social media analytics in an ex-ante configuration. We use sentiment analysis techniques and Information Theory measures to demonstrate that social media message sentiment can contain statistically-significant ex-ante information on the future prices of the S&P500 index and a limited set of stocks, in excess of what is achievable using solely message volumes.

When Can Social Media Lead Financial Markets?

NASA Astrophysics Data System (ADS)

Zheludev, Ilya; Smith, Robert; Aste, Tomaso

2014-02-01

Social media analytics is showing promise for the prediction of financial markets. However, the true value of such data for trading is unclear due to a lack of consensus on which instruments can be predicted and how. Current approaches are based on the evaluation of message volumes and are typically assessed via retrospective (ex-post facto) evaluation of trading strategy returns. In this paper, we present instead a sentiment analysis methodology to quantify and statistically validate which assets could qualify for trading from social media analytics in an ex-ante configuration. We use sentiment analysis techniques and Information Theory measures to demonstrate that social media message sentiment can contain statistically-significant ex-ante information on the future prices of the S&P500 index and a limited set of stocks, in excess of what is achievable using solely message volumes.

When Can Social Media Lead Financial Markets?

PubMed Central

Zheludev, Ilya; Smith, Robert; Aste, Tomaso

2014-01-01

Social media analytics is showing promise for the prediction of financial markets. However, the true value of such data for trading is unclear due to a lack of consensus on which instruments can be predicted and how. Current approaches are based on the evaluation of message volumes and are typically assessed via retrospective (ex-post facto) evaluation of trading strategy returns. In this paper, we present instead a sentiment analysis methodology to quantify and statistically validate which assets could qualify for trading from social media analytics in an ex-ante configuration. We use sentiment analysis techniques and Information Theory measures to demonstrate that social media message sentiment can contain statistically-significant ex-ante information on the future prices of the S&P500 index and a limited set of stocks, in excess of what is achievable using solely message volumes. PMID:24572909

Real-time forecasting and predictability of catastrophic failure events: from rock failure to volcanoes and earthquakes

NASA Astrophysics Data System (ADS)

Main, I. G.; Bell, A. F.; Naylor, M.; Atkinson, M.; Filguera, R.; Meredith, P. G.; Brantut, N.

2012-12-01

Accurate prediction of catastrophic brittle failure in rocks and in the Earth presents a significant challenge on theoretical and practical grounds. The governing equations are not known precisely, but are known to produce highly non-linear behavior similar to those of near-critical dynamical systems, with a large and irreducible stochastic component due to material heterogeneity. In a laboratory setting mechanical, hydraulic and rock physical properties are known to change in systematic ways prior to catastrophic failure, often with significant non-Gaussian fluctuations about the mean signal at a given time, for example in the rate of remotely-sensed acoustic emissions. The effectiveness of such signals in real-time forecasting has never been tested before in a controlled laboratory setting, and previous work has often been qualitative in nature, and subject to retrospective selection bias, though it has often been invoked as a basis in forecasting natural hazard events such as volcanoes and earthquakes. Here we describe a collaborative experiment in real-time data assimilation to explore the limits of predictability of rock failure in a best-case scenario. Data are streamed from a remote rock deformation laboratory to a user-friendly portal, where several proposed physical/stochastic models can be analysed in parallel in real time, using a variety of statistical fitting techniques, including least squares regression, maximum likelihood fitting, Markov-chain Monte-Carlo and Bayesian analysis. The results are posted and regularly updated on the web site prior to catastrophic failure, to ensure a true and and verifiable prospective test of forecasting power. Preliminary tests on synthetic data with known non-Gaussian statistics shows how forecasting power is likely to evolve in the live experiments. In general the predicted failure time does converge on the real failure time, illustrating the bias associated with the 'benefit of hindsight' in retrospective analyses. Inference techniques that account explicitly for non-Gaussian statistics significantly reduce the bias, and increase the reliability and accuracy, of the forecast failure time in prospective mode.

Strategies for Reduced-Order Models in Uncertainty Quantification of Complex Turbulent Dynamical Systems

NASA Astrophysics Data System (ADS)

Qi, Di

Turbulent dynamical systems are ubiquitous in science and engineering. Uncertainty quantification (UQ) in turbulent dynamical systems is a grand challenge where the goal is to obtain statistical estimates for key physical quantities. In the development of a proper UQ scheme for systems characterized by both a high-dimensional phase space and a large number of instabilities, significant model errors compared with the true natural signal are always unavoidable due to both the imperfect understanding of the underlying physical processes and the limited computational resources available. One central issue in contemporary research is the development of a systematic methodology for reduced order models that can recover the crucial features both with model fidelity in statistical equilibrium and with model sensitivity in response to perturbations. In the first part, we discuss a general mathematical framework to construct statistically accurate reduced-order models that have skill in capturing the statistical variability in the principal directions of a general class of complex systems with quadratic nonlinearity. A systematic hierarchy of simple statistical closure schemes, which are built through new global statistical energy conservation principles combined with statistical equilibrium fidelity, are designed and tested for UQ of these problems. Second, the capacity of imperfect low-order stochastic approximations to model extreme events in a passive scalar field advected by turbulent flows is investigated. The effects in complicated flow systems are considered including strong nonlinear and non-Gaussian interactions, and much simpler and cheaper imperfect models with model error are constructed to capture the crucial statistical features in the stationary tracer field. Several mathematical ideas are introduced to improve the prediction skill of the imperfect reduced-order models. Most importantly, empirical information theory and statistical linear response theory are applied in the training phase for calibrating model errors to achieve optimal imperfect model parameters; and total statistical energy dynamics are introduced to improve the model sensitivity in the prediction phase especially when strong external perturbations are exerted. The validity of reduced-order models for predicting statistical responses and intermittency is demonstrated on a series of instructive models with increasing complexity, including the stochastic triad model, the Lorenz '96 model, and models for barotropic and baroclinic turbulence. The skillful low-order modeling methods developed here should also be useful for other applications such as efficient algorithms for data assimilation.

Prediction/discussion-based learning cycle versus conceptual change text: comparative effects on students' understanding of genetics

NASA Astrophysics Data System (ADS)

khawaldeh, Salem A. Al

2013-07-01

Background and purpose: The purpose of this study was to investigate the comparative effects of a prediction/discussion-based learning cycle (HPD-LC), conceptual change text (CCT) and traditional instruction on 10th grade students' understanding of genetics concepts. Sample: Participants were 112 10th basic grade male students in three classes of the same school located in an urban area. The three classes taught by the same biology teacher were randomly assigned as a prediction/discussion-based learning cycle class (n = 39), conceptual change text class (n = 37) and traditional class (n = 36). Design and method: A quasi-experimental research design of pre-test-post-test non-equivalent control group was adopted. Participants completed the Genetics Concept Test as pre-test-post-test, to examine the effects of instructional strategies on their genetics understanding. Pre-test scores and Test of Logical Thinking scores were used as covariates. Results: The analysis of covariance showed a statistically significant difference between the experimental and control groups in the favor of experimental groups after treatment. However, no statistically significant difference between the experimental groups (HPD-LC versus CCT instruction) was found. Conclusions: Overall, the findings of this study support the use of the prediction/discussion-based learning cycle and conceptual change text in both research and teaching. The findings may be useful for improving classroom practices in teaching science concepts and for the development of suitable materials promoting students' understanding of science.

Cluster-based analysis improves predictive validity of spike-triggered receptive field estimates

PubMed Central

Malone, Brian J.

2017-01-01

Spectrotemporal receptive field (STRF) characterization is a central goal of auditory physiology. STRFs are often approximated by the spike-triggered average (STA), which reflects the average stimulus preceding a spike. In many cases, the raw STA is subjected to a threshold defined by gain values expected by chance. However, such correction methods have not been universally adopted, and the consequences of specific gain-thresholding approaches have not been investigated systematically. Here, we evaluate two classes of statistical correction techniques, using the resulting STRF estimates to predict responses to a novel validation stimulus. The first, more traditional technique eliminated STRF pixels (time-frequency bins) with gain values expected by chance. This correction method yielded significant increases in prediction accuracy, including when the threshold setting was optimized for each unit. The second technique was a two-step thresholding procedure wherein clusters of contiguous pixels surviving an initial gain threshold were then subjected to a cluster mass threshold based on summed pixel values. This approach significantly improved upon even the best gain-thresholding techniques. Additional analyses suggested that allowing threshold settings to vary independently for excitatory and inhibitory subfields of the STRF resulted in only marginal additional gains, at best. In summary, augmenting reverse correlation techniques with principled statistical correction choices increased prediction accuracy by over 80% for multi-unit STRFs and by over 40% for single-unit STRFs, furthering the interpretational relevance of the recovered spectrotemporal filters for auditory systems analysis. PMID:28877194

Trend extraction using empirical mode decomposition and statistical empirical mode decomposition: Case study: Kuala Lumpur stock market

NASA Astrophysics Data System (ADS)

Jaber, Abobaker M.

2014-12-01

Two nonparametric methods for prediction and modeling of financial time series signals are proposed. The proposed techniques are designed to handle non-stationary and non-linearity behave and to extract meaningful signals for reliable prediction. Due to Fourier Transform (FT), the methods select significant decomposed signals that will be employed for signal prediction. The proposed techniques developed by coupling Holt-winter method with Empirical Mode Decomposition (EMD) and it is Extending the scope of empirical mode decomposition by smoothing (SEMD). To show performance of proposed techniques, we analyze daily closed price of Kuala Lumpur stock market index.

The impacts of renewable energy policies on renewable energy sources for electricity generating capacity

NASA Astrophysics Data System (ADS)

Koo, Bryan Bonsuk

Electricity generation from non-hydro renewable sources has increased rapidly in the last decade. For example, Renewable Energy Sources for Electricity (RES-E) generating capacity in the U.S. almost doubled for the last three year from 2009 to 2012. Multiple papers point out that RES-E policies implemented by state governments play a crucial role in increasing RES-E generation or capacity. This study examines the effects of state RES-E policies on state RES-E generating capacity, using a fixed effects model. The research employs panel data from the 50 states and the District of Columbia, for the period 1990 to 2011, and uses a two-stage approach to control endogeneity embedded in the policies adopted by state governments, and a Prais-Winsten estimator to fix any autocorrelation in the panel data. The analysis finds that Renewable Portfolio Standards (RPS) and Net-metering are significantly and positively associated with RES-E generating capacity, but neither Public Benefit Funds nor the Mandatory Green Power Option has a statistically significant relation to RES-E generating capacity. Results of the two-stage model are quite different from models which do not employ predicted policy variables. Analysis using non-predicted variables finds that RPS and Net-metering policy are statistically insignificant and negatively associated with RES-E generating capacity. On the other hand, Green Energy Purchasing policy is insignificant in the two-stage model, but significant in the model without predicted values.

Changing patterns of microcalcification on screening mammography for prediction of breast cancer.

PubMed

Kim, Kwan Il; Lee, Kyung Hee; Kim, Tae Ryung; Chun, Yong Soon; Lee, Tae Hoon; Choi, Hye Young; Park, Heung Kyu

2016-05-01

The presence of microcalcification on mammography is one of the earliest signs in breast cancer detection. However, it is difficult to distinguish malignant calcifications from benign calcifications. The aim of this study is to evaluate correlation between changing patterns of microcalcification on screening mammography and malignant breast lesions. Medical records and diagnostic images of 67 women who had previously undergone at least two digital mammograms at least 6 months apart and underwent mammography-guided needle localization and surgical excision between 2011 and 2013 were retrospectively reviewed and analyzed. Breast cancer was detected in the surgical specimens of 20 patients (29.9 %). Annual change of extent of microcalcification on mammography showed statistically significant correlation with pathologic outcome (P = 0.023). The changing pattern of new appearance or increased extent of microcalcification on mammography had positive predictive value of 54.8 % for breast cancer, and it was a statistically significant predictor for breast cancer (P = 0.012). Shape or number change of microcalcification without increased extent had less accurate predictive value for breast cancer, particularly in women younger than 50 years (P < 0.001). This study showed that the pattern of increased extent of microcalcification on screening mammography was a significant predictor for breast cancer. We suggest that mammography-guided needle localization and surgical excision should be considered when increased extent of microcalcification is observed on screening mammography and closed follow-up without pathologic confirmation can be permitted if absence of extension of microcalcification was confirmed in women younger than 50 years.

Biological risk factors for suicidal behaviors: a meta-analysis

PubMed Central

Chang, B P; Franklin, J C; Ribeiro, J D; Fox, K R; Bentley, K H; Kleiman, E M; Nock, M K

2016-01-01

Prior studies have proposed a wide range of potential biological risk factors for future suicidal behaviors. Although strong evidence exists for biological correlates of suicidal behaviors, it remains unclear if these correlates are also risk factors for suicidal behaviors. We performed a meta-analysis to integrate the existing literature on biological risk factors for suicidal behaviors and to determine their statistical significance. We conducted a systematic search of PubMed, PsycInfo and Google Scholar for studies that used a biological factor to predict either suicide attempt or death by suicide. Inclusion criteria included studies with at least one longitudinal analysis using a biological factor to predict either of these outcomes in any population through 2015. From an initial screen of 2541 studies we identified 94 cases. Random effects models were used for both meta-analyses and meta-regression. The combined effect of biological factors produced statistically significant but relatively weak prediction of suicide attempts (weighted mean odds ratio (wOR)=1.41; CI: 1.09–1.81) and suicide death (wOR=1.28; CI: 1.13–1.45). After accounting for publication bias, prediction was nonsignificant for both suicide attempts and suicide death. Only two factors remained significant after accounting for publication bias—cytokines (wOR=2.87; CI: 1.40–5.93) and low levels of fish oil nutrients (wOR=1.09; CI: 1.01–1.19). Our meta-analysis revealed that currently known biological factors are weak predictors of future suicidal behaviors. This conclusion should be interpreted within the context of the limitations of the existing literature, including long follow-up intervals and a lack of tests of interactions with other risk factors. Future studies addressing these limitations may more effectively test for potential biological risk factors. PMID:27622931

The intracranial number of foreign bodies as a predictor of mortality after penetrating brain injury.

PubMed

Bolatkale, Mustafa; Acara, Ahmet Cagdas

2018-06-02

Penetrating brain injury (PBI) is the most lethal form of traumatic brain injury, which is a leading cause of mortality. PBI has a mortality rate of 23%-93% and 87%-100% with poor neurological status. Despite the use of various prognostic factors there is still a need for a specific prognostic factor for early prediction of mortality in PBI to reduce mortality and provide good outcomes with cost-effective surgical treatments. The aim of this study was to investigate the predictive value of the number of intracranial foreign bodies (FBs) on mortality in PBI in the Emergency Department. The study included 95 patients admitted with PBI caused by barrel bomb explosion. The intracranial number of FB was examined by brain computed tomography. Logistic regression was used to assess the association of the intracranial number of FB on mortality. Correlation analyses were performed to investigate the association of Glasgow Coma Scale (GCS) with intracranial number of FB. The optimal cut-off value of the intracranial number of FB calculated for mortality was 2, which was effective for predicting mortality (p < .001). In patients with >2 intracranial FB, the mortality rate was statistically significantly 51-fold higher than those with ≤2 (p < .001). A statistically significant negative correlation was determined between GCS and number of. FB (r = -0.697;p < .001). When the intracranial number of FB was >2, mortality significantly increased in patients with PBI. The intracranial number of FBs may be considered as a novel prognostic factor for the prediction of mortality in PBI. Penetrating brain injury, mortality, foreign body, barrel bomb. Copyright © 2018 Elsevier Inc. All rights reserved.

Behavioral and Social Science Research: A National Resource. Part II.

ERIC Educational Resources Information Center

Adams, Robert McC., Ed.; And Others

Areas of behavioral and social science research that have achieved significant breakthroughs in knowledge or application or that show future promise of achieving such breakthroughs are discussed in 12 papers. For example, the paper on formal demography shows how mathematical or statistical techniques can be used to explain and predict change in…

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

Jin, C.; Potts, I.; Reeks, M. W., E-mail: mike.reeks@ncl.ac.uk

We present a simple stochastic quadrant model for calculating the transport and deposition of heavy particles in a fully developed turbulent boundary layer based on the statistics of wall-normal fluid velocity fluctuations obtained from a fully developed channel flow. Individual particles are tracked through the boundary layer via their interactions with a succession of random eddies found in each of the quadrants of the fluid Reynolds shear stress domain in a homogeneous Markov chain process. In this way, we are able to account directly for the influence of ejection and sweeping events as others have done but without resorting tomore » the use of adjustable parameters. Deposition rate predictions for a wide range of heavy particles predicted by the model compare well with benchmark experimental measurements. In addition, deposition rates are compared with those obtained from continuous random walk models and Langevin equation based ejection and sweep models which noticeably give significantly lower deposition rates. Various statistics related to the particle near wall behavior are also presented. Finally, we consider the model limitations in using the model to calculate deposition in more complex flows where the near wall turbulence may be significantly different.« less

Predicting the effect of maternal docosahexaenoic acid (DHA) supplementation to reduce early preterm birth in Australia and the United States using results of within country randomized controlled trials

PubMed Central

Yelland, LN; Gajewski, BJ; Colombo, J; Gibson, RA; Makrides, M; Carlson, SE

2016-01-01

SUMMARY The DHA to Optimize Mother Infant Outcome (DOMInO) and Kansas DHA Outcomes Study (KUDOS) were randomized controlled trials that supplemented mothers with 800 and 600 mg DHA/day, respectively, or a placebo during pregnancy. DOMInO was conducted in Australia and KUDOS in the United States. Both trials found an unanticipated and statistically significant reduction in early preterm birth (ePTB; i.e., birth before 34 weeks gestation). However, in each trial, the number of ePTBs were small. We used a novel Bayesian approach and an arbitrary sample of 120,000 pregnancies to estimate statistically derived low, moderate or high risk for ePTB, and to test for differences between the DHA and placebo groups. In both trials, the model predicted DHA would significantly reduce the expected proportion of deliveries in the high risk group under the trial conditions of the parent studies. From these proportions we estimated the number of ePTB that could be prevented. PMID:27637340

Comparison of the accuracy of radiography and ultrasonography for detection of articular lesions in horses.

PubMed

Hinz, Antje; Fischer, Andrew T

2011-10-01

To compare the accuracy of ultrasonographic and radiographic examination for evaluation of articular lesions in horses. Cross-sectional study. Horses (n = 137) with articular lesions. Radiographic and ultrasonographic examinations of the affected joint(s) were performed before diagnostic or therapeutic arthroscopic surgery. Findings were recorded and compared to lesions identified during arthroscopy. In 254 joints, 432 lesions were identified by arthroscopy. The overall accuracy was 82.9% for ultrasonography and 62.2% for radiography (P < .0001) with a sensitivity of 91.4% for ultrasonography and 66.7% for radiography (P < .0001). The difference in specificity was not statistically significant (P = .2628). The negative predictive value for ultrasonography was 31.5% and 13.2% for radiography (P = .0022), the difference for the positive predictive value was not statistically significant (P = .3898). The accuracy for ultrasonography and radiography for left versus right joints was equal and corresponded with the overall results. Ultrasonographic evaluation of articular lesions was more accurate than radiographic evaluation. © Copyright 2011 by The American College of Veterinary Surgeons.

The Association of CHA2DS2-VASc Score and Blood Biomarkers with Ischemic Stroke Outcomes: The Belgrade Stroke Study

PubMed Central

Potpara, Tatjana S.; Polovina, Marija M.; Djikic, Dijana; Marinkovic, Jelena M.; Kocev, Nikola; Lip, Gregory Y. H.

2014-01-01

Background Many blood biomarkers have a positive association with stroke outcome, but adding blood biomarkers to the National Institutes of Health Stroke Scale (NIHSS) did not significantly improve its discriminatory ability. We investigated the association of the CHA2DS2-VASc score with unfavourable functional outcome (defined as a 30-day modified Rankin Scale [mRS] ≥3) in patients presenting with acute ischemic stroke (AIS), and examined whether the addition of blood biomarkers (troponin I [TnI], fibrinogen, C-reactive protein [CRP]) affects the model discriminatory ability. Methods We conducted an observational single-centre study of consecutive patients with AIS. All patients were admitted to hospital within 24 hours from the neurological symptoms onset. Results Of 240 patients (mean age 70.0±8.9 years), unfavourable 30-day outcome occurred in 92 (38.3%). Patients with mRS≥3 were older and more likely to have atrial fibrillation or other comorbidities (all p<0.001). They had higher levels of CRP, fibrinogen, TnI and higher CHA2DS2-VASc and CHADS2 scores (all p<0.05). The adjusted CHA2DS2-VASc score had excellent predictive ability for poor stroke outcome (c-statistic 0.982;95%CI,0.964–1.000, p<0.001). Whilst CRP had the highest sensitivity (83.7%), cardiac TnI was the most specific (97.3%) for prediction of poor stroke outcome (cut-off: >0.09µg/L). Compared with each of these biomarkers, CHA2DS2-VASc score had significantly better predictive ability for poor stroke outcome (c-statistic for CRP, Fibrinogen and TnI was 0.853;95%CI,0.802–0.895, 0.848;95%CI,0.796–0.891, and 0.792;95%CI,0.736–0.842, all p<0.001, respectively, versus 0.932;95%CI,0.892–0.960, p<0.001 for the CHA2DS2-VASc, all p for the comparisons<0.01). There was no significant difference in the predictive ability of the CHA2DS2-VASc score vs. combinations of the CHA2DS2-VASc and TnI or TnI, fibrinogen and CRP (z statistic 0.369, p = 0.7119; integrated discrimination index 0.00801 and 0.00172, respectively, both p>0.05). Conclusions The CHA2DS2-VASc score alone reliably predicts 30-day unfavourable outcome of stroke. Adding blood biomarkers to the CHA2DS2-VASc score did not significantly increase the predictive ability of the model. PMID:25184809

Assessment of Current Jet Noise Prediction Capabilities

NASA Technical Reports Server (NTRS)

Hunter, Craid A.; Bridges, James E.; Khavaran, Abbas

2008-01-01

An assessment was made of the capability of jet noise prediction codes over a broad range of jet flows, with the objective of quantifying current capabilities and identifying areas requiring future research investment. Three separate codes in NASA s possession, representative of two classes of jet noise prediction codes, were evaluated, one empirical and two statistical. The empirical code is the Stone Jet Noise Module (ST2JET) contained within the ANOPP aircraft noise prediction code. It is well documented, and represents the state of the art in semi-empirical acoustic prediction codes where virtual sources are attributed to various aspects of noise generation in each jet. These sources, in combination, predict the spectral directivity of a jet plume. A total of 258 jet noise cases were examined on the ST2JET code, each run requiring only fractions of a second to complete. Two statistical jet noise prediction codes were also evaluated, JeNo v1, and Jet3D. Fewer cases were run for the statistical prediction methods because they require substantially more resources, typically a Reynolds-Averaged Navier-Stokes solution of the jet, volume integration of the source statistical models over the entire plume, and a numerical solution of the governing propagation equation within the jet. In the evaluation process, substantial justification of experimental datasets used in the evaluations was made. In the end, none of the current codes can predict jet noise within experimental uncertainty. The empirical code came within 2dB on a 1/3 octave spectral basis for a wide range of flows. The statistical code Jet3D was within experimental uncertainty at broadside angles for hot supersonic jets, but errors in peak frequency and amplitude put it out of experimental uncertainty at cooler, lower speed conditions. Jet3D did not predict changes in directivity in the downstream angles. The statistical code JeNo,v1 was within experimental uncertainty predicting noise from cold subsonic jets at all angles, but did not predict changes with heating of the jet and did not account for directivity changes at supersonic conditions. Shortcomings addressed here give direction for future work relevant to the statistical-based prediction methods. A full report will be released as a chapter in a NASA publication assessing the state of the art in aircraft noise prediction.

Statistical Learning of Probabilistic Nonadjacent Dependencies by Multiple-Cue Integration

ERIC Educational Resources Information Center

van den Bos, Esther; Christiansen, Morten H.; Misyak, Jennifer B.

2012-01-01

Previous studies have indicated that dependencies between nonadjacent elements can be acquired by statistical learning when each element predicts only one other element (deterministic dependencies). The present study investigates statistical learning of probabilistic nonadjacent dependencies, in which each element predicts several other elements…

Plasma inflammatory and immune proteins as predictors of intra-amniotic infection and spontaneous preterm delivery in women with preterm labor: a retrospective study.

PubMed

Park, Hyunsoo; Park, Kyo Hoon; Kim, Yu Mi; Kook, Song Yi; Jeon, Se Jeong; Yoo, Ha-Na

2018-05-09

We investigated whether various inflammatory and immune proteins in plasma predict intra-amniotic infection and imminent preterm delivery in women with preterm labor and compared their predictive ability with that of amniotic fluid (AF) interleukin (IL)-6 and serum C-reactive protein (CRP). This retrospective cohort study included 173 consecutive women with preterm labor who underwent amniocentesis for diagnosis of infection and/or inflammation in the AF. The AF was cultured, and assayed for IL-6. CRP levels and cervical length by transvaginal ultrasound were measured at the time of amniocentesis. The stored maternal plasma was assayed for IL-6, matrix metalloproteinase (MMP)-9, and complements C3a and C5a using ELISA kits. The primary and secondary outcome criteria were positive AF cultures and spontaneous preterm delivery (SPTD) within 48 h, respectively. Univariate, multivariate, and receiver operating characteristic analysis were used for the statistical analysis. In bivariate analyses, elevated plasma IL-6 level was significantly associated with intra-amniotic infection and imminent preterm delivery, whereas elevated plasma levels of MMP-9, C3a, and C5a were not associated with these two outcomes. On multivariate analyses, an elevated plasma IL-6 level was significantly associated with intra-amniotic infection and imminent preterm delivery after adjusting for confounders, including high serum CRP levels and short cervical length. In predicting intra-amniotic infection, the area under the curve (AUC) was significantly lower for plasma IL-6 than for AF IL-6 but was similar to that for serum CRP. Differences in the AUCs between plasma IL-6, AF IL-6, and serum CRP were not statistically significant in predicting imminent preterm delivery. Maternal plasma IL-6 independently predicts intra-amniotic infection in women with preterm labor; however, it has worse diagnostic performance than that of AF IL-6 and similar performance to that of serum CRP. To predict imminent preterm delivery, plasma IL-6 had an overall diagnostic performance similar to that of AF IL-6 and serum CRP. Plasma MMP-9, C3a, and C5a levels could not predict intra-amniotic infection or imminent preterm delivery.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Outcome Prediction of Consciousness Disorders in the Acute Stage Based on a Complementary Motor Behavioural Tool.

PubMed

Pignat, Jean-Michel; Mauron, Etienne; Jöhr, Jane; Gilart de Keranflec'h, Charlotte; Van De Ville, Dimitri; Preti, Maria Giulia; Meskaldji, Djalel E; Hömberg, Volker; Laureys, Steven; Draganski, Bogdan; Frackowiak, Richard; Diserens, Karin

2016-01-01

Attaining an accurate diagnosis in the acute phase for severely brain-damaged patients presenting Disorders of Consciousness (DOC) is crucial for prognostic validity; such a diagnosis determines further medical management, in terms of therapeutic choices and end-of-life decisions. However, DOC evaluation based on validated scales, such as the Revised Coma Recovery Scale (CRS-R), can lead to an underestimation of consciousness and to frequent misdiagnoses particularly in cases of cognitive motor dissociation due to other aetiologies. The purpose of this study is to determine the clinical signs that lead to a more accurate consciousness assessment allowing more reliable outcome prediction. From the Unit of Acute Neurorehabilitation (University Hospital, Lausanne, Switzerland) between 2011 and 2014, we enrolled 33 DOC patients with a DOC diagnosis according to the CRS-R that had been established within 28 days of brain damage. The first CRS-R assessment established the initial diagnosis of Unresponsive Wakefulness Syndrome (UWS) in 20 patients and a Minimally Consciousness State (MCS) in the remaining13 patients. We clinically evaluated the patients over time using the CRS-R scale and concurrently from the beginning with complementary clinical items of a new observational Motor Behaviour Tool (MBT). Primary endpoint was outcome at unit discharge distinguishing two main classes of patients (DOC patients having emerged from DOC and those remaining in DOC) and 6 subclasses detailing the outcome of UWS and MCS patients, respectively. Based on CRS-R and MBT scores assessed separately and jointly, statistical testing was performed in the acute phase using a non-parametric Mann-Whitney U test; longitudinal CRS-R data were modelled with a Generalized Linear Model. Fifty-five per cent of the UWS patients and 77% of the MCS patients had emerged from DOC. First, statistical prediction of the first CRS-R scores did not permit outcome differentiation between classes; longitudinal regression modelling of the CRS-R data identified distinct outcome evolution, but not earlier than 19 days. Second, the MBT yielded a significant outcome predictability in the acute phase (p<0.02, sensitivity>0.81). Third, a statistical comparison of the CRS-R subscales weighted by MBT became significantly predictive for DOC outcome (p<0.02). The association of MBT and CRS-R scoring improves significantly the evaluation of consciousness and the predictability of outcome in the acute phase. Subtle motor behaviour assessment provides accurate insight into the amount and the content of consciousness even in the case of cognitive motor dissociation.

Risk models for post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP): smoking and chronic liver disease are predictors of protection against PEP.

PubMed

DiMagno, Matthew J; Spaete, Joshua P; Ballard, Darren D; Wamsteker, Erik-Jan; Saini, Sameer D

2013-08-01

We investigated which variables independently associated with protection against or development of postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) and severity of PEP. Subsequently, we derived predictive risk models for PEP. In a case-control design, 6505 patients had 8264 ERCPs, 211 patients had PEP, and 22 patients had severe PEP. We randomly selected 348 non-PEP controls. We examined 7 established- and 9 investigational variables. In univariate analysis, 7 variables predicted PEP: younger age, female sex, suspected sphincter of Oddi dysfunction (SOD), pancreatic sphincterotomy, moderate-difficult cannulation (MDC), pancreatic stent placement, and lower Charlson score. Protective variables were current smoking, former drinking, diabetes, and chronic liver disease (CLD, biliary/transplant complications). Multivariate analysis identified seven independent variables for PEP, three protective (current smoking, CLD-biliary, CLD-transplant/hepatectomy complications) and 4 predictive (younger age, suspected SOD, pancreatic sphincterotomy, MDC). Pre- and post-ERCP risk models of 7 variables have a C-statistic of 0.74. Removing age (seventh variable) did not significantly affect the predictive value (C-statistic of 0.73) and reduced model complexity. Severity of PEP did not associate with any variables by multivariate analysis. By using the newly identified protective variables with 3 predictive variables, we derived 2 risk models with a higher predictive value for PEP compared to prior studies.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Empirical Research of Micro-blog Information Transmission Range by Guard nodes

NASA Astrophysics Data System (ADS)

Chen, Shan; Ji, Ling; Li, Guang

2018-03-01

The prediction and evaluation of information transmission in online social networks is a challenge. It is significant to solve this issue for monitoring public option and advertisement communication. First, the prediction process is described by a set language. Then with Sina Microblog system as used as the case object, the relationship between node influence and coverage rate is analyzed by using the topology structure of information nodes. A nonlinear model is built by a statistic method in a specific, bounded and controlled Microblog network. It can predict the message coverage rate by guard nodes. The experimental results show that the prediction model has higher accuracy to the source nodes which have lower influence in social network and practical application.

Nanoindentation of orthodontic archwires: The effect of decontamination and clinical use on hardness, elastic modulus and surface roughness.

PubMed

Alcock, Joseph P; Barbour, Michele E; Sandy, Jonathan R; Ireland, Anthony J

2009-08-01

The purpose of this research was to investigate the effects of decontamination and clinical exposure on the elastic moduli, hardness and surface roughness of two frequently used orthodontic archwires, namely 0.020in.x0.020in. heat activated (martensitic active) nickel titanium archwires and 0.019in.x0.025in. austenitic stainless steel archwires. This study was a prospective clinical trial in which 20 consecutive patients requiring an archwire change as part of their course of orthodontic fixed appliance therapy, had either a nickel titanium or stainless steel archwire fitted as deemed clinically necessary. The effect of clinical use was determined by comparing distal end cuts of the "as received" archwires before and after decontamination, with the same retrieved archwires following clinical use and decontamination. Hardness, elastic modulus and surface roughness were determined using an atomic force microscope (AFM) coupled with a nanoindenter. The results showed that the decontamination regimen and clinical use had no statistically significant effect on the nickel titanium archwires, but did have a statistically significant effect on the steel archwires. Decontamination of the steel wires significantly increased the observed surface hardness (p=0.01) and reduced the surface roughness (p=0.02). Clinical use demonstrated a statistically significant increase in the observed elastic modulus (p<0.001) and a decrease in surface roughness (p=0.001). At present it is difficult to predict the clinical significance of these statistically significant changes in archwire properties on orthodontic tooth movement.

The Prehospital Sepsis Project: out-of-hospital physiologic predictors of sepsis outcomes.

PubMed

Baez, Amado Alejandro; Hanudel, Priscilla; Wilcox, Susan Renee

2013-12-01

Severe sepsis and septic shock are common, expensive and often fatal medical problems. The care of the critically sick and injured often begins in the prehospital setting; there is limited data available related to predictors and interventions specific to sepsis in the prehospital arena. The objective of this study was to assess the predictive effect of physiologic elements commonly reported in the out-of-hospital setting in the outcomes of patients transported with sepsis. This was a cross-sectional descriptive study. Data from the years 2004-2006 were collected. Adult cases (≥18 years of age) transported by Emergency Medical Services to a major academic center with the diagnosis of sepsis as defined by ICD-9-CM diagnostic codes were included. Descriptive statistics and standard deviations were used to present group characteristics. Chi-square was used for statistical significance and odds ratio (OR) to assess strength of association. Statistical significance was set at the .05 level. Physiologic variables studied included mean arterial pressure (MAP), heart rate (HR), respiratory rate (RR) and shock index (SI). Sixty-three (63) patients were included. Outcome variables included a mean hospital length of stay (HLOS) of 13.75 days (SD = 9.97), mean ventilator days of 4.93 (SD = 7.87), in-hospital mortality of 22 out of 63 (34.9%), and mean intensive care unit length-of-stay (ICU-LOS) of 7.02 days (SD = 7.98). Although SI and RR were found to predict intensive care unit (ICU) admissions, [OR 5.96 (CI, 1.49-25.78; P = .003) and OR 4.81 (CI, 1.16-21.01; P = .0116), respectively] none of the studied variables were found to predict mortality (MAP <65 mmHg: P = .39; HR >90: P = .60; RR >20 P = .11; SI >0.7 P = .35). This study demonstrated that the out-of-hospital shock index and respiratory rate have high predictability for ICU admission. Further studies should include the development of an out-of-hospital sepsis score.

Population activity statistics dissect subthreshold and spiking variability in V1.

PubMed

Bányai, Mihály; Koman, Zsombor; Orbán, Gergő

2017-07-01

Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject of choice. We investigate two competing models, the doubly stochastic Poisson (DSP) model assuming stochasticity at spike generation, and the rectified Gaussian (RG) model tracing variability back to membrane potential variance, to analyze stimulus-dependent modulation of both single-neuron and pairwise response statistics. Using a pair of model neurons, we demonstrate that the two models predict similar single-cell statistics. However, DSP and RG models have contradicting predictions on the joint statistics of spiking responses. To test the models against data, we build a population model to simulate stimulus change-related modulations in pairwise response statistics. We use single-unit data from the primary visual cortex (V1) of monkeys to show that while model predictions for variance are qualitatively similar to experimental data, only the RG model's predictions are compatible with joint statistics. These results suggest that models using Poisson-like variability might fail to capture important properties of response statistics. We argue that membrane potential-level modeling of stochasticity provides an efficient strategy to model correlations. NEW & NOTEWORTHY Neural variability and covariability are puzzling aspects of cortical computations. For efficient decoding and prediction, models of information encoding in neural populations hinge on an appropriate model of variability. Our work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability. Contrasting model predictions with neuronal data provides hints on the noise sources in spiking and provides constraints on statistical models of population activity. Copyright © 2017 the American Physiological Society.

Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

DTIC Science & Technology

2015-07-15

Long-term effects on cancer survivors’ quality of life of physical training versus physical training combined with cognitive-behavioral therapy ...COMPARISON OF NEURAL NETWORK AND LINEAR REGRESSION MODELS IN STATISTICALLY PREDICTING MENTAL AND PHYSICAL HEALTH STATUS OF BREAST...34Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

Syndromic surveillance models using Web data: the case of scarlet fever in the UK.

PubMed

Samaras, Loukas; García-Barriocanal, Elena; Sicilia, Miguel-Angel

2012-03-01

Recent research has shown the potential of Web queries as a source for syndromic surveillance, and existing studies show that these queries can be used as a basis for estimation and prediction of the development of a syndromic disease, such as influenza, using log linear (logit) statistical models. Two alternative models are applied to the relationship between cases and Web queries in this paper. We examine the applicability of using statistical methods to relate search engine queries with scarlet fever cases in the UK, taking advantage of tools to acquire the appropriate data from Google, and using an alternative statistical method based on gamma distributions. The results show that using logit models, the Pearson correlation factor between Web queries and the data obtained from the official agencies must be over 0.90, otherwise the prediction of the peak and the spread of the distributions gives significant deviations. In this paper, we describe the gamma distribution model and show that we can obtain better results in all cases using gamma transformations, and especially in those with a smaller correlation factor.

The skeletal maturation status estimated by statistical shape analysis: axial images of Japanese cervical vertebra.

PubMed

Shin, S M; Kim, Y-I; Choi, Y-S; Yamaguchi, T; Maki, K; Cho, B-H; Park, S-B

2015-01-01

To evaluate axial cervical vertebral (ACV) shape quantitatively and to build a prediction model for skeletal maturation level using statistical shape analysis for Japanese individuals. The sample included 24 female and 19 male patients with hand-wrist radiographs and CBCT images. Through generalized Procrustes analysis and principal components (PCs) analysis, the meaningful PCs were extracted from each ACV shape and analysed for the estimation regression model. Each ACV shape had meaningful PCs, except for the second axial cervical vertebra. Based on these models, the smallest prediction intervals (PIs) were from the combination of the shape space PCs, age and gender. Overall, the PIs of the male group were smaller than those of the female group. There was no significant correlation between centroid size as a size factor and skeletal maturation level. Our findings suggest that the ACV maturation method, which was applied by statistical shape analysis, could confirm information about skeletal maturation in Japanese individuals as an available quantifier of skeletal maturation and could be as useful a quantitative method as the skeletal maturation index.

The skeletal maturation status estimated by statistical shape analysis: axial images of Japanese cervical vertebra

PubMed Central

Shin, S M; Choi, Y-S; Yamaguchi, T; Maki, K; Cho, B-H; Park, S-B

2015-01-01

Objectives: To evaluate axial cervical vertebral (ACV) shape quantitatively and to build a prediction model for skeletal maturation level using statistical shape analysis for Japanese individuals. Methods: The sample included 24 female and 19 male patients with hand–wrist radiographs and CBCT images. Through generalized Procrustes analysis and principal components (PCs) analysis, the meaningful PCs were extracted from each ACV shape and analysed for the estimation regression model. Results: Each ACV shape had meaningful PCs, except for the second axial cervical vertebra. Based on these models, the smallest prediction intervals (PIs) were from the combination of the shape space PCs, age and gender. Overall, the PIs of the male group were smaller than those of the female group. There was no significant correlation between centroid size as a size factor and skeletal maturation level. Conclusions: Our findings suggest that the ACV maturation method, which was applied by statistical shape analysis, could confirm information about skeletal maturation in Japanese individuals as an available quantifier of skeletal maturation and could be as useful a quantitative method as the skeletal maturation index. PMID:25411713

Early rectal stenosis following stapled rectal mucosectomy for hemorrhoids

PubMed Central

Petersen, Sven; Hellmich, Gunter; Schumann, Dietrich; Schuster, Anja; Ludwig, Klaus

2004-01-01

Background Within the last years, stapled rectal mucosectomy (SRM) has become a widely accepted procedure for second and third degree hemorrhoids. One of the delayed complications is a stenosis of the lower rectum. In order to evaluate the specific problem of rectal stenosis following SRM we reviewed our data with special respect to potential predictive factors or stenotic events. Methods A retrospective analysis of 419 consecutive patients, which underwent SRM from December 1998 to August 2003 was performed. Only patients with at least one follow-up check were evaluated, thus the analysis includes 289 patients with a mean follow-up of 281 days (±18 days). For statistic analysis the groups with and without stenosis were evaluated using the Chi-Square Test, using the Kaplan-Meier statistic the actuarial incidence for rectal stenosis was plotted. Results Rectal stenosis was observed in 9 patients (3.1%), eight of these stenoses were detected within the first 100 days after surgery; the median time to stenosis was 95 days. Only one patient had a rectal stenosis after more than one year. 8 of the 9 patients had no obstructive symptoms, however the remaining patients complained of obstructive defecation and underwent surgery for transanal strictureplasty with electrocautery. A statistical analysis revealed that patients with stenosis had significantly more often prior treatment for hemorrhoids (p < 0.01). According to the SRM only severe postoperative pain was significantly associated with stenoses (p < 0.01). Other factors, such as gender (p = 0.11), surgical technique (p = 0.25), revision (p = 0.79) or histological evidence of squamous skin (p = 0.69) showed no significance. Conclusion Rectal stenosis is an uncommon event after SRM. Early stenosis will occur within the first three months after surgery. The majority of the stenoses are without clinical relevance. Only one of nine patients had to undergo surgery for a relevant stenosis. The predictive factor for stenosis in the patient-characteristics is previous interventions for hemorrhoids, severe postoperative pain might also predict rectal stenosis. PMID:15153248

Predicting the 10-year risk of hip and major osteoporotic fracture in rheumatoid arthritis and in the general population: an independent validation and update of UK FRAX without bone mineral density

PubMed Central

Klop, Corinne; de Vries, Frank; Bijlsma, Johannes W J; Leufkens, Hubert G M; Welsing, Paco M J

2016-01-01

Objectives FRAX incorporates rheumatoid arthritis (RA) as a dichotomous predictor for predicting the 10-year risk of hip and major osteoporotic fracture (MOF). However, fracture risk may deviate with disease severity, duration or treatment. Aims were to validate, and if needed to update, UK FRAX for patients with RA and to compare predictive performance with the general population (GP). Methods Cohort study within UK Clinical Practice Research Datalink (CPRD) (RA: n=11 582, GP: n=38 755), also linked to hospital admissions for hip fracture (CPRD-Hospital Episode Statistics, HES) (RA: n=7221, GP: n=24 227). Predictive performance of UK FRAX without bone mineral density was assessed by discrimination and calibration. Updating methods included recalibration and extension. Differences in predictive performance were assessed by the C-statistic and Net Reclassification Improvement (NRI) using the UK National Osteoporosis Guideline Group intervention thresholds. Results UK FRAX significantly overestimated fracture risk in patients with RA, both for MOF (mean predicted vs observed 10-year risk: 13.3% vs 8.4%) and hip fracture (CPRD: 5.5% vs 3.1%, CPRD-HES: 5.5% vs 4.1%). Calibration was good for hip fracture in the GP (CPRD-HES: 2.7% vs 2.4%). Discrimination was good for hip fracture (RA: 0.78, GP: 0.83) and moderate for MOF (RA: 0.69, GP: 0.71). Extension of the recalibrated UK FRAX using CPRD-HES with duration of RA disease, glucocorticoids (>7.5 mg/day) and secondary osteoporosis did not improve the NRI (0.01, 95% CI −0.04 to 0.05) or C-statistic (0.78). Conclusions UK FRAX overestimated fracture risk in RA, but performed well for hip fracture in the GP after linkage to hospitalisations. Extension of the recalibrated UK FRAX did not improve predictive performance. PMID:26984006

Post-fire debris flow prediction in Western United States: Advancements based on a nonparametric statistical technique

NASA Astrophysics Data System (ADS)

Nikolopoulos, E. I.; Destro, E.; Bhuiyan, M. A. E.; Borga, M., Sr.; Anagnostou, E. N.

2017-12-01

Fire disasters affect modern societies at global scale inducing significant economic losses and human casualties. In addition to their direct impacts they have various adverse effects on hydrologic and geomorphologic processes of a region due to the tremendous alteration of the landscape characteristics (vegetation, soil properties etc). As a consequence, wildfires often initiate a cascade of hazards such as flash floods and debris flows that usually follow the occurrence of a wildfire thus magnifying the overall impact in a region. Post-fire debris flows (PFDF) is one such type of hazards frequently occurring in Western United States where wildfires are a common natural disaster. Prediction of PDFD is therefore of high importance in this region and over the last years a number of efforts from United States Geological Survey (USGS) and National Weather Service (NWS) have been focused on the development of early warning systems that will help mitigate PFDF risk. This work proposes a prediction framework that is based on a nonparametric statistical technique (random forests) that allows predicting the occurrence of PFDF at regional scale with a higher degree of accuracy than the commonly used approaches that are based on power-law thresholds and logistic regression procedures. The work presented is based on a recently released database from USGS that reports a total of 1500 storms that triggered and did not trigger PFDF in a number of fire affected catchments in Western United States. The database includes information on storm characteristics (duration, accumulation, max intensity etc) and other auxiliary information of land surface properties (soil erodibility index, local slope etc). Results show that the proposed model is able to achieve a satisfactory prediction accuracy (threat score > 0.6) superior of previously published prediction frameworks highlighting the potential of nonparametric statistical techniques for development of PFDF prediction systems.

Socio-Demographic and Clinical Characteristics are Not Clinically Useful Predictors of Refill Adherence in Patients with Hypertension

PubMed Central

Steiner, John F.; Ho, P. Michael; Beaty, Brenda L.; Dickinson, L. Miriam; Hanratty, Rebecca; Zeng, Chan; Tavel, Heather M.; Havranek, Edward P.; Davidson, Arthur J.; Magid, David J.; Estacio, Raymond O.

2009-01-01

Background Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in two health care delivery systems. Methods and Results Retrospective cohort studies of hypertension registries in an inner-city health care delivery system (N = 17176) and a health maintenance organization (N = 94297) in Denver, Colorado. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than non-adherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed less than three antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from non-adherent patients (C-statistic = 0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from non-adherent individuals (C-statistic = 0.576). Conclusions Although certain socio-demographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment. PMID:20031876

The relationship between twelve-month home stimulation and school achievement.

PubMed

van Doorninck, W J; Caldwell, B M; Wright, C; Frankenburg, W K

1981-09-01

Home Observation for Measurement of the Environment (HOME) was designed to reflect parental support of early cognitive and socioemotional development. 12-month HOME scores were correlated with elementary school achievement, 5--9 years later. 50 low-income children were rank ordered by a weighted average of centile estimates of achievement test scores, letter grades, and curriculum levels in reading and math. 24 children were classified as having significant school achievement problems. The HOME total score correlated significantly, r = .37, with school centile scores among the low-income families. The statistically more appropriate contingency table analysis revealed a 68% correct classification rate and a significantly reduced error rate over random or blanket prediction. The results supported the predictive value of the 12-month HOME for school achievement among low-income families. In an additional sample of 21 middle-income families, there was insufficient variability among HOME scores to allow prediction. The HOME total scores were highly correlated, r = .86, among siblings tested at least 10 months apart.

Visumax femtolasik versus Moria M2 microkeratome in mild to moderate myopia: efficacy, safety, predictability, aberrometric changes and flap thickness predictability.

PubMed

Torky, Magda A; Al Zafiri, Yousif A; Khattab, Abeer M; Farag, Rania K; Awad, Eman A

2017-07-17

This is an interventional prospective clinical study which was conducted to evaluate the efficacy, safety, predictability, ocular aberrations, and flap thickness predictability of Visumax femtosecond laser (FSL) compared to Moria M2 microkeratome (MK) in mild to moderate myopia. This study included 60 eyes who were divided into two groups. Thirty eyes in group (I) in which the flap was created with Visumax FSL, while in group II (30 eyes) the Moria M2 MK was used. Keratometric, refractive, and aberrometric measurements were compared preoperatively and 3 months postoperatively. The intraoperative subtraction pachymetry (the SP 100 Handy pachymeter (Tomey, Nagoya, Japan) was used for preoperative pachymetry and flap thickness measurement. No significant difference was found between the two groups in regards to postoperative manifest sphere, spherical equivalent, astigmatism, safety indices nor ocular aberrations. Twenty six eyes (86.6%) in group I and 23 eyes in group II (76.6%) were within ±0.5D of the intended correction and 23 eyes (76.6%) in group I and 15 eyes in group II (50%) were within ±0.25D of the intended correction. In group I, the mean postoperative actual flap thickness was 100.12 ± 16.1 μm (81 to 122 μm), while in group II, it was 104.6 ± 20.1 μm (62 to 155 μm). The difference was statistically significant (p = 0.001). Both Visumax and Moria M2 MK are safe and effective in treating myopia with no statistically significant difference in induction of ocular aberrations but with potential advantage for Visumax regarding predictability. More accurate flap thickness is achieved with Visumax femtolasik. This study was retrospectively registered on 19/6/2017. Trial registration number NCT03193411 , clinicalTrials.gov .

Cosmic shear measurements with Dark Energy Survey Science Verification data

DOE PAGES

Becker, M. R.

2016-07-06

Here, we present measurements of weak gravitational lensing cosmic shear two-point statistics using Dark Energy Survey Science Verification data. We demonstrate that our results are robust to the choice of shear measurement pipeline, either ngmix or im3shape, and robust to the choice of two-point statistic, including both real and Fourier-space statistics. Our results pass a suite of null tests including tests for B-mode contamination and direct tests for any dependence of the two-point functions on a set of 16 observing conditions and galaxy properties, such as seeing, airmass, galaxy color, galaxy magnitude, etc. We use a large suite of simulationsmore » to compute the covariance matrix of the cosmic shear measurements and assign statistical significance to our null tests. We find that our covariance matrix is consistent with the halo model prediction, indicating that it has the appropriate level of halo sample variance. We also compare the same jackknife procedure applied to the data and the simulations in order to search for additional sources of noise not captured by the simulations. We find no statistically significant extra sources of noise in the data. The overall detection significance with tomography for our highest source density catalog is 9.7σ. Cosmological constraints from the measurements in this work are presented in a companion paper.« less

Observation of the rare Bs0 →µ+µ- decay from the combined analysis of CMS and LHCb data

NASA Astrophysics Data System (ADS)

Cms Collaboration; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Krätschmer, I.; Liko, D.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Bansal, S.; Cornelis, T.; de Wolf, E. A.; Janssen, X.; Knutsson, A.; Lauwers, J.; Luyckx, S.; Ochesanu, S.; Rougny, R.; van de Klundert, M.; van Haevermaet, H.; van Mechelen, P.; van Remortel, N.; van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Daci, N.; Heracleous, N.; Keaveney, J.; Lowette, S.; Maes, M.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; van Doninck, W.; van Mulders, P.; van Onsem, G. P.; Villella, I.; Caillol, C.; Clerbaux, B.; de Lentdecker, G.; Dobur, D.; Favart, L.; Gay, A. P. R.; Grebenyuk, A.; Léonard, A.; Mohammadi, A.; Perniè, L.; Randle-Conde, A.; Reis, T.; Seva, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Zenoni, F.; Adler, V.; Beernaert, K.; Benucci, L.; Cimmino, A.; Costantini, S.; Crucy, S.; Dildick, S.; Fagot, A.; Garcia, G.; McCartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Salva Diblen, S.; Sigamani, M.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bruno, G.; Castello, R.; Caudron, A.; Ceard, L.; da Silveira, G. G.; Delaere, C.; Du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Nuttens, C.; Pagano, D.; Perrini, L.; Pin, A.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Aldá Júnior, W. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Dos Reis Martins, T.; Mora Herrera, C.; Pol, M. E.; Rebello Teles, P.; Carvalho, W.; Chinellato, J.; Custódio, A.; da Costa, E. M.; de Jesus Damiao, D.; de Oliveira Martins, C.; Fonseca de Souza, S.; Malbouisson, H.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Prado da Silva, W. L.; Santaolalla, J.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Bernardes, C. A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Aleksandrov, A.; Genchev, V.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Piperov, S.; Rodozov, M.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Cheng, T.; Du, R.; Jiang, C. H.; Plestina, R.; Romeo, F.; Tao, J.; Wang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Zou, W.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; Gomez Moreno, B.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Polic, D.; Puljak, I.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Kadija, K.; Luetic, J.; Mekterovic, D.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Bodlak, M.; Finger, M.; Finger, M., Jr.; Assran, Y.; Ellithi Kamel, A.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Murumaa, M.; Raidal, M.; Tiko, A.; Eerola, P.; Fedi, G.; Voutilainen, M.; Härkönen, J.; Karimäki, V.; Kinnunen, R.; Kortelainen, M. J.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Baffioni, S.; Beaudette, F.; Busson, P.; Charlot, C.; Dahms, T.; Dalchenko, M.; Dobrzynski, L.; Filipovic, N.; Florent, A.; Granier de Cassagnac, R.; Mastrolorenzo, L.; Miné, P.; Mironov, C.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Regnard, S.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Veelken, C.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Chabert, E. C.; Collard, C.; Conte, E.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A.-C.; Skovpen, K.; van Hove, P.; Gadrat, S.; Beauceron, S.; Beaupere, N.; Boudoul, G.; Bouvier, E.; Brochet, S.; Carrillo Montoya, C. 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I.; Wardle, N.; Wöhri, H. K.; Wollny, H.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Renker, D.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Buchmann, M. A.; Casal, B.; Chanon, N.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dünser, M.; Eller, P.; Grab, C.; Hits, D.; Hoss, J.; Lustermann, W.; Mangano, B.; Marini, A. C.; Marionneau, M.; Martinez Ruiz Del Arbol, P.; Masciovecchio, M.; Meister, D.; Mohr, N.; Musella, P.; Nägeli, C.; Nessi-Tedaldi, F.; Pandolfi, F.; Pauss, F.; Peruzzi, M.; Quittnat, M.; Rebane, L.; Rossini, M.; Starodumov, A.; Takahashi, M.; Theofilatos, K.; Wallny, R.; Weber, H. A.; Amsler, C.; Canelli, M. F.; Chiochia, V.; de Cosa, A.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Lange, C.; Millan Mejias, B.; Ngadiuba, J.; Pinna, D.; Robmann, P.; Ronga, F. J.; Taroni, S.; Verzetti, M.; Yang, Y.; Cardaci, M.; Chen, K. H.; Ferro, C.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Volpe, R.; Yu, S. 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Y.; de Bruyn, K.; Farinelli, C.; Heijne, V.; Hulsbergen, W.; Jans, E.; Koppenburg, P.; Kozlinskiy, A.; van Leerdam, J.; Merk, M.; Oggero, S.; Pellegrino, A.; Snoek, H.; van Tilburg, J.; Tsopelas, P.; Tuning, N.; de Vries, J. A.; Ketel, T.; Koopman, R. F.; Lambert, R. W.; Martinez Santos, D.; Raven, G.; Schiller, M.; Syropoulos, V.; Tolk, S.; Dovbnya, A.; Kandybei, S.; Raniuk, I.; Okhrimenko, O.; Pugatch, V.; Bifani, S.; Farley, N.; Griffith, P.; Kenyon, I. R.; Lazzeroni, C.; Mazurov, A.; McCarthy, J.; Pescatore, L.; Watson, N. K.; Williams, M. P.; Adinolfi, M.; Benton, J.; Brook, N. H.; Cook, A.; Coombes, M.; Dalseno, J.; Hampson, T.; Harnew, S. T.; Naik, P.; Price, E.; Prouve, C.; Rademacker, J. H.; Richards, S.; Saunders, D. M.; Skidmore, N.; Souza, D.; Velthuis, J. J.; Voong, D.; Barter, W.; Bettler, M.-O.; Cliff, H. V.; Evans, H.-M.; Garra Tico, J.; Gibson, V.; Gregson, S.; Haines, S. C.; Jones, C. R.; Sirendi, M.; Smith, J.; Ward, D. R.; Wotton, S. A.; Wright, S.; Back, J. J.; Blake, T.; Craik, D. C.; Crocombe, A. C.; Dossett, D.; Gershon, T.; Kreps, M.; Langenbruch, C.; Latham, T.; O'Hanlon, D. P.; Pilař, T.; Poluektov, A.; Reid, M. M.; Silva Coutinho, R.; Wallace, C.; Whitehead, M.; Easo, S.; Nandakumar, R.; Papanestis, A.; Ricciardi, S.; Wilson, F. F.; Carson, L.; Clarke, P. E. L.; Cowan, G. A.; Eisenhardt, S.; Ferguson, D.; Lambert, D.; Luo, H.; Morris, A.-B.; Muheim, F.; Needham, M.; Playfer, S.; Alexander, M.; Beddow, J.; Dean, C.-T.; Eklund, L.; Hynds, D.; Karodia, S.; Longstaff, I.; Ogilvy, S.; Pappagallo, M.; Sail, P.; Skillicorn, I.; Soler, F. J. P.; Spradlin, P.; Affolder, A.; Bowcock, T. J. V.; Brown, H.; Casse, G.; Donleavy, S.; Dreimanis, K.; Farry, S.; Fay, R.; Hennessy, K.; Hutchcroft, D.; Liles, M.; McSkelly, B.; Patel, G. D.; Price, J. D.; Pritchard, A.; Rinnert, K.; Shears, T.; Smith, N. A.; Ciezarek, G.; Cunliffe, S.; Currie, R.; Egede, U.; Fol, P.; Golutvin, A.; Hall, S.; McCann, M.; Owen, P.; Patel, M.; Petridis, K.; Redi, F.; Sepp, I.; Smith, E.; Sutcliffe, W.; Websdale, D.; Appleby, R. B.; Barlow, R. J.; Bird, T.; Bjørnstad, P. M.; Borghi, S.; Brett, D.; Brodzicka, J.; Capriotti, L.; Chen, S.; de Capua, S.; Dujany, G.; Gersabeck, M.; Harrison, J.; Hombach, C.; Klaver, S.; Lafferty, G.; McNab, A.; Parkes, C.; Pearce, A.; Reichert, S.; Rodrigues, E.; Rodriguez Perez, P.; Smith, M.; Cheung, S.-F.; Derkach, D.; Evans, T.; Gauld, R.; Greening, E.; Harnew, N.; Hill, D.; Hunt, P.; Hussain, N.; Jalocha, J.; John, M.; Lupton, O.; Malde, S.; Smith, E.; Stevenson, S.; Thomas, C.; Topp-Joergensen, S.; Torr, N.; Wilkinson, G.; Counts, I.; Ilten, P.; Williams, M.; Andreassen, R.; Davis, A.; de Silva, W.; Meadows, B.; Sokoloff, M. D.; Sun, L.; Todd, J.; Andrews, J. E.; Hamilton, B.; Jawahery, A.; Wimberley, J.; Artuso, M.; Blusk, S.; Borgia, A.; Britton, T.; Ely, S.; Gandini, P.; Garofoli, J.; Gui, B.; Hadjivasiliou, C.; Jurik, N.; Kelsey, M.; Mountain, R.; Pal, B. K.; Skwarnicki, T.; Stone, S.; Wang, J.; Xing, Z.; Zhang, L.; Baesso, C.; Cruz Torres, M.; Göbel, C.; Molina Rodriguez, J.; Xie, Y.; Milanes, D. A.; Grünberg, O.; Heß, M.; Voß, C.; Waldi, R.; Likhomanenko, T.; Malinin, A.; Shevchenko, V.; Ustyuzhanin, A.; Martinez Vidal, F.; Oyanguren, A.; Ruiz Valls, P.; Sanchez Mayordomo, C.; Onderwater, C. J. G.; Wilschut, H. W.; Pesen, E.

2015-06-01

The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. The probabilities, or branching fractions, of the strange B meson () and the B0 meson decaying into two oppositely charged muons (μ+ and μ-) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the and decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B0 mesons. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton-proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the µ+µ- decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. Furthermore, we obtained evidence for the µ+µ- decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton-proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of and B0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.

Surgery residency curriculum examination scores predict future American Board of Surgery in-training examination performance.

PubMed

Webb, Travis P; Paul, Jasmeet; Treat, Robert; Codner, Panna; Anderson, Rebecca; Redlich, Philip

2014-01-01

A protected block curriculum (PBC) with postcurriculum examinations for all surgical residents has been provided to assure coverage of core curricular topics. Biannual assessment of resident competency will soon be required by the Next Accreditation System. To identify opportunities for early medical knowledge assessment and interventions, we examined whether performance in postcurriculum multiple-choice examinations (PCEs) is predictive of performance in the American Board of Surgery In-Training Examination (ABSITE) and clinical service competency assessments. Retrospective single-institutional education research study. Academic general surgery residency program. A total of 49 surgical residents. Data for PGY1 and PGY2 residents participating in the 2008 to 2012 PBC are included. Each resident completed 6 PCEs during each year. The results of 6 examinations were correlated to percentage-correct ABSITE scores and clinical assessments based on the 6 Accreditation Council for Graduate Medical Education core competencies. Individual ABSITE performance was compared between PGY1 and PGY2. Statistical analysis included multivariate linear regression and bivariate Pearson correlations. A total of 49 residents completed the PGY1 PBC and 36 completed the PGY2 curriculum. Linear regression analysis of percentage-correct ABSITE and PCE scores demonstrated a statistically significant correlation between the PGY1 PCE 1 score and the subsequent PGY1 ABSITE score (p = 0.037, β = 0.299). Similarly, the PGY2 PCE 1 score predicted performance in the PGY2 ABSITE (p = 0.015, β = 0.383). The ABSITE scores correlated between PGY1 and PGY2 with statistical significance, r = 0.675, p = 0.001. Performance on the 6 Accreditation Council for Graduate Medical Education core competencies correlated between PGY1 and PGY2, r = 0.729, p = 0.001, but did not correlate with PCE scores during either years. Within a mature PBC, early performance in a PGY1 and PGY2 PCE is predictive of performance in the respective ABSITE. This information can be used for formative assessment and early remediation of residents who are predicted to be at risk for poor performance in the ABSITE. Copyright © 2014 Association of Program Directors in Surgery. All rights reserved.

The role of descriptive norm within the theory of planned behavior in predicting Korean Americans' exercise behavior.

PubMed

Lee, Hyo

2011-08-01

There are few studies investigating psychosocial mechanisms in Korean Americans' exercise behavior. The present study tested the usefulness of the theory of planned behavior in predicting Korean American's exercise behavior and whether the descriptive norm (i.e., perceptions of what others do) improved the predictive validity of the theory of planned behavior. Using a retrospective design and self-report measures, web-survey responses from 198 Korean-American adults were analyzed using hierarchical regression analyses. The theory of planned behavior constructs accounted for 31% of exercise behavior and 43% of exercise intention. Intention and perceived behavioral control were significant predictors of exercise behavior. Although the descriptive norm did not augment the theory of planned behavior, all original constructs--attitude, injunctive norm (a narrow definition of subjective norm), and perceived behavioral control--statistically significantly predicted leisure-time physical activity intention. Future studies should consider random sampling, prospective design, and objective measures of physical activity.

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

DOE PAGES

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

2017-05-30

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

Carotid Artery Plaque Morphology and Composition in Relation to Incident Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis (MESA)

PubMed Central

Zavodni, Anna E. H.; Wasserman, Bruce A.; McClelland, Robyn L.; Gomes, Antoinette S.; Folsom, Aaron R.; Polak, Joseph F.; Lima, João A. C.

2014-01-01

Purpose To determine if carotid plaque morphology and composition with magnetic resonance (MR) imaging can be used to identify asymptomatic subjects at risk for cardiovascular events. Materials and Methods Institutional review boards at each site approved the study, and all sites were Health Insurance Portability and Accountability Act (HIPAA) compliant. A total of 946 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) were evaluated with MR imaging and ultrasonography (US). MR imaging was used to define carotid plaque composition and remodeling index (wall area divided by the sum of wall area and lumen area), while US was used to assess carotid wall thickness. Incident cardiovascular events, including myocardial infarction, resuscitated cardiac arrest, angina, stroke, and death, were ascertained for an average of 5.5 years. Multivariable Cox proportional hazards models, C statistics, and net reclassification improvement (NRI) for event prediction were determined. Results Cardiovascular events occurred in 59 (6%) of participants. Carotid IMT as well as MR imaging remodeling index, lipid core, and calcium in the internal carotid artery were significant predictors of events in univariate analysis (P < .001 for all). For traditional risk factors, the C statistic for event prediction was 0.696. For MR imaging remodeling index and lipid core, the C statistic was 0.734 and the NRI was 7.4% and 15.8% for participants with and those without cardiovascular events, respectively (P = .02). The NRI for US IMT in addition to traditional risk factors was not significant. Conclusion The identification of vulnerable plaque characteristics with MR imaging aids in cardiovascular disease prediction and improves the reclassification of baseline cardiovascular risk. © RSNA, 2014 PMID:24592924

Reliability of the Watch-PAT 200 in Detecting Sleep Apnea in Highway Bus Drivers

PubMed Central

Yuceege, Melike; Firat, Hikmet; Demir, Ahmet; Ardic, Sadik

2013-01-01

Objective: To predict the validity of Watch-PAT (WP) device for sleep disordered breathing (SDB) among highway bus drivers. Method: A total number of 90 highway bus drivers have undergone polysomnography (PSG) and Watch-PAT test simultaneously. Routine blood tests and the routine ear-nose-throat (ENT) exams have been done as well. Results: The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 89.1%, 76.9%, 82% and 85.7% for RDI > 15, respectively. WRDI, WODI, W < 90% duration and Wmean SaO2 results were well correlated with the PSG results. In the sensitivity and specificity analysis, when diagnosis of sleep apnea was defined for different cut-off values of RDI of 5, 10 and 15, AUC (95%CI) were found as 0.84 (0.74-0.93), 0.87 (95%CI: 0.79-0.94) and 0.91 (95%CI: 0.85-0.97), respectively. There were no statistically significant differences between Stage1+2/Wlight and Stage REM/WREM. The percentage of Stage 3 sleep had difference significant statistically from the percentage of Wdeep. Total sleep times in PSG and WP showed no statistically important difference. Total NREM duration and total WNREM duration had no difference either. Conclusion: Watch-PAT device is helpful in detecting SDB with RDI > 15 in highway bus drivers, especially in drivers older than 45 years, but has limited value in drivers younger than 45 years old who have less risk for OSA. Therefore, WP can be used in the former group when PSG is not easily available. Citation: Yuceege M; Firat F; Demir A; Ardic S. Reliability of the Watch-PAT 200 in detecting sleep apnea in highway bus drivers. J Clin Sleep Med 2013;9(4):339-344. PMID:23585749

Predictive importance of anthropometric and training data in recreational male Ironman triathletes and marathon runners: comment on the study by Gianoli, et al. (2012).

PubMed

Burtscher, Martin; Gatterer, Hannes

2013-04-01

Anthropometric and training data have been reported as statistically significant predictors of race performance in endurance events. However, it is well established that physiological characteristics, i.e., maximal oxygen uptake (VO2max), the use of a high percentage of VO2max during sustained exercise, and work efficiency are predominant predictors of performance in those events. Thus, the essential issue is whether the anthropometric and training data give additional predictive power beyond these other measures.

A prospective cohort study on the clinical utility of second trimester mean arterial blood pressure in the prediction of late-onset preeclampsia among Nigerian women.

PubMed

Udenze, I C; Arikawe, A P; Makwe, C C; Olowoselu, O F

2017-06-01

Early detection of preeclampsia will help reduce the morbidities and mortalities associated with the disorder. Late-onset preeclampsia was the predominant presentation in this cohort. The search for biomarkers for predicting preeclampsia is still ongoing. Mean arterial blood pressure (MABP), which has the advantage of presenting a single cutoff value compared with the use of systolic and diastolic blood pressure measurements, merits evaluation. The study aims to evaluate the clinical utility of second trimester MABP in the prediction of preeclampsia. This was a prospective cohort study of 155 normotensive, nonproteinuric pregnant women without prior history of gestational hypertension. The women were booked patients attending the antenatal clinic at the Lagos University Teaching Hospital and were all in their second trimesters of pregnancy. The outcome measures were systolic blood pressure, diastolic blood pressure, and MABP. The end point of the study was the development of preeclampsia. The diagnosis of preeclampsia was made by the attending obstetrician. The data were analyzed using the IBM SPSS statistical software. Statistical significance was set at P < 0.05. One hundred and fifty-five pregnant women participated in the study. Eleven (7.1%) of the women developed preeclampsia after 34 weeks gestation and 144 (92.9%) had normal pregnancy. The mean gestational age at the time of assessment was 18.88 ± 3.15 weeks with a range of 14 weeks to 27 completed weeks. There was a statistically significant increase in the systolic blood pressure, diastolic blood pressure, and MABP values in the group of women who later developed preeclampsia, P = 0.005, 0.001, and <0.001, respectively. At a false-positive rate of 10%, MABP value of 88.33 mmHg predicted preeclampsia with a specificity of 90% and a sensitivity of 45.5%, P <0.05. The area under the receiver-operative characteristics curve (AUC) was 0.732 (95% confidence interval, 0.544-0.919, P = 0.011). The negative predictive value (NPV) was 88.88% and the positive predictive value (PPV) was 45.45%, P < 0.05. At an MABP cutoff of 88.33 mmHg, preeclampsia was predicted with a relative risk of 4.44 and a positive likelihood ratio of 6.46, P < 0.05. With an AUC of 0.732, MABP performed moderately (considering that excellent performance has an AUC of 1.0) in the prediction of late-onset preeclampsia in Nigerian women. Its high NPV suggests a strong ability to rule out preeclampsia and help to appropriate management.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

Improved accuracy of supervised CRM discovery with interpolated Markov models and cross-species comparison

PubMed Central

Kazemian, Majid; Zhu, Qiyun; Halfon, Marc S.; Sinha, Saurabh

2011-01-01

Despite recent advances in experimental approaches for identifying transcriptional cis-regulatory modules (CRMs, ‘enhancers’), direct empirical discovery of CRMs for all genes in all cell types and environmental conditions is likely to remain an elusive goal. Effective methods for computational CRM discovery are thus a critically needed complement to empirical approaches. However, existing computational methods that search for clusters of putative binding sites are ineffective if the relevant TFs and/or their binding specificities are unknown. Here, we provide a significantly improved method for ‘motif-blind’ CRM discovery that does not depend on knowledge or accurate prediction of TF-binding motifs and is effective when limited knowledge of functional CRMs is available to ‘supervise’ the search. We propose a new statistical method, based on ‘Interpolated Markov Models’, for motif-blind, genome-wide CRM discovery. It captures the statistical profile of variable length words in known CRMs of a regulatory network and finds candidate CRMs that match this profile. The method also uses orthologs of the known CRMs from closely related genomes. We perform in silico evaluation of predicted CRMs by assessing whether their neighboring genes are enriched for the expected expression patterns. This assessment uses a novel statistical test that extends the widely used Hypergeometric test of gene set enrichment to account for variability in intergenic lengths. We find that the new CRM prediction method is superior to existing methods. Finally, we experimentally validate 12 new CRM predictions by examining their regulatory activity in vivo in Drosophila; 10 of the tested CRMs were found to be functional, while 6 of the top 7 predictions showed the expected activity patterns. We make our program available as downloadable source code, and as a plugin for a genome browser installed on our servers. PMID:21821659

Risk prediction models of breast cancer: a systematic review of model performances.

PubMed

Anothaisintawee, Thunyarat; Teerawattananon, Yot; Wiratkapun, Chollathip; Kasamesup, Vijj; Thakkinstian, Ammarin

2012-05-01

The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87-1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53-0.66) and in external validation (concordance statistics: 0.56-0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.

Vitamin D and ferritin correlation with chronic neck pain using standard statistics and a novel artificial neural network prediction model.

PubMed

Eloqayli, Haytham; Al-Yousef, Ali; Jaradat, Raid

2018-02-15

Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled. Patients and control demographic parameters, height, weight and single measurement of serum vitamin D, Vitamin B12, ferritin, calcium, phosphorus, zinc were obtained. An ANN prediction model was developed. The statistical analysis reveals that patients with chronic neck pain have significantly lower serum Vitamin D and Ferritin (p-value <.05). 90% of patients with chronic neck pain were females. Multilayer Feed Forward Neural Network with Back Propagation(MFFNN) prediction model were developed and designed based on vitamin D and ferritin as input variables and CNP as output. The ANN model output results show that, 92 out of 108 samples were correctly classified with 85% classification accuracy. Although Iron and vitamin D deficiency cannot be isolated as the sole risk factors of chronic neck pain, they should be considered as two modifiable risk. The high prevalence of chronic neck pain, hypovitaminosis D and low ferritin amongst women is of concern. Bioinformatics predictions with artificial neural network can be of future benefit in classification and prediction models for chronic neck pain. We hope this initial work will encourage a future larger cohort study addressing vitamin D and iron correction as modifiable factors and the application of artificial intelligence models in clinical practice.

Statistical validation of a solar wind propagation model from 1 to 10 AU

NASA Astrophysics Data System (ADS)

Zieger, Bertalan; Hansen, Kenneth C.

2008-08-01

A one-dimensional (1-D) numerical magnetohydrodynamic (MHD) code is applied to propagate the solar wind from 1 AU through 10 AU, i.e., beyond the heliocentric distance of Saturn's orbit, in a non-rotating frame of reference. The time-varying boundary conditions at 1 AU are obtained from hourly solar wind data observed near the Earth. Although similar MHD simulations have been carried out and used by several authors, very little work has been done to validate the statistical accuracy of such solar wind predictions. In this paper, we present an extensive analysis of the prediction efficiency, using 12 selected years of solar wind data from the major heliospheric missions Pioneer, Voyager, and Ulysses. We map the numerical solution to each spacecraft in space and time, and validate the simulation, comparing the propagated solar wind parameters with in-situ observations. We do not restrict our statistical analysis to the times of spacecraft alignment, as most of the earlier case studies do. Our superposed epoch analysis suggests that the prediction efficiency is significantly higher during periods with high recurrence index of solar wind speed, typically in the late declining phase of the solar cycle. Among the solar wind variables, the solar wind speed can be predicted to the highest accuracy, with a linear correlation of 0.75 on average close to the time of opposition. We estimate the accuracy of shock arrival times to be as high as 10-15 hours within ±75 d from apparent opposition during years with high recurrence index. During solar activity maximum, there is a clear bias for the model to predicted shocks arriving later than observed in the data, suggesting that during these periods, there is an additional acceleration mechanism in the solar wind that is not included in the model.

Risk prediction score for death of traumatised and injured children

PubMed Central

2014-01-01

Background Injury prediction scores facilitate the development of clinical management protocols to decrease mortality. However, most of the previously developed scores are limited in scope and are non-specific for use in children. We aimed to develop and validate a risk prediction model of death for injured and Traumatised Thai children. Methods Our cross-sectional study included 43,516 injured children from 34 emergency services. A risk prediction model was derived using a logistic regression analysis that included 15 predictors. Model performance was assessed using the concordance statistic (C-statistic) and the observed per expected (O/E) ratio. Internal validation of the model was performed using a 200-repetition bootstrap analysis. Results Death occurred in 1.7% of the injured children (95% confidence interval [95% CI]: 1.57–1.82). Ten predictors (i.e., age, airway intervention, physical injury mechanism, three injured body regions, the Glasgow Coma Scale, and three vital signs) were significantly associated with death. The C-statistic and the O/E ratio were 0.938 (95% CI: 0.929–0.947) and 0.86 (95% CI: 0.70–1.02), respectively. The scoring scheme classified three risk stratifications with respective likelihood ratios of 1.26 (95% CI: 1.25–1.27), 2.45 (95% CI: 2.42–2.52), and 4.72 (95% CI: 4.57–4.88) for low, intermediate, and high risks of death. Internal validation showed good model performance (C-statistic = 0.938, 95% CI: 0.926–0.952) and a small calibration bias of 0.002 (95% CI: 0.0005–0.003). Conclusions We developed a simplified Thai pediatric injury death prediction score with satisfactory calibrated and discriminative performance in emergency room settings. PMID:24575982

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Addition of 24-Hour Heart Rate Variability Parameters to the Cardiovascular Health Study Stroke Risk Score and Prediction of Incident Stroke: The Cardiovascular Health Study.

PubMed

Bodapati, Rohan K; Kizer, Jorge R; Kop, Willem J; Kamel, Hooman; Stein, Phyllis K

2017-07-21

Heart rate variability (HRV) characterizes cardiac autonomic functioning. The association of HRV with stroke is uncertain. We examined whether 24-hour HRV added predictive value to the Cardiovascular Health Study clinical stroke risk score (CHS-SCORE), previously developed at the baseline examination. N=884 stroke-free CHS participants (age 75.3±4.6), with 24-hour Holters adequate for HRV analysis at the 1994-1995 examination, had 68 strokes over ≤8 year follow-up (median 7.3 [interquartile range 7.1-7.6] years). The value of adding HRV to the CHS-SCORE was assessed with stepwise Cox regression analysis. The CHS-SCORE predicted incident stroke (HR=1.06 per unit increment, P =0.005). Two HRV parameters, decreased coefficient of variance of NN intervals (CV%, P =0.031) and decreased power law slope (SLOPE, P =0.033) also entered the model, but these did not significantly improve the c-statistic ( P =0.47). In a secondary analysis, dichotomization of CV% (LOWCV% ≤12.8%) was found to maximally stratify higher-risk participants after adjustment for CHS-SCORE. Similarly, dichotomizing SLOPE (LOWSLOPE <-1.4) maximally stratified higher-risk participants. When these HRV categories were combined (eg, HIGHCV% with HIGHSLOPE), the c-statistic for the model with the CHS-SCORE and combined HRV categories was 0.68, significantly higher than 0.61 for the CHS-SCORE alone ( P =0.02). In this sample of older adults, 2 HRV parameters, CV% and power law slope, emerged as significantly associated with incident stroke when added to a validated clinical risk score. After each parameter was dichotomized based on its optimal cut point in this sample, their composite significantly improved prediction of incident stroke during ≤8-year follow-up. These findings will require validation in separate, larger cohorts. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

A simple rain attenuation model for earth-space radio links operating at 10-35 GHz

NASA Technical Reports Server (NTRS)

Stutzman, W. L.; Yon, K. M.

1986-01-01

The simple attenuation model has been improved from an earlier version and now includes the effect of wave polarization. The model is for the prediction of rain attenuation statistics on earth-space communication links operating in the 10-35 GHz band. Simple calculations produce attenuation values as a function of average rain rate. These together with rain rate statistics (either measured or predicted) can be used to predict annual rain attenuation statistics. In this paper model predictions are compared to measured data from a data base of 62 experiments performed in the U.S., Europe, and Japan. Comparisons are also made to predictions from other models.

Analysis Code - Data Analysis in 'Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications' (LMSMIPNFA) v. 1.0

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

Lewis, John R

R code that performs the analysis of a data set presented in the paper ‘Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications’ by Lewis, J., Zhang, A., Anderson-Cook, C. It provides functions for doing inverse predictions in this setting using several different statistical methods. The data set is a publicly available data set from a historical Plutonium production experiment.

Comparison of Predictive Factors for Postoperative Incontinence of Holmium Laser Enucleation of the Prostate by the Surgeons' Experience During Learning Curve.

PubMed

Shigemura, Katsumi; Tanaka, Kazushi; Yamamichi, Fukashi; Chiba, Koji; Fujisawa, Masato

2016-03-01

To detect predictive factors for postoperative incontinence following holmium laser enucleation of the prostate (HoLEP) according to surgeon experience (beginner or experienced) and preoperative clinical data. Of 224 patients, a total of 203 with available data on incontinence were investigated. The potential predictive factors for post-HoLEP incontinence included clinical factors, such as patient age, and preoperative urodynamic study results, including detrusor overactivity (DO). We also classified the surgeons performing the procedure according to their HoLEP experience: beginner (<21 cases) and experienced (≥21 cases). Our statistical data showed DO was a significant predictive factor at the super-short period (the next day of catheter removal: odds ratio [OR], 3.375; P=0.000). Additionally, patient age, surgeon mentorship (inverse correlation), and prostate volume were significant predictive factors at the 1-month interval after HoLEP (OR, 1.072; P=0.004; OR, 0.251; P=0.002; and OR, 1.008; P=0.049, respectively). With regards to surgeon experience, DO and preoperative International Prostate Symptom Score (inverse) at the super-short period, and patient age and mentorship (inverse correlation) at the 1-month interval after HoLEP (OR, 3.952; P=0.002; OR, 1.084; P=0.015; and OR,1.084; P=0.015; OR, 0.358; P=0.003, respectively) were significant predictive factors for beginners, and first desire to void (FDV) at 1 month after HoLEP (OR, 1.009; P=0.012) was a significant predictive factor for experienced surgeons in multivariate analysis. Preoperative DO, IPSS, patient age, and surgeon mentorship were significant predictive factors of postoperative patient incontinence for beginner surgeons, while FDV was a significant predictive factors for experienced surgeons. These findings should be taken into account by surgeons performing HoLEP to maximize the patient's quality of life with regards to urinary continence.

Does affective organizational commitment and experience of meaning at work predict long-term sickness absence? An analysis of register-based outcomes using pooled data on 61,302 observations in four occupational groups.

PubMed

Clausen, Thomas; Burr, Hermann; Borg, Vilhelm

2014-02-01

To investigate whether experience of low meaning at work (MAW) and low affective organizational commitment (AOC) predicts long-term sickness absence (LTSA) for more than 3 consecutive weeks and whether this association is dependent on the occupational group. Survey data pooling 61,302 observations were fitted to the DREAM register containing information on payments of sickness absence compensation. Using multiadjusted Cox regression, observations were followed for an 18-month follow-up period to assess the risk for LTSA. Low levels of MAW and AOC significantly increased the risk for LTSA during follow-up. Subgroup analyses showed that associations were statistically significant for employees working with clients and office workers but not for employees working with customers and manual workers. Further analyses showed that associations between MAW and LTSA varied significantly across the four occupational groups. Meaning at work and affective organizational commitment significantly predict LTSA. Promoting MAW and AOC may contribute toward reducing LTSA in contemporary workplaces.

Reader performance in visual assessment of breast density using visual analogue scales: Are some readers more predictive of breast cancer?

NASA Astrophysics Data System (ADS)

Rayner, Millicent; Harkness, Elaine F.; Foden, Philip; Wilson, Mary; Gadde, Soujanya; Beetles, Ursula; Lim, Yit Y.; Jain, Anil; Bundred, Sally; Barr, Nicky; Evans, D. Gareth; Howell, Anthony; Maxwell, Anthony; Astley, Susan M.

2018-03-01

Mammographic breast density is one of the strongest risk factors for breast cancer, and is used in risk prediction and for deciding appropriate imaging strategies. In the Predicting Risk Of Cancer At Screening (PROCAS) study, percent density estimated by two readers on Visual Analogue Scales (VAS) has shown a strong relationship with breast cancer risk when assessed against automated methods. However, this method suffers from reader variability. This study aimed to assess the performance of PROCAS readers using VAS, and to identify those most predictive of breast cancer. We selected the seven readers who had estimated density on over 6,500 women including at least 100 cancer cases, analysing their performance using multivariable logistic regression and Receiver Operator Characteristic (ROC) analysis. All seven readers showed statistically significant odds ratios (OR) for cancer risk according to VAS score after adjusting for classical risk factors. The OR was greatest for reader 18 at 1.026 (95% Cl 1.018-1.034). Adjusted Area Under the ROC Curves (AUCs) were statistically significant for all readers, but greatest for reader 14 at 0.639. Further analysis of the VAS scores for these two readers showed reader 14 had higher sensitivity (78.0% versus 42.2%), whereas reader 18 had higher specificity (78.0% versus 46.0%). Our results demonstrate individual differences when assigning VAS scores; one better identified those with increased risk, whereas another better identified low risk individuals. However, despite their different strengths, both readers showed similar predictive abilities overall. Standardised training for VAS may improve reader variability and consistency of VAS scoring.

An interactive Bayesian model for prediction of lymph node ratio and survival in pancreatic cancer patients.

PubMed

Smith, Brian J; Mezhir, James J

2014-10-01

Regional lymph node status has long been used as a dichotomous predictor of clinical outcomes in cancer patients. More recently, interest has turned to the prognostic utility of lymph node ratio (LNR), quantified as the proportion of positive nodes examined. However, statistical tools for the joint modeling of LNR and its effect on cancer survival are lacking. Data were obtained from the NCI SEER cancer registry on 6400 patients diagnosed with pancreatic ductal adenocarcinoma from 2004 to 2010 and who underwent radical oncologic resection. A novel Bayesian statistical approach was developed and applied to model simultaneously patients' true, but unobservable, LNR statuses and overall survival. New web development tools were then employed to create an interactive web application for individualized patient prediction. Histologic grade and T and M stages were important predictors of LNR status. Significant predictors of survival included age, gender, marital status, grade, histology, T and M stages, tumor size, and radiation therapy. LNR was found to have a highly significant, non-linear effect on survival. Furthermore, predictive performance of the survival model compared favorably to those from studies with more homogeneous patients and individualized predictors. We provide a new approach and tool set for the prediction of LNR and survival that are generally applicable to a host of cancer types, including breast, colon, melanoma, and stomach. Our methods are illustrated with the development of a validated model and web applications for the prediction of survival in a large set of pancreatic cancer patients. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Evaluation of the Risk of Grade 3 Oral and Pharyngeal Dysphagia Using Atlas-Based Method and Multivariate Analyses of Individual Patient Dose Distributions

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

Otter, Sophie; Schick, Ulrike; Gulliford, Sarah

Purpose: The study aimed to apply the atlas of complication incidence (ACI) method to patients receiving radical treatment for head and neck squamous cell carcinomas (HNSCC), to generate constraints based on dose-volume histograms (DVHs), and to identify clinical and dosimetric parameters that predict the risk of grade 3 oral mucositis (g3OM) and pharyngeal dysphagia (g3PD). Methods and Materials: Oral and pharyngeal mucosal DVHs were generated for 253 patients who received radiation (RT) or chemoradiation (CRT). They were used to produce ACI for g3OM and g3PD. Multivariate analysis (MVA) of the effect of dosimetry, clinical, and patient-related variables was performed usingmore » logistic regression and bootstrapping. Receiver operating curve (ROC) analysis was also performed, and the Youden index was used to find volume constraints that discriminated between volumes that predicted for toxicity. Results: We derived statistically significant dose-volume constraints for g3OM over the range v28 to v70. Only 3 statistically significant constraints were derived for g3PD v67, v68, and v69. On MVA, mean dose to the oral mucosa predicted for g3OM and concomitant chemotherapy and mean dose to the inferior constrictor (IC) predicted for g3PD. Conclusions: We have used the ACI method to evaluate incidences of g3OM and g3PD and ROC analysis to generate constraints to predict g3OM and g3PD derived from entire individual patient DVHs. On MVA, the strongest predictors were radiation dose (for g3OM) and concomitant chemotherapy (for g3PD).« less

TGFbeta1 (Leu10Pro), p53 (Arg72Pro) can predict for increased risk for breast cancer in south Indian women and TGFbeta1 Pro (Leu10Pro) allele predicts response to neo-adjuvant chemo-radiotherapy.

PubMed

Rajkumar, Thangarajan; Samson, Mani; Rama, Ranganathan; Sridevi, Veluswami; Mahji, Urmila; Swaminathan, Rajaraman; Nancy, Nirmala K

2008-11-01

The breast cancer incidence has been increasing in the south Indian women. A case (n=250)-control (n=500) study was undertaken to investigate the role of Single Nucleotide Polymorphisms (SNP's) in GSTM1 (Present/Null); GSTP1 (Ile105Val), p53 (Arg72Pro), TGFbeta1 (Leu10Pro), c-erbB2 (Ile655Val), and GSTT1 (Null/Present) in breast cancer. In addition, the value of the SNP's in predicting primary tumor's pathologic response following neo-adjuvant chemo-radiotherapy was assessed. Genotyping was done using PCR (GSTM1, GSTT1), Taqman Allelic discrimination assay (GSTP1, c-erbB2) and PCR-CTPP (p53 and TGFbeta1). None of the gene SNP's studied were associated with a statistically significant increased risk for the breast cancer. However, combined analysis of the SNP's showed that p53 (Arg/Arg and Arg/Pro) with TGFbeta1 (Pro/Pro and Leu/Pro) were associated with greater than 2 fold increased risk for breast cancer in Univariate (P=0.01) and Multivariate (P=0.003) analysis. There was no statistically significant association for the GST family members with the breast cancer risk. TGFbeta1 (Pro/Pro) allele was found to predict complete pathologic response in the primary tumour following neo-adjuvant chemo-radiotherapy (OR=6.53 and 10.53 in Univariate and Multivariate analysis respectively) (P=0.004) and was independent of stage. This study suggests that SNP's can help predict breast cancer risk in south Indian women and that TGFbeta1 (Pro/Pro) allele is associated with a better pCR in the primary tumour.

Risk factors of exocrine and endocrine pancreatic insufficiency after pancreatic resection: A multi-center prospective study.

PubMed

Maignan, A; Ouaïssi, M; Turrini, O; Regenet, N; Loundou, A; Louis, G; Moutardier, V; Dahan, L; Pirrò, N; Sastre, B; Delpero, J-R; Sielezneff, I

2018-01-26

Management of functional consequences after pancreatic resection has become a new therapeutic challenge. The goal of our study is to evaluate the risk factors for exocrine (ExoPI) and endocrine (EndoPI) pancreatic insufficiency after pancreatic surgery and to establish a predictive model for their onset. Between January 1, 2014 and June 19, 2015, 91 consecutive patients undergoing pancreatoduodenectomy (PD) or left pancreatectomy (LP) (72% and 28%, respectively) were followed prospectively. ExoPI was defined as fecal elastase content<200μg per gram of feces while EndoPI was defined as fasting glucose>126mg/dL or aggravation of preexisting diabetes. The volume of residual pancreas was measured according to the same principles as liver volumetry. The ExoPI and EndoPI rates at 6 months were 75.9% and 30.8%, respectively. The rate of ExoPI after PD was statistically significantly higher than after LP (98% vs. 21%; P<0.001), while the rate of EndoPI was lower after PD vs. LP, but this difference did not reach statistical significance (28% vs. 38.5%; P=0.412). There was no statistically significant difference in ExoPI found between pancreatico-gastrostomy (PG) and pancreatico-jejunostomy (PJ) (100% vs. 98%; P=1.000). Remnant pancreatic volume less than 39.5% was predictive of ExoPI. ExoPI occurs quasi-systematically after PD irrespective of the reconstruction scheme. The rate of EndoPI did not differ between PD and LP. Copyright © 2017. Published by Elsevier Masson SAS.

Role of socioeconomic status measures in long-term mortality risk prediction after myocardial infarction.

PubMed

Molshatzki, Noa; Drory, Yaacov; Myers, Vicki; Goldbourt, Uri; Benyamini, Yael; Steinberg, David M; Gerber, Yariv

2011-07-01

The relationship of risk factors to outcomes has traditionally been assessed by measures of association such as odds ratio or hazard ratio and their statistical significance from an adjusted model. However, a strong, highly significant association does not guarantee a gain in stratification capacity. Using recently developed model performance indices, we evaluated the incremental discriminatory power of individual and neighborhood socioeconomic status (SES) measures after myocardial infarction (MI). Consecutive patients aged ≤65 years (N=1178) discharged from 8 hospitals in central Israel after incident MI in 1992 to 1993 were followed-up through 2005. A basic model (demographic variables, traditional cardiovascular risk factors, and disease severity indicators) was compared with an extended model including SES measures (education, income, employment, living with a steady partner, and neighborhood SES) in terms of Harrell c statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). During the 13-year follow-up, 326 (28%) patients died. Cox proportional hazards models showed that all SES measures were significantly and independently associated with mortality. Furthermore, compared with the basic model, the extended model yielded substantial gains (all P<0.001) in c statistic (0.723 to 0.757), NRI (15.2%), IDI (5.9%), and relative IDI (32%). Improvement was observed both for sensitivity (classification of events) and specificity (classification of nonevents). This study illustrates the additional insights that can be gained from considering the IDI and NRI measures of model performance and suggests that, among community patients with incident MI, incorporating SES measures into a clinical-based model substantially improves long-term mortality risk prediction.

Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

PubMed

van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

2018-06-01

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

Skillful prediction of northern climate provided by the ocean

NASA Astrophysics Data System (ADS)

Årthun, Marius; Eldevik, Tor; Viste, Ellen; Drange, Helge; Furevik, Tore; Johnson, Helen L.; Keenlyside, Noel S.

2017-06-01

It is commonly understood that a potential for skillful climate prediction resides in the ocean. It nevertheless remains unresolved to what extent variable ocean heat is imprinted on the atmosphere to realize its predictive potential over land. Here we assess from observations whether anomalous heat in the Gulf Stream's northern extension provides predictability of northwestern European and Arctic climate. We show that variations in ocean temperature in the high latitude North Atlantic and Nordic Seas are reflected in the climate of northwestern Europe and in winter Arctic sea ice extent. Statistical regression models show that a significant part of northern climate variability thus can be skillfully predicted up to a decade in advance based on the state of the ocean. Particularly, we predict that Norwegian air temperature will decrease over the coming years, although staying above the long-term (1981-2010) average. Winter Arctic sea ice extent will remain low but with a general increase towards 2020.

The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing

PubMed Central

Weber, Kirsten; Lau, Ellen F.; Stillerman, Benjamin; Kuperberg, Gina R.

2016-01-01

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions—a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment. PMID:27010386

The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing.

PubMed

Weber, Kirsten; Lau, Ellen F; Stillerman, Benjamin; Kuperberg, Gina R

2016-01-01

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions-a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment.

Schizophrenia classification using functional network features

NASA Astrophysics Data System (ADS)

Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle

2012-03-01

This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

Deriving inertial wave characteristics from surface drifter velocities: Frequency variability in the Tropical Pacific

NASA Astrophysics Data System (ADS)

Poulain, Pierre-Marie; Luther, Douglas S.; Patzert, William C.

1992-11-01

Two techniques have been developed for estimating statistics of inertial oscillations from satellite-tracked drifters. These techniques overcome the difficulties inherent in estimating such statistics from data dependent upon space coordinates that are a function of time. Application of these techniques to tropical surface drifter data collected during the NORPAX, EPOCS, and TOGA programs reveals a latitude-dependent, statistically significant "blue shift" of inertial wave frequency. The latitudinal dependence of the blue shift is similar to predictions based on "global" internal wave spectral models, with a superposition of frequency shifting due to modification of the effective local inertial frequency by the presence of strongly sheared zonal mean currents within 12° of the equator.

Statistical shear lag model - unraveling the size effect in hierarchical composites.

PubMed

Wei, Xiaoding; Filleter, Tobin; Espinosa, Horacio D

2015-05-01

Numerous experimental and computational studies have established that the hierarchical structures encountered in natural materials, such as the brick-and-mortar structure observed in sea shells, are essential for achieving defect tolerance. Due to this hierarchy, the mechanical properties of natural materials have a different size dependence compared to that of typical engineered materials. This study aimed to explore size effects on the strength of bio-inspired staggered hierarchical composites and to define the influence of the geometry of constituents in their outstanding defect tolerance capability. A statistical shear lag model is derived by extending the classical shear lag model to account for the statistics of the constituents' strength. A general solution emerges from rigorous mathematical derivations, unifying the various empirical formulations for the fundamental link length used in previous statistical models. The model shows that the staggered arrangement of constituents grants composites a unique size effect on mechanical strength in contrast to homogenous continuous materials. The model is applied to hierarchical yarns consisting of double-walled carbon nanotube bundles to assess its predictive capabilities for novel synthetic materials. Interestingly, the model predicts that yarn gauge length does not significantly influence the yarn strength, in close agreement with experimental observations. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Solar Radio Burst Statistics and Implications for Space Weather Effects

NASA Astrophysics Data System (ADS)

Giersch, O. D.; Kennewell, J.; Lynch, M.

2017-11-01

Solar radio bursts have the potential to affect space and terrestrial navigation, communication, and other technical systems that are sometimes overlooked. However, over the last decade a series of extreme L band solar radio bursts in December 2006 have renewed interest in these effects. In this paper we point out significant deficiencies in the solar radio data archives of the National Centers for Environmental Information (NCEI) that are used by most researchers in analyzing and producing statistics on solar radio burst phenomena. In particular, we examine the records submitted by the United States Air Force (USAF) Radio Solar Telescope Network (RSTN) and its predecessors from the period 1966 to 2010. Besides identifying substantial missing burst records we show that different observatories can have statistically different burst distributions, particularly at 245 MHz. We also point out that different solar cycles may show statistically different distributions and that it is a mistake to assume that the Sun shows similar behavior in different sunspot cycles. Large solar radio bursts are not confined to the period around sunspot maximum, and prediction of such events that utilize historical data will invariably be an underestimate due to archive data deficiencies. It is important that researchers and forecasters use historical occurrence frequency with caution in attempting to predict future cycles.

No-reference image quality assessment based on statistics of convolution feature maps

NASA Astrophysics Data System (ADS)

Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

2018-04-01

We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

The predictive power of local properties of financial networks

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2017-01-01

The literature on analyzing the dynamics of financial networks has focused so far on the predictive power of global measures of networks like entropy or index cohesive force. In this paper, I show that the local network properties have similar predictive power. I focus on key network measures like average path length, average degree or cluster coefficient, and also consider the diameter and the s-metric. Using Granger causality tests, I show that some of these measures have statistically significant prediction power with respect to the dynamics of aggregate stock market. Average path length is most robust relative to the frequency of data used or specification (index or growth rate). Most measures are found to have predictive power only for monthly frequency. Further evidences that support this view are provided through a simple regression model.

Investigating the intrinsic and extrinsic work values of 10th grade students in science-oriented charter schools

NASA Astrophysics Data System (ADS)

Ozer, Ozgur

The purpose of this study was to investigate to what extent gender, achievement level, and income level predict the intrinsic and extrinsic work values of 10th grade students. The study explored whether group differences were good predictors of scores in work values. The research was a descriptive, cross-sectional study conducted on 131 10th graders who attended science-oriented charter schools. Students took Super's Work Values Instrument, a Likert-type test that links to 15 work values, which can be categorized as intrinsic and extrinsic values (Super, 1970). Multiple regression analysis was employed as the main analysis followed by ANCOVA. Multiple regression analysis results indicated that there is evidence that 8.9% of the variance in intrinsic work values and 10.2% of the variance in extrinsic work values can be explained by the independent variables ( p < .05). Achievement Level and Income Level may help predict intrinsic work value scores; Achievement Level may also help predict extrinsic work values. Achievement Level was the covariate in ANCOVA. Results indicated that males (M = .174) in this sample have a higher mean of extrinsic work values than that of females (M = -.279). However, there was no statistically significant difference between the intrinsic work values by gender. One possible interpretation of this might be school choice; students in these science-oriented charter schools may have higher intrinsic work values regardless of gender. Results indicated that there was no statistically significant difference among the means of extrinsic work values by income level (p < .05). However, free lunch students (M = .268) have a higher mean of intrinsic work values than that of paid lunch students ( M = -.279). A possible interpretation of this might be that lower income students benefit greatly from the intrinsic work values in overcoming obstacles. Further research is needed in each of these areas. The study produced statistically significant results with little practical significance. Students, parents, teachers, and counselors may still be advised to consider the work value orientations of students during the career choice process.

Using the Theory of Planned Behavior to Predict College Students' Intention to Intervene With a Suicidal Individual.

PubMed

Aldrich, Rosalie S

2015-01-01

Suicide among college students is an issue of serious concern. College peers may effectively intervene with at-risk persons due to their regular contact and close personal relationships with others in this population of significantly enhanced risk. The current study was designed to investigate whether the theory of planned behavior constructs predicted intention to intervene when a college peer is suicidal. Undergraduate students (n = 367) completed an on-line questionnaire; they answered questions about their attitudes, subjective norms, perceived behavioral control regarding suicide and suicide intervention, as well as their intention to intervene when someone is suicidal. The data were analyzed using multiple regression. The statistical significance of this cross-sectional study indicates that the theory of planned behavior constructs predicts self-reported intention to intervene with a suicidal individual. Theory of planned behavior is an effective framework for understanding peers' intention to intervene with a suicidal individual.

Predicting Operator Execution Times Using CogTool

NASA Technical Reports Server (NTRS)

Santiago-Espada, Yamira; Latorella, Kara A.

2013-01-01

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

Statistical trend analysis and extreme distribution of significant wave height from 1958 to 1999 - an application to the Italian Seas

NASA Astrophysics Data System (ADS)

Martucci, G.; Carniel, S.; Chiggiato, J.; Sclavo, M.; Lionello, P.; Galati, M. B.

2010-06-01

The study is a statistical analysis of sea states timeseries derived using the wave model WAM forced by the ERA-40 dataset in selected areas near the Italian coasts. For the period 1 January 1958 to 31 December 1999 the analysis yields: (i) the existence of a negative trend in the annual- and winter-averaged sea state heights; (ii) the existence of a turning-point in late 80's in the annual-averaged trend of sea state heights at a site in the Northern Adriatic Sea; (iii) the overall absence of a significant trend in the annual-averaged mean durations of sea states over thresholds; (iv) the assessment of the extreme values on a time-scale of thousand years. The analysis uses two methods to obtain samples of extremes from the independent sea states: the r-largest annual maxima and the peak-over-threshold. The two methods show statistical differences in retrieving the return values and more generally in describing the significant wave field. The r-largest annual maxima method provides more reliable predictions of the extreme values especially for small return periods (<100 years). Finally, the study statistically proves the existence of decadal negative trends in the significant wave heights and by this it conveys useful information on the wave climatology of the Italian seas during the second half of the 20th century.

Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method.

PubMed

Petukh, Marharyta; Li, Minghui; Alexov, Emil

2015-07-01

A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).

A statistical method for predicting seizure onset zones from human single-neuron recordings

NASA Astrophysics Data System (ADS)

Valdez, André B.; Hickman, Erin N.; Treiman, David M.; Smith, Kris A.; Steinmetz, Peter N.

2013-02-01

Objective. Clinicians often use depth-electrode recordings to localize human epileptogenic foci. To advance the diagnostic value of these recordings, we applied logistic regression models to single-neuron recordings from depth-electrode microwires to predict seizure onset zones (SOZs). Approach. We collected data from 17 epilepsy patients at the Barrow Neurological Institute and developed logistic regression models to calculate the odds of observing SOZs in the hippocampus, amygdala and ventromedial prefrontal cortex, based on statistics such as the burst interspike interval (ISI). Main results. Analysis of these models showed that, for a single-unit increase in burst ISI ratio, the left hippocampus was approximately 12 times more likely to contain a SOZ; and the right amygdala, 14.5 times more likely. Our models were most accurate for the hippocampus bilaterally (at 85% average sensitivity), and performance was comparable with current diagnostics such as electroencephalography. Significance. Logistic regression models can be combined with single-neuron recording to predict likely SOZs in epilepsy patients being evaluated for resective surgery, providing an automated source of clinically useful information.

Gene-expression programming for flip-bucket spillway scour.

PubMed

Guven, Aytac; Azamathulla, H Md

2012-01-01

During the last two decades, researchers have noticed that the use of soft computing techniques as an alternative to conventional statistical methods based on controlled laboratory or field data, gave significantly better results. Gene-expression programming (GEP), which is an extension to genetic programming (GP), has nowadays attracted the attention of researchers in prediction of hydraulic data. This study presents GEP as an alternative tool in the prediction of scour downstream of a flip-bucket spillway. Actual field measurements were used to develop GEP models. The proposed GEP models are compared with the earlier conventional GP results of others (Azamathulla et al. 2008b; RMSE = 2.347, δ = 0.377, R = 0.842) and those of commonly used regression-based formulae. The predictions of GEP models were observed to be in strictly good agreement with measured ones, and quite a bit better than conventional GP and the regression-based formulae. The results are tabulated in terms of statistical error measures (GEP1; RMSE = 1.596, δ = 0.109, R = 0.917) and illustrated via scatter plots.

Climate Change Assessment of Precipitation in Tandula Reservoir System

NASA Astrophysics Data System (ADS)

Jaiswal, Rahul Kumar; Tiwari, H. L.; Lohani, A. K.

2018-02-01

The precipitation is the principle input of hydrological cycle affect availability of water in spatial and temporal scale of basin due to widely accepted climate change. The present study deals with the statistical downscaling using Statistical Down Scaling Model for rainfall of five rain gauge stations (Ambagarh, Bhanpura, Balod, Chamra and Gondli) in Tandula, Kharkhara and Gondli reservoirs of Chhattisgarh state of India to forecast future rainfall in three different periods under SRES A1B and A2 climatic forcing conditions. In the analysis, twenty-six climatic variables obtained from National Centers for Environmental Prediction were used and statistically tested for selection of best-fit predictors. The conditional process based statistical correlation was used to evolve multiple linear relations in calibration for period of 1981-1995 was tested with independent data of 1996-2003 for validation. The developed relations were further used to predict future rainfall scenarios for three different periods 2020-2035 (FP-1), 2046-2064 (FP-2) and 2081-2100 (FP-3) and compared with monthly rainfalls during base period (1981-2003) for individual station and all three reservoir catchments. From the analysis, it has been found that most of the rain gauge stations and all three reservoir catchments may receive significant less rainfall in future. The Thiessen polygon based annual and seasonal rainfall for different catchments confirmed a reduction of seasonal rainfall from 5.1 to 14.1% in Tandula reservoir, 11-19.2% in Kharkhara reservoir and 15.1-23.8% in Gondli reservoir. The Gondli reservoir may be affected the most in term of water availability in future prediction periods.

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

PubMed

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

2018-02-01

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

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

PubMed Central

Duta, Mihaela; Thompson, Paul

2018-01-01

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

The Relationship among Strategic Writing Behavior, Writing Motivation, and Writing Performance with Young, Developing Writers

ERIC Educational Resources Information Center

Graham, Steve; Harris, Karen R.; Kiuhara, Sharlene A.; Fishman, Evan J.

2017-01-01

Our study tested whether learning is shaped by fundamental cognitive and motivational forces in the academic domain of writing. We examined whether strategic writing behavior and motivation (attitudes toward writing and self-efficacy) made a statistically significant and unique contribution to the prediction of writing quality and number of words…

Applying Ajzen's Theory of Planned Behavior to a Study of Online Course Adoption in Public Relations Education

ERIC Educational Resources Information Center

Knabe, Ann Peru

2012-01-01

This study used Icek Ajzen's Theory of Planned Behavior to research public relations faculty intentions of teaching online. All of the main predictor variables (Subjective Norms, Attitude toward the Act and Perceived Behavioral Control) were statistically significant at varying degrees in predicting intent to teach public relations online. Of the…

Gender Differences in Research Patterns among PhD Economists

ERIC Educational Resources Information Center

Barbezat, Debra A.

2006-01-01

This study is based on a 1996 survey of PhD economists working in the academic and nonacademic sectors since 1989. Despite a raw gender difference in all types of research output, the male dummy variable proves statistically significant in predicting only one publication measure. In a full sample and faculty subsample, number of years since…

An Investigation on Value Orientations, Attitudes and Concern towards the Environment: The Case of Turkish Elementary School Students

ERIC Educational Resources Information Center

Onur, Arzu; Sahin, Elvan; Tekkaya, Ceren

2012-01-01

Environmental attitudes depend on the relative importance that individuals attach to themselves, other people, or all living things. These distinct bases have been found to predict environmental concern, and may act as statistically significant determinants of pro-environmental behaviours. We claim that examining the complex nature of value…

Predictors of Working Alliance Efficacy among State VR Counselors as a Function of Ex-Offender Status

ERIC Educational Resources Information Center

Bates, Julie K.

2012-01-01

The purpose of this study was to determine if statistically significant relationships existed between burnout, stigma, flourishing, caseload size, experience, and working alliance self-efficacy and to assess the predictive power of these variables on levels of working alliance self-efficacy with clients with disabilities alone and clients with…

Motivational Correlates of Academic Success in an Educational Psychology Course

ERIC Educational Resources Information Center

Herman, William E.

2011-01-01

The variables of class attendance and the institution-wide Early Alert Grading System were employed to predict academic success at the end of the semester. Classroom attendance was found to be statistically and significantly related to final average and accounted for 14-16% of the variance in academic performance. Class attendance was found to…

Understanding Diversity in Suburban School Districts: The Impact of Culturally Proficient Professional Development and School Leadership

ERIC Educational Resources Information Center

Jones, Eva L.

2017-01-01

According to the National Center for Educational Statistics (NCES), minority populations are increasing significantly and are predicted to become the majority nationwide by 2024 (Kena et al., 2015). Frankenberg & Orfield (2012) explain that suburban schools are struggling with the realization that their schools are more racially/ethnically and…

Does Individual Secularism Promote Life Satisfaction? The Moderating Role of Societal Development

ERIC Educational Resources Information Center

Li, Liman Man Wai; Bond, Michael H.

2010-01-01

This study was designed to examine the link between values and life satisfaction, examining the role of culture in this process. Secularism was found to predict life satisfaction scores at a small but statistically very significant level in persons from all nations participating in all four waves of the World Values Survey. The direction and…

Antenatal assessment of liver position, rather than lung-to-head ratio (LHR) or observed/expected LHR, is predictive of outcome in fetuses with isolated left-sided congenital diaphragmatic hernia.

PubMed

Straňák, Zbyněk; Krofta, Ladislav; Haak, Lucia Anna; Vojtěch, Jiří; Hašlík, Luboš; Rygl, Michal; Pýcha, Karel; Feyereisl, Jaroslav

2017-01-01

Respiratory morbidity in congenital diaphragmatic hernia (CDH) is associated with high mortality and adverse outcome. Accurate prenatal diagnosis is essential for prognosis and potential treatment in utero. The aim was to evaluate the prenatal ultrasound findings in assessing the respiratory prognosis in fetuses with isolated left-sided CDH. We retrospectively analyzed the medical records of 59 prenatally diagnosed left-sided CDH cases managed at a tertiary perinatal center. Survival rate in the study group was 73% (43/59). We found no statistically significant relationship between survival and the presence of polyhydramnios, gestational age at diagnosis, lung-to-head ratio (LHR) and observed/expected LHR (O/E LHR) values, gestational age at birth and birth weight. Intrathoracic liver herniation was a statistically significant parameter adversely affecting survival (37.2% in survivors, 68.8% in non-survivors, p = 0.031) and logistic regression confirmed this relationship. The presence of pneumothorax and severe pulmonary hypertension were significantly associated with mortality (82% non-survivors versus 15% in survivors, p = 0.0001). Intrathoracic liver herniation seems to be a reliable parameter in the prediction of survival and neonatal respiratory morbidity in fetuses with isolated left-sided CDH. In contrast, we found no significant correlation between perinatal outcome and LHR, O/E LHR values, birth weight and gestational age.

Teacher Quality Indicators as Predictors of Instructional Assessment Practices in Science Classrooms in Secondary Schools in Barbados

NASA Astrophysics Data System (ADS)

Ogunkola, Babalola J.; Archer-Bradshaw, Ramona E.

2013-02-01

This study investigated the self-reported instructional assessment practices of a selected sample of secondary school science teachers in Barbados. The study sought to determine if there were statistically significant differences in the instructional assessment practices of teachers based on their sex and teacher quality (teaching experience, professional qualification and teacher academic qualification). It also sought to determine the extent to which each of these four selected variables individually and jointly affected the teachers' report of their instructional assessment practices. A sample of 55 science teachers from nine secondary schools in Barbados was randomly selected to participate in this study. Data was collected by means of a survey and was analyzed using the means and standard deviations of the instructional assessment practices scores and linear, multiple and binary logistic regression. The results of the study were such that the majority of the sample reported good overall instructional assessment practices while only a few participants reported moderate assessment practices. The instructional assessment practices in the area of student knowledge were mostly moderate as indicated by the sample. There were no statistically significant differences between or among the mean scores of the teachers' reported instructional assessment practices based on sex ( t = 0.10; df = 53; p = 0.992), teaching experience ( F[4,50] = 1.766; p = 0.150), the level of professional qualification (F[3,45] = 0.2117; p = 0.111) or the level of academic qualification (F[2,52] = 0.504; p = 0.607). The independent variables (teacher sex, teaching experience, teacher professional qualification or teacher academic qualification) were not significant predictors of the instructional assessment practices scores. However, teacher sex was a significant predictor of the teachers' report of good instructional assessment practices. The study also found that the joint effect of the variables teacher sex, teaching experience, teacher professional qualification and teacher academic qualification was not significant in predicting the instructional assessment practices scores of the science teachers. However, the joint effect of these variables was statistically significant ( X 2 = 18.482; df = 10; p = 0.047) in predicting the teachers' reported use of good instructional assessment practices. The best predictor of teachers' report of good instructional assessment practices, though not statistically significant, was the diploma in education professional qualification.

The predictive value of FDG-PET with 3D-SSP for surgical outcomes in patients with temporal lobe epilepsy.

PubMed

Higo, Takuma; Sugano, Hidenori; Nakajima, Madoka; Karagiozov, Kostadin; Iimura, Yasushi; Suzuki, Masaru; Sato, Kiyoshi; Arai, Hajime

2016-10-01

We retrospectively evaluated the diagnostic value of (18)F-2-fluorodeoxy-d-glucose positron emission tomography (FDG-PET) with statistical analysis for the foci detection and predictive utility for postsurgical seizure outcome of patients with mesial temporal lobe epilepsy (mTLE). We evaluated 40 patients who were diagnosed mTLE and underwent selective amygdalohippocampectomy (SAH) or anterior temporal lobectomy (ATL) in our institute. Preoperative interictal FDG-PET with statistical analysis using three-dimensional stereotactic surface projection (3D-SSP) was detected with several clinical data including seizure semiology, MRI, scalp electroencephalography, surgical procedure with SAH or ATL and postsurgical outcome. The region of interest (ROI) was defined on 'Hippocampus & Amygdala', 'Parahippocampal gyrus & Uncus', 'T1 & T2', and 'T3 & Fusiform gyrus'. We obtained the ratio of hypometabolism difference (RHD) by 3D-SSP, and evaluated the relation among hypometabolic extent, surgical outcome and surgical procedure. The RHD in each ROIs ipsilateral to operative side was significantly higher than that of contralateral side in good outcome group. Hypometabolism of 'Hippocampus & Amygdala' was most reliable prognostic factor. Patients of discordant with presurgical examinations hardly showed obvious lateralized hypometabolism. Nevertheless, when they have significantly high RHD in mesial temporal lobe, good surgical outcome was expected. There was not significant difference of RHD distribution between SAH and ATL in good outcome group. Significant hypometabolism in mesial temporal lobe on FDG-PET with 3D-SSP is useful to predict good surgical outcome for patients with mTLE, particularly in discordant patients with hypometabolism in mesial temporal structure. However, FDG-PET is not indicative of surgical procedure. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Evaluation of Respiratory Muscle Strength in the Acute Phase of Stroke: The Role of Aging and Anthropometric Variables.

PubMed

Luvizutto, Gustavo José; Dos Santos, Maria Regina Lopes; Sartor, Lorena Cristina Alvarez; da Silva Rodrigues, Josiela Cristina; da Costa, Rafael Dalle Molle; Braga, Gabriel Pereira; de Oliveira Antunes, Letícia Cláudia; Souza, Juli Thomaz; de Carvalho Nunes, Hélio Rubens; Bazan, Silméia Garcia Zanati; Bazan, Rodrigo

2017-10-01

During hospitalization, stroke patients are bedridden due to neurologic impairment, leading to loss of muscle mass, weakness, and functional limitation. There have been few studies examining respiratory muscle strength (RMS) in the acute phase of stroke. This study aimed to evaluate the RMS of patients with acute stroke compared with predicted values and to relate this to anthropometric variables, risk factors, and neurologic severity. This is a cross-sectional study in the acute phase of stroke. After admission, RMS was evaluated by maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP); anthropometric data were collected; and neurologic severity was evaluated by the National Institutes of Health Stroke Scale. The analysis of MIP and MEP with predicted values was performed by chi-square test, and the relationship between anthropometric variables, risk factors, and neurologic severity was determined through multiple linear regression followed by residue analysis by the Shapiro-Wilk test; P < .05 was considered statistically significant. In the 32 patients studied, MIP and MEP were reduced when compared with the predicted values. MIP declined significantly by 4.39 points for each 1 kg/m 2 increase in body mass index (BMI), and MEP declined significantly by an average of 3.89 points for each 1 kg/m 2 increase in BMI. There was no statistically significant relationship between MIP or MEP and risk factors, and between MIP or MIP and neurologic severity in acute phase of stroke. There is a reduction of RMS in the acute phase of stroke, and RMS was lower in individuals with increased age and BMI. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Predicting early signs of dyslexia at a preliterate age by combining behavioral assessment with structural MRI.

PubMed

Kraft, Indra; Schreiber, Jan; Cafiero, Riccardo; Metere, Riccardo; Schaadt, Gesa; Brauer, Jens; Neef, Nicole E; Müller, Bent; Kirsten, Holger; Wilcke, Arndt; Boltze, Johannes; Friederici, Angela D; Skeide, Michael A

2016-12-01

Recent studies suggest that neurobiological anomalies are already detectable in pre-school children with a family history of developmental dyslexia (DD). However, there is a lack of longitudinal studies showing a direct link between those differences at a preliterate age and the subsequent literacy difficulties seen in school. It is also not clear whether the prediction of DD in pre-school children can be significantly improved when considering neurobiological predictors, compared to models based on behavioral literacy precursors only. We recruited 53 pre-reading children either with (N=25) or without a family risk of DD (N=28). Quantitative T1 MNI data and literacy precursor abilities were assessed at kindergarten age. A subsample of 35 children was tested for literacy skills either one or two years later, that is, either in first or second grade. The group comparison of quantitative T1 measures revealed significantly higher T1 intensities in the left anterior arcuate fascicle (AF), suggesting reduced myelin concentration in preliterate children at risk of DD. A logistic regression showed that DD can be predicted significantly better (p=.024) when neuroanatomical differences between groups are used as predictors (80%) compared to a model based on behavioral predictors only (63%). The Wald statistic confirmed that the T1 intensity of the left AF is a statistically significant predictor of DD (p<.05). Our longitudinal results provide evidence for the hypothesis that neuroanatomical anomalies in children with a family risk of DD are related to subsequent problems in acquiring literacy. Particularly, solid white matter organization in the left anterior arcuate fascicle seems to play a pivotal role. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluation of the clinical and allergen specific serum immunoglobulin E responses to oral challenge with cornstarch, corn, soy and a soy hydrolysate diet in dogs with spontaneous food allergy.

PubMed

Jackson, H A; Jackson, M W; Coblentz, L; Hammerberg, B

2003-08-01

Fourteen dogs with known clinical hypersensitivity to soy and corn were maintained on a limited antigen duck and rice diet until cutaneous manifestations of pruritus were minimal (78 days). Sequential oral challenges with cornstarch, corn and soy were then performed. Subsequently, the dogs were fed a diet containing hydrolysed soy protein and cornstarch. Throughout the study period the dogs were examined for cutaneous manifestations of pruritus and, additionally, serum was collected for measurement of allergen-specific and total immunoglobulin (Ig)E concentrations. Intradermal testing with food antigens was performed prior to entry into the study and after 83 days. A statistically significant clinical improvement was measured between days 0 and 83. Significant pruritus was induced after oral challenge with cornstarch, corn and soy (P = 0.04, 0.002, 0.01, respectively) but not with the hydrolysed diet (P = 0.5). The positive predictive value of the skin test for soy and corn allergy was reduced after feeding a soy and corn free diet. Although increases in soy and corn-specific serum IgE concentrations were measured in individual dogs post challenge they were not statistically significant and could not be used to predict clinical hypersensitivity.

Comparison of the effectiveness of some common animal data scaling techniques in estimating human radiation dose

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

Sparks, R.B.; Aydogan, B.

In the development of new radiopharmaceuticals, animal studies are typically performed to get a first approximation of the expected radiation dose in humans. This study evaluates the performance of some commonly used data extrapolation techniques to predict residence times in humans using data collected from animals. Residence times were calculated using animal and human data, and distributions of ratios of the animal results to human results were constructed for each extrapolation method. Four methods using animal data to predict human residence times were examined: (1) using no extrapolation, (2) using relative organ mass extrapolation, (3) using physiological time extrapolation, andmore » (4) using a combination of the mass and time methods. The residence time ratios were found to be log normally distributed for the nonextrapolated and extrapolated data sets. The use of relative organ mass extrapolation yielded no statistically significant change in the geometric mean or variance of the residence time ratios as compared to using no extrapolation. Physiologic time extrapolation yielded a statistically significant improvement (p < 0.01, paired t test) in the geometric mean of the residence time ratio from 0.5 to 0.8. Combining mass and time methods did not significantly improve the results of using time extrapolation alone. 63 refs., 4 figs., 3 tabs.« less

The Relationship Between Procrastination, Learning Strategies and Statistics Anxiety Among Iranian College Students: A Canonical Correlation Analysis

PubMed Central

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Objective: Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. Methods: A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Results: Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. Conclusion: These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction. PMID:24644468

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

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

Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au; Ebert, Martin A.; Bulsara, Max

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥more » 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.« less

Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

PubMed

Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

2018-07-12

The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic determination of the DP relies on chemometric models, which are usually sensitive to drifts caused by instrumental and/or sample-associated changes occurring over time. In order to detect the time point when drifts start causing prediction bias, we here explore a universal drift detector based on a faded version of the Page-Hinkley (PH) statistic, which we test in three data streams from an industrial MF resin production process. We employ committee disagreement (CD), computed as the variance of model predictions from an ensemble of partial least squares (PLS) models, as a measure for sample-wise prediction uncertainty and use the PH statistic to detect changes in this quantity. We further explore supervised and unsupervised strategies for (semi-)automatic model adaptation upon detection of a drift. For the former, manual reference measurements are requested whenever statistical thresholds on Hotelling's T 2 and/or Q-Residuals are violated. Models are subsequently re-calibrated using weighted partial least squares in order to increase the influence of newer samples, which increases the flexibility when adapting to new (drifted) states. Unsupervised model adaptation is carried out exploiting the dual antecedent-consequent structure of a recently developed fuzzy systems variant of PLS termed FLEXFIS-PLS. In particular, antecedent parts are updated while maintaining the internal structure of the local linear predictors (i.e. the consequents). We found improved drift detection capability of the CD compared to Hotelling's T 2 and Q-Residuals when used in combination with the proposed PH test. Furthermore, we found that active selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space. Copyright © 2018 Elsevier B.V. All rights reserved.

Early prediction of olanzapine-induced weight gain for schizophrenia patients.

PubMed

Lin, Ching-Hua; Lin, Shih-Chi; Huang, Yu-Hui; Wang, Fu-Chiang; Huang, Chun-Jen

2018-05-01

The aim of this study was to determine whether weight changes at week 2 or other factors predicted weight gain at week 6 for schizophrenia patients receiving olanzapine. This study was the secondary analysis of a six-week trial for 94 patients receiving olanzapine (5 mg/d) plus trifluoperazine (5 mg/d), or olanzapine (10 mg/d) alone. Patients were included in analysis only if they had completed the 6-week trial (per protocol analysis). Weight gain was defined as a 7% or greater increase of the patient's baseline weight. The receiver operating characteristic curve was employed to determine the optimal cutoff points of statistically significant predictors. Eleven of the 67 patients completing the 6-week trial were classified as weight gainers. Weight change at week 2 was the statistically significant predictor for ultimate weight gain at week 6. A weight change of 1.0 kg at week 2 appeared to be the optimal cutoff point, with a sensitivity of 0.92, a specificity of 0.75, and an AUC of 0.85. Using weight change at week 2 to predict weight gain at week 6 is favorable in terms of both specificity and sensitivity. Weight change of 1.0 kg or more at 2 weeks is a reliable predictor. Copyright © 2018 Elsevier B.V. All rights reserved.

[Influence of educational status, burn area and coping behaviors on the complication of psychological disorders in severely burned patients].

PubMed

Cheng, Hua; Li, Xiao-jian; Cao, Wen-juan; Chen, Li-ying; Zhang, Zhi; Liu, Zhi-he; Yi, Xian-feng; Lai, Wen

2013-04-01

To discuss how the educational status, burn area and coping behaviors influence the psychological disorders in severely burned patients. Sixty-four severely burned patients hospitalized in Guangzhou Red Cross Hospital, Guangdong Provincial Work Injury Rehabilitation Center, and Guangdong General Hospital were enrolled with cluster random sampling method. Data of their demography and situation of burns were collected. Then their coping behavior, psychological disorders including anxiety, depression and post-traumatic stress disorder (PTSD) plus its core symptoms of flashback, avoidance, and hypervigilance were assessed by medical coping modes questionnaire, self-rating anxiety scale (SAS), self-rating depression scale (SDS), PTSD checklist-civilian version (PCL-C) respectively. Correlation was analyzed between demography, burn area, coping behavior and psychological disorders. The predictive powers of educational status, burn area and coping behaviors on the psychological disorders were analyzed. The qualitative variables were assigned values. Data were processed with t test, Spearman rank correlation analysis, and multiple linear regression analysis. (1) The patients scored (19.0 ± 3.4) points in confrontation coping behavior, which showed no statistically significant difference from the domestic norm score (19.5 ± 3.8) points (t = -1.13, P > 0.05). The patients scored (16.6 ± 2.4) and (11.0 ± 2.2) points in avoidance and resignation coping behaviors, which were significantly higher than the domestic norm score (14.4 ± 3.0), (8.8 ± 3.2) points (with t values respectively 7.06 and 7.76, P values both below 0.01). The patients' standard score of SAS, SDS, PCL-C were (50 ± 11), (54 ± 11), and (38 ± 12) points. Respectively 89.1% (57/64), 60.9% (39/64), 46.9% (30/64) of the patients showed anxiety, depression, and PTSD symptoms. (2) Four independent variables: age, gender, marital status, and time after burns, were correlated with the psychological disorders, but the correlativity was not statistically significant (with rs values from -0.089 to 0.245, P values all above 0.05). Educational status was significantly negatively correlated with anxiety, depression, PTSD and its core symptoms of flashback, avoidance (with rs values from -0.361 to -0.253, P values all below 0.05). Educational status was negatively correlated with hypervigilance, but the correlativity was not statistically significant (rs = -0.187, P > 0.05). Burn area was significantly positively correlated with the psychological disorders (with rs values from 0.306 to 0.478, P values all below 0.05). Confrontation coping behavior was positively correlated with the psychological disorders, but the correlativity was not statistically significant (with rs values from 0.121 to 0.550, P values all above 0.05). Avoidance coping behavior was correlated with the psychological disorders, but the correlativity was not statistically significant (with rs values from -0.144 to 0.193, P values all above 0.05). Resignation coping behavior was significantly positively correlated with the psychological disorder (with rs values from 0.377 to 0.596, P values all below 0.01). (3) Educational status had predictive power on the anxiety, PTSD and flash back symptoms of patients (with t values from -2.19 to -2.02, P values all below 0.05), but not on depression, avoidance and hypervigilance (with t values from -1.95 to -0.99, P values all above 0.05). Burn area had no predictive power on the psychological disorders (with t values from 0.55 to 1.78, P values all above 0.05). Resignation coping behavior had predictive power on the psychological disorders (with t values from 3.10 to 6.46, P values below 0.01). Confrontation and avoidance coping behaviors had no predictive power on the psychological disorders (with t values from 0.46 to 2.32 and -0.89 and 1.75 respectively, P values all above 0.05). The severely burned patients with lower educational status, larger burn area, and the more frequently adapted resignation coping behavior are more likely to suffer from anxiety, depression, and PTSD.

IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies.

PubMed

Dai, Mingwei; Ming, Jingsi; Cai, Mingxuan; Liu, Jin; Yang, Can; Wan, Xiang; Xu, Zongben

2017-09-15

Results from genome-wide association studies (GWAS) suggest that a complex phenotype is often affected by many variants with small effects, known as 'polygenicity'. Tens of thousands of samples are often required to ensure statistical power of identifying these variants with small effects. However, it is often the case that a research group can only get approval for the access to individual-level genotype data with a limited sample size (e.g. a few hundreds or thousands). Meanwhile, summary statistics generated using single-variant-based analysis are becoming publicly available. The sample sizes associated with the summary statistics datasets are usually quite large. How to make the most efficient use of existing abundant data resources largely remains an open question. In this study, we propose a statistical approach, IGESS, to increasing statistical power of identifying risk variants and improving accuracy of risk prediction by i ntegrating individual level ge notype data and s ummary s tatistics. An efficient algorithm based on variational inference is developed to handle the genome-wide analysis. Through comprehensive simulation studies, we demonstrated the advantages of IGESS over the methods which take either individual-level data or summary statistics data as input. We applied IGESS to perform integrative analysis of Crohns Disease from WTCCC and summary statistics from other studies. IGESS was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.2% ( ±0.4% ) to 69.4% ( ±0.1% ) using about 240 000 variants. The IGESS software is available at https://github.com/daviddaigithub/IGESS . zbxu@xjtu.edu.cn or xwan@comp.hkbu.edu.hk or eeyang@hkbu.edu.hk. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Composite Overwrapped Pressure Vessel (COPV) Stress Rupture Testing

NASA Technical Reports Server (NTRS)

Greene, Nathanael J.; Saulsberry, Regor L.; Leifeste, Mark R.; Yoder, Tommy B.; Keddy, Chris P.; Forth, Scott C.; Russell, Rick W.

2010-01-01

This paper reports stress rupture testing of Kevlar(TradeMark) composite overwrapped pressure vessels (COPVs) at NASA White Sands Test Facility. This 6-year test program was part of the larger effort to predict and extend the lifetime of flight vessels. Tests were performed to characterize control parameters for stress rupture testing, and vessel life was predicted by statistical modeling. One highly instrumented 102-cm (40-in.) diameter Kevlar(TradeMark) COPV was tested to failure (burst) as a single-point model verification. Significant data were generated that will enhance development of improved NDE methods and predictive modeling techniques, and thus better address stress rupture and other composite durability concerns that affect pressure vessel safety, reliability and mission assurance.

Model Update of a Micro Air Vehicle (MAV) Flexible Wing Frame with Uncertainty Quantification

NASA Technical Reports Server (NTRS)

Reaves, Mercedes C.; Horta, Lucas G.; Waszak, Martin R.; Morgan, Benjamin G.

2004-01-01

This paper describes a procedure to update parameters in the finite element model of a Micro Air Vehicle (MAV) to improve displacement predictions under aerodynamics loads. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with Multidisciplinary Design Optimization (MDO) is used to modify key model parameters. Static test data collected using photogrammetry are used to correlate with model predictions. Results show significant improvements in model predictions after parameters are updated; however, computed probabilities values indicate low confidence in updated values and/or model structure errors. Lessons learned in the areas of wing design, test procedures, modeling approaches with geometric nonlinearities, and uncertainties quantification are all documented.

Dark matter candidate with well-defined mass and couplings

NASA Astrophysics Data System (ADS)

Allen, Roland

2017-01-01

There is as yet no confirmed and statistically significant evidence for direct, indirect, or collider-based detection of dark matter. However, several indirect searches, including AMS-02, Fermi-LAT, and PAMELA, have shown an intriguing excess of positrons when compared to expectations. Here we predict a Higgs-related but spin 1/2 dark matter candidate with a mass of 125 GeV. Since an initially reported 130 GeV gamma-ray excess has been abandoned by the full Fermi-LAT collaboration, this is a genuine prediction rather than postdiction. It would be consistent with a prediction of 125 GeV freshly-created positrons and antiprotons, but the complicated propagation of charged particles makes a comparison problematical.

Statistical evaluation of surrogate endpoints with examples from cancer clinical trials.

PubMed

Buyse, Marc; Molenberghs, Geert; Paoletti, Xavier; Oba, Koji; Alonso, Ariel; Van der Elst, Wim; Burzykowski, Tomasz

2016-01-01

A surrogate endpoint is intended to replace a clinical endpoint for the evaluation of new treatments when it can be measured more cheaply, more conveniently, more frequently, or earlier than that clinical endpoint. A surrogate endpoint is expected to predict clinical benefit, harm, or lack of these. Besides the biological plausibility of a surrogate, a quantitative assessment of the strength of evidence for surrogacy requires the demonstration of the prognostic value of the surrogate for the clinical outcome, and evidence that treatment effects on the surrogate reliably predict treatment effects on the clinical outcome. We focus on these two conditions, and outline the statistical approaches that have been proposed to assess the extent to which these conditions are fulfilled. When data are available from a single trial, one can assess the "individual level association" between the surrogate and the true endpoint. When data are available from several trials, one can additionally assess the "trial level association" between the treatment effect on the surrogate and the treatment effect on the true endpoint. In the latter case, the "surrogate threshold effect" can be estimated as the minimum effect on the surrogate endpoint that predicts a statistically significant effect on the clinical endpoint. All these concepts are discussed in the context of randomized clinical trials in oncology, and illustrated with two meta-analyses in gastric cancer. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linear regression analysis of Hospital Episode Statistics predicts a large increase in demand for elective hand surgery in England.

PubMed

Bebbington, Emily; Furniss, Dominic

2015-02-01

We integrated two factors, demographic population shifts and changes in prevalence of disease, to predict future trends in demand for hand surgery in England, to facilitate workforce planning. We analysed Hospital Episode Statistics data for Dupuytren's disease, carpal tunnel syndrome, cubital tunnel syndrome, and trigger finger from 1998 to 2011. Using linear regression, we estimated trends in both diagnosis and surgery until 2030. We integrated this regression with age specific population data from the Office for National Statistics in order to estimate how this will contribute to a change in workload over time. There has been a significant increase in both absolute numbers of diagnoses and surgery for all four conditions. Combined with future population data, we calculate that the total operative burden for these four conditions will increase from 87,582 in 2011 to 170,166 (95% confidence interval 144,517-195,353) in 2030. The prevalence of these diseases in the ageing population, and increasing prevalence of predisposing factors such as obesity and diabetes, may account for the predicted increase in workload. The most cost effective treatments must be sought, which requires high quality clinical trials. Our methodology can be applied to other sub-specialties to help anticipate the need for future service provision. Copyright © 2014 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

The proposed 'concordance-statistic for benefit' provided a useful metric when modeling heterogeneous treatment effects.

PubMed

van Klaveren, David; Steyerberg, Ewout W; Serruys, Patrick W; Kent, David M

2018-02-01

Clinical prediction models that support treatment decisions are usually evaluated for their ability to predict the risk of an outcome rather than treatment benefit-the difference between outcome risk with vs. without therapy. We aimed to define performance metrics for a model's ability to predict treatment benefit. We analyzed data of the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) trial and of three recombinant tissue plasminogen activator trials. We assessed alternative prediction models with a conventional risk concordance-statistic (c-statistic) and a novel c-statistic for benefit. We defined observed treatment benefit by the outcomes in pairs of patients matched on predicted benefit but discordant for treatment assignment. The 'c-for-benefit' represents the probability that from two randomly chosen matched patient pairs with unequal observed benefit, the pair with greater observed benefit also has a higher predicted benefit. Compared to a model without treatment interactions, the SYNTAX score II had improved ability to discriminate treatment benefit (c-for-benefit 0.590 vs. 0.552), despite having similar risk discrimination (c-statistic 0.725 vs. 0.719). However, for the simplified stroke-thrombolytic predictive instrument (TPI) vs. the original stroke-TPI, the c-for-benefit (0.584 vs. 0.578) was similar. The proposed methodology has the potential to measure a model's ability to predict treatment benefit not captured with conventional performance metrics. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning predictive statistics from temporal sequences: Dynamics and strategies.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Predicting and evaluation the severity in acute pancreatitis using a new modeling built on body mass index and intra-abdominal pressure.

PubMed

Fei, Yang; Gao, Kun; Tu, Jianfeng; Wang, Wei; Zong, Guang-Quan; Li, Wei-Qin

2017-06-03

Acute pancreatitis (AP) keeps as severe medical diagnosis and treatment problem. Early evaluation for severity and risk stratification in patients with AP is very important. Some scoring system such as acute physiology and chronic health evaluation-II (APACHE-II), the computed tomography severity index (CTSI), Ranson's score and the bedside index of severity of AP (BISAP) have been used, nevertheless, there're a few shortcomings in these methods. The aim of this study was to construct a new modeling including intra-abdominal pressure (IAP) and body mass index (BMI) to evaluate the severity in AP. The study comprised of two independent cohorts of patients with AP, one set was used to develop modeling from Jinling hospital in the period between January 2013 and October 2016, 1073 patients were included in it; another set was used to validate modeling from the 81st hospital in the period between January 2012 and December 2016, 326 patients were included in it. The association between risk factors and severity of AP were assessed by univariable analysis; multivariable modeling was explored through stepwise selection regression. The change in IAP and BMI were combined to generate a regression equation as the new modeling. Statistical indexes were used to evaluate the value of the prediction in the new modeling. Univariable analysis confirmed change in IAP and BMI to be significantly associated with severity of AP. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by the new modeling for severity of AP were 77.6%, 82.6%, 71.9%, 87.5% and 74.9% respectively in the developing dataset. There were significant differences between the new modeling and other scoring systems in these parameters (P < 0.05). In addition, a comparison of the area under receiver operating characteristic curves of them showed a statistically significant difference (P < 0.05). The same results could be found in the validating dataset. A new modeling based on IAP and BMI is more likely to predict the severity of AP. Copyright © 2017. Published by Elsevier Inc.

Nonlinear dynamic analysis of voices before and after surgical excision of vocal polyps

NASA Astrophysics Data System (ADS)

Zhang, Yu; McGilligan, Clancy; Zhou, Liang; Vig, Mark; Jiang, Jack J.

2004-05-01

Phase space reconstruction, correlation dimension, and second-order entropy, methods from nonlinear dynamics, are used to analyze sustained vowels generated by patients before and after surgical excision of vocal polyps. Two conventional acoustic perturbation parameters, jitter and shimmer, are also employed to analyze voices before and after surgery. Presurgical and postsurgical analyses of jitter, shimmer, correlation dimension, and second-order entropy are statistically compared. Correlation dimension and second-order entropy show a statistically significant decrease after surgery, indicating reduced complexity and higher predictability of postsurgical voice dynamics. There is not a significant postsurgical difference in shimmer, although jitter shows a significant postsurgical decrease. The results suggest that jitter and shimmer should be applied to analyze disordered voices with caution; however, nonlinear dynamic methods may be useful for analyzing abnormal vocal function and quantitatively evaluating the effects of surgical excision of vocal polyps.

Effects of vibratory stimulation on sexual response in women with spinal cord injury.

PubMed

Sipski, Marca L; Alexander, Craig J; Gomez-Marin, Orlando; Grossbard, Marissa; Rosen, Raymond

2005-01-01

Women with spinal cord injuries (SCIs) have predictable alterations in sexual responses. They commonly have a decreased ability to achieve genital sexual arousal. This study determined whether the use of vibratory stimulation would result in increased genital arousal as measured by vaginal pulse amplitude in women with SCIs. Subjects included 46 women with SCIs and 11 nondisabled control subjects. Results revealed vibratory clitoral stimulation resulted in increased vaginal pulse amplitude as compared with manual clitoral stimulation in both SCI and nondisabled subjects; however, these differences were not statistically significant. Subjective levels of arousal were also compared between SCI and nondisabled control subjects. Both vibratory and manual clitoral stimulation resulted in significantly increased arousal levels in both groups of subjects; however, statistically significant differences between the two conditions were only noted in nondisabled subjects. Further studies of the effects of repetitive vibratory stimulation are underway.

Predictors of Adolescents’ Health- promoting Behaviors Guided by Primary Socialization Theory

PubMed Central

Rew, Lynn; Arheart, Kristopher L.; Thompson, Sanna; Johnson, Karen

2013-01-01

Purpose The purpose of this study was to determine the influence of parents and peers on adolescents’ health-promoting behaviors, framed by Primary Socialization Theory. Design and Method Longitudinal data collected annually from 1,081 rural youth (mean age = 17 ±.7; 43.5% males; 44% Hispanic) and once from their parents were analyzed using generalized linear models. Results Parental monitoring and adolescent’s religious commitment significantly predicted all health-promoting behaviors (nutrition, physical activity, safety, health practices awareness, stress management). Other statistically significant predictors were parent’s responsiveness and health-promoting behaviors. Peer influence predicted safety and stress management. Practice Implications Nurses may facilitate adolescents’ development of health-promoting behaviors through family-focused interventions. PMID:24094123

Development and Evaluation of Season-ahead Precipitation and Streamflow Predictions for Sectoral Management in Western Ethiopia

NASA Astrophysics Data System (ADS)

Block, P. J.; Alexander, S.; WU, S.

2017-12-01

Skillful season-ahead predictions conditioned on local and large-scale hydro-climate variables can provide valuable knowledge to farmers and reservoir operators, enabling informed water resource allocation and management decisions. In Ethiopia, the potential for advancing agriculture and hydropower management, and subsequently economic growth, is substantial, yet evidence suggests a weak adoption of prediction information by sectoral audiences. To address common critiques, including skill, scale, and uncertainty, probabilistic forecasts are developed at various scales - temporally and spatially - for the Finchaa hydropower dam and the Koga agricultural scheme in an attempt to promote uptake and application. Significant prediction skill is evident across scales, particularly for statistical models. This raises questions regarding other potential barriers to forecast utilization at community scales, which are also addressed.

On the incremental validity of irrational beliefs to predict subjective well-being while controlling for personality factors.

PubMed

Spörrle, Matthias; Strobel, Maria; Tumasjan, Andranik

2010-11-01

This research examines the incremental validity of irrational thinking as conceptualized by Albert Ellis to predict diverse aspects of subjective well-being while controlling for the influence of personality factors. Rational-emotive behavior therapy (REBT) argues that irrational beliefs result in maladaptive emotions leading to reduced well-being. Although there is some early scientific evidence for this relation, it has never been investigated whether this connection would still persist when statistically controlling for the Big Five personality factors, which were consistently found to be important determinants of well-being. Regression analyses revealed significant incremental validity of irrationality over personality factors when predicting life satisfaction, but not when predicting subjective happiness. Results are discussed with respect to conceptual differences between these two aspects of subjective well-being.

Early water intake restriction to prevent inappropriate antidiuretic hormone secretion following transsphenoidal surgery: low BMI predicts postoperative SIADH.

PubMed

Matsuyama, Junko; Ikeda, Hidetoshi; Sato, Shunsuke; Yamamoto, Koh; Ohashi, Genichiro; Watanabe, Kazuo

2014-12-01

The goals of this study were to assess the incidence of and risk factors for the syndrome of inappropriate antidiuretic hormone secretion (SIADH) in patients following transsphenoidal surgery (TSS), and to validate the effectiveness of early prophylactic restriction of water intake. Retrospective analysis was performed for 207 patients who had undergone TSS, including 156 patients not placed on early prophylactic water restriction. Sixty-four patients received treatment for SIADH. We compared the incidence of SIADH between patients with and without early water intake restriction, and analyzed various risk factors for SIADH using statistical analyses. BMI was significantly lower for patients with SIADH than for those patients without SIADH. Statistical analysis revealed that the threshold BMI predicting SIADH was 26. Serum sodium levels on postoperative days 5-10 and daily urine volumes on postoperative days 5-10 were significantly lower in patients with SIADH than in those without SIADH. Postoperative body weight loss on days 6, 8, 10, and 11 was significantly higher in patients with SIADH. The incidence of SIADH after starting prophylactic water intake restriction (14%) was significantly lower than the rate before early water restriction (38%; P<0.05). SIADH is relatively common after TSS, and serum sodium concentrations and daily urine volumes should be carefully monitored. Patients with low preoperative BMI should be closely observed, as this represented a significant preoperative risk factor for SIADH. Early prophylactic water intake restriction appears effective at preventing postoperative SIADH. © 2014 European Society of Endocrinology.

Evaluation of airborne lidar data to predict vegetation Presence/Absence

USGS Publications Warehouse

Palaseanu-Lovejoy, M.; Nayegandhi, A.; Brock, J.; Woodman, R.; Wright, C.W.

2009-01-01

This study evaluates the capabilities of the Experimental Advanced Airborne Research Lidar (EAARL) in delineating vegetation assemblages in Jean Lafitte National Park, Louisiana. Five-meter-resolution grids of bare earth, canopy height, canopy-reflection ratio, and height of median energy were derived from EAARL data acquired in September 2006. Ground-truth data were collected along transects to assess species composition, canopy cover, and ground cover. To decide which model is more accurate, comparisons of general linear models and generalized additive models were conducted using conventional evaluation methods (i.e., sensitivity, specificity, Kappa statistics, and area under the curve) and two new indexes, net reclassification improvement and integrated discrimination improvement. Generalized additive models were superior to general linear models in modeling presence/absence in training vegetation categories, but no statistically significant differences between the two models were achieved in determining the classification accuracy at validation locations using conventional evaluation methods, although statistically significant improvements in net reclassifications were observed. ?? 2009 Coastal Education and Research Foundation.

Predicting stroke through genetic risk functions: The CHARGE risk score project

PubMed Central

Ibrahim-Verbaas, Carla A; Fornage, Myriam; Bis, Joshua C; Choi, Seung Hoan; Psaty, Bruce M; Meigs, James B; Rao, Madhu; Nalls, Mike; Fontes, Joao D; O’Donnell, Christopher J.; Kathiresan, Sekar; Ehret, Georg B.; Fox, Caroline S; Malik, Rainer; Dichgans, Martin; Schmidt, Helena; Lahti, Jari; Heckbert, Susan R; Lumley, Thomas; Rice, Kenneth; Rotter, Jerome I; Taylor, Kent D; Folsom, Aaron R; Boerwinkle, Eric; Rosamond, Wayne D; Shahar, Eyal; Gottesman, Rebecca F.; Koudstaal, Peter J; Amin, Najaf; Wieberdink, Renske G.; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G; DeStefano, Anita L.; Debette, Stephanie; Xue, Luting; Beiser, Alexa; Wolf, Philip A.; DeCarli, Charles; Ikram, M. Arfan; Seshadri, Sudha; Mosley, Thomas H; Longstreth, WT; van Duijn, Cornelia M; Launer, Lenore J

2014-01-01

Background and Purpose Beyond the Framingham Stroke Risk Score (FSRS), prediction of future stroke may improve with a genetic risk score (GRS) based on Single nucleotide polymorphisms (SNPs) associated with stroke and its risk factors. Methods The study includes four population-based cohorts with 2,047 first incident strokes from 22,720 initially stroke-free European origin participants aged 55 years and older, who were followed for up to 20 years. GRS were constructed with 324 SNPs implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with Area under the curve (AUC) statistics comparing the GRS to age sex, and FSRS models, and with reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke (IS). Results In the meta-analysis, adding the GRS to the FSRS, age and sex model resulted in a significant improvement in discrimination (All stroke: Δjoint AUC =0.016, p-value=2.3*10-6; IS: Δ joint AUC =0.021, p-value=3.7*10−7), although the overall AUC remained low. In all studies there was a highly significantly improved net reclassification index (p-values <10−4). Conclusions The SNPs associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared to the classical epidemiological risk factors for stroke. PMID:24436238

The predictive value of mean serum uric acid levels for developing prediabetes.

PubMed

Zhang, Qing; Bao, Xue; Meng, Ge; Liu, Li; Wu, Hongmei; Du, Huanmin; Shi, Hongbin; Xia, Yang; Guo, Xiaoyan; Liu, Xing; Li, Chunlei; Su, Qian; Gu, Yeqing; Fang, Liyun; Yu, Fei; Yang, Huijun; Yu, Bin; Sun, Shaomei; Wang, Xing; Zhou, Ming; Jia, Qiyu; Zhao, Honglin; Huang, Guowei; Song, Kun; Niu, Kaijun

2016-08-01

We aimed to assess the predictive value of mean serum uric acid (SUA) levels for incident prediabetes. Normoglycemic adults (n=39,353) were followed for a median of 3.0years. Prediabetes is defined as impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or impaired HbA1c (IA1c), based on the American Diabetes Association criteria. Serum SUA levels were measured annually. Four diagnostic strategies were used to detect prediabetes in four separate analyses (Analysis 1: IFG. Analysis 2: IFG+IGT. Analysis 3: IFG+IA1c. Analysis 4: IFG+IGT+IA1c). Cox proportional hazards regression models were used to assess the relationship between SUA quintiles and prediabetes. C-statistic was additionally used in the final analysis to assess the accuracy of predictions based upon baseline SUA and mean SUA, respectively. After adjustment for potential confounders, the hazard ratios (95% confidence interval) of prediabetes for the highest versus lowest quintile of mean SUA were 1.22 (1.10, 1.36) in analysis 1; 1.59 (1.23, 2.05) in analysis 2; 1.62 (1.34, 1.95) in analysis 3 and 1.67 (1.31, 2.13) in analysis 4. In contrast, for baseline SUA, significance was only reached in analyses 3 and 4. Moreover, compared with baseline SUA, mean SUA value was associated with a significant increase in the C-statistic (P<0.001). Mean SUA value was strongly and positively related to prediabetes risk, and showed better predictive ability for prediabetes than baseline SUA. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Predictive Factors for Response of Intracranial Dural Arteriovenous Fistulas to Transarterial Onyx Embolization: Angiographic Subgroup Analysis of Treatment Outcomes.

PubMed

Kim, Byungjun; Jeon, Pyoung; Kim, Keonha; Kim, Sungtae; Kim, Hyungjin; Byun, Hong Sik; Jo, Kyung-Il

2016-04-01

Endovascular treatment using Onyx has been increasingly used to treat intracranial dural arteriovenous fistulas (DAVFs). This study evaluated predictive factors for favorable treatment outcome in patients with intracranial noncavernous DAVFs treated by transarterial Onyx embolization. Between August 2008 and August 2014, 55 patients who underwent transarterial Onyx embolization for noncavernous DAVFs were retrospectively reviewed. Patients' demographic, clinical, and procedural data were analyzed to find statistically significant predictive factors for favorable treatment outcomes after Onyx embolization. Fistulas were classified angiographically according to the relationship between fistulas and dural venous sinuses and the presence of leptomeningeal venous reflux. Sixty-eight Onyx embolizations were performed in 55 patients. Immediate angiographic cure was achieved in 28 patients, and 14 of 27 patients with residual shunts showed progressive occlusion at follow-up imaging studies. Therefore, the overall favorable treatment outcome was 76.4% (42/55). The remaining 13 patients (23.6%) showed persistent residual shunts, and 3 (5.5%) of them showed aggravation of residual lesion on follow-up studies. Of 25 patients with non-sinus fistulas, 23 patients (92%) showed favorable treatment outcomes, and 19 of 30 patients (63.3%) with sinus fistulas showed favorable outcomes. Among the evaluated variables, non-sinus DAVFs was a statistically significant predictive factor for favorable response to transarterial Onyx embolization (P < 0.05). Transarterial Onyx embolization is a highly effective treatment method for non-sinus DAVFs. Careful consideration of angiographic features and multimodal embolization strategies are required for treatment of sinus DAVFs. Copyright © 2016 Elsevier Inc. All rights reserved.

A known-groups evaluation of the response bias scale in a neuropsychological setting.

PubMed

Sullivan, Karen A; Elliott, Cameron D; Lange, Rael T; Anderson, Deborah S

2013-01-01

We evaluated the Minnesota Multiphasic Personality Inventory-Second Edition (MMPI-2) Response Bias Scale (RBS). Archival data from 83 individuals who were referred for neuropsychological assessment with no formal diagnosis (n = 10), following a known or suspected traumatic brain injury (n = 36), with a psychiatric diagnosis (n = 20), or with a history of both trauma and a psychiatric condition (n = 17) were retrieved. The criteria for malingered neurocognitive dysfunction (MNCD) were applied, and two groups of participants were formed: poor effort (n = 15) and genuine responders (n = 68). Consistent with previous studies, the difference in scores between groups was greatest for the RBS (d = 2.44), followed by two established MMPI-2 validity scales, F (d = 0.25) and K (d = 0.23), and strong significant correlations were found between RBS and F (rs = .48) and RBS and K (r = -.41). When MNCD group membership was predicted using logistic regression, the RBS failed to add incrementally to F. In a separate regression to predict group membership, K added significantly to the RBS. Receiver-operating curve analysis revealed a nonsignificant area under the curve statistic, and at the ideal cutoff in this sample of >12, specificity was moderate (.79), sensitivity was low (.47), and positive and negative predictive power values at a 13% base rate were .25 and .91, respectively. Although the results of this study require replication because of a number of limitations, this study has made an important first attempt to report RBS classification accuracy statistics for predicting poor effort at a range of base rates.

Analgesics use in competitive triathletes: its relationship to doping and on predicting its usage.

PubMed

Dietz, Pavel; Dalaker, Robert; Letzel, Stephan; Ulrich, Rolf; Simon, Perikles

2016-10-01

The two major objectives of this study were (i) to assess variables that predict the use of analgesics in competitive athletes and (ii) to test whether the use of analgesics is associated with the use of doping. A questionnaire primarily addressing the use of analgesics and doping was distributed among 2,997 triathletes. Binary logistic regression analysis was performed to predict the use of analgesics. Moreover, the randomised response technique (RRT) was used to estimate the prevalence of doping in order to assess whether users of analgesics have a higher potential risk for doping than non-users. Statistical power analyses were performed to determine sample size. The bootstrap method was used to assess the statistical significance of the prevalence difference for doping between users and non-users of analgesics. Four variables from a pool of 16 variables were identified that predict the use of analgesics. These were: "version of questionnaire (English)", "gender (female)", "behaviour in case of pain (continue training)", and "hours of training per week (>12 h/week)". The 12-month prevalence estimate for the use of doping substances (overall estimate 13.0%) was significantly higher in athletes that used analgesics (20.4%) than in those athletes who did not use analgesics (12.4%). The results of this study revealed that athletes who use analgesics prior to competition may be especially prone to using doping substances. The predictors of analgesic use found in the study may be of importance to prepare education material and prevention models against the misuse of drugs in athletes.

Contemporary model for cardiovascular risk prediction in people with type 2 diabetes.

PubMed

Kengne, Andre Pascal; Patel, Anushka; Marre, Michel; Travert, Florence; Lievre, Michel; Zoungas, Sophia; Chalmers, John; Colagiuri, Stephen; Grobbee, Diederick E; Hamet, Pavel; Heller, Simon; Neal, Bruce; Woodward, Mark

2011-06-01

Existing cardiovascular risk prediction equations perform non-optimally in different populations with diabetes. Thus, there is a continuing need to develop new equations that will reliably estimate cardiovascular disease (CVD) risk and offer flexibility for adaptation in various settings. This report presents a contemporary model for predicting cardiovascular risk in people with type 2 diabetes mellitus. A 4.5-year follow-up of the Action in Diabetes and Vascular disease: preterax and diamicron-MR controlled evaluation (ADVANCE) cohort was used to estimate coefficients for significant predictors of CVD using Cox models. Similar Cox models were used to fit the 4-year risk of CVD in 7168 participants without previous CVD. The model's applicability was tested on the same sample and another dataset. A total of 473 major cardiovascular events were recorded during follow-up. Age at diagnosis, known duration of diabetes, sex, pulse pressure, treated hypertension, atrial fibrillation, retinopathy, HbA1c, urinary albumin/creatinine ratio and non-HDL cholesterol at baseline were significant predictors of cardiovascular events. The model developed using these predictors displayed an acceptable discrimination (c-statistic: 0.70) and good calibration during internal validation. The external applicability of the model was tested on an independent cohort of individuals with type 2 diabetes, where similar discrimination was demonstrated (c-statistic: 0.69). Major cardiovascular events in contemporary populations with type 2 diabetes can be predicted on the basis of routinely measured clinical and biological variables. The model presented here can be used to quantify risk and guide the intensity of treatment in people with diabetes.

Long-term predictive models of risk factors for early chronic kidney disease: a longitudinal study.

PubMed

Wu, Wen-Chih; Hsieh, Po-Chien; Hu, Fu-Kang; Kuan, Jen-Chun; Chu, Chi-Ming; Sun, Chien-An; Yang, Tsan; Su, Sui-Lung; Chou, Yu-Ching

2018-04-13

The high incidence and prevalence of chronic kidney disease (CKD) in Taiwan have produced tremendous burdens on health care resources. The work environment of air force special operations personnel engenders high psychological stress, and the resulting increased blood pressure can lead to glomerular hypertension and accelerated glomerular injury in the long term. The aim of the study was to establish the predictive models to define the predictors of CKD. The results indicated that the prevalence of CKD over 4 consecutive years was 3.8%, 9.4%, 9.0%, and 9.4%. The capability of using occult blood in urine to predict the risk of CKD after 1, 2, and 3 years was statistically significant. The age-adjusted odds ratio (OR) and 95% confidence interval (CI) were 7.94 (95% CI: 2.61-24.14), 12.35 (95% CI: 4.02-37.94) and 4.25 (95% CI: 1.32-13.70), respectively. The predictive power of occult blood in urine for the risk of CKD in each model was statistically significant. Future investigations can explore the feasibility of implementing simple and accurate urine dipsticks for preliminary testing besides annual aircrew physical examinations to facilitate early detection and treatment. This study was a longitudinal study, in which air force special operations personnel who received physical examinations at military hospitals between 2004 and 2010 were selected. CKD was determined based on the definition provided by the US National Kidney Foundation. Overall, 212 participants that could be followed continuously for 4 years were analyzed.

Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.

PubMed

Ibrahim-Verbaas, Carla A; Fornage, Myriam; Bis, Joshua C; Choi, Seung Hoan; Psaty, Bruce M; Meigs, James B; Rao, Madhu; Nalls, Mike; Fontes, Joao D; O'Donnell, Christopher J; Kathiresan, Sekar; Ehret, Georg B; Fox, Caroline S; Malik, Rainer; Dichgans, Martin; Schmidt, Helena; Lahti, Jari; Heckbert, Susan R; Lumley, Thomas; Rice, Kenneth; Rotter, Jerome I; Taylor, Kent D; Folsom, Aaron R; Boerwinkle, Eric; Rosamond, Wayne D; Shahar, Eyal; Gottesman, Rebecca F; Koudstaal, Peter J; Amin, Najaf; Wieberdink, Renske G; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G; Destefano, Anita L; Debette, Stephanie; Xue, Luting; Beiser, Alexa; Wolf, Philip A; Decarli, Charles; Ikram, M Arfan; Seshadri, Sudha; Mosley, Thomas H; Longstreth, W T; van Duijn, Cornelia M; Launer, Lenore J

2014-02-01

Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors. The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke. In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)). The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India.

PubMed

Kumar, Rajesh; Dash, Chinmaya; Rani, Khushbu

2017-09-01

Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover ( p  < 0.05). The efficient predictive model include the forest cover [IRR-1.033 (1.024-1.042)], maximum humidity [IRR-1.016 (1.013-1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231-1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974-0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state.

Quantitative structure-retention relationships of polycyclic aromatic hydrocarbons gas-chromatographic retention indices.

PubMed

Drosos, Juan Carlos; Viola-Rhenals, Maricela; Vivas-Reyes, Ricardo

2010-06-25

Polycyclic aromatic compounds (PAHs) are of concern in environmental chemistry and toxicology. In the present work, a QSRR study was performed for 209 previously reported PAHs using quantum mechanics and other sources descriptors estimated by different approaches. The B3LYP/6-31G* level of theory was used for geometrical optimization and quantum mechanics related variables. A good linear relationship between gas-chromatographic retention index and electronic or topologic descriptors was found by stepwise linear regression analysis. The molecular polarizability (alpha) and the second order molecular connectivity Kier and Hall index ((2)chi) showed evidence of significant correlation with retention index by means of important squared coefficient of determination, (R(2)), values (R(2)=0.950 and 0.962, respectively). A one variable QSRR model is presented for each descriptor and both models demonstrates a significant predictive capacity established using the leave-many-out LMO (excluding 25% of rows) cross validation method's q(2) cross-validation coefficients q(2)(CV-LMO25%), (obtained q(2)(CV-LMO25%) 0.947 and 0.960, respectively). Furthermore, the physicochemical interpretation of selected descriptors allowed detailed explanation of the source of the observed statistical correlation. The model analysis suggests that only one descriptor is sufficient to establish a consistent retention index-structure relationship. Moderate or non-significant improve was observed for quantitative results or statistical validation parameters when introducing more terms in predictive equation. The one parameter QSRR proposed model offers a consistent scheme to predict chromatographic properties of PAHs compounds. Copyright 2010 Elsevier B.V. All rights reserved.

Predicting lettuce canopy photosynthesis with statistical and neural network models

NASA Technical Reports Server (NTRS)

Frick, J.; Precetti, C.; Mitchell, C. A.

1998-01-01

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

Mixture EMOS model for calibrating ensemble forecasts of wind speed.

PubMed

Baran, S; Lerch, S

2016-03-01

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.

Evaluating the effect of disturbed ensemble distributions on SCFG based statistical sampling of RNA secondary structures.

PubMed

Scheid, Anika; Nebel, Markus E

2012-07-09

Over the past years, statistical and Bayesian approaches have become increasingly appreciated to address the long-standing problem of computational RNA structure prediction. Recently, a novel probabilistic method for the prediction of RNA secondary structures from a single sequence has been studied which is based on generating statistically representative and reproducible samples of the entire ensemble of feasible structures for a particular input sequence. This method samples the possible foldings from a distribution implied by a sophisticated (traditional or length-dependent) stochastic context-free grammar (SCFG) that mirrors the standard thermodynamic model applied in modern physics-based prediction algorithms. Specifically, that grammar represents an exact probabilistic counterpart to the energy model underlying the Sfold software, which employs a sampling extension of the partition function (PF) approach to produce statistically representative subsets of the Boltzmann-weighted ensemble. Although both sampling approaches have the same worst-case time and space complexities, it has been indicated that they differ in performance (both with respect to prediction accuracy and quality of generated samples), where neither of these two competing approaches generally outperforms the other. In this work, we will consider the SCFG based approach in order to perform an analysis on how the quality of generated sample sets and the corresponding prediction accuracy changes when different degrees of disturbances are incorporated into the needed sampling probabilities. This is motivated by the fact that if the results prove to be resistant to large errors on the distinct sampling probabilities (compared to the exact ones), then it will be an indication that these probabilities do not need to be computed exactly, but it may be sufficient and more efficient to approximate them. Thus, it might then be possible to decrease the worst-case time requirements of such an SCFG based sampling method without significant accuracy losses. If, on the other hand, the quality of sampled structures can be observed to strongly react to slight disturbances, there is little hope for improving the complexity by heuristic procedures. We hence provide a reliable test for the hypothesis that a heuristic method could be implemented to improve the time scaling of RNA secondary structure prediction in the worst-case - without sacrificing much of the accuracy of the results. Our experiments indicate that absolute errors generally lead to the generation of useless sample sets, whereas relative errors seem to have only small negative impact on both the predictive accuracy and the overall quality of resulting structure samples. Based on these observations, we present some useful ideas for developing a time-reduced sampling method guaranteeing an acceptable predictive accuracy. We also discuss some inherent drawbacks that arise in the context of approximation. The key results of this paper are crucial for the design of an efficient and competitive heuristic prediction method based on the increasingly accepted and attractive statistical sampling approach. This has indeed been indicated by the construction of prototype algorithms.

Evaluating the effect of disturbed ensemble distributions on SCFG based statistical sampling of RNA secondary structures

PubMed Central

2012-01-01

Background Over the past years, statistical and Bayesian approaches have become increasingly appreciated to address the long-standing problem of computational RNA structure prediction. Recently, a novel probabilistic method for the prediction of RNA secondary structures from a single sequence has been studied which is based on generating statistically representative and reproducible samples of the entire ensemble of feasible structures for a particular input sequence. This method samples the possible foldings from a distribution implied by a sophisticated (traditional or length-dependent) stochastic context-free grammar (SCFG) that mirrors the standard thermodynamic model applied in modern physics-based prediction algorithms. Specifically, that grammar represents an exact probabilistic counterpart to the energy model underlying the Sfold software, which employs a sampling extension of the partition function (PF) approach to produce statistically representative subsets of the Boltzmann-weighted ensemble. Although both sampling approaches have the same worst-case time and space complexities, it has been indicated that they differ in performance (both with respect to prediction accuracy and quality of generated samples), where neither of these two competing approaches generally outperforms the other. Results In this work, we will consider the SCFG based approach in order to perform an analysis on how the quality of generated sample sets and the corresponding prediction accuracy changes when different degrees of disturbances are incorporated into the needed sampling probabilities. This is motivated by the fact that if the results prove to be resistant to large errors on the distinct sampling probabilities (compared to the exact ones), then it will be an indication that these probabilities do not need to be computed exactly, but it may be sufficient and more efficient to approximate them. Thus, it might then be possible to decrease the worst-case time requirements of such an SCFG based sampling method without significant accuracy losses. If, on the other hand, the quality of sampled structures can be observed to strongly react to slight disturbances, there is little hope for improving the complexity by heuristic procedures. We hence provide a reliable test for the hypothesis that a heuristic method could be implemented to improve the time scaling of RNA secondary structure prediction in the worst-case – without sacrificing much of the accuracy of the results. Conclusions Our experiments indicate that absolute errors generally lead to the generation of useless sample sets, whereas relative errors seem to have only small negative impact on both the predictive accuracy and the overall quality of resulting structure samples. Based on these observations, we present some useful ideas for developing a time-reduced sampling method guaranteeing an acceptable predictive accuracy. We also discuss some inherent drawbacks that arise in the context of approximation. The key results of this paper are crucial for the design of an efficient and competitive heuristic prediction method based on the increasingly accepted and attractive statistical sampling approach. This has indeed been indicated by the construction of prototype algorithms. PMID:22776037

Guidelines 13 and 14—Prediction uncertainty

USGS Publications Warehouse

Hill, Mary C.; Tiedeman, Claire

2005-01-01

An advantage of using optimization for model development and calibration is that optimization provides methods for evaluating and quantifying prediction uncertainty. Both deterministic and statistical methods can be used. Guideline 13 discusses using regression and post-audits, which we classify as deterministic methods. Guideline 14 discusses inferential statistics and Monte Carlo methods, which we classify as statistical methods.

COMPARING MID-INFRARED GLOBULAR CLUSTER COLORS WITH POPULATION SYNTHESIS MODELS

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

Barmby, P.; Jalilian, F. F.

2012-04-15

Several population synthesis models now predict integrated colors of simple stellar populations in the mid-infrared bands. To date, the models have not been extensively tested in this wavelength range. In a comparison of the predictions of several recent population synthesis models, the integrated colors are found to cover approximately the same range but to disagree in detail, for example, on the effects of metallicity. To test against observational data, globular clusters (GCs) are used as the closest objects to idealized groups of stars with a single age and single metallicity. Using recent mass estimates, we have compiled a sample ofmore » massive, old GCs in M31 which contain enough stars to guard against the stochastic effects of small-number statistics, and measured their integrated colors in the Spitzer/IRAC bands. Comparison of the cluster photometry in the IRAC bands with the model predictions shows that the models reproduce the cluster colors reasonably well, except for a small (not statistically significant) offset in [4.5] - [5.8]. In this color, models without circumstellar dust emission predict bluer values than are observed. Model predictions of colors formed from the V band and the IRAC 3.6 and 4.5 {mu}m bands are redder than the observed data at high metallicities and we discuss several possible explanations. In agreement with model predictions, V - [3.6] and V - [4.5] colors are found to have metallicity sensitivity similar to or slightly better than V - K{sub s}.« less

Sirc-cvs cytotoxicity test: an alternative for predicting rodent acute systemic toxicity.

PubMed

Kitagaki, Masato; Wakuri, Shinobu; Hirota, Morihiko; Tanaka, Noriho; Itagaki, Hiroshi

2006-10-01

An in vitro crystal violet staining method using the rabbit cornea-derived cell line (SIRC-CVS) has been developed as an alternative to predict acute systemic toxicity in rodents. Seventy-nine chemicals, the in vitro cytotoxicity of which was already reported by the Multicenter Evaluation of In vitro Toxicity (MEIC) and ICCVAM/ECVAM, were selected as test compounds. The cells were incubated with the chemicals for 72 hrs and the IC(50) and IC(35) values (microg/mL) were obtained. The results were compared to the in vivo (rat or mouse) "most toxic" oral, intraperitoneal, subcutaneous and intravenous LD(50) values (mg/kg) taken from the RTECS database for each of the chemicals by using Pearson's correlation statistics. The following parameters were calculated: accuracy, sensitivity, specificity, prevalence, positive predictability, and negative predictability. Good linear correlations (Pearson's coefficient; r>0.6) were observed between either the IC(50) or the IC(35) values and all the LD(50) values. Among them, a statistically significant high correlation (r=0.8102, p<0.001) required for acute systemic toxicity prediction was obtained between the IC(50) values and the oral LD(50) values. By using the cut-off concentrations of 2,000 mg/kg (LD(50)) and 4,225 microg/mL (IC(50)), no false negatives were observed, and the accuracy was 84.8%. From this, it is concluded that this method could be used to predict the acute systemic toxicity potential of chemicals in rodents.

Prediction of shock initiation thresholds and ignition probability of polymer-bonded explosives using mesoscale simulations

NASA Astrophysics Data System (ADS)

Kim, Seokpum; Wei, Yaochi; Horie, Yasuyuki; Zhou, Min

2018-05-01

The design of new materials requires establishment of macroscopic measures of material performance as functions of microstructure. Traditionally, this process has been an empirical endeavor. An approach to computationally predict the probabilistic ignition thresholds of polymer-bonded explosives (PBXs) using mesoscale simulations is developed. The simulations explicitly account for microstructure, constituent properties, and interfacial responses and capture processes responsible for the development of hotspots and damage. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, and heat conduction. The probabilistic analysis uses sets of statistically similar microstructure samples to directly mimic relevant experiments for quantification of statistical variations of material behavior due to inherent material heterogeneities. The particular thresholds and ignition probabilities predicted are expressed in James type and Walker-Wasley type relations, leading to the establishment of explicit analytical expressions for the ignition probability as function of loading. Specifically, the ignition thresholds corresponding to any given level of ignition probability and ignition probability maps are predicted for PBX 9404 for the loading regime of Up = 200-1200 m/s where Up is the particle speed. The predicted results are in good agreement with available experimental measurements. A parametric study also shows that binder properties can significantly affect the macroscopic ignition behavior of PBXs. The capability to computationally predict the macroscopic engineering material response relations out of material microstructures and basic constituent and interfacial properties lends itself to the design of new materials as well as the analysis of existing materials.

Publication bias in animal research presented at the 2008 Society of Critical Care Medicine Conference.

PubMed

Conradi, Una; Joffe, Ari R

2017-07-07

To determine a direct measure of publication bias by determining subsequent full-paper publication (P) of studies reported in animal research abstracts presented at an international conference (A). We selected 100 random (using a random-number generator) A from the 2008 Society of Critical Care Medicine Conference. Using a data collection form and study manual, we recorded methodology and result variables from A. We searched PubMed and EMBASE to June 2015, and DOAJ and Google Scholar to May 2017 to screen for subsequent P. Methodology and result variables were recorded from P to determine changes in reporting from A. Predictors of P were examined using Fisher's Exact Test. 62% (95% CI 52-71%) of studies described in A were subsequently P after a median 19 [IQR 9-33.3] months from conference presentation. Reporting of studies in A was of low quality: randomized 27% (the method of randomization and allocation concealment not described), blinded 0%, sample-size calculation stated 0%, specifying the primary outcome 26%, numbers given with denominators 6%, and stating number of animals used 47%. Only being an orally presented (vs. poster presented) A (14/16 vs. 48/84, p = 0.025) predicted P. Reporting of studies in P was of poor quality: randomized 39% (the method of randomization and allocation concealment not described), likely blinded 6%, primary outcome specified 5%, sample size calculation stated 0%, numbers given with denominators 34%, and number of animals used stated 56%. Changes in reporting from A to P occurred: from non-randomized to randomized 19%, from non-blinded to blinded 6%, from negative to positive outcomes 8%, from having to not having a stated primary outcome 16%, and from non-statistically to statistically significant findings 37%. Post-hoc, using publication data, P was predicted by having positive outcomes (published 62/62, unpublished 33/38; p = 0.003), or statistically significant results (published 58/62, unpublished 20/38; p < 0.001). Only 62% (95% CI 52-71%) of animal research A are subsequently P; this was predicted by oral presentation of the A, finally having positive outcomes, and finally having statistically significant results. Publication bias is prevalent in critical care animal research.

Football experts versus sports economists: Whose forecasts are better?

PubMed

Frick, Bernd; Wicker, Pamela

2016-08-01

Given the uncertainty of outcome in sport, predicting the outcome of sporting contests is a major topic in sport sciences. This study examines the accuracy of expert predictions in the German Bundesliga and compares their predictions to those of sports economists. Prior to the start of each season, a set of distinguished experts (head coaches and players) express their subjective evaluations of the teams in school grades. While experts may be driven by irrational sentiments and may therefore systematically over- or underestimate specific teams, sports economists use observable characteristics to predict season outcomes. The latter typically use team wage bills given the positive pay-performance relationship as well as other factors (average team age, tenure, appearances on national team, and attendance). Using data from 15 consecutive Bundesliga seasons, the predictive accuracy of expert evaluations and sports economists is analysed. The results of separate estimations show that relative grade and relative wage bill significantly affect relative points, while age, tenure, appearances, and attendance are insignificant. In a joint model, relative grade and relative wage bill are still statistically significant, suggesting that the two types of predictions are complements rather than substitutes. Consequently, football experts and sports economists seem to rely on completely different sources of information when making their predictions.

Diagnostic value of fibronectin discriminant score for predicting liver fibrosis stages in chronic hepatitis C virus patients.

PubMed

Attallah, Abdelfattah M; Abdallah, Sanaa O; Attallah, Ahmed A; Omran, Mohamed M; Farid, Khaled; Nasif, Wesam A; Shiha, Gamal E; Abdel-Aziz, Abdel-Aziz F; Rasafy, Nancy; Shaker, Yehia M

2013-01-01

Several noninvasive predictive models were developed to substitute liver biopsy for fibrosis assessment. To evaluate the diagnostic value of fibronectin which reflect extracellular matrix metabolism and standard liver functions tests which reflect alterations in hepatic functions. Chronic hepatitis C (CHC) patients (n = 145) were evaluated using ROC curves and stepwise multivariate discriminant analysis (MDA) and was validated in 180 additional patients. Liver biochemical profile including transaminases, bilirubin, alkaline phosphatase, albumin, complete blood count were estimated. Fibronectin concentration was determined using monoclonal antibody and ELISA. A novel index named fibronectin discriminant score (FDS) based on fibronectin, APRI and albumin was developed. FDS produced areas under ROC curves (AUC) of 0.91 for significant fibrosis and 0.81 for advanced fibrosis. The FDS correctly classified 79% of the significant liver fibrosis patients (F2-F4) with 87% sensitivity and 75% specificity. The relative risk [odds ratio (OR)] of having significant liver fibrosis using the cut-off values determined by ROC curve analyses were 6.1 for fibronectin, 4.9 for APRI, and 4.2 for albumin. FDS predicted liver fibrosis with an OR of 16.8 for significant fibrosis and 8.6 for advanced fibrosis. The FDS had similar AUC and OR in the validation group to the estimation group without statistically significant difference. FDS predicted liver fibrosis with high degree of accuracy, potentially decreasing the number of liver biopsy required.

Application of GIS to predict malaria hotspots based on Anopheles arabiensis habitat suitability in Southern Africa

NASA Astrophysics Data System (ADS)

Gwitira, Isaiah; Murwira, Amon; Zengeya, Fadzai M.; Shekede, Munyaradzi Davis

2018-02-01

Malaria remains a major public health problem and a principal cause of morbidity and mortality in most developing countries. Although malaria still presents health problems, significant successes have been recorded in reducing deaths resulting from the disease. As malaria transmission continues to decline, control interventions will increasingly depend on the ability to define high-risk areas known as malaria hotspots. Therefore, there is urgent need to use geospatial tools such as geographic information system to detect spatial patterns of malaria and delineate disease hot spots for better planning and management. Thus, accurate mapping and prediction of seasonality of malaria hotspots is an important step towards developing strategies for effective malaria control. In this study, we modelled seasonal malaria hotspots as a function of habitat suitability of Anopheles arabiensis (A. Arabiensis) as a first step towards predicting likely seasonal malaria hotspots that could provide guidance in targeted malaria control. We used Geographical information system (GIS) and spatial statistic methods to identify seasonal hotspots of malaria cases at the country level. In order to achieve this, we first determined the spatial distribution of seasonal malaria hotspots using the Getis Ord Gi* statistic based on confirmed positive malaria cases recorded at health facilities in Zimbabwe over four years (1996-1999). We then used MAXENT technique to model habitat suitability of A. arabiensis from presence data collected from 1990 to 2002 based on bioclimatic variables and altitude. Finally, we used autologistic regression to test the extent to which malaria hotspots can be predicted using A. arabiensis habitat suitability. Our results show that A. arabiensis habitat suitability consistently and significantly (p < 0.05) predicts malaria hotspots from 1996 to 1999. Overall, our results show that malaria hotspots can be predicted using A. arabiensis habitat suitability, suggesting the possibility of developing models for malaria early warning based on vector habitat suitability.

Incorporating Midbrain Adaptation to Mean Sound Level Improves Models of Auditory Cortical Processing

PubMed Central

Schoppe, Oliver; King, Andrew J.; Schnupp, Jan W.H.; Harper, Nicol S.

2016-01-01

Adaptation to stimulus statistics, such as the mean level and contrast of recently heard sounds, has been demonstrated at various levels of the auditory pathway. It allows the nervous system to operate over the wide range of intensities and contrasts found in the natural world. Yet current standard models of the response properties of auditory neurons do not incorporate such adaptation. Here we present a model of neural responses in the ferret auditory cortex (the IC Adaptation model), which takes into account adaptation to mean sound level at a lower level of processing: the inferior colliculus (IC). The model performs high-pass filtering with frequency-dependent time constants on the sound spectrogram, followed by half-wave rectification, and passes the output to a standard linear–nonlinear (LN) model. We find that the IC Adaptation model consistently predicts cortical responses better than the standard LN model for a range of synthetic and natural stimuli. The IC Adaptation model introduces no extra free parameters, so it improves predictions without sacrificing parsimony. Furthermore, the time constants of adaptation in the IC appear to be matched to the statistics of natural sounds, suggesting that neurons in the auditory midbrain predict the mean level of future sounds and adapt their responses appropriately. SIGNIFICANCE STATEMENT An ability to accurately predict how sensory neurons respond to novel stimuli is critical if we are to fully characterize their response properties. Attempts to model these responses have had a distinguished history, but it has proven difficult to improve their predictive power significantly beyond that of simple, mostly linear receptive field models. Here we show that auditory cortex receptive field models benefit from a nonlinear preprocessing stage that replicates known adaptation properties of the auditory midbrain. This improves their predictive power across a wide range of stimuli but keeps model complexity low as it introduces no new free parameters. Incorporating the adaptive coding properties of neurons will likely improve receptive field models in other sensory modalities too. PMID:26758822

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

Boutilier, Justin J., E-mail: j.boutilier@mail.utoronto.ca; Lee, Taewoo; Craig, Tim

Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and appliedmore » three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs.« less

The utility of Bayesian predictive probabilities for interim monitoring of clinical trials

PubMed Central

Connor, Jason T.; Ayers, Gregory D; Alvarez, JoAnn

2014-01-01

Background Bayesian predictive probabilities can be used for interim monitoring of clinical trials to estimate the probability of observing a statistically significant treatment effect if the trial were to continue to its predefined maximum sample size. Purpose We explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods. Results For interim analyses that address prediction hypotheses, such as futility monitoring and efficacy monitoring with lagged outcomes, only predictive probabilities properly account for the amount of data remaining to be observed in a clinical trial and have the flexibility to incorporate additional information via auxiliary variables. Limitations Computational burdens limit the feasibility of predictive probabilities in many clinical trial settings. The specification of prior distributions brings additional challenges for regulatory approval. Conclusions The use of Bayesian predictive probabilities enables the choice of logical interim stopping rules that closely align with the clinical decision making process. PMID:24872363

Predicting umbilical artery pH during labour: Development and validation of a nomogram using fetal heart rate patterns.

PubMed

Ramanah, Rajeev; Omar, Sikiyah; Guillien, Alicia; Pugin, Aurore; Martin, Alain; Riethmuller, Didier; Mottet, Nicolas

2018-06-01

Nomograms are statistical models that combine variables to obtain the most accurate and reliable prediction for a particular risk. Fetal heart rate (FHR) interpretation alone has been found to be poorly predictive for fetal acidosis while other clinical risk factors exist. The aim of this study was to create and validate a nomogram based on FHR patterns and relevant clinical parameters to provide a non-invasive individualized prediction of umbilical artery pH during labour. A retrospective observational study was conducted on 4071 patients in labour presenting singleton pregnancies at >34 gestational weeks and delivering vaginally. Clinical characteristics, FHR patterns and umbilical cord gas of 1913 patients were used to construct a nomogram predicting an umbilical artery (Ua) pH <7.18 (10th centile of the study population) after an univariate and multivariate stepwise logistic regression analysis. External validation was obtained from an independent cohort of 2158 patients. Area under the receiver operating characteristics (ROC) curve, sensitivity, specificity, positive and negative predictive values of the nomogram were determined. Upon multivariate analysis, parity (p < 0.01), induction of labour (p = 0.01), a prior uterine scar (p = 0.02), maternal fever (p = 0.02) and the type of FHR (p < 0.01) were significantly associated with an Ua pH <7.18 (p < 0.05). Apgar score at 1, 5 and 10 min were significantly lower in the group with an Ua pH <7.18 (p < 0.01). The nomogram constructed had a Concordance Index of 0.75 (area under the curve) with a sensitivity of 57%, a specificity of 91%, a negative predictive value of 5% and a positive predictive value of 99%. Calibration found no difference between the predicted probabilities and the observed rate of Ua pH <7.18 (p = 0.63). The validation set had a Concordance Index of 0.72 and calibration with a p < 0.77. We successfully developed and validated a nomogram to predict Ua pH by combining easily available clinical variables and FHR. Discrimination and calibration of the model were statistically good. This mathematical tool can help clinicians in the management of labour by predicting umbilical artery pH based on FHR tracings. Copyright © 2018 Elsevier B.V. All rights reserved.

A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Zack, J. W.

2015-12-01

Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble, which is a case-matching scheme. The presentation will provide (1) an overview of each method and the experimental design, (2) performance comparisons based on standard metrics such as bias, MAE and RMSE, (3) a summary of the performance characteristics of each approach and (4) a preview of further experiments to be conducted.

Predicting Subsequent Myopia in Initially Pilot-Qualified USAFA Cadets.

DTIC Science & Technology

1985-12-27

Refraction Measurement 14 Accesion For . 4.0 RESULTS NTIS CRA&I 15 4.1 Descriptive Statistics DTIC TAB 0 15i ~ ~Unannoutwced [ 4.2 Predictive Statistics ...mentioned), and three were missing a status. The data of the subject who was commissionable were dropped from the statistical analyses. Of the 91...relatively equal numbers of participants from all classes will become obvious ’’" - within the results. J 4.1 Descriptive Statistics In the original plan

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Predictors of Latina/o Community College Student Vocational Choice of STEM Fields: Testing of the STEM-Vocational Choice Model

ERIC Educational Resources Information Center

Johnson, Joel D.

2013-01-01

This study confirmed appropriate measurement model fit for a theoretical model, the STEM vocational choice (STEM-VC) model. This model identifies exogenous factors that successfully predicted, at a statistically significant level, a student's vocational choice decision to pursue a STEM degree at transfer. The student population examined for this…

CEA, CA 15-3, and TPS as Prognostic Factors in the Follow-up Monitoring of Patients After Radical Surgery for Breast Cancer.

PubMed

Svobodova, Sarka; Kucera, Radek; Fiala, Ondrej; Marie, Karlikova; Narsanska, Andrea; Zedníková, Ilona; Treska, Vladislav; Slouka, David; Milena, Rousarova; Topolcan, Ondrej; Finek, Jindrich

2018-01-01

The aim of this study was to evaluate the ability of tissue polypeptide-specific antigen (TPS), carcinoembryonic antigen (CEA), and cancer antigen 15-3 (CA 15-3) to predict relapse in breast cancer patients, when the measurement of biomarkers is performed within 6 months after surgery. Four hundred and seventy-two patients with breast cancer were evaluated. TPS, CEA, and CA 15-3 were measured in months 1, 3, and 6, after surgery. Disease recurrence was recorded between 7-12 months after surgery. Disease recurrence occurred in 60 patients, while 412 patients remained in recurrence-free status. TPS levels of the recurrence group differed statistically significantly in the first and sixth month after surgery compared to recurrence-free group (p=0.0339, AUC=0.6056; p<0.0001, AUC=0.7196). CEA and CA 15-3 measurements did not achieve a statistically significant difference for any month examined. TPS level in the sixth month after surgery is the best candidate biomarker to predict disease recurrence. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

The risk of cancer as a result of elevated levels of nitrate in drinking water and vegetables in Central India.

PubMed

Taneja, Pinky; Labhasetwar, Pawan; Nagarnaik, Pranav; Ensink, Jeroen H J

2017-08-01

The objective of the present study was to determine the effect of nitrates on the incidence of gastrointestinal (GI) cancer development. Nitrate converted to nitrite under reducing conditions of gut results in the formation of N-nitrosamines which are linked to an increased gastric cancer risk. A population of 234 individuals with 78 cases of GI cancer and 156 controls residing at urban and rural settings in Nagpur and Bhandara districts of India were studied for 2 years using a case-control study. A detailed survey of 16 predictor variables using Formhub software was carried out. Nitrate concentrations in vegetables and primary drinking water supplies were measured. The logistic regression model showed that nitrate was statistically significant in predicting increasing risk of cancer when potential confounders were kept at base level (P value of 0.001 nitrate in drinking water; 0.003 for nitrate in vegetable) at P < 0.01. Exposure to nitrate in drinking water at >45 mg/L level of nitrate was associated with a higher risk of GI cancers. Analysis suggests that nitrate concentration in drinking water was found statistically significant in predicting cancer risk with an odds ratio of 1.20.

Prediction of neurodevelopmental outcome after hypoxic-ischemic encephalopathy treated with hypothermia by diffusion tensor imaging analyzed using tract-based spatial statistics.

PubMed

Tusor, Nora; Wusthoff, Courtney; Smee, Natalie; Merchant, Nazakat; Arichi, Tomoki; Allsop, Joanna M; Cowan, Frances M; Azzopardi, Denis; Edwards, A David; Counsell, Serena J

2012-07-01

Objective biomarkers are needed to assess neuroprotective therapies after perinatal hypoxic-ischemic encephalopathy (HIE). We tested the hypothesis that, in infants who underwent therapeutic hypothermia after perinatal HIE, neurodevelopmental performance was predicted by fractional anisotropy (FA) values in the white matter (WM) on early diffusion tensor imaging (DTI) as assessed by means of tract-based spatial statistics (TBSS). We studied 43 term infants with HIE. Developmental assessments were carried out at a median (range) age of 24 (12-28) mo. As compared with infants with favorable outcomes, those with unfavorable outcomes had significantly lower FA values (P < 0.05) in the centrum semiovale, corpus callosum (CC), anterior and posterior limbs of the internal capsule, external capsules, fornix, cingulum, cerebral peduncles, optic radiations, and inferior longitudinal fasciculus. In a second analysis in 32 assessable infants, the Griffiths Mental Development Scales (Revised) (GMDS-R) showed a significant linear correlation (P < 0.05) between FA values and developmental quotient (DQ) and all its component subscale scores. DTI analyzed by TBSS provides a qualified biomarker that can be used to assess the efficacy of additional neuroprotective therapies after HIE.

Comparison of mean climate trends in the Northern Hemisphere between National Centers for Environmental Prediction and two atmosphere-ocean model forced runs

NASA Astrophysics Data System (ADS)

Lucarini, Valerio; Russell, Gary L.

2002-08-01

Results are presented for two greenhouse gas experiments of the Goddard Institute for Space Studies atmosphere-ocean model (AOM). The computed trends of surface pressure; surface temperature; 850, 500, and 200 mbar geopotential heights; and related temperatures of the model for the time frame 1960-2000 are compared with those obtained from the National Centers for Enviromental Prediction (NCEP) observations. The domain of interest is the Northern Hemisphere because of the higher reliability of both the model results and the observations. A spatial correlation analysis and a mean value comparison are performed, showing good agreement in terms of statistical significance for most of the variables considered in the winter and annual means. However, the 850 mbar temperature trends do not show significant positive correlation, and the surface pressure and 850 mbar geopotential height mean trends confidence intervals do not overlap. A brief general discussion about the statistics of trend detection is presented. The accuracy that this AOM has in describing the regional and NH mean climate trends inferred from NCEP through the atmosphere suggests that it may be reliable in forecasting future climate changes.

Salivary pH and Buffering Capacity as Risk Markers for Early Childhood Caries: A Clinical Study.

PubMed

Jayaraj, D; Ganesan, S

2015-01-01

The diagnostic utility of saliva is currently being explored in various branches of dentistry, remarkably in the field of caries research. This study was aimed to determine if assessment of salivary pH and buffering capacity would serve as reliable tools in risk prediction of early childhood caries (ECC). Paraffin-stimulated salivary samples were collected from 50 children with ECC (group I) and 50 caries free children (group II). Salivary pH and buffering capacity (by titration with 0.1 N hydrochloric acid) were assessed using a handheld digital pH meter in both groups. The data obtained were subjected to statistical analysis. Statistically, no significant difference was observed between both the groups for all salivary parameters assessed, except for the buffering capacity level at 150 μl titration of 0.1 N hydrochloric acid (p = 0.73; significant at 1% level). Salivary pH and buffering capacity may not serve as reliable markers for risk prediction of ECC. How to cite this article: Jayaraj D, Ganesan S. Salivary pH and Buffering Capacity as Risk Markers for Early Childhood Caries: A Clinical Study. Int J Clin Pediatr Dent 2015;8(3):167-171.

A digital spatial predictive model of land-use change using economic and environmental inputs and a statistical tree classification approach: Thailand, 1970s--1990s

NASA Astrophysics Data System (ADS)

Felkner, John Sames

The scale and extent of global land use change is massive, and has potentially powerful effects on the global climate and global atmospheric composition (Turner & Meyer, 1994). Because of this tremendous change and impact, there is an urgent need for quantitative, empirical models of land use change, especially predictive models with an ability to capture the trajectories of change (Agarwal, Green, Grove, Evans, & Schweik, 2000; Lambin et al., 1999). For this research, a spatial statistical predictive model of land use change was created and run in two provinces of Thailand. The model utilized an extensive spatial database, and used a classification tree approach for explanatory model creation and future land use (Breiman, Friedman, Olshen, & Stone, 1984). Eight input variables were used, and the trees were run on a dependent variable of land use change measured from 1979 to 1989 using classified satellite imagery. The derived tree models were used to create probability of change surfaces, and these were then used to create predicted land cover maps for 1999. These predicted 1999 maps were compared with actual 1999 landcover derived from 1999 Landsat 7 imagery. The primary research hypothesis was that an explanatory model using both economic and environmental input variables would better predict future land use change than would either a model using only economic variables or a model using only environmental. Thus, the eight input variables included four economic and four environmental variables. The results indicated a very slight superiority of the full models to predict future agricultural change and future deforestation, but a slight superiority of the economic models to predict future built change. However, the margins of superiority were too small to be statistically significant. The resulting tree structures were used, however, to derive a series of principles or "rules" governing land use change in both provinces. The model was able to predict future land use, given a series of assumptions, with 90 percent overall accuracies. The model can be used in other developing or developed country locations for future land use prediction, determination of future threatened areas, or to derive "rules" or principles driving land use change.

Statistical comparisons of AGDISP prediction with Mission III data

Treesearch

Baozhong Duan; Karl Mierzejewski; William G. Yendol

1991-01-01

Statistical comparison of AGDISP prediction were made against data obtained during aerial spray field trials ("Mission III") conducted in March 1987 at the APHIS Facility, Moore Air Base, Edinburg, Texas, by the NEFAAT group (Northeast Forest Aerial Application Technology). Seven out of twenty one runs were observed and predicted means (O and P), mean bias...

Lagrangian acceleration statistics in a turbulent channel flow

NASA Astrophysics Data System (ADS)

Stelzenmuller, Nickolas; Polanco, Juan Ignacio; Vignal, Laure; Vinkovic, Ivana; Mordant, Nicolas

2017-05-01

Lagrangian acceleration statistics in a fully developed turbulent channel flow at Reτ=1440 are investigated, based on tracer particle tracking in experiments and direct numerical simulations. The evolution with wall distance of the Lagrangian velocity and acceleration time scales is analyzed. Dependency between acceleration components in the near-wall region is described using cross-correlations and joint probability density functions. The strong streamwise coherent vortices typical of wall-bounded turbulent flows are shown to have a significant impact on the dynamics. This results in a strong anisotropy at small scales in the near-wall region that remains present in most of the channel. Such statistical properties may be used as constraints in building advanced Lagrangian stochastic models to predict the dispersion and mixing of chemical components for combustion or environmental studies.

Noise level and MPEG-2 encoder statistics

NASA Astrophysics Data System (ADS)

Lee, Jungwoo

1997-01-01

Most software in the movie and broadcasting industries are still in analog film or tape format, which typically contains random noise that originated from film, CCD camera, and tape recording. The performance of the MPEG-2 encoder may be significantly degraded by the noise. It is also affected by the scene type that includes spatial and temporal activity. The statistical property of noise originating from camera and tape player is analyzed and the models for the two types of noise are developed. The relationship between the noise, the scene type, and encoder statistics of a number of MPEG-2 parameters such as motion vector magnitude, prediction error, and quant scale are discussed. This analysis is intended to be a tool for designing robust MPEG encoding algorithms such as preprocessing and rate control.

Understanding Short-Term Nonmigrating Tidal Variability in the Ionospheric Dynamo Region from SABER Using Information Theory and Bayesian Statistics

NASA Astrophysics Data System (ADS)

Kumari, K.; Oberheide, J.

2017-12-01

Nonmigrating tidal diagnostics of SABER temperature observations in the ionospheric dynamo region reveal a large amount of variability on time-scales of a few days to weeks. In this paper, we discuss the physical reasons for the observed short-term tidal variability using a novel approach based on Information theory and Bayesian statistics. We diagnose short-term tidal variability as a function of season, QBO, ENSO, and solar cycle and other drivers using time dependent probability density functions, Shannon entropy and Kullback-Leibler divergence. The statistical significance of the approach and its predictive capability is exemplified using SABER tidal diagnostics with emphasis on the responses to the QBO and solar cycle. Implications for F-region plasma density will be discussed.

Prediction of body mass index status from voice signals based on machine learning for automated medical applications.

PubMed

Lee, Bum Ju; Kim, Keun Ho; Ku, Boncho; Jang, Jun-Su; Kim, Jong Yeol

2013-05-01

The body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of diseases such as cardiovascular disease, diabetes, and stroke. We propose a method for the prediction of normal, overweight, and obese classes based only on the combination of voice features that are associated with BMI status, independently of weight and height measurements. A total of 1568 subjects were divided into 4 groups according to age and gender differences. We performed statistical analyses by analysis of variance (ANOVA) and Scheffe test to find significant features in each group. We predicted BMI status (normal, overweight, and obese) by a logistic regression algorithm and two ensemble classification algorithms (bagging and random forests) based on statistically significant features. In the Female-2030 group (females aged 20-40 years), classification experiments using an imbalanced (original) data set gave area under the receiver operating characteristic curve (AUC) values of 0.569-0.731 by logistic regression, whereas experiments using a balanced data set gave AUC values of 0.893-0.994 by random forests. AUC values in Female-4050 (females aged 41-60 years), Male-2030 (males aged 20-40 years), and Male-4050 (males aged 41-60 years) groups by logistic regression in imbalanced data were 0.585-0.654, 0.581-0.614, and 0.557-0.653, respectively. AUC values in Female-4050, Male-2030, and Male-4050 groups in balanced data were 0.629-0.893 by bagging, 0.707-0.916 by random forests, and 0.695-0.854 by bagging, respectively. In each group, we found discriminatory features showing statistical differences among normal, overweight, and obese classes. The results showed that the classification models built by logistic regression in imbalanced data were better than those built by the other two algorithms, and significant features differed according to age and gender groups. Our results could support the development of BMI diagnosis tools for real-time monitoring; such tools are considered helpful in improving automated BMI status diagnosis in remote healthcare or telemedicine and are expected to have applications in forensic and medical science. Copyright © 2013 Elsevier B.V. All rights reserved.

Prediction of Osteopathic Medical School Performance on the basis of MCAT score, GPA, sex, undergraduate major, and undergraduate institution.

PubMed

Dixon, Donna

2012-04-01

The relationships of students' preadmission academic variables, sex, undergraduate major, and undergraduate institution to academic performance in medical school have not been thoroughly examined. To determine the ability of students' preadmission academic variables to predict osteopathic medical school performance and whether students' sex, undergraduate major, or undergraduate institution influence osteopathic medical school performance. The study followed students who graduated from New York College of Osteopathic Medicine of New York Institute of Technology in Old Westbury between 2003 and 2006. Student preadmission data were Medical College Admission Test (MCAT) scores, undergraduate grade point averages (GPAs), sex, undergraduate major, and undergraduate institutional selectivity. Medical school performance variables were GPAs, clinical performance (ie, clinical subject examinations and clerkship evaluations), and scores on the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 and Level 2-Clinical Evaluation (CE). Data were analyzed with Pearson product moment correlation coefficients and multivariate linear regression analyses. Differences between student groups were compared with the independent-samples, 2-tailed t test. A total of 737 students were included. All preadmission academic variables, except nonscience undergraduate GPA, were statistically significant predictors of performance on COMLEX-USA Level 1, and all preadmission academic variables were statistically significant predictors of performance on COMLEX-USA Level 2-CE. The MCAT score for biological sciences had the highest correlation among all variables with COMLEX-USA Level 1 performance (Pearson r=0.304; P<.001) and Level 2-CE performance (Pearson r=0.272; P<.001). All preadmission variables were moderately correlated with the mean clinical subject examination scores. The mean clerkship evaluation score was moderately correlated with mean clinical examination results (Pearson r=0.267; P<.001) and COMLEX-USA Level 2-CE performance (Pearson r=0.301; P<.001). Clinical subject examination scores were highly correlated with COMLEX-USA Level 2-CE scores (Pearson r=0.817; P<.001). No statistically significant difference in medical school performance was found between students with science and nonscience undergraduate majors, nor was undergraduate institutional selectivity a factor influencing performance. Students' preadmission academic variables were predictive of osteopathic medical school performance, including GPAs, clinical performance, and COMLEX-USA Level 1 and Level 2-CE results. Clinical performance was predictive of COMLEX-USA Level 2-CE performance.

Association between leukocyte telomere length and bone mineral density in women 25-93 years of age.

PubMed

Nielsen, Barbara Rubek; Linneberg, Allan; Bendix, Laila; Harboe, Maria; Christensen, Kaare; Schwarz, Peter

2015-06-01

Leukocyte telomere length (LTL) and bone mineral density (BMD) are associated with health and mortality. Because osteoporosis is an age-related condition and LTL is considered to be a biomarker of aging, we hypothesized that shorter LTL could predict lower BMD. The aim of our study was to assess whether there is an association of LTL with BMD and to determine whether this possible association is independent of age. The BMDs of the lumbar spine (LS), femoral neck (FN) and total hip (TH) were evaluated in 460 women using DXA. LTL was analyzed using quantitative polymerase chain reaction. The women completed a health and lifestyle questionnaire. The associations were estimated by regression models that considered age, body mass index (BMI), menopause, physical activity, alcohol consumption and smoking habits. We found a statistically significant unadjusted association between LTL and age (estimate and 95% confidence interval (CI): -0.003 (-0.005; -0.002)); and between BMI adjusted age and logarithmic transformed BMD. Estimates and 95% CI were as follows: LS: -0.13 (-0.26; -0.01); right TH: -0.44 (-0.53; -0.34); left TH: -0.38 (-0.48; -0.28); right FN: -0.57 (-0.67; -0.46) and left FN: -0.51 (-0.62; -0.40). There were no statistically significant associations between BMD and LTL (both logarithmically transformed) with or without age adjustments. The age-adjusted estimates and CI were as follows: LS: -0.10 (-0.71; 0.52); right TH: -0.13 (-0.66; 0.41); left TH: -0.13 (-0.67; 0.42); right FN: -0.03 (-0.58; 0.52) and left FN: 0.09 (-0.47; 0.66). In conclusion, we found no statistically significant associations between BMD and LTL, although the estimates of the crude associations were all positive, indicating hypothesis consistency; that shorter LTL predict lower BMD values. This positive association was no longer apparent after adjusting for age. As expected, age was statistically significantly associated with both telomere length and BMI adjusted BMD. Copyright © 2015 Elsevier Inc. All rights reserved.

Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

NASA Technical Reports Server (NTRS)

Ashrafi, S.

1991-01-01

K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

The Necessity of the Hippocampus for Statistical Learning

PubMed Central

Covington, Natalie V.; Brown-Schmidt, Sarah; Duff, Melissa C.

2018-01-01

Converging evidence points to a role for the hippocampus in statistical learning, but open questions about its necessity remain. Evidence for necessity comes from Schapiro and colleagues who report that a single patient with damage to hippocampus and broader medial temporal lobe cortex was unable to discriminate new from old sequences in several statistical learning tasks. The aim of the current study was to replicate these methods in a larger group of patients who have either damage localized to hippocampus or a broader medial temporal lobe damage, to ascertain the necessity of the hippocampus in statistical learning. Patients with hippocampal damage consistently showed less learning overall compared with healthy comparison participants, consistent with an emerging consensus for hippocampal contributions to statistical learning. Interestingly, lesion size did not reliably predict performance. However, patients with hippocampal damage were not uniformly at chance and demonstrated above-chance performance in some task variants. These results suggest that hippocampus is necessary for statistical learning levels achieved by most healthy comparison participants but significant hippocampal pathology alone does not abolish such learning. PMID:29308986

Prediction and monitoring of clear air turbulence: An evaluation of the applicability of the rawinsonde system

NASA Technical Reports Server (NTRS)

Keller, J. L.

1978-01-01

The applicability of rawinsonde data for either detection or prediction of clear air turbulence is discussed. The mechanism currently believed responsible for the development of CAT is reviewed. Since previous studies determined that the most significant factor for the existence of turbulent layers is the magnitude of the vertical shear, certain theoretically derived shear criteria was applied to statistical and diagnostic comparisons of rawinsonde and Jimsphere/Jimsonde conjunctive vertical profiles. It is determined that the Rawinsonde system cannot reliably be used as a CAT detector in a single-station sense.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

PubMed

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

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

PubMed Central

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

2013-01-01

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

Applications of Principled Search Methods in Climate Influences and Mechanisms

NASA Technical Reports Server (NTRS)

Glymour, Clark

2005-01-01

Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used.

Seroepidemiology of Varicella and value of self-reported history of Varicella infection in Iranian medical students.

PubMed

Allami, Abbas; Mohammadi, Navid; Najar, Azade

2014-04-01

We conducted this study to assess the seroprevalence of Varicella zoster virus (VZV) antibodies in a group of Iranian medical sciences students that were at risk of Varicella and the value of self-reported history as a predictor of immunity. 255 medical, nursing and obstetrics students who had not entered as a student or worked in a hospital from 3 different schools were enrolled in the study in 2012 (Qazvin province, Iran). Demographics and other information as well as the history of Varicella were obtained through a self-administered questionnaire. Blood samples were collected to determine the Varicella IgG levels via an enzyme-linked immunosorbent assay. A statistical analysis was performed by calculating prevalences and their 95% confidence intervals. Sensitivity, specificity, positive and negative predictive values, Cohen's kappa and positive and negative likelihood ratios of recalled history were determined. p < 0.05 was considered statistically significant. The mean age of participants was 21.3 ± 4.3 years. Seropositivity rate was 74.5%. The relationships between marital status, number of family members, and acquired VZV history with immunity against the virus were statistically significant. The overall rate of reported history was 57%. The positive and negative predictive values of self-reported history of Varicella were 91% and 47.3%, respectively. Immunization of students of Iranian medical sciences seems logical in the near future. Also, they should be tested for Varicella immunity regardless of the history of previous infection.

Efficacy and Physicochemical Evaluation of an Optimized Semisolid Formulation of Povidone Iodine Proposed by Extreme Vertices Statistical Design; a Practical Approach

PubMed Central

Lotfipour, Farzaneh; Valizadeh, Hadi; Shademan, Shahin; Monajjemzadeh, Farnaz

2015-01-01

One of the most significant issues in pharmaceutical industries, prior to commercialization of a pharmaceutical preparation is the "preformulation" stage. However, far too attention has been paid to verification of the software assisted statistical designs in preformulation studies. The main aim of this study was to report a step by step preformulation approach for a semisolid preparation based on a statistical mixture design and to verify the predictions made by the software with an in-vitro efficacy bioassay test. Extreme vertices mixture design (4 factors, 4 levels) was applied for preformulation of a semisolid Povidone Iodine preparation as Water removable ointment using different PolyEthylenGlycoles. Software Assisted (Minitab) analysis was then performed using four practically assessed response values including; Available iodine, viscosity (N index and yield value) and water absorption capacity. Subsequently mixture analysis was performed and finally, an optimized formulation was proposed. The efficacy of this formulation was bio-assayed using microbial tests in-vitro and MIC values were calculated for Escherichia coli, pseudomonaaeruginosa, staphylococcus aureus and Candida albicans. Results indicated the acceptable conformity of the measured responses. Thus, it can be concluded that the proposed design had an adequate power to predict the responses in practice. Stability studies, proved no significant change during the one year study for the optimized formulation. Efficacy was eligible on all tested species and in the case of staphylococcus aureus; the prepared semisolid formulation was even more effective. PMID:26664368

Clinical and histopathologic independent prognostic factors in oral squamous cell carcinoma: a retrospective study of 334 cases.

PubMed

Arduino, Paolo G; Carrozzo, Marco; Chiecchio, Andrea; Broccoletti, Roberto; Tirone, Federico; Borra, Eleonora; Bertolusso, Giorgio; Gandolfo, Sergio

2008-08-01

This retrospective hospital-based study reviewed and evaluated the outcome of patients with oral squamous cell carcinoma (OSCC) with the aim of identifying factors affecting the clinical course and survival rate. Patients with a follow-up of at least 12 months were included. The data collected were statistically analyzed for the presence of factors valuable for prognosis; survival curves were processed in accordance with the Kaplan-Meier method. Differences in the expression of variables in different grading levels were investigated. Cox's proportional hazard model for Z(i) covariates (grading, age, T, N) also was calculated. Mean patient age was 67.7 years in women (n = 152) and 62.4 years in men (n = 182). A total of 98 patients were identified with Broder's/World Health Organization grade 1 histology, 176 with grade 2, and 55 with grade 3; 5 patients were identified as grade 4 (carcinoma in situ). Gender and risk factors seemed to be unrelated to prognosis, whereas a significant increase in mortality was seen in patients over age 70. Histological grading, tumor size, and neck involvement were related, as independent factors, in predicting survival in patients with OSCC (QM-H > 3.9). Gender, age, and risk factors had no statistical relationship with cancer histological differentiation. Our analysis reveals a statistically significant relationship among histological Broder's grading of malignancy, tumor size, locoregional involvement, and survival rates, underscoring the utility of tumor differentiation in predicting the clinical course and outcome of OSCC.

Statistical prediction of dynamic distortion of inlet flow using minimum dynamic measurement. An application to the Melick statistical method and inlet flow dynamic distortion prediction without RMS measurements

NASA Technical Reports Server (NTRS)

Schweikhard, W. G.; Chen, Y. S.

1986-01-01

The Melick method of inlet flow dynamic distortion prediction by statistical means is outlined. A hypothetic vortex model is used as the basis for the mathematical formulations. The main variables are identified by matching the theoretical total pressure rms ratio with the measured total pressure rms ratio. Data comparisons, using the HiMAT inlet test data set, indicate satisfactory prediction of the dynamic peak distortion for cases with boundary layer control device vortex generators. A method for the dynamic probe selection was developed. Validity of the probe selection criteria is demonstrated by comparing the reduced-probe predictions with the 40-probe predictions. It is indicated that the the number of dynamic probes can be reduced to as few as two and still retain good accuracy.

Predictive and Reactive Locomotor Adaptability in Healthy Elderly: A Systematic Review and Meta-Analysis.

PubMed

Bohm, Sebastian; Mademli, Lida; Mersmann, Falk; Arampatzis, Adamantios

2015-12-01

Locomotor adaptability is based on the implementation of error-feedback information from previous perturbations to predictively adapt to expected perturbations (feedforward) and to facilitate reactive responses in recurring unexpected perturbations ('savings'). The effect of aging on predictive and reactive adaptability is yet unclear. However, such understanding is fundamental for the design and application of effective interventions targeting fall prevention. We systematically searched the Web of Science, MEDLINE, Embase and Science Direct databases as well as the reference lists of the eligible articles. A study was included if it addressed an investigation of the locomotor adaptability in response to repeated mechanical movement perturbations of healthy older adults (≥60 years). The weighted average effect size (WAES) of the general adaptability (adaptive motor responses to repeated perturbations) as well as predictive (after-effects) and reactive adaptation (feedback responses to a recurring unexpected perturbation) was calculated and tested for an overall effect. A subgroup analysis was performed regarding the factor age group [i.e., young (≤35 years) vs. older adults]. Furthermore, the methodological study quality was assessed. The review process yielded 18 studies [1009 participants, 613 older adults (70 ± 4 years)], which used various kinds of locomotor tasks and perturbations. The WAES for the general locomotor adaptability was 1.21 [95% confidence interval (CI) 0.68-1.74, n = 11] for the older and 1.39 (95% CI 0.90-1.89, n = 10) for the young adults with a significant (p < 0.05) overall effect for both age groups and no significant subgroup differences. Similar results were found for the predictive (older: WAES 1.10, 95% CI 0.37-1.83, n = 8; young: WAES 1.54, 95% CI 0.11-2.97, n = 7) and reactive (older: WAES 1.09, 95% CI 0.22-1.96, n = 5; young: WAES 1.35, 95% CI 0.60-2.09, n = 5) adaptation featuring significant (p < 0.05) overall effects without subgroup differences. The average score of the methodological quality was 67 ± 8 %. The present meta-analysis provides elaborate statistical evidence that locomotor adaptability in general and predictive and reactive adaptation in particular remain highly effective in the elderly, showing only minor, not statistically significant age-related deficits. Consequently, interventions which use adaptation and learning paradigms including the application of the mechanisms responsible for an effective predictive and reactive dynamic stability control may progressively improve older adults' recovery performance and, thus, reduce their risk of falling.

Application of statistical classification methods for predicting the acceptability of well-water quality

NASA Astrophysics Data System (ADS)

Cameron, Enrico; Pilla, Giorgio; Stella, Fabio A.

2018-06-01

The application of statistical classification methods is investigated—in comparison also to spatial interpolation methods—for predicting the acceptability of well-water quality in a situation where an effective quantitative model of the hydrogeological system under consideration cannot be developed. In the example area in northern Italy, in particular, the aquifer is locally affected by saline water and the concentration of chloride is the main indicator of both saltwater occurrence and groundwater quality. The goal is to predict if the chloride concentration in a water well will exceed the allowable concentration so that the water is unfit for the intended use. A statistical classification algorithm achieved the best predictive performances and the results of the study show that statistical classification methods provide further tools for dealing with groundwater quality problems concerning hydrogeological systems that are too difficult to describe analytically or to simulate effectively.

Cognitive predictors of balance in Parkinson's disease.

PubMed

Fernandes, Ângela; Mendes, Andreia; Rocha, Nuno; Tavares, João Manuel R S

2016-06-01

Postural instability is one of the most incapacitating symptoms of Parkinson's disease (PD) and appears to be related to cognitive deficits. This study aims to determine the cognitive factors that can predict deficits in static and dynamic balance in individuals with PD. A sociodemographic questionnaire characterized 52 individuals with PD for this work. The Trail Making Test, Rule Shift Cards Test, and Digit Span Test assessed the executive functions. The static balance was assessed using a plantar pressure platform, and dynamic balance was based on the Timed Up and Go Test. The results were statistically analysed using SPSS Statistics software through linear regression analysis. The results show that a statistically significant model based on cognitive outcomes was able to explain the variance of motor variables. Also, the explanatory value of the model tended to increase with the addition of individual and clinical variables, although the resulting model was not statistically significant The model explained 25-29% of the variability of the Timed Up and Go Test, while for the anteroposterior displacement it was 23-34%, and for the mediolateral displacement it was 24-39%. From the findings, we conclude that the cognitive performance, especially the executive functions, is a predictor of balance deficit in individuals with PD.

Prediction Methods in Solar Sunspots Cycles

PubMed Central

Ng, Kim Kwee

2016-01-01

An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle. PMID:26868269

Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate.

PubMed

Switanek, Matthew; Crailsheim, Karl; Truhetz, Heimo; Brodschneider, Robert

2017-02-01

Insect pollinators are essential to global food production. For this reason, it is alarming that honey bee (Apis mellifera) populations across the world have recently seen increased rates of mortality. These changes in colony mortality are often ascribed to one or more factors including parasites, diseases, pesticides, nutrition, habitat dynamics, weather and/or climate. However, the effect of climate on colony mortality has never been demonstrated. Therefore, in this study, we focus on longer-term weather conditions and/or climate's influence on honey bee winter mortality rates across Austria. Statistical correlations between monthly climate variables and winter mortality rates were investigated. Our results indicate that warmer and drier weather conditions in the preceding year were accompanied by increased winter mortality. We subsequently built a statistical model to predict colony mortality using temperature and precipitation data as predictors. Our model reduces the mean absolute error between predicted and observed colony mortalities by 9% and is statistically significant at the 99.9% confidence level. This is the first study to show clear evidence of a link between climate variability and honey bee winter mortality. Copyright © 2016 British Geological Survey, NERC. Published by Elsevier B.V. All rights reserved.

Child posture and shoulder belt fit during extended night-time traveling: an in-transit observational study.

PubMed

Forman, Jason L; Segui-Gomez, Maria; Ash, Joseph H; Lopez-Valdes, Francisco J

2011-01-01

Understanding pediatric occupant postures can help researchers indentify injury risk factors, and provide information for prospective injury prediction. This study sought to observe lateral head positions and shoulder belt fit among older child automobile occupants during a scenario likely to result in sleeping - extended travel during the night. An observational, volunteer, in-transit study was performed with 30 pediatric rear-seat passengers, ages 7 to 14. Each was restrained by a three-point seatbelt and was driven for seventy-five minutes at night. Ten subjects used a high-back booster seat, ten used a low-back booster seat, and ten used none (based on the subject height and weight). The subjects were recorded with a low-light video camera, and one frame was analyzed per each minute of video. The high-back booster group exhibited a statistically significant (p<0.05) decrease in the mean frequency of poor shoulder belt fit compared to the no-booster and low-back booster groups. The high-back booster group also exhibited statistically significant decreases in the 90(th) percentile of the absolute value of the relative lateral motion of the head. The low-back booster group did not result in statistically significant decreases in poor shoulder belt fit or lateral head motion compared to the no-booster group. These results are consistent with the presence of large lateral supports of the high-back booster which provided support to the head while sleeping, reducing voluntary lateral occupant motion and improving shoulder belt fit. Future work includes examining lap belt fit in-transit, and examining the effects of these observations on predicted injury risk.

Child Posture and Shoulder Belt Fit During Extended Night-Time Traveling: An In-Transit Observational Study.

PubMed Central

Forman, Jason L.; Segui-Gomez, Maria; Ash, Joseph H.; Lopez-Valdes, Francisco J.

2011-01-01

Understanding pediatric occupant postures can help researchers indentify injury risk factors, and provide information for prospective injury prediction. This study sought to observe lateral head positions and shoulder belt fit among older child automobile occupants during a scenario likely to result in sleeping - extended travel during the night. An observational, volunteer, in-transit study was performed with 30 pediatric rear-seat passengers, ages 7 to 14. Each was restrained by a three-point seatbelt and was driven for seventy-five minutes at night. Ten subjects used a high-back booster seat, ten used a low-back booster seat, and ten used none (based on the subject height and weight). The subjects were recorded with a low-light video camera, and one frame was analyzed per each minute of video. The high-back booster group exhibited a statistically significant (p<0.05) decrease in the mean frequency of poor shoulder belt fit compared to the no-booster and low-back booster groups. The high-back booster group also exhibited statistically significant decreases in the 90th percentile of the absolute value of the relative lateral motion of the head. The low-back booster group did not result in statistically significant decreases in poor shoulder belt fit or lateral head motion compared to the no-booster group. These results are consistent with the presence of large lateral supports of the high-back booster which provided support to the head while sleeping, reducing voluntary lateral occupant motion and improving shoulder belt fit. Future work includes examining lap belt fit in-transit, and examining the effects of these observations on predicted injury risk. PMID:22105378

Comparison of diagnostic capability of macular ganglion cell complex and retinal nerve fiber layer among primary open angle glaucoma, ocular hypertension, and normal population using Fourier-domain optical coherence tomography and determining their functional correlation in Indian population

PubMed Central

Barua, Nabanita; Sitaraman, Chitra; Goel, Sonu; Chakraborti, Chandana; Mukherjee, Sonai; Parashar, Hemandra

2016-01-01

Context: Analysis of diagnostic ability of macular ganglionic cell complex and retinal nerve fiber layer (RNFL) in glaucoma. Aim: To correlate functional and structural parameters and comparing predictive value of each of the structural parameters using Fourier-domain (FD) optical coherence tomography (OCT) among primary open angle glaucoma (POAG) and ocular hypertension (OHT) versus normal population. Setting and Design: Single centric, cross-sectional study done in 234 eyes. Materials and Methods: Patients were enrolled in three groups: POAG, ocular hypertensive and normal (40 patients in each group). After comprehensive ophthalmological examination, patients underwent standard automated perimetry and FD-OCT scan in optic nerve head and ganglion cell mode. The relationship was assessed by correlating ganglion cell complex (GCC) parameters with mean deviation. Results were compared with RNFL parameters. Statistical Analysis: Data were analyzed with SPSS, analysis of variance, t-test, Pearson's coefficient, and receiver operating curve. Results: All parameters showed strong correlation with visual field (P < 0.001). Inferior GCC had highest area under curve (AUC) for detecting glaucoma (0.827) in POAG from normal population. However, the difference was not statistically significant (P > 0.5) when compared with other parameters. None of the parameters showed significant diagnostic capability to detect OHT from normal population. In diagnosing early glaucoma from OHT and normal population, only inferior GCC had statistically significant AUC value (0.715). Conclusion: In this study, GCC and RNFL parameters showed equal predictive capability in perimetric versus normal group. In early stage, inferior GCC was the best parameter. In OHT population, single day cross-sectional imaging was not valuable. PMID:27221682

Prediction versus aetiology: common pitfalls and how to avoid them.

PubMed

van Diepen, Merel; Ramspek, Chava L; Jager, Kitty J; Zoccali, Carmine; Dekker, Friedo W

2017-04-01

Prediction research is a distinct field of epidemiologic research, which should be clearly separated from aetiological research. Both prediction and aetiology make use of multivariable modelling, but the underlying research aim and interpretation of results are very different. Aetiology aims at uncovering the causal effect of a specific risk factor on an outcome, adjusting for confounding factors that are selected based on pre-existing knowledge of causal relations. In contrast, prediction aims at accurately predicting the risk of an outcome using multiple predictors collectively, where the final prediction model is usually based on statistically significant, but not necessarily causal, associations in the data at hand.In both scientific and clinical practice, however, the two are often confused, resulting in poor-quality publications with limited interpretability and applicability. A major problem is the frequently encountered aetiological interpretation of prediction results, where individual variables in a prediction model are attributed causal meaning. This article stresses the differences in use and interpretation of aetiological and prediction studies, and gives examples of common pitfalls. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S.

PubMed

Hu, Xuefei; Waller, Lance A; Lyapustin, Alexei; Wang, Yujie; Liu, Yang

2014-10-16

Multiple studies have developed surface PM 2.5 (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM 2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM 2.5 . In this paper, we examined whether remotely sensed fire count data could improve PM 2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM 2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM 2.5 across the models considered. Cross validation (CV) generated an R 2 of 0.69, a mean prediction error of 2.75 µg/m 3 , and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m 3 , indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m 3 , exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM 2.5 concentration estimation, especially in areas and seasons prone to fire events.

Development of a prognostic model for predicting spontaneous singleton preterm birth.

PubMed

Schaaf, Jelle M; Ravelli, Anita C J; Mol, Ben Willem J; Abu-Hanna, Ameen

2012-10-01

To develop and validate a prognostic model for prediction of spontaneous preterm birth. Prospective cohort study using data of the nationwide perinatal registry in The Netherlands. We studied 1,524,058 singleton pregnancies between 1999 and 2007. We developed a multiple logistic regression model to estimate the risk of spontaneous preterm birth based on maternal and pregnancy characteristics. We used bootstrapping techniques to internally validate our model. Discrimination (AUC), accuracy (Brier score) and calibration (calibration graphs and Hosmer-Lemeshow C-statistic) were used to assess the model's predictive performance. Our primary outcome measure was spontaneous preterm birth at <37 completed weeks. Spontaneous preterm birth occurred in 57,796 (3.8%) pregnancies. The final model included 13 variables for predicting preterm birth. The predicted probabilities ranged from 0.01 to 0.71 (IQR 0.02-0.04). The model had an area under the receiver operator characteristic curve (AUC) of 0.63 (95% CI 0.63-0.63), the Brier score was 0.04 (95% CI 0.04-0.04) and the Hosmer Lemeshow C-statistic was significant (p<0.0001). The calibration graph showed overprediction at higher values of predicted probability. The positive predictive value was 26% (95% CI 20-33%) for the 0.4 probability cut-off point. The model's discrimination was fair and it had modest calibration. Previous preterm birth, drug abuse and vaginal bleeding in the first half of pregnancy were the most important predictors for spontaneous preterm birth. Although not applicable in clinical practice yet, this model is a next step towards early prediction of spontaneous preterm birth that enables caregivers to start preventive therapy in women at higher risk. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S

PubMed Central

Hu, Xuefei; Waller, Lance A.; Lyapustin, Alexei; Wang, Yujie; Liu, Yang

2017-01-01

Multiple studies have developed surface PM2.5 (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM2.5. In this paper, we examined whether remotely sensed fire count data could improve PM2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM2.5 across the models considered. Cross validation (CV) generated an R2 of 0.69, a mean prediction error of 2.75 µg/m3, and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m3, indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m3, exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM2.5 concentration estimation, especially in areas and seasons prone to fire events. PMID:28967648

Evaporation residue cross-section measurements for 48Ti-induced reactions

NASA Astrophysics Data System (ADS)

Sharma, Priya; Behera, B. R.; Mahajan, Ruchi; Thakur, Meenu; Kaur, Gurpreet; Kapoor, Kushal; Rani, Kavita; Madhavan, N.; Nath, S.; Gehlot, J.; Dubey, R.; Mazumdar, I.; Patel, S. M.; Dhibar, M.; Hosamani, M. M.; Khushboo, Kumar, Neeraj; Shamlath, A.; Mohanto, G.; Pal, Santanu

2017-09-01

Background: A significant research effort is currently aimed at understanding the synthesis of heavy elements. For this purpose, heavy ion induced fusion reactions are used and various experimental observations have indicated the influence of shell and deformation effects in the compound nucleus (CN) formation. There is a need to understand these two effects. Purpose: To investigate the effect of proton shell closure and deformation through the comparison of evaporation residue (ER) cross sections for the systems involving heavy compound nuclei around the ZCN=82 region. Methods: A systematic study of ER cross-section measurements was carried out for the 48Ti+Nd,150142 , 144Sm systems in the energy range of 140 -205 MeV . The measurement has been performed using the gas-filled mode of the hybrid recoil mass analyzer present at the Inter University Accelerator Centre (IUAC), New Delhi. Theoretical calculations based on a statistical model were carried out incorporating an adjustable barrier scaling factor to fit the experimental ER cross section. Coupled-channel calculations were also performed using the ccfull code to obtain the spin distribution of the CN, which was used as an input in the calculations. Results: Experimental ER cross sections for 48Ti+Nd,150142 were found to be considerably smaller than the statistical model predictions whereas experimental and statistical model predictions for 48Ti+144Sm were of comparable magnitudes. Conclusion: Though comparison of experimental ER cross sections with statistical model predictions indicate considerable non-compound-nuclear processes for 48Ti+Nd,150142 reactions, no such evidence is found for the 48Ti+144Sm system. Further investigations are required to understand the difference in fusion probabilities of 48Ti+142Nd and 48Ti+144Sm systems.

Methods for estimating selected low-flow frequency statistics and harmonic mean flows for streams in Iowa

USGS Publications Warehouse

Eash, David A.; Barnes, Kimberlee K.

2017-01-01

A statewide study was conducted to develop regression equations for estimating six selected low-flow frequency statistics and harmonic mean flows for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include: the annual 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years, the annual 30-day mean low flow for a recurrence interval of 5 years, and the seasonal (October 1 through December 31) 1- and 7-day mean low flows for a recurrence interval of 10 years. Estimation equations also were developed for the harmonic-mean-flow statistic. Estimates of these seven selected statistics are provided for 208 U.S. Geological Survey continuous-record streamgages using data through September 30, 2006. The study area comprises streamgages located within Iowa and 50 miles beyond the State's borders. Because trend analyses indicated statistically significant positive trends when considering the entire period of record for the majority of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. The median number of years of record used to compute each of these seven selected statistics was 35. Geographic information system software was used to measure 54 selected basin characteristics for each streamgage. Following the removal of two streamgages from the initial data set, data collected for 206 streamgages were compiled to investigate three approaches for regionalization of the seven selected statistics. Regionalization, a process using statistical regression analysis, provides a relation for efficiently transferring information from a group of streamgages in a region to ungaged sites in the region. The three regionalization approaches tested included statewide, regional, and region-of-influence regressions. For the regional regression, the study area was divided into three low-flow regions on the basis of hydrologic characteristics, landform regions, and soil regions. A comparison of root mean square errors and average standard errors of prediction for the statewide, regional, and region-of-influence regressions determined that the regional regression provided the best estimates of the seven selected statistics at ungaged sites in Iowa. Because a significant number of streams in Iowa reach zero flow as their minimum flow during low-flow years, four different types of regression analyses were used: left-censored, logistic, generalized-least-squares, and weighted-least-squares regression. A total of 192 streamgages were included in the development of 27 regression equations for the three low-flow regions. For the northeast and northwest regions, a censoring threshold was used to develop 12 left-censored regression equations to estimate the 6 low-flow frequency statistics for each region. For the southern region a total of 12 regression equations were developed; 6 logistic regression equations were developed to estimate the probability of zero flow for the 6 low-flow frequency statistics and 6 generalized least-squares regression equations were developed to estimate the 6 low-flow frequency statistics, if nonzero flow is estimated first by use of the logistic equations. A weighted-least-squares regression equation was developed for each region to estimate the harmonic-mean-flow statistic. Average standard errors of estimate for the left-censored equations for the northeast region range from 64.7 to 88.1 percent and for the northwest region range from 85.8 to 111.8 percent. Misclassification percentages for the logistic equations for the southern region range from 5.6 to 14.0 percent. Average standard errors of prediction for generalized least-squares equations for the southern region range from 71.7 to 98.9 percent and pseudo coefficients of determination for the generalized-least-squares equations range from 87.7 to 91.8 percent. Average standard errors of prediction for weighted-least-squares equations developed for estimating the harmonic-mean-flow statistic for each of the three regions range from 66.4 to 80.4 percent. The regression equations are applicable only to stream sites in Iowa with low flows not significantly affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. If the equations are used at ungaged sites on regulated streams, or on streams affected by water-supply and agricultural withdrawals, then the estimates will need to be adjusted by the amount of regulation or withdrawal to estimate the actual flow conditions if that is of interest. Caution is advised when applying the equations for basins with characteristics near the applicable limits of the equations and for basins located in karst topography. A test of two drainage-area ratio methods using 31 pairs of streamgages, for the annual 7-day mean low-flow statistic for a recurrence interval of 10 years, indicates a weighted drainage-area ratio method provides better estimates than regional regression equations for an ungaged site on a gaged stream in Iowa when the drainage-area ratio is between 0.5 and 1.4. These regression equations will be implemented within the U.S. Geological Survey StreamStats web-based geographic-information-system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the seven selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these seven selected statistics are provided for the streamgage.