WorldWideScience

Sample records for variables significantly predicted

  1. Predicting significant torso trauma.

    Science.gov (United States)

    Nirula, Ram; Talmor, Daniel; Brasel, Karen

    2005-07-01

    Identification of motor vehicle crash (MVC) characteristics associated with thoracoabdominal injury would advance the development of automatic crash notification systems (ACNS) by improving triage and response times. Our objective was to determine the relationships between MVC characteristics and thoracoabdominal trauma to develop a torso injury probability model. Drivers involved in crashes from 1993 to 2001 within the National Automotive Sampling System were reviewed. Relationships between torso injury and MVC characteristics were assessed using multivariate logistic regression. Receiver operating characteristic curves were used to compare the model to current ACNS models. There were a total of 56,466 drivers. Age, ejection, braking, avoidance, velocity, restraints, passenger-side impact, rollover, and vehicle weight and type were associated with injury (p < 0.05). The area under the receiver operating characteristic curve (83.9) was significantly greater than current ACNS models. We have developed a thoracoabdominal injury probability model that may improve patient triage when used with ACNS.

  2. Validation of three noninvasive laboratory variables to predict significant fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia

    International Nuclear Information System (INIS)

    Ado, Ayman A.; Al-Swat, Khalid; Azzam, N.; Al-Faleh, Faleh; Ahmed, S.

    2007-01-01

    We tested the clinical utility of the platelet count, aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio, and the AST to platelet ratio index (APRI) score in predicting the presence or absence of advanced fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia. Liver biopsy procedures performed on chronic hepatitis C patients in our gastroenterology unit at King Khalid University Hospital were traced form records between 1998 to 2003. The hospital computer database was then accessed and detailed laboratory parameters obtained. By plotting receiver operating characteristic curves (ROC), three selected models (platelet count, AST/ALT ratio and the APRI score) were compared in terms of the best variable to predict significant fibrosis. Two hundred and forty-six patients with hepatitis C were included in this analysis. Overall, 26% of patients had advanced fibrosis. When comparing the three above mentioned prediction models, APRI score was the one associated with the highest area under the curve (AUC) = 0.812 (95%Cl, 0.756-0.868) on the ROC curves, compared to the platelet count and AST/ALT ratio, which yielded an AUC of 0.783 (0.711-0.855) and 0.716 (0.642-0.789), respectively. The APRI score seemed to be the best predictive variable for the presence or absence of advanced fibrosis in Saudi hepatitis C patients. (author)

  3. Significance of blood pressure variability in patients with sepsis.

    Science.gov (United States)

    Pandey, Nishant Raj; Bian, Yu-Yao; Shou, Song-Tao

    2014-01-01

    This study was undertaken to observe the characteristics of blood pressure variability (BPV) and sepsis and to investigate changes in blood pressure and its value on the severity of illness in patients with sepsis. Blood parameters, APACHE II score, and 24-hour ambulatory BP were analyzed in 89 patients with sepsis. In patients with APACHE II score>19, the values of systolic blood pressure (SBPV), diasystolic blood pressure (DBPV), non-dipper percentage, cortisol (COR), lactate (LAC), platelet count (PLT) and glucose (GLU) were significantly higher than in those with APACHE II score ≤19 (Pblood cell (WBC), creatinine (Cr), PaO2, C-reactive protein (CRP), adrenocorticotropic hormone (ACTH) and tumor necrosis factor α (TNF-α) were not statistically significant (P>0.05). Correlation analysis showed that APACHE II scores correlated significantly with SBPV and DBPV (P0.05). Logistic regression analysis of SBPV, DBPV, APACHE II score, and LAC was used to predict prognosis in terms of survival and non-survival rates. Receiver operating characteristics curve (ROC) showed that DBPV was a better predictor of survival rate with an AUC value of 0.890. However, AUC of SBPV, APACHE II score, and LAC was 0.746, 0.831 and 0.915, respectively. The values of SBPV, DBPV and non-dipper percentage are higher in patients with sepsis. DBPV and SBPV can be used to predict the survival rate of patients with sepsis.

  4. The Real World Significance of Performance Prediction

    Science.gov (United States)

    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…

  5. Fatty liver incidence and predictive variables

    International Nuclear Information System (INIS)

    Tsuneto, Akira; Seto, Shinji; Maemura, Koji; Hida, Ayumi; Sera, Nobuko; Imaizumi, Misa; Ichimaru, Shinichiro; Nakashima, Eiji; Akahoshi, Masazumi

    2010-01-01

    Although fatty liver predicts ischemic heart disease, the incidence and predictors of fatty liver need examination. The objective of this study was to determine fatty liver incidence and predictive variables. Using abdominal ultrasonography, we followed biennially through 2007 (mean follow-up, 11.6±4.6 years) 1635 Nagasaki atomic bomb survivors (606 men) without fatty liver at baseline (November 1990 through October 1992). We examined potential predictive variables with the Cox proportional hazard model and longitudinal trends with the Wilcoxon rank-sum test. In all, 323 (124 men) new fatty liver cases were diagnosed. The incidence was 19.9/1000 person-years (22.3 for men, 18.6 for women) and peaked in the sixth decade of life. After controlling for age, sex, and smoking and drinking habits, obesity (relative risk (RR), 2.93; 95% confidence interval (CI), 2.33-3.69, P<0.001), low high-density lipoprotein-cholesterol (RR, 1.87; 95% CI, 1.42-2.47; P<0.001), hypertriglyceridemia (RR, 2.49; 95% CI, 1.96-3.15; P<0.001), glucose intolerance (RR, 1.51; 95% CI, 1.09-2.10; P=0.013) and hypertension (RR, 1.63; 95% CI, 1.30-2.04; P<0.001) were predictive of fatty liver. In multivariate analysis including all variables, obesity (RR, 2.55; 95% CI, 1.93-3.38; P<0.001), hypertriglyceridemia (RR, 1.92; 95% CI, 1.41-2.62; P<0.001) and hypertension (RR, 1.31; 95% CI, 1.01-1.71; P=0.046) remained predictive. In fatty liver cases, body mass index and serum triglycerides, but not systolic or diastolic blood pressure, increased significantly and steadily up to the time of the diagnosis. Obesity, hypertriglyceridemia and, to a lesser extent, hypertension might serve as predictive variables for fatty liver. (author)

  6. Classification and prediction of port variables

    Energy Technology Data Exchange (ETDEWEB)

    Molina Serrano, B.

    2016-07-01

    Many variables are included in planning and management of port terminals. They can beeconomic, social, environmental and institutional. Agent needs to know relationshipbetween these variables to modify planning conditions. Use of Bayesian Networks allowsfor classifying, predicting and diagnosing these variables. Bayesian Networks allow forestimating subsequent probability of unknown variables, basing on know variables.In planning level, it means that it is not necessary to know all variables because theirrelationships are known. Agent can know interesting information about how port variablesare connected. It can be interpreted as cause-effect relationship. Bayesian Networks can beused to make optimal decisions by introduction of possible actions and utility of theirresults.In proposed methodology, a data base has been generated with more than 40 port variables.They have been classified in economic, social, environmental and institutional variables, inthe same way that smart port studies in Spanish Port System make. From this data base, anetwork has been generated using a non-cyclic conducted grafo which allows for knowingport variable relationships - parents-children relationships-. Obtained network exhibits thateconomic variables are – in cause-effect terms- cause of rest of variable typologies.Economic variables represent parent role in the most of cases. Moreover, whenenvironmental variables are known, obtained network allows for estimating subsequentprobability of social variables.It has been concluded that Bayesian Networks allow for modeling uncertainty in aprobabilistic way, even when number of variables is high as occurs in planning andmanagement of port terminals. (Author)

  7. Predictability and Variability of Wave and Wind

    DEFF Research Database (Denmark)

    Chozas, Julia Fernandez; Kofoed, Jens Peter; Sørensen, Hans Christian

    This project covers two fields of study: a) Wave energy predictability and electricity markets. b) Variability of the power output of WECs in diversified systems : diversified renewable systems with wave and offshore wind production. See page 2-4 in the report for a executive summery....

  8. Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

    Science.gov (United States)

    Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

    2016-07-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. Identify the dominant variables to predict stream water temperature

    Science.gov (United States)

    Chien, H.; Flagler, J.

    2016-12-01

    Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

  10. Significant Variables in the Combustion Process of Natural Gas

    OpenAIRE

    Villaflor, Gloria; Morales, Graciela V; Velasco, Jorge

    2008-01-01

    Se determinan las variables significativas del proceso de combustión de gas natural, aquellas más sensibles para producir cambios importantes desde punto de vista económico y medioambiental. Con este fin se realiza la simulación del proceso de combustión de gas natural, utilizando el simulador comercial HYSYS. Se determina que las variables de operación más sensibles para este proceso son la temperatura del aire, la temperatura de los gases de combustión y el exceso de aire usado en la combus...

  11. Value of Construction Company and its Dependence on Significant Variables

    Science.gov (United States)

    Vítková, E.; Hromádka, V.; Ondrušková, E.

    2017-10-01

    The paper deals with the value of the construction company assessment respecting usable approaches and determinable variables. The reasons of the value of the construction company assessment are different, but the most important reasons are the sale or the purchase of the company, the liquidation of the company, the fusion of the company with another subject or the others. According the reason of the value assessment it is possible to determine theoretically different approaches for valuation, mainly it concerns about the yield method of valuation and the proprietary method of valuation. Both approaches are dependant of detailed input variables, which quality will influence the final assessment of the company´s value. The main objective of the paper is to suggest, according to the analysis, possible ways of input variables, mainly in the form of expected cash-flows or the profit, determination. The paper is focused mainly on methods of time series analysis, regression analysis and mathematical simulation utilization. As the output, the results of the analysis on the case study will be demonstrated.

  12. US Climate Variability and Predictability Project

    Energy Technology Data Exchange (ETDEWEB)

    Patterson, Mike [University Corporation for Atmospheric Research (UCAR), Boulder, CO (United States)

    2017-11-14

    The US CLIVAR Project Office administers the US CLIVAR Program with its mission to advance understanding and prediction of climate variability and change across timescales with an emphasis on the role of the ocean and its interaction with other elements of the Earth system. The Project Office promotes and facilitates scientific collaboration within the US and international climate and Earth science communities, addressing priority topics from subseasonal to centennial climate variability and change; the global energy imbalance; the ocean’s role in climate, water, and carbon cycles; climate and weather extremes; and polar climate changes. This project provides essential one-year support of the Project Office, enabling the participation of US scientists in the meetings of the US CLIVAR bodies that guide scientific planning and implementation, including the scientific steering committee that establishes program goals and evaluates progress of activities to address them, the science team of funded investigators studying the ocean overturning circulation in the Atlantic, and two working groups tackling the priority research topics of Arctic change influence on midlatitude climate and weather extremes and the decadal-scale widening of the tropical belt.

  13. Predictive Variable Gain Iterative Learning Control for PMSM

    Directory of Open Access Journals (Sweden)

    Huimin Xu

    2015-01-01

    Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

  14. Significance of Demographic Variables for Targeting of Internet Advertisements

    Directory of Open Access Journals (Sweden)

    Václav Stříteský

    2016-06-01

    Full Text Available Broad ad targeting options belong among the major advantages of internet advertising. Demographic targeting has become a standard option in most of on-line advertising systems. There are more ways how to target on-line advertisements by using demographic variables. In some cases, e.g., social media, we can use data from user registrations. Modern technologies enable to estimate the demographic profile of internet users using on behavioural data. The traditional approach to the demographic targeting of advertisements based on affinity targeting assumes the existence of internet servers with sufficient homogeneity of visits. The aim of this article is to identify the differences in the internet content consumption habits of Czech internet users based on gender and age. The analysis is based on the data from the extensive research which was carried out by the Netmonitor project, and which was provided for the purposes of this study by the Association for Internet Development (SPIR. The research results show that the traditional affinity-based method of targeting according to gender and age is still suitable on the Czech internet. On the other hand, in some cases, the traditional approach of ad targeting based on affinity leads to wasted ad impressions that miss defined target group.

  15. European Randomized Study of Screening for Prostate Cancer Risk Calculator: External Validation, Variability, and Clinical Significance.

    Science.gov (United States)

    Gómez-Gómez, Enrique; Carrasco-Valiente, Julia; Blanca-Pedregosa, Ana; Barco-Sánchez, Beatriz; Fernandez-Rueda, Jose Luis; Molina-Abril, Helena; Valero-Rosa, Jose; Font-Ugalde, Pilar; Requena-Tapia, Maria José

    2017-04-01

    To externally validate the European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator (RC) and to evaluate its variability between 2 consecutive prostate-specific antigen (PSA) values. We prospectively catalogued 1021 consecutive patients before prostate biopsy for suspicion of prostate cancer (PCa). The risk of PCa and significant PCa (Gleason score ≥7) from 749 patients was calculated according to ERSPC-RC (digital rectal examination-based version 3 of 4) for 2 consecutive PSA tests per patient. The calculators' predictions were analyzed using calibration plots and the area under the receiver operating characteristic curve (area under the curve). Cohen kappa coefficient was used to compare the ability and variability. Of 749 patients, PCa was detected in 251 (33.5%) and significant PCa was detected in 133 (17.8%). Calibration plots showed an acceptable parallelism and similar discrimination ability for both PSA levels with an area under the curve of 0.69 for PCa and 0.74 for significant PCa. The ERSPC showed 226 (30.2%) unnecessary biopsies with the loss of 10 significant PCa. The variability of the RC was 16% for PCa and 20% for significant PCa, and a higher variability was associated with a reduced risk of significant PCa. We can conclude that the performance of the ERSPC-RC in the present cohort shows a high similitude between the 2 PSA levels; however, the RC variability value is associated with a decreased risk of significant PCa. The use of the ERSPC in our cohort detects a high number of unnecessary biopsies. Thus, the incorporation of ERSPC-RC could help the clinical decision to carry out a prostate biopsy. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Interobserver variability of sonography for prediction of placenta accreta.

    Science.gov (United States)

    Bowman, Zachary S; Eller, Alexandra G; Kennedy, Anne M; Richards, Douglas S; Winter, Thomas C; Woodward, Paula J; Silver, Robert M

    2014-12-01

    The sensitivity of sonography to predict accreta has been reported as higher than 90%. However, most studies are from single expert investigators. Our objective was to analyze interobserver variability of sonography for prediction of placenta accreta. Patients with previa with and without accreta were ascertained, and images with placental views were collected, deidentified, and placed in random sequence. Three radiologists and 3 maternal-fetal medicine specialists interpreted each study for the presence of accreta and specific findings reported to be associated with its diagnosis. Investigator-specific sensitivity, specificity, and accuracy were calculated. κ statistics were used to assess variability between individuals and types of investigators. A total of 229 sonographic studies from 55 patients with accreta and 56 control patients were examined. Accuracy ranged from 55.9% to 76.4%. Of imaging studies yielding diagnoses, sensitivity ranged from 53.4% to 74.4%, and specificity ranged from 70.8% to 94.8%. Overall interobserver agreement was moderate (mean κ ± SD = 0.47 ± 0.12). κ values between pairs of investigators ranged from 0.32 (fair agreement) to 0.73 (substantial agreement). Average individual agreement ranged from fair (κ = 0.35) to moderate (κ = 0.53). Blinded from clinical data, sonography has significant interobserver variability for the diagnosis of placenta accreta. © 2013 by the American Institute of Ultrasound in Medicine.

  17. Variable importance and prediction methods for longitudinal problems with missing variables.

    Directory of Open Access Journals (Sweden)

    Iván Díaz

    Full Text Available We present prediction and variable importance (VIM methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient's physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed. Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner, which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO. In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC for a receiver-operator curve (ROC. Thus, given that 1 our VIM

  18. Predictive modeling and reducing cyclic variability in autoignition engines

    Science.gov (United States)

    Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

    2016-08-30

    Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

  19. Analysis and Prediction of Micromilling Stability with Variable Tool Geometry

    Directory of Open Access Journals (Sweden)

    Ziyang Cao

    2014-11-01

    Full Text Available Micromilling can fabricate miniaturized components using micro-end mill at high rotational speeds. The analysis of machining stability in micromilling plays an important role in characterizing the cutting process, estimating the tool life, and optimizing the process. A numerical analysis and experimental method are presented to investigate the chatter stability in micro-end milling process with variable milling tool geometry. The schematic model of micromilling process is constructed and the calculation formula to predict cutting force and displacements is derived. This is followed by a detailed numerical analysis on micromilling forces between helical ball and square end mills through time domain and frequency domain method and the results are compared. Furthermore, a detailed time domain simulation for micro end milling with straight teeth and helical teeth end mill is conducted based on the machine-tool system frequency response function obtained through modal experiment. The forces and displacements are predicted and the simulation result between variable cutter geometry is deeply compared. The simulation results have important significance for the actual milling process.

  20. Analyst-to-Analyst Variability in Simulation-Based Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Glickman, Matthew R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Romero, Vicente J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-02-01

    This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and open one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.

  1. Predicting Bond Betas using Macro-Finance Variables

    DEFF Research Database (Denmark)

    Aslanidis, Nektarios; Christiansen, Charlotte; Cipollini, Andrea

    We conduct in-sample and out-of-sample forecasting using the new approach of combining explanatory variables through complete subset regressions (CSR). We predict bond CAPM betas and bond returns conditioning on various macro-fi…nance variables. We explore differences across long-term government ...... bonds, investment grade corporate bonds, and high-yield corporate bonds. The CSR method performs well in predicting bond betas, especially in-sample, and, mainly high-yield bond betas when the focus is out-of-sample. Bond returns are less predictable than bond betas....

  2. An analysis of prediction skill of monthly mean climate variability

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Arun; Chen, Mingyue; Wang, Wanqiu [Climate Prediction Center, National Centers for Environmental Prediction (CPC/NCEP), Camp Springs, MD (United States)

    2011-09-15

    In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30-40 days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability. (orig.)

  3. Leg pain and psychological variables predict outcome 2-3 years after lumbar fusion surgery.

    Science.gov (United States)

    Abbott, Allan D; Tyni-Lenné, Raija; Hedlund, Rune

    2011-10-01

    Prediction studies testing a thorough range of psychological variables in addition to demographic, work-related and clinical variables are lacking in lumbar fusion surgery research. This prospective cohort study aimed at examining predictions of functional disability, back pain and health-related quality of life (HRQOL) 2-3 years after lumbar fusion by regressing nonlinear relations in a multivariate predictive model of pre-surgical variables. Before and 2-3 years after lumbar fusion surgery, patients completed measures investigating demographics, work-related variables, clinical variables, functional self-efficacy, outcome expectancy, fear of movement/(re)injury, mental health and pain coping. Categorical regression with optimal scaling transformation, elastic net regularization and bootstrapping were used to investigate predictor variables and address predictive model validity. The most parsimonious and stable subset of pre-surgical predictor variables explained 41.6, 36.0 and 25.6% of the variance in functional disability, back pain intensity and HRQOL 2-3 years after lumbar fusion. Pre-surgical control over pain significantly predicted functional disability and HRQOL. Pre-surgical catastrophizing and leg pain intensity significantly predicted functional disability and back pain while the pre-surgical straight leg raise significantly predicted back pain. Post-operative psychomotor therapy also significantly predicted functional disability while pre-surgical outcome expectations significantly predicted HRQOL. For the median dichotomised classification of functional disability, back pain intensity and HRQOL levels 2-3 years post-surgery, the discriminative ability of the prediction models was of good quality. The results demonstrate the importance of pre-surgical psychological factors, leg pain intensity, straight leg raise and post-operative psychomotor therapy in the predictions of functional disability, back pain and HRQOL-related outcomes.

  4. Predictive Maintenance (PdM) Centralization for Significant Energy Savings

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Dale

    2010-09-15

    Cost effective predictive maintenance (PdM) technologies and basic energy calculations can mine energy savings form processes or maintenance activities. Centralizing and packaging this information correctly empowers facility maintenance and reliability professionals to build financial justification and support for strategies and personnel to weather global economic downturns and competition. Attendees will learn how to: Systematically build a 'pilot project' for applying PdM and tracking systems; Break down a typical electrical bill to calculate energy savings; Use return on investment (ROI) calculations to identify the best and highest value options, strategies and tips for substantiating your energy reduction maintenance strategies.

  5. Early signs that predict later haemodynamically significant patent ductus arteriosus.

    Science.gov (United States)

    Engür, Defne; Deveci, Murat; Türkmen, Münevver K

    2016-03-01

    Our aim was to determine the optimal cut-off values, sensitivity, specificity, and diagnostic power of 12 echocardiographic parameters on the second day of life to predict subsequent ductal patency. We evaluated preterm infants, born at ⩽32 weeks of gestation, starting on their second day of life, and they were evaluated every other day until ductal closure or until there were clinical signs of re-opening. We measured transductal diameter; pulmonary arterial diastolic flow; retrograde aortic diastolic flow; pulsatility index of the left pulmonary artery and descending aorta; left atrium and ventricle/aortic root ratio; left ventricular output; left ventricular flow velocity time integral; mitral early/late diastolic flow; and superior caval vein diameter and flow as well as performed receiver operating curve analysis. Transductal diameter (>1.5 mm); pulmonary arterial diastolic flow (>25.6 cm/second); presence of retrograde aortic diastolic flow; ductal diameter by body weight (>1.07 mm/kg); left pulmonary arterial pulsatility index (⩽0.71); and left ventricle to aortic root ratio (>2.2) displayed high sensitivity and specificity (p0.9). Parameters with moderate sensitivity and specificity were as follows: left atrial to aortic root ratio; left ventricular output; left ventricular flow velocity time integral; and mitral early/late diastolic flow ratio (p0.05) had low diagnostic value. Left pulmonary arterial pulsatility index, left ventricle/aortic root ratio, and ductal diameter by body weight are useful adjuncts offering a broader outlook for predicting ductal patency.

  6. Marine heatwaves off eastern Tasmania: Trends, interannual variability, and predictability

    Science.gov (United States)

    Oliver, Eric C. J.; Lago, Véronique; Hobday, Alistair J.; Holbrook, Neil J.; Ling, Scott D.; Mundy, Craig N.

    2018-02-01

    Surface waters off eastern Tasmania are a global warming hotspot. Here, mean temperatures have been rising over several decades at nearly four times the global average rate, with concomitant changes in extreme temperatures - marine heatwaves. These changes have recently caused the marine biodiversity, fisheries and aquaculture industries off Tasmania's east coast to come under stress. In this study we quantify the long-term trends, variability and predictability of marine heatwaves off eastern Tasmania. We use a high-resolution ocean model for Tasmania's eastern continental shelf. The ocean state over the 1993-2015 period is hindcast, providing daily estimates of the three-dimensional temperature and circulation fields. Marine heatwaves are identified at the surface and subsurface from ocean temperature time series using a consistent definition. Trends in marine heatwave frequency are positive nearly everywhere and annual marine heatwave days and penetration depths indicate significant positive changes, particularly off southeastern Tasmania. A decomposition into modes of variability indicates that the East Australian Current is the dominant driver of marine heatwaves across the domain. Self-organising maps are used to identify 12 marine heatwave types, each with its own regionality, seasonality, and associated large-scale oceanic and atmospheric circulation patterns. The implications of this work for marine ecosystems and their management were revealed through review of past impacts and stakeholder discussions regarding use of these data.

  7. Statistical Significance of the Contribution of Variables to the PCA Solution: An Alternative Permutation Strategy

    Science.gov (United States)

    Linting, Marielle; van Os, Bart Jan; Meulman, Jacqueline J.

    2011-01-01

    In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix…

  8. Variable context Markov chains for HIV protease cleavage site prediction.

    Science.gov (United States)

    Oğul, Hasan

    2009-06-01

    Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.

  9. Days on radiosensitivity: individual variability and predictive tests

    International Nuclear Information System (INIS)

    2008-01-01

    The radiosensitivity is a part of usual clinical observations. It is already included in the therapy protocols. however, some questions stay on its individual variability and on the difficulty to evaluate it. The point will be stocked on its origin and its usefulness in predictive medicine. Through examples on the use of predictive tests and ethical and legal questions that they raise, concrete cases will be presented by specialists such radio biologists, geneticists, immunologists, jurists and occupational physicians. (N.C.)

  10. Hydroclimatic variability and predictability: a survey of recent research

    Directory of Open Access Journals (Sweden)

    R. D. Koster

    2017-07-01

    Full Text Available Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i variability in general, (ii droughts, (iii floods, (iv land–atmosphere coupling, and (v hydroclimatic prediction. Each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Taken together, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted.

  11. Modeling Chronic Toxicity: A Comparison of Experimental Variability With (QSAR/Read-Across Predictions

    Directory of Open Access Journals (Sweden)

    Christoph Helma

    2018-04-01

    Full Text Available This study compares the accuracy of (QSAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.

  12. Measuring psychosocial variables that predict older persons' oral health behaviour.

    Science.gov (United States)

    Kiyak, H A

    1996-12-01

    The importance of recognising psychosocial characteristics of older people that influence their oral health behaviours and the potential success of dental procedures is discussed. Three variables and instruments developed and tested by the author and colleagues are presented. A measure of perceived importance of oral health behaviours has been found to be a significant predictor of dental service utilization in three studies. Self-efficacy regarding oral health has been found to be lower than self-efficacy regarding general health and medication use among older adults, especially among non-Western ethnic minorities. The significance of self-efficacy for predicting changes in caries and periodontal disease is described. Finally, a measure of expectations regarding specific dental procedures has been used with older people undergoing implant therapy. Studies with this instrument reveal that patients have concerns about the procedure far different than those focused on by dental providers. All three instruments can be used in clinical practice as a means of understanding patients' values, perceived oral health abilities, and expectations from dental care. These instruments can enhance dentist-patient rapport and improve the chances of successful dental outcomes for older patients.

  13. Relative Contributions of Socio-Cultural Variables to the Prediction ...

    African Journals Online (AJOL)

    Erah

    the Prediction of Maternal Mortality in Edo South. Senatorial ... variables across the two locations (rural and urban) was early marriage/early child bearing (R2 = 0.200;. F = 401.40 ... severe bleeding, infections, obstructed or prolonged .... Analytical System (SAS) mode. Descriptive .... incontinence of urine and faeces due to.

  14. Predicting travel time variability for cost-benefit analysis

    NARCIS (Netherlands)

    Peer, S.; Koopmans, C.; Verhoef, E.T.

    2010-01-01

    Unreliable travel times cause substantial costs to travelers. Nevertheless, they are not taken into account in many cost-benefit-analyses (CBA), or only in very rough ways. This paper aims at providing simple rules on how variability can be predicted, based on travel time data from Dutch highways.

  15. Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism.

    Science.gov (United States)

    Chervyakov, Alexander V; Sinitsyn, Dmitry O; Piradov, Michael A

    2016-01-01

    HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), "genuine harmful" (noise), "genuine neutral" (synonyms, repeats), and "genuine useful" (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.

  16. Predicting sun protection behaviors using protection motivation variables.

    Science.gov (United States)

    Ch'ng, Joanne W M; Glendon, A Ian

    2014-04-01

    Protection motivation theory components were used to predict sun protection behaviors (SPBs) using four outcome measures: typical reported behaviors, previous reported behaviors, current sunscreen use as determined by interview, and current observed behaviors (clothing worn) to control for common method bias. Sampled from two SE Queensland public beaches during summer, 199 participants aged 18-29 years completed a questionnaire measuring perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation (PM). Personal perceived risk (similar to threat appraisal) and response likelihood (similar to coping appraisal) were derived from their respective PM components. Protection motivation predicted all four SPB criterion variables. Personal perceived risk and response likelihood predicted protection motivation. Protection motivation completely mediated the effect of response likelihood on all four criterion variables. Alternative models are considered. Strengths and limitations of the study are outlined and suggestions made for future research.

  17. Survey of French spine surgeons reveals significant variability in spine trauma practices in 2013.

    Science.gov (United States)

    Lonjon, G; Grelat, M; Dhenin, A; Dauzac, C; Lonjon, N; Kepler, C K; Vaccaro, A R

    2015-02-01

    In France, attempts to define common ground during spine surgery meetings have revealed significant variability in clinical practices across different schools of surgery and the two specialities involved in spine surgery, namely, neurosurgery and orthopaedic surgery. To objectively characterise this variability by performing a survey based on a fictitious spine trauma case. Our working hypothesis was that significant variability existed in trauma practices and that this variability was related to a lack of strong scientific evidence in spine trauma care. We performed a cross-sectional survey based on a clinical vignette describing a 31-year-old male with an L1 burst fracture and neurologic symptoms (numbness). Surgeons received the vignette and a 14-item questionnaire on the management of this patient. For each question, surgeons had to choose among five possible answers. Differences in answers across surgeons were assessed using the Index of Qualitative Variability (IQV), in which 0 indicates no variability and 1 maximal variability. Surgeons also received a questionnaire about their demographics and surgical experience. Of 405 invited spine surgeons, 200 responded to the survey. Five questions had an IQV greater than 0.9, seven an IQV between 0.5 and 0.9, and two an IQV lower than 0.5. Variability was greatest about the need for MRI (IQV=0.93), degree of urgency (IQV=0.93), need for fusion (IQV=0.92), need for post-operative bracing (IQV=0.91), and routine removal of instrumentation (IQV=0.94). Variability was lowest for questions about the need for surgery (IQV=0.42) and use of the posterior approach (IQV=0.36). Answers were influenced by surgeon specialty, age, experience level, and type of centre. Clinical practice regarding spine trauma varies widely in France. Little published evidence is available on which to base recommendations that would diminish this variability. Copyright © 2015. Published by Elsevier Masson SAS.

  18. Artificial neural networks to predict presence of significant pathology in patients presenting to routine colorectal clinics.

    Science.gov (United States)

    Maslekar, S; Gardiner, A B; Monson, J R T; Duthie, G S

    2010-12-01

    Artificial neural networks (ANNs) are computer programs used to identify complex relations within data. Routine predictions of presence of colorectal pathology based on population statistics have little meaning for individual patient. This results in large number of unnecessary lower gastrointestinal endoscopies (LGEs - colonoscopies and flexible sigmoidoscopies). We aimed to develop a neural network algorithm that can accurately predict presence of significant pathology in patients attending routine outpatient clinics for gastrointestinal symptoms. Ethics approval was obtained and the study was monitored according to International Committee on Harmonisation - Good Clinical Practice (ICH-GCP) standards. Three-hundred patients undergoing LGE prospectively completed a specifically developed questionnaire, which included 40 variables based on clinical symptoms, signs, past- and family history. Complete data sets of 100 patients were used to train the ANN; the remaining data was used for internal validation. The primary output used was positive finding on LGE, including polyps, cancer, diverticular disease or colitis. For external validation, the ANN was applied to data from 50 patients in primary care and also compared with the predictions of four clinicians. Clear correlation between actual data value and ANN predictions were found (r = 0.931; P = 0.0001). The predictive accuracy of ANN was 95% in training group and 90% (95% CI 84-96) in the internal validation set and this was significantly higher than the clinical accuracy (75%). ANN also showed high accuracy in the external validation group (89%). Artificial neural networks offer the possibility of personal prediction of outcome for individual patients presenting in clinics with colorectal symptoms, making it possible to make more appropriate requests for lower gastrointestinal endoscopy. © 2010 The Authors. Colorectal Disease © 2010 The Association of Coloproctology of Great Britain and Ireland.

  19. Making oneself predictable: Reduced temporal variability facilitates joint action coordination

    DEFF Research Database (Denmark)

    Vesper, Cordula; van der Wel, Robrecht; Knoblich, Günther

    2011-01-01

    Performing joint actions often requires precise temporal coordination of individual actions. The present study investigated how people coordinate their actions at discrete points in time when continuous or rhythmic information about others’ actions is not available. In particular, we tested...... the hypothesis that making oneself predictable is used as a coordination strategy. Pairs of participants were instructed to coordinate key presses in a two-choice reaction time task, either responding in synchrony (Experiments 1 and 2) or in close temporal succession (Experiment 3). Across all experiments, we...... found that coactors reduced the variability of their actions in the joint context compared with the same task performed individually. Correlation analyses indicated that the less variable the actions were, the better was interpersonal coordination. The relation between reduced variability and improved...

  20. Predictive coding of dynamical variables in balanced spiking networks.

    Science.gov (United States)

    Boerlin, Martin; Machens, Christian K; Denève, Sophie

    2013-01-01

    Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

  1. Understanding the significance variables for fabrication of fish gelatin nanoparticles by Plackett-Burman design

    Science.gov (United States)

    Subara, Deni; Jaswir, Irwandi; Alkhatib, Maan Fahmi Rashid; Noorbatcha, Ibrahim Ali

    2018-01-01

    The aim of this experiment is to screen and to understand the process variables on the fabrication of fish gelatin nanoparticles by using quality-design approach. The most influencing process variables were screened by using Plackett-Burman design. Mean particles size, size distribution, and zeta potential were found in the range 240±9.76 nm, 0.3, and -9 mV, respectively. Statistical results explained that concentration of acetone, pH of solution during precipitation step and volume of cross linker had a most significant effect on particles size of fish gelatin nanoparticles. It was found that, time and chemical consuming is lower than previous research. This study revealed the potential of quality-by design in understanding the effects of process variables on the fish gelatin nanoparticles production.

  2. Predicting Teacher Retention Using Stress and Support Variables

    Science.gov (United States)

    Sass, Daniel A.; Seal, Andrea K.; Martin, Nancy K.

    2011-01-01

    Purpose: Teacher attrition is a significant international concern facing administrators. Although a considerable amount of literature exists related to the causes of job dissatisfaction and teachers leaving the profession, relatively few theoretical models test the complex interrelationships between these variables. The goal of this paper is to…

  3. Dynamic Variables Fail to Predict Fluid Responsiveness in an Animal Model With Pericardial Effusion.

    Science.gov (United States)

    Broch, Ole; Renner, Jochen; Meybohm, Patrick; Albrecht, Martin; Höcker, Jan; Haneya, Assad; Steinfath, Markus; Bein, Berthold; Gruenewald, Matthias

    2016-10-01

    The reliability of dynamic and volumetric variables of fluid responsiveness in the presence of pericardial effusion is still elusive. The aim of the present study was to investigate their predictive power in a porcine model with hemodynamic relevant pericardial effusion. A single-center animal investigation. Twelve German domestic pigs. Pigs were studied before and during pericardial effusion. Instrumentation included a pulmonary artery catheter and a transpulmonary thermodilution catheter in the femoral artery. Hemodynamic variables like cardiac output (COPAC) and stroke volume (SVPAC) derived from pulmonary artery catheter, global end-diastolic volume (GEDV), stroke volume variation (SVV), and pulse-pressure variation (PPV) were obtained. At baseline, SVV, PPV, GEDV, COPAC, and SVPAC reliably predicted fluid responsiveness (area under the curve 0.81 [p = 0.02], 0.82 [p = 0.02], 0.74 [p = 0.07], 0.74 [p = 0.07], 0.82 [p = 0.02]). After establishment of pericardial effusion the predictive power of dynamic variables was impaired and only COPAC and SVPAC and GEDV allowed significant prediction of fluid responsiveness (area under the curve 0.77 [p = 0.04], 0.76 [p = 0.05], 0.83 [p = 0.01]) with clinically relevant changes in threshold values. In this porcine model, hemodynamic relevant pericardial effusion abolished the ability of dynamic variables to predict fluid responsiveness. COPAC, SVPAC, and GEDV enabled prediction, but their threshold values were significantly changed. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Protein construct storage: Bayesian variable selection and prediction with mixtures.

    Science.gov (United States)

    Clyde, M A; Parmigiani, G

    1998-07-01

    Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

  5. Significance of the impact of motion compensation on the variability of PET image features

    Science.gov (United States)

    Carles, M.; Bach, T.; Torres-Espallardo, I.; Baltas, D.; Nestle, U.; Martí-Bonmatí, L.

    2018-03-01

    In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40% of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40% contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, p\\ll 0.001 ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the

  6. Variability, Predictability, and Race Factors Affecting Performance in Elite Biathlon.

    Science.gov (United States)

    Skattebo, Øyvind; Losnegard, Thomas

    2018-03-01

    To investigate variability, predictability, and smallest worthwhile performance enhancement in elite biathlon sprint events. In addition, the effects of race factors on performance were assessed. Data from 2005 to 2015 including >10,000 and >1000 observations for each sex for all athletes and annual top-10 athletes, respectively, were included. Generalized linear mixed models were constructed based on total race time, skiing time, shooting time, and proportions of targets hit. Within-athlete race-to-race variability was expressed as coefficient of variation of performance times and standard deviation (SD) in proportion units (%) of targets hit. The models were adjusted for random and fixed effects of subject identity, season, event identity, and race factors. The within-athlete variability was independent of sex and performance standard of athletes: 2.5-3.2% for total race time, 1.5-1.8% for skiing time, and 11-15% for shooting times. The SD of the proportion of hits was ∼10% in both shootings combined (meaning ±1 hit in 10 shots). The predictability in total race time was very high to extremely high for all athletes (ICC .78-.84) but trivial for top-10 athletes (ICC .05). Race times during World Championships and Olympics were ∼2-3% faster than in World Cups. Moreover, race time increased by ∼2% per 1000 m of altitude, by ∼5% per 1% of gradient, by 1-2% per 1 m/s of wind speed, and by ∼2-4% on soft vs hard tracks. Researchers and practitioners should focus on strategies that improve biathletes' performance by at least 0.8-0.9%, corresponding to the smallest worthwhile enhancement (0.3 × within-athlete variability).

  7. Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.

    Science.gov (United States)

    Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

    2011-10-31

    significantly smaller numbers of informative variables than the existing SVR-PPRV, UVE-GA-PLS and UVE-iPLS methods without loss of prediction ability. Contrary to UVE-GA-PLS and UVE-iPLS, there is no variability in the number of retained variables in each PPRV(R) method. Renewed variable ranking, after deletion of a variable, followed by remodelling, combined with the possibility to decrease the PLS model complexity, is beneficial. A preferred PPRVR-CAM method is proposed. Copyright © 2011 Elsevier B.V. All rights reserved.

  8. Can biomechanical variables predict improvement in crouch gait?

    Science.gov (United States)

    Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

    2011-01-01

    Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

  9. LASR-Guided Variability Subtraction: The Linear Algorithm for Significance Reduction of Stellar Seismic Activity

    Science.gov (United States)

    Horvath, Sarah; Myers, Sam; Ahlers, Johnathon; Barnes, Jason W.

    2017-10-01

    Stellar seismic activity produces variations in brightness that introduce oscillations into transit light curves, which can create challenges for traditional fitting models. These oscillations disrupt baseline stellar flux values and potentially mask transits. We develop a model that removes these oscillations from transit light curves by minimizing the significance of each oscillation in frequency space. By removing stellar variability, we prepare each light curve for traditional fitting techniques. We apply our model to $\\delta$-Scuti KOI-976 and demonstrate that our variability subtraction routine successfully allows for measuring bulk system characteristics using traditional light curve fitting. These results open a new window for characterizing bulk system parameters of planets orbiting seismically active stars.

  10. On the selection of significant variables in a model for the deteriorating process of facades

    Science.gov (United States)

    Serrat, C.; Gibert, V.; Casas, J. R.; Rapinski, J.

    2017-10-01

    In previous works the authors of this paper have introduced a predictive system that uses survival analysis techniques for the study of time-to-failure in the facades of a building stock. The approach is population based, in order to obtain information on the evolution of the stock across time, and to help the manager in the decision making process on global maintenance strategies. For the decision making it is crutial to determine those covariates -like materials, morphology and characteristics of the facade, orientation or environmental conditions- that play a significative role in the progression of different failures. The proposed platform also incorporates an open source GIS plugin that includes survival and test moduli that allow the investigator to model the time until a lesion taking into account the variables collected during the inspection process. The aim of this paper is double: a) to shortly introduce the predictive system, as well as the inspection and the analysis methodologies and b) to introduce and illustrate the modeling strategy for the deteriorating process of an urban front. The illustration will be focused on the city of L’Hospitalet de Llobregat (Barcelona, Spain) in which more than 14,000 facades have been inspected and analyzed.

  11. On the predictability of land surface fluxes from meteorological variables

    Science.gov (United States)

    Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

    2018-01-01

    Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

  12. It's the People, Stupid: The Role of Personality and Situational Variable in Predicting Decisionmaker Behavior

    National Research Council Canada - National Science Library

    Sticha, Paul J; Buede, Dennis M; Rees, Richard L

    2006-01-01

    .... to identity assumptions and determinant variables, and to quantify each variable's relative contribution to the prediction, producing a graphical representation of the analysis with explicit levels of uncertainty...

  13. Biological variability in biomechanical engineering research: Significance and meta-analysis of current modeling practices.

    Science.gov (United States)

    Cook, Douglas; Julias, Margaret; Nauman, Eric

    2014-04-11

    Biological systems are characterized by high levels of variability, which can affect the results of biomechanical analyses. As a review of this topic, we first surveyed levels of variation in materials relevant to biomechanics, and compared these values to standard engineered materials. As expected, we found significantly higher levels of variation in biological materials. A meta-analysis was then performed based on thorough reviews of 60 research studies from the field of biomechanics to assess the methods and manner in which biological variation is currently handled in our field. The results of our meta-analysis revealed interesting trends in modeling practices, and suggest a need for more biomechanical studies that fully incorporate biological variation in biomechanical models and analyses. Finally, we provide some case study example of how biological variability may provide valuable insights or lead to surprising results. The purpose of this study is to promote the advancement of biomechanics research by encouraging broader treatment of biological variability in biomechanical modeling. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Identification of significant process variables for a flow-through supercritical water oxidation reactor

    International Nuclear Information System (INIS)

    Rossi, R.E.

    1992-05-01

    The effects of four process variables on the destruction efficiency of a flow-through supercritical water oxidation reactor were investigated. These process variables included: (1) reactor throughput (GPH), (2) concentration of the surrogate waste (% acetone), (3) maximum reactor tube-wall temperature (OC), and (4) applied stoichiometric oxygen. The analysis was conducted utilizing two-level factorial experiments, steepest ascent methods, and central composite designs. This experimental protocol assures efficient experimentation and allows for an empirical response surface model of the system to be developed. This experimentation identified a significant positive effect for stoichiometric oxygen applied and temperature variations between 400 to 500 degrees C. The increase in destruction efficiency due to stoichiometric 0 2 provides strong evidence that supercritical water oxidations are catalyzed by excess oxygen, and the strong temperature effect is a result of large increases in the kinetic rates for this temperature range. However, increasing temperature between 550 to 650 degrees C does not provide substantial increases in destruction efficiency. In addition, destruction efficiency is significantly unproved by increasing the Reynolds number and residence time. The destruction efficiency of the reactor is also dependent upon the initial concentration of surrogate waste. This concentration dependence may indicate first-order supercritical CO kinetics is inadequate for describing all waste types and reactor configurations. Alternatively, it may indicate reactant mixing, caused by local turbulence at the oxidation fronts of these higher concentration waste streams, results in higher destruction efficiencies

  15. Variability in anatomical features of human clavicle: Its forensic anthropological and clinical significance

    Directory of Open Access Journals (Sweden)

    Jagmahender Singh Sehrawat

    2016-06-01

    Full Text Available Bones can reflect the basic framework of human body and may provide valuable information about the biological identity of the deceased. They, often, survive the morphological alterations, taphonomic destructions, decay/mutilation and decomposition insults. In-depth knowledge of variations in clavicular shape, size and its dimensions is very important from both clinical (fixation of clavicular fractures using external or inter-medullary devices, designing orthopedic fixation devices as well as forensic anthropological perspectives. Human clavicle is the most frequently fractured bone of human skeleton, possessing high degree of variability in its anatomical, biomechanical and morphological features. Extended period of skeletal growth (up to third decade in clavicle imparts it an additional advantage for forensic identification purposes. In present study, five categories of clavicular features like lengths, diameters, angles, indices and robustness were examined to explore the suitability of collarbone for forensic and clinical purposes. For this purpose, 263 pairs of adult clavicles (195 Males and 68 Females were collected from autopsied cadavers and were studied for 13 anatomical features. Gender and occupational affiliations of cadavers were found to have significant influences on anatomical dimensions of their clavicles. Product index, weight and circumference of collarbone were found the best univariate variables, discriminating sex of more than 80% individuals. The best multivariate Function-I (DF: -17.315 + 0.054 CL-L+0.196 CC-R+0.184 DM-L could identify sex and occupation of 89.4% (89.2% Male and 89.7% Female and 65.4% individuals, respectively. All clavicular variables were found bilaterally asymmetric; left clavicles being significantly longer in length, lighter in weight, smooth in texture and less curved than the right side bones. Among non-metric traits, sub-clavian groove, nutrient foramina and ‘type’ of clavicle exhibited

  16. Vigorous physical activity predicts higher heart rate variability among younger adults.

    Science.gov (United States)

    May, Richard; McBerty, Victoria; Zaky, Adam; Gianotti, Melino

    2017-06-14

    Baseline heart rate variability (HRV) is linked to prospective cardiovascular health. We tested intensity and duration of weekly physical activity as predictors of heart rate variability in young adults. Time and frequency domain indices of HRV were calculated based on 5-min resting electrocardiograms collected from 82 undergraduate students. Hours per week of both moderate and vigorous activity were estimated using the International Physical Activity Questionnaire. In regression analyses, hours of vigorous physical activity, but not moderate activity, significantly predicted greater time domain and frequency domain indices of heart rate variability. Adjusted for weekly frequency, greater daily duration of vigorous activity failed to predict HRV indices. Future studies should test direct measurements of vigorous activity patterns as predictors of autonomic function in young adulthood.

  17. Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indices

    International Nuclear Information System (INIS)

    Magagnin, Valentina; Bassani, Tito; Bari, Vlasta; Turiel, Maurizio; Porta, Alberto; Maestri, Roberto; Pinna, Gian Domenico

    2011-01-01

    The autonomic regulation is non-invasively estimated from heart rate variability (HRV). Many methods utilized to assess autonomic regulation require stationarity of HRV recordings. However, non-stationarities are frequently present even during well-controlled experiments, thus potentially biasing HRV indices. The aim of our study is to quantify the potential bias of spectral, symbolic and entropy HRV indices due to non-stationarities. We analyzed HRV series recorded in healthy subjects during uncontrolled daily life activities typical of 24 h Holter recordings and during predetermined levels of robotic-assisted treadmill-based physical exercise. A stationarity test checking the stability of the mean and variance over short HRV series (about 300 cardiac beats) was utilized to distinguish stationary periods from non-stationary ones. Spectral, symbolic and entropy indices evaluated solely over stationary periods were contrasted with those derived from all the HRV segments. When indices were calculated solely over stationary series, we found that (i) during both uncontrolled daily life activities and controlled physical exercise, the entropy-based complexity indices were significantly larger; (ii) during uncontrolled daily life activities, the spectral and symbolic indices linked to sympathetic modulation were significantly smaller and those associated with vagal modulation were significantly larger; (iii) while during uncontrolled daily life activities, the variance of spectral, symbolic and entropy rate indices was significantly larger, during controlled physical exercise, it was smaller. The study suggests that non-stationarities increase the likelihood to overestimate the contribution of sympathetic control and affect the power of statistical tests utilized to discriminate conditions and/or groups

  18. Droughts in Amazonia: Spatiotemporal Variability, Teleconnections, and Seasonal Predictions

    Science.gov (United States)

    Lima, Carlos H. R.; AghaKouchak, Amir

    2017-12-01

    Most Amazonia drought studies have focused on rainfall deficits and their impact on river discharges, while the analysis of other important driver variables, such as temperature and soil moisture, has attracted less attention. Here we try to better understand the spatiotemporal dynamics of Amazonia droughts and associated climate teleconnections as characterized by the Palmer Drought Severity Index (PDSI), which integrates information from rainfall deficit, temperature anomalies, and soil moisture capacity. The results reveal that Amazonia droughts are most related to one dominant pattern across the entire region, followed by two seesaw kind of patterns: north-south and east-west. The main two modes are correlated with sea surface temperature (SST) anomalies in the tropical Pacific and Atlantic oceans. The teleconnections associated with global SST are then used to build a seasonal forecast model for PDSI over Amazonia based on predictors obtained from a sparse canonical correlation analysis approach. A unique feature of the presented drought prediction method is using only a few number of predictors to avoid excessive noise in the predictor space. Cross-validated results show correlations between observed and predicted spatial average PDSI up to 0.60 and 0.45 for lead times of 5 and 9 months, respectively. To the best of our knowledge, this is the first study in the region that, based on cross-validation results, leads to appreciable forecast skills for lead times beyond 4 months. This is a step forward in better understanding the dynamics of Amazonia droughts and improving risk assessment and management, through improved drought forecasting.

  19. Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.

    Science.gov (United States)

    Sepehri, Bakhtyar; Omidikia, Nematollah; Kompany-Zareh, Mohsen; Ghavami, Raouf

    2018-01-01

    Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

    Science.gov (United States)

    Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

    2016-11-01

    Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

  1. Prediction of university student’s addictability based on some demographic variables, academic procrastination, and interpersonal variables

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Tavakoli

    2014-02-01

    Full Text Available Objectives: This study aimed to predict addictability among the students, based on demographic variables, academic procrastination, and interpersonal variables, and also to study the prevalence of addictability among these students. Method: The participants were 500 students (260 females, 240 males selected through a stratified random sampling among the students in Islamic Azad University Branch Abadan. The participants were assessed through Individual specification inventory, addiction potential scale and Aitken procrastination Inventory. Findings: The findings showed %23/6 of students’ readiness for addiction. Men showed higher addictability than women, but age wasn’t an issue. Also variables such as economic status, age, major, and academic procrastination predicted %13, and among interpersonal variables, the variables of having friends who use drugs and dissociated family predicted %13/2 of the variance in addictability. Conclusion: This study contains applied implications for addiction prevention.

  2. Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malaysia

    Science.gov (United States)

    Dom, Nazri Che; Hassan, A Abu; Latif, Z Abd; Ismail, Rodziah

    2013-01-01

    Objective To develop a forecasting model for the incidence of dengue cases in Subang Jaya using time series analysis. Methods The model was performed using the Autoregressive Integrated Moving Average (ARIMA) based on data collected from 2005 to 2010. The fitted model was then used to predict dengue incidence for the year 2010 by extrapolating dengue patterns using three different approaches (i.e. 52, 13 and 4 weeks ahead). Finally cross correlation between dengue incidence and climate variable was computed over a range of lags in order to identify significant variables to be included as external regressor. Results The result of this study revealed that the ARIMA (2,0,0) (0,0,1)52 model developed, closely described the trends of dengue incidence and confirmed the existence of dengue fever cases in Subang Jaya for the year 2005 to 2010. The prediction per period of 4 weeks ahead for ARIMA (2,0,0)(0,0,1)52 was found to be best fit and consistent with the observed dengue incidence based on the training data from 2005 to 2010 (Root Mean Square Error=0.61). The predictive power of ARIMA (2,0,0) (0,0,1)52 is enhanced by the inclusion of climate variables as external regressor to forecast the dengue cases for the year 2010. Conclusions The ARIMA model with weekly variation is a useful tool for disease control and prevention program as it is able to effectively predict the number of dengue cases in Malaysia.

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

    Science.gov (United States)

    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

  4. On the significance of bell's inequality for hidden-variable theories

    International Nuclear Information System (INIS)

    De Baere, W.

    1984-01-01

    It is explicitly shown that Bell's derivation of the generalized Bell inequality and its subsequent interpretation depend on an implicit hypothesis concerning the reproducibility of some set of hidden variables in different runs of the same experiment

  5. The role of socio-cognitive variables in predicting learning satisfaction in smart schools

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Firoozi

    2017-03-01

    Full Text Available The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003, Performance Expectation Questionnaire developed by Compeau and Higgins (1995, System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006, Interaction Questionnaire developed by Johnston, Killion and Oomen (2005, Learning Climate Questionnaire developed by Chou` and Liu (2005 and Learning Satisfaction Questionnaire developed by Chou and Liu (2005. In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.

  6. The Role of Socio-Cognitive Variables in Predicting Learning Satisfaction in Smart Schools

    Directory of Open Access Journals (Sweden)

    Mohammad Reza FIROOZI

    2017-03-01

    Full Text Available The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003, Performance Expectation Questionnaire developed by Compeau and Higgins (1995, System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006, Interaction Questionnaire developed by Johnston, Killion and Oomen (2005, Learning Climate Questionnaire developed by Chou` and Liu (2005 and Learning Satisfaction Questionnaire developed by Chou and Liu (2005. In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.

  7. How well do financial and macroeconomic variables predict stock returns

    DEFF Research Database (Denmark)

    Rasmussen, Anne-Sofie Reng

    Recent evidence of mean reversion in stock returns has led to an explosion in the development of forecasting variables. This paper evaluates the relative performance of these many variables in both time-series and cross-sectional setups. We collect the different measures and compare their forecas......Recent evidence of mean reversion in stock returns has led to an explosion in the development of forecasting variables. This paper evaluates the relative performance of these many variables in both time-series and cross-sectional setups. We collect the different measures and compare...... their forecasting ability for stock returns, and we examine the forecasting variables' ability to reduce pricing errors in the conditional C-CAPM. A key result of the analysis is that the traditional pricedividend ratio performs surprisingly well compared to the many new forecasting variables. We also find...

  8. Predicting farm-level animal populations using environmental and socioeconomic variables.

    Science.gov (United States)

    van Andel, Mary; Jewell, Christopher; McKenzie, Joanna; Hollings, Tracey; Robinson, Andrew; Burgman, Mark; Bingham, Paul; Carpenter, Tim

    2017-09-15

    Accurate information on the geographic distribution of domestic animal populations helps biosecurity authorities to efficiently prepare for and rapidly eradicate exotic diseases, such as Foot and Mouth Disease (FMD). Developing and maintaining sufficiently high-quality data resources is expensive and time consuming. Statistical modelling of population density and distribution has only begun to be applied to farm animal populations, although it is commonly used in wildlife ecology. We developed zero-inflated Poisson regression models in a Bayesian framework using environmental and socioeconomic variables to predict the counts of livestock units (LSUs) and of cattle on spatially referenced farm polygons in a commercially available New Zealand farm database, Agribase. Farm-level counts of cattle and of LSUs varied considerably by region, because of the heterogeneous farming landscape in New Zealand. The amount of high quality pasture per farm was significantly associated with the presence of both cattle and LSUs. Internal model validation (predictive performance) showed that the models were able to predict the count of the animal population on groups of farms that were located in randomly selected 3km zones with a high level of accuracy. Predicting cattle or LSU counts on individual farms was less accurate. Predicted counts were statistically significantly more variable for farms that were contract grazing dry stock, such as replacement dairy heifers and dairy cattle not currently producing milk, compared with other farm types. This analysis presents a way to predict numbers of LSUs and cattle for farms using environmental and socio-economic data. The technique has the potential to be extrapolated to predicting other pastoral based livestock species. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Clinical significance of mobile health assessed sleep duration and variability in bipolar disorder.

    Science.gov (United States)

    Kaufmann, Christopher N; Gershon, Anda; Eyler, Lisa T; Depp, Colin A

    2016-10-01

    Sleep disturbances are prevalent, persistent, and impairing features of bipolar disorder. However, the near-term and cumulative impact of the severity and variability of sleep disturbances on symptoms and functioning remains unclear. We examined self-reported daily sleep duration and variability in relation to mood symptoms, medication adherence, cognitive functioning, and concurrent daily affect. Forty-one outpatients diagnosed with bipolar disorder were asked to provide daily reports of sleep duration and affect collected via ecological momentary assessment with smartphones over eleven weeks. Measures of depressive and manic symptoms, medication adherence, and cognitive function were collected at baseline and concurrent assessment of affect were collected daily. Analyses examined whether sleep duration or variability were associated with baseline measures and changes in same-day or next-day affect. Greater sleep duration variability (but not average sleep duration) was associated with greater depressive and manic symptom severity, and lower medication adherence at baseline, and with lower and more variable ratings of positive affect and higher ratings of negative affect. Sleep durations shorter than 7-8 h were associated with lower same-day ratings of positive and higher same-day ratings of negative affect, however this did not extend to next-day affect. Greater cumulative day-to-day sleep duration variability, but not average sleep duration, was related to more severe mood symptoms, lower self-reported medication adherence and higher levels of negative affect. Bouts of short- or long-duration sleep had transient impact on affect. Day-to-day sleep variability may be important to incorporate into clinical assessment of sleep disturbances in bipolar disorder. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Predictive Variables of Success for Latino Enrollment in Higher Education

    Science.gov (United States)

    Sanchez, Jafeth E.; Usinger, Janet; Thornton, Bill W.

    2015-01-01

    It is necessary to better understand the unique variables that serve as predictors of Latino students' postsecondary enrollment and success. Impacts of various variables were examined among 850 Latino and Caucasian students (76% and 24% of the sample, respectively). Gender, ethnicity, perceived affordability, high school grade point average, and…

  11. Exploring the significance of human mobility patterns in social link prediction

    KAUST Repository

    Alharbi, Basma Mohammed

    2014-01-01

    Link prediction is a fundamental task in social networks. Recently, emphasis has been placed on forecasting new social ties using user mobility patterns, e.g., investigating physical and semantic co-locations for new proximity measure. This paper explores the effect of in-depth mobility patterns. Specifically, we study individuals\\' movement behavior, and quantify mobility on the basis of trip frequency, travel purpose and transportation mode. Our hybrid link prediction model is composed of two modules. The first module extracts mobility patterns, including travel purpose and mode, from raw trajectory data. The second module employs the extracted patterns for link prediction. We evaluate our method on two real data sets, GeoLife [15] and Reality Mining [5]. Experimental results show that our hybrid model significantly improves the accuracy of social link prediction, when comparing to primary topology-based solutions. Copyright 2014 ACM.

  12. A variable capacitance based modeling and power capability predicting method for ultracapacitor

    Science.gov (United States)

    Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

    2018-01-01

    Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

  13. Variables that predict academic procrastination behavior in prospective primary school teachers

    Directory of Open Access Journals (Sweden)

    Asuman Seda SARACALOĞLU

    2016-04-01

    Full Text Available This study aimed to examine the variables predicting academic procrastination behavior of prospective primary school teachers and is conducted using the correlational survey model. The study group is composed of 294 undergraduate students studying primary school teaching programs in faculties of education at Adnan Menderes, Pamukkale, and Muğla Sıtkı Koçman Universities in Turkey. The data collection instruments used were the Procrastination Assessment Scale Students (PASS, Academic Self-Efficacy Scale (ASES, and Academic Motivation Scale (AMS. While analyzing the gathered data, descriptive analysis techniques were utilized. Moreover, while analyzing the data, power of variables namely reasons of academic procrastination, academic motivation, and academic efficacy to predict prospective primary school teachers’ academic procrastination tendencies were tested. For that purpose, stepwise regression analysis was employed. It was found that nearly half of the prospective primary school teachers displayed no academic procrastination behavior. Participants’ reasons for procrastination were fear of failure, laziness, taking risks, and rebellion against control. An average level significant correlation was found between participants’ academic procrastination and other variables. As a result, it was identified that prospective primary school teachers had less academic procrastination than reported in literature and laziness, fear of failure, academic motivation predicted academic procrastination.

  14. Variable Origin of the Superior Laryngeal Artery and Its Clinical Significance

    OpenAIRE

    Soubhagya R. Nayak1*, Ashwin Krishnamurthy2, Latha V. Prabhu2, Bhagath Kumar Potu3, Ishwar B. Bagoji4, Jiji PJ2 and Ganesh Kumar Chettiar2

    2011-01-01

    The superior laryngeal artery (SLA) is the dominant arterial supply of the laryngeal muscles, mucosa and glands. The purpose of the present study was to document the variable origin of the SLA in the carotid triangle. Although the variation in the SLA origin and morphology is important during the partial laryngectomy and reconstruction surgery of the larynx, the description of the SLA in modern literature is vague. The anatomy of SLA was studied in 37 adult South Indian preserved cadavers age...

  15. relationship of some variables in predicting pre service teachers

    African Journals Online (AJOL)

    Preferred Customer

    and gender could predict their problem solving performance in chemistry. The sample for the study ... accessible methods for obtaining the solution to the question, goal or objective. These ... the selected quantitative problems. Secondly ...

  16. Biographical and demographical variables as moderators in the prediction of turnover intentions

    Directory of Open Access Journals (Sweden)

    Janine du Plooy

    2013-04-01

    Full Text Available Orientation: The aim of the study was to explore the possible moderation effects of biographical and demographical variables on a prediction model of turnover intention (TI. Research purpose: The main purpose of the study was to determine how biographical and demographical variables have an impact on predictors of turnover intentions. Motivation for the study: Twenty-first century organisations face significant challenges in the management of talent and human capital. One in particular is voluntary employee turnover and the lack of appropriate business models to track this process. Research design, approach, and method: A secondary data analysis (SDA was performed in a quantitative research tradition on the cross-sectional survey sample (n = 2429. Data were collected from a large South African Information and Communication Technologies (ICT sector company (N = 23 134. Main findings: The results of the study confirmed significant moderation effects regarding race, age, and marital status in the prediction equations of TIs. Practical and managerial implications: Practical implications of the study suggested increased understanding of workforce diversity effects within the human resource (HR value chain, with resultant evidence-based, employee retention strategies and interventions. Issues concerning talent management could also be addressed. Contribution and value-add: The study described in this article took Industrial/Organisational (I/O psychological concepts and linked them in unique combinations to establish better predictive validity of a more comprehensive turnover intentions model.

  17. The Significance of the Spatial Variability of Rainfall on the Numerical Simulation of Urban Floods

    Directory of Open Access Journals (Sweden)

    Laurent Guillaume Courty

    2018-02-01

    Full Text Available The growth of urban population, combined with an increase of extreme events due to climate change call for a better understanding and representation of urban floods. The uncertainty in rainfall distribution is one of the most important factors that affects the watershed response to a given precipitation event. However, most of the investigations on this topic have considered theoretical scenarios, with little reference to case studies in the real world. This paper incorporates the use of spatially-variable precipitation data from a long-range radar in the simulation of the severe floods that impacted the city of Hull, U.K., in June 2007. This radar-based rainfall field is merged with rain gauge data using a Kriging with External Drift interpolation technique. The utility of this spatially-variable information is investigated through the comparison of computed flooded areas (uniform and radar against those registered by public authorities. Both results show similar skills at reproducing the real event, but differences in the total precipitated volumes, water depths and flooded areas are illustrated. It is envisaged that in urban areas and with the advent of higher resolution radars, these differences will be more important and call for further investigation.

  18. Recent and Past Musical Activity Predicts Cognitive Aging Variability: Direct Comparison with Leisure Activities

    Directory of Open Access Journals (Sweden)

    Brenda eHanna-Pladdy

    2012-07-01

    Full Text Available Studies evaluating the impact of modifiable lifestyle factors on cognition offer potential insights into sources of cognitive aging variability. Recently, we reported an association between extent of musical instrumental practice throughout the life span (greater than 10 years on preserved cognitive functioning in advanced age . These findings raise the question of whether there are training-induced brain changes in musicians that can transfer to nonmusical cognitive abilities to allow for compensation of age-related cognitive declines. However, because of the relationship between engagement in lifestyle activities and preserved cognition, it remains unclear whether these findings are specifically driven by musical training or the types of individuals likely to engage in greater activities in general. The current study examined the type of leisure activity (musical versus other as well as the timing of engagement (age of acquisition, past versus recent in predictive models of successful cognitive aging. Seventy age and education matched older musicians (> 10 years and nonmusicians (ages 59-80 were evaluated on neuropsychological tests and life-style activities (AAP. Partition analyses were conducted on significant cognitive measures to explain performance variance in musicians. Musicians scored higher on tests of phonemic fluency, verbal immediate recall, judgment of line orientation (JLO, and Letter Number Sequencing (LNS, but not the AAP. The first partition analysis revealed education best predicted JLO in musicians, followed by recent musical engagement which offset low education. In the second partition analysis, early age of musical acquisition (< 9 years predicted enhanced LNS in musicians, while analyses for AAP, verbal recall and fluency were not predictive. Recent and past musical activity, but not leisure activity, predicted variability across verbal and visuospatial domains in aging. Early musical acquisition predicted auditory

  19. Appraisal and Reliability of Variable Engagement Model Prediction ...

    African Journals Online (AJOL)

    The variable engagement model based on the stress - crack opening displacement relationship and, which describes the behaviour of randomly oriented steel fibres composite subjected to uniaxial tension has been evaluated so as to determine the safety indices associated when the fibres are subjected to pullout and with ...

  20. The role of socio demographic variables in predicting patients ...

    African Journals Online (AJOL)

    Background: Radiological examination remains a vital and integral aspect of health services delivery and patient satisfaction with radiological service remains beneficial both to patients and hospitals. Aim: To evaluate the influence of patient's socio demographic variables on satisfaction with radiological services. Subjects ...

  1. Child Support Payment: A Structural Model of Predictive Variables.

    Science.gov (United States)

    Wright, David W.; Price, Sharon J.

    A major area of concern in divorced families is compliance with child support payments. Aspects of the former spouse relationship that are predictive of compliance with court-ordered payment of child support were investigated in a sample of 58 divorced persons all of whom either paid or received child support. Structured interviews and…

  2. Variable input parameter influence on river corridor prediction

    NARCIS (Netherlands)

    Zerfu, T.; Beevers, L.; Crosato, A.; Wright, N.

    2015-01-01

    This paper considers the erodible river corridor, which is the area in which the main river channel is free to migrate over a period of time. Due to growing anthropogenic pressure, predicting the corridor width has become increasingly important for the planning of development along rivers. Several

  3. Use of data mining to predict significant factors and benefits of bilateral cochlear implantation.

    Science.gov (United States)

    Ramos-Miguel, Angel; Perez-Zaballos, Teresa; Perez, Daniel; Falconb, Juan Carlos; Ramosb, Angel

    2015-11-01

    Data mining (DM) is a technique used to discover pattern and knowledge from a big amount of data. It uses artificial intelligence, automatic learning, statistics, databases, etc. In this study, DM was successfully used as a predictive tool to assess disyllabic speech test performance in bilateral implanted patients with a success rate above 90%. 60 bilateral sequentially implanted adult patients were included in the study. The DM algorithms developed found correlations between unilateral medical records and Audiological test results and bilateral performance by establishing relevant variables based on two DM techniques: the classifier and the estimation. The nearest neighbor algorithm was implemented in the first case, and the linear regression in the second. The results showed that patients with unilateral disyllabic test results below 70% benefited the most from a bilateral implantation. Finally, it was observed that its benefits decrease as the inter-implant time increases.

  4. Clinical Significance of Hemostatic Parameters in the Prediction for Type 2 Diabetes Mellitus and Diabetic Nephropathy

    Directory of Open Access Journals (Sweden)

    Lianlian Pan

    2018-01-01

    Full Text Available It would be important to predict type 2 diabetes mellitus (T2DM and diabetic nephropathy (DN. This study was aimed at evaluating the predicting significance of hemostatic parameters for T2DM and DN. Plasma coagulation and hematologic parameters before treatment were measured in 297 T2DM patients. The risk factors and their predicting power were evaluated. T2DM patients without complications exhibited significantly different activated partial thromboplastin time (aPTT, platelet (PLT, and D-dimer (D-D levels compared with controls (P<0.01. Fibrinogen (FIB, PLT, and D-D increased in DN patients compared with those without complications (P<0.001. Both aPTT and PLT were the independent risk factors for T2DM (OR: 1.320 and 1.211, P<0.01, resp., and FIB and PLT were the independent risk factors for DN (OR: 1.611 and 1.194, P<0.01, resp.. The area under ROC curve (AUC of aPTT and PLT was 0.592 and 0.647, respectively, with low sensitivity in predicting T2DM. AUC of FIB was 0.874 with high sensitivity (85% and specificity (76% for DN, and that of PLT was 0.564, with sensitivity (60% and specificity (89% based on the cutoff values of 3.15 g/L and 245 × 109/L, respectively. This study suggests that hemostatic parameters have a low predicting value for T2DM, whereas fibrinogen is a powerful predictor for DN.

  5. Can Social History Variables Predict Prison Inmates’ Risk for Latent Tuberculosis Infection?

    Directory of Open Access Journals (Sweden)

    Tyler E. Weant

    2012-01-01

    Full Text Available Improved screening and treatment of latent tuberculosis infection (LTBI in correctional facilities may improve TB control. The Ohio Department of Rehabilitation and Correction (ODRC consists of 32 prisons. Inmates are screened upon entry to ODRC and yearly thereafter. The objective of the study was to determine if social history factors such as tobacco, alcohol, and drug use are significant predictors of LTBI and treatment outcomes. We reviewed the medical charts of inmates and randomly selected age-matched controls at one ODRC facility for 2009. We used a conditional logistic regression to assess associations between selected social history variables and LTBI diagnosis. Eighty-nine inmates with a history of LTBI and 88 controls were identified. No social history variable was a significant predictor of LTBI. Medical comorbidities such as asthma, rheumatoid arthritis, and hepatitis C were significantly higher in inmates with LTBI. 84% of inmates diagnosed with LTBI had either completed or were on treatment. Annual TB screening may not be cost-effective in all inmate populations. Identification of factors to help target screening populations at risk for TB is critical. Social history variables did not predict LTBI in our inmate population. Additional studies are needed to identify inmates for the targeted TB testing.

  6. Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.

    Science.gov (United States)

    Danner, Omar K; Hendren, Sandra; Santiago, Ethel; Nye, Brittany; Abraham, Prasad

    2017-04-01

    Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation. We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock. Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients. Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Surface tensions of multi-component mixed inorganic/organic aqueous systems of atmospheric significance: measurements, model predictions and importance for cloud activation predictions

    Directory of Open Access Journals (Sweden)

    D. O. Topping

    2007-01-01

    Full Text Available In order to predict the physical properties of aerosol particles, it is necessary to adequately capture the behaviour of the ubiquitous complex organic components. One of the key properties which may affect this behaviour is the contribution of the organic components to the surface tension of aqueous particles in the moist atmosphere. Whilst the qualitative effect of organic compounds on solution surface tensions has been widely reported, our quantitative understanding on mixed organic and mixed inorganic/organic systems is limited. Furthermore, it is unclear whether models that exist in the literature can reproduce the surface tension variability for binary and higher order multi-component organic and mixed inorganic/organic systems of atmospheric significance. The current study aims to resolve both issues to some extent. Surface tensions of single and multiple solute aqueous solutions were measured and compared with predictions from a number of model treatments. On comparison with binary organic systems, two predictive models found in the literature provided a range of values resulting from sensitivity to calculations of pure component surface tensions. Results indicate that a fitted model can capture the variability of the measured data very well, producing the lowest average percentage deviation for all compounds studied. The performance of the other models varies with compound and choice of model parameters. The behaviour of ternary mixed inorganic/organic systems was unreliably captured by using a predictive scheme and this was dependent on the composition of the solutes present. For more atmospherically representative higher order systems, entirely predictive schemes performed poorly. It was found that use of the binary data in a relatively simple mixing rule, or modification of an existing thermodynamic model with parameters derived from binary data, was able to accurately capture the surface tension variation with concentration. Thus

  8. Recent and past musical activity predicts cognitive aging variability: direct comparison with general lifestyle activities.

    Science.gov (United States)

    Hanna-Pladdy, Brenda; Gajewski, Byron

    2012-01-01

    Studies evaluating the impact of modifiable lifestyle factors on cognition offer potential insights into sources of cognitive aging variability. Recently, we reported an association between extent of musical instrumental practice throughout the life span (greater than 10 years) on preserved cognitive functioning in advanced age. These findings raise the question of whether there are training-induced brain changes in musicians that can transfer to non-musical cognitive abilities to allow for compensation of age-related cognitive declines. However, because of the relationship between engagement in general lifestyle activities and preserved cognition, it remains unclear whether these findings are specifically driven by musical training or the types of individuals likely to engage in greater activities in general. The current study controlled for general activity level in evaluating cognition between musicians and nomusicians. Also, the timing of engagement (age of acquisition, past versus recent) was assessed in predictive models of successful cognitive aging. Seventy age and education matched older musicians (>10 years) and non-musicians (ages 59-80) were evaluated on neuropsychological tests and general lifestyle activities. Musicians scored higher on tests of phonemic fluency, verbal working memory, verbal immediate recall, visuospatial judgment, and motor dexterity, but did not differ in other general leisure activities. Partition analyses were conducted on significant cognitive measures to determine aspects of musical training predictive of enhanced cognition. The first partition analysis revealed education best predicted visuospatial functions in musicians, followed by recent musical engagement which offset low education. In the second partition analysis, early age of musical acquisition (memory in musicians, while analyses for other measures were not predictive. Recent and past musical activity, but not general lifestyle activities, predicted variability

  9. Significant and variable linear polarization during the prompt optical flash of GRB 160625B.

    Science.gov (United States)

    Troja, E.; Lipunov, V. M.; Mundell, C. G.; Butler, N. R.; Watson, A. M.; Kobayashi, S.; Cenko, S. B.; Marshall, F. E.; Ricci, R.; Fruchter, A.; Wieringa, M. H.; Gorbovskoy, E. S.; Kornilov, V.; Kutyrev, A.; Lee, W. H.; Toy, V.; Tyurina, N. V.; Budnev, N. M.; Buckley, D. A. H.; González, J.; Gress, O.; Horesh, A.; Panasyuk, M. I.; Prochaska, J. X.; Ramirez-Ruiz, E.; Rebolo Lopez, R.; Richer, M. G.; Roman-Zuniga, C.; Serra-Ricart, M.; Yurkov, V.; Gehrels, N.

    2017-07-01

    Newly formed black holes of stellar mass launch collimated outflows (jets) of ionized matter that approach the speed of light. These outflows power prompt, brief and intense flashes of γ-rays known as γ-ray bursts (GRBs), followed by longer-lived afterglow radiation that is detected across the electromagnetic spectrum. Measuring the polarization of the observed GRB radiation provides a direct probe of the magnetic fields in the collimated jets. Rapid-response polarimetric observations of newly discovered bursts have probed the initial afterglow phase, and show that, minutes after the prompt emission has ended, the degree of linear polarization can be as high as 30 per cent - consistent with the idea that a stable, globally ordered magnetic field permeates the jet at large distances from the central source. By contrast, optical and γ-ray observations during the prompt phase have led to discordant and often controversial results, and no definitive conclusions have been reached regarding the origin of the prompt radiation or the configuration of the magnetic field. Here we report the detection of substantial (8.3 ± 0.8 per cent from our most conservative simulation), variable linear polarization of a prompt optical flash that accompanied the extremely energetic and long-lived prompt γ-ray emission from GRB 160625B. Our measurements probe the structure of the magnetic field at an early stage of the jet, closer to its central black hole, and show that the prompt phase is produced via fast-cooling synchrotron radiation in a large-scale magnetic field that is advected from the black hole and distorted by dissipation processes within the jet.

  10. Variability of Cenococcum colonization and its ecophysiological significance for young conifers at alpine-treeline.

    Science.gov (United States)

    Hasselquist, Niles; Germino, Matthew J; McGonigle, Terence; Smith, William K

    2005-03-01

    * Plants establishing in environments that are marginal for growth could be particularly sensitive to mycorrhizal associations. We investigated ectomycorrhizal colonization and its significance for young conifers growing at, or above, their normal limits for growth, in the alpine-treeline ecotone. * Colonization of seedlings (treeline may include a below-ground, mycorrhizal component that complements previously reported effects of trees on the microclimate and ecophysiology of seedlings.

  11. Identifying psychosocial variables that predict safer-sex intentions in adolescents and young adults

    Directory of Open Access Journals (Sweden)

    Phil eBrüll

    2016-04-01

    Full Text Available Young people are especially vulnerable to sexually transmitted infections. The triad of deliberate and effective safer-sex behavior encompasses condom use, combined with additional information about a partner’s sexual health, and the kind of sex acts usually performed. To identify psychosocial predictors of young people’s intentions to have safer sex, as related to this triad we conducted an online study with 211 sexually active participants aged between 18 and 24 years. Predictors (i.e. perceived behavioural control, subjective norms and intention taken from Fishbein and Ajzen’s Reasoned Action Approach (RAA, were combined with more distal variables (e.g. behavioral inhibition, sensation seeking, parental monitoring, and knowledge about sexually transmitted infections. Beyond the highly predictive power of RAA variables, additional variance was explained by the number of instances of unprotected sexual intercourse during the last twelve months and reasons for using barrier protection during first sexual intercourse. In particular, past condom nonuse behavior moderated perceived behavioral control related to intended condom use. Further, various distal variables showed significant univariate associations with intentions related to the three behaviors of interest. It may, therefore, be helpful to include measures of past behavior as well as certain additional distal variables in future safer-sex programs designed to promote health sustaining sexual behavior.

  12. It is possible to predict Sangiovese wine quality through a limited number of variables measured on the vines

    Directory of Open Access Journals (Sweden)

    Pierluigi Bucelli

    2010-12-01

    Significance and impact of the study: It is now possible to predict the quality of Sangiovese wines with a few selected grape parameters. Because of the wide variability in soil and climatic condition of the viticultural areas of the Province of Siena, where the method was developed, and the strong climatic contrast between the years when the method was validated, the use of both matching table and multiple regression is recommended for VPS prediction in Mediterranean environments.

  13. Contact parameter identification for vibrational response variability prediction

    DEFF Research Database (Denmark)

    Creixell Mediante, Ester; Brunskog, Jonas; Jensen, Jakob Søndergaard

    2018-01-01

    industry, where the vibrational behavior of the structures within the hearing frequency range is critical for the performance of the devices. A procedure to localize the most probable contact areas and determine the most sensitive contact points with respect to variations in the modes of vibration......Variability in the dynamic response of assembled structures can arise due to variations in the contact conditions between the parts that conform them. Contact conditions are difficult to model accurately due to randomness in physical properties such as contact surface, load distribution...... or geometric details. Those properties can vary for a given structure due to the assembly and disassembly process, and also across nominally equal items that are produced in series. This work focuses on modeling the contact between small light-weight plastic pieces such as those used in the hearing aid...

  14. Physical attraction to reliable, low variability nervous systems: Reaction time variability predicts attractiveness.

    Science.gov (United States)

    Butler, Emily E; Saville, Christopher W N; Ward, Robert; Ramsey, Richard

    2017-01-01

    The human face cues a range of important fitness information, which guides mate selection towards desirable others. Given humans' high investment in the central nervous system (CNS), cues to CNS function should be especially important in social selection. We tested if facial attractiveness preferences are sensitive to the reliability of human nervous system function. Several decades of research suggest an operational measure for CNS reliability is reaction time variability, which is measured by standard deviation of reaction times across trials. Across two experiments, we show that low reaction time variability is associated with facial attractiveness. Moreover, variability in performance made a unique contribution to attractiveness judgements above and beyond both physical health and sex-typicality judgements, which have previously been associated with perceptions of attractiveness. In a third experiment, we empirically estimated the distribution of attractiveness preferences expected by chance and show that the size and direction of our results in Experiments 1 and 2 are statistically unlikely without reference to reaction time variability. We conclude that an operating characteristic of the human nervous system, reliability of information processing, is signalled to others through facial appearance. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Relationship of Some Variables in Predicting Pre-Service Teachers ...

    African Journals Online (AJOL)

    ) and mathematics skill test (MST). Data were analysed using one-way ANOVA, ttest and multiple regression. The results showed that, based on academic level and college specialization, there was a significant difference between the mean ...

  16. Statistical significance of theoretical predictions: A new dimension in nuclear structure theories (I)

    International Nuclear Information System (INIS)

    DUDEK, J; SZPAK, B; FORNAL, B; PORQUET, M-G

    2011-01-01

    In this and the follow-up article we briefly discuss what we believe represents one of the most serious problems in contemporary nuclear structure: the question of statistical significance of parametrizations of nuclear microscopic Hamiltonians and the implied predictive power of the underlying theories. In the present Part I, we introduce the main lines of reasoning of the so-called Inverse Problem Theory, an important sub-field in the contemporary Applied Mathematics, here illustrated on the example of the Nuclear Mean-Field Approach.

  17. Predicting Students' Skills in the Context of Scientific Inquiry with Cognitive, Motivational, and Sociodemographic Variables

    Science.gov (United States)

    Nehring, Andreas; Nowak, Kathrin H.; Belzen, Annette Upmeier zu; Tiemann, Rüdiger

    2015-06-01

    Research on predictors of achievement in science is often targeted on more traditional content-based assessments and single student characteristics. At the same time, the development of skills in the field of scientific inquiry constitutes a focal point of interest for science education. Against this background, the purpose of this study was to investigate to which extent multiple student characteristics contribute to skills of scientific inquiry. Based on a theoretical framework describing nine epistemological acts, we constructed and administered a multiple-choice test that assesses these skills in lower and upper secondary school level (n = 780). The test items contained problem-solving situations that occur during chemical investigations in school and had to be solved by choosing an appropriate inquiry procedure. We collected further data on 12 cognitive, motivational, and sociodemographic variables such as conceptual knowledge, enjoyment of chemistry, or language spoken at home. Plausible values were drawn to quantify students' inquiry skills. The results show that students' characteristics predict their inquiry skills to a large extent (55%), whereas 9 out of 12 variables contribute significantly on a multivariate level. The influence of sociodemographic traits such as gender or the social background becomes non-significant after controlling for cognitive and motivational variables. Furthermore, the performance advance of students from upper secondary school level can be explained by controlling for cognitive covariates. We discuss our findings with regard to curricular aspects and raise the question whether the inquiry skills can be considered as an autonomous trait in science education research.

  18. Review of some advances of the literature about predictive variables concerning subjective well-being

    Directory of Open Access Journals (Sweden)

    Gloria Cajiao

    2013-06-01

    Full Text Available This review of scientific literature presents some tendencies, conceptual advances, empirical findings and tests that measure the predictive variables of subjective well-being. It was done through the search in bibliographical database like ProQuest, PsycArticles, Psyctest, OVID SP, books and Thesis. Two types of predictive variables were recognized- internal and external to the individual-. Both of them influence the achievement of the subjective well-being. Besides, the studies and conceptualization about Subjetive well-being and some of the Predictive Variables were analyzed in the conclusion.

  19. Model Predictive Control of a Nonlinear System with Known Scheduling Variable

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2012-01-01

    Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...... the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

  20. Resting heart rate variability predicts safety learning and fear extinction in an interoceptive fear conditioning paradigm.

    Directory of Open Access Journals (Sweden)

    Meike Pappens

    Full Text Available This study aimed to investigate whether interindividual differences in autonomic inhibitory control predict safety learning and fear extinction in an interoceptive fear conditioning paradigm. Data from a previously reported study (N = 40 were extended (N = 17 and re-analyzed to test whether healthy participants' resting heart rate variability (HRV - a proxy of cardiac vagal tone - predicts learning performance. The conditioned stimulus (CS was a slight sensation of breathlessness induced by a flow resistor, the unconditioned stimulus (US was an aversive short-lasting suffocation experience induced by a complete occlusion of the breathing circuitry. During acquisition, the paired group received 6 paired CS-US presentations; the control group received 6 explicitly unpaired CS-US presentations. In the extinction phase, both groups were exposed to 6 CS-only presentations. Measures included startle blink EMG, skin conductance responses (SCR and US-expectancy ratings. Resting HRV significantly predicted the startle blink EMG learning curves both during acquisition and extinction. In the unpaired group, higher levels of HRV at rest predicted safety learning to the CS during acquisition. In the paired group, higher levels of HRV were associated with better extinction. Our findings suggest that the strength or integrity of prefrontal inhibitory mechanisms involved in safety- and extinction learning can be indexed by HRV at rest.

  1. Contextual Variability in Personality from Significant-Other Knowledge and Relational Selves

    Directory of Open Access Journals (Sweden)

    Susan M Andersen

    2016-01-01

    Full Text Available We argue that the self is intrinsically embedded in an interpersonal context such that it varies in IF-THEN terms, as the relational self. We have demonstrated that representations of the significant other and the relationship with that other are automatically activated by situational cues and that this activation affects both experienced and expressed aspects of the self and personality. Here, we expand on developments of the IF–THEN cognitive-affective framework of personality (CAPS, Mischel & Shoda, 1995, by extending it to the domain of interpersonal relationships at the dyadic level (Andersen & Chen, 2002. Going beyond Mischel’s early research (Mischel, 1968, our framework combines social cognition and learning theory with a learning-based psychodynamic approach, which provides the basis for extensive research on the social-cognitive process of transference and the relational self as it arises in everyday social interactions (Andersen & Cole, 1990, evidence from which contributes to a modern conceptualization of personality that emphasizes the centrality of the situation.

  2. Contextual Variability in Personality From Significant-Other Knowledge and Relational Selves.

    Science.gov (United States)

    Andersen, Susan M; Tuskeviciute, Rugile; Przybylinski, Elizabeth; Ahn, Janet N; Xu, Joy H

    2015-01-01

    We argue that the self is intrinsically embedded in an interpersonal context such that it varies in IF-THEN terms, as the relational self. We have demonstrated that representations of the significant other and the relationship with that other are automatically activated by situational cues and that this activation affects both experienced and expressed aspects of the self and personality. Here, we expand on developments of the IF-THEN cognitive-affective framework of personality system (Mischel and Shoda, 1995), by extending it to the domain of interpersonal relationships at the dyadic level (Andersen and Chen, 2002). Going beyond Mischel's early research (Mischel, 1968), our framework combines social cognition and learning theory with a learning-based psychodynamic approach, which provides the basis for extensive research on the social-cognitive process of transference and the relational self as it arises in everyday social interactions (Andersen and Cole, 1990), evidence from which contributes to a modern conceptualization of personality that emphasizes the centrality of the situation.

  3. Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach

    DEFF Research Database (Denmark)

    Asgharian, Hossein; Hou, Ai Jun; Javed, Farrukh

    2013-01-01

    This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term compone......This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long...

  4. Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System.

    Science.gov (United States)

    Doyle, Andy; Katz, Graham; Summers, Kristen; Ackermann, Chris; Zavorin, Ilya; Lim, Zunsik; Muthiah, Sathappan; Butler, Patrick; Self, Nathan; Zhao, Liang; Lu, Chang-Tien; Khandpur, Rupinder Paul; Fayed, Youssef; Ramakrishnan, Naren

    2014-12-01

    Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It is deployed on Amazon Web Services using an entirely automated deployment process. We describe the architecture of the system, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. We also present a detailed prospective evaluation of EMBERS in forecasting significant societal events in the past 2 years.

  5. 10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables.

    Science.gov (United States)

    Abad, Cesar C C; Barros, Ronaldo V; Bertuzzi, Romulo; Gagliardi, João F L; Lima-Silva, Adriano E; Lambert, Mike I; Pires, Flavio O

    2016-06-01

    The aim of this study was to verify the power of VO 2max , peak treadmill running velocity (PTV), and running economy (RE), unadjusted or allometrically adjusted, in predicting 10 km running performance. Eighteen male endurance runners performed: 1) an incremental test to exhaustion to determine VO 2max and PTV; 2) a constant submaximal run at 12 km·h -1 on an outdoor track for RE determination; and 3) a 10 km running race. Unadjusted (VO 2max , PTV and RE) and adjusted variables (VO 2max 0.72 , PTV 0.72 and RE 0.60 ) were investigated through independent multiple regression models to predict 10 km running race time. There were no significant correlations between 10 km running time and either the adjusted or unadjusted VO 2max . Significant correlations (p 0.84 and power > 0.88. The allometrically adjusted predictive model was composed of PTV 0.72 and RE 0.60 and explained 83% of the variance in 10 km running time with a standard error of the estimate (SEE) of 1.5 min. The unadjusted model composed of a single PVT accounted for 72% of the variance in 10 km running time (SEE of 1.9 min). Both regression models provided powerful estimates of 10 km running time; however, the unadjusted PTV may provide an uncomplicated estimation.

  6. Analysis of variability and predictability challenges of wind and solar power

    NARCIS (Netherlands)

    Haan, de J.E.S.; Virag, A.; Kling, W.L.

    2013-01-01

    In power systems, reserves are essential to ensure system security, certainly when challenges of predictability (inaccurate forecast) and variability (imperfect correlation of renewable generation and system load) are causing power imbalances. Different techniques can be used to size and allocate

  7. Significant interarm blood pressure difference predicts cardiovascular risk in hypertensive patients: CoCoNet study.

    Science.gov (United States)

    Kim, Su-A; Kim, Jang Young; Park, Jeong Bae

    2016-06-01

    There has been a rising interest in interarm blood pressure difference (IAD), due to its relationship with peripheral arterial disease and its possible relationship with cardiovascular disease. This study aimed to characterize hypertensive patients with a significant IAD in relation to cardiovascular risk. A total of 3699 patients (mean age, 61 ± 11 years) were prospectively enrolled in the study. Blood pressure (BP) was measured simultaneously in both arms 3 times using an automated cuff-oscillometric device. IAD was defined as the absolute difference in averaged BPs between the left and right arm, and an IAD ≥ 10 mm Hg was considered to be significant. The Framingham risk score was used to calculate the 10-year cardiovascular risk. The mean systolic IAD (sIAD) was 4.3 ± 4.1 mm Hg, and 285 (7.7%) patients showed significant sIAD. Patients with significant sIAD showed larger body mass index (P < 0.001), greater systolic BP (P = 0.050), more coronary artery disease (relative risk = 1.356, P = 0.034), and more cerebrovascular disease (relative risk = 1.521, P = 0.072). The mean 10-year cardiovascular risk was 9.3 ± 7.7%. By multiple regression, sIAD was significantly but weakly correlated with the 10-year cardiovascular risk (β = 0.135, P = 0.008). Patients with significant sIAD showed a higher prevalence of coronary artery disease, as well as an increase in 10-year cardiovascular risk. Therefore, accurate measurements of sIAD may serve as a simple and cost-effective tool for predicting cardiovascular risk in clinical settings.

  8. Significant interarm blood pressure difference predicts cardiovascular risk in hypertensive patients

    Science.gov (United States)

    Kim, Su-A; Kim, Jang Young; Park, Jeong Bae

    2016-01-01

    Abstract There has been a rising interest in interarm blood pressure difference (IAD), due to its relationship with peripheral arterial disease and its possible relationship with cardiovascular disease. This study aimed to characterize hypertensive patients with a significant IAD in relation to cardiovascular risk. A total of 3699 patients (mean age, 61 ± 11 years) were prospectively enrolled in the study. Blood pressure (BP) was measured simultaneously in both arms 3 times using an automated cuff-oscillometric device. IAD was defined as the absolute difference in averaged BPs between the left and right arm, and an IAD ≥ 10 mm Hg was considered to be significant. The Framingham risk score was used to calculate the 10-year cardiovascular risk. The mean systolic IAD (sIAD) was 4.3 ± 4.1 mm Hg, and 285 (7.7%) patients showed significant sIAD. Patients with significant sIAD showed larger body mass index (P < 0.001), greater systolic BP (P = 0.050), more coronary artery disease (relative risk = 1.356, P = 0.034), and more cerebrovascular disease (relative risk = 1.521, P = 0.072). The mean 10-year cardiovascular risk was 9.3 ± 7.7%. By multiple regression, sIAD was significantly but weakly correlated with the 10-year cardiovascular risk (β = 0.135, P = 0.008). Patients with significant sIAD showed a higher prevalence of coronary artery disease, as well as an increase in 10-year cardiovascular risk. Therefore, accurate measurements of sIAD may serve as a simple and cost-effective tool for predicting cardiovascular risk in clinical settings. PMID:27310982

  9. Bilirubin nomogram for prediction of significant hyperbilirubinemia in north Indian neonates.

    Science.gov (United States)

    Pathak, Umesh; Chawla, Deepak; Kaur, Saranjit; Jain, Suksham

    2013-04-01

    (i) To construct hour-specific serum total bilirubin (STB) nomogram in neonates born at =35 weeks of gestation; (ii)To evaluate efficacy of pre-discharge bilirubin measurement in predicting hyperbilirubinemia needing treatment. Diagnostic test performance in a prospective cohort study. Teaching hospital in Northern India. Healthy neonates with gestation =35 weeks or birth weight =2000 g. Serum total bilirubin was measured in all enrolled neonates at 24 ± 6, 72-96 and 96-144 h of postnatal age and when indicated clinically. Neonates were followed up during hospital stay and after discharge till completion of 7th postnatal day. Key outcome was significant hyperbilirubinemia (SHB) defined as need of phototherapy based on modified American Academy of Pediatrics (AAP) guidelines. In neonates born at 38 or more weeks of gestation middle line and in neonates born at 37 or less completed weeks of gestation, lower line of phototherapy thresholds were used to initiate phototherapy. For construction of nomogram, STB values were clubbed in six-hour epochs (age ± 3 hours) for postnatal age up to 48 h and twelve-hour epochs (age ± 6 hours) for age beyond 48 h. Predictive ability of the nomogram was assessed by calculating sensitivity, specificity, positive predictive value, negative predictive value and likelihood ratio, by plotting receiver-operating characteristics (ROC) curve and calculating c-statistic. 997 neonates (birth weight: 2627 ± 536 g, gestation: 37.8 ± 1.5 weeks) were enrolled, of which 931 completed followup. Among enrolled neonates 344 (34.5%) were low birth weight. Rate of exclusive breastfeeding during hospital stay was more than 80%. Bilirubin nomogram was constructed using 40th, 75th and 95th percentile values of hour-specific bilirubin. Pre-discharge STB of =95th percentile was assigned to be in high-risk zone, between 75th and 94th centile in upper-intermediate risk zone, between 40th and 74th centile in lower-intermediate risk zone and below 40th

  10. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions.

    Directory of Open Access Journals (Sweden)

    Tomislav Hengl

    Full Text Available 80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na. We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring

  11. The significance of collateral vessels, as seen on chest CT, in predicting SVC obstruction

    International Nuclear Information System (INIS)

    Yeouk, Young Soo; Kim, Sung Jin; Bae, Il Hun; Kim, Jae Youn; Hwang, Seung Min; Han, Gi Seok; Park, Kil Sun; Kim, Dae Young

    1998-01-01

    To evaluate the significance of collateral veins, as seen on chest CT, in the diagnosis of superior vena cava obstruction. We retrospectively the records of 81 patients in whom collateral veins were seen on chest CT. On spiral CT(n=49), contrast material was infused via power injector, and on conventional CT(n=32), 50 ml bolus infusion was followed by 50 ml drip infusion. Obstruction of the SVC was evaluated on chest CT; if, however, evaluation of the SVC of its major tributaries was difficult, as in five cases, the patient underwent SVC phlebography. Collateral vessels were assigned to one of ten categories. On conventional CT, the jugular venous arch in the only collateral vessel to predict SVC obstruction; on spiral CT, however, collateral vessels are not helpful in the diagnosis of SVC obstruction, but are a nonspecific finding. (author). 12 refs., 2 tab., 2 figs

  12. The prognostic significance of UCA1 for predicting clinical outcome in patients with digestive system malignancies.

    Science.gov (United States)

    Liu, Fang-Teng; Dong, Qing; Gao, Hui; Zhu, Zheng-Ming

    2017-06-20

    Urothelial Carcinoma Associated 1 (UCA1) was an originally identified lncRNA in bladder cancer. Previous studies have reported that UCA1 played a significant role in various types of cancer. This study aimed to clarify the prognostic value of UCA1 in digestive system cancers. The meta-analysis of 15 studies were included, comprising 1441 patients with digestive system cancers. The pooled results of 14 studies indicated that high expression of UCA1 was significantly associated with poorer OS in patients with digestive system cancers (HR: 1.89, 95 % CI: 1.52-2.26). In addition, UCA1 could be as an independent prognostic factor for predicting OS of patients (HR: 1.85, 95 % CI: 1.45-2.25). The pooled results of 3 studies indicated a significant association between UCA1 and DFS in patients with digestive system cancers (HR = 2.50; 95 % CI = 1.30-3.69). Statistical significance was also observed in subgroup meta-analysis. Furthermore, the clinicopathological values of UCA1 were discussed in esophageal cancer, colorectal cancer and pancreatic cancer. A comprehensive retrieval was performed to search studies evaluating the prognostic value of UCA1 in digestive system cancers. Many databases were involved, including PubMed, Web of Science, Embase and Chinese National Knowledge Infrastructure and Wanfang database. Quantitative meta-analysis was performed with standard statistical methods and the prognostic significance of UCA1 in digestive system cancers was qualified. Elevated level of UCA1 indicated the poor clinical outcome for patients with digestive system cancers. It may serve as a new biomarker related to prognosis in digestive system cancers.

  13. Do Assault-Related Variables Predict Response to Cognitive Behavioral Treatment for PTSD?

    Science.gov (United States)

    Hembree, Elizabeth A.; Street, Gordon P.; Riggs, David S.; Foa, Edna B.

    2004-01-01

    This study examined the hypothesis that variables such as history of prior trauma, assault severity, and type of assault, previously found to be associated with natural recovery, would also predict treatment outcome. Trauma-related variables were examined as predictors of posttreatment posttraumatic stress disorder (PTSD) severity in a sample of…

  14. Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

    Science.gov (United States)

    Akbulut, Yavuz

    2007-01-01

    Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

  15. Intraindividual variability in reaction time predicts cognitive outcomes 5 years later.

    Science.gov (United States)

    Bielak, Allison A M; Hultsch, David F; Strauss, Esther; Macdonald, Stuart W S; Hunter, Michael A

    2010-11-01

    Building on results suggesting that intraindividual variability in reaction time (inconsistency) is highly sensitive to even subtle changes in cognitive ability, this study addressed the capacity of inconsistency to predict change in cognitive status (i.e., cognitive impairment, no dementia [CIND] classification) and attrition 5 years later. Two hundred twelve community-dwelling older adults, initially aged 64-92 years, remained in the study after 5 years. Inconsistency was calculated from baseline reaction time performance. Participants were assigned to groups on the basis of their fluctuations in CIND classification over time. Logistic and Cox regressions were used. Baseline inconsistency significantly distinguished among those who remained or transitioned into CIND over the 5 years and those who were consistently intact (e.g., stable intact vs. stable CIND, Wald (1) = 7.91, p < .01, Exp(β) = 1.49). Average level of inconsistency over time was also predictive of study attrition, for example, Wald (1) = 11.31, p < .01, Exp(β) = 1.24. For both outcomes, greater inconsistency was associated with a greater likelihood of being in a maladaptive group 5 years later. Variability based on moderately cognitively challenging tasks appeared to be particularly sensitive to longitudinal changes in cognitive ability. Mean rate of responding was a comparable predictor of change in most instances, but individuals were at greater relative risk of being in a maladaptive outcome group if they were more inconsistent rather than if they were slower in responding. Implications for the potential utility of intraindividual variability in reaction time as an early marker of cognitive decline are discussed. (c) 2010 APA, all rights reserved

  16. Using lexical variables to predict picture-naming errors in jargon aphasia

    Directory of Open Access Journals (Sweden)

    Catherine Godbold

    2015-04-01

    Full Text Available Introduction Individuals with jargon aphasia produce fluent output which often comprises high proportions of non-word errors (e.g., maf for dog. Research has been devoted to identifying the underlying mechanisms behind such output. Some accounts posit a reduced flow of spreading activation between levels in the lexical network (e.g., Robson et al., 2003. If activation level differences across the lexical network are a cause of non-word outputs, we would predict improved performance when target items reflect an increased flow of activation between levels (e.g. more frequently-used words are often represented by higher resting levels of activation. This research investigates the effect of lexical properties of targets (e.g., frequency, imageability on accuracy, error type (real word vs. non-word and target-error overlap of non-word errors in a picture naming task by individuals with jargon aphasia. Method Participants were 17 individuals with Wernicke’s aphasia, who produced a high proportion of non-word errors (>20% of errors on the Philadelphia Naming Test (PNT; Roach et al., 1996. The data were retrieved from the Moss Aphasic Psycholinguistic Database Project (MAPPD, Mirman et al., 2010. We used a series of mixed models to test whether lexical variables predicted accuracy, error type (real word vs. non-word and target-error overlap for the PNT data. As lexical variables tend to be highly correlated, we performed a principal components analysis to reduce the variables into five components representing variables associated with phonology (length, phonotactic probability, neighbourhood density and neighbourhood frequency, semantics (imageability and concreteness, usage (frequency and age-of-acquisition, name agreement and visual complexity. Results and Discussion Table 1 shows the components that made a significant contribution to each model. Individuals with jargon aphasia produced more correct responses and fewer non-word errors relative to

  17. Predicting Eight Grade Students' Equation Solving Performances via Concepts of Variable and Equality

    Science.gov (United States)

    Ertekin, Erhan

    2017-01-01

    This study focused on how two algebraic concepts- equality and variable- predicted 8th grade students' equation solving performance. In this study, predictive design as a correlational research design was used. Randomly selected 407 eight-grade students who were from the central districts of a city in the central region of Turkey participated in…

  18. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables.

    Science.gov (United States)

    Roelen, Corné; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bültmann, Ute; Bjørner, Jakob

    2018-01-01

    The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for

  19. Collaborative Research: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic

    Energy Technology Data Exchange (ETDEWEB)

    Gutowski, William J. [Iowa State Univ., Ames, IA (United States)

    2017-12-28

    This project developed and applied a regional Arctic System model for enhanced decadal predictions. It built on successful research by four of the current PIs with support from the DOE Climate Change Prediction Program, which has resulted in the development of a fully coupled Regional Arctic Climate Model (RACM) consisting of atmosphere, land-hydrology, ocean and sea ice components. An expanded RACM, a Regional Arctic System Model (RASM), has been set up to include ice sheets, ice caps, mountain glaciers, and dynamic vegetation to allow investigation of coupled physical processes responsible for decadal-scale climate change and variability in the Arctic. RASM can have high spatial resolution (~4-20 times higher than currently practical in global models) to advance modeling of critical processes and determine the need for their explicit representation in Global Earth System Models (GESMs). The pan-Arctic region is a key indicator of the state of global climate through polar amplification. However, a system-level understanding of critical arctic processes and feedbacks needs further development. Rapid climate change has occurred in a number of Arctic System components during the past few decades, including retreat of the perennial sea ice cover, increased surface melting of the Greenland ice sheet, acceleration and thinning of outlet glaciers, reduced snow cover, thawing permafrost, and shifts in vegetation. Such changes could have significant ramifications for global sea level, the ocean thermohaline circulation and heat budget, ecosystems, native communities, natural resource exploration, and commercial transportation. The overarching goal of the RASM project has been to advance understanding of past and present states of arctic climate and to improve seasonal to decadal predictions. To do this the project has focused on variability and long-term change of energy and freshwater flows through the arctic climate system. The three foci of this research are: - Changes

  20. Correction of the significance level when attempting multiple transformations of an explanatory variable in generalized linear models

    Science.gov (United States)

    2013-01-01

    Background In statistical modeling, finding the most favorable coding for an exploratory quantitative variable involves many tests. This process involves multiple testing problems and requires the correction of the significance level. Methods For each coding, a test on the nullity of the coefficient associated with the new coded variable is computed. The selected coding corresponds to that associated with the largest statistical test (or equivalently the smallest pvalue). In the context of the Generalized Linear Model, Liquet and Commenges (Stat Probability Lett,71:33–38,2005) proposed an asymptotic correction of the significance level. This procedure, based on the score test, has been developed for dichotomous and Box-Cox transformations. In this paper, we suggest the use of resampling methods to estimate the significance level for categorical transformations with more than two levels and, by definition those that involve more than one parameter in the model. The categorical transformation is a more flexible way to explore the unknown shape of the effect between an explanatory and a dependent variable. Results The simulations we ran in this study showed good performances of the proposed methods. These methods were illustrated using the data from a study of the relationship between cholesterol and dementia. Conclusion The algorithms were implemented using R, and the associated CPMCGLM R package is available on the CRAN. PMID:23758852

  1. Significance of SYT8 For the Detection, Prediction, and Treatment of Peritoneal Metastasis From Gastric Cancer.

    Science.gov (United States)

    Kanda, Mitsuro; Shimizu, Dai; Tanaka, Haruyoshi; Tanaka, Chie; Kobayashi, Daisuke; Hayashi, Masamichi; Iwata, Naoki; Niwa, Yukiko; Yamada, Suguru; Fujii, Tsutomu; Sugimoto, Hiroyuki; Murotani, Kenta; Fujiwara, Michitaka; Kodera, Yasuhiro

    2018-03-01

    To develop novel diagnostic and therapeutic targets specific for peritoneal metastasis of gastric cancer (GC). Advanced GC frequently recurs because of undetected micrometastases even after curative resection. Peritoneal metastasis has been the most frequent recurrent pattern after gastrectomy and is incurable. We conducted a recurrence pattern-specific transcriptome analysis in an independent cohort of 16 patients with stage III GC who underwent curative gastrectomy and adjuvant S-1 for screening candidate molecules specific for peritoneal metastasis of GC. Next, another 340 patients were allocated to discovery and validation sets (1:2) to evaluate the diagnostic and predictive value of the candidate molecule. The results of quantitative reverse-transcription PCR and immunohistochemical analysis were correlated with clinical characteristics and survival. The effects of siRNA-mediated knockdown on phenotype and fluorouracil sensitivity of GC cells were evaluated in vitro, and the therapeutic effects of siRNAs were evaluated using a mouse xenograft model. Synaptotagmin VIII (SYT8) was identified as a candidate biomarker specific to peritoneal metastasis. In the discovery set, the optimal cut-off of SYT8 expression was established as 0.005. Expression levels of SYT8 mRNA in GC tissues were elevated in the validation set comprising patients with peritoneal recurrence or metastasis. SYT8 levels above the cut-off value were significantly and specifically associated with peritoneal metastasis, and served as an independent prognostic marker for peritoneal recurrence-free survival of patients with stage II/III GC. The survival difference between patients with SYT8 levels above and below the cut-off was associated with patients who received adjuvant chemotherapy. Inhibition of SYT8 expression by GC cells correlated with decreased invasion, migration, and fluorouracil resistance. Intraperitoneal administration of SYT8-siRNA inhibited the growth of peritoneal nodules and

  2. Predicting Intentions of a Familiar Significant Other Beyond the Mirror Neuron System

    Directory of Open Access Journals (Sweden)

    Stephanie Cacioppo

    2017-08-01

    Full Text Available Inferring intentions of others is one of the most intriguing issues in interpersonal interaction. Theories of embodied cognition and simulation suggest that this mechanism takes place through a direct and automatic matching process that occurs between an observed action and past actions. This process occurs via the reactivation of past self-related sensorimotor experiences within the inferior frontoparietal network (including the mirror neuron system, MNS. The working model is that the anticipatory representations of others' behaviors require internal predictive models of actions formed from pre-established, shared representations between the observer and the actor. This model suggests that observers should be better at predicting intentions performed by a familiar actor, rather than a stranger. However, little is known about the modulations of the intention brain network as a function of the familiarity between the observer and the actor. Here, we combined functional magnetic resonance imaging (fMRI with a behavioral intention inference task, in which participants were asked to predict intentions from three types of actors: A familiar actor (their significant other, themselves (another familiar actor, and a non-familiar actor (a stranger. Our results showed that the participants were better at inferring intentions performed by familiar actors than non-familiar actors and that this better performance was associated with greater activation within and beyond the inferior frontoparietal network i.e., in brain areas related to familiarity (e.g., precuneus. In addition, and in line with Hebbian principles of neural modulations, the more the participants reported being cognitively close to their partner, the less the brain areas associated with action self-other comparison (e.g., inferior parietal lobule, attention (e.g., superior parietal lobule, recollection (hippocampus, and pair bond (ventral tegmental area, VTA were recruited, suggesting that the

  3. Fuel temperature prediction using a variable bypass gap size in the prismatic VHTR

    International Nuclear Information System (INIS)

    Lee, Sung Nam; Tak, Nam-il; Kim, Min Hwan

    2016-01-01

    Highlights: • The bypass flow of the prismatic very high temperature reactor is analyzed. • The bypass gap sizes are calculated considering the effect of the neutron fluences and thermal expansion. • The fuel hot spot temperature and temperature profiles are calculated using the variable gap size. • The BOC, MOC and EOC condition at the cycle 07 and 14 are applied. - Abstract: The temperature gradient and hot spot temperatures were calculated in the prismatic very high temperature reactor as a function of the variable bypass gap size. Many previous studies have predicted the temperature of the reactor core based on a fixed bypass gap size. The graphite matrix of the assemblies in the reactor core undergoes a dimensional change during the operation due to thermal expansion and neutron fluence. The expansion and shrinkage of the bypass gaps change the coolant flow fractions into the coolant channels, the control rod holes, and the bypass gaps. Therefore, the temperature of the assemblies may differ compared to those for the fixed bypass gap case. The temperature gradient and the hot spot temperatures are important for the design of reactor structures to ensure their safety and efficiency. In the present study, the temperature variation of the PMR200 is studied at the beginning (BOC), middle (MOC), and end (EOC) of cycles 07 and 14. CORONA code which has been developed in KAERI is applied to solve the thermal-hydraulics of the reactor core of the PMR200. CORONA solves a fluid region using a one-dimensional formulation and a solid region using a three-dimensional formulation to enhance the computational speed and still obtain a reasonable accuracy. The maximum temperatures in the fuel assemblies using the variable bypass gaps did not differ much from the corresponding temperatures using the fixed bypass gaps. However, the maximum temperatures in the reflector assemblies using the variable bypass gaps differ significantly from the corresponding temperatures

  4. Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2012-01-01

    Robust model predictive control (RMPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Because...... of the special structure of the problem, uncertainty is only in the B matrix (gain) of the state space model. Therefore by taking advantage of this structure, we formulate a tractable minimax optimization problem to solve robust model predictive control problem. Wind turbine is chosen as the case study and we...... choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

  5. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables

    DEFF Research Database (Denmark)

    Roelen, Corné; Thorsen, Sannie; Heymans, Martijn

    2018-01-01

    LTSA during follow-up. Results: The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC...... population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between...... employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for new variables, based on the knowledge and experience...

  6. Spatially resolved flux measurements of NOx from London suggest significantly higher emissions than predicted by inventories.

    Science.gov (United States)

    Vaughan, Adam R; Lee, James D; Misztal, Pawel K; Metzger, Stefan; Shaw, Marvin D; Lewis, Alastair C; Purvis, Ruth M; Carslaw, David C; Goldstein, Allen H; Hewitt, C Nicholas; Davison, Brian; Beevers, Sean D; Karl, Thomas G

    2016-07-18

    To date, direct validation of city-wide emissions inventories for air pollutants has been difficult or impossible. However, recent technological innovations now allow direct measurement of pollutant fluxes from cities, for comparison with emissions inventories, which are themselves commonly used for prediction of current and future air quality and to help guide abatement strategies. Fluxes of NOx were measured using the eddy-covariance technique from an aircraft flying at low altitude over London. The highest fluxes were observed over central London, with lower fluxes measured in suburban areas. A footprint model was used to estimate the spatial area from which the measured emissions occurred. This allowed comparison of the flux measurements to the UK's National Atmospheric Emissions Inventory (NAEI) for NOx, with scaling factors used to account for the actual time of day, day of week and month of year of the measurement. The comparison suggests significant underestimation of NOx emissions in London by the NAEI, mainly due to its under-representation of real world road traffic emissions. A comparison was also carried out with an enhanced version of the inventory using real world driving emission factors and road measurement data taken from the London Atmospheric Emissions Inventory (LAEI). The measurement to inventory agreement was substantially improved using the enhanced version, showing the importance of fully accounting for road traffic, which is the dominant NOx emission source in London. In central London there was still an underestimation by the inventory of 30-40% compared with flux measurements, suggesting significant improvements are still required in the NOx emissions inventory.

  7. Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

    Science.gov (United States)

    Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

    2017-10-27

    Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

  8. Predicting Community College Outcomes: Does High School CTE Participation Have a Significant Effect?

    Science.gov (United States)

    Dietrich, Cecile; Lichtenberger, Eric; Kamalludeen, Rosemaliza

    2016-01-01

    This study explored the relative importance of participation in high school career and technical education (CTE) programs in predicting community college outcomes. A hierarchical generalized linear model (HGLM) was used to predict community college outcome attainment among a random sample of direct community college entrants. Results show that…

  9. Significant change of predictions related to the future of nuclear power

    International Nuclear Information System (INIS)

    Dumitrache, Ion

    2002-01-01

    During the last two decades of the 20th century, nuclear power contribution increased slowly in the world. This trend was mainly determined by the commissioning of new nuclear power plants, NPP, in the non-developed countries, except for Japan and South Korea. Almost all the forecasts offered the image of the stagnant nuclear power business. Sweden, Germany, Holland and Belgium Governments made clear the intention to stop the production of electricity based on fission. Recently, despite the negative effects on nuclear power of the terrorism events of September 11, 2001, the predictions related to the nuclear power future become much more optimistic. USA, Japan, South Korea and Canada made clear that new NPPs will offer their significant electricity contribution several decades, even after years 2020-2030. Moreover, several old NPP from USA obtained the license for an additional 20 years period of operation. The analysis indicated that most of the existing NPP in USA may increase the level of the maximum global power defined by the initial design. In the European Union the situation is much more complicated. About 35% of the electricity is based now on fission. Several countries, like Sweden and Germany, maintain the position of phasing out the NPPs, as soon as the licensed life-time is over. Finland decided to build a new power plant. France is very favorable to nuclear power, but does not need more energy. In the UK several very old NPP will be shut down, and companies like BNFL and British Energy intend to build new NPP, based on Westinghouse or AECL-Canada advanced reactors. Switzerland and Spain are favorable to the future use of nuclear power. In the eastern part of Europe, almost all the countries intend to base their electricity production on coal, fission, hydro and gas, nuclear contribution being significant. The most impressive increases of nuclear power output are related to Asia; in China, from 2.2 Gwe in 1999, to 18.7 Gwe in 2020, reference case, or 10

  10. Are Macro variables good predictors? A prediction based on the number of total medals acquired

    Directory of Open Access Journals (Sweden)

    Shahram Shafiee

    2012-01-01

    Full Text Available A large amount of effort is spent on forecasting the outcome of sporting events. Moreover, there are large quantities of data regarding the outcomes of sporting events and the factors which are assumed to contribute to those outcomes. In this paper we tried to predict the success of nations at the Asian Games through macro-economic, political, social and cultural variables. we used the information of variables include urban population, Education Expenditures, Age Structure, GDP Real Growth Rate, GDP Per Capita, Unemployment Rate, Population, Inflation Average, current account balance, life expectancy at birth and Merchandise Trade for all of the participating countries in Asian Games from 1970 to 2006 in order to build the model and then this model was tested by the information of variables in 2010. The prediction is based on the number of total medals acquired each country. In this research we used WEKA software that is a popular suite of machine learning software written in Java. The value of correlation coefficient between the predicted and original ranks is 90.42%. Neural Network Model, between 28 countries mentioned, predicts their ranks according to the maximum difference between predicted and original ranks of 19 countries (67.85% is 3, the maximum difference between predicted and original ranks of 8 countries (28.57% is between 4 to 6 and the difference between predicted and original ranks of 1 countries (3.57% is more than 6.

  11. Fatigue life prediction of rotor blade composites: Validation of constant amplitude formulations with variable amplitude experiments

    International Nuclear Information System (INIS)

    Westphal, T; Nijssen, R P L

    2014-01-01

    The effect of Constant Life Diagram (CLD) formulation on the fatigue life prediction under variable amplitude (VA) loading was investigated based on variable amplitude tests using three different load spectra representative for wind turbine loading. Next to the Wisper and WisperX spectra, the recently developed NewWisper2 spectrum was used. Based on these variable amplitude fatigue results the prediction accuracy of 4 CLD formulations is investigated. In the study a piecewise linear CLD based on the S-N curves for 9 load ratios compares favourably in terms of prediction accuracy and conservativeness. For the specific laminate used in this study Boerstra's Multislope model provides a good alternative at reduced test effort

  12. Fatigue life prediction of rotor blade composites: Validation of constant amplitude formulations with variable amplitude experiments

    Science.gov (United States)

    Westphal, T.; Nijssen, R. P. L.

    2014-12-01

    The effect of Constant Life Diagram (CLD) formulation on the fatigue life prediction under variable amplitude (VA) loading was investigated based on variable amplitude tests using three different load spectra representative for wind turbine loading. Next to the Wisper and WisperX spectra, the recently developed NewWisper2 spectrum was used. Based on these variable amplitude fatigue results the prediction accuracy of 4 CLD formulations is investigated. In the study a piecewise linear CLD based on the S-N curves for 9 load ratios compares favourably in terms of prediction accuracy and conservativeness. For the specific laminate used in this study Boerstra's Multislope model provides a good alternative at reduced test effort.

  13. Optimal no-go theorem on hidden-variable predictions of effect expectations

    Science.gov (United States)

    Blass, Andreas; Gurevich, Yuri

    2018-03-01

    No-go theorems prove that, under reasonable assumptions, classical hidden-variable theories cannot reproduce the predictions of quantum mechanics. Traditional no-go theorems proved that hidden-variable theories cannot predict correctly the values of observables. Recent expectation no-go theorems prove that hidden-variable theories cannot predict the expectations of observables. We prove the strongest expectation-focused no-go theorem to date. It is optimal in the sense that the natural weakenings of the assumptions and the natural strengthenings of the conclusion make the theorem fail. The literature on expectation no-go theorems strongly suggests that the expectation-focused approach is more general than the value-focused one. We establish that the expectation approach is not more general.

  14. The prognostic significance of HOTAIR for predicting clinical outcome in patients with digestive system tumors.

    Science.gov (United States)

    Ma, Gaoxiang; Wang, Qiaoyan; Lv, Chunye; Qiang, Fulin; Hua, Qiuhan; Chu, Haiyan; Du, Mulong; Tong, Na; Jiang, Yejuan; Wang, Meilin; Zhang, Zhengdong; Wang, Jian; Gong, Weida

    2015-12-01

    Although some studies have assessed the prognostic value of HOTAIR in patients with digestive system tumors, the relationship between the HOTAIR and outcome of digestive system tumors remains unknown. The PubMed was searched to identify the eligible studies. Here, we performed a meta-analysis with 11 studies, including a total of 903 cases. Pooled hazard ratios (HRs) and 95 % confidence interval (CI) of HOTAIR for cancer survival were calculated. We found that the pooled HR elevated HOTAIR expression in tumor tissues was 2.36 (95 % CI 1.88-2.97) compared with patients with low HOTAIR expression. Moreover, subgroup analysis revealed that HOTAIR overexpression was also markedly associated with short survival for esophageal squamous cell carcinoma (HR 2.19, 95 % CI 1.62-2.94) and gastric cancer (HR 1.66, 95 % CI 1.02-2.68). In addition, up-regulated HOTAIR was significantly related to survival of digestive system cancer among the studies with more follow-up time (follow time ≥ 5 years) (HR 2.51, 95 % CI 1.99-3.17). When stratified by HR resource and number of patients, the result indicated consistent results with the overall analysis. Subgroup analysis on ethnicities did not change the prognostic influence of elevated HOTAIR expression. Additionally, we conducted an independent validation cohort including 71 gastric cancer cases, in which patients with up-regulated HOTAIR expression had an unfavorable outcome with HR of 2.10 (95 % CI 1.10-4.03). The results suggest that aberrant HOTAIR expression may serve as a candidate positive marker to predict the prognosis of patients with carcinoma of digestive system.

  15. Faculty Decisions on Serials Subscriptions Differ Significantly from Decisions Predicted by a Bibliometric Tool.

    Directory of Open Access Journals (Sweden)

    Sue F. Phelps

    2016-03-01

    Full Text Available Objective – To compare faculty choices of serials subscription cancellations to the scores of a bibliometric tool. Design – Natural experiment. Data was collected about faculty valuations of serials. The California Digital Library Weighted Value Algorithm (CDL-WVA was used to measure the value of journals to a particular library. These two sets of scores were then compared. Setting – A public research university in the United States of America. Subjects – Teaching and research faculty, as well as serials data. Methods – Experimental methodology was used to compare faculty valuations of serials (based on their journal cancellation choices to bibliometric valuations of the same journal titles (determined by CDL-WVA scores to identify the match rate between the faculty choices and the bibliographic data. Faculty were asked to select titles to cancel that totaled approximately 30% of the budget for their disciplinary fund code. This “keep” or “cancel” choice was the binary variable for the study. Usage data was gathered for articles downloaded through the link resolver for titles in each disciplinary dataset, and the CDL-WVA scores were determined for each journal title based on utility, quality, and cost effectiveness. Titles within each dataset were ranked highest to lowest using the CDL-WVA scores within each fund code, and then by subscription cost for titles with the same CDL-WVA score. The journal titles selected for comparison were those that ranked above the approximate 30% of titles chosen for cancellation by faculty and CDL-WVA scores. Researchers estimated an odds ratio of faculty choosing to keep a title and a CDL-WVA score that indicated the title should be kept. The p-value for that result was less than 0.0001, indicating that there was a negligible probability that the results were by chance. They also applied logistic regression to quantify the association between the numeric score of CDL-WVA and the binary variable

  16. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    The understanding and mitigation of downhole vibration has been a heavily researched subject in the oil industry as it results in more expensive drilling operations, as vibrations significantly diminish the amount of effective drilling energy available to the bit and generate forces that can push the bit or the Bottom Hole Assembly (BHA) off its concentric axis of rotation, producing high magnitude impacts with the borehole wall. In order to drill ahead, a sufficient amount of energy must be supplied by the rig to overcome the resistance of the drilling system, including the reactive torque of the system, drag forces, fluid pressure losses and energy dissipated by downhole vibrations, then providing the bit with the energy required to fail the rock. If the drill string enters resonant modes of vibration, not only does it decreases the amount of available energy to drill, but increases the potential for catastrophic downhole equipment and drilling bit failures. In this sense, the mitigation of downhole vibrations will result in faster, smoother, and cheaper drilling operations. A software tool using Finite Element Analysis (FEA) has been developed to provide better understanding of downhole vibration phenomena in drilling environments. The software tool calculates the response of the drilling system at various input conditions, based on the design of the wellbore along with the geometry of the Bottom Hole Assembly (BHA) and the drill string. It identifies where undesired levels of resonant vibration will be driven by certain combinations of specific drilling parameters, and also which combinations of drilling parameters will result in lower levels of vibration, so the least shocks, the highest penetration rate and the lowest cost per foot can be achieved. With the growing performance of personal computers, complex software systems modeling the drilling vibrations using FEA has been accessible to a wider audience of field users, further complimenting with real time

  17. Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation dissipation theorem

    Science.gov (United States)

    Frank, T. D.; Patanarapeelert, K.; Beek, P. J.

    2008-05-01

    We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the explicit motor unit properties and of the dynamical features of isometric force production. A constant coefficient of variation in the asymptotic regime and a nonequilibrium fluctuation-dissipation theorem for optimal isometric force are predicted.

  18. Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation-dissipation theorem

    International Nuclear Information System (INIS)

    Frank, T.D.; Patanarapeelert, K.; Beek, P.J.

    2008-01-01

    We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the explicit motor unit properties and of the dynamical features of isometric force production. A constant coefficient of variation in the asymptotic regime and a nonequilibrium fluctuation-dissipation theorem for optimal isometric force are predicted

  19. Multi-pentad prediction of precipitation variability over Southeast Asia during boreal summer using BCC_CSM1.2

    Science.gov (United States)

    Li, Chengcheng; Ren, Hong-Li; Zhou, Fang; Li, Shuanglin; Fu, Joshua-Xiouhua; Li, Guoping

    2018-06-01

    Precipitation is highly variable in space and discontinuous in time, which makes it challenging for models to predict on subseasonal scales (10-30 days). We analyze multi-pentad predictions from the Beijing Climate Center Climate System Model version 1.2 (BCC_CSM1.2), which are based on hindcasts from 1997 to 2014. The analysis focus on the skill of the model to predict precipitation variability over Southeast Asia from May to September, as well as its connections with intraseasonal oscillation (ISO). The effective precipitation prediction length is about two pentads (10 days), during which the skill measured by anomaly correlation is greater than 0.1. In order to further evaluate the performance of the precipitation prediction, the diagnosis results of the skills of two related circulation fields show that the prediction skills for the circulation fields exceed that of precipitation. Moreover, the prediction skills tend to be higher when the amplitude of ISO is large, especially for a boreal summer intraseasonal oscillation. The skills associated with phases 2 and 5 are higher, but that of phase 3 is relatively lower. Even so, different initial phases reflect the same spatial characteristics, which shows higher skill of precipitation prediction in the northwest Pacific Ocean. Finally, filter analysis is used on the prediction skills of total and subseasonal anomalies. The results of the two anomaly sets are comparable during the first two lead pentads, but thereafter the skill of the total anomalies is significantly higher than that of the subseasonal anomalies. This paper should help advance research in subseasonal precipitation prediction.

  20. Exploring the significance of human mobility patterns in social link prediction

    KAUST Repository

    Alharbi, Basma Mohammed; Zhang, Xiangliang

    2014-01-01

    Link prediction is a fundamental task in social networks. Recently, emphasis has been placed on forecasting new social ties using user mobility patterns, e.g., investigating physical and semantic co-locations for new proximity measure. This paper

  1. Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

    Directory of Open Access Journals (Sweden)

    B. M. Brentan

    2017-01-01

    Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

  2. Prognostic Significance of Blood Pressure Variability on Beat-to-Beat Monitoring After Transient Ischemic Attack and Stroke.

    Science.gov (United States)

    Webb, Alastair J S; Mazzucco, Sara; Li, Linxin; Rothwell, Peter M

    2018-01-01

    Visit-to-visit and day-to-day blood pressure (BP) variability (BPV) predict an increased risk of cardiovascular events but only reflect 1 form of BPV. Beat-to-beat BPV can be rapidly assessed and might also be predictive. In consecutive patients within 6 weeks of transient ischemic attack or nondisabling stroke (Oxford Vascular Study), BPV (coefficient of variation) was measured beat-to-beat for 5 minutes (Finometer), day-to-day for 1 week on home monitoring (3 readings, 3× daily), and on awake ambulatory BP monitoring. BPV after 1-month standard treatment was related (Cox proportional hazards) to recurrent stroke and cardiovascular events for 2 to 5 years, adjusted for mean systolic BP. Among 520 patients, 26 had inadequate beat-to-beat recordings, and 22 patients were in atrial fibrillation. Four hundred five patients had all forms of monitoring. Beat-to-beat BPV predicted recurrent stroke and cardiovascular events independently of mean systolic BP (hazard ratio per group SD, stroke: 1.47 [1.12-1.91]; P =0.005; cardiovascular events: 1.41 [1.08-1.83]; P =0.01), including after adjustment for age and sex (stroke: 1.47 [1.12-1.92]; P =0.005) and all risk factors (1.40 [1.00-1.94]; P =0.047). Day-to-day BPV was less strongly associated with stroke (adjusted hazard ratio, 1.29 [0.97-1.71]; P =0.08) but similarly with cardiovascular events (1.41 [1.09-1.83]; P =0.009). BPV on awake ambulatory BP monitoring was nonpredictive (stroke: 0.89 [0.59-1.35]; P =0.59; cardiovascular events: 1.08 [0.77-1.52]; P =0.65). Despite a weak correlation ( r =0.119; P =0.02), beat-to-beat BPV was associated with risk of recurrent stroke independently of day-to-day BPV (1.41 [1.05-1.90]; P =0.02). Beat-to-beat BPV predicted recurrent stroke and cardiovascular events, independently of mean systolic BP and risk factors but short-term BPV on ambulatory BP monitoring did not. Beat-to-beat BPV may be a useful additional marker of cardiovascular risk. © 2017 The Authors.

  3. Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

    Science.gov (United States)

    Gengler, Sarah; Bogaert, Patrick

    2014-12-01

    First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression.

  4. Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

    International Nuclear Information System (INIS)

    Gengler, Sarah; Bogaert, Patrick

    2014-01-01

    First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression

  5. Days on radiosensitivity: individual variability and predictive tests; Radiosensibilite: variabilite individuelle et tests predictifs

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2008-07-01

    The radiosensitivity is a part of usual clinical observations. It is already included in the therapy protocols. however, some questions stay on its individual variability and on the difficulty to evaluate it. The point will be stocked on its origin and its usefulness in predictive medicine. Through examples on the use of predictive tests and ethical and legal questions that they raise, concrete cases will be presented by specialists such radio biologists, geneticists, immunologists, jurists and occupational physicians. (N.C.)

  6. Scaling prediction errors to reward variability benefits error-driven learning in humans.

    Science.gov (United States)

    Diederen, Kelly M J; Schultz, Wolfram

    2015-09-01

    Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. Copyright © 2015 the American Physiological Society.

  7. Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

    Directory of Open Access Journals (Sweden)

    Sers Christine T

    2010-12-01

    Full Text Available Abstract Background While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species. Results We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (MLH1, PMS2, EPHB4 and could confirm more than 73% of them based on evidence in the literature. Conclusions The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.

  8. Genome-wide prediction of traits with different genetic architecture through efficient variable selection.

    Science.gov (United States)

    Wimmer, Valentin; Lehermeier, Christina; Albrecht, Theresa; Auinger, Hans-Jürgen; Wang, Yu; Schön, Chris-Carolin

    2013-10-01

    In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.

  9. Heart rate variability in prediction of individual adaptation to endurance training in recreational endurance runners.

    Science.gov (United States)

    Vesterinen, V; Häkkinen, K; Hynynen, E; Mikkola, J; Hokka, L; Nummela, A

    2013-03-01

    The aim of this study was to investigate whether nocturnal heart rate variability (HRV) can be used to predict changes in endurance performance during 28 weeks of endurance training. The training was divided into 14 weeks of basic training (BTP) and 14 weeks of intensive training periods (ITP). Endurance performance characteristics, nocturnal HRV, and serum hormone concentrations were measured before and after both training periods in 28 recreational endurance runners. During the study peak treadmill running speed (Vpeak ) improved by 7.5 ± 4.5%. No changes were observed in HRV indices after BTP, but after ITP, these indices increased significantly (HFP: 1.9%, P=0.026; TP: 1.7%, P=0.007). Significant correlations were observed between the change of Vpeak and HRV indices (TP: r=0.75, PHRV among recreational endurance runners, it seems that moderate- and high-intensity training are needed. This study showed that recreational endurance runners with a high HRV at baseline improved their endurance running performance after ITP more than runners with low baseline HRV. © 2011 John Wiley & Sons A/S.

  10. The clinical significance of detection to heart rate deceleration capacity and heart rate variability in patients with chronic heart failure

    Directory of Open Access Journals (Sweden)

    Jiang-rong Zhou

    2015-01-01

    Full Text Available Objective: To study the change of heart rate deceleration capacity ( DC and heart rate variability in patients with chronic heart failure (CHF and its relationship with left ventricular ejection fraction (LVEF. Methods: DC, LVEF, time and frequency domain parameters of HRV were measured in 66 patients with CHF and 34 healthy adults (control group by using 24h Holter recordings and Echocardiography. The standard deviation of normal R-R intervals( SDNN, squares of differences between adjacent NN intervals ( RMSSD,low frequency power( LFn and high frequency power( HFn and the changes of LVEF were compared between  the two groups,the relationship between DC,LVEF and HRV were studied in patients with CHF. Results: The median value of DC in the patients with CHF was significantly lower than that in control group( 3.1 ± 2.4 ms vs 7.2 ± 1.3 ms,P <0.01.Incidence of abnormal DC in the CHF group was 57.5%,which was significantly higher than that in the control group (P <0.01.The HRV index, including SDNN、RMSSD、LFn、HFn, in the CHF group was significantly lower than that in normal control group (P < 0.01. Significant positive correlation between HRV index and LVEF were confirmed (P < 0.01. Conclusions: DC and HRV index are lower in patients with CHF and have a good correlation with the left ventricular ejection fraction.

  11. Significance of High Resolution GHRSST on prediction of Indian Summer Monsoon

    KAUST Repository

    Jangid, Buddhi Prakash

    2017-02-24

    In this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4 K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.

  12. Significant increase of Echinococcus multilocularis prevalencein foxes, but no increased predicted risk for humans

    NARCIS (Netherlands)

    Maas, M.; Dam-Deisz, W.D.C.; Roon, van A.M.; Takumi, K.; Giessen, van der J.W.B.

    2014-01-01

    The emergence of the zoonotic tapeworm Echinococcus multilocularis, causative agent ofalveolar echinococcosis (AE), poses a public health risk. A previously designed risk mapmodel predicted a spread of E. multilocularis and increasing numbers of alveolar echinococ-cosis patients in the province of

  13. The significance of parenchymal changes of acute cellular rejection in predicting chronic liver graft rejection

    NARCIS (Netherlands)

    Gouw, ASH; van den Heuvel, MC; van den Berg, AP; Slooff, NJH; de Jong, KP; Poppema, S

    2002-01-01

    Background. Chronic rejection (CR) in liver allografts shows a rapid onset and progressive course, leading to graft failure within the first year after transplantation. Most cases are preceded by episodes of acute cellular rejection (AR), but histological features predictive for the transition

  14. Significance of High Resolution GHRSST on prediction of Indian Summer Monsoon

    KAUST Repository

    Jangid, Buddhi Prakash; Kumar, Prashant; Attada, Raju; Kumar, Raj

    2017-01-01

    In this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4 K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.

  15. Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables.

    Science.gov (United States)

    Jordan, Pascal; Shedden-Mora, Meike C; Löwe, Bernd

    To obtain predictors of suicidal ideation, which can also be used for an indirect assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the Patient Health Questionnaire (PHQ) and sociodemographic variables, and to obtain an upper bound on the best possible performance of a predictor based on those variables. From a consecutive sample of 9025 primary care patients, 6805 eligible patients (60% female; mean age = 51.5 years) participated. Advanced methods of machine learning were used to derive the prediction equation. Various classifiers were applied and the area under the curve (AUC) was computed as a performance measure. Classifiers based on methods of machine learning outperformed ordinary regression methods and achieved AUCs around 0.87. The key variables in the prediction equation comprised four items - namely feelings of depression/hopelessness, low self-esteem, worrying, and severe sleep disturbances. The generalized anxiety disorder scale (GAD-7) and the somatic symptom subscale (PHQ-15) did not enhance prediction substantially. In predicting suicidal ideation researchers should refrain from using ordinary regression tools. The relevant information is primarily captured by the depression subscale and should be incorporated in a nonlinear model. For clinical practice, a classification tree using only four items of the whole PHQ may be advocated. Copyright © 2018 Elsevier Inc. All rights reserved.

  16. Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

    Science.gov (United States)

    Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

    2018-02-22

    Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

  17. Beyond the mean: the role of variability in predicting ecological effects of stream temperature on salmon

    Science.gov (United States)

    E. Ashley Steel; Abby Tillotson; Donald A. Larson; Aimee H. Fullerton; Keith P. Denton; Brian R. Beckman

    2012-01-01

    Alterations in variance of riverine thermal regimes have been observed and are predicted with climate change and human development. We tested whether changes in daily or seasonal thermal variability, aside from changes in mean temperature, could have biological consequences by exposing Chinook salmon (Oncorhynchus tshawytscha) eggs to eight...

  18. New considerations on variability of creep rupture data and life prediction

    International Nuclear Information System (INIS)

    Kim, Seon Jin; Jeong, Won Taek; Kong, Yu Sik

    2009-01-01

    This paper deals with the variability analysis of short term creep rupture test data based on the previous creep rupture tests and the possibility of the creep life prediction. From creep tests performed by constant uniaxial stresses at 600, 650 and 700 .deg. C elevated temperature, in order to investigate the variability of short-term creep rupture data, the creep curves were analyzed for normalized creep strain divided by initial strain. There are some variability in thee creep rupture data. And, the difference between general creep curves and normalized creep curves were obtained. The effects of the creep rupture time and state steady creep rate on the Weibull distribution parameters were investigated. There were good relation between normal Weibull parameters and normalized Weibull parameters. Finally, the predicted creep life were compared with the Monkman-Grant model.

  19. New Considerations on Variability of Creep Rupture Data and Life Prediction

    International Nuclear Information System (INIS)

    Jung, Won Taek; Kong, Yu Sik; Kim, Seon Jin

    2009-01-01

    This paper deals with the variability analysis of short term creep rupture test data based on the previous creep rupture tests and the possibility of the creep life prediction. From creep tests performed by constant uniaxial stresses at 600, 650 and 700 .deg. C elevated temperature, in order to investigate the variability of short-term creep rupture data, the creep curves were analyzed for normalized creep strain divided by initial strain. There are some variability in the creep rupture data. And, the difference between general creep curves and normalized creep curves were obtained. The effects of the creep rupture time (RT) and steady state creep rate (SSCR) on the Weibull distribution parameters were investigated. There were good relation between normal Weibull parameters and normalized Weibull parameters. Finally, the predicted creep life were compared with the Monkman-Grant model

  20. THE RELATIVE IMPORTANCE OF FINANCIAL RATIOS AND NONFINANCIAL VARIABLES IN PREDICTING OF INSOLVENCY

    Directory of Open Access Journals (Sweden)

    Ivica Pervan

    2013-02-01

    Full Text Available One of the most important decisions in every bank is approving loans to firms, which is based on evaluated credit risk and collateral. Namely, it is necessary to evaluate the risk that client will be unable to repay the obligations according to the contract. After Beaver's (1967 and Altman's (1968 seminal papers many authors extended the initial research by changing the methodology, samples, countries, etc. But majority of business failure papers as predictors use financial ratios, while in the real life banks combine financial and nonfinancial variables. In order to test predictive power of nonfinancial variables authors in the paper compare two insolvency prediction models. The first model that used financial rations resulted with classification accuracy of 82.8%, while the combined model with financial and nonfinancial variables resulted with classification accuracy of 88.1%.

  1. Are Simulated and Observed Twentieth Century Tropical Pacific Sea Surface Temperature Trends Significant Relative to Internal Variability?

    Science.gov (United States)

    Coats, S.; Karnauskas, K. B.

    2017-10-01

    Historical trends in the tropical Pacific zonal sea surface temperature gradient (SST gradient) are analyzed herein using 41 climate models (83 simulations) and 5 observational data sets. A linear inverse model is trained on each simulation and observational data set to assess if trends in the SST gradient are significant relative to the stationary statistics of internal variability, as would suggest an important role for external forcings such as anthropogenic greenhouse gasses. None of the 83 simulations have a positive trend in the SST gradient, a strengthening of the climatological SST gradient with more warming in the western than eastern tropical Pacific, as large as the mean trend across the five observational data sets. If the observed trends are anthropogenically forced, this discrepancy suggests that state-of-the-art climate models are not capturing the observed response of the tropical Pacific to anthropogenic forcing, with serious implications for confidence in future climate projections. There are caveats to this interpretation, however, as some climate models have a significant strengthening of the SST gradient between 1900 and 2013 Common Era, though smaller in magnitude than the observational data sets, and the strengthening in three out of five observational data sets is insignificant. When combined with observational uncertainties and the possibility of centennial time scale internal variability not sampled by the linear inverse model, this suggests that confident validation of anthropogenic SST gradient trends in climate models will require further emergence of anthropogenic trends. Regardless, the differences in SST gradient trends between climate models and observational data sets are concerning and motivate the need for process-level validation of the atmosphere-ocean dynamics relevant to climate change in the tropical Pacific.

  2. Asymptotically Constant-Risk Predictive Densities When the Distributions of Data and Target Variables Are Different

    Directory of Open Access Journals (Sweden)

    Keisuke Yano

    2014-05-01

    Full Text Available We investigate the asymptotic construction of constant-risk Bayesian predictive densities under the Kullback–Leibler risk when the distributions of data and target variables are different and have a common unknown parameter. It is known that the Kullback–Leibler risk is asymptotically equal to a trace of the product of two matrices: the inverse of the Fisher information matrix for the data and the Fisher information matrix for the target variables. We assume that the trace has a unique maximum point with respect to the parameter. We construct asymptotically constant-risk Bayesian predictive densities using a prior depending on the sample size. Further, we apply the theory to the subminimax estimator problem and the prediction based on the binary regression model.

  3. Uncertainty in wave energy resource assessment. Part 2: Variability and predictability

    International Nuclear Information System (INIS)

    Mackay, Edward B.L.; Bahaj, AbuBakr S.; Challenor, Peter G.

    2010-01-01

    The uncertainty in estimates of the energy yield from a wave energy converter (WEC) is considered. The study is presented in two articles. The first article considered the accuracy of the historic data and the second article, presented here, considers the uncertainty which arises from variability in the wave climate. Mean wave conditions exhibit high levels of interannual variability. Moreover, many previous studies have demonstrated longer-term decadal changes in wave climate. The effect of interannual and climatic changes in wave climate on the predictability of long-term mean WEC power is examined for an area off the north coast of Scotland. In this location anomalies in mean WEC power are strongly correlated with the North Atlantic Oscillation (NAO) index. This link enables the results of many previous studies on the variability of the NAO and its sensitivity to climate change to be applied to WEC power levels. It is shown that the variability in 5, 10 and 20 year mean power levels is greater than if annual power anomalies were uncorrelated noise. It is also shown that the change in wave climate from anthropogenic climate change over the life time of a wave farm is likely to be small in comparison to the natural level of variability. Finally, it is shown that despite the uncertainty related to variability in the wave climate, improvements in the accuracy of historic data will improve the accuracy of predictions of future WEC yield. (author)

  4. The presence, predictive utility, and clinical significance of body dysmorphic symptoms in women with eating disorders

    Science.gov (United States)

    2013-01-01

    Background Both eating disorders (EDs) and body dysmorphic disorder (BDD) are disorders of body image. This study aimed to assess the presence, predictive utility, and impact of clinical features commonly associated with BDD in women with EDs. Methods Participants recruited from two non-clinical cohorts of women, symptomatic and asymptomatic of EDs, completed a survey on ED (EDE-Q) and BDD (BDDE-SR) psychopathology, psychological distress (K-10), and quality of life (SF-12). Results A strong correlation was observed between the total BDDE-SR and the global EDE-Q scores (r = 0.79, p 0.05) measured appearance checking, reassurance-seeking, camouflaging, comparison-making, and social avoidance. In addition to these behaviors, inspection of sensitivity (Se) and specificity (Sp) revealed that BDDE-SR items measuring preoccupation and dissatisfaction with appearance were most predictive of ED cases (Se and Sp > 0.60). Higher total BDDE-SR scores were associated with greater distress on the K-10 and poorer quality of life on the SF-12 (all p < 0.01). Conclusions Clinical features central to the model of BDD are common in, predictive of, and associated with impairment in women with EDs. Practice implications are that these features be included in the assessment and treatment of EDs. PMID:24999401

  5. Interannual Variability, Global Teleconnection, and Potential Predictability Associated with the Asian Summer Monsoon

    Science.gov (United States)

    Lau, K. M.; Kim, K. M.; Li, J. Y.

    2001-01-01

    In this Chapter, aspects of global teleconnections associated with the interannual variability of the Asian summer monsoon (ASM) are discussed. The basic differences in the basic dynamics of the South Asian Monsoon and the East Asian monsoon, and their implications on global linkages are discussed. Two teleconnection modes linking ASM variability to summertime precipitation over the continental North America were identified. These modes link regional circulation and precipitation anomalies over East Asia and continental North America, via coupled atmosphere-ocean variations over the North Pacific. The first mode has a large zonally symmetrical component and appears to be associated with subtropical jetstream variability and the second mode with Rossby wave dispersion. Both modes possess strong sea surface temperature (SST) expressions in the North Pacific. Results show that the two teleconnection modes may have its origin in intrinsic modes of sea surface temperature variability in the extratropical oceans, which are forced in part by atmospheric variability and in part by air-sea interaction. The potential predictability of the ASM associated with SST variability in different ocean basins is explored using a new canonical ensemble correlation prediction scheme. It is found that SST anomalies in tropical Pacific, i.e., El Nino, is the most dominant forcing for the ASM, especially over the maritime continent and eastern Australia. SST anomalies in the India Ocean may trump the influence from El Nino in western Australia and western maritime continent. Both El Nino, and North Pacific SSTs contribute to monsoon precipitation anomalies over Japan, southern Korea, northern and central China. By optimizing SST variability signals from the world ocean basins using CEC, the overall predictability of ASM can be substantially improved.

  6. The seasonal predictability of blocking frequency in two seasonal prediction systems (CMCC, Met-Office) and the associated representation of low-frequency variability.

    Science.gov (United States)

    Athanasiadis, Panos; Gualdi, Silvio; Scaife, Adam A.; Bellucci, Alessio; Hermanson, Leon; MacLachlan, Craig; Arribas, Alberto; Materia, Stefano; Borelli, Andrea

    2014-05-01

    Low-frequency variability is a fundamental component of the atmospheric circulation. Extratropical teleconnections, the occurrence of blocking and the slow modulation of the jet streams and storm tracks are all different aspects of low-frequency variability. Part of the latter is attributed to the chaotic nature of the atmosphere and is inherently unpredictable. On the other hand, primarily as a response to boundary forcings, tropospheric low-frequency variability includes components that are potentially predictable. Seasonal forecasting faces the difficult task of predicting these components. Particularly referring to the extratropics, the current generation of seasonal forecasting systems seem to be approaching this target by realistically initializing most components of the climate system, using higher resolution and utilizing large ensemble sizes. Two seasonal prediction systems (Met-Office GloSea and CMCC-SPS-v1.5) are analyzed in terms of their representation of different aspects of extratropical low-frequency variability. The current operational Met-Office system achieves unprecedented high scores in predicting the winter-mean phase of the North Atlantic Oscillation (NAO, corr. 0.74 at 500 hPa) and the Pacific-N. American pattern (PNA, corr. 0.82). The CMCC system, considering its small ensemble size and course resolution, also achieves good scores (0.42 for NAO, 0.51 for PNA). Despite these positive features, both models suffer from biases in low-frequency variance, particularly in the N. Atlantic. Consequently, it is found that their intrinsic variability patterns (sectoral EOFs) differ significantly from the observed, and the known teleconnections are underrepresented. Regarding the representation of N. hemisphere blocking, after bias correction both systems exhibit a realistic climatology of blocking frequency. In this assessment, instantaneous blocking and large-scale persistent blocking events are identified using daily geopotential height fields at

  7. Significance of satellite sign and spot sign in predicting hematoma expansion in spontaneous intracerebral hemorrhage.

    Science.gov (United States)

    Yu, Zhiyuan; Zheng, Jun; Ali, Hasan; Guo, Rui; Li, Mou; Wang, Xiaoze; Ma, Lu; Li, Hao; You, Chao

    2017-11-01

    Hematoma expansion is related to poor outcome in spontaneous intracerebral hemorrhage (ICH). Recently, a non-enhanced computed tomography (CT) based finding, termed the 'satellite sign', was reported to be a novel predictor for poor outcome in spontaneous ICH. However, it is still unclear whether the presence of the satellite sign is related to hematoma expansion. Initial computed tomography angiography (CTA) was conducted within 6h after ictus. Satellite sign on non-enhanced CT and spot sign on CTA were detected by two independent reviewers. The sensitivity and specificity of both satellite sign and spot sign were calculated. Receiver-operator analysis was conducted to evaluate their predictive accuracy for hematoma expansion. This study included 153 patients. Satellite sign was detected in 58 (37.91%) patients and spot sign was detected in 38 (24.84%) patients. Among 37 patients with hematoma expansion, 22 (59.46%) had satellite sign and 23 (62.16%) had spot sign. The sensitivity and specificity of satellite sign for prediction of hematoma expansion were 59.46% and 68.97%, respectively. The sensitivity and specificity of spot sign were 62.16% and 87.07%, respectively. The area under the curve (AUC) of satellite sign was 0.642 and the AUC of spot sign was 0.746. (P=0.157) CONCLUSION: Our results suggest that the satellite sign is an independent predictor for hematoma expansion in spontaneous ICH. Although spot sign has the higher predictive accuracy, satellite sign is still an acceptable predictor for hematoma expansion when CTA is unavailable. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

    Science.gov (United States)

    Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

    2017-12-01

    In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

  9. Hemodynamic variables predict outcome of emergency thoracotomy in the pediatric trauma population.

    Science.gov (United States)

    Wyrick, Deidre L; Dassinger, Melvin S; Bozeman, Andrew P; Porter, Austin; Maxson, R Todd

    2014-09-01

    Limited data exist regarding indications for resuscitative emergency thoracotomy (ETR) in the pediatric population. We attempt to define the presenting hemodynamic parameters that predict survival for pediatric patients undergoing ETR. We reviewed all pediatric patients (age <18years), entered into the National Trauma Data Bank from 2007 to 2010, who underwent ETR within one hour of ED arrival. Mechanism of injury and hemodynamics were analyzed using Chi squared and Wilcoxon tests. 316 children (70 blunt, 240 penetrating) underwent ETR, 31% (98/316) survived to discharge. Less than 5% of patients survived when presenting SBP was ≤50mmHg or heart rate was ≤70bpm. For blunt injuries there were no survivors with a pulse ≤80bpm or SBP ≤60mmHg. When survivors were compared to nonsurvivors, blood pressure, pulse, and injury type were statistically significant when treated as independent variables and in a logistic regression model. When ETR was performed for SBP ≤50mmHg or for heart rate ≤70bpm less than 5% of patients survived. There were no survivors of blunt trauma when SBP was ≤60mmHg or pulse was ≤80bpm. This review suggests that ETR may have limited benefit in these patients. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Can illness perceptions predict lower heart rate variability following acute myocardial infarction?

    Directory of Open Access Journals (Sweden)

    Mary Princip

    2016-11-01

    Full Text Available Objective: Decreased heart rate variability (HRV has been reported to be a predictor of mortality after myocardial infarction (MI. Patients’ beliefs and perceptions concerning their illness may play a role in decreased HRV. This study investigated if illness perceptions predict HRV at three months following acute MI. Methods: 130 patients referred to a tertiary cardiology centre, were examined within 48 hours and three months following acute MI. At admission, patients’ cognitive representations of their MI were assessed using the German version of the self-rated Brief Illness Perception Questionnaire (Brief IPQ. At admission and after three months (follow-up, frequency and time domain measures of HRV were obtained from 5-min electrocardiogram (ECG recordings during stable supine resting. Results: Linear hierarchical regression showed that the Brief IPQ dimensions timeline (β coefficient = -0.29; p = .044, personal control (β = 0.47; p = .008 and illness understanding (β = 0.43; p = .014 were significant predictors of HRV, adjusted for age, gender, baseline HRV, diabetes, beta-blockers, left ventricular ejection fraction (LVEF, attendance of cardiac rehabilitation, and depressive symptoms. Conclusions: As patients’ negative perceptions of their illness are associated with lower HRV following acute MI, a brief illness perception questionnaire may help to identify patients who might benefit from a specific illness perceptions intervention.

  11. Dynamic interactions between hydrogeological and exposure parameters in daily dose prediction under uncertainty and temporal variability

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Vikas, E-mail: vikas.kumar@urv.cat [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Barros, Felipe P.J. de [Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles 90089, CA (United States); Schuhmacher, Marta [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier [Hydrogeology Group, Department of Geotechnical Engineering and Geosciences, University Politècnica de Catalunya-BarcelonaTech, Barcelona 08034 (Spain)

    2013-12-15

    Highlights: • Dynamic parametric interaction in daily dose prediction under uncertainty. • Importance of temporal dynamics associated with the dose. • Different dose experienced by different population cohorts as a function of time. • Relevance of uncertainty reduction in the input parameters shows temporal dynamism. -- Abstract: We study the time dependent interaction between hydrogeological and exposure parameters in daily dose predictions due to exposure of humans to groundwater contamination. Dose predictions are treated stochastically to account for an incomplete hydrogeological and geochemical field characterization, and an incomplete knowledge of the physiological response. We used a nested Monte Carlo framework to account for uncertainty and variability arising from both hydrogeological and exposure variables. Our interest is in the temporal dynamics of the total dose and their effects on parametric uncertainty reduction. We illustrate the approach to a HCH (lindane) pollution problem at the Ebro River, Spain. The temporal distribution of lindane in the river water can have a strong impact in the evaluation of risk. The total dose displays a non-linear effect on different population cohorts, indicating the need to account for population variability. We then expand the concept of Comparative Information Yield Curves developed earlier (see de Barros et al. [29]) to evaluate parametric uncertainty reduction under temporally variable exposure dose. Results show that the importance of parametric uncertainty reduction varies according to the temporal dynamics of the lindane plume. The approach could be used for any chemical to aid decision makers to better allocate resources towards reducing uncertainty.

  12. Variables that Predict Serve Efficacy in Elite Men's Volleyball with Different Quality of Opposition Sets.

    Science.gov (United States)

    Valhondo, Álvaro; Fernández-Echeverría, Carmen; González-Silva, Jara; Claver, Fernando; Moreno, M Perla

    2018-03-01

    The objective of this study was to determine the variables that predicted serve efficacy in elite men's volleyball, in sets with different quality of opposition. 3292 serve actions were analysed, of which 2254 were carried out in high quality of opposition sets and 1038 actions were in low quality of opposition sets, corresponding to a total of 24 matches played during the Men's European Volleyball Championships held in 2011. The independent variables considered in this study were the serve zone, serve type, serving player, serve direction, reception zone, receiving player and reception type; the dependent variable was serve efficacy and the situational variable was quality of opposition sets. The variables that acted as predictors in both high and low quality of opposition sets were the serving player, reception zone and reception type. The serve type variable only acted as a predictor in high quality of opposition sets, while the serve zone variable only acted as a predictor in low quality of opposition sets. These results may provide important guidance in men's volleyball training processes.

  13. Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

    Science.gov (United States)

    Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

    2018-01-01

    To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute

  14. Co-relation of variables as determined from panoramic radiograph and evaluating their significance in eruption of permanent mandibular third molar

    Directory of Open Access Journals (Sweden)

    Kushal Amin

    2008-01-01

    Full Text Available Purpose of the Study: Purpose of the study is to investigate whether the variables associated with the permanent mandibular third molar (PMM3 and arch dimensions could be co-related and significantly differentiated between a fully erupted and mesially impacted PMM3 among a set of Indian population. Study Design: A standardized panoramic radiograph was taken of subjects of age 21 years and above. Patients with missing tooth from mandibular arch, subjects undergoing or having history of orthodontic treatment, subjects having disto-angular, horizontal or vertical impacted PMM3 were excluded from the study. Subjects were divided into 2 groups: (1 mesially impacted PMM3 and (2 vertically erupted PMM3. Following measurements were taken from acetate paper tracing of standardized panoramic radiograph: (1 Angulation of long axis of PMM3 to permanent mandibular second molar (theta (2 Angulation of PMM 3 to base of mandible (theta 2 (3 Gonial angle (theta 3 (4 Mesio-distal width of PMM 3 (5 Retro molar space. From these measurements Ganss ratio (retro molar space /PMM3 crown width. was calculated. Results and Conclusion: Results revealed that angle theta 1, angle theta 2, retro molar space and Ganss ratio were positively co-related and highly significant variables associated with the mesially and vertically erupted teeth as measured on panoramic radiograph. Using these variables a long-term study can be carried out to predict the ultimate position of lower third molar in the arch so that if there is a probability of the tooth being impacted at a later age, a prophylactic germectomy can be performed at an early age.

  15. Both Reaction Time and Accuracy Measures of Intraindividual Variability Predict Cognitive Performance in Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Björn U. Christ

    2018-04-01

    Full Text Available Dementia researchers around the world prioritize the urgent need for sensitive measurement tools that can detect cognitive and functional change at the earliest stages of Alzheimer's disease (AD. Sensitive indicators of underlying neural pathology assist in the early detection of cognitive change and are thus important for the evaluation of early-intervention clinical trials. One method that may be particularly well-suited to help achieve this goal involves the quantification of intraindividual variability (IIV in cognitive performance. The current study aimed to directly compare two methods of estimating IIV (fluctuations in accuracy-based scores vs. those in latency-based scores to predict cognitive performance in AD. Specifically, we directly compared the relative sensitivity of reaction time (RT—and accuracy-based estimates of IIV to cognitive compromise. The novelty of the present study, however, centered on the patients we tested [a group of patients with Alzheimer's disease (AD] and the outcome measures we used (a measure of general cognitive function and a measure of episodic memory function. Hence, we compared intraindividual standard deviations (iSDs from two RT tasks and three accuracy-based memory tasks in patients with possible or probable Alzheimer's dementia (n = 23 and matched healthy controls (n = 25. The main analyses modeled the relative contributions of RT vs. accuracy-based measures of IIV toward the prediction of performance on measures of (a overall cognitive functioning, and (b episodic memory functioning. Results indicated that RT-based IIV measures are superior predictors of neurocognitive impairment (as indexed by overall cognitive and memory performance than accuracy-based IIV measures, even after adjusting for the timescale of measurement. However, one accuracy-based IIV measure (derived from a recognition memory test also differentiated patients with AD from controls, and significantly predicted episodic memory

  16. Unprecedented drought over tropical South America in 2016: significantly under-predicted by tropical SST.

    Science.gov (United States)

    Erfanian, Amir; Wang, Guiling; Fomenko, Lori

    2017-07-19

    Tropical and sub-tropical South America are highly susceptible to extreme droughts. Recent events include two droughts (2005 and 2010) exceeding the 100-year return value in the Amazon and recurrent extreme droughts in the Nordeste region, with profound eco-hydrological and socioeconomic impacts. In 2015-2016, both regions were hit by another drought. Here, we show that the severity of the 2015-2016 drought ("2016 drought" hereafter) is unprecedented based on multiple precipitation products (since 1900), satellite-derived data on terrestrial water storage (since 2002) and two vegetation indices (since 2004). The ecohydrological consequences from the 2016 drought are more severe and extensive than the 2005 and 2010 droughts. Empirical relationships between rainfall and sea surface temperatures (SSTs) over the tropical Pacific and Atlantic are used to assess the role of tropical oceanic variability in the observed precipitation anomalies. Our results indicate that warmer-than-usual SSTs in the Tropical Pacific (including El Niño events) and Atlantic were the main drivers of extreme droughts in South America, but are unable to explain the severity of the 2016 observed rainfall deficits for a substantial portion of the Amazonia and Nordeste regions. This strongly suggests potential contribution of non-oceanic factors (e.g., land cover change and CO2-induced warming) to the 2016 drought.

  17. Significance of collateral vessels on the prediction of superior vena cava syndrome on CT

    International Nuclear Information System (INIS)

    Kim, Hyun Sook; Kim, Hyung Jin; Lee, Hyeng Gon; Ahn, In Oak; Chung, Sung Hoon

    1993-01-01

    Although visible collateral vessels on computed tomography (CT) has been considered as an important finding in superior vena cava (SVC) syndrome, there is no systematical analysis concerning correlation between the CT evidence of collateral vessels and clinical evidence of SVC syndrome. The purpose of this study is to evaluate how accurately we predict the clinical presence of SVC syndrome by the collateral vessels in patients with apparent SVC obstruction in CT. Forty seven patients having a CT evidence of obstruction or compression of SVC and/or its major tributaries were included in this study. Lung cancer was the most common underlying disease (n=40). The enhanced CT scans were obtained through either arm vein using a combined bolus and drip-infusion technique. Analyzing the CT scans, we particularly paid attention to the site and pattern of venous compromise, presence of collateral vessels, and if present, their location, without knowing whether symptoms and sign were present or nor, and then compared them with clinical data by a thorough review of charts, To verify the frequency of visible collateral vessels in normal subjects, we also evaluated the CT scans of 50 patients without mediastinal disease and clinical SVC syndrome as a control group. On CT, collateral vessels were found in 24 patients, among whom three patient had a single collateral and 21 patients had two or more collateral channels. There were two false positive cases, in which clinically overt SVC syndrome appeared 10 days and three months after CT examination respectively, and one false negative case. The presence of collateral vessels on CT, respectively, and one false negative case. The presence of collateral vessels on CT, regardless of the number and location of collateral vessels and pattern of venous obstruction, was a good clue for predicting the presence of clinical SVC syndrome with the sensitivity and the specificity of 95.7% and 91.7%, respectively. In control group, collateral

  18. Examining spatial-temporal variability and prediction of rainfall in North-eastern Nigeria

    Science.gov (United States)

    Muhammed, B. U.; Kaduk, J.; Balzter, H.

    2012-12-01

    In the last 50 years rainfall in North-eastern Nigeria under the influence of the West African Monsoon (WAM) has been characterised by large annual variations with severe droughts recorded in 1967-1973, and 1983-1987. This variability in rainfall has a large impact on the regions agricultural output, economy and security where the majority of the people depend on subsistence agriculture. In the 1990s there was a sign of recovery with higher annual rainfall totals compared to the 1961-1990 period but annual totals were slightly above the long term mean for the century. In this study we examine how significant this recovery is by analysing medium-term (1980-2006) rainfall of the region using the Climate Research Unit (CRU) and National Centre for Environment Prediction (NCEP) precipitation ½ degree, 6 hourly reanalysis data set. Percentage coefficient of variation increases northwards for annual rainfall (10%-35%) and the number of rainy days (10%-50%). The standardized precipitation index (SPI) of the area shows 7 years during the period as very wet (1996, 1999, 2003 and 2004) with SPI≥1.5 and moderately wet (1993, 1998, and 2006) with values of 1.0≥SPI≤1.49. Annual rainfall indicates a recovery from the 1990s and onwards but significant increases (in the amount of rainfall and number of days recorded with rainfall) is only during the peak of the monsoon season in the months of August and September (pARIMA) model. The model is further evaluated using 24 months rainfall data yielding r=0.79 (regression slope=0.8; pARIMA model and the rainfall data used for this study indicates that the model can be satisfactorily used in forecasting rainfall in the in the sub-humid part of North-eastern Nigeria over a 24 months period.

  19. Impact of vegetation variability on potential predictability and skill of EC-Earth simulations

    Energy Technology Data Exchange (ETDEWEB)

    Weiss, Martina; Hurk, Bart van den; Haarsma, Reindert; Hazeleger, Wilco [Royal Netherlands Meteorological Institute (KNMI), De Bilt (Netherlands)

    2012-12-15

    Climate models often use a simplified and static representation of vegetation characteristics to determine fluxes of energy, momentum and water vapour between surface and lower atmosphere. In order to analyse the impact of short term variability in vegetation phenology, we use remotely-sensed leaf area index and albedo products to examine the role of vegetation in the coupled land-atmosphere system. Perfect model experiments are carried out to determine the impact of realistic temporal variability of vegetation on potential predictability of evaporation and temperature, as well as model skill of EC-Earth simulations. The length of the simulation period is hereby limited by the availability of satellite products to 2000-2010. While a realistic representation of vegetation positively influences the simulation of evaporation and its potential predictability, a positive impact on 2 m temperature is of smaller magnitude, regionally confined and more pronounced in climatically extreme years. (orig.)

  20. Testing predictive models of positive and negative affect with psychosocial, acculturation, and coping variables in a multiethnic undergraduate sample.

    Science.gov (United States)

    Kuo, Ben Ch; Kwantes, Catherine T

    2014-01-01

    Despite the prevalence and popularity of research on positive and negative affect within the field of psychology, there is currently little research on affect involving the examination of cultural variables and with participants of diverse cultural and ethnic backgrounds. To the authors' knowledge, currently no empirical studies have comprehensively examined predictive models of positive and negative affect based specifically on multiple psychosocial, acculturation, and coping variables as predictors with any sample populations. Therefore, the purpose of the present study was to test the predictive power of perceived stress, social support, bidirectional acculturation (i.e., Canadian acculturation and heritage acculturation), religious coping and cultural coping (i.e., collective, avoidance, and engagement coping) in explaining positive and negative affect in a multiethnic sample of 301 undergraduate students in Canada. Two hierarchal multiple regressions were conducted, one for each affect as the dependent variable, with the above described predictors. The results supported the hypotheses and showed the two overall models to be significant in predicting affect of both kinds. Specifically, a higher level of positive affect was predicted by a lower level of perceived stress, less use of religious coping, and more use of engagement coping in dealing with stress by the participants. Higher level of negative affect, however, was predicted by a higher level of perceived stress and more use of avoidance coping in responding to stress. The current findings highlight the value and relevance of empirically examining the stress-coping-adaptation experiences of diverse populations from an affective conceptual framework, particularly with the inclusion of positive affect. Implications and recommendations for advancing future research and theoretical works in this area are considered and presented.

  1. MEDEX 2015: Heart Rate Variability Predicts Development of Acute Mountain Sickness.

    Science.gov (United States)

    Sutherland, Angus; Freer, Joseph; Evans, Laura; Dolci, Alberto; Crotti, Matteo; Macdonald, Jamie Hugo

    2017-09-01

    Sutherland, Angus, Joseph Freer, Laura Evans, Alberto Dolci, Matteo Crotti, and Jamie Hugo Macdonald. MEDEX 2015: Heart rate variability predicts development of acute mountain sickness. High Alt Med Biol. 18: 199-208, 2017. Acute mountain sickness (AMS) develops when the body fails to acclimatize to atmospheric changes at altitude. Preascent prediction of susceptibility to AMS would be a useful tool to prevent subsequent harm. Changes to peripheral oxygen saturation (SpO 2 ) on hypoxic exposure have previously been shown to be of poor predictive value. Heart rate variability (HRV) has shown promise in the early prediction of AMS, but its use pre-expedition has not previously been investigated. We aimed to determine whether pre- and intraexpedition HRV assessment could predict susceptibility to AMS at high altitude with better diagnostic accuracy than SpO 2 . Forty-four healthy volunteers undertook an expedition in the Nepali Himalaya to >5000 m. SpO 2 and HRV parameters were recorded at rest in normoxia and in a normobaric hypoxic chamber before the expedition. On the expedition HRV parameters and SpO 2 were collected again at 3841 m. A daily Lake Louise Score was obtained to assess AMS symptomology. Low frequency/high frequency (LF/HF) ratio in normoxia (cutpoint ≤2.28 a.u.) and LF following 15 minutes of exposure to normobaric hypoxia had moderate (area under the curve ≥0.8) diagnostic accuracy. LF/HF ratio in normoxia had the highest sensitivity (85%) and specificity (88%) for predicting AMS on subsequent ascent to altitude. In contrast, pre-expedition SpO 2 measurements had poor (area under the curve <0.7) diagnostic accuracy and inferior sensitivity and specificity. Pre-ascent measurement of HRV in normoxia was found to be of better diagnostic accuracy for AMS prediction than all measures of HRV in hypoxia, and better than peripheral oxygen saturation monitoring.

  2. Utility of Childhood Glucose Homeostasis Variables in Predicting Adult Diabetes and Related Cardiometabolic Risk Factors

    OpenAIRE

    Nguyen, Quoc Manh; Srinivasan, Sathanur R.; Xu, Ji-Hua; Chen, Wei; Kieltyka, Lyn; Berenson, Gerald S.

    2009-01-01

    OBJECTIVE This study examines the usefulness of childhood glucose homeostasis variables (glucose, insulin, and insulin resistance index [homeostasis model assessment of insulin resistance {HOMA-IR}]) in predicting pre-diabetes and type 2 diabetes and related cardiometabolic risk factors in adulthood. RESEARCH DESIGN AND METHODS This retrospective cohort study consisted of normoglycemic (n = 1,058), pre-diabetic (n = 37), and type 2 diabetic (n = 25) adults aged 19–39 years who were followed o...

  3. Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking.

    Science.gov (United States)

    Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

    2013-01-14

    The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative variables. Many variable-reduction methods are based on so-called predictor-variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the variable-reduction process. Recently, a new predictive-property-ranked variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward variable elimination method applied on the predictive-property-ranked variables. The variable number is first reduced, with constant PLS1 model complexity A, until A variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of variables. In this study for three data sets the utility and effectiveness of six individual and nine combined predictor-variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a predictor variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are

  4. [Prediction of mathematics achievement: effect of personal, socioeducational and contextual variables].

    Science.gov (United States)

    Rosário, Pedro; Lourenço, Abílio; Paiva, Olímpia; Rodrigues, Adriana; Valle, Antonio; Tuero-Herrero, Ellián

    2012-05-01

    Based upon the self-regulated learning theoretical framework this study examined to what extent students' Math school achievement (fifth to ninth graders from compulsory education) can be explained by different cognitive-motivational, social, educational, and contextual variables. A sample of 571 students (10 to 15 year old) enrolled in the study. Findings suggest that Math achievement can be predicted by self-efficacy in Math, school success and self-regulated learning and that these same variables can be explained by other motivational (ej., achievement goals) and contextual variables (school disruption) stressing this way the main importance of self-regulated learning processes and the role context can play in the promotion of school success. The educational implications of the results to the school levels taken are also discussed in the present paper.

  5. Variability in Cadence During Forced Cycling Predicts Motor Improvement in Individuals With Parkinson’s Disease

    Science.gov (United States)

    Ridgel, Angela L.; Abdar, Hassan Mohammadi; Alberts, Jay L.; Discenzo, Fred M.; Loparo, Kenneth A.

    2014-01-01

    Variability in severity and progression of Parkinson’s disease symptoms makes it challenging to design therapy interventions that provide maximal benefit. Previous studies showed that forced cycling, at greater pedaling rates, results in greater improvements in motor function than voluntary cycling. The precise mechanism for differences in function following exercise is unknown. We examined the complexity of biomechanical and physiological features of forced and voluntary cycling and correlated these features to improvements in motor function as measured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Heart rate, cadence, and power were analyzed using entropy signal processing techniques. Pattern variability in heart rate and power were greater in the voluntary group when compared to forced group. In contrast, variability in cadence was higher during forced cycling. UPDRS Motor III scores predicted from the pattern variability data were highly correlated to measured scores in the forced group. This study shows how time series analysis methods of biomechanical and physiological parameters of exercise can be used to predict improvements in motor function. This knowledge will be important in the development of optimal exercise-based rehabilitation programs for Parkinson’s disease. PMID:23144045

  6. Meta-Analysis of Predictive Significance of the Black Hole Sign for Hematoma Expansion in Intracerebral Hemorrhage.

    Science.gov (United States)

    Zheng, Jun; Yu, Zhiyuan; Guo, Rui; Li, Hao; You, Chao; Ma, Lu

    2018-04-27

    Hematoma expansion is related to unfavorable prognosis in intracerebral hemorrhage (ICH). The black hole sign is a novel marker on non-contrast computed tomography for predicting hematoma expansion. However, its predictive values are different in previous studies. Thus, this meta-analysis was conducted to evaluate the predictive significance of the black hole sign for hematoma expansion in ICH. A systematic literature search was performed. Original researches on the association between the black hole sign and hematoma expansion in ICH were included. Sensitivity and specificity were pooled to assess the predictive accuracy. Summary receiver operating characteristics curve (SROC) was developed. Deeks' funnel plot asymmetry test was used to assess the publication bias. Five studies with a total of 1495 patients were included in this study. The pooled sensitivity and specificity of the black hole sign for predicting hematoma expansion were 0.30 and 0.91, respectively. The area under the curve was 0.78 in SROC curve. There was no significant publication bias. This meta-analysis shows that the black hole sign is a helpful imaging marker for predicting hematoma expansion in ICH. Although the black hole sign has a relatively low sensitivity, its specificity is relatively high. Copyright © 2018 Elsevier Inc. All rights reserved.

  7. A new model using routinely available clinical parameters to predict significant liver fibrosis in chronic hepatitis B.

    Directory of Open Access Journals (Sweden)

    Wai-Kay Seto

    Full Text Available OBJECTIVE: We developed a predictive model for significant fibrosis in chronic hepatitis B (CHB based on routinely available clinical parameters. METHODS: 237 treatment-naïve CHB patients [58.4% hepatitis B e antigen (HBeAg-positive] who had undergone liver biopsy were randomly divided into two cohorts: training group (n = 108 and validation group (n = 129. Liver histology was assessed for fibrosis. All common demographics, viral serology, viral load and liver biochemistry were analyzed. RESULTS: Based on 12 available clinical parameters (age, sex, HBeAg status, HBV DNA, platelet, albumin, bilirubin, ALT, AST, ALP, GGT and AFP, a model to predict significant liver fibrosis (Ishak fibrosis score ≥3 was derived using the five best parameters (age, ALP, AST, AFP and platelet. Using the formula log(index+1 = 0.025+0.0031(age+0.1483 log(ALP+0.004 log(AST+0.0908 log(AFP+1-0.028 log(platelet, the PAPAS (Platelet/Age/Phosphatase/AFP/AST index predicts significant fibrosis with an area under the receiving operating characteristics (AUROC curve of 0.776 [0.797 for patients with ALT <2×upper limit of normal (ULN] The negative predictive value to exclude significant fibrosis was 88.4%. This predictive power is superior to other non-invasive models using common parameters, including the AST/platelet/GGT/AFP (APGA index, AST/platelet ratio index (APRI, and the FIB-4 index (AUROC of 0.757, 0.708 and 0.723 respectively. Using the PAPAS index, 67.5% of liver biopsies for patients being considered for treatment with ALT <2×ULN could be avoided. CONCLUSION: The PAPAS index can predict and exclude significant fibrosis, and may reduce the need for liver biopsy in CHB patients.

  8. Patterns and predictability in the intra-annual organic carbon variability across the boreal and hemiboreal landscape

    Science.gov (United States)

    Hytteborn, Julia K.; Temnerud, Johan; Alexander, Richard B.; Boyer, Elizabeth W.; Futter, Martyn N.; Fröberg, Mats; Dahné, Joel; Bishop, Kevin H.

    2015-01-01

    Factors affecting total organic carbon (TOC) concentrations in 215 watercourses across Sweden were investigated using parameter parsimonious regression approaches to explain spatial and temporal variabilities of the TOC water quality responses. We systematically quantified the effects of discharge, seasonality, and long-term trend as factors controlling intra-annual (among year) and inter-annual (within year) variabilities of TOC by evaluating the spatial variability in model coefficients and catchment characteristics (e.g. land cover, retention time, soil type).Catchment area (0.18–47,000 km2) and land cover types (forests, agriculture and alpine terrain) are typical for the boreal and hemiboreal zones across Fennoscandia. Watercourses had at least 6 years of monthly water quality observations between 1990 and 2010. Statistically significant models (p characteristics explained 21% of the spatial variation in the linear trend coefficient, less than 20% of the variation in the discharge coefficient and 73% of the spatial variation in mean TOC. Specific discharge, water residence time, the variance of daily precipitation, and lake area, explained 45% of the spatial variation in the amplitude of the TOC seasonality.Because the main drivers of temporal variability in TOC are seasonality and discharge, first-order estimates of the influences of climatic variability and change on TOC concentration should be predictable if the studied catchments continue to respond similarly.

  9. Removing batch effects for prediction problems with frozen surrogate variable analysis

    Directory of Open Access Journals (Sweden)

    Hilary S. Parker

    2014-09-01

    Full Text Available Batch effects are responsible for the failure of promising genomic prognostic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to remove these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where samples are analyzed one at a time for diagnostic, prognostic, and predictive applications. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction accuracy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.

  10. Prevalence, significance and predictive value of antiphospholipid antibodies in Crohn’s disease

    Science.gov (United States)

    Sipeki, Nora; Davida, Laszlo; Palyu, Eszter; Altorjay, Istvan; Harsfalvi, Jolan; Antal Szalmas, Peter; Szabo, Zoltan; Veres, Gabor; Shums, Zakera; Norman, Gary L; Lakatos, Peter L; Papp, Maria

    2015-01-01

    AIM: To assess the prevalence and stability of different antiphospholipid antibodies (APLAs) and their association with disease phenotype and progression in inflammatory bowel diseases (IBD) patients. METHODS: About 458 consecutive patients [Crohn’s disease (CD): 271 and ulcerative colitis (UC): 187] were enrolled into a follow-up cohort study in a tertiary IBD referral center in Hungary. Detailed clinical phenotypes were determined at enrollment by reviewing the patients’ medical charts. Disease activity, medical treatment and data about evolvement of complications or surgical interventions were determined prospectively during the follow-up. Disease course (development f complicated disease phenotype and need for surgery), occurrence of thrombotic events, actual state of disease activity according to clinical, laboratory and endoscopic scores and accurate treatment regime were recorded during the follow-up, (median, 57.4 and 61.6 mo for CD and UC). Sera of IBD patients and 103 healthy controls (HC) were tested on individual anti-β2-Glycoprotein-I (anti-β2-GPI IgA/M/G), anti-cardiolipin (ACA IgA/M/G) and anti-phosphatidylserine/prothrombin (anti-PS/PT IgA/M/G) antibodies and also anti-Saccharomyces cerevisiae antibodies (ASCA IgA/G) by enzyme-linked immunosorbent assay (ELISA). In a subgroup of CD (n = 198) and UC patients (n = 103), obtaining consecutive samples over various arbitrary time-points during the disease course, we evaluated the intraindividual stability of the APLA status. Additionally, we provide an overview of studies, performed so far, in which significance of APLAs in IBD were assessed. RESULTS: Patients with CD had significantly higher prevalence of both ACA (23.4%) and anti-PS/PT (20.4%) antibodies than UC (4.8%, P < 0.0001 and 10.2%, P = 0.004) and HC (2.9%, P < 0.0001 and 15.5%, P = NS). No difference was found for the prevalence of anti-β2-GPI between different groups (7.2%-9.7%). In CD, no association was found between APLA and ASCA

  11. Ecological Momentary Assessment of Pain, Fatigue, Depressive, and Cognitive Symptoms Reveals Significant Daily Variability in Multiple Sclerosis.

    Science.gov (United States)

    Kratz, Anna L; Murphy, Susan L; Braley, Tiffany J

    2017-11-01

    To describe the daily variability and patterns of pain, fatigue, depressed mood, and cognitive function in persons with multiple sclerosis (MS). Repeated-measures observational study of 7 consecutive days of home monitoring, including ecological momentary assessment (EMA) of symptoms. Multilevel mixed models were used to analyze data. General community. Ambulatory adults (N=107) with MS recruited through the University of Michigan and surrounding community. Not applicable. EMA measures of pain, fatigue, depressed mood, and cognitive function rated on a 0 to 10 scale, collected 5 times a day for 7 days. Cognitive function and depressed mood exhibited more stable within-person patterns than pain and fatigue, which varied considerably within person. All symptoms increased in intensity across the day (all Pfatigue showing the most substantial increase. Notably, this diurnal increase varied by sex and age; women showed a continuous increase from wake to bedtime, whereas fatigue plateaued after 7 pm for men (wake-bed B=1.04, P=.004). For the oldest subgroup, diurnal increases were concentrated to the middle of the day compared with younger subgroups, which showed an earlier onset of fatigue increase and sustained increases until bed time (wake-3 pm B=.04, P=.01; wake-7 pm B=.03, P=.02). Diurnal patterns of cognitive function varied by education; those with advanced college degrees showed a more stable pattern across the day, with significant differences compared with those with bachelor-level degrees in the evening (wake-7 pm B=-.47, P=.02; wake-bed B=-.45, P=.04). Findings suggest that chronic symptoms in MS are not static, even over a short time frame; rather, symptoms-fatigue and pain in particular-vary dynamically across and within days. Incorporation of EMA methods should be considered in the assessment of these chronic MS symptoms to enhance assessment and treatment strategies. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier

  12. Accurate prediction of the functional significance of single nucleotide polymorphisms and mutations in the ABCA1 gene.

    Directory of Open Access Journals (Sweden)

    Liam R Brunham

    2005-12-01

    Full Text Available The human genome contains an estimated 100,000 to 300,000 DNA variants that alter an amino acid in an encoded protein. However, our ability to predict which of these variants are functionally significant is limited. We used a bioinformatics approach to define the functional significance of genetic variation in the ABCA1 gene, a cholesterol transporter crucial for the metabolism of high density lipoprotein cholesterol. To predict the functional consequence of each coding single nucleotide polymorphism and mutation in this gene, we calculated a substitution position-specific evolutionary conservation score for each variant, which considers site-specific variation among evolutionarily related proteins. To test the bioinformatics predictions experimentally, we evaluated the biochemical consequence of these sequence variants by examining the ability of cell lines stably transfected with the ABCA1 alleles to elicit cholesterol efflux. Our bioinformatics approach correctly predicted the functional impact of greater than 94% of the naturally occurring variants we assessed. The bioinformatics predictions were significantly correlated with the degree of functional impairment of ABCA1 mutations (r2 = 0.62, p = 0.0008. These results have allowed us to define the impact of genetic variation on ABCA1 function and to suggest that the in silico evolutionary approach we used may be a useful tool in general for predicting the effects of DNA variation on gene function. In addition, our data suggest that considering patterns of positive selection, along with patterns of negative selection such as evolutionary conservation, may improve our ability to predict the functional effects of amino acid variation.

  13. Quantifying Net Synergy/Redundancy of Spontaneous Variability Regulation via Predictability and Transfer Entropy Decomposition Frameworks.

    Science.gov (United States)

    Porta, Alberto; Bari, Vlasta; De Maria, Beatrice; Takahashi, Anielle C M; Guzzetti, Stefano; Colombo, Riccardo; Catai, Aparecida M; Raimondi, Ferdinando; Faes, Luca

    2017-11-01

    Objective: Indexes assessing the balance between redundancy and synergy were hypothesized to be helpful in characterizing cardiovascular control from spontaneous beat-to-beat variations of heart period (HP), systolic arterial pressure (SAP), and respiration (R). Methods: Net redundancy/synergy indexes were derived according to predictability and transfer entropy decomposition strategies via a multivariate linear regression approach. Indexes were tested in two protocols inducing modifications of the cardiovascular regulation via baroreflex loading/unloading (i.e., head-down tilt at -25° and graded head-up tilt at 15°, 30°, 45°, 60°, 75°, and 90°, respectively). The net redundancy/synergy of SAP and R to HP and of HP and R to SAP were estimated over stationary sequences of 256 successive values. Results: We found that: 1) regardless of the target (i.e., HP or SAP) redundancy was prevalent over synergy and this prevalence was independent of type and magnitude of the baroreflex challenge; 2) the prevalence of redundancy disappeared when decoupling inputs from output via a surrogate approach; 3) net redundancy was under autonomic control given that it varied in proportion to the vagal withdrawal during graded head-up tilt; and 4) conclusions held regardless of the decomposition strategy. Conclusion: Net redundancy indexes can monitor changes of cardiovascular control from a perspective completely different from that provided by more traditional univariate and multivariate methods. Significance: Net redundancy measures might provide a practical tool to quantify the reservoir of effective cardiovascular regulatory mechanisms sharing causal influences over a target variable. Objective: Indexes assessing the balance between redundancy and synergy were hypothesized to be helpful in characterizing cardiovascular control from spontaneous beat-to-beat variations of heart period (HP), systolic arterial pressure (SAP), and respiration (R). Methods: Net redundancy

  14. Prediction of significant conduction disease through noninvasive assessment of cardiac calcification.

    Science.gov (United States)

    Mainigi, Sumeet K; Chebrolu, Lakshmi Hima Bindu; Romero-Corral, Abel; Mehta, Vinay; Machado, Rodolfo Rozindo; Konecny, Tomas; Pressman, Gregg S

    2012-10-01

    Cardiac calcification is associated with coronary artery disease, arrhythmias, conduction disease, and adverse cardiac events. Recently, we have described an echocardiographic-based global cardiac calcification scoring system. The objective of this study was to evaluate the severity of cardiac calcification in patients with permanent pacemakers as based on this scoring system. Patients with a pacemaker implanted within the 2-year study period with a previous echocardiogram were identified and underwent blinded global cardiac calcium scoring. These patients were compared to matched control patients without a pacemaker who also underwent calcium scoring. The study group consisted of 49 patients with pacemaker implantation who were compared to 100 matched control patients. The mean calcium score in the pacemaker group was 3.3 ± 2.9 versus 1.8 ± 2.0 (P = 0.006) in the control group. Univariate and multivariate analysis revealed glomerular filtration rate and calcium scoring to be significant predictors of the presence of a pacemaker. Echocardiographic-based calcium scoring correlates with the presence of severe conduction disease requiring a pacemaker. © 2012, Wiley Periodicals, Inc.

  15. Drivers and potential predictability of summer time North Atlantic polar front jet variability

    Science.gov (United States)

    Hall, Richard J.; Jones, Julie M.; Hanna, Edward; Scaife, Adam A.; Erdélyi, Róbert

    2017-06-01

    The variability of the North Atlantic polar front jet stream is crucial in determining summer weather around the North Atlantic basin. Recent extreme summers in western Europe and North America have highlighted the need for greater understanding of this variability, in order to aid seasonal forecasting and mitigate societal, environmental and economic impacts. Here we find that simple linear regression and composite models based on a few predictable factors are able to explain up to 35 % of summertime jet stream speed and latitude variability from 1955 onwards. Sea surface temperature forcings impact predominantly on jet speed, whereas solar and cryospheric forcings appear to influence jet latitude. The cryospheric associations come from the previous autumn, suggesting the survival of an ice-induced signal through the winter season, whereas solar influences lead jet variability by a few years. Regression models covering the earlier part of the twentieth century are much less effective, presumably due to decreased availability of data, and increased uncertainty in observational reanalyses. Wavelet coherence analysis identifies that associations fluctuate over the study period but it is not clear whether this is just internal variability or genuine non-stationarity. Finally we identify areas for future research.

  16. Social group dynamics predict stress variability among children in a New Zealand classroom.

    Science.gov (United States)

    Spray, Julie; Floyd, Bruce; Littleton, Judith; Trnka, Susanna; Mattison, Siobhan

    2018-03-27

    Previous research proposes stress as a mechanism for linking social environments and biological bodies. In particular, non-human primate studies investigate relationships between cortisol as a measure of stress response and social hierarchies. Because human social structures often include hierarchies of dominance and social status, humans may exhibit similar patterns. Studies of non-human primates, however, have not reached consistent conclusions with respect to relationships between social position and levels of cortisol. While human studies report associations between cortisol and various aspects of social environments, studies that consider social status as a predictor of stress response also report mixed results. Others have argued that perceptions of social status may have different implications for stress response depending upon social context. We propose here that characteristics of children's social networks may be a better predictor of central tendencies and variability of stress response than their perceptions of social status. This is evaluated among 24 children from 9.4 to 11.3 years of age in one upper middle-class New Zealand primary school classroom, assessed through observation within the classroom, self-reports during semi-structured interviews and 221 serial saliva samples provided daily over 10 consecutive school days. A synthetic assessment of the children's networks and peer-relationships was developed prior to saliva-cortisol analysis. We found that greater stability of peer-relationships within groups significantly predicts lower within-group variation in mid-morning cortisol over the two-week period, but not overall within-group differences in mean cortisol. Copyright © 2018 Elsevier GmbH. All rights reserved.

  17. Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

    Science.gov (United States)

    Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

    2016-09-01

    As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for

  18. Using a predictive model to evaluate spatiotemporal variability in streamflow permanence across the Pacific Northwest region

    Science.gov (United States)

    Jaeger, K. L.

    2017-12-01

    The U.S. Geological Survey (USGS) has developed the PRObability Of Streamflow PERmanence (PROSPER) model, a GIS-based empirical model that provides predictions of the annual probability of a stream channel having year-round flow (Streamflow permanence probability; SPP) for any unregulated and minimally-impaired stream channel in the Pacific Northwest (Washington, Oregon, Idaho, western Montana). The model provides annual predictions for 2004-2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions, and static physiographic variables associated with the upstream basin. Prediction locations correspond to the channel network consistent with the National Hydrography Dataset stream grid and are publicly available through the USGS StreamStats platform (https://water.usgs.gov/osw/streamstats/). In snowmelt-driven systems, the most informative predictor variable was mean upstream snow water equivalent on May 1, which highlights the influence of late spring snow cover for supporting streamflow in mountain river networks. In non-snowmelt-driven systems, the most informative variable was mean annual precipitation. Streamflow permanence probabilities varied across the study area by geography and from year-to-year. Notably lower SPP corresponded to the climatically drier subregions of the study area. Higher SPP were concentrated in coastal and higher elevation mountain regions. In addition, SPP appeared to trend with average hydroclimatic conditions, which were also geographically coherent. The year-to-year variability lends support for the growing recognition of the spatiotemporal dynamism of streamflow permanence. An analysis of three focus basins located in contrasting geographical and hydroclimatic settings demonstrates differences in the sensitivity of streamflow permanence to antecedent climate conditions as a function of geography. Consequently, results suggest that PROSPER model can be a useful tool to evaluate regions of the

  19. New pulmonary vein Doppler echocardiographic index predicts significant interatrial shunting in secundum atrial septal defect.

    Science.gov (United States)

    Lam, Yat-Yin; Fang, Fang; Yip, Gabriel Wai-Kwok; Li, Zhi-An; Yang, Ya; Yu, Cheuk-Man

    2012-09-20

    The relation between pulmonary venous flow (PVF) pattern and degree of left-to-right interatrial shunting (IAS) in patients with secundum atrial septal defect (ASD) is unknown. Fifty consecutive ASD patients (14 males, 36 ± 17 years) received transthoracic echocardiography (TTE) before and 1 day after transcatheter closure and their results were compared to 40 controls. The ratio of pulmonary-to-systemic flows (Qp/Qs) was assessed by TTE and invasive oximetry. Pre-closure PV systolic (PVs), diastolic (PVd) velocities and velocity-time integral (PV-VTI) increased, time from onset of ECG Q-wave to the peak PV diastolic wave (Q-PVd) shortened and atrial reversal (PVar) velocity significantly decreased as compared to normals. These findings normalized after closure. Patients with large IAS (defined as invasive Qp/Qs ≥ 2) had higher PVs, PVd and PV-VTI, shorter Q-PVd but lower PVar (all pIAS. Invasive Qp/Qs ratios correlated with PVs, PVd, PV-VTI, Q-PVd and TTE-derived Qp/Qs ratios, ASD sizes and RV end-diastolic dimensions (all pIAS after multivariate analysis. The corresponding sensitivity, specificity and AUC were 89%, 82% and 0.90 respectively for a PV-VTI of 30 cm (pIAS have distinguishable PVF features. Doppler evaluation of PV-VTI is a novel additional tool for assessing the magnitude of shunting in these patients non-invasively. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  20. PREDICTIVE SIGNIFICANCE OF ANTI-HLA AUTOANTIBODIES IN HEART TRANSPLANT RECIPIENTS

    Directory of Open Access Journals (Sweden)

    O. P. Shevchenko

    2013-01-01

    Full Text Available Aim. The aim of this study was to define the role of preformed anti-HLA antibodies (anti-HLA in antibody-mediated rejection (AMR and cardiac allograft vasculopathy (CAV after heart transplantation. Materials and Methods. 140 heart transplant recipients were followed after heart transplantation performed for 106 dilated and 34 – ischemic cardiomyopathy. Anti-HLA was determined before transplantation by ELISA. Results. Recipients were divided into 2 groups: anti-HLA positive (n = 45, 32,1% and anti-HLA negative (n = 95, 67,9%. The incidence of AMR in anti-HLA positive group was 12 (26,67% and 11 (11,58% in anti-HLA negative group. Risk of AMR was significantly higher in anti-HLA positive recipients (RR 2,3: 95% CI 1,02–4,81, р = 0,03. During first three years after transplantation CAV was diagnosed in 9 (20% of anti-HLA positive recipients and in 7 (6,8% of patients without anti-HLA. (RR 2,7: 95% CI 1,08–6,82, р = 0,03. Survival in freedom from CAV in anti-HLA negative recipients was much higher than in anti-HLA positive recipients (0,89 ± 0,07, 0,72 ± 0,06, resp. (p = 0,02.Conclusions. The presence of preformed anti-HLA antibodies in candidates for heart transplantation increase the risk of AMR and CAV post transplantation in 2,3 and 2,7 times, respectively. 

  1. Amniotic fluid index predicts the relief of variable decelerations after amnioinfusion bolus.

    Science.gov (United States)

    Spong, C Y; McKindsey, F; Ross, M G

    1996-10-01

    Our purpose was to determine whether intrapartum amniotic fluid index before amnioinfusion can be used to predict response to therapeutic amnioinfusion. Intrapartum patients (n = 85) with repetitive variable decelerations in fetal heart rate that necessitated amnioinfusion (10 ml/min for 60 minutes) underwent determination of amniotic fluid index before and after bolus amnioinfusion. The fetal heart tracing was scored (scorer blinded to amniotic fluid index values) for number and characteristics of variable decelerations before and 1 hour after initiation of amnioinfusion. The amnioinfusion was considered successful if it resulted in a decrease of > or = 50% in total number of variable decelerations or a decrease of > or = 50% in the rate of atypical or severe variable decelerations after administration of the bolus. Spontaneous vaginal births before completion of administration of the bolus (n = 18) were excluded from analysis. The probability of success of amnioinfusion in relation to amniotic fluid index was analyzed with the chi(2) test for progressive sequence. The mean amniotic fluid index before amnioinfusion was 6.2 +/- 3.3 cm. An amniotic fluid index of amnioinfusion decreased with increasing amniotic fluid index before amnioinfusion (76% [16/21] when initial amniotic fluid index was 0 to 4 cm, 63% [17/27] when initial amniotic fluid index was 4 to 8 cm, 44% [7/16] when initial amniotic fluid index was 8 to 12 cm, and 33% [1/3] when initial amniotic fluid index was > 12 cm, p = 0.03). The incidence of nuchal cords or true umbilical cord knots increased in relation to amniotic fluid index before amnioinfusion. Amniotic fluid index before amnioinfusion can be used to predict the success of amnioinfusion for relief of variable decelerations in fetal heart rate. Failure of amnioinfusion at a high amniotic fluid index before amnioinfusion may be explained by the increased prevalence of nuchal cords or true knots in the umbilical cord.

  2. Interrater and intrarater agreements of magnetic resonance imaging findings in the lumbar spine: significant variability across degenerative conditions.

    Science.gov (United States)

    Fu, Michael C; Buerba, Rafael A; Long, William D; Blizzard, Daniel J; Lischuk, Andrew W; Haims, Andrew H; Grauer, Jonathan N

    2014-10-01

    agreement. However, when stratified by condition, absolute interrater agreement ranged from 65.1% to 92.0%. Disc hydration, disc space height, and bone marrow changes exhibited the lowest absolute interrater agreements. The absolute intrarater agreement had a narrower range, from 74.5% to 91.5%. Fleiss kappa coefficients ranged from fair-to-substantial agreement (0.282-0.618). Even in a study using standardized evaluation criteria, there was significant variability in the interrater and intrarater agreements of MRI in assessing different degenerative conditions of the lumbar spine. Clinicians should be aware of the condition-specific diagnostic limitations of MRI interpretation. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

    Science.gov (United States)

    Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

    2017-07-15

    New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier

  4. A critical review of predictive models for the onset of significant void in forced-convection subcooled boiling

    International Nuclear Information System (INIS)

    Dorra, H.; Lee, S.C.; Bankoff, S.G.

    1993-06-01

    This predictive models for the onset of significant void (OSV) in forced-convection subcooled boiling are reviewed and compared with extensive data. Three analytical models and seven empirical correlations are considered in this review. These models and correlations are put onto a common basis and are compared, again on a common basis, with a variety of data. The evaluation of their range of validity and applicability under various operating conditions are discussed. The results show that the correlations of Saha-Zuber seems to be the best model to predict OSV in vertical subcooled boiling flow

  5. Prediction of Placental Barrier Permeability: A Model Based on Partial Least Squares Variable Selection Procedure

    Directory of Open Access Journals (Sweden)

    Yong-Hong Zhang

    2015-05-01

    Full Text Available Assessing the human placental barrier permeability of drugs is very important to guarantee drug safety during pregnancy. Quantitative structure–activity relationship (QSAR method was used as an effective assessing tool for the placental transfer study of drugs, while in vitro human placental perfusion is the most widely used method. In this study, the partial least squares (PLS variable selection and modeling procedure was used to pick out optimal descriptors from a pool of 620 descriptors of 65 compounds and to simultaneously develop a QSAR model between the descriptors and the placental barrier permeability expressed by the clearance indices (CI. The model was subjected to internal validation by cross-validation and y-randomization and to external validation by predicting CI values of 19 compounds. It was shown that the model developed is robust and has a good predictive potential (r2 = 0.9064, RMSE = 0.09, q2 = 0.7323, rp2 = 0.7656, RMSP = 0.14. The mechanistic interpretation of the final model was given by the high variable importance in projection values of descriptors. Using PLS procedure, we can rapidly and effectively select optimal descriptors and thus construct a model with good stability and predictability. This analysis can provide an effective tool for the high-throughput screening of the placental barrier permeability of drugs.

  6. What variables are important in predicting bovine viral diarrhea virus? A random forest approach.

    Science.gov (United States)

    Machado, Gustavo; Mendoza, Mariana Recamonde; Corbellini, Luis Gustavo

    2015-07-24

    Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.

  7. Heart Rate Variability Density Analysis (Dyx) and Prediction of Long-Term Mortality after Acute Myocardial Infarction

    DEFF Research Database (Denmark)

    Jørgensen, Rikke Mørch; Abildstrøm, Steen Z; Levitan, Jacob

    2016-01-01

    AIMS: The density HRV parameter Dyx is a new heart rate variability (HRV) measure based on multipole analysis of the Poincaré plot obtained from RR interval time series, deriving information from both the time and frequency domain. Preliminary results have suggested that the parameter may provide...... new predictive information on mortality in survivors of acute myocardial infarction (MI). This study compares the prognostic significance of Dyx to that of traditional linear and nonlinear measures of HRV. METHODS AND RESULTS: In the Nordic ICD pilot study, patients with an acute MI were screened...... with 2D echocardiography and 24-hour Holter recordings. The study was designed to assess the power of several HRV measures to predict mortality. Dyx was tested in a subset of 206 consecutive Danish patients with analysable Holter recordings. After a median follow-up of 8.5 years 70 patients had died...

  8. Simple models to predict grassland ecosystem C exchange and actual evapotranspiration using NDVI and environmental variables

    Science.gov (United States)

    Semiarid grasslands contribute significantly to net terrestrial carbon flux as plant productivity and heterotrophic respiration in these moisture-limited systems are correlated with metrics related to water availability (e.g., precipitation, Actual EvapoTranspiration or AET). These variables are als...

  9. Prediction of spatially variable unsaturated hydraulic conductivity using scaled particle-size distribution functions

    NARCIS (Netherlands)

    Nasta, P.; Romano, N.; Assouline, S; Vrugt, J.A.; Hopmans, J.W.

    2013-01-01

    Simultaneous scaling of soil water retention and hydraulic conductivity functions provides an effective means to characterize the heterogeneity and spatial variability of soil hydraulic properties in a given study area. The statistical significance of this approach largely depends on the number of

  10. Identification of cognitive and non-cognitive predictive variables related to attrition in baccalaureate nursing education programs in Mississippi

    Science.gov (United States)

    Hayes, Catherine

    2005-07-01

    This study sought to identify a variable or variables predictive of attrition among baccalaureate nursing students. The study was quantitative in design and multivariate correlational statistics and discriminant statistical analysis were used to identify a model for prediction of attrition. The analysis then weighted variables according to their predictive value to determine the most parsimonious model with the greatest predictive value. Three public university nursing education programs in Mississippi offering a Bachelors Degree in Nursing were selected for the study. The population consisted of students accepted and enrolled in these three programs for the years 2001 and 2002 and graduating in the years 2003 and 2004 (N = 195). The categorical dependent variable was attrition (includes academic failure or withdrawal) from the program of nursing education. The ten independent variables selected for the study and considered to have possible predictive value were: Grade Point Average for Pre-requisite Course Work; ACT Composite Score, ACT Reading Subscore, and ACT Mathematics Subscore; Letter Grades in the Courses: Anatomy & Physiology and Lab I, Algebra I, English I (101), Chemistry & Lab I, and Microbiology & Lab I; and Number of Institutions Attended (Universities, Colleges, Junior Colleges or Community Colleges). Descriptive analysis was performed and the means of each of the ten independent variables was compared for students who attrited and those who were retained in the population. The discriminant statistical analysis performed created a matrix using the ten variable model that was able to correctly predicted attrition in the study's population in 77.6% of the cases. Variables were then combined and recombined to produce the most efficient and parsimonious model for prediction. A six variable model resulted which weighted each variable according to predictive value: GPA for Prerequisite Coursework, ACT Composite, English I, Chemistry & Lab I, Microbiology

  11. Personal and situational variables, and career concerns: predicting career adaptability in young adults.

    Science.gov (United States)

    Yousefi, Zahra; Abedi, Mohammadreza; Baghban, Iran; Eatemadi, Ozra; Abedi, Ahmade

    2011-05-01

    This study examined relationships among career adaptability and career concerns, social support and goal orientation. We surveyed 304 university students using measures of career concerns, adaptability (career planning, career exploration, self-exploration, decision-making, self-regulation), goal-orientation (learning, performance-prove, performance-avoid) and social support (family, friends, significant others). Multiple regression analysis revealed career concerns, learning and performance-prove goal orientations emerged relatively as the most important contributors. Other variables did not contribute significantly.

  12. Prediction of autoignition in a lifted methane/air flame using an unsteady flamelet/progress variable model

    Energy Technology Data Exchange (ETDEWEB)

    Ihme, Matthias; See, Yee Chee [Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109 (United States)

    2010-10-15

    An unsteady flamelet/progress variable (UFPV) model has been developed for the prediction of autoignition in turbulent lifted flames. The model is a consistent extension to the steady flamelet/progress variable (SFPV) approach, and employs an unsteady flamelet formulation to describe the transient evolution of all thermochemical quantities during the flame ignition process. In this UFPV model, all thermochemical quantities are parameterized by mixture fraction, reaction progress parameter, and stoichiometric scalar dissipation rate, eliminating the explicit dependence on a flamelet time scale. An a priori study is performed to analyze critical modeling assumptions that are associated with the population of the flamelet state space. For application to LES, the UFPV model is combined with a presumed PDF closure to account for subgrid contributions of mixture fraction and reaction progress variable. The model was applied in LES of a lifted methane/air flame. Additional calculations were performed to quantify the interaction between turbulence and chemistry a posteriori. Simulation results obtained from these calculations are compared with experimental data. Compared to the SFPV results, the unsteady flamelet/progress variable model captures the autoignition process, and good agreement with measurements is obtained for mixture fraction, temperature, and species mass fractions. From the analysis of scatter data and mixture fraction-conditional results it is shown that the turbulence/chemistry interaction delays the ignition process towards lower values of scalar dissipation rate, and a significantly larger region in the flamelet state space is occupied during the ignition process. (author)

  13. Glycemic variability is an independent predictive factor for development of hepatic fibrosis in nonalcoholic fatty liver disease.

    Directory of Open Access Journals (Sweden)

    Motoi Hashiba

    Full Text Available Patients with nonalcoholic fatty liver disease (NAFLD and nonalcoholic steatohepatitis (NASH often have metabolic disorders including insulin resistance and type 2 diabetes mellitus (T2DM. We clarified the predictive factors in glucose metabolism for progression of hepatic fibrosis in patients with NAFLD by the 75-g oral glucose tolerance test (75gOGTT and a continuous glucose monitoring system (CGMS. One hundred sixty-nine patients (68 female and 101 male patients with biopsy-proven NAFLD with performance with 75gOGTT were enrolled and divided into four groups according to the stage of hepatic fibrosis (F0-3. The proportion of patients with T2DM significantly gradually increased, HbA1c and the homeostasis model assessment of insulin resistance were significantly elevated, and 1,5-anhydroglucitol (1,5-AG was remarkably decreased with the progression of fibrosis. In the 75gOGTT, both plasma glucose and insulin secretion were remarkably increased with the progression of fibrosis. The only factor significantly associated with advanced fibrosis was 1,5-AG (P = 0.008 as determined by multivariate logistic regression analysis. We next evaluated the changes in blood glucose during 24 hours by monitoring with the CGMS to confirm the relationship between glycemic variability and progression of fibrosis. Variability of median glucose, standard deviation of median glucose (P = 0.0022, maximum blood glucose (P = 0.0019, and ΔMin-max blood glucose (P = 0.0029 were remarkably higher in severe fibrosis than in mild fibrosis.Hyperinsulinemia and hyperglycemia, especially glycemic variability, are important predictive factors in glucose impairment for the progression of hepatic fibrosis in NAFLD.

  14. Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea

    Science.gov (United States)

    Serreze, Mark C.; Crawford, Alex D.; Stroeve, Julienne C.; Barrett, Andrew P.; Woodgate, Rebecca A.

    2016-10-01

    As assessed over the period 1979-2014, the date that sea ice retreats to the shelf break (150 m contour) of the Chukchi Sea has a linear trend of -0.7 days per year. The date of seasonal ice advance back to the shelf break has a steeper trend of about +1.5 days per year, together yielding an increase in the open water period of 80 days. Based on detrended time series, we ask how interannual variability in advance and retreat dates relate to various forcing parameters including radiation fluxes, temperature and wind (from numerical reanalyses), and the oceanic heat inflow through the Bering Strait (from in situ moorings). Of all variables considered, the retreat date is most strongly correlated (r ˜ 0.8) with the April through June Bering Strait heat inflow. After testing a suite of statistical linear models using several potential predictors, the best model for predicting the date of retreat includes only the April through June Bering Strait heat inflow, which explains 68% of retreat date variance. The best model predicting the ice advance date includes the July through September inflow and the date of retreat, explaining 67% of advance date variance. We address these relationships by discussing heat balances within the Chukchi Sea, and the hypothesis of oceanic heat transport triggering ocean heat uptake and ice-albedo feedback. Developing an operational prediction scheme for seasonal retreat and advance would require timely acquisition of Bering Strait heat inflow data. Predictability will likely always be limited by the chaotic nature of atmospheric circulation patterns.

  15. Predictions of Tropospheric Zenithal Delay for South America : Seasonal Variability and Quality Evaluation

    Directory of Open Access Journals (Sweden)

    Luiz Augusto Toledo Machado

    2006-12-01

    Full Text Available The Zenithal Tropospheric Delay (Z TD is an important error source in the observable involved in the positioning methods using artificial satellite. Frequently, the Z TD influence in the positioning is minimized by applying empirical models. However, such models are not able to supply the precision required to some real time applications, such as navigation and steak out. In 2010 it will be implanted the new navigation and administration system of the air traffic, denominated CNS-ATM (Communication Navigation Surveillance - Air Traffic Management. In this new system the application of positioning techniques by satellites in the air traffic will be quite explored because they provide good precision in real time. The predictions of Z TD values from Numeric Weather Prediction (NWP, denominated dynamic modeling, is an alternative to model the atmospheric gases effects in the radio-frequency signals in real time. The Center for Weather Forecasting and Climate Studies (CPTEC has generated operationally prediction of Z TD values to South American Continent since March, 2004. The aims of the present paper are to investigate the Z TD seasonal variability and evaluate the quality of predicted Z TD values. One year of GPS data from Brazilian Continuous GPS Network (RBMC was used in this evaluation. The RMS values resulting from this evaluation were in the range of 4 to 11 cm. Considering the Z TDtemporal variability, the advantages provide by this modeling, the results obtained in this evaluation and the future improvements, this work shows that the dynamic modeling has great potential to become the most appropriate alternative to model Z TD in real time.

  16. A minimal model of the Atlantic Multidecadal Variability: its genesis and predictability

    Energy Technology Data Exchange (ETDEWEB)

    Ou, Hsien-Wang [Lamont-Doherty Earth Observatory of Columbia University, Department of Earth and Environmental Sciences, Palisades, NY (United States)

    2012-02-15

    Through a box model of the subpolar North Atlantic, we examine the genesis and predictability of the Atlantic Multidecadal Variability (AMV), posited as a linear perturbation sustained by the stochastic atmosphere. Postulating a density-dependent thermohaline circulation (THC), the latter would strongly differentiate the thermal and saline damping, and facilitate a negative feedback between the two fields. This negative feedback preferentially suppresses the low-frequency thermal variance to render a broad multidecadal peak bounded by the thermal and saline damping time. We offer this ''differential variance suppression'' as an alternative paradigm of the AMV in place of the ''damped oscillation'' - the latter generally not allowed by the deterministic dynamics and in any event bears no relation to the thermal peak. With the validated dynamics, we then assess the AMV predictability based on the relative entropy - a difference of the forecast and climatological probability distributions, which decays through both error growth and dynamical damping. Since the stochastic forcing is mainly in the surface heat flux, the thermal noise grows rapidly and together with its climatological variance limited by the THC-aided thermal damping, they strongly curtail the thermal predictability. The latter may be prolonged if the initial thermal and saline anomalies are of the same sign, but even rare events of less than 1% chance of occurrence yield a predictable time that is well short of a decade; we contend therefore that the AMV is in effect unpredictable. (orig.)

  17. Brief Communication: Upper Air Relaxation in RACMO2 Significantly Improves Modelled Interannual Surface Mass Balance Variability in Antarctica

    Science.gov (United States)

    van de Berg, W. J.; Medley, B.

    2016-01-01

    The Regional Atmospheric Climate Model (RACMO2) has been a powerful tool for improving surface mass balance (SMB) estimates from GCMs or reanalyses. However, new yearly SMB observations for West Antarctica show that the modelled interannual variability in SMB is poorly simulated by RACMO2, in contrast to ERA-Interim, which resolves this variability well. In an attempt to remedy RACMO2 performance, we included additional upper-air relaxation (UAR) in RACMO2. With UAR, the correlation to observations is similar for RACMO2 and ERA-Interim. The spatial SMB patterns and ice-sheet-integrated SMB modelled using UAR remain very similar to the estimates of RACMO2 without UAR. We only observe an upstream smoothing of precipitation in regions with very steep topography like the Antarctic Peninsula. We conclude that UAR is a useful improvement for regional climate model simulations, although results in regions with steep topography should be treated with care.

  18. IDENTIFICATION OF THOSE VARIABLES THAT HAVE A SIGNIFICANT INFLUENCE ON THE EXPECTED NUMBER OF DAYS OF STAYING IN THE CENTRE DEVELOPMENT REGION OF ROMANIA

    Directory of Open Access Journals (Sweden)

    Erika KULCSÁR

    2010-06-01

    Full Text Available I started from the assumption that there are more variables that have a significant influence on the expected number of days of staying in the Centre Development Region. To identify those variables this paper includes the analysis of variance with two variables that are not interacting, in this case the dependent variable is the question "How many days did you plan to stay in Centre Development Region?" and the independent variables are: "What is the purpose of your stay?" "What is the highest level of education?". Given that there are cases when interactions occur between variables, I also analyzed the interaction effects between the two independent variables. The paper also includes an ANOVA analysis with three variables between which interactions relationships occur. After identifying the dependency relations between the variables I found that the inclusion of the third variable, namely the "Marital status" of respondents, adds value to the model. Following the results obtained by ANOVA analysis, I identified those socio-demographic characteristics that, in my opinion, companies that operate on tourist market in the Center Development Region should consider when fundamenting marketing strategies in tourism.

  19. Clinical significance of power spectral analysis of heart rate variability and {sup 123}I-metaiodobenzylguanidine (MIBG) myocardial imaging for assessing the severity of heart failure

    Energy Technology Data Exchange (ETDEWEB)

    Ishida, Yoshio; Fukuoka, Shuji; Shimotsu, Yoriko; Sasaki, Tatsuya; Kamakura, Shiro; Yasumura, Yoshio; Miyatake, Kunio; Shimomura, Katsuro [National Cardiovascular Center, Suita, Osaka (Japan); Tani, Akihiro

    1997-04-01

    The significance of power spectral analysis of heart rate variability and of MIBG myocardial imaging to see the sympathetic nervous function was evaluated in patients with congestive heart failure due to dilated cardiomyopathy. Subjects were 10 normal volunteers and 8 patients with severity NYHA II; 10 normals and 25 patients with NYHA II and III; and 17 patients treated with a beta-blocker (metoprolol 5-40 mg). ECG was recorded with a portable ECG recorder for measuring RR intervals for 24 hr, which were applied for power spectral analysis. Early and delayed imagings with 111 MBq of {sup 123}I-MIBG were performed at 15 min and 4 hr, respectively, after its intravenous administration for acquisition of anterior planar and SPECT images. Myocardial blood flow SPECT was also done with 111 MBq of {sup 201}Tl given intravenously, and difference of total defect scores between MIBG and Tl images was computed. MIBG myocardial sympathetic nerve imaging in those patients was found useful to assess the severity of heart failure, to predict the risk patients for beta-blocker treatment and to assess the risk in complicated ventricular tachycardia. (K.H.)

  20. Intraindividual Variability in Basic Reaction Time Predicts Middle-Aged and Older Pilots’ Flight Simulator Performance

    Science.gov (United States)

    2013-01-01

    Objectives. Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Method. Two-hundred and thirty-six pilots (40–69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Results. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%–12% of the negative age effect on initial flight performance. Discussion. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance. PMID:23052365

  1. Intraindividual variability in basic reaction time predicts middle-aged and older pilots' flight simulator performance.

    Science.gov (United States)

    Kennedy, Quinn; Taylor, Joy; Heraldez, Daniel; Noda, Art; Lazzeroni, Laura C; Yesavage, Jerome

    2013-07-01

    Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Two-hundred and thirty-six pilots (40-69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%-12% of the negative age effect on initial flight performance. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance.

  2. Predictive value of clinical and laboratory variables for vesicoureteral reflux in children.

    Science.gov (United States)

    Soylu, Alper; Kasap, Belde; Demir, Korcan; Türkmen, Mehmet; Kavukçu, Salih

    2007-06-01

    We aimed to determine the predictability of clinical and laboratory variables for vesicoureteral reflux (VUR) in children with urinary tract infection (UTI). Data of children with febrile UTI who underwent voiding cystoureterography between 2002 and 2005 were evaluated retrospectively for clinical (age, gender, fever > or = 38.5 degrees C, recurrent UTI), laboratory [leukocytosis, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), pyuria, serum creatinine (S(Cr))] and imaging (renal ultrasonography) variables. Children with VUR (group 1) vs. no VUR (group 2) and children with high-grade (III-V) VUR (group 3) vs. no or low-grade (I-II) VUR (group 4) were compared. Among 88 patients (24 male), 38 had VUR and 21 high-grade VUR. Fever > or = 38.5 degrees C was associated with VUR [odds ratio (OR): 7.5]. CRP level of 50 mg/l was the best cut-off level for predicting high-grade VUR (OR 15.5; discriminative ability 0.89 +/- 0.05). Performing voiding cystourethrography based on this CRP level would result in failure to notice 9% of patients with high-grade VUR, whereas 69% of children with no/low-grade VUR would be spared from this invasive test. In conclusion, fever > or = 38 degrees C and CRP > 50 mg/l seem to be potentially useful clinical predictors of VUR and high-grade VUR, respectively, in pediatric patients with UTI. Further validation of these findings could limit unnecessary voiding cystourethrography.

  3. Spatial Variability and Geostatistical Prediction of Some Soil Hydraulic Coefficients of a Calcareous Soil

    Directory of Open Access Journals (Sweden)

    Ali Akbar Moosavi

    2017-02-01

    Full Text Available Introduction: Saturated hydraulic conductivity and the other hydraulic properties of soils are essential vital soil attributes that play role in the modeling of hydrological phenomena, designing irrigation-drainage systems, transportation of salts and chemical and biological pollutants within the soil. Measurement of these hydraulic properties needs some special instruments, expert technician, and are time consuming and expensive and due to their high temporal and spatial variability, a large number of measurements are needed. Nowadays, prediction of these attributes using the readily available soil data using pedotransfer functions or using the limited measurement with applying the geostatistical approaches has been receiving high attention. The study aimed to determine the spatial variability and prediction of saturated (Ks and near saturated (Kfs hydraulic conductivity, the power of Gardner equation (α, sorptivity (S, hydraulic diffusivity (D and matric flux potential (Фm of a calcareous soil. Material and Methods: The study was carried out on the soil series of Daneshkadeh located in the Bajgah Agricultural Experimental Station of Agricultural College, Shiraz University, Shiraz, Iran (1852 m above the mean sea level. This soil series with about 745 ha is a deep yellowish brow calcareous soil with textural classes of loam to clay. In the studied soil series 50 sampling locations with the sampling distances of 16, 8 , and 4 m were selected on the relatively regular sampling design. The saturated hydraulic conductivity (Ks, near saturated hydraulic conductivity (Kfs, the power of Gardner equation (α, sorptivity (S, hydraulic diffusivity (D and matric flux potential (Фm of the aforementioned sampling locations was determined using the Single Ring and Droplet methods. After, initial statistical processing, including a normality test of data, trend and stationary analysis of data, the semivariograms of each studied hydraulic attributes were

  4. Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography

    Energy Technology Data Exchange (ETDEWEB)

    Hell, Michaela M., E-mail: michaela.hell@uk-erlangen.de [Department of Cardiology, University of Erlangen (Germany); Dey, Damini [Department of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Taper Building, Room A238, 8700 Beverly Boulevard, Los Angeles, CA 90048 (United States); Marwan, Mohamed; Achenbach, Stephan; Schmid, Jasmin; Schuhbaeck, Annika [Department of Cardiology, University of Erlangen (Germany)

    2015-08-15

    Highlights: • Overestimation of coronary lesions by coronary computed tomography angiography and subsequent unnecessary invasive coronary angiography and revascularization is a concern. • Differences in plaque characteristics and contrast density difference between hemodynamically significant and non-significant stenoses, as defined by invasive fractional flow reserve, were assessed. • At a threshold of ≥24%, contrast density difference predicted hemodynamically significant lesions with a specificity of 75%, sensitivity of 33%, PPV of 35% and NPV of 73%. • The determination of contrast density difference required less time than transluminal attenuation gradient measurement. - Abstract: Objectives: Coronary computed tomography angiography (CTA) allows the detection of obstructive coronary artery disease. However, its ability to predict the hemodynamic significance of stenoses is limited. We assessed differences in plaque characteristics and contrast density difference between hemodynamically significant and non-significant stenoses, as defined by invasive fractional flow reserve (FFR). Methods: Lesion characteristics of 59 consecutive patients (72 lesions) in whom invasive FFR was performed in at least one coronary artery with moderate to high-grade stenoses in coronary CTA were evaluated by two experienced readers. Coronary CTA data sets were acquired on a second-generation dual-source CT scanner using retrospectively ECG-gated spiral acquisition or prospectively ECG-triggered axial acquisition mode. Plaque volume and composition (non-calcified, calcified), remodeling index as well as contrast density difference (defined as the percentage decline in luminal CT attenuation/cross-sectional area over the lesion) were assessed using a semi-automatic software tool (Autoplaq). Additionally, the transluminal attenuation gradient (defined as the linear regression coefficient between intraluminal CT attenuation and length from the ostium) was determined

  5. Modelling and prediction of pig iron variables in the blast furnace

    Energy Technology Data Exchange (ETDEWEB)

    Saxen, H; Laaksonen, M; Waller, M [Aabo Akademi, Turku (Finland). Heat Engineering Lab.

    1997-12-31

    The blast furnace, where pig iron for steelmaking is produced, is an extremely complicated process, with heat and mass transfer and chemical reactions between several phases. Very few direct measurements on the internal state are available in the operation of the process. A main problem in on-line analysis and modelling is that the state of the furnace may undergo spontaneous changes, which alter the dynamic behaviour of the process. Moreover, large internal disturbances frequently occur, which affect the product quality. The work in this research project focuses on a central problem in the control of the blast furnace process, i.e., short-term prediction of pig iron variables. The problem is of considerable importance for fuel economy, product quality, and for an optimal decision making in integrated steel plants. The operation of the blast furnace aims at producing a product (hot metal) with variables maintained on a stable level (close to their setpoints) without waste of expensive fuel (metallurgical coke). The hot metal temperature and composition affect the downstream (steelmaking) processes, so fluctuations in the pig iron quality must be `corrected` in the steel plant. The goal is to develop a system which predicts the evolution of the hot metal variables (temperature, chemical composition) during the next few taps, and that can be used for decision-making in the operation of the blast furnace. Because of the complicated behaviour of the process, it is considered important to include both deterministic and stochastic components in the modelling: Mathematical models, which on the basis of measurements describe the physical state of the process, and statistical (black-box) models will be combined in the system. Moreover, different models will be applied in different domains in order to capture structural changes in the dynamics of the process SULA 2 Research Programme; 17 refs.

  6. Modelling and prediction of pig iron variables in the blast furnace

    Energy Technology Data Exchange (ETDEWEB)

    Saxen, H.; Laaksonen, M.; Waller, M. [Aabo Akademi, Turku (Finland). Heat Engineering Lab.

    1996-12-31

    The blast furnace, where pig iron for steelmaking is produced, is an extremely complicated process, with heat and mass transfer and chemical reactions between several phases. Very few direct measurements on the internal state are available in the operation of the process. A main problem in on-line analysis and modelling is that the state of the furnace may undergo spontaneous changes, which alter the dynamic behaviour of the process. Moreover, large internal disturbances frequently occur, which affect the product quality. The work in this research project focuses on a central problem in the control of the blast furnace process, i.e., short-term prediction of pig iron variables. The problem is of considerable importance for fuel economy, product quality, and for an optimal decision making in integrated steel plants. The operation of the blast furnace aims at producing a product (hot metal) with variables maintained on a stable level (close to their setpoints) without waste of expensive fuel (metallurgical coke). The hot metal temperature and composition affect the downstream (steelmaking) processes, so fluctuations in the pig iron quality must be `corrected` in the steel plant. The goal is to develop a system which predicts the evolution of the hot metal variables (temperature, chemical composition) during the next few taps, and that can be used for decision-making in the operation of the blast furnace. Because of the complicated behaviour of the process, it is considered important to include both deterministic and stochastic components in the modelling: Mathematical models, which on the basis of measurements describe the physical state of the process, and statistical (black-box) models will be combined in the system. Moreover, different models will be applied in different domains in order to capture structural changes in the dynamics of the process SULA 2 Research Programme; 17 refs.

  7. Sensitivity, Specificity and Predictive Value of Heart Rate Variability Indices in Type 1 Diabetes Mellitus

    Directory of Open Access Journals (Sweden)

    Anne Kastelianne França da Silva

    Full Text Available Abstract Background: Heart rate variability (HRV indices may detect autonomic changes with good diagnostic accuracy. Type diabetes mellitus (DM individuals may have changes in autonomic modulation; however, studies of this nature in this population are still scarce. Objective: To compare HRV indices between and assess their prognostic value by measurements of sensitivity, specificity and predictive values in young individuals with type 1 DM and healthy volunteers. Methods: In this cross-sectional study, physical and clinical assessment was performed in 39 young patients with type 1 DM and 43 young healthy controls. For HRV analysis, beat-to-beat heart rate variability was measured in dorsal decubitus, using a Polar S810i heart rate monitor, for 30 minutes. The following indices were calculated: SDNN, RMSSD, PNN50, TINN, RRTri, LF ms2, HF ms2, LF un, HF un, LF/HF, SD1, SD2, SD1/SD2, and ApEn. Results: Type 1 DM subjects showed a decrease in sympathetic and parasympathetic activities, and overall variability of autonomic nervous system. The RMSSD, SDNN, PNN50, LF ms2, HF ms2, RRTri, SD1 and SD2 indices showed greater diagnostic accuracy in discriminating diabetic from healthy individuals. Conclusion: Type 1 DM individuals have changes in autonomic modulation. The SDNN, RMSSD, PNN50, RRtri, LF ms2, HF ms2, SD1 and SD2 indices may be alternative tools to discriminate individuals with type 1 DM.

  8. Clinical and angiographic predictors of haemodynamically significant angiographic lesions: development and validation of a risk score to predict positive fractional flow reserve.

    Science.gov (United States)

    Sareen, Nishtha; Baber, Usman; Kezbor, Safwan; Sayseng, Sonny; Aquino, Melissa; Mehran, Roxana; Sweeny, Joseph; Barman, Nitin; Kini, Annapoorna; Sharma, Samin K

    2017-04-07

    Coronary revascularisation based upon physiological evaluation of lesions improves clinical outcomes. Angiographic or visual stenosis assessment alone is insufficient in predicting haemodynamic stenosis severity by fractional flow reserve (FFR) and therefore cannot be used to guide revascularisation, particularly in the lesion subset system formulated. Of 1,023 consecutive lesions (883 patients), 314 (31%) were haemodynamically significant. Characteristics associated with FFR ≤0.8 include male gender, higher SYNTAX score, lesions ≥20 mm, stenosis >50%, bifurcation, calcification, absence of tortuosity and smaller reference diameter. A user-friendly integer score was developed with the five variables demonstrating the strongest association. On prospective validation (in 279 distinct lesions), the increasing value of the score correlated well with increasing haemodynamic significance (C-statistic 0.85). We identified several clinical and angiographic characteristics and formulated a scoring system to guide the approach to intermediate lesions. This may translate into cost savings. Larger studies with prospective validation are required to confirm our results.

  9. A model for estimating pathogen variability in shellfish and predicting minimum depuration times.

    Science.gov (United States)

    McMenemy, Paul; Kleczkowski, Adam; Lees, David N; Lowther, James; Taylor, Nick

    2018-01-01

    Norovirus is a major cause of viral gastroenteritis, with shellfish consumption being identified as one potential norovirus entry point into the human population. Minimising shellfish norovirus levels is therefore important for both the consumer's protection and the shellfish industry's reputation. One method used to reduce microbiological risks in shellfish is depuration; however, this process also presents additional costs to industry. Providing a mechanism to estimate norovirus levels during depuration would therefore be useful to stakeholders. This paper presents a mathematical model of the depuration process and its impact on norovirus levels found in shellfish. Two fundamental stages of norovirus depuration are considered: (i) the initial distribution of norovirus loads within a shellfish population and (ii) the way in which the initial norovirus loads evolve during depuration. Realistic assumptions are made about the dynamics of norovirus during depuration, and mathematical descriptions of both stages are derived and combined into a single model. Parameters to describe the depuration effect and norovirus load values are derived from existing norovirus data obtained from U.K. harvest sites. However, obtaining population estimates of norovirus variability is time-consuming and expensive; this model addresses the issue by assuming a 'worst case scenario' for variability of pathogens, which is independent of mean pathogen levels. The model is then used to predict minimum depuration times required to achieve norovirus levels which fall within possible risk management levels, as well as predictions of minimum depuration times for other water-borne pathogens found in shellfish. Times for Escherichia coli predicted by the model all fall within the minimum 42 hours required for class B harvest sites, whereas minimum depuration times for norovirus and FRNA+ bacteriophage are substantially longer. Thus this study provides relevant information and tools to assist

  10. Predictability of the intra-seasonal rainfall characteristics variables over South Africa

    CSIR Research Space (South Africa)

    Phakula, S

    2015-09-01

    Full Text Available for the homogeneous rainfall regions. Keywords: Retro-active validation, Forecast skill, Area-averaged ROC scores, Reliability diagrams. Introduction Southern Africa is a region of significant rainfall variability on a range of temporal and spacial scales... are evaluated using retro-actively generated hindcasts through canonical correlation analysis (CCA). Retro-active forecast validation is a robust method to assess forecast model performance and give unbiased skill levels (Landman et al., 2001). Two...

  11. A predictability study of Lorenz's 28-variable model as a dynamical system

    Science.gov (United States)

    Krishnamurthy, V.

    1993-01-01

    The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.

  12. Value of Serial Heart Rate Variability Measurement for Prediction of Appropriate ICD Discharge in Patients with Heart Failure

    NARCIS (Netherlands)

    ten Sande, Judith N.; Damman, Peter; Tijssen, Jan G. P.; de Groot, Joris R.; Knops, Reinoud E.; Wilde, Arthur A. M.; van Dessel, Pascal F. H. M.

    2014-01-01

    HRV and Appropriate ICD Shock in Heart Failure Introduction Decreased heart rate variability (HRV) is associated with adverse outcomes in patients with heart failure. Our objective was to examine whether decreased HRV predicts appropriate implantable cardioverter defibrillator (ICD) shocks. Methods

  13. Clinical significance and predictive factors of early massive recurrence after radiofrequency ablation in patients with a single small hepatocellular carcinoma

    Directory of Open Access Journals (Sweden)

    Ju-Yeon Cho

    2016-12-01

    Full Text Available Background/Aims Radiofrequency ablation (RFA is one of the most frequently applied curative treatments in patients with a single small hepatocellular carcinoma (HCC. However, the clinical significance of and risk factors for early massive recurrence after RFA—a dreadful event limiting further curative treatment—have not been fully evaluated. Methods In total, 438 patients with a single HCC of size ≤3 cm who underwent percutaneous RFA as an initial treatment between 2006 and 2009 were included. Baseline patient characteristics, overall survival, predictive factors, and recurrence after RFA were evaluated. In addition, the incidence, impact on survival, and predictive factors of early massive recurrence, and initial recurrence beyond the Milan criteria within 2 years were also investigated. Results During the median follow-up of 68.4 months, recurrent HCC was confirmed in 302 (68.9% patients, with early massive recurrence in 27 patients (6.2%. The 1-, 3-, and 5-year overall survival rates were 95.4%, 84.7%, and 81.8%, respectively, in patients with no recurrence, 99.6%, 86.4%, and 70.1% in patients with recurrence within the Milan criteria or late recurrence, and 92.6%, 46.5%, and 0.05% in patients with early massive recurrence. Multivariable analysis identified older age, Child-Pugh score B or C, and early massive recurrence as predictive of poor overall survival. A tumor size of ≥2 cm and tumor location adjacent to the colon were independent risk factors predictive of early massive recurrence. Conclusions Early massive recurrence is independently predictive of poor overall survival after RFA in patients with a single small HCC. Tumors sized ≥2 cm and located adjacent to the colon appear to be independent risk factors for early massive recurrence.

  14. Using k-dependence causal forest to mine the most significant dependency relationships among clinical variables for thyroid disease diagnosis.

    Directory of Open Access Journals (Sweden)

    LiMin Wang

    Full Text Available Numerous data mining models have been proposed to construct computer-aided medical expert systems. Bayesian network classifiers (BNCs are more distinct and understandable than other models. To graphically describe the dependency relationships among clinical variables for thyroid disease diagnosis and ensure the rationality of the diagnosis results, the proposed k-dependence causal forest (KCF model generates a series of submodels in the framework of maximum spanning tree (MST and demonstrates stronger dependence representation. Friedman test on 12 UCI datasets shows that KCF has classification accuracy advantage over the other state-of-the-art BNCs, such as Naive Bayes, tree augmented Naive Bayes, and k-dependence Bayesian classifier. Our extensive experimental comparison on 4 medical datasets also proves the feasibility and effectiveness of KCF in terms of sensitivity and specificity.

  15. Chromosomal radiosensitivity: a study of the chromosomal G2 assay in human blood lymphocytes indicating significant inter-individual variability

    International Nuclear Information System (INIS)

    Smart, V.; Curwen, G.B.; Whitehouse, C.A.; Edwards, A.; Tawn, E.J.

    2003-01-01

    The G 2 chromosomal radiosensitivity assay is a technically demanding assay. To ensure that it is reproducible in our laboratory, we have examined the effects of storage and culture conditions by applying the assay to a group of healthy controls and determined the extent of intra- and inter-individual variations. Nineteen different individuals provided one or more blood samples resulting in a total of 57 successful tests. Multiple cultures from a single blood sample showed no statistically significant difference in the number of chromatid type aberrations between cultures. A 24 h delay prior to culturing the lymphocytes did not significantly affect the induced G 2 score. Intra-individual variation was not statistically significant in seven out of nine individuals. Inter-individual variation was highly statistically significant (P<0.001), indicating that there is a real difference between individuals in the response to radiation using this assay

  16. Variability and predictability of decadal mean temperature and precipitation over China in the CCSM4 last millennium simulation

    Science.gov (United States)

    Ying, Kairan; Frederiksen, Carsten S.; Zheng, Xiaogu; Lou, Jiale; Zhao, Tianbao

    2018-02-01

    The modes of variability that arise from the slow-decadal (potentially predictable) and intra-decadal (unpredictable) components of decadal mean temperature and precipitation over China are examined, in a 1000 year (850-1850 AD) experiment using the CCSM4 model. Solar variations, volcanic aerosols, orbital forcing, land use, and greenhouse gas concentrations provide the main forcing and boundary conditions. The analysis is done using a decadal variance decomposition method that identifies sources of potential decadal predictability and uncertainty. The average potential decadal predictabilities (ratio of slow-to-total decadal variance) are 0.62 and 0.37 for the temperature and rainfall over China, respectively, indicating that the (multi-)decadal variations of temperature are dominated by slow-decadal variability, while precipitation is dominated by unpredictable decadal noise. Possible sources of decadal predictability for the two leading predictable modes of temperature are the external radiative forcing, and the combined effects of slow-decadal variability of the Arctic oscillation (AO) and the Pacific decadal oscillation (PDO), respectively. Combined AO and PDO slow-decadal variability is associated also with the leading predictable mode of precipitation. External radiative forcing as well as the slow-decadal variability of PDO are associated with the second predictable rainfall mode; the slow-decadal variability of Atlantic multi-decadal oscillation (AMO) is associated with the third predictable precipitation mode. The dominant unpredictable decadal modes are associated with intra-decadal/inter-annual phenomena. In particular, the El Niño-Southern Oscillation and the intra-decadal variability of the AMO, PDO and AO are the most important sources of prediction uncertainty.

  17. Seasonal Variability of Aragonite Saturation State in the North Pacific Ocean Predicted by Multiple Linear Regression

    Science.gov (United States)

    Kim, T. W.; Park, G. H.

    2014-12-01

    Seasonal variation of aragonite saturation state (Ωarag) in the North Pacific Ocean (NPO) was investigated, using multiple linear regression (MLR) models produced from the PACIFICA (Pacific Ocean interior carbon) dataset. Data within depth ranges of 50-1200m were used to derive MLR models, and three parameters (potential temperature, nitrate, and apparent oxygen utilization (AOU)) were chosen as predictor variables because these parameters are associated with vertical mixing, DIC (dissolved inorganic carbon) removal and release which all affect Ωarag in water column directly or indirectly. The PACIFICA dataset was divided into 5° × 5° grids, and a MLR model was produced in each grid, giving total 145 independent MLR models over the NPO. Mean RMSE (root mean square error) and r2 (coefficient of determination) of all derived MLR models were approximately 0.09 and 0.96, respectively. Then the obtained MLR coefficients for each of predictor variables and an intercept were interpolated over the study area, thereby making possible to allocate MLR coefficients to data-sparse ocean regions. Predictability from the interpolated coefficients was evaluated using Hawaiian time-series data, and as a result mean residual between measured and predicted Ωarag values was approximately 0.08, which is less than the mean RMSE of our MLR models. The interpolated MLR coefficients were combined with seasonal climatology of World Ocean Atlas 2013 (1° × 1°) to produce seasonal Ωarag distributions over various depths. Large seasonal variability in Ωarag was manifested in the mid-latitude Western NPO (24-40°N, 130-180°E) and low-latitude Eastern NPO (0-12°N, 115-150°W). In the Western NPO, seasonal fluctuations of water column stratification appeared to be responsible for the seasonal variation in Ωarag (~ 0.5 at 50 m) because it closely followed temperature variations in a layer of 0-75 m. In contrast, remineralization of organic matter was the main cause for the seasonal

  18. Predictability experiments for the Asian summer monsoon: impact of SST anomalies on interannual and intraseasonal variability

    International Nuclear Information System (INIS)

    Molteni, Franco; Corti, Susanna; Ferranti, Laura; Slingo, Julia M.

    2003-07-01

    The effects of SST anomalies on the interannual and intraseasonal variability of the Asian summer monsoon have been studied by multivariate statistical analyses of 850-hPa wind and rainfall fields simulated in a set of ensemble integrations of the ECMWF atmospheric GCM, referred to as the PRISM experiments. The simulations used observed SSTs (PRISM-O), covering 9 years characterised by large variations of the ENSO phenomenon in the 1980's and the early 1990's. A parallel set of simulations was also performed with climatological SSTs (PRISM-C), thus enabling the influence of SST forcing on the modes of interannual and intraseasonal variability to be investigated. As in observations, the model's interannual variability is dominated by a zonally-oriented mode which describes the north-south movement of the tropical convergence zone (TCZ). This mode appears to be independent of SST forcing and its robustness between the PRISM-O and PRISM-C simulations suggests that it is driven by internal atmospheric dynamics. On the other hand, the second mode of variability, which again has a good correspondence with observed patterns, shows a clear relationship with the ENSO cycle. Since the mode related to ENSO accounts for only a small part of the total variance, the notion of a quasi-linear superposition of forced and unforced modes of variability may not provide an appropriate interpretation of monsoon interannual variability. Consequently, the possibility of a non-linear influence has been investigated by exploring the relationship between interannual and intraseasonal variability. As in other studies, a common mode of interannual and intraseasonal variability has been found, in this case describing the north-south transition of the TCZ associated with monsoon active/break cycles. Although seasonal-mean values of the Principal Component (PC) timeseries associated with the leading intraseasonal mode shows no significant correlation with ENSO, the 2-dimensional probability

  19. Predictability experiments for the Asian summer monsoon: Impact of SST anomalies on interannual and intraseasonal variability

    International Nuclear Information System (INIS)

    Molteni, F.; Corti, S.; Ferranti, L.; Slingo, J.M.

    2002-04-01

    The effects of SST anomalies on the interannual and intraseasonal variability of the Asian summer monsoon have been studied by multivariate statistical analyses of 850-hPa wind and rainfall yields simulated in a set of ensemble integrations of the ECMWF atmospheric GCM, referred to as the PRISM experiments. The simulations used observed SSTs (PRISM-O), covering 9 years characterised by large variations of the ENSO phenomenon in the 1980's and the early 1990's. A parallel set of simulations was also performed with climatological SSTs (PRISM-C), thus enabling the influence of SST forcing on the modes of interannual and intraseasonal variability to be investigated. As in observations, the model's interannual variability is dominated by a zonally-oriented mode which describes the north-south movement of the tropical convergence zone (TCZ). This mode appears to be independent of SST forcing and its robustness between the PRISM-O and PRISM-C simulations suggests that it is driven by internal atmospheric dynamics. On the other hand, the second mode of variability, which again has a good correspondence with observed patterns, shows a clear relationship with the ENSO cycle. Since the mode related to ENSO accounts for only a small part of the total variance, the notion of a quasi-linear superposition of forced and unforced modes of variability may not provide an appropriate interpretation of monsoon interannual variability. Consequently, the possibility of a non-linear influence has been investigated by exploring the relationship between interannual and intraseasonal variability. As in other studies, a common mode of interannual and intraseasonal variability has been found, in this case describing the north-south transition of the TCZ associated with monsoon active/break cycles. Although seasonal-mean values of the Principal Component (PC) timeseries associated with the leading intraseasonal mode shows no significant correlation with ENSO, the 2-dimensional probability

  20. Histological variability in the limb bones of the Asiatic wild ass and its significance for life history inferences.

    Science.gov (United States)

    Nacarino-Meneses, Carmen; Jordana, Xavier; Köhler, Meike

    2016-01-01

    The study of bone growth marks (BGMs) and other histological traits of bone tissue provides insights into the life history of present and past organisms. Important life history traits like longevity or age at maturity, which could be inferred from the analysis of these features, form the basis for estimations of demographic parameters that are essential in ecological and evolutionary studies of vertebrates. Here, we study the intraskeletal histological variability in an ontogenetic series of Asiatic wild ass ( Equus hemionus ) in order to assess the suitability of several skeletal elements to reconstruct the life history strategy of the species. Bone tissue types, vascular canal orientation and BGMs have been analyzed in 35 cross-sections of femur, tibia and metapodial bones of 9 individuals of different sexes, ages and habitats. Our results show that the number of BGMs recorded by the different limb bones varies within the same specimen. Our study supports that the femur is the most reliable bone for skeletochronology, as already suggested. Our findings also challenge traditional beliefs with regard to the meaning of deposition of the external fundamental system (EFS). In the Asiatic wild ass, this bone tissue is deposited some time after skeletal maturity and, in the case of the femora, coinciding with the reproductive maturity of the species. The results obtained from this research are not only relevant for future studies in fossil Equus , but could also contribute to improve the conservation strategies of threatened equid species.

  1. Role of hyaluronic acid and laminin as serum markers for predicting significant fibrosis in patients with chronic hepatitis B

    Directory of Open Access Journals (Sweden)

    Feng Li

    Full Text Available OBJECTIVES: The aim of this study was to evaluate the diagnostic performance of serum HA and LN as serum markers for predicting significant fibrosis in CHB patients. METHODS: Serum HA and LN levels of 87 patients with chronic hepatitis B and 19 blood donors were assayed by RIA. Liver fibrosis stages were determined according to the Metavir scoring-system. The diagnostic performances of all indexes were evaluated by the receiver operating characteristic (ROC curves. RESULTS: Serum HA and LN concentrations increased significantly with the stage of hepatic fibrosis, which showed positive correlation with the stages of liver fibrosis (HA: r = 0.875, p < 0.001; LN: r = 0.610, p < 0.001. There were significant differences of serum HA and LN levels between F2-4 group in comparison with those in F0-F1 group (p < 0.001 and controls (p < 0.001, respectively. From ROC curves, 185.3 ng/mL as the optimal cut-off value of serum HA for diagnosis of significant fibrosis, giving its sensitivity, specificity, PPV, NPV, LR+, LR- and AC of 84.2%, 83.3%, 90.6%, 73.5%, 5.04, 0.19 and 83.9, respectively. While 132.7 ng/mL was the optimal cut-off value of serum LN, the sensitivity, specificity, PPV, NPV, LR+, LR- and AC were 71.9%, 80.0%, 87.2%, 60.0%, 3.59%, 0.35% and 74.7, respectively. Combinations of HA and LN by serial tests showed a perfect specificity and PPV of 100%, at the same time sensitivity declined to 63.2% and LR+ increased to 18.9, while parallel tests revealed a good sensitivity of 94.7%, NPV to 86.4%, and LR- declined to 0.08. CONCLUSIONS: Serum HA and LN concentrations showed positive correlation with the stages of liver fibrosis. Detection of serum HA and LN in predicting significant fibrosis showed good diagnostic performance, which would be further optimized by combination of the two indices. HA and LN would be clinically useful serum markers for predicting significant fibrosis in patients with chronic hepatitis B, when liver biopsy is

  2. Flow prediction models using macroclimatic variables and multivariate statistical techniques in the Cauca River Valley

    International Nuclear Information System (INIS)

    Carvajal Escobar Yesid; Munoz, Flor Matilde

    2007-01-01

    The project this centred in the revision of the state of the art of the ocean-atmospheric phenomena that you affect the Colombian hydrology especially The Phenomenon Enos that causes a socioeconomic impact of first order in our country, it has not been sufficiently studied; therefore it is important to approach the thematic one, including the variable macroclimates associated to the Enos in the analyses of water planning. The analyses include revision of statistical techniques of analysis of consistency of hydrological data with the objective of conforming a database of monthly flow of the river reliable and homogeneous Cauca. Statistical methods are used (Analysis of data multivariante) specifically The analysis of principal components to involve them in the development of models of prediction of flows monthly means in the river Cauca involving the Lineal focus as they are the model autoregressive AR, ARX and Armax and the focus non lineal Net Artificial Network.

  3. Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

    Science.gov (United States)

    Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

    2018-03-27

    Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

  4. Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

    Science.gov (United States)

    Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

    2017-12-01

    Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in

  5. High Interannual Variability in Connectivity and Genetic Pool of a Temperate Clingfish Matches Oceanographic Transport Predictions

    Science.gov (United States)

    Teixeira, Sara; Assis, Jorge; Serrão, Ester A.; Gonçalves, Emanuel J.; Borges, Rita

    2016-01-01

    Adults of most marine benthic and demersal fish are site-attached, with the dispersal of their larval stages ensuring connectivity among populations. In this study we aimed to infer spatial and temporal variation in population connectivity and dispersal of a marine fish species, using genetic tools and comparing these with oceanographic transport. We focused on an intertidal rocky reef fish species, the shore clingfish Lepadogaster lepadogaster, along the southwest Iberian Peninsula, in 2011 and 2012. We predicted high levels of self-recruitment and distinct populations, due to short pelagic larval duration and because all its developmental stages have previously been found near adult habitats. Genetic analyses based on microsatellites countered our prediction and a biophysical dispersal model showed that oceanographic transport was a good explanation for the patterns observed. Adult sub-populations separated by up to 300 km of coastline displayed no genetic differentiation, revealing a single connected population with larvae potentially dispersing long distances over hundreds of km. Despite this, parentage analysis performed on recruits from one focal site within the Marine Park of Arrábida (Portugal), revealed self-recruitment levels of 2.5% and 7.7% in 2011 and 2012, respectively, suggesting that both long- and short-distance dispersal play an important role in the replenishment of these populations. Population differentiation and patterns of dispersal, which were highly variable between years, could be linked to the variability inherent in local oceanographic processes. Overall, our measures of connectivity based on genetic and oceanographic data highlight the relevance of long-distance dispersal in determining the degree of connectivity, even in species with short pelagic larval durations. PMID:27911952

  6. The predicted CLARREO sampling error of the inter-annual SW variability

    Science.gov (United States)

    Doelling, D. R.; Keyes, D. F.; Nguyen, C.; Macdonnell, D.; Young, D. F.

    2009-12-01

    The NRC Decadal Survey has called for SI traceability of long-term hyper-spectral flux measurements in order to monitor climate variability. This mission is called the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and is currently defining its mission requirements. The requirements are focused on the ability to measure decadal change of key climate variables at very high accuracy. The accuracy goals are set using anticipated climate change magnitudes, but the accuracy achieved for any given climate variable must take into account the temporal and spatial sampling errors based on satellite orbits and calibration accuracy. The time period to detect a significant trend in the CLARREO record depends on the magnitude of the sampling calibration errors relative to the current inter-annual variability. The largest uncertainty in climate feedbacks remains the effect of changing clouds on planetary energy balance. Some regions on earth have strong diurnal cycles, such as maritime stratus and afternoon land convection; other regions have strong seasonal cycles, such as the monsoon. However, when monitoring inter-annual variability these cycles are only important if the strength of these cycles vary on decadal time scales. This study will attempt to determine the best satellite constellations to reduce sampling error and to compare the error with the current inter-annual variability signal to ensure the viability of the mission. The study will incorporate Clouds and the Earth's Radiant Energy System (CERES) (Monthly TOA/Surface Averages) SRBAVG product TOA LW and SW climate quality fluxes. The fluxes are derived by combining Terra (10:30 local equator crossing time) CERES fluxes with 3-hourly 5-geostationary satellite estimated broadband fluxes, which are normalized using the CERES fluxes, to complete the diurnal cycle. These fluxes were saved hourly during processing and considered the truth dataset. 90°, 83° and 74° inclination precessionary orbits as

  7. HIV-1 infection during pregnancy and in children : significance of HIV-1 variability and the placental barrier

    OpenAIRE

    Casper, Charlotte

    2001-01-01

    With the global increase in human immunodeficiency virus 1 (HIV-1) infection in women of childbearing age, there has also been an alarming increase in the number of mother-to-child transmissions of HIV-1. Although antiretroviral therapy and Cesarian section have been demonstrated to significantly decrease the vertical transmission rate of , these interventions are not widely available in the developing world. Therefore, studies of the mechanisms of vertical transmission are ...

  8. Study of prognostic significance of antenatal ultrasonography and renin angiotensin system activation in predicting disease severity in posterior urethral valves

    Directory of Open Access Journals (Sweden)

    Divya Bhadoo

    2015-01-01

    Full Text Available Aims: Study on prognostic significance of antenatal ultrasonography and renin angiotensin system activation in predicting disease severity in posterior urethral valves. Materials and Methods: Antenatally diagnosed hydronephrosis patients were included. Postnatally, they were divided into two groups, posterior urethral valve (PUV and non-PUV. The studied parameters were: Gestational age at detection, surgical intervention, ultrasound findings, cord blood and follow up plasma renin activity (PRA values, vesico-ureteric reflux (VUR, renal scars, and glomerular filtration rate (GFR. Results: A total of 25 patients were included, 10 PUV and 15 non-PUV. All infants with PUV underwent primary valve incision. GFR was less than 60 ml/min/1.73 m 2 body surface area in 4 patients at last follow-up. Keyhole sign, oligoamnios, absent bladder cycling, and cortical cysts were not consistent findings on antenatal ultrasound in PUV. Cord blood PRA was significantly higher (P < 0.0001 in PUV compared to non-PUV patients. Gestational age at detection of hydronephrosis, cortical cysts, bladder wall thickness, and amniotic fluid index were not significantly correlated with GFR while PRA could differentiate between poor and better prognosis cases with PUV. Conclusions: Ultrasound was neither uniformly useful in diagnosing PUV antenatally, nor differentiating it from cases with non-PUV hydronephrosis. In congenital hydronephrosis, cord blood PRA was significantly higher in cases with PUV compared to non-PUV cases and fell significantly after valve ablation. Cord blood PRA could distinguish between poor and better prognosis cases with PUV.

  9. Emotionally Excited Eyeblink-Rate Variability Predicts an Experience of Transportation into the Narrative World

    Directory of Open Access Journals (Sweden)

    Ryota eNomura

    2015-04-01

    Full Text Available Collective spectator communications such as oral presentations, movies, and storytelling performances are ubiquitous in human culture. This study investigated the effects of past viewing experiences and differences in expressive performance on an audience’s transportive experience into a created world of a storytelling performance. In the experiment, 60 participants (mean age = 34.12 yrs., SD = 13.18 yrs., range 18–63 yrs. were assigned to watch one of two videotaped performances that were played (1 in an orthodox way for frequent viewers and (2 in a modified way aimed at easier comprehension for first-time viewers. Eyeblink synchronization among participants was quantified by employing distance-based measurements of spike trains, Dspike and Dinterval (Victor & Purpura, 1997. The results indicated that even non-familiar participants’ eyeblinks were synchronized as the story progressed and that the effect of the viewing experience on transportation was weak. Rather, the results of a multiple regression analysis demonstrated that the degrees of transportation could be predicted by a retrospectively reported humor experience and higher real-time variability (i.e., logarithmic transformed standard deviation of inter blink intervals during a performance viewing. The results are discussed from the viewpoint in which the extent of eyeblink synchronization and eyeblink-rate variability acts as an index of the inner experience of audience members.

  10. Dynamic Network Communication in the Human Functional Connectome Predicts Perceptual Variability in Visual Illusion.

    Science.gov (United States)

    Wang, Zhiwei; Zeljic, Kristina; Jiang, Qinying; Gu, Yong; Wang, Wei; Wang, Zheng

    2018-01-01

    Ubiquitous variability between individuals in visual perception is difficult to standardize and has thus essentially been ignored. Here we construct a quantitative psychophysical measure of illusory rotary motion based on the Pinna-Brelstaff figure (PBF) in 73 healthy volunteers and investigate the neural circuit mechanisms underlying perceptual variation using functional magnetic resonance imaging (fMRI). We acquired fMRI data from a subset of 42 subjects during spontaneous and 3 stimulus conditions: expanding PBF, expanding modified-PBF (illusion-free) and expanding modified-PBF with physical rotation. Brain-wide graph analysis of stimulus-evoked functional connectivity patterns yielded a functionally segregated architecture containing 3 discrete hierarchical networks, commonly shared between rest and stimulation conditions. Strikingly, communication efficiency and strength between 2 networks predominantly located in visual areas robustly predicted individual perceptual differences solely in the illusory stimulus condition. These unprecedented findings demonstrate that stimulus-dependent, not spontaneous, dynamic functional integration between distributed brain networks contributes to perceptual variability in humans. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  11. Sleep-disordered breathing in patients with COPD and mild hypoxemia: prevalence and predictive variables.

    Science.gov (United States)

    Silva, José Laerte Rodrigues; Conde, Marcus Barreto; Corrêa, Krislainy de Sousa; Rabahi, Helena; Rocha, Arthur Alves; Rabahi, Marcelo Fouad

    2017-01-01

    To infer the prevalence and variables predictive of isolated nocturnal hypoxemia and obstructive sleep apnea (OSA) in patients with COPD and mild hypoxemia. This was a cross-sectional study involving clinically stable COPD outpatients with mild hypoxemia (oxygen saturation = 90-94%) at a clinical center specializing in respiratory diseases, located in the city of Goiânia, Brazil. The patients underwent clinical evaluation, spirometry, polysomnography, echocardiography, arterial blood gas analysis, six-minute walk test assessment, and chest X-ray. The sample included 64 patients with COPD and mild hypoxemia; 39 (61%) were diagnosed with sleep-disordered breathing (OSA, in 14; and isolated nocturnal hypoxemia, in 25). Correlation analysis showed that PaO2 correlated moderately with mean sleep oxygen saturation (r = 0.45; p = 0.0002), mean rapid eye movement (REM) sleep oxygen saturation (r = 0.43; p = 0.001), and mean non-REM sleep oxygen saturation (r = 0.42; p = 0.001). A cut-off point of PaO2 ≤ 70 mmHg in the arterial blood gas analysis was significantly associated with sleep-disordered breathing (OR = 4.59; 95% CI: 1.54-13.67; p = 0.01). The model showed that, for identifying sleep-disordered breathing, the cut-off point had a specificity of 73.9% (95% CI: 51.6-89.8%), a sensitivity of 63.4% (95% CI: 46.9-77.9%), a positive predictive value of 81.3% (95% CI: 67.7-90.0%), and a negative predictive value of 53.1% (95% CI: 41.4-64.4%), with an area under the ROC curve of 0.69 (95% CI: 0.57-0.80), correctly classifying the observations in 67.2% of the cases. In our sample of patients with COPD and mild hypoxemia, the prevalence of sleep-disordered breathing was high (61%), suggesting that such patients would benefit from sleep studies. Inferir a prevalência e as variáveis preditivas de hipoxemia noturna e apneia obstrutiva do sono (AOS) em pacientes portadores de DPOC com hipoxemia leve. Estudo transversal realizado em pacientes ambulatoriais, clinicamente est

  12. Increasing work-time influence: consequences for flexibility, variability, regularity and predictability.

    Science.gov (United States)

    Nabe-Nielsen, Kirsten; Garde, Anne Helene; Aust, Birgit; Diderichsen, Finn

    2012-01-01

    This quasi-experimental study investigated how an intervention aiming at increasing eldercare workers' influence on their working hours affected the flexibility, variability, regularity and predictability of the working hours. We used baseline (n = 296) and follow-up (n = 274) questionnaire data and interviews with intervention-group participants (n = 32). The work units in the intervention group designed their own intervention comprising either implementation of computerised self-scheduling (subgroup A), collection of information about the employees' work-time preferences by questionnaires (subgroup B), or discussion of working hours (subgroup C). Only computerised self-scheduling changed the working hours and the way they were planned. These changes implied more flexible but less regular working hours and an experience of less predictability and less continuity in the care of clients and in the co-operation with colleagues. In subgroup B and C, the participants ended up discussing the potential consequences of more work-time influence without actually implementing any changes. Employee work-time influence may buffer the adverse effects of shift work. However, our intervention study suggested that while increasing the individual flexibility, increasing work-time influence may also result in decreased regularity of the working hours and less continuity in the care of clients and co-operation with colleagues.

  13. Quantifying the effect of microstructure variability on the yield strength predictions of Ni-base superalloys

    Energy Technology Data Exchange (ETDEWEB)

    Tiley, J.S. [Air Force Research Laboratory, Wright Patterson AFB, OH 45433 (United States); Kim, S.L.; Parthasarathy, T.A. [UES, Inc., Wright Patterson AFB, OH 45433 (United States); Loughnane, G.T. [Wright State University, Dayton, OH 45435 (United States); Kublik, R.; Salem, A.A. [Materials Resources LLC, Dayton, OH 45402 (United States)

    2017-02-08

    Physics-based models for predicting the mechanical behavior of Ni-based superalloys as a function of microstructure features require the use of microstructure data for calibration and verification. Accurate representation of the heterogeneity of microstructure features requires accurate selection of the representative microstructure data size (i.e. image size). Thus, this work is carried out to address the influence of microstructure data size on the accuracy of a discrete dislocation dynamic model in predicting the critical resolved share stress (CRSS) of IN100. Microstructure features from backscattered electron images were extracted using image processing techniques. Single point statistics (e.g. area fraction, precipitate size, and distance between γ' particles) and higher order statistics using two-point correlations were calculated from segmented 2-D images. Modified Bhattacharyya Coefficient analysis techniques were employed to calculate three-dimensional particle size distributions. Results indicate a significant influence of the microstructure data size on the calculated CRSS.

  14. Pathogen prevalence predicts human cross-cultural variability in individualism/collectivism.

    Science.gov (United States)

    Fincher, Corey L; Thornhill, Randy; Murray, Damian R; Schaller, Mark

    2008-06-07

    Pathogenic diseases impose selection pressures on the social behaviour of host populations. In humans (Homo sapiens), many psychological phenomena appear to serve an antipathogen defence function. One broad implication is the existence of cross-cultural differences in human cognition and behaviour contingent upon the relative presence of pathogens in the local ecology. We focus specifically on one fundamental cultural variable: differences in individualistic versus collectivist values. We suggest that specific behavioural manifestations of collectivism (e.g. ethnocentrism, conformity) can inhibit the transmission of pathogens; and so we hypothesize that collectivism (compared with individualism) will more often characterize cultures in regions that have historically had higher prevalence of pathogens. Drawing on epidemiological data and the findings of worldwide cross-national surveys of individualism/collectivism, our results support this hypothesis: the regional prevalence of pathogens has a strong positive correlation with cultural indicators of collectivism and a strong negative correlation with individualism. The correlations remain significant even when controlling for potential confounding variables. These results help to explain the origin of a paradigmatic cross-cultural difference, and reveal previously undocumented consequences of pathogenic diseases on the variable nature of human societies.

  15. Prediction of employer-employee relationships from sociodemographic variables and social values in Brunei public and private sector workers.

    Science.gov (United States)

    Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

    2017-01-01

    The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer-employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer-employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer-employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer-employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer-employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer-employee relationship problems. Other significant social value correlates, but not predictors of employer-employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer-employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its

  16. Prediction of employer–employee relationships from sociodemographic variables and social values in Brunei public and private sector workers

    Science.gov (United States)

    Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

    2017-01-01

    The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer–employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer–employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer–employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer–employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer–employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer–employee relationship problems. Other significant social value correlates, but not predictors of employer–employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer–employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the

  17. Unbiased proteomics analysis demonstrates significant variability in mucosal immune factor expression depending on the site and method of collection.

    Directory of Open Access Journals (Sweden)

    Kenzie M Birse

    Full Text Available Female genital tract secretions are commonly sampled by lavage of the ectocervix and vaginal vault or via a sponge inserted into the endocervix for evaluating inflammation status and immune factors critical for HIV microbicide and vaccine studies. This study uses a proteomics approach to comprehensively compare the efficacy of these methods, which sample from different compartments of the female genital tract, for the collection of immune factors. Matching sponge and lavage samples were collected from 10 healthy women and were analyzed by tandem mass spectrometry. Data was analyzed by a combination of differential protein expression analysis, hierarchical clustering and pathway analysis. Of the 385 proteins identified, endocervical sponge samples collected nearly twice as many unique proteins as cervicovaginal lavage (111 vs. 61 with 55% of proteins common to both (213. Each method/site identified 73 unique proteins that have roles in host immunity according to their gene ontology. Sponge samples enriched for specific inflammation pathways including acute phase response proteins (p = 3.37×10(-24 and LXR/RXR immune activation pathways (p = 8.82×10(-22 while the role IL-17A in psoriasis pathway (p = 5.98×10(-4 and the complement system pathway (p = 3.91×10(-3 were enriched in lavage samples. Many host defense factors were differentially enriched (p<0.05 between sites including known/potential antimicrobial factors (n = 21, S100 proteins (n = 9, and immune regulatory factors such as serpins (n = 7. Immunoglobulins (n = 6 were collected at comparable levels in abundance in each site although 25% of those identified were unique to sponge samples. This study demonstrates significant differences in types and quantities of immune factors and inflammation pathways collected by each sampling technique. Therefore, clinical studies that measure mucosal immune activation or factors assessing HIV transmission should utilize

  18. Towards an improved prediction of the free radical scavenging potency of flavonoids: the significance of double PCET mechanisms.

    Science.gov (United States)

    Amić, Ana; Marković, Zoran; Dimitrić Marković, Jasmina M; Stepanić, Višnja; Lučić, Bono; Amić, Dragan

    2014-01-01

    The 1H(+)/1e(-) and 2H(+)/2e(-) proton-coupled electron transfer (PCET) processes of free radical scavenging by flavonoids were theoretically studied for aqueous and lipid environments using the PM6 and PM7 methods. The results reported here indicate that the significant contribution of the second PCET mechanism, resulting in the formation of a quinone/quinone methide, effectively discriminates the active from inactive flavonoids. The predictive potency of descriptors related to the energetics of second PCET mechanisms (the second O-H bond dissociation enthalpy (BDE2) related to hydrogen atom transfer (HAT) mechanism, and the second electron transfer enthalpy (ETE2) related to sequential proton loss electron transfer (SPLET) mechanism) are superior to the currently used indices, which are related to the first 1H(+)/1e(-) processes, and could serve as primary descriptors in development of the QSAR (quantitative structure-activity relationships) of flavonoids. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Significance of uncertainties derived from settling tank model structure and parameters on predicting WWTP performance - A global sensitivity analysis study

    DEFF Research Database (Denmark)

    Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen

    2011-01-01

    Uncertainty derived from one of the process models – such as one-dimensional secondary settling tank (SST) models – can impact the output of the other process models, e.g., biokinetic (ASM1), as well as the integrated wastewater treatment plant (WWTP) models. The model structure and parameter...... and from the last aerobic bioreactor upstream to the SST (Garrett/hydraulic method). For model structure uncertainty, two one-dimensional secondary settling tank (1-D SST) models are assessed, including a first-order model (the widely used Takács-model), in which the feasibility of using measured...... uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based...

  20. The co registration of initial PET on the CT-radiotherapy reduces significantly the variabilities of anatomo-clinical target volume in the child hodgkin disease

    International Nuclear Information System (INIS)

    Metwally, H.; Blouet, A.; David, I.; Rives, M.; Izar, F.; Courbon, F.; Filleron, T.; Laprie, A.; Plat, G.; Vial, J.

    2009-01-01

    It exists a great interobserver variability for the anatomo-clinical target volume (C.T.V.) definition in children suffering of Hodgkin disease. In this study, the co-registration of the PET with F.D.G. on the planning computed tomography has significantly lead to a greater coherence in the clinical target volume definition. (N.C.)

  1. Predictive significance of standardized uptake value parameters of FDG-PET in patients with non-small cell lung carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Duan, X-Y.; Wang, W.; Li, M.; Li, Y.; Guo, Y-M. [PET-CT Center, The First Affiliated Hospital of Xi' an, Jiaotong University, Xi' an, Shaanxi (China)

    2015-02-03

    {sup 18}F-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is widely used to diagnose and stage non-small cell lung cancer (NSCLC). The aim of this retrospective study was to evaluate the predictive ability of different FDG standardized uptake values (SUVs) in 74 patients with newly diagnosed NSCLC. {sup 18}F-FDG PET/CT scans were performed and different SUV parameters (SUV{sub max}, SUV{sub avg}, SUV{sub T/L}, and SUV{sub T/A}) obtained, and their relationship with clinical characteristics were investigated. Meanwhile, correlation and multiple stepwise regression analyses were performed to determine the primary predictor of SUVs for NSCLC. Age, gender, and tumor size significantly affected SUV parameters. The mean SUVs of squamous cell carcinoma were higher than those of adenocarcinoma. Poorly differentiated tumors exhibited higher SUVs than well-differentiated ones. Further analyses based on the pathologic type revealed that the SUV{sub max}, SUV{sub avg}, and SUV{sub T/L} of poorly differentiated adenocarcinoma tumors were higher than those of moderately or well-differentiated tumors. Among these four SUV parameters, SUV{sub T/L} was the primary predictor for tumor differentiation. However, in adenocarcinoma, SUV{sub max} was the determining factor for tumor differentiation. Our results showed that these four SUV parameters had predictive significance related to NSCLC tumor differentiation; SUV{sub T/L} appeared to be most useful overall, but SUV{sub max} was the best index for adenocarcinoma tumor differentiation.

  2. How the choice of safety performance function affects the identification of important crash prediction variables.

    Science.gov (United States)

    Wang, Ketong; Simandl, Jenna K; Porter, Michael D; Graettinger, Andrew J; Smith, Randy K

    2016-03-01

    Across the nation, researchers and transportation engineers are developing safety performance functions (SPFs) to predict crash rates and develop crash modification factors to improve traffic safety at roadway segments and intersections. Generalized linear models (GLMs), such as Poisson or negative binomial regression, are most commonly used to develop SPFs with annual average daily traffic as the primary roadway characteristic to predict crashes. However, while more complex to interpret, data mining models such as boosted regression trees have improved upon GLMs crash prediction performance due to their ability to handle more data characteristics, accommodate non-linearities, and include interaction effects between the characteristics. An intersection data inventory of 36 safety relevant parameters for three- and four-legged non-signalized intersections along state routes in Alabama was used to study the importance of intersection characteristics on crash rate and the interaction effects between key characteristics. Four different SPFs were investigated and compared: Poisson regression, negative binomial regression, regularized generalized linear model, and boosted regression trees. The models did not agree on which intersection characteristics were most related to the crash rate. The boosted regression tree model significantly outperformed the other models and identified several intersection characteristics as having strong interaction effects. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. The importance of histopathological and clinical variables in predicting the evolution of colon cancer.

    Science.gov (United States)

    Diculescu, Mircea; Iacob, Răzvan; Iacob, Speranţa; Croitoru, Adina; Becheanu, Gabriel; Popeneciu, Valentin

    2002-09-01

    It has been a consensus that prognostic factors should always be taken into account before planning treatment in colorectal cancer. A 5 year prospective study was conducted, in order to assess the importance of several histopathological and clinical prognostic variables in the prediction of evolution in colon cancer. Some of the factors included in the analysis are still subject to dispute by different authors. 46 of 53 screened patients qualified to enter the study and underwent a potentially curative resection of the tumor, followed, when necessary, by adjuvant chemotherapy. Univariate and multivariate analyses were carried out in order to identify independent prognostic indicators. The endpoint of the study was considered the recurrence of the tumor or the detection of metastases. 65.2% of the patients had a good evolution during the follow up period. Multivariate survival analysis performed by Cox proportional hazard model identified 3 independent prognostic factors: Dukes stage (p = 0.00002), the grade of differentiation (p = 0.0009) and the weight loss index, representing the weight loss of the patient divided by the number of months when it was actually lost (p = 0.02). Age under 40 years, sex, microscopic aspect of the tumor, tumor location, anemia degree were not identified by our analysis as having prognostic importance. Histopathological factors continue to be the most valuable source of information regarding the possible evolution of patients with colorectal cancer. Individual clinical symptoms or biological parameters such as erytrocyte sedimentation rate or hemoglobin level are of little or no prognostic value. More research is required relating to the impact of a performance status index (which could include also weight loss index) as another reliable prognostic variable.

  4. Significance of cardiac sympathetic nervous system abnormality for predicting vascular events in patients with idiopathic paroxysmal atrial fibrillation

    International Nuclear Information System (INIS)

    Akutsu, Yasushi; Kaneko, Kyouichi; Kodama, Yusuke; Li, Hui-Ling; Kawamura, Mitsuharu; Asano, Taku; Hamazaki, Yuji; Tanno, Kaoru; Kobayashi, Youichi; Suyama, Jumpei; Shinozuka, Akira; Gokan, Takehiko

    2010-01-01

    Neuronal system activity plays an important role for the prognosis of patients with atrial fibrillation (AF). Using 123 I metaiodobenzylguanidine ( 123 I-MIBG) scintigraphy, we investigated whether a cardiac sympathetic nervous system (SNS) abnormality would be associated with an increased risk of vascular events in patients with paroxysmal AF. 123 I-MIBG scintigraphy was performed in 69 consecutive patients (67 ± 13 years, 62% men) with paroxysmal AF who did not have structural heart disease. SNS integrity was assessed from the heart to mediastinum (H/M) ratio on delayed imaging. Serum concentration of C-reactive protein (CRP) was measured before 123 I-MIBG study. During a mean of 4.5 ± 3.6 years follow-up, 19 patients had myocardial infarction, stroke or heart failure (range: 0.2-11.5 years). SNS abnormality (H/M ratio <2.7) and high CRP (≥0.3 mg/dl) were associated with the vascular events (58.3% in 14 of 24 patients with SNS abnormality vs 11.1% in 5 of 45 patients without SNS abnormality, p < 0.0001, 52.4% in 11 of 21 patients with high CRP vs 16.7% in 8 of 48 patients without high CRP, p < 0.0001). After adjustment for potential confounding variables such as age, left atrial dimension and left ventricular function, SNS abnormality was an independent predictor of vascular events with a hazard ratio of 4.1 [95% confidence interval (CI): 1.3-12.6, p = 0.014]. Further, SNS abnormality had an incremental and additive prognostic power in combination with high CRP with an adjusted hazard ratio of 4.1 (95% CI: 1.5-10.9, p = 0.006). SNS abnormality is predictive of vascular events in patients with idiopathic paroxysmal AF. (orig.)

  5. Significance of cardiac sympathetic nervous system abnormality for predicting vascular events in patients with idiopathic paroxysmal atrial fibrillation

    Energy Technology Data Exchange (ETDEWEB)

    Akutsu, Yasushi; Kaneko, Kyouichi; Kodama, Yusuke; Li, Hui-Ling; Kawamura, Mitsuharu; Asano, Taku; Hamazaki, Yuji; Tanno, Kaoru; Kobayashi, Youichi [Showa University School of Medicine, Division of Cardiology, Department of Medicine, Tokyo (Japan); Suyama, Jumpei; Shinozuka, Akira; Gokan, Takehiko [Showa University School of Medicine, Department of Radiology, Tokyo (Japan)

    2010-04-15

    Neuronal system activity plays an important role for the prognosis of patients with atrial fibrillation (AF). Using {sup 123}I metaiodobenzylguanidine ({sup 123}I-MIBG) scintigraphy, we investigated whether a cardiac sympathetic nervous system (SNS) abnormality would be associated with an increased risk of vascular events in patients with paroxysmal AF. {sup 123}I-MIBG scintigraphy was performed in 69 consecutive patients (67 {+-} 13 years, 62% men) with paroxysmal AF who did not have structural heart disease. SNS integrity was assessed from the heart to mediastinum (H/M) ratio on delayed imaging. Serum concentration of C-reactive protein (CRP) was measured before {sup 123}I-MIBG study. During a mean of 4.5 {+-} 3.6 years follow-up, 19 patients had myocardial infarction, stroke or heart failure (range: 0.2-11.5 years). SNS abnormality (H/M ratio <2.7) and high CRP ({>=}0.3 mg/dl) were associated with the vascular events (58.3% in 14 of 24 patients with SNS abnormality vs 11.1% in 5 of 45 patients without SNS abnormality, p < 0.0001, 52.4% in 11 of 21 patients with high CRP vs 16.7% in 8 of 48 patients without high CRP, p < 0.0001). After adjustment for potential confounding variables such as age, left atrial dimension and left ventricular function, SNS abnormality was an independent predictor of vascular events with a hazard ratio of 4.1 [95% confidence interval (CI): 1.3-12.6, p = 0.014]. Further, SNS abnormality had an incremental and additive prognostic power in combination with high CRP with an adjusted hazard ratio of 4.1 (95% CI: 1.5-10.9, p = 0.006). SNS abnormality is predictive of vascular events in patients with idiopathic paroxysmal AF. (orig.)

  6. Prediction of whole-body fat percentage and visceral adipose tissue mass from five anthropometric variables.

    Directory of Open Access Journals (Sweden)

    Michelle G Swainson

    Full Text Available The conventional measurement of obesity utilises the body mass index (BMI criterion. Although there are benefits to this method, there is concern that not all individuals at risk of obesity-associated medical conditions are being identified. Whole-body fat percentage (%FM, and specifically visceral adipose tissue (VAT mass, are correlated with and potentially implicated in disease trajectories, but are not fully accounted for through BMI evaluation. The aims of this study were (a to compare five anthropometric predictors of %FM and VAT mass, and (b to explore new cut-points for the best of these predictors to improve the characterisation of obesity.BMI, waist circumference (WC, waist-to-hip ratio (WHR, waist-to-height ratio (WHtR and waist/height0.5 (WHT.5R were measured and calculated for 81 adults (40 women, 41 men; mean (SD age: 38.4 (17.5 years; 94% Caucasian. Total body dual energy X-ray absorptiometry with Corescan (GE Lunar iDXA, Encore version 15.0 was also performed to quantify %FM and VAT mass. Linear regression analysis, stratified by sex, was applied to predict both %FM and VAT mass for each anthropometric variable. Within each sex, we used information theoretic methods (Akaike Information Criterion; AIC to compare models. For the best anthropometric predictor, we derived tentative cut-points for classifying individuals as obese (>25% FM for men or >35% FM for women, or > highest tertile for VAT mass.The best predictor of both %FM and VAT mass in men and women was WHtR. Derived cut-points for predicting whole body obesity were 0.53 in men and 0.54 in women. The cut-point for predicting visceral obesity was 0.59 in both sexes.In the absence of more objective measures of central obesity and adiposity, WHtR is a suitable proxy measure in both women and men. The proposed DXA-%FM and VAT mass cut-offs require validation in larger studies, but offer potential for improvement of obesity characterisation and the identification of individuals

  7. Logistic Regression for Seismically Induced Landslide Predictions: Using Uniform Hazard and Geophysical Layers as Predictor Variables

    Science.gov (United States)

    Nowicki, M. A.; Hearne, M.; Thompson, E.; Wald, D. J.

    2012-12-01

    Seismically induced landslides present a costly and often fatal threats in many mountainous regions. Substantial effort has been invested to understand where seismically induced landslides may occur in the future. Both slope-stability methods and, more recently, statistical approaches to the problem are described throughout the literature. Though some regional efforts have succeeded, no uniformly agreed-upon method is available for predicting the likelihood and spatial extent of seismically induced landslides. For use in the U. S. Geological Survey (USGS) Prompt Assessment of Global Earthquakes for Response (PAGER) system, we would like to routinely make such estimates, in near-real time, around the globe. Here we use the recently produced USGS ShakeMap Atlas of historic earthquakes to develop an empirical landslide probability model. We focus on recent events, yet include any digitally-mapped landslide inventories for which well-constrained ShakeMaps are also available. We combine these uniform estimates of the input shaking (e.g., peak acceleration and velocity) with broadly available susceptibility proxies, such as topographic slope and surface geology. The resulting database is used to build a predictive model of the probability of landslide occurrence with logistic regression. The landslide database includes observations from the Northridge, California (1994); Wenchuan, China (2008); ChiChi, Taiwan (1999); and Chuetsu, Japan (2004) earthquakes; we also provide ShakeMaps for moderate-sized events without landslide for proper model testing and training. The performance of the regression model is assessed with both statistical goodness-of-fit metrics and a qualitative review of whether or not the model is able to capture the spatial extent of landslides for each event. Part of our goal is to determine which variables can be employed based on globally-available data or proxies, and whether or not modeling results from one region are transferrable to

  8. The role of Personal Self-Regulation and Regulatory Teaching to predict motivational-affective variables, achievement and satisfaction: A structural model

    Directory of Open Access Journals (Sweden)

    Jesus ede la Fuente

    2015-04-01

    Full Text Available The present investigation examines how personal self-regulation (presage variable and regulatory teaching (process variable of teaching relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning and, finally, how they relate to performance and satisfaction with the learning process (product variables. The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models. A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching-learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching-learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching-learning context at university.

  9. The role of personal self-regulation and regulatory teaching to predict motivational-affective variables, achievement, and satisfaction: a structural model

    Science.gov (United States)

    De la Fuente, Jesus; Zapata, Lucía; Martínez-Vicente, Jose M.; Sander, Paul; Cardelle-Elawar, María

    2014-01-01

    The present investigation examines how personal self-regulation (presage variable) and regulatory teaching (process variable of teaching) relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning) and, finally, how they relate to performance and satisfaction with the learning process (product variables). The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models). A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection) variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching–learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching–learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching–learning context at university. PMID:25964764

  10. Significant variables associated with epilepsy

    International Nuclear Information System (INIS)

    Cheema, F.A.; Qayyum, K.; Ahmad, N.; Makhdoomi, A.; Safdar, A.; Asif, A.; Chaudhry, H.R.

    2003-01-01

    Objective: To study the characteristics of the epileptics and the risk factors contributing to the development of epilepsy. Results: Majority of the subjects were single (77.84%), 1st born among their siblings (25.95%), belonged to low social class (50.63%), and unemployed(25.31%). The major risk factors were family history of illness (23.52%) and positive medical problem around birth (12.66%). The presence of family history of illness, positive medical problem around birth and advanced maternal age at birth were associated with early onset of epilepsy. Vulnerability for the epilepsy also increases among hospital deliveries. Conclusion: Although the present study has identified various risk factors, yet the results need to be further confirmed through case-control studies. (author)

  11. Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System

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    Thamiris B. Campos

    2018-03-01

    Full Text Available Due to the high spatial and temporal variability of atmospheric water vapor associated with the deficient methodologies used in its quantification and the imperfect physics parameterizations incorporated in the models, there are significant uncertainties in characterizing the moisture field. The process responsible for incorporating the information provided by observation into the numerical weather prediction is denominated data assimilation. The best result in atmospheric moisture depend on the correct choice of the moisture control variable. Normalized relative humidity and pseudo-relative humidity are the variables usually used by the main weather prediction centers. The objective of this study is to assess the sensibility of the Center for Weather Forecast and Climate Studies to choose moisture control variable in the data assimilation scheme. Experiments using these variables are carried out. The results show that the pseudo-relative humidity improves the variables that depend on temperature values but damage the moisture field. The opposite results show when the simulation used the normalized relative humidity. These experiments suggest that the pseudo-relative humidity should be used in the cyclical process of data assimilation and the normalized relative humidity should be used in non-cyclic process (e.g., nowcasting application in high resolution.

  12. GIS Based Distributed Runoff Predictions in Variable Source Area Watersheds Employing the SCS-Curve Number

    Science.gov (United States)

    Steenhuis, T. S.; Mendoza, G.; Lyon, S. W.; Gerard Marchant, P.; Walter, M. T.; Schneiderman, E.

    2003-04-01

    Because the traditional Soil Conservation Service Curve Number (SCS-CN) approach continues to be ubiquitously used in GIS-BASED water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed within an integrated GIS modeling environment a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Spatial representation of hydrologic processes is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point source pollution. The methodology presented here uses the traditional SCS-CN method to predict runoff volume and spatial extent of saturated areas and uses a topographic index to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was incorporated in an existing GWLF water quality model and applied to sub-watersheds of the Delaware basin in the Catskill Mountains region of New York State. We found that the distributed CN-VSA approach provided a physically-based method that gives realistic results for watersheds with VSA hydrology.

  13. Neural network and wavelets in prediction of cosmic ray variability: The North Africa as study case

    Science.gov (United States)

    Zarrouk, Neïla; Bennaceur, Raouf

    2010-04-01

    Since the Earth is permanently bombarded with energetic cosmic rays particles, cosmic ray flux has been monitored by ground based neutron monitors for decades. In this work an attempt is made to investigate the decomposition and reconstructions provided by Morlet wavelet technique, using data series of cosmic rays variabilities, then to constitute from this wavelet analysis an input data base for the neural network system with which we can then predict decomposition coefficients and all related parameters for other points. Thus the latter are used for the recomposition step in which the plots and curves describing the relative cosmic rays intensities are obtained in any points on the earth in which we do not have any information about cosmic rays intensities. Although neural network associated with wavelets are not frequently used for cosmic rays time series, they seems very suitable and are a good choice to obtain these results. In fact we have succeeded to derive a very useful tool to obtain the decomposition coefficients, the main periods for each point on the Earth and on another hand we have now a kind of virtual NM for these locations like North Africa countries, Maroc, Algeria, Tunisia, Libya and Cairo. We have found the aspect of very known 11-years cycle: T1, we have also revealed the variation type of T2 and especially T3 cycles which seem to be induced by particular Earth's phenomena.

  14. Prediction of the Chemoreflex Gain by Common Clinical Variables in Heart Failure.

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    Gianluca Mirizzi

    Full Text Available Peripheral and central chemoreflex sensitivity, assessed by the hypoxic or hypercapnic ventilatory response (HVR and HCVR, respectively, is enhanced in heart failure (HF patients, is involved in the pathophysiology of the disease, and is under investigation as a potential therapeutic target. Chemoreflex sensitivity assessment is however demanding and, therefore, not easily applicable in the clinical setting. We aimed at evaluating whether common clinical variables, broadly obtained by routine clinical and instrumental evaluation, could predict increased HVR and HCVR.191 patients with systolic HF (left ventricular ejection fraction--LVEF--<50% underwent chemoreflex assessment by rebreathing technique to assess HVR and HCVR. All patients underwent clinical and neurohormonal evaluation, comprising: echocardiogram, cardiopulmonary exercise test (CPET, daytime cardiorespiratory monitoring for breathing pattern evaluation. Regarding HVR, multivariate penalized logistic regression, Bayesian Model Averaging (BMA logistic regression and random forest analysis identified, as predictors, the presence of periodic breathing and increased slope of the relation between ventilation and carbon dioxide production (VE/VCO2 during exercise. Again, the above-mentioned statistical tools identified as HCVR predictors plasma levels of N-terminal fragment of proBNP and VE/VCO2 slope.In HF patients, the simple assessment of breathing pattern, alongside with ventilatory efficiency during exercise and natriuretic peptides levels identifies a subset of patients presenting with increased chemoreflex sensitivity to either hypoxia or hypercapnia.

  15. Higher resting heart rate variability predicts skill in expressing some emotions.

    Science.gov (United States)

    Tuck, Natalie L; Grant, Rosemary C I; Sollers, John J; Booth, Roger J; Consedine, Nathan S

    2016-12-01

    Vagally mediated heart rate variability (vmHRV) is a measure of cardiac vagal tone, and is widely viewed as a physiological index of the capacity to regulate emotions. However, studies have not directly tested whether vmHRV is associated with the ability to facially express emotions. In extending prior work, the current report tested links between resting vmHRV and the objectively assessed ability to facially express emotions, hypothesizing that higher vmHRV would predict greater expressive skill. Eighty healthy women completed self-reported measures, before attending a laboratory session in which vmHRV and the ability to express six emotions in the face were assessed. A repeated measures analysis of variance revealed a marginal main effect for vmHRV on skill overall; individuals with higher resting vmHRV were only better able to deliberately facially express anger and interest. Findings suggest that differences in resting vmHRV are associated with the objectively assessed ability to facially express some, but not all, emotions, with potential implications for health and well-being. © 2016 Society for Psychophysiological Research.

  16. NERI PROJECT 99-119. TASK 2. DATA-DRIVEN PREDICTION OF PROCESS VARIABLES. FINAL REPORT

    Energy Technology Data Exchange (ETDEWEB)

    Upadhyaya, B.R.

    2003-04-10

    This report describes the detailed results for task 2 of DOE-NERI project number 99-119 entitled ''Automatic Development of Highly Reliable Control Architecture for Future Nuclear Power Plants''. This project is a collaboration effort between the Oak Ridge National Laboratory (ORNL,) The University of Tennessee, Knoxville (UTK) and the North Carolina State University (NCSU). UTK is the lead organization for Task 2 under contract number DE-FG03-99SF21906. Under task 2 we completed the development of data-driven models for the characterization of sub-system dynamics for predicting state variables, control functions, and expected control actions. We have also developed the ''Principal Component Analysis (PCA)'' approach for mapping system measurements, and a nonlinear system modeling approach called the ''Group Method of Data Handling (GMDH)'' with rational functions, and includes temporal data information for transient characterization. The majority of the results are presented in detailed reports for Phases 1 through 3 of our research, which are attached to this report.

  17. Prediction of Internet Addiction of University Students Based on Various Variables

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    atma Gizem Karaoglan Yilmaz

    2014-04-01

    Full Text Available That internet is developing fast and its cost is becoming cheaper rapidly increases the number of people using this technology. Although internet provides miscellaneous benefits for the users, it also causes them to encounter certain difficulties. Particularly, those young people, who leave their families to study at a university spend most of their time on the internet because of such personal and social problems as having low satisfaction from life, having social anxiety, not being able to communicate or establish relationships and feeling lonely. And this could lead to internet addiction in young people. The aim of this study is to discuss the internet addiction levels of freshmen and sophomores at university within the scope of educational theories and to predict addiction according to various variables. Survey method is used in the study. The study was carried out on 329 freshmen and sophomores studying at economics, science teaching, primary school mathematics education, primary school teaching and social sciences teaching departments of Bartın University in the second term of 2012-2013 academic year. As an end of the study, factors that cause to internet addiction and what can be done to remove these factors are discussed within theoretical framework.

  18. Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

    Directory of Open Access Journals (Sweden)

    Shaowei Sang

    Full Text Available Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF, a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue's control and prevention purpose.Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8% imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags.Imported DF cases and mosquito

  19. Testing predictive models of positive and negative affect with psychosocial, acculturation, and coping variables in a multiethnic undergraduate sample

    OpenAIRE

    Kuo, Ben CH; Kwantes, Catherine T

    2014-01-01

    Despite the prevalence and popularity of research on positive and negative affect within the field of psychology, there is currently little research on affect involving the examination of cultural variables and with participants of diverse cultural and ethnic backgrounds. To the authors’ knowledge, currently no empirical studies have comprehensively examined predictive models of positive and negative affect based specifically on multiple psychosocial, acculturation, and coping variables as pr...

  20. Prediction of employer–employee relationships from sociodemographic variables and social values in Brunei public and private sector workers

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    Mundia L

    2017-07-01

    Full Text Available Lawrence Mundia, Salwa Mahalle, Rohani Matzin, Gamal Abdul Nasir Zakaria, Nor Zaiham Midawati Abdullah, Siti Norhedayah Abdul Latif Psychological Studies and Human Development Academic Group, Sultan Hassanal Bolkiah Institute of Education, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam Abstract: The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer–employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer–employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer–employee relationship problems were seeking help from the Bomo (traditional healer; obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer–employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer–employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer–employee relationship problems. Other significant social value correlates, but not predictors of employer–employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated

  1. Integrated genomic and immunophenotypic classification of pancreatic cancer reveals three distinct subtypes with prognostic/predictive significance.

    Science.gov (United States)

    Wartenberg, Martin; Cibin, Silvia; Zlobec, Inti; Vassella, Erik; Eppenberger-Castori, Serenella M M; Terracciano, Luigi; Eichmann, Micha; Worni, Mathias; Gloor, Beat; Perren, Aurel; Karamitopoulou, Eva

    2018-04-16

    Current clinical classification of pancreatic ductal adenocarcinoma (PDAC) is unable to predict prognosis or response to chemo- or immunotherapy and does not take into account the host reaction to PDAC-cells. Our aim is to classify PDAC according to host- and tumor-related factors into clinically/biologically relevant subtypes by integrating molecular and microenvironmental findings. A well-characterized PDAC-cohort (n=110) underwent next-generation sequencing with a hotspot cancer panel, while Next-generation Tissue-Microarrays were immunostained for CD3, CD4, CD8, CD20, PD-L1, p63, hyaluronan-mediated motility receptor (RHAMM) and DNA mismatch-repair proteins. Previous data on FOXP3 were integrated. Immune-cell counts and protein expression were correlated with tumor-derived driver mutations, clinicopathologic features (TNM 8. 2017), survival and epithelial-mesenchymal-transition (EMT)-like tumor budding.  Results: Three PDAC-subtypes were identified: the "immune-escape" (54%), poor in T- and B-cells and enriched in FOXP3+Tregs, with high-grade budding, frequent CDKN2A- , SMAD4- and PIK3CA-mutations and poor outcome; the "immune-rich" (35%), rich in T- and B-cells and poorer in FOXP3+Tregs, with infrequent budding, lower CDKN2A- and PIK3CA-mutation rate and better outcome and a subpopulation with tertiary lymphoid tissue (TLT), mutations in DNA damage response genes (STK11, ATM) and the best outcome; and the "immune-exhausted" (11%) with immunogenic microenvironment and two subpopulations: one with PD-L1-expression and high PIK3CA-mutation rate and a microsatellite-unstable subpopulation with high prevalence of JAK3-mutations. The combination of low budding, low stromal FOXP3-counts, presence of TLTs and absence of CDKN2A-mutations confers significant survival advantage in PDAC-patients. Immune host responses correlate with tumor characteristics leading to morphologically recognizable PDAC-subtypes with prognostic/predictive significance. Copyright ©2018

  2. Preoperative Metabolic Syndrome Is Predictive of Significant Gastric Cancer Mortality after Gastrectomy: The Fujian Prospective Investigation of Cancer (FIESTA Study

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    Dan Hu

    2017-02-01

    Full Text Available Metabolic syndrome (MetS has been shown to be associated with an increased risk of gastric cancer. However, the impact of MetS on gastric cancer mortality remains largely unknown. Here, we prospectively examined the prediction of preoperative MetS for gastric cancer mortality by analyzing a subset of data from the ongoing Fujian prospective investigation of cancer (FIESTA study. This study was conducted among 3012 patients with gastric cancer who received radical gastrectomy between 2000 and 2010. The latest follow-up was completed in 2015. Blood/tissue specimens, demographic and clinicopathologic characteristics were collected at baseline. During 15-year follow-up, 1331 of 3012 patients died of gastric cancer. The median survival time (MST of patients with MetS was 31.3 months, which was significantly shorter than that of MetS-free patients (157.1 months. The coexistence of MetS before surgery was associated with a 2.3-fold increased risk for gastric cancer mortality (P < 0.001. The multivariate-adjusted hazard ratios (HRs were increased with invasion depth T1/T2 (HR = 2.78, P < 0.001, regional lymph node metastasis N0 (HR = 2.65, P < 0.001, positive distant metastasis (HR = 2.53, P < 0.001, TNM stage I/II (HR = 3.00, P < 0.001, intestinal type (HR = 2.96, P < 0.001, negative tumor embolus (HR = 2.34, P < 0.001, and tumor size ≤4.5 cm (HR = 2.49, P < 0.001. Further survival tree analysis confirmed the top splitting role of TNM stage, followed by MetS or hyperglycemia with remarkable discrimination ability. In this large cohort study, preoperative MetS, especially hyperglycemia, was predictive of significant gastric cancer mortality in patients with radical gastrectomy, especially for early stage of gastric cancer.

  3. Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

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    G Khalili-Zadeh-Mahani

    2016-07-01

    Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

  4. Validation of DAB2IP methylation and its relative significance in predicting outcome in renal cell carcinoma

    Science.gov (United States)

    Zhao, Liang-Yun; Kapur, Payal; Wu, Kai-Jie; Wang, Bin; Yu, Yan-Hong; Liao, Bing; He, Da-Lin; Chen, Wei; Margulis, Vitaly; Hsieh, Jer-Tsong; Luo, Jun-Hang

    2016-01-01

    We have recently reported tumor suppressive role of DAB2IP in RCC development. In this study, We identified one CpG methylation biomarker (DAB2IP CpG1) located UTSS of DAB2IP that was associated with poor overall survival in a cohort of 318 ccRCC patients from the Cancer Genome Atlas (TCGA). We further validated the prognostic accuracy of DAB2IP CpG methylation by pyrosequencing quantitative methylation assay in 224 ccRCC patients from multiple Chinese centers (MCHC set), and 239 patients from University of Texas Southwestern Medical Center at Dallas (UTSW set) by using FFPE samples. DAB2IP CpG1 can predict the overall survival of patients in TCGA, MCHC, and UTSW sets independent of patient age, Fuhrman grade and TNM stage (all p<0.05). DAB2IP CpG1 successfully categorized patients into high-risk and low-risk groups with significant differences of clinical outcome in respective clinical subsets, regardless of age, sex, grade, stage, or race (HR: 1.63-7.83; all p<0.05). The detection of DAB2IP CpG1 methylation was minimally affected by ITH in ccRCC. DAB2IP mRNA expression was regulated by DNA methylation in vitro. DAB2IP CpG1 methylation is a practical and repeatable biomarker for ccRCC, which can provide prognostic value that complements the current staging system. PMID:27129174

  5. Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables.

    Science.gov (United States)

    Seddon, Johanna M; Reynolds, Robyn; Maller, Julian; Fagerness, Jesen A; Daly, Mark J; Rosner, Bernard

    2009-05-01

    The joint effects of genetic, ocular, and environmental variables were evaluated and predictive models for prevalence and incidence of AMD were assessed. Participants in the multicenter Age-Related Eye Disease Study (AREDS) were included in a prospective evaluation of 1446 individuals, of which 279 progressed to advanced AMD (geographic atrophy or neovascular disease) and 1167 did not progress during 6.3 years of follow-up. For prevalent AMD, 509 advanced cases were compared with 222 controls. Covariates for the incidence analysis included age, sex, education, smoking, body mass index (BMI), baseline AMD grade, and the AREDS vitamin-mineral treatment assignment. DNA specimens were evaluated for six variants in five genes related to AMD. Unconditional logistic regression analyses were performed for prevalent and incident advanced AMD. An algorithm was developed and receiver operating characteristic curves and C statistics were calculated to assess the predictive ability of risk scores to discriminate progressors from nonprogressors. All genetic polymorphisms were independently related to prevalence of advanced AMD, controlling for genetic factors, smoking, BMI, and AREDS treatment. Multivariate odds ratios (ORs) were 3.5 (95% confidence interval [CI], 1.7-7.1) for CFH Y402H; 3.7 (95% CI, 1.6-8.4) for CFH rs1410996; 25.4 (95% CI, 8.6-75.1) for LOC387715 A69S (ARMS2); 0.3 (95% CI, 0.1-0.7) for C2 E318D; 0.3 (95% CI, 0.1-0.5) for CFB; and 3.6 (95% CI, 1.4-9.4) for C3 R102G, comparing the homozygous risk/protective genotypes to the referent genotypes. For incident AMD, all these variants except CFB were significantly related to progression to advanced AMD, after controlling for baseline AMD grade and other factors, with ORs from 1.8 to 4.0 for presence of two risk alleles and 0.4 for the protective allele. An interaction was seen between CFH402H and treatment, after controlling for all genotypes. Smoking was independently related to AMD, with a multiplicative joint

  6. Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis.

    Science.gov (United States)

    Srimath-Tirumula-Peddinti, Ravi Chandra Pavan Kumar; Neelapu, Nageswara Rao Reddy; Sidagam, Naresh

    2015-01-01

    Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM). Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis. Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission. Changes in climatic factors influence malaria directly by

  7. Evaluation of heat transfer mathematical models and multiple linear regression to predict the inside variables in semi-solar greenhouse

    Directory of Open Access Journals (Sweden)

    M Taki

    2017-05-01

    Full Text Available Introduction Controlling greenhouse microclimate not only influences the growth of plants, but also is critical in the spread of diseases inside the greenhouse. The microclimate parameters were inside air, greenhouse roof and soil temperature, relative humidity and solar radiation intensity. Predicting the microclimate conditions inside a greenhouse and enabling the use of automatic control systems are the two main objectives of greenhouse climate model. The microclimate inside a greenhouse can be predicted by conducting experiments or by using simulation. Static and dynamic models are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Some works were done in past to 2015 year to simulation and predict the inside variables in different greenhouse structures. Usually simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. The main objective of this paper is comparison between heat transfer and regression models to evaluate them to predict inside air and roof temperature in a semi-solar greenhouse in Tabriz University. Materials and Methods In this study, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (geographical location of 38°10′ N and 46°18′ E with elevation of 1364 m above the sea level. In this research, shape and orientation of the greenhouse, selected between some greenhouses common shapes and according to receive maximum solar radiation whole the year. Also internal thermal screen and cement north wall was used to store and prevent of heat lost during the cold period of year. So we called this structure, ‘semi-solar’ greenhouse. It was covered with glass (4 mm thickness. It occupies a surface of approximately 15.36 m2 and 26.4 m3. The orientation of this greenhouse was East–West and perpendicular to the direction of the wind prevailing

  8. Predicting health literacy of students in Kermanshah University of Medical Sciences in 2016: The role of demographic variables

    Directory of Open Access Journals (Sweden)

    Arash Ziapoor

    2016-12-01

    Full Text Available Background and objective: Health literacy is a key outcome measures of health education that should be in the context of broader health promotion. This study aims to predict the health literacy of students in Kermanshah University of Medical Sciences in 1395: the role of demographic variables was performed. Methods: A descriptive correlational study on 350 students of Kermanshah University of Medical Sciences was done. Sampling was random. Data collection was conducted through a questionnaire of health literacy Montazeri et al. Information collected through software SPSS 23 and using t-tests, ANOVA and Pearson correlation coefficient were analyzed. Results: The mean (SD total score of health literacy in students was 4.04 ± 0.43. T-test and ANOVA between health literacy by gender, age, profession, education level and location have a significant relationship. Pearson correlation coefficient between the components of health literacy in research samples showed high correlation was statistically significant (P <0.01. Conclusion: The importance and need for attention to students' health literacy for health promotion as an essential factor in the impact-transition seems to be. Paper Type: Research Article.

  9. Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse

    Directory of Open Access Journals (Sweden)

    Behzad Mohammadi

    2018-06-01

    Full Text Available Greenhouses are one of the most effective cultivation methods with a yield per cultivated area up to 10 times more than free land cultivation but the use of fossil fuels in this production field is very high. The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it. There are many control methods, such as adaptive, feedback and intelligent control and they require a precise model. Therefore, many modeling methods have been proposed for this purpose; including physical, transfer function and black-box modeling. The objective of this paper is to modeling and experimental validation of some inside environment variables in an innovative greenhouse structure (semi-solar greenhouse. For this propose, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (38°10′N and 46°18′E with elevation of 1364 m above the sea level. The main inside environment factors include inside air temperature (Ta and inside soil temperature (Ts were collected as the experimental data samples. The dynamic heat transfer model used to estimate the temperature in two different points of semi-solar greenhouse with initial values. The results showed that dynamic model can predict the inside temperatures in two different points (Ta and Ts with RMSE, MAPE and EF about 5.3 °C, 10.2% and 0.78% and 3.45 °C, 7.7% and 0.86%, respectively. Keywords: Semi-solar greenhouse, Dynamic model, Commercial greenhouse

  10. An Investigation of the Variables Predicting Faculty of Education Students' Speaking Anxiety through Ordinal Logistic Regression Analysis

    Science.gov (United States)

    Bozpolat, Ebru

    2017-01-01

    The purpose of this study is to determine whether Cumhuriyet University Faculty of Education students' levels of speaking anxiety are predicted by the variables of gender, department, grade, such sub-dimensions of "Speaking Self-Efficacy Scale for Pre-Service Teachers" as "public speaking," "effective speaking,"…

  11. Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

    Science.gov (United States)

    Methods are needed improve the timeliness and accuracy of recreational water‐quality assessments. Traditional culture methods require 18–24 h to obtain results and may not reflect current conditions. Predictive models, based on environmental and water quality variables, have been...

  12. The predictive value of baseline variables in the treatment of benign prostatic hyperplasia using high-energy transurethral microwave thermotherapy

    NARCIS (Netherlands)

    D'Ancona, F. C.; Francisca, E. A.; Hendriks, J. C.; Debruyne, F. M.; de la Rosette, J. J.

    1998-01-01

    To evaluate the combination of patient age, prostate size, grade of outlet obstruction and total amount of energy, all independent predictive variables of treatment outcome in patients with lower urinary tract symptoms (LUTS) and benign prostatic hyperplasia (BPH) treated with high-energy

  13. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, Corne A. M.; Stapelfeldt, Christina M.; Heymans, Martijn W.; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V.; Bultmann, Ute; Jensen, Chris

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. Methods 2,562 municipal eldercare

  14. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, C.A.M.; Stapelfeldt, C.M.; Heijmans, M.W.; van Rhenen, W.; Labriola, M.; Nielsen, C.V.; Bultmann, U.; Jensen, C.

    2015-01-01

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models’ risk discrimination was also investigated. Methods 2,562 municipal eldercare

  15. Cephalometric variables predicting the long-term success or failure of combined rapid maxillary expansion and facial mask therapy.

    Science.gov (United States)

    Baccetti, Tiziano; Franchi, Lorenzo; McNamara, James A

    2004-07-01

    The aim of this study was to select a model of cephalometric variables to predict the results of early treatment of Class III malocclusion with rapid maxillary expansion and facemask therapy followed by comprehensive treatment with fixed appliances. Lateral cephalograms of 42 patients (20 boys, 22 girls) with Class III malocclusion were analyzed at the start of treatment (mean age 8 years 6 months +/- 2 years, at stage I in cervical vertebral maturation). All patients were reevaluated after a mean period of 6 years 6 months (at stage IV or V in cervical vertebral maturation) that included active treatment plus retention. At this time, the sample was divided into 2 groups according to occlusal criteria: a successful group (30 patients) and an unsuccessful group (12 patients). Discriminant analysis was applied to select pretreatment predictive variables of long-term treatment outcome. Stepwise variable selection of the cephalometric measurements at the first observation identified 3 predictive variables. Orthopedic treatment of Class III malocclusion might be unfavorable over the long term when a patient's pretreatment cephalometric records exhibit a long mandibular ramus (ie, increased posterior facial height), an acute cranial base angle, and a steep mandibular plane angle. On the basis of the equation generated by the multivariate statistical method, the outcome of interceptive orthopedic treatment for each new patient with Class III malocclusion can be predicted with a probability error of 16.7%.

  16. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables

    NARCIS (Netherlands)

    Roelen, Corne; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bultmann, Ute; Bjorner, Jakob

    2018-01-01

    Purpose: The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Materials and methods: Based on the literature, 15 predictor

  17. Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

    Directory of Open Access Journals (Sweden)

    Alejandro Baldominos Gómez

    2016-03-01

    Full Text Available This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies.

  18. Surgeon and type of anesthesia predict variability in surgical procedure times.

    Science.gov (United States)

    Strum, D P; Sampson, A R; May, J H; Vargas, L G

    2000-05-01

    Variability in surgical procedure times increases the cost of healthcare delivery by increasing both the underutilization and overutilization of expensive surgical resources. To reduce variability in surgical procedure times, we must identify and study its sources. Our data set consisted of all surgeries performed over a 7-yr period at a large teaching hospital, resulting in 46,322 surgical cases. To study factors associated with variability in surgical procedure times, data mining techniques were used to segment and focus the data so that the analyses would be both technically and intellectually feasible. The data were subdivided into 40 representative segments of manageable size and variability based on headers adopted from the common procedural terminology classification. Each data segment was then analyzed using a main-effects linear model to identify and quantify specific sources of variability in surgical procedure times. The single most important source of variability in surgical procedure times was surgeon effect. Type of anesthesia, age, gender, and American Society of Anesthesiologists risk class were additional sources of variability. Intrinsic case-specific variability, unexplained by any of the preceding factors, was found to be highest for shorter surgeries relative to longer procedures. Variability in procedure times among surgeons was a multiplicative function (proportionate to time) of surgical time and total procedure time, such that as procedure times increased, variability in surgeons' surgical time increased proportionately. Surgeon-specific variability should be considered when building scheduling heuristics for longer surgeries. Results concerning variability in surgical procedure times due to factors such as type of anesthesia, age, gender, and American Society of Anesthesiologists risk class may be extrapolated to scheduling in other institutions, although specifics on individual surgeons may not. This research identifies factors associated

  19. Triaging TIA/minor stroke patients using the ABCD2 score does not predict those with significant carotid disease.

    Science.gov (United States)

    Walker, J; Isherwood, J; Eveson, D; Naylor, A R

    2012-05-01

    'Rapid Access' TIA Clinics use the ABCD(2) score to triage patients as it is not possible to see everyone with a suspected TIA TIA/minor stroke or 'carotid territory' TIA/minor stroke. Between 1.10.2008 and 31.04.2011, 2452 patients were referred to the Leicester Rapid Access TIA Service. After Stroke Physician review, 1273 (52%) were thought to have suffered a minor stroke/TIA. Of these, both FD/ED referrer and Specialist Stroke Consultant ABCD(2) scores and carotid Duplex ultrasound studies were available for 843 (66%). The yield for identifying a ≥50% stenosis or carotid occlusion was 109/843 (12.9%) in patients with 'any territory' TIA/minor stroke and 101/740 (13.6%) in those with a clinical diagnosis of 'carotid territory' TIA/minor stroke. There was no association between ABCD(2) score and the likelihood of encountering significant carotid disease and analyses of the area under the receiver operating characteristic curve (AUC) for FD/ED referrer and stroke specialist ABCD(2) scores showed no prediction of carotid stenosis (FD/ED: AUC 0.50 (95%CI 0.44-0.55, p = 0.9), Specialist: AUC 0.51 (95%CI 0.45-0.57, p = 0.78). The ABCD(2) score was unable to identify TIA/minor stroke patients with a higher prevalence of clinically important ipsilateral carotid disease. Copyright © 2012 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

  20. Patient-specific metrics of invasiveness reveal significant prognostic benefit of resection in a predictable subset of gliomas.

    Directory of Open Access Journals (Sweden)

    Anne L Baldock

    Full Text Available Malignant gliomas are incurable, primary brain neoplasms noted for their potential to extensively invade brain parenchyma. Current methods of clinical imaging do not elucidate the full extent of brain invasion, making it difficult to predict which, if any, patients are likely to benefit from gross total resection. Our goal was to apply a mathematical modeling approach to estimate the overall tumor invasiveness on a patient-by-patient basis and determine whether gross total resection would improve survival in patients with relatively less invasive gliomas.In 243 patients presenting with contrast-enhancing gliomas, estimates of the relative invasiveness of each patient's tumor, in terms of the ratio of net proliferation rate of the glioma cells to their net dispersal rate, were derived by applying a patient-specific mathematical model to routine pretreatment MR imaging. The effect of varying degrees of extent of resection on overall survival was assessed for cohorts of patients grouped by tumor invasiveness.We demonstrate that patients with more diffuse tumors showed no survival benefit (P = 0.532 from gross total resection over subtotal/biopsy, while those with nodular (less diffuse tumors showed a significant benefit (P = 0.00142 with a striking median survival benefit of over eight months compared to sub-totally resected tumors in the same cohort (an 80% improvement in survival time for GTR only seen for nodular tumors.These results suggest that our patient-specific, model-based estimates of tumor invasiveness have clinical utility in surgical decision making. Quantification of relative invasiveness assessed from routinely obtained pre-operative imaging provides a practical predictor of the benefit of gross total resection.

  1. Survival prediction algorithms miss significant opportunities for improvement if used for case selection in trauma quality improvement programs.

    Science.gov (United States)

    Heim, Catherine; Cole, Elaine; West, Anita; Tai, Nigel; Brohi, Karim

    2016-09-01

    Quality improvement (QI) programs have shown to reduce preventable mortality in trauma care. Detailed review of all trauma deaths is a time and resource consuming process and calculated probability of survival (Ps) has been proposed as audit filter. Review is limited on deaths that were 'expected to survive'. However no Ps-based algorithm has been validated and no study has examined elements of preventability associated with deaths classified as 'expected'. The objective of this study was to examine whether trauma performance review can be streamlined using existing mortality prediction tools without missing important areas for improvement. We conducted a retrospective study of all trauma deaths reviewed by our trauma QI program. Deaths were classified into non-preventable, possibly preventable, probably preventable or preventable. Opportunities for improvement (OPIs) involve failure in the process of care and were classified into clinical and system deviations from standards of care. TRISS and PS were used for calculation of probability of survival. Peer-review charts were reviewed by a single investigator. Over 8 years, 626 patients were included. One third showed elements of preventability and 4% were preventable. Preventability occurred across the entire range of the calculated Ps band. Limiting review to unexpected deaths would have missed over 50% of all preventability issues and a third of preventable deaths. 37% of patients showed opportunities for improvement (OPIs). Neither TRISS nor PS allowed for reliable identification of OPIs and limiting peer-review to patients with unexpected deaths would have missed close to 60% of all issues in care. TRISS and PS fail to identify a significant proportion of avoidable deaths and miss important opportunities for process and system improvement. Based on this, all trauma deaths should be subjected to expert panel review in order to aim at a maximal output of performance improvement programs. Copyright © 2016 Elsevier

  2. A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares.

    Science.gov (United States)

    Li, Xu; Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

    2018-01-01

    To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.

  3. Initial sociometric impressions of attention-deficit hyperactivity disorder and comparison boys: predictions from social behaviors and from nonbehavioral variables.

    Science.gov (United States)

    Erhardt, Drew; Hinshaw, Stephen P

    1994-08-01

    This study systematically compared the influence of naturalistic social behaviors and nonbehavioral variables on the development of peer status in 49 previously unfamiliar boys, aged 6-12 years, who attended a summer research program. Twenty-five boys with attention-deficit hyperactivity disorder (ADHD) and 24 comparison boys participated. Physical attractiveness, motor competence, intelligence, and academic achievement constituted the nonbehavioral variables; social behaviors included noncompliance, aggression, prosocial actions, and isolation, measured by live observations of classroom and playground interactions. As early as the first day of interaction, ADHD and comparison boys displayed clear differences in social behaviors, and the ADHD youngsters were overwhelmingly rejected. Whereas prosocial behavior independently predicted friendship ratings during the first week, the magnitude of prediction was small. In contrast, the boys' aggression (or noncompliance) strongly predicted negative nominations, even with nonbehavioral factors, group status (ADHD versus comparison), and other social behaviors controlled statistically. Implications for understanding and remediating negative peer reputations are discussed.

  4. Predicting word-recognition performance in noise by young listeners with normal hearing using acoustic, phonetic, and lexical variables.

    Science.gov (United States)

    McArdle, Rachel; Wilson, Richard H

    2008-06-01

    To analyze the 50% correct recognition data that were from the Wilson et al (this issue) study and that were obtained from 24 listeners with normal hearing; also to examine whether acoustic, phonetic, or lexical variables can predict recognition performance for monosyllabic words presented in speech-spectrum noise. The specific variables are as follows: (a) acoustic variables (i.e., effective root-mean-square sound pressure level, duration), (b) phonetic variables (i.e., consonant features such as manner, place, and voicing for initial and final phonemes; vowel phonemes), and (c) lexical variables (i.e., word frequency, word familiarity, neighborhood density, neighborhood frequency). The descriptive, correlational study will examine the influence of acoustic, phonetic, and lexical variables on speech recognition in noise performance. Regression analysis demonstrated that 45% of the variance in the 50% point was accounted for by acoustic and phonetic variables whereas only 3% of the variance was accounted for by lexical variables. These findings suggest that monosyllabic word-recognition-in-noise is more dependent on bottom-up processing than on top-down processing. The results suggest that when speech-in-noise testing is used in a pre- and post-hearing-aid-fitting format, the use of monosyllabic words may be sensitive to changes in audibility resulting from amplification.

  5. Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

    Science.gov (United States)

    Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

    2015-04-01

    Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for

  6. Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability

    Science.gov (United States)

    Liu, Sijun; Chen, Jiaping; Wang, Jianming; Wu, Zhuchao; Wu, Weihua; Xu, Zhiwei; Hu, Wenbiao; Xu, Fei; Tong, Shilu; Shen, Hongbing

    2017-10-01

    Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.

  7. Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases.

    Science.gov (United States)

    Hwang, Eui Jin; Goo, Jin Mo; Kim, Jihye; Park, Sang Joon; Ahn, Soyeon; Park, Chang Min; Shin, Yeong-Gil

    2017-08-01

    To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth. For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE). SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004) CONCLUSION: Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold. • The 95 % percentile APE of a particular nodule can be predicted. • Estimated 95 % percentile APE can be utilized as an individualized threshold. • More sensitive diagnosis of metastasis can be made with an individualized threshold. • Tailored nodule management can be provided during nodule growth follow-up.

  8. Variables predicting elevated portal pressure in alcoholic liver disease. Results of a multivariate analysis

    DEFF Research Database (Denmark)

    Krogsgaard, K; Christensen, E; Gluud, C

    1987-01-01

    In 46 alcoholic patients the association of wedged-to-free hepatic-vein pressure with other variables (clinical, histologic, hemodynamic, and liver function data) was studied by means of multiple regression analysis, taking the wedged-to-free hepatic-vein pressure as the dependent variable. Four ...

  9. Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation-dissipation theorem

    NARCIS (Netherlands)

    Frank, T.D.; Patanarapeelert, K.; Beek, P.J.

    2008-01-01

    We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the

  10. Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

    Science.gov (United States)

    Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

    2018-04-01

    Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

  11. The use of Chernobyl data to test model predictions for interindividual variability of 137Cs concentrations in humans

    International Nuclear Information System (INIS)

    Hoffman, F. Owen; Thiessen, Kathleen M.

    1996-01-01

    Data sets assembled in the aftermath of the Chernobyl accident as a part of the International Atomic Energy Agency's model testing program (VAMP) have provided a rare opportunity for 'blind-testing' predictions made with exposure assessment models. Measurements of Chernobyl-derived 137 Cs in Central Bohemia (Czech Republic) and southern Finland were used to test model predictions for a number of endpoints, including the distribution of whole-body concentrations of 137 Cs in adults in these regions at specified time points. This test endpoint required separation of uncertainty due to stochastic variability (aleatoric uncertainty) and uncertainty due to lack of knowledge about fixed but unknown values (epistemic uncertainty). Predictions of the distribution of whole-body 137 Cs concentrations were made by a minority of the participants in these model-testing exercises. Major reasons for misprediction included bias in the bioavailability of 137 Cs in soil and misestimation of the total intake of 137 Cs in the diet. Overestimation of the amount of interindividual variability often resulted from confusion of uncertainty with variability. The spreads of the distributions for parameters describing interindividual variability were frequently increased to compensate for lack of knowledge about the uptake and metabolism of 137 Cs in the population. Accurate results produced by participants are attributable both to a participant's access to additional site-specific data or choice of appropriate site-specific assumptions and to the effects of compensatory errors

  12. It's the People, Stupid: The Role of Personality and Situational Variable in Predicting Decisionmaker Behavior

    National Research Council Canada - National Science Library

    Sticha, Paul J; Buede, Dennis M; Rees, Richard L

    2006-01-01

    .... The analyst builds Bayesian networks that integrate situational information with the Subject's personality and culture to provide a probabilistic prediction of the hypothesized actions a Subject might choose...

  13. The estimation of soil parameters using observations on crop biophysical variables and the crop model STICS improve the predictions of agro environmental variables.

    Science.gov (United States)

    Varella, H.-V.

    2009-04-01

    Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters, especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless, observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In this work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters is made, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, a Bayesian data assimilation method is chosen (because of available prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prior information and the result with the posterior information provided by the Bayesian data assimilation method. The result of the

  14. Variables that Predict Serve Efficacy in Elite Men’s Volleyball with Different Quality of Opposition Sets

    Science.gov (United States)

    Valhondo, Álvaro; Fernández-Echeverría, Carmen; González-Silva, Jara; Claver, Fernando; Moreno, M. Perla

    2018-01-01

    Abstract The objective of this study was to determine the variables that predicted serve efficacy in elite men’s volleyball, in sets with different quality of opposition. 3292 serve actions were analysed, of which 2254 were carried out in high quality of opposition sets and 1038 actions were in low quality of opposition sets, corresponding to a total of 24 matches played during the Men’s European Volleyball Championships held in 2011. The independent variables considered in this study were the serve zone, serve type, serving player, serve direction, reception zone, receiving player and reception type; the dependent variable was serve efficacy and the situational variable was quality of opposition sets. The variables that acted as predictors in both high and low quality of opposition sets were the serving player, reception zone and reception type. The serve type variable only acted as a predictor in high quality of opposition sets, while the serve zone variable only acted as a predictor in low quality of opposition sets. These results may provide important guidance in men’s volleyball training processes. PMID:29599869

  15. The Role of Individual and Social Variables in Predicting Body Dissatisfaction and Eating Disorder Symptoms among Iranian Adolescent Girls: An Expanding of the Tripartite Influence Mode.

    Science.gov (United States)

    Shahyad, Shima; Pakdaman, Shahla; Shokri, Omid; Saadat, Seyed Hassan

    2018-01-12

    The aim of the present study was to examine the causal relationships between psychological and social factors, being independent variables and body image dissatisfaction plus symptoms of eating disorders as dependent variables through the mediation of social comparison and thin-ideal internalization. To conduct the study, 477 high-school students from Tehran were recruited by method of cluster sampling. Next, they filled out Rosenberg Self-esteem Scale (RSES), Physical Appearance Comparison Scale (PACS), Self-Concept Clarity Scale (SCCS), Appearance Perfectionism Scale (APS), Eating Disorder Inventory (EDI), Multidimensional Body Self Relations Questionnaire (MBSRQ) and Sociocultural Attitudes towards Appearance Questionnaire (SATAQ-4). In the end, collected data were analyzed using structural equation modeling. Findings showed that the assumed model perfectly fitted the data after modification and as a result, all the path-coefficients of latent variables (except for the path between self-esteem and thin-ideal internalization) were statistically significant (p>0.05). Also, in this model, 75% of scores' distribution of body dissatisfaction was explained through psychological variables, socio-cultural variables, social comparison and internalization of the thin ideal. The results of the present study provid experimental basis for the confirmation of proposed causal model. The combination of psychological, social and cultural variables could efficiently predict body image dissatisfaction of young girls in Iran.

  16. The role of individual and social variables in predicting body dissatisfaction and eating disorder symptoms among Iranian adolescent girls: an expanding of the tripartite influence model

    Directory of Open Access Journals (Sweden)

    Shima Shahyad

    2018-03-01

    Full Text Available The aim of the present study was to examine the causal relationships between psychological and social factors, being independent variables and body image dissatisfaction plus symptoms of eating disorders as dependent variables through the mediation of social comparison and thin-ideal internalization. To conduct the study, 477 high-school students from Tehran were recruited by method of cluster sampling. Next, they filled out Rosenberg Self-esteem Scale (RSES, Physical Appearance Comparison Scale (PACS, Self-Concept Clarity Scale (SCCS, Appearance Perfectionism Scale (APS, Eating Disorder Inventory (EDI, Multidimensional Body Self Relations Questionnaire (MBSRQ and Sociocultural Attitudes towards Appearance Questionnaire (SATAQ-4. In the end, collected data were analyzed using structural equation modeling. Findings showed that the assumed model perfectly fitted the data after modification and as a result, all the path-coefficients of latent variables (except for the path between self-esteem and thin-ideal internalization were statistically significant (p<0.05. Also, in this model, 75% of scores' distribution of body dissatisfaction was explained through psychological variables, socio-cultural variables, social comparison and internalization of the thin ideal. The results of the present study provid experimental basis for the confirmation of proposed causal model. The combination of psychological, social and cultural variables could efficiently predict body image dissatisfaction of young girls in Iran. Key Words: Thin-ideal Internalization, Social comparison, Body image dissatisfaction, mediating effects model, eating disorder symptoms, psychological factors.

  17. Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

    Science.gov (United States)

    Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

    2015-06-01

    To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

  18. Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation

    Science.gov (United States)

    Vybiral, T.; Glaeser, D. H.; Goldberger, A. L.; Rigney, D. R.; Hess, K. R.; Mietus, J.; Skinner, J. E.; Francis, M.; Pratt, C. M.

    1993-01-01

    OBJECTIVES. The purpose of this report was to study heart rate variability in Holter recordings of patients who experienced ventricular fibrillation during the recording. BACKGROUND. Decreased heart rate variability is recognized as a long-term predictor of overall and arrhythmic death after myocardial infarction. It was therefore postulated that heart rate variability would be lowest when measured immediately before ventricular fibrillation. METHODS. Conventional indexes of heart rate variability were calculated from Holter recordings of 24 patients with structural heart disease who had ventricular fibrillation during monitoring. The control group consisted of 19 patients with coronary artery disease, of comparable age and left ventricular ejection fraction, who had nonsustained ventricular tachycardia but no ventricular fibrillation. RESULTS. Heart rate variability did not differ between the two groups, and no consistent trends in heart rate variability were observed before ventricular fibrillation occurred. CONCLUSIONS. Although conventional heart rate variability is an independent long-term predictor of adverse outcome after myocardial infarction, its clinical utility as a short-term predictor of life-threatening arrhythmias remains to be elucidated.

  19. Sensitivity, specificity and predictive values of linear and nonlinear indices of heart rate variability in stable angina patients

    Directory of Open Access Journals (Sweden)

    Pivatelli Flávio

    2012-10-01

    Full Text Available Abstract Background Decreased heart rate variability (HRV is related to higher morbidity and mortality. In this study we evaluated the linear and nonlinear indices of the HRV in stable angina patients submitted to coronary angiography. Methods We studied 77 unselected patients for elective coronary angiography, which were divided into two groups: coronary artery disease (CAD and non-CAD groups. For analysis of HRV indices, HRV was recorded beat by beat with the volunteers in the supine position for 40 minutes. We analyzed the linear indices in the time (SDNN [standard deviation of normal to normal], NN50 [total number of adjacent RR intervals with a difference of duration greater than 50ms] and RMSSD [root-mean square of differences] and frequency domains ultra-low frequency (ULF ≤ 0,003 Hz, very low frequency (VLF 0,003 – 0,04 Hz, low frequency (LF (0.04–0.15 Hz, and high frequency (HF (0.15–0.40 Hz as well as the ratio between LF and HF components (LF/HF. In relation to the nonlinear indices we evaluated SD1, SD2, SD1/SD2, approximate entropy (−ApEn, α1, α2, Lyapunov Exponent, Hurst Exponent, autocorrelation and dimension correlation. The definition of the cutoff point of the variables for predictive tests was obtained by the Receiver Operating Characteristic curve (ROC. The area under the ROC curve was calculated by the extended trapezoidal rule, assuming as relevant areas under the curve ≥ 0.650. Results Coronary arterial disease patients presented reduced values of SDNN, RMSSD, NN50, HF, SD1, SD2 and -ApEn. HF ≤ 66 ms2, RMSSD ≤ 23.9 ms, ApEn ≤−0.296 and NN50 ≤ 16 presented the best discriminatory power for the presence of significant coronary obstruction. Conclusion We suggest the use of Heart Rate Variability Analysis in linear and nonlinear domains, for prognostic purposes in patients with stable angina pectoris, in view of their overall impairment.

  20. Intraindividual Stepping Reaction Time Variability Predicts Falls in Older Adults With Mild Cognitive Impairment.

    Science.gov (United States)

    Bunce, David; Haynes, Becky I; Lord, Stephen R; Gschwind, Yves J; Kochan, Nicole A; Reppermund, Simone; Brodaty, Henry; Sachdev, Perminder S; Delbaere, Kim

    2017-06-01

    Reaction time measures have considerable potential to aid neuropsychological assessment in a variety of health care settings. One such measure, the intraindividual reaction time variability (IIV), is of particular interest as it is thought to reflect neurobiological disturbance. IIV is associated with a variety of age-related neurological disorders, as well as gait impairment and future falls in older adults. However, although persons diagnosed with Mild Cognitive Impairment (MCI) are at high risk of falling, the association between IIV and prospective falls is unknown. We conducted a longitudinal cohort study in cognitively intact (n = 271) and MCI (n = 154) community-dwelling adults aged 70-90 years. IIV was assessed through a variety of measures including simple and choice hand reaction time and choice stepping reaction time tasks (CSRT), the latter administered as a single task and also with a secondary working memory task. Logistic regression did not show an association between IIV on the hand-held tasks and falls. Greater IIV in both CSRT tasks, however, did significantly increase the risk of future falls. This effect was specific to the MCI group, with a stronger effect in persons exhibiting gait, posture, or physiological impairment. The findings suggest that increased stepping IIV may indicate compromised neural circuitry involved in executive function, gait, and posture in persons with MCI increasing their risk of falling. IIV measures have potential to assess neurobiological disturbance underlying physical and cognitive dysfunction in old age, and aid fall risk assessment and routine care in community and health care settings. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  1. Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia

    NARCIS (Netherlands)

    Ionescu, Clara A.; De Keyser, Robin; Torrico, Bismark Claure; De Smet, Tom; Struys, Michel M. R. F.; Normey-Rico, Julio E.

    This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The

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

    Science.gov (United States)

    Francq, Bernard G; Govaerts, Bernadette

    2016-06-30

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

  3. Quality of life after surgical treatment of coarctation in long-term follow-up (CoAFU): Predictive value of clinical variables.

    Science.gov (United States)

    Bambul Heck, Pinar; Pabst von Ohain, Jelena; Kaemmerer, Harald; Ewert, Peter; Hager, Alfred

    2018-01-01

    We sought to analyze the quality of life and the predictive value of clinical variables from previous follow-up study in patients late after surgical treatment of aortic coarctation on the quality of life. All patients, who have participated in the prospective cross-sectional COALA Study in 2000 with a structural clinical investigation including blood pressure measurement and symptom-limited exercise test were contacted for the health-related quality of life questionnaire SF-36 from January 2013 through December 2014. From 273 eligible patients, we received data from 135 patients, 9 of them died during the follow-up time at the median age of 46years (range 30-64years). Seventy-four patients did not participate in the study, other 64 patients moved to remote or unknown areas and could not be contacted. Quality of life was good in the fields of physical role and pain. However, patients reported a significant impairment in general health and in health transition, depending on the age. Arterial hypertension and variables from echocardiography or exercise testing from the COALA study were not predictive on functional health status. Quality of life in patients late after aortic coarctation repair is fairly good compared with healthy controls. Impairments in general health and health transition depend mainly on age, can be explained due to numerous comorbidities and reinterventions in long-term. The predictive value of the commonly assessed clinical variables on quality of life is limited. Copyright © 2017. Published by Elsevier B.V.

  4. Start time variability and predictability in railroad train and engine freight and passenger service employees.

    Science.gov (United States)

    2014-04-01

    Start time variability in work schedules is often hypothesized to be a cause of railroad employee fatigue because unpredictable work start times prevent employees from planning sleep and personal activities. This report examines work start time diffe...

  5. Baseline Chromatin Modification Levels May Predict Interindividual Variability in Ozone-Induced Gene Expression

    Science.gov (United States)

    Traditional toxicological paradigms have relied on factors such as age, genotype, and disease status to explain variability in responsiveness to toxicant exposure; however, these are neither sufficient to faithfully identify differentially responsive individuals nor are they modi...

  6. Intraindividual variability in executive functions but not speed of processing or conflict resolution predicts performance differences in gait speed in older adults.

    Science.gov (United States)

    Holtzer, Roee; Mahoney, Jeannette; Verghese, Joe

    2014-08-01

    The relationship between executive functions (EF) and gait speed is well established. However, with the exception of dual tasking, the key components of EF that predict differences in gait performance have not been determined. Therefore, the current study was designed to determine whether processing speed, conflict resolution, and intraindividual variability in EF predicted variance in gait performance in single- and dual-task conditions. Participants were 234 nondemented older adults (mean age 76.48 years; 55% women) enrolled in a community-based cohort study. Gait speed was assessed using an instrumented walkway during single- and dual-task conditions. The flanker task was used to assess EF. Results from the linear mixed effects model showed that (a) dual-task interference caused a significant dual-task cost in gait speed (estimate = 35.99; 95% CI = 33.19-38.80) and (b) of the cognitive predictors, only intraindividual variability was associated with gait speed (estimate = -.606; 95% CI = -1.11 to -.10). In unadjusted analyses, the three EF measures were related to gait speed in single- and dual-task conditions. However, in fully adjusted linear regression analysis, only intraindividual variability predicted performance differences in gait speed during dual tasking (B = -.901; 95% CI = -1.557 to -.245). Among the three EF measures assessed, intraindividual variability but not speed of processing or conflict resolution predicted performance differences in gait speed. © The Author 2013. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  7. Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

    OpenAIRE

    Alejandro Baldominos Gómez; Esperanza Albacete; Ignacio Merrero; Yago Saez

    2016-01-01

    This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user's actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to ...

  8. Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

    Directory of Open Access Journals (Sweden)

    Su Yang

    Full Text Available Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1 Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2 The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3 The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

  9. Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

    Science.gov (United States)

    Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

    2015-01-01

    Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

  10. Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences.

    Science.gov (United States)

    Mi, Tian; Merlin, Jerlin Camilus; Deverasetty, Sandeep; Gryk, Michael R; Bill, Travis J; Brooks, Andrew W; Lee, Logan Y; Rathnayake, Viraj; Ross, Christian A; Sargeant, David P; Strong, Christy L; Watts, Paula; Rajasekaran, Sanguthevar; Schiller, Martin R

    2012-01-01

    Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.

  11. A prediction score for significant coronary artery disease in Chinese patients ≥50 years old referred for rheumatic valvular heart disease surgery.

    Science.gov (United States)

    Xu, Zhenjun; Pan, Jun; Chen, Tao; Zhou, Qing; Wang, Qiang; Cao, Hailong; Fan, Fudong; Luo, Xuan; Ge, Min; Wang, Dongjin

    2018-04-01

    Our goal was to establish a prediction score and protocol for the preoperative prediction of significant coronary artery disease (CAD) in patients with rheumatic valvular heart disease. Using multivariate logistic regression analysis, we validated the model based on 490 patients without a history of myocardial infarction and who underwent preoperative screening coronary angiography. Significant CAD was defined as ≥50% narrowing of the diameter of the lumen of the left main coronary artery or ≥70% narrowing of the diameter of the lumen of the left anterior descending coronary artery, left circumflex artery or right coronary artery. Significant CAD was present in 9.8% of patients. Age, smoking, diabetes mellitus, diastolic blood pressure, low-density lipoprotein cholesterol and ischaemia evident on an electrocardiogram were independently associated with significant CAD and were entered into the multivariate model. According to the logistic regression predictive risk score, preoperative coronary angiography is recommended in (i) postmenopausal women between 50 and 59 years of age with ≥9.1% logistic regression predictive risk score; (ii) postmenopausal women who are ≥60 years old with a logistic regression predictive risk score ≥6.6% and (iii) men ≥50 years old whose logistic regression predictive risk score was ≥2.8%. Based on this predictive model, 246 (50.2%) preoperative coronary angiograms could be safely avoided. The negative predictive value of the model was 98.8% (246 of 249). This model was accurate for the preoperative prediction of significant CAD in patients with rheumatic valvular heart disease. This model must be validated in larger cohorts and various populations.

  12. Probabilistic approaches to accounting for data variability in the practical application of bioavailability in predicting aquatic risks from metals.

    Science.gov (United States)

    Ciffroy, Philippe; Charlatchka, Rayna; Ferreira, Daniel; Marang, Laura

    2013-07-01

    The biotic ligand model (BLM) theoretically enables the derivation of environmental quality standards that are based on true bioavailable fractions of metals. Several physicochemical variables (especially pH, major cations, dissolved organic carbon, and dissolved metal concentrations) must, however, be assigned to run the BLM, but they are highly variable in time and space in natural systems. This article describes probabilistic approaches for integrating such variability during the derivation of risk indexes. To describe each variable using a probability density function (PDF), several methods were combined to 1) treat censored data (i.e., data below the limit of detection), 2) incorporate the uncertainty of the solid-to-liquid partitioning of metals, and 3) detect outliers. From a probabilistic perspective, 2 alternative approaches that are based on log-normal and Γ distributions were tested to estimate the probability of the predicted environmental concentration (PEC) exceeding the predicted non-effect concentration (PNEC), i.e., p(PEC/PNEC>1). The probabilistic approach was tested on 4 real-case studies based on Cu-related data collected from stations on the Loire and Moselle rivers. The approach described in this article is based on BLM tools that are freely available for end-users (i.e., the Bio-Met software) and on accessible statistical data treatments. This approach could be used by stakeholders who are involved in risk assessments of metals for improving site-specific studies. Copyright © 2013 SETAC.

  13. Blood pressure variability predicts cardiovascular events independently of traditional cardiovascular risk factors and target organ damage

    DEFF Research Database (Denmark)

    Vishram, Julie K K; Dahlöf, Björn; Devereux, Richard B

    2015-01-01

    ). METHODS: In 8505 patients randomized to losartan vs. atenolol-based treatment in the LIFE study, we tested whether BP variability assessed as SD and range for BP6-24months measured at 6, 12, 18 and 24 months of treatment was associated with target organ damage (TOD) defined by LVH on ECG and urine albumin......BACKGROUND: Assessment of antihypertensive treatment is normally based on the mean value of a number of blood pressure (BP) measurements. However, it is uncertain whether high in-treatment visit-to-visit BP variability may be harmful in hypertensive patients with left ventricular hypertrophy (LVH.......05), but MI was not. CONCLUSION: In LIFE patients, higher in-treatment BP6-24months variability was independently of mean BP6-24months associated with later CEP and stroke, but not with MI or TOD after 24 months....

  14. Straight line fitting and predictions: On a marginal likelihood approach to linear regression and errors-in-variables models

    Science.gov (United States)

    Christiansen, Bo

    2015-04-01

    Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.

  15. Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

    Science.gov (United States)

    Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul A.

    2017-08-01

    The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over

  16. Predicting aboveground forest biomass with topographic variables in human-impacted tropical dry forest landscapes

    NARCIS (Netherlands)

    Salinas-Melgoza, Miguel A.; Skutsch, Margaret; Lovett, Jon C.

    2018-01-01

    Topographic variables such as slope and elevation partially explain spatial variations in aboveground biomass (AGB) within landscapes. Human activities that impact vegetation, such as cattle grazing and shifting cultivation, often follow topographic features and also play a key role in determining

  17. Undergraduate Nurse Variables that Predict Academic Achievement and Clinical Competence in Nursing

    Science.gov (United States)

    Blackman, Ian; Hall, Margaret; Darmawan, I Gusti Ngurah.

    2007-01-01

    A hypothetical model was formulated to explore factors that influenced academic and clinical achievement for undergraduate nursing students. Sixteen latent variables were considered including the students' background, gender, type of first language, age, their previous successes with their undergraduate nursing studies and status given for…

  18. Predictivity strength of the spatial variability of phenanthrene sorption across two sandy loam fields

    DEFF Research Database (Denmark)

    Soares, Antonio; Paradelo Pérez, Marcos; Møldrup, Per

    2015-01-01

    Sorption is commonly suggested as the major process underlying the transport and fate of polycyclic aromatic hydrocarbons (PAHs) in soils. However, studies focusing in spatial variability at the field scale in particular are still scarce. In order to investigate the sorption of phenanthrene...

  19. Feasibility, Reliability and Predictive Value Of In-Ambulance Heart Rate Variability Registration

    NARCIS (Netherlands)

    Yperzeele, Laetitia; van Hooff, Robbert-Jan; De Smedt, Ann; Nagels, Guy; Hubloue, Ives; De Keyser, Jacques; Brouns, Raf

    2016-01-01

    Background Heart rate variability (HRV) is a parameter of autonomic nervous system function. A decrease of HRV has been associated with disease severity, risk of complications and prognosis in several conditions. Objective We aim to investigate the feasibility and the reliability of in-ambulance HRV

  20. Using small area estimation and Lidar-derived variables for multivariate prediction of forest attributes

    Science.gov (United States)

    F. Mauro; Vicente Monleon; H. Temesgen

    2015-01-01

    Small area estimation (SAE) techniques have been successfully applied in forest inventories to provide reliable estimates for domains where the sample size is small (i.e. small areas). Previous studies have explored the use of either Area Level or Unit Level Empirical Best Linear Unbiased Predictors (EBLUPs) in a univariate framework, modeling each variable of interest...

  1. Does Response Variability Predict Distractibility among Adults with Attention-Deficit/Hyperactivity Disorder?

    Science.gov (United States)

    Adams, Zachary W.; Roberts, Walter M.; Milich, Richard; Fillmore, Mark T.

    2011-01-01

    Increased intraindividual variability in response time (RTSD) has been observed reliably in attention-deficit/hyperactivity disorder (ADHD) and has often been used as a measure of inattention. RTSD is assumed to reflect attentional lapses and distractibility, though evidence for the validity of this connection is lacking. We assessed whether RTSD…

  2. A Source Area Approach Demonstrates Moderate Predictive Ability but Pronounced Variability of Invasive Species Traits.

    Directory of Open Access Journals (Sweden)

    Günther Klonner

    Full Text Available The search for traits that make alien species invasive has mostly concentrated on comparing successful invaders and different comparison groups with respect to average trait values. By contrast, little attention has been paid to trait variability among invaders. Here, we combine an analysis of trait differences between invasive and non-invasive species with a comparison of multidimensional trait variability within these two species groups. We collected data on biological and distributional traits for 1402 species of the native, non-woody vascular plant flora of Austria. We then compared the subsets of species recorded and not recorded as invasive aliens anywhere in the world, respectively, first, with respect to the sampled traits using univariate and multiple regression models; and, second, with respect to their multidimensional trait diversity by calculating functional richness and dispersion metrics. Attributes related to competitiveness (strategy type, nitrogen indicator value, habitat use (agricultural and ruderal habitats, occurrence under the montane belt, and propagule pressure (frequency were most closely associated with invasiveness. However, even the best multiple model, including interactions, only explained a moderate fraction of the differences in invasive success. In addition, multidimensional variability in trait space was even larger among invasive than among non-invasive species. This pronounced variability suggests that invasive success has a considerable idiosyncratic component and is probably highly context specific. We conclude that basing risk assessment protocols on species trait profiles will probably face hardly reducible uncertainties.

  3. Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables

    International Nuclear Information System (INIS)

    Sun, Huaiwei; Zhao, Na; Zeng, Xiaofan; Yan, Dong

    2015-01-01

    Highlights: • We investigate relationships between solar radiation and meteorological variables. • A strong relationship exists between solar radiation and sunshine duration. • Daily global radiation can be estimated accurately with ARMAX–GARCH models. • MGARCH model was applied to investigate time-varying relationships. - Abstract: The traditional approaches that employ the correlations between solar radiation and other measured meteorological variables are commonly utilized in studies. It is important to investigate the time-varying relationships between meteorological variables and solar radiation to determine which variables have the strongest correlations with solar radiation. In this study, the nonlinear autoregressive moving average with exogenous variable–generalized autoregressive conditional heteroscedasticity (ARMAX–GARCH) and multivariate GARCH (MGARCH) time-series approaches were applied to investigate the associations between solar radiation and several meteorological variables. For these investigations, the long-term daily global solar radiation series measured at three stations from January 1, 2004 until December 31, 2007 were used in this study. Stronger relationships were observed to exist between global solar radiation and sunshine duration than between solar radiation and temperature difference. The results show that 82–88% of the temporal variations of the global solar radiation were captured by the sunshine-duration-based ARMAX–GARCH models and 55–68% of daily variations were captured by the temperature-difference-based ARMAX–GARCH models. The advantages of the ARMAX–GARCH models were also confirmed by comparison of Auto-Regressive and Moving Average (ARMA) and neutral network (ANN) models in the estimation of daily global solar radiation. The strong heteroscedastic persistency of the global solar radiation series was revealed by the AutoRegressive Conditional Heteroscedasticity (ARCH) and Generalized Auto

  4. Surface Complexation Modeling in Variable Charge Soils: Prediction of Cadmium Adsorption

    Directory of Open Access Journals (Sweden)

    Giuliano Marchi

    2015-10-01

    Full Text Available ABSTRACT Intrinsic equilibrium constants for 22 representative Brazilian Oxisols were estimated from a cadmium adsorption experiment. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. Intrinsic equilibrium constants were optimized by FITEQL and by hand calculation using Visual MINTEQ in sweep mode, and Excel spreadsheets. Data from both models were incorporated into Visual MINTEQ. Constants estimated by FITEQL and incorporated in Visual MINTEQ software failed to predict observed data accurately. However, FITEQL raw output data rendered good results when predicted values were directly compared with observed values, instead of incorporating the estimated constants into Visual MINTEQ. Intrinsic equilibrium constants optimized by hand calculation and incorporated in Visual MINTEQ reliably predicted Cd adsorption reactions on soil surfaces under changing environmental conditions.

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

    Science.gov (United States)

    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.

  6. Significant correlation between spleen volume and thrombocytopenia in liver transplant patients: a concept for predicting persistent thrombocytopenia.

    Science.gov (United States)

    Ohira, Masahiro; Ishifuro, Minoru; Ide, Kentaro; Irei, Toshimitsu; Tashiro, Hirotaka; Itamoto, Toshiyuki; Ito, Katsuhide; Chayama, Kazuaki; Asahara, Toshimasa; Ohdan, Hideki

    2009-02-01

    Interferon (IFN) therapy with or without ribavirin treatment is well established as a standard antiviral treatment for hepatitis C virus (HCV)-infected patients. However, susceptibility to thrombocytopenia is a major obstacle for initiating or continuing this therapy, particularly in liver transplant (LTx) recipients with HCV. Studies have reported that splenectomy performed concurrently with LTx is a feasible strategy for conditioning patients for anti-HCV IFN therapy. However, the relationship between the severity of splenomegaly and alterations in the blood cytopenia in LTx recipients remains to be clarified. Here, we analyzed the relationship between spleen volume (SV) and thrombocytopenia in 45 patients who underwent LTx at Hiroshima University Hospital. The extent of pre-LTx splenomegaly [the SV to body surface area (BSA) ratio in an individual] was inversely correlated with both the post-LTx white blood cell count and platelet (PLT) count (P or= 400), persistent thrombocytopenia is predictable after LTx. (c) 2009 AASLD.

  7. Noncognitive Variables to Predict Academic Success among Junior Year Baccalaureate Nursing Students

    Science.gov (United States)

    Smith, Ellen M. T.

    2017-01-01

    An equitable predictor of academic success is needed as nursing education strives toward comprehensive preparation of diverse nursing students. The purpose of this study was to discover how Sedlacek's (2004a) Noncognitive Questionnaire (NCQ) and Duckworth & Quinn's (2009) Grit-S predicted baccalaureate nursing student academic performance and…

  8. Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms

    NARCIS (Netherlands)

    Bianchi, E.; Doppelbauer, G.; Filion, L.C.; Dijkstra, M.; Kahl, G.

    2012-01-01

    We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the

  9. Intraindividual Stepping Reaction Time Variability Predicts Falls in Older Adults With Mild Cognitive Impairment

    OpenAIRE

    Bunce, D; Haynes, BI; Lord, SR; Gschwind, YJ; Kochan, NA; Reppermund, S; Brodaty, H; Sachdev, PS; Delbaere, K

    2017-01-01

    Background: Reaction time measures have considerable potential to aid neuropsychological assessment in a variety of health care settings. One such measure, the intraindividual reaction time variability (IIV), is of particular interest as it is thought to reflect neurobiological disturbance. IIV is associated with a variety of age-related neurological disorders, as well as gait impairment and future falls in older adults. However, although persons diagnosed with Mild Cognitive Impairment (MCI)...

  10. Pathogen prevalence predicts human cross-cultural variability in individualism/collectivism

    OpenAIRE

    Fincher, Corey L; Thornhill, Randy; Murray, Damian R; Schaller, Mark

    2008-01-01

    Pathogenic diseases impose selection pressures on the social behaviour of host populations. In humans (Homo sapiens), many psychological phenomena appear to serve an antipathogen defence function. One broad implication is the existence of cross-cultural differences in human cognition and behaviour contingent upon the relative presence of pathogens in the local ecology. We focus specifically on one fundamental cultural variable: differences in individualistic versus collectivist values. We sug...

  11. Evaluation of standardized and applied variables in predicting treatment outcomes of polytrauma patients.

    Science.gov (United States)

    Aksamija, Goran; Mulabdic, Adi; Rasic, Ismar; Muhovic, Samir; Gavric, Igor

    2011-01-01

    Polytrauma is defined as an injury where they are affected by at least two different organ systems or body, with at least one life-threatening injuries. Given the multilevel model care of polytrauma patients within KCUS are inevitable weaknesses in the management of this category of patients. To determine the dynamics of existing procedures in treatment of polytrauma patients on admission to KCUS, and based on statistical analysis of variables applied to determine and define the factors that influence the final outcome of treatment, and determine their mutual relationship, which may result in eliminating the flaws in the approach to the problem. The study was based on 263 polytrauma patients. Parametric and non-parametric statistical methods were used. Basic statistics were calculated, based on the calculated parameters for the final achievement of research objectives, multicoleration analysis, image analysis, discriminant analysis and multifactorial analysis were used. From the universe of variables for this study we selected sample of n = 25 variables, of which the first two modular, others belong to the common measurement space (n = 23) and in this paper defined as a system variable methods, procedures and assessments of polytrauma patients. After the multicoleration analysis, since the image analysis gave a reliable measurement results, we started the analysis of eigenvalues, that is defining the factors upon which they obtain information about the system solve the problem of the existing model and its correlation with treatment outcome. The study singled out the essential factors that determine the current organizational model of care, which may affect the treatment and better outcome of polytrauma patients. This analysis has shown the maximum correlative relationships between these practices and contributed to development guidelines that are defined by isolated factors.

  12. Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China.

    Science.gov (United States)

    Du, Zhicheng; Xu, Lin; Zhang, Wangjian; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao

    2017-10-06

    Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Ecological study. Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011-2014. Analyses were conducted at aggregate level and no confidential information was involved. A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. A high correlation between HFMD incidence and BDI ( r =0.794, pengine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  13. Predictability experiments for the Asian summer monsoon impact of SST anomalies on interannual and intraseasonal variability

    CERN Document Server

    Molteni, F; Ferranti, L; Slingo, J M

    2003-01-01

    The effects of SST anomalies on the interannual and intraseasonal variability of the Asian summer monsoon have been studied by multivariate statistical analyses of 850-hPa wind and rainfall fields simulated in a set of ensemble integrations of the ECMWF atmospheric GCM, referred to as the PRISM experiments. The simulations used observed SSTs (PRISM-O), covering 9 years characterised by large variations of the ENSO phenomenon in the 1980's and the early 1990's. A parallel set of simulations was also performed with climatological SSTs (PRISM-C), thus enabling the influence of SST forcing on the modes of interannual and intraseasonal variability to be investigated. As in observations, the model's interannual variability is dominated by a zonally-oriented mode which describes the north-south movement of the tropical convergence zone (TCZ). This mode appears to be independent of SST forcing and its robustness between the PRISM-O and PRISM-C simulations suggests that it is driven by internal atmospheric dynamics. O...

  14. Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

    Science.gov (United States)

    He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

    2016-03-01

    In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Personality, emotion-related variables, and media pressure predict eating disorders via disordered eating in Lebanese university students.

    Science.gov (United States)

    Sanchez-Ruiz, Maria Jose; El-Jor, Claire; Abi Kharma, Joelle; Bassil, Maya; Zeeni, Nadine

    2017-04-18

    Disordered eating behaviors are on the rise among youth. The present study investigates psychosocial and weight-related variables as predictors of eating disorders (ED) through disordered eating (DE) dimensions (namely restrained, external, and emotional eating) in Lebanese university students. The sample consisted of 244 undergraduates (143 female) aged from 18 to 31 years (M = 20.06; SD = 1.67). Using path analysis, two statistical models were built separately with restrained and emotional eating as dependent variables, and all possible direct and indirect pathways were tested for mediating effects. The variables tested for were media influence, perfectionism, trait emotional intelligence, and the Big Five dimensions. In the first model, media pressure, self-control, and extraversion predicted eating disorders via emotional eating. In the second model, media pressure and perfectionism predicted eating disorders via restrained eating. Findings from this study provide an understanding of the dynamics between DE, ED, and key personality, emotion-related, and social factors in youth. Lastly, implications and recommendations for future studies are advanced.

  16. The significance and predictive value of free light chains in the urine of patients with chronic inflammatory rheumatic disease.

    Science.gov (United States)

    Bramlage, Carsten Paul; Froelich, Britta; Wallbach, Manuel; Minguet, Joan; Grupp, Clemens; Deutsch, Cornelia; Bramlage, Peter; Koziolek, Michael; Müller, Gerhard Anton

    2016-12-01

    In patients with rheumatic diseases, reliable markers for determining disease activity are scarce. One potential parameter is the level of immunoglobulin free light chains (FLCs), which is known to be elevated in the blood of patients with certain rheumatic diseases. Few studies have quantified FLCs in urine, a convenient source of test sample, in patients with different rheumatic diseases. We carried out a retrospective analysis of patients with rheumatic disease attending the University hospital of Goettingen, Germany. Subjects were included if they had urine levels of both κ and λ FLCs available and did not have myeloma. Data regarding systemic inflammation and kidney function were recorded, and FLC levels were correlated with inflammatory markers. Of the 382 patients with rheumatic disease, 40.1 % had chronic polyarthritis, 21.2 % connective tissue disease, 18.6 % spondyloarthritis and 15.7 % vasculitis. Elevated levels of κ FLCs were found for 84 % of patients and elevated λ for 52.7 %. For the patients with rheumatoid arthritis, FLCs correlated with C-reactive protein (κ, r = 0.368, p rheumatic disease, but not in κ/λ ratio. The correlation between FLCs and inflammatory markers in patients with rheumatoid arthritis demonstrates their potential for predicting disease activity.

  17. Combined use of serum MCP-1/IL-10 ratio and uterine artery Doppler index significantly improves the prediction of preeclampsia.

    Science.gov (United States)

    Cui, Shihong; Gao, Yanan; Zhang, Linlin; Wang, Yuan; Zhang, Lindong; Liu, Pingping; Liu, Ling; Chen, Juan

    2017-10-01

    Monocyte chemotactic protein-1 (MCP-1, or CCL2) is a member of the chemokine subfamily involved in recruitment of monocytes in inflammatory tissues. IL-10 is a key regulator for maintaining the balance of anti-inflammatory and pro-inflammatory milieu at the feto-maternal interface. Doppler examination has been routinely performed for the monitoring and management of preeclampsia patients. This study evaluates the efficiency of these factors alone, or in combination, for the predication of preeclampsia. The serum levels of MCP-1 and IL-10 in 78 preeclampsia patients and 143 age-matched normal controls were measured. The Doppler ultrasonography was performed and Artery Pulsatility Index (PI) and Resistance Index (RI) were calculated for the same subjects. It was found that while the second-trimester serum MCP-1, IL-10, MCP-1/IL-10 ratio, PI, and RI showed some power in predicting preeclampsia, the combination of MCP-1/IL-10 and PI and RI accomplishes the highest efficiency, achieving an AUC of 0.973 (95% CI, 0.000-1.000, Ppreeclampsia. Future studies using a larger sample can be conducted to construct an algorithm capable of quantitative assessment on the risk of preeclampsia. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Cultural values predict coping using culture as an individual difference variable in multi-cultural samples.

    OpenAIRE

    Bardi, Anat; Guerra, V. M.

    2011-01-01

    Three studies establish the relations between cultural values and coping using multicultural samples of international students. Study 1 established the cross-cultural measurement invariance of subscales of the Cope inventory (Carver, Scheier, & Weintraub, 1989) used in the paper. The cultural value dimensions of embeddedness vs. autonomy and hierarchy vs. egalitarianism predicted how international students from 28 (Study 2) and 38 (Study 3) countries coped with adapting to living in a new cou...

  19. Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.

    Science.gov (United States)

    Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard

    2012-06-07

    We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems.

  20. Performance prediction for silicon photonics integrated circuits with layout-dependent correlated manufacturing variability.

    Science.gov (United States)

    Lu, Zeqin; Jhoja, Jaspreet; Klein, Jackson; Wang, Xu; Liu, Amy; Flueckiger, Jonas; Pond, James; Chrostowski, Lukas

    2017-05-01

    This work develops an enhanced Monte Carlo (MC) simulation methodology to predict the impacts of layout-dependent correlated manufacturing variations on the performance of photonics integrated circuits (PICs). First, to enable such performance prediction, we demonstrate a simple method with sub-nanometer accuracy to characterize photonics manufacturing variations, where the width and height for a fabricated waveguide can be extracted from the spectral response of a racetrack resonator. By measuring the spectral responses for a large number of identical resonators spread over a wafer, statistical results for the variations of waveguide width and height can be obtained. Second, we develop models for the layout-dependent enhanced MC simulation. Our models use netlist extraction to transfer physical layouts into circuit simulators. Spatially correlated physical variations across the PICs are simulated on a discrete grid and are mapped to each circuit component, so that the performance for each component can be updated according to its obtained variations, and therefore, circuit simulations take the correlated variations between components into account. The simulation flow and theoretical models for our layout-dependent enhanced MC simulation are detailed in this paper. As examples, several ring-resonator filter circuits are studied using the developed enhanced MC simulation, and statistical results from the simulations can predict both common-mode and differential-mode variations of the circuit performance.

  1. Recent developments on SMA actuators: predicting the actuation fatigue life for variable loading schemes

    Science.gov (United States)

    Wheeler, Robert W.; Lagoudas, Dimitris C.

    2017-04-01

    Shape memory alloys (SMAs), due to their ability to repeatably recover substantial deformations under applied mechanical loading, have the potential to impact the aerospace, automotive, biomedical, and energy industries as weight and volume saving replacements for conventional actuators. While numerous applications of SMA actuators have been flight tested and can be found in industrial applications, these actuators are generally limited to non-critical components, are not widely implemented and frequently one-off designs, and are generally overdesigned due to a lack of understanding of the effect of the loading path on the fatigue life and the lack of an accurate method for predicting actuator lifetimes. In recent years, multiple research efforts have increased our understanding of the actuation fatigue process of SMAs. These advances can be utilized to predict the fatigue lives and failure loads in SMA actuators. Additionally, these prediction methods can be implemented in order to intelligently design actuators in accordance with their fatigue and failure limits. In the following paper, both simple and complex thermomechanical loading paths have been considered. Experimental data was utilized from two material systems: equiatomic Nickel-Titanium and Nickelrich Nickel-Titanium.

  2. Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

    Energy Technology Data Exchange (ETDEWEB)

    Maslowski, Wieslaw [Naval Postgraduate School, Monterey, CA (United States)

    2016-10-17

    This project aims to develop, apply and evaluate a regional Arctic System model (RASM) for enhanced decadal predictions. Its overarching goal is to advance understanding of the past and present states of arctic climate and to facilitate improvements in seasonal to decadal predictions. In particular, it will focus on variability and long-term change of energy and freshwater flows through the arctic climate system. The project will also address modes of natural climate variability as well as extreme and rapid climate change in a region of the Earth that is: (i) a key indicator of the state of global climate through polar amplification and (ii) which is undergoing environmental transitions not seen in instrumental records. RASM will readily allow the addition of other earth system components, such as ecosystem or biochemistry models, thus allowing it to facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts. As such, RASM is expected to become a foundation for more complete Arctic System models and part of a model hierarchy important for improving climate modeling and predictions.

  3. Significant pre-accession factors predicting success or failure during a Marine Corps officer’s initial service obligation

    OpenAIRE

    Johnson, Jacob A.

    2015-01-01

    Approved for public release; distribution is unlimited Increasing diversity and equal opportunity in the military is a congressional and executive priority. At the same time, improving recruiting practices is a priority of the commandant of the Marine Corps. In an effort to provide information to the Marine Corps that may improve recruiting practice and enable retention of a higher quality and more diverse officer corps, probit econometric models are estimated to identify significant facto...

  4. The Epidemiological Significance and Temporal Stability of Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats-Based Method Applied to Mycobacterium tuberculosis in China

    Directory of Open Access Journals (Sweden)

    Yang Li

    2018-04-01

    Full Text Available This study aimed to validate the epidemiological significance and temporal stability of Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats (MIRU-VNTR typing in a genetically and geographically diverse set of clinical isolates from patients diagnosed with pulmonary tuberculosis in China. Between 2010 and 2013, a total of 982 Mycobacterium tuberculosis isolates were collected from four population-based investigations in China. Apart from the currently applied 24-locus MIRU-VNTR, six additional hypervariable loci were analyzed in order to validate the MIRU-VNTR combinations in terms of their epidemiological links, clustering time span, and paired geographic distance. In vitro temporal stability was analyzed for both individual MIRU-VNTR loci, and for several combinations of loci. In the present study, four MIRU-VNTR combinations, including the hypervariable loci 3820, 3232, 2163a, and 4120, were evaluated. All of these combinations obtained a Hunter-Gaston discriminatory index (HGDI value over 0.9900 with a reduced clustering proportion (from 32.0% to 25.6%. By comparing epidemiological links, clustering time span, and paired geographic distance, we found that the performances of the four MIRU-VNTR combinations were comparable to the insertion sequence 6110 restriction fragment length polymorphism (IS6110-RFLP, and significantly better than that of 24-locus MIRU-VNTR genotyping alone. The proportion of temporally stable loci ranged from 90.5% to 92.5% within the combined MIRU-VNTR genotyping, which is higher than IS6110-RFLP (85.4%. By adding four hypervariable loci to the standard 24-locus MIRU-VNTR genotyping, we obtained a high discriminatory power, stability and epidemiological significance. This algorithm could therefore be used to improve tuberculosis transmission surveillance and outbreak investigation in China.

  5. Total levels of hippocampal histone acetylation predict normal variability in mouse behavior.

    Directory of Open Access Journals (Sweden)

    Addie May I Nesbitt

    Full Text Available Genetic, pharmacological, and environmental interventions that alter total levels of histone acetylation in specific brain regions can modulate behaviors and treatment responses. Efforts have been made to identify specific genes that are affected by alterations in total histone acetylation and to propose that such gene specific modulation could explain the effects of total histone acetylation levels on behavior - the implication being that under naturalistic conditions variability in histone acetylation occurs primarily around the promoters of specific genes.Here we challenge this hypothesis by demonstrating with a novel flow cytometry based technique that normal variability in open field exploration, a hippocampus-related behavior, was associated with total levels of histone acetylation in the hippocampus but not in other brain regions.Results suggest that modulation of total levels of histone acetylation may play a role in regulating biological processes. We speculate in the discussion that endogenous regulation of total levels of histone acetylation may be a mechanism through which organisms regulate cellular plasticity. Flow cytometry provides a useful approach to measure total levels of histone acetylation at the single cell level. Relating such information to behavioral measures and treatment responses could inform drug delivery strategies to target histone deacetylase inhibitors and other chromatin modulators to places where they may be of benefit while avoiding areas where correction is not needed and could be harmful.

  6. Spatial prediction of water quality variables along a main river channel, in presence of pollution hotspots.

    Science.gov (United States)

    Rizo-Decelis, L D; Pardo-Igúzquiza, E; Andreo, B

    2017-12-15

    In order to treat and evaluate the available data of water quality and fully exploit monitoring results (e.g. characterize regional patterns, optimize monitoring networks, infer conditions at unmonitored locations, etc.), it is crucial to develop improved and efficient methodologies. Accordingly, estimation of water quality along fluvial ecosystems is a frequent task in environment studies. In this work, a particular case of this problem is examined, namely, the estimation of water quality along a main stem of a large basin (where most anthropic activity takes place), from observational data measured along this river channel. We adapted topological kriging to this case, where each watershed contains all the watersheds of the upstream observed data ("nested support effect"). Data analysis was additionally extended by taking into account the upstream distance to the closest contamination hotspot as an external drift. We propose choosing the best estimation method by cross-validation. The methodological approach in spatial variability modeling may be used for optimizing the water quality monitoring of a given watercourse. The methodology presented is applied to 28 water quality variables measured along the Santiago River in Western Mexico. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

    Science.gov (United States)

    Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

    2017-12-01

    Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

  8. Evaluation of ischemic corticospinal tract damage by diffusion tensor MRI. Its significance to predict functional outcome of corona radiata infarct

    International Nuclear Information System (INIS)

    Tanaka, Hideki

    2010-01-01

    Motor impairment is one of the most frequent symptoms among stroke patients and often leads to poststroke dependency. Recent advances of diffusion tensor MR imaging made it possible to identify corticospinal tract (CST) three-dimensionally and evaluate structural damage, so precise evaluation of the ischemic CST damage became feasible.Motor impairment, lesion size and location upon diffusion weighted MR image and clinical outcome were assessed in 23 acute to subacute capsular and corona radiata infarct patients. According to the lesion size, patients were grouped into A, maximal diameter below 15 mm and B, that above 15 mm. Motor impairment was graded severe: limb movement synergy level, moderate: selective muscle activity possible and mild: isolated movements well co-ordinated, each corresponding to Brunnstrom stage 1-3, 4-5, and 6, respectively. Outcome at the time of discharge was assessed by modified Rankin Scale (mRS), discharge destination and length of hospital stay were also registered. Diffusion tensor MR imaging was conducted in 15 corona radiata infarct patients at 2.3+-2.2 days from the onset of the clinical symptoms. CST was 3-dimensionally identified with dTV. II. SR and Volume-one 1.72 and CST-FA ratio (ipsi-/contralesional CST-FA) and CST-Area% (CST lesion free area/whole CST area) were obtained at the level where ischemic damage was most prominent and correlation of these parameters to motor impairment and clinical outcome was studied. CST-FA ratio and CST-Area% were in good correlation to motor impairment at presentation. Patients with severe motor impairment had lower CST-FA ratio and CSF-Area% than those with moderate or mild. CST-FA ratio was 0.73+-0.22 in patients with poor clinical outcome (mRS 3-6) and 0.93+-0.09 with good clinical outcome (mRS 0-2) (p=0.038). Diffusion tensor MR imaging is useful in evaluating motor impairment and predicting functional outcome of corona radiata infarct patient in the acute to subacute stage. (author)

  9. Sebum and Hydration Levels in Specific Regions of Human Face Significantly Predict the Nature and Diversity of Facial Skin Microbiome.

    Science.gov (United States)

    Mukherjee, Souvik; Mitra, Rupak; Maitra, Arindam; Gupta, Satyaranjan; Kumaran, Srikala; Chakrabortty, Amit; Majumder, Partha P

    2016-10-27

    The skin microbiome varies across individuals. The causes of these variations are inadequately understood. We tested the hypothesis that inter-individual variation in facial skin microbiome can be significantly explained by variation in sebum and hydration levels in specific facial regions of humans. We measured sebum and hydration from forehead and cheek regions of healthy female volunteers (n = 30). Metagenomic DNA from skin swabs were sequenced for V3-V5 regions of 16S rRNA gene. Altogether, 34 phyla were identified; predominantly Actinobacteria (66.3%), Firmicutes (17.7%), Proteobacteria (13.1%) and Bacteroidetes (1.4%). About 1000 genera were identified; predominantly Propionibacterium (58.6%), Staphylococcus (8.6%), Streptococcus (4.0%), Corynebacterium (3.6%) and Paracoccus (3.3%). A subset (n = 24) of individuals were sampled two months later. Stepwise multiple regression analysis showed that cheek sebum level was the most significant predictor of microbiome composition and diversity followed by forehead hydration level; forehead sebum and cheek hydration levels were not. With increase in cheek sebum, the prevalence of Actinobacteria (p = 0.001)/Propionibacterium (p = 0.002) increased, whereas microbiome diversity decreased (Shannon Index, p = 0.032); this was opposite for other phyla/genera. These trends were reversed for forehead hydration levels. Therefore, the nature and diversity of facial skin microbiome is jointly determined by site-specific lipid and water levels in the stratum corneum.

  10. Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

    Directory of Open Access Journals (Sweden)

    Feng Zhong-xiang

    2014-01-01

    Full Text Available In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

  11. Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

    Science.gov (United States)

    Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

    2014-01-01

    In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

  12. Review: The variability of the eating quality of beef can be reduced by predicting consumer satisfaction.

    Science.gov (United States)

    Bonny, S P F; Hocquette, J-F; Pethick, D W; Legrand, I; Wierzbicki, J; Allen, P; Farmer, L J; Polkinghorne, R J; Gardner, G E

    2018-04-02

    The Meat Standards Australia (MSA) grading scheme has the ability to predict beef eating quality for each 'cut×cooking method combination' from animal and carcass traits such as sex, age, breed, marbling, hot carcass weight and fatness, ageing time, etc. Following MSA testing protocols, a total of 22 different muscles, cooked by four different cooking methods and to three different degrees of doneness, were tasted by over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia. Consumers scored the sensory characteristics (tenderness, flavor liking, juiciness and overall liking) and then allocated samples to one of four quality grades: unsatisfactory, good-every-day, better-than-every-day and premium. We observed that 26% of the beef was unsatisfactory. As previously reported, 68% of samples were allocated to the correct quality grades using the MSA grading scheme. Furthermore, only 7% of the beef unsatisfactory to consumers was misclassified as acceptable. Overall, we concluded that an MSA-like grading scheme could be used to predict beef eating quality and hence underpin commercial brands or labels in a number of European countries, and possibly the whole of Europe. In addition, such an eating quality guarantee system may allow the implementation of an MSA genetic index to improve eating quality through genetics as well as through management. Finally, such an eating quality guarantee system is likely to generate economic benefits to be shared along the beef supply chain from farmers to retailors, as consumers are willing to pay more for a better quality product.

  13. Variability of dose predictions for cesium-137 and radium-226 using the PRISM method

    International Nuclear Information System (INIS)

    Bergstroem, U.; Andersson, K.; Roejder, B.

    1984-01-01

    The uncertainty associated with dose predictions for cesium-137 and radium-226 in a specific ecosystem has been studied. The method used is a systematic method for determining the effect of parameter uncertainties on model prediction called PRISM. The ecosystems studied are different types of lakes where the following transport processes are included: runoff of water in the lake, irrigation, transport in soil, in groundwater and in sediment. The ecosystems are modelled by the compartment principle, using the BIOPATH-code. Seven different internal exposure pathways are included. The total dose commitment for both nuclides varies about two orders of magnitude. For cesium-137 the total dose and the uncertainty are dominated by the consumption of fish. The most important factor to the total uncertainty is the concentration factor water-fish. For radium-226 the largest contributions to the total dose are the exposure pathways, fish, milk and drinking-water. Half of the uncertainty lies in the milk dose. This uncertainty is dominated by the distribution factor for milk. (orig.)

  14. Beyond clay: Towards an improved set of variables for predicting soil organic matter content

    Science.gov (United States)

    Rasmussen, Craig; Heckman, Katherine; Wieder, William R.; Keiluweit, Marco; Lawrence, Corey R.; Berhe, Asmeret Asefaw; Blankinship, Joseph C.; Crow, Susan E.; Druhan, Jennifer; Hicks Pries, Caitlin E.; Marin-Spiotta, Erika; Plante, Alain F.; Schadel, Christina; Schmiel, Joshua P.; Sierra, Carlos A.; Thompson, Aaron; Wagai, Rota

    2018-01-01

    Improved quantification of the factors controlling soil organic matter (SOM) stabilization at continental to global scales is needed to inform projections of the largest actively cycling terrestrial carbon pool on Earth, and its response to environmental change. Biogeochemical models rely almost exclusively on clay content to modify rates of SOM turnover and fluxes of climate-active CO2 to the atmosphere. Emerging conceptual understanding, however, suggests other soil physicochemical properties may predict SOM stabilization better than clay content. We addressed this discrepancy by synthesizing data from over 5,500 soil profiles spanning continental scale environmental gradients. Here, we demonstrate that other physicochemical parameters are much stronger predictors of SOM content, with clay content having relatively little explanatory power. We show that exchangeable calcium strongly predicted SOM content in water-limited, alkaline soils, whereas with increasing moisture availability and acidity, iron- and aluminum-oxyhydroxides emerged as better predictors, demonstrating that the relative importance of SOM stabilization mechanisms scales with climate and acidity. These results highlight the urgent need to modify biogeochemical models to better reflect the role of soil physicochemical properties in SOM cycling.

  15. Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates.

    Science.gov (United States)

    Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P

    2017-03-01

    How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  16. Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

    Science.gov (United States)

    Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T.; Yahn, Jeremiah; Porter, Warren P.

    2017-01-01

    How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  17. A Review of Spectral Methods for Variable Amplitude Fatigue Prediction and New Results

    Science.gov (United States)

    Larsen, Curtis E.; Irvine, Tom

    2013-01-01

    A comprehensive review of the available methods for estimating fatigue damage from variable amplitude loading is presented. The dependence of fatigue damage accumulation on power spectral density (psd) is investigated for random processes relevant to real structures such as in offshore or aerospace applications. Beginning with the Rayleigh (or narrow band) approximation, attempts at improved approximations or corrections to the Rayleigh approximation are examined by comparison to rainflow analysis of time histories simulated from psd functions representative of simple theoretical and real world applications. Spectral methods investigated include corrections by Wirsching and Light, Ortiz and Chen, the Dirlik formula, and the Single-Moment method, among other more recent proposed methods. Good agreement is obtained between the spectral methods and the time-domain rainflow identification for most cases, with some limitations. Guidelines are given for using the several spectral methods to increase confidence in the damage estimate.

  18. Social integration prospectively predicts changes in heart rate variability among individuals undergoing migration stress.

    Science.gov (United States)

    Gouin, Jean-Philippe; Zhou, Biru; Fitzpatrick, Stephanie

    2015-04-01

    Poor social integration increases risk for poor health. The psychobiological pathways underlying this effect are not well-understood. This study utilized a migration stress model to prospectively investigate the impact of social integration on change in high-frequency heart rate variability (HF-HRV), a marker of autonomic functioning. Sixty new international students were recruited shortly after their arrival in the host country and assessed 2 and 5 months later. At each assessment period, participants provided information on social integration and loneliness and had their resting HF-HRV evaluated. There was an overall decrease in HF-HRV over time. The magnitude of the within-person and between-person effects of social integration on HRV increased over time, such that greater social integration was associated with higher HF-HRV at later follow-ups. These results suggest that altered autonomic functioning might represent a key pathway linking social integration to health outcomes.

  19. Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration.

    Science.gov (United States)

    Jović, Ozren; Smrečki, Neven; Popović, Zora

    2016-04-01

    A novel quantitative prediction and variable selection method called interval ridge regression (iRR) is studied in this work. The method is performed on six data sets of FTIR, two data sets of UV-vis and one data set of DSC. The obtained results show that models built with ridge regression on optimal variables selected with iRR significantly outperfom models built with ridge regression on all variables in both calibration (6 out of 9 cases) and validation (2 out of 9 cases). In this study, iRR is also compared with interval partial least squares regression (iPLS). iRR outperfomed iPLS in validation (insignificantly in 6 out of 9 cases and significantly in one out of 9 cases for poil, a well known health beneficial nutrient, is studied in this work by mixing it with cheap and widely used oils such as soybean (So) oil, rapeseed (R) oil and sunflower (Su) oil. Binary mixture sets of hempseed oil with these three oils (HSo, HR and HSu) and a ternary mixture set of H oil, R oil and Su oil (HRSu) were considered. The obtained accuracy indicates that using iRR on FTIR and UV-vis data, each particular oil can be very successfully quantified (in all 8 cases RMSEPoil (R(2)>0.99). Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Multiple Off-Ice Performance Variables Predict On-Ice Skating Performance in Male and Female Division III Ice Hockey Players

    Directory of Open Access Journals (Sweden)

    effrey M. Janot, Nicholas M. Beltz, Lance D. Dalleck

    2015-09-01

    Full Text Available The purpose of this study was to determine if off-ice performance variables could predict on-ice skating performance in Division III collegiate hockey players. Both men (n = 15 and women (n = 11 hockey players (age = 20.5 ± 1.4 years participated in the study. The skating tests were agility cornering S-turn, 6.10 m acceleration, 44.80 m speed, modified repeat skate, and 15.20 m full speed. Off-ice variables assessed were years of playing experience, height, weight and percent body fat and off-ice performance variables included vertical jump (VJ, 40-yd dash (36.58m, 1-RM squat, pro-agility, Wingate peak power and peak power percentage drop (% drop, and 1.5 mile (2.4km run. Results indicated that 40-yd dash (36.58m, VJ, 1.5 mile (2.4km run, and % drop were significant predictors of skating performance for repeat skate (slowest, fastest, and average time and 44.80 m speed time, respectively. Four predictive equations were derived from multiple regression analyses: 1 slowest repeat skate time = 2.362 + (1.68 x 40-yd dash time + (0.005 x 1.5 mile run, 2 fastest repeat skate time = 9.762 - (0.089 x VJ - (0.998 x 40-yd dash time, 3 average repeat skate time = 7.770 + (1.041 x 40-yd dash time - (0.63 x VJ + (0.003 x 1.5 mile time, and 4 47.85 m speed test = 7.707 - (0.050 x VJ - (0.01 x % drop. It was concluded that selected off-ice tests could be used to predict on-ice performance regarding speed and recovery ability in Division III male and female hockey players.

  1. Successful emotion regulation is predicted by amygdala activity and aspects of personality: A latent variable approach.

    Science.gov (United States)

    Morawetz, Carmen; Alexandrowicz, Rainer W; Heekeren, Hauke R

    2017-04-01

    The experience of emotions and their cognitive control are based upon neural responses in prefrontal and subcortical regions and could be affected by personality and temperamental traits. Previous studies established an association between activity in reappraisal-related brain regions (e.g., inferior frontal gyrus and amygdala) and emotion regulation success. Given these relationships, we aimed to further elucidate how individual differences in emotion regulation skills relate to brain activity within the emotion regulation network on the one hand, and personality/temperamental traits on the other. We directly examined the relationship between personality and temperamental traits, emotion regulation success and its underlying neuronal network in a large sample (N = 82) using an explicit emotion regulation task and functional MRI (fMRI). We applied a multimethodological analysis approach, combing standard activation-based analyses with structural equation modeling. First, we found that successful downregulation is predicted by activity in key regions related to emotion processing. Second, the individual ability to successfully upregulate emotions is strongly associated with the ability to identify feelings, conscientiousness, and neuroticism. Third, the successful downregulation of emotion is modulated by openness to experience and habitual use of reappraisal. Fourth, the ability to regulate emotions is best predicted by a combination of brain activity and personality as well temperamental traits. Using a multimethodological analysis approach, we provide a first step toward a causal model of individual differences in emotion regulation ability by linking biological systems underlying emotion regulation with descriptive constructs. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  2. Multiple Off-Ice Performance Variables Predict On-Ice Skating Performance in Male and Female Division III Ice Hockey Players.

    Science.gov (United States)

    Janot, Jeffrey M; Beltz, Nicholas M; Dalleck, Lance D

    2015-09-01

    The purpose of this study was to determine if off-ice performance variables could predict on-ice skating performance in Division III collegiate hockey players. Both men (n = 15) and women (n = 11) hockey players (age = 20.5 ± 1.4 years) participated in the study. The skating tests were agility cornering S-turn, 6.10 m acceleration, 44.80 m speed, modified repeat skate, and 15.20 m full speed. Off-ice variables assessed were years of playing experience, height, weight and percent body fat and off-ice performance variables included vertical jump (VJ), 40-yd dash (36.58m), 1-RM squat, pro-agility, Wingate peak power and peak power percentage drop (% drop), and 1.5 mile (2.4km) run. Results indicated that 40-yd dash (36.58m), VJ, 1.5 mile (2.4km) run, and % drop were significant predictors of skating performance for repeat skate (slowest, fastest, and average time) and 44.80 m speed time, respectively. Four predictive equations were derived from multiple regression analyses: 1) slowest repeat skate time = 2.362 + (1.68 x 40-yd dash time) + (0.005 x 1.5 mile run), 2) fastest repeat skate time = 9.762 - (0.089 x VJ) - (0.998 x 40-yd dash time), 3) average repeat skate time = 7.770 + (1.041 x 40-yd dash time) - (0.63 x VJ) + (0.003 x 1.5 mile time), and 4) 47.85 m speed test = 7.707 - (0.050 x VJ) - (0.01 x % drop). It was concluded that selected off-ice tests could be used to predict on-ice performance regarding speed and recovery ability in Division III male and female hockey players. Key pointsThe 40-yd dash (36.58m) and vertical jump tests are significant predictors of on-ice skating performance specific to speed.In addition to 40-yd dash and vertical jump, the 1.5 mile (2.4km) run for time and percent power drop from the Wingate anaerobic power test were also significant predictors of skating performance that incorporates the aspect of recovery from skating activity.Due to the specificity of selected off-ice variables as predictors of on-ice performance, coaches can

  3. Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

    Directory of Open Access Journals (Sweden)

    David M. Makori

    2017-02-01

    Full Text Available Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and influences are not well established. In this study, we used remotely sensed data to improve the reliability of pest ecological niche (EN models to attain reliable pest distribution maps. Occurrence data on four pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor were collected from apiaries within four main agro-ecological regions responsible for over 80% of Kenya’s bee keeping. Africlim bioclimatic and derived normalized difference vegetation index (NDVI variables were used to model their ecological niches using Maximum Entropy (MaxEnt. Combined precipitation variables had a high positive logit influence on all remotely sensed and biotic models’ performance. Remotely sensed vegetation variables had a substantial effect on the model, contributing up to 40.8% for G. mellonella and regions with high rainfall seasonality were predicted to be high-risk areas. Projections (to 2055 indicated that, with the current climate change trend, these regions will experience increased honeybee pest risk. We conclude that honeybee pests could be modelled using bioclimatic data and remotely sensed variables in MaxEnt. Although the bioclimatic data were most relevant in all model results, incorporating vegetation seasonality variables to improve mapping the ‘actual’ habitat of key honeybee pests and to identify risk and containment zones needs to be further investigated.

  4. Application of Artificial Neural Networks (ANNs for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896 Using Predictor Variables

    Directory of Open Access Journals (Sweden)

    C. TURELI BILEN

    2011-10-01

    Full Text Available An evaluation of the performance of artificial networks (ANNs to estimate the weights of blue crab (Callinectes sapidus catches in Yumurtalık Cove (Iskenderun Bay that uses measured predictor variables is presented, including carapace width (CW, sex (male, female and female with eggs, and sampling month. Blue crabs (n=410 were collected each month between 15 September 1996 and 15 May 1998. Sex, CW, and sampling month were used and specified in the input layer of the network. The weights of the blue crabs were utilized in the output layer of the network. A multi-layer perception architecture model was used and was calibrated with the Levenberg Marguardt (LM algorithm. Finally, the values were determined by the ANN model using the actual data. The mean square error (MSE was measured as 3.3, and the best results had a correlation coefficient (R of 0.93. We compared the predictive capacity of the general linear model (GLM versus the Artificial Neural Network model (ANN for the estimation of the weights of blue crabs from independent field data. The results indicated the higher performance capacity of the ANN to predict weights compared to the GLM (R=0.97 vs. R=0.95, raw variable when evaluated against independent field data.

  5. Cephalometric variables used to predict the success of interceptive treatment with rapid maxillary expansion and face mask. A longitudinal study

    Directory of Open Access Journals (Sweden)

    Daniele Nóbrega Nardoni

    2015-02-01

    Full Text Available INTRODUCTION: Prognosis is the main limitation of interceptive treatment of Class III malocclusions. The interceptive procedures of rapid maxillary expansion (RME and face mask therapy performed in early mixed dentition are capable of achieving immediate overcorrection and maintenance of facial and occlusal morphology for a few years. Individuals presenting minimal acceptable faces at growth completion are potential candidates for compensatory orthodontic treatment, while those with facial involvement should be submitted to orthodontic decompensation for orthognathic surgery. OBJECTIVES: To investigate cephalometric variables that might predict the outcomes of orthopedic treatment with RME and face mask therapy (FM. METHODS: Cephalometric analysis of 26 Class III patients (mean age of 8 years and 4 months was performed at treatment onset and after a mean period of 6 years and 10 months at pubertal growth completion, including a subjective facial analysis. Patients was divided into two groups: success group (21 individuals and failure group (5 individuals. Discriminant analysis was applied to the cephalometric values at treatment onset. Two predictor variables were found by stepwise procedure. RESULTS: Orthopedic treatment of Class III malocclusion may have unfavorable prognosis at growth completion whenever initial cephalometric analysis reveals increased lower anterior facial height (LAFH combined with reduced angle between the condylar axis and the mandibular plane (CondAx.MP. CONCLUSION: The results of treatment with RME and face mask therapy at growth completion in Class III patients could be predicted with a probability of 88.5%.

  6. Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

    Science.gov (United States)

    Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul

    2018-01-01

    Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

  7. Predicting The Variability And The Severity Of The “Little Dry Season ...

    African Journals Online (AJOL)

    Linear regression algorithm and K-means cluster analyses were employed to generate the models. Results show that LDS rainfall amount and rainy days have significant positive relationship with SSTs of the Gulf Guinea and the source locations of the Guinea and Benguela currents. Very Cold, Cold, Average, Warm and ...

  8. Effect of heart rate correction on pre- and post-exercise heart rate variability to predict risk of mortality – an experimental study on the FINCAVAS cohort

    Directory of Open Access Journals (Sweden)

    Paruthi ePradhapan

    2014-06-01

    Full Text Available The non-linear inverse relationship between RR-intervals and heart rate (HR contributes significantly to the heart rate variability (HRV parameters and their performance in mortality prediction. To determine the level of influence HR exerts over HRV parameters’ prognostic power, we studied the predictive performance for different HR levels by applying eight correction procedures, multiplying or dividing HRV parameters by the mean RR-interval (RRavg to the power 0.5-16. Data collected from 1288 patients in The Finnish Cardiovascular Study (FINCAVAS, who satisfied the inclusion criteria, was used for the analyses. HRV parameters (RMSSD, VLF Power and LF Power were calculated from 2-minute segment in the rest phase before exercise and 2-minute recovery period immediately after peak exercise. Area under the receiver operating characteristic curve (AUC was used to determine the predictive performance for each parameter with and without HR corrections in rest and recovery phases. The division of HRV parameters by segment’s RRavg to the power 2 (HRVDIV-2 showed the highest predictive performance under the rest phase (RMSSD: 0.67/0.66; VLF Power: 0.70/0.62; LF Power: 0.79/0.65; cardiac mortality/non-cardiac mortality with minimum correlation to HR (r = -0.15 to 0.15. In the recovery phase, Kaplan-Meier (KM survival analysis revealed good risk stratification capacity at HRVDIV-2 in both groups (cardiac and non-cardiac mortality. Although higher powers of correction (HRVDIV-4 and HRVDIV-8 improved predictive performance during recovery, they induced an increased positive correlation to HR. Thus, we inferred that predictive capacity of HRV during rest and recovery is augmented when its dependence on HR is weakened by applying appropriate correction procedures.

  9. Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China

    Science.gov (United States)

    Du, Zhicheng; Xu, Lin; Zhang, Wangjian; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao

    2017-01-01

    Objectives Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Design Ecological study. Setting and participants Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011–2014. Analyses were conducted at aggregate level and no confidential information was involved. Outcome measures A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. Results A high correlation between HFMD incidence and BDI (r=0.794, pmodel. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of −345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%. Conclusions An ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings. PMID:28988169

  10. Intrinsic spontaneous brain activity predicts individual variability in associative memory in older adults.

    Science.gov (United States)

    Zheng, Zhiwei; Li, Rui; Xiao, Fengqiu; He, Rongqiao; Zhang, Shouzi; Li, Juan

    2018-04-19

    Older adults demonstrate notable individual differences in associative memory. Here, resting-state functional magnetic resonance imaging (rsfMRI) was used to investigate whether intrinsic brain activity at rest could predict individual differences in associative memory among cognitively healthy older adults. Regional amplitude of low-frequency fluctuations (ALFF) analysis and a correlation-based resting-state functional connectivity (RSFC) approach were used to analyze data acquired from 102 cognitively normal elderly who completed the paired-associative learning test (PALT) and underwent fMRI scans. Participants were divided into two groups based on the retrospective self-reports on whether or not they utilized encoding strategies during the PALT. The behavioral results revealed better associative memory performance in the participants who reported utilizing memory strategies compared with participants who reported not doing so. The fMRI results showed that higher associative memory performance was associated with greater functional connectivity between the right superior frontal gyrus and the right posterior cerebellum lobe in the strategy group. The regional ALFF values in the right superior frontal gyrus were linked to associative memory performance in the no-strategy group. These findings suggest that the regional spontaneous fluctuations and functional connectivity during rest may subserve the individual differences in the associative memory in older adults, and that this is modulated by self-initiated memory strategy use. © 2018 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

  11. Predicting abundance and variability of ice nucleating particles in precipitation at the high-altitude observatory Jungfraujoch

    Directory of Open Access Journals (Sweden)

    E. Stopelli

    2016-07-01

    Full Text Available Nucleation of ice affects the properties of clouds and the formation of precipitation. Quantitative data on how ice nucleating particles (INPs determine the distribution, occurrence and intensity of precipitation are still scarce. INPs active at −8 °C (INPs−8 were observed for 2 years in precipitation samples at the High-Altitude Research Station Jungfraujoch (Switzerland at 3580 m a.s.l. Several environmental parameters were scanned for their capability to predict the observed abundance and variability of INPs−8. Those singularly presenting the best correlations with observed number of INPs−8 (residual fraction of water vapour, wind speed, air temperature, number of particles with diameter larger than 0.5 µm, season, and source region of particles were implemented as potential predictor variables in statistical multiple linear regression models. These models were calibrated with 84 precipitation samples collected during the first year of observations; their predictive power was successively validated on the set of 15 precipitation samples collected during the second year. The model performing best in calibration and validation explains more than 75 % of the whole variability of INPs−8 in precipitation and indicates that a high abundance of INPs−8 is to be expected whenever high wind speed coincides with air masses having experienced little or no precipitation prior to sampling. Such conditions occur during frontal passages, often accompanied by precipitation. Therefore, the circumstances when INPs−8 could be sufficiently abundant to initiate the ice phase in clouds may frequently coincide with meteorological conditions favourable to the onset of precipitation events.

  12. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV).

    Science.gov (United States)

    Poblete, Tomas; Ortega-Farías, Samuel; Moreno, Miguel Angel; Bardeen, Matthew

    2017-10-30

    Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψ stem ). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500-800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψ stem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R²) obtained between ANN outputs and ground-truth measurements of Ψ stem were between 0.56-0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψ stem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of -9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26-0.27 MPa, 0.32-0.34 MPa and -24.2-25.6%, respectively.

  13. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV

    Directory of Open Access Journals (Sweden)

    Tomas Poblete

    2017-10-01

    Full Text Available Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem. However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2 obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE of 0.1 MPa, root mean square error (RMSE of 0.12 MPa, and relative error (RE of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.

  14. State-Space Modeling and Performance Analysis of Variable-Speed Wind Turbine Based on a Model Predictive Control Approach

    Directory of Open Access Journals (Sweden)

    H. Bassi

    2017-04-01

    Full Text Available Advancements in wind energy technologies have led wind turbines from fixed speed to variable speed operation. This paper introduces an innovative version of a variable-speed wind turbine based on a model predictive control (MPC approach. The proposed approach provides maximum power point tracking (MPPT, whose main objective is to capture the maximum wind energy in spite of the variable nature of the wind’s speed. The proposed MPC approach also reduces the constraints of the two main functional parts of the wind turbine: the full load and partial load segments. The pitch angle for full load and the rotating force for the partial load have been fixed concurrently in order to balance power generation as well as to reduce the operations of the pitch angle. A mathematical analysis of the proposed system using state-space approach is introduced. The simulation results using MATLAB/SIMULINK show that the performance of the wind turbine with the MPC approach is improved compared to the traditional PID controller in both low and high wind speeds.

  15. Spatiotemporal predictions of soil properties and states in variably saturated landscapes

    Science.gov (United States)

    Franz, Trenton E.; Loecke, Terrance D.; Burgin, Amy J.; Zhou, Yuzhen; Le, Tri; Moscicki, David

    2017-07-01

    Understanding greenhouse gas (GHG) fluxes from landscapes with variably saturated soil conditions is challenging given the highly dynamic nature of GHG fluxes in both space and time, dubbed hot spots, and hot moments. On one hand, our ability to directly monitor these processes is limited by sparse in situ and surface chamber observational networks. On the other hand, remote sensing approaches provide spatial data sets but are limited by infrequent imaging over time. We use a robust statistical framework to merge sparse sensor network observations with reconnaissance style hydrogeophysical mapping at a well-characterized site in Ohio. We find that combining time-lapse electromagnetic induction surveys with empirical orthogonal functions provides additional environmental covariates related to soil properties and states at high spatial resolutions ( 5 m). A cross-validation experiment using eight different spatial interpolation methods versus 120 in situ soil cores indicated an 30% reduction in root-mean-square error for soil properties (clay weight percent and total soil carbon weight percent) using hydrogeophysical derived environmental covariates with regression kriging. In addition, the hydrogeophysical derived environmental covariates were found to be good predictors of soil states (soil temperature, soil water content, and soil oxygen). The presented framework allows for temporal gap filling of individual sensor data sets as well as provides flexible geometric interpolation to complex areas/volumes. We anticipate that the framework, with its flexible temporal and spatial monitoring options, will be useful in designing future monitoring networks as well as support the next generation of hyper-resolution hydrologic and biogeochemical models.

  16. An Object-Based Approach to Evaluation of Climate Variability Projections and Predictions

    Science.gov (United States)

    Ammann, C. M.; Brown, B.; Kalb, C. P.; Bullock, R.

    2017-12-01

    Evaluations of the performance of earth system model predictions and projections are of critical importance to enhance usefulness of these products. Such evaluations need to address specific concerns depending on the system and decisions of interest; hence, evaluation tools must be tailored to inform about specific issues. Traditional approaches that summarize grid-based comparisons of analyses and models, or between current and future climate, often do not reveal important information about the models' performance (e.g., spatial or temporal displacements; the reason behind a poor score) and are unable to accommodate these specific information needs. For example, summary statistics such as the correlation coefficient or the mean-squared error provide minimal information to developers, users, and decision makers regarding what is "right" and "wrong" with a model. New spatial and temporal-spatial object-based tools from the field of weather forecast verification (where comparisons typically focus on much finer temporal and spatial scales) have been adapted to more completely answer some of the important earth system model evaluation questions. In particular, the Method for Object-based Diagnostic Evaluation (MODE) tool and its temporal (three-dimensional) extension (MODE-TD) have been adapted for these evaluations. More specifically, these tools can be used to address spatial and temporal displacements in projections of El Nino-related precipitation and/or temperature anomalies, ITCZ-associated precipitation areas, atmospheric rivers, seasonal sea-ice extent, and other features of interest. Examples of several applications of these tools in a climate context will be presented, using output of the CESM large ensemble. In general, these tools provide diagnostic information about model performance - accounting for spatial, temporal, and intensity differences - that cannot be achieved using traditional (scalar) model comparison approaches. Thus, they can provide more

  17. Variability of the Cyclin-Dependent Kinase 2 Flexibility Without Significant Change in the Initial Conformation of the Protein or Its Environment; a Computational Study.

    Science.gov (United States)

    Taghizadeh, Mohammad; Goliaei, Bahram; Madadkar-Sobhani, Armin

    2016-06-01

    Protein flexibility, which has been referred as a dynamic behavior has various roles in proteins' functions. Furthermore, for some developed tools in bioinformatics, such as protein-protein docking software, considering the protein flexibility, causes a higher degree of accuracy. Through undertaking the present work, we have accomplished the quantification plus analysis of the variations in the human Cyclin Dependent Kinase 2 (hCDK2) protein flexibility without affecting a significant change in its initial environment or the protein per se. The main goal of the present research was to calculate variations in the flexibility for each residue of the hCDK2, analysis of their flexibility variations through clustering, and to investigate the functional aspects of the residues with high flexibility variations. Using Gromacs package (version 4.5.4), three independent molecular dynamics (MD) simulations of the hCDK2 protein (PDB ID: 1HCL) was accomplished with no significant changes in their initial environments, structures, or conformations, followed by Root Mean Square Fluctuations (RMSF) calculation of these MD trajectories. The amount of variations in these three curves of RMSF was calculated using two formulas. More than 50% of the variation in the flexibility (the distance between the maximum and the minimum amount of the RMSF) was found at the region of Val-154. As well, there are other major flexibility fluctuations in other residues. These residues were mostly positioned in the vicinity of the functional residues. The subsequent works were done, as followed by clustering all hCDK2 residues into four groups considering the amount of their variability with respect to flexibility and their position in the RMSF curves. This work has introduced a new class of flexibility aspect of the proteins' residues. It could also help designing and engineering proteins, with introducing a new dynamic aspect of hCDK2, and accordingly, for the other similar globular proteins. In

  18. Investigation of Predictive Power of Mathematics Anxiety on Mathematics Achievement in Terms of Gender and Class Variables

    Directory of Open Access Journals (Sweden)

    Mustafa İLHAN

    2013-12-01

    Full Text Available This research aims to explore predictive power of mathematics anxiety in terms of gender and class variables. For this purpose relational model was used in the study. Working group of the research consists of 348 secondary school second stage students, 175 of whom are girls and 175 are boys, having education in four elementary schools in central district of Diyarbakır province, during 2011-2012 Academic Year, first Semester. “Math Anxiety Scale for Primary School Students” to determine students’ mathematics anxiety was used. Averages of students’ mathematics notes in the first term of 2011- 2012 academic year are taken as the achievement scores of mathematics. The collected data has been analyzed by SPSS 17.0. The relationship between mathematics achievement and math anxiety was analyzed with pearson correlation. The predictor power of math anxiety for mathematics achievement was determined by the regression analysis. According the research findings %17 of the total variance of mathematics achievement can be explained by math anxiety. It has been determined that predictive power of mathematics anxiety on mathematics success is higher in girls than boys. Furthermore, it has been determined in the research that predictive power of mathematics anxiety on mathematics success increases, as students proceed towards the next grade.

  19. Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

    Science.gov (United States)

    Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

    2018-04-01

    Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

  20. Relative value of clinical variables, bicycle ergometry, rest radionuclide ventriculography and 24 hour ambulatory electrocardiographic monitoring at discharge to predict 1 year survival after myocardial infarction

    NARCIS (Netherlands)

    P.M. Fioretti (Paolo); R.W. Brower (Ronald); M.L. Simoons (Maarten); H.J. ten Katen (Harald); A. Beelen (Anita); T. Baardman (Taco); J. Lubsen (Jacob); P.G. Hugenholtz (Paul)

    1986-01-01

    textabstractThe relative value of predischarge clinical variables, bicycle ergometry, radionuclide ventriculography and 24 hour ambulatory electrocardiographic monitoring for predicting survival during the first year in 351 hospital survivors of acute myocardial infarction was assessed. Discriminant

  1. The role of clinical variables, neuropsychological performance and SLC6A4 and COMT gene polymorphisms on the prediction of early response to fluoxetine in major depressive disorder.

    Science.gov (United States)

    Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Cruz, David; Hernández, Sandra; Genis, Alma; Carrillo-Guerrero, Mariana Y; Avilés Reyes, Rubén; Guàrdia-Olmos, Joan

    2010-12-01

    Major depressive disorder (MDD) is treated with antidepressants, but only between 50% and 70% of the patients respond to the initial treatment. Several authors suggested different factors that could predict antidepressant response, including clinical, psychophysiological, neuropsychological, neuroimaging, and genetic variables. However, these different predictors present poor prognostic sensitivity and specificity by themselves. The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms in the prediction of the response to fluoxetine after 4weeks of treatment in a sample of patient with MDD. 64 patients with MDD were genotyped according to the above-mentioned polymorphisms, and were clinically and neuropsychologically assessed before a 4-week fluoxetine treatment. Fluoxetine response was assessed by using the Hamilton Depression Rating Scale. We carried out a binary logistic regression model for the potential predictive variables. Out of the clinical variables studied, only the number of anxiety disorders comorbid with MDD have predicted a poor response to the treatment. A combination of a good performance in variables of attention and low performance in planning could predict a good response to fluoxetine in patients with MDD. None of the genetic variables studied had predictive value in our model. The possible placebo effect has not been controlled. Our study is focused on response prediction but not in remission prediction. Our work suggests that the combination of the number of comorbid anxiety disorders, an attentional variable, and two planning variables makes it possible to correctly classify 82% of the depressed patients who responded to the treatment with fluoxetine, and 74% of the patients who did not respond to that treatment. Copyright © 2010 Elsevier B.V. All rights reserved.

  2. Specific psychological variables predict quality of diet in women of lower, but not higher, educational attainment

    DEFF Research Database (Denmark)

    Lawrence, Wendy; Schlotz, Wolff; Crozier, Sarah

    2011-01-01

    Our previous work found that perceived control over life was a significant predictor of the quality of diet of women of lower educational attainment. In this paper, we explore the influence on quality of diet of a range of psychological and social factors identified during focus group discussions......, and specify the way this differs in women of lower and higher educational attainment. We assessed educational attainment, quality of diet, and psycho-social factors in 378 women attending Sure Start Children's Centres and baby clinics in Southampton, UK. Multiple-group path analysis showed that in women...... of self-efficacy, perceived control or outcome expectancies on the quality of diet of women of higher educational attainment, though having more social support and food involvement were associated with improved quality of diet in these women. Our analysis confirms our hypothesis that control...

  3. Norepinephrine genes predict response time variability and methylphenidate-induced changes in neuropsychological function in attention deficit hyperactivity disorder.

    Science.gov (United States)

    Kim, Bung-Nyun; Kim, Jae-Won; Cummins, Tarrant D R; Bellgrove, Mark A; Hawi, Ziarih; Hong, Soon-Beom; Yang, Young-Hui; Kim, Hyo-Jin; Shin, Min-Sup; Cho, Soo-Churl; Kim, Ji-Hoon; Son, Jung-Woo; Shin, Yun-Mi; Chung, Un-Sun; Han, Doug-Hyun

    2013-06-01

    Noradrenergic dysfunction may be associated with cognitive impairments in attention-deficit/hyperactivity disorder (ADHD), including increased response time variability, which has been proposed as a leading endophenotype for ADHD. The aim of this study was to examine the relationship between polymorphisms in the α-2A-adrenergic receptor (ADRA2A) and norepinephrine transporter (SLC6A2) genes and attentional performance in ADHD children before and after pharmacological treatment.One hundred one medication-naive ADHD children were included. All subjects were administered methylphenidate (MPH)-OROS for 12 weeks. The subjects underwent a computerized comprehensive attention test to measure the response time variability at baseline before MPH treatment and after 12 weeks. Additive regression analyses controlling for ADHD symptom severity, age, sex, IQ, and final dose of MPH examined the association between response time variability on the comprehensive attention test measures and allelic variations in single-nucleotide polymorphisms of the ADRA2A and SLC6A2 before and after MPH treatment.Increasing possession of an A allele at the G1287A polymorphism of SLC6A2 was significantly related to heightened response time variability at baseline in the sustained (P = 2.0 × 10) and auditory selective attention (P = 1.0 × 10) tasks. Response time variability at baseline increased additively with possession of the T allele at the DraI polymorphism of the ADRA2A gene in the auditory selective attention task (P = 2.0 × 10). After medication, increasing possession of a G allele at the MspI polymorphism of the ADRA2A gene was associated with increased MPH-related change in response time variability in the flanker task (P = 1.0 × 10).Our study suggested an association between norepinephrine gene variants and response time variability measured at baseline and after MPH treatment in children with ADHD. Our results add to a growing body of evidence, suggesting that response time

  4. Predicting long-term streamflow variability in moist eucalypt forests using forest growth models and a sapwood area index

    Science.gov (United States)

    Jaskierniak, D.; Kuczera, G.; Benyon, R.

    2016-04-01

    A major challenge in surface hydrology involves predicting streamflow in ungauged catchments with heterogeneous vegetation and spatiotemporally varying evapotranspiration (ET) rates. We present a top-down approach for quantifying the influence of broad-scale changes in forest structure on ET and hence streamflow. Across three catchments between 18 and 100 km2 in size and with regenerating Eucalyptus regnans and E. delegatensis forest, we demonstrate how variation in ET can be mapped in space and over time using LiDAR data and commonly available forest inventory data. The model scales plot-level sapwood area (SA) to the catchment-level using basal area (BA) and tree stocking density (N) estimates in forest growth models. The SA estimates over a 69 year regeneration period are used in a relationship between SA and vegetation induced streamflow loss (L) to predict annual streamflow (Q) with annual rainfall (P) estimates. Without calibrating P, BA, N, SA, and L to Q data, we predict annual Q with R2 between 0.68 and 0.75 and Nash Sutcliffe efficiency (NSE) between 0.44 and 0.48. To remove bias, the model was extended to allow for runoff carry-over into the following year as well as minor correction to rainfall bias, which produced R2 values between 0.72 and 0.79, and NSE between 0.70 and 0.79. The model under-predicts streamflow during drought periods as it lacks representation of ecohydrological processes that reduce L with either reduced growth rates or rainfall interception during drought. Refining the relationship between sapwood thickness and forest inventory variables is likely to further improve results.

  5. Effect of variable consumption habits in the Nordic populations on ECOSYS model predictions of ingestion dose

    International Nuclear Information System (INIS)

    Nielsen, Sven P.; Andersson, Kasper G.; Hansen, Hanne S.; Thoerring, Haavard; Joensen, Hans P.; Isaksson, Mats; Kostiainen, Eila; Suolanen, Vesa; Sigurgeirsson, Magnus A.; Palsson, Sigurour E.

    2008-01-01

    Full text: The two European standard decision support systems, ARGOS and RODOS, have in recent years become increasingly integrated in the Nordic preparedness against nuclear and radiological accidents and incidents. In the event of an emergency, decision making will rest heavily on the reliability of these tools. The ECOSYS model is the ingestion dose module in both decision support systems. This module is highly sensitive to variation in a number of input parameters, food production patterns, diets and environmental transfer data. With regard to for instance consumption habits, the ECOSYS default values, based on data from Southern Germany, have shown to be inadequate for Nordic conditions. We have thus collected recent data describing the human diets for four different age groups in each of the Nordic countries. Also the fractions of the consumed food items that have national origin and the animal feeding regimes in each of the Nordic countries have been examined. For a particular contamination scenario of atmospheric deposition of caesium-137, country specific data regarding consumption habits were used for dose calculations. Resulting 'country specific' doses were then compared among the participating countries and with the doses calculated using the default values of the parameters.The collected data for diets demonstrated that the average consumption of milk varied by a factor of 4-5 among the Nordic countries, and consumption of leafy vegetables varied by a factor of almost 4. Calculated ingestion doses based on country specific data for diets, with all other parameters being default values, varied by a factor of 1.8 among the countries. When also the import fractions were taken into account the calculated doses varied by a factor of 2. Due to the differences in the climate among the Nordic countries, and between these countries and Southern Germany, there were also very significant differences in the production regimes of some food items. In countries in

  6. A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data

    NARCIS (Netherlands)

    Wynants, L.; Bouwmeester, W.; Moons, K. G. M.; Moerbeek, M.; Timmerman, D.; Van Huffel, S.; Van Calster, B.; Vergouwe, Y.

    2015-01-01

    Objectives: This study aims to investigate the influence of the amount of clustering [intraclass correlation (ICC) = 0%, 5%, or 20%], the number of events per variable (EPV) or candidate predictor (EPV = 5, 10, 20, or 50), and backward variable selection on the performance of prediction models.

  7. The macrophage activation marker sCD163 combined with markers of the Enhanced Liver Fibrosis (ELF) score predicts clinically significant portal hypertension in patients with cirrhosis

    DEFF Research Database (Denmark)

    Sandahl, T D; McGrail, R; Møller, H J

    2016-01-01

    BACKGROUND: Noninvasive identification of significant portal hypertension in patients with cirrhosis is needed in hepatology practice. AIM: To investigate whether the combination of sCD163 as a hepatic inflammation marker and the fibrosis markers of the Enhanced Liver Fibrosis score (ELF) can...... predict portal hypertension in patients with cirrhosis. METHODS: We measured sCD163 and the ELF components (hyaluronic acid, tissue inhibitor of metalloproteinase-1 and procollagen-III aminopeptide) in two separate cohorts of cirrhosis patients that underwent hepatic vein catheterisation. To test...... the predictive accuracy we developed a CD163-fibrosis portal hypertension score in an estimation cohort (n = 80) and validated the score in an independent cohort (n = 80). A HVPG ≥10 mmHg was considered clinically significant. RESULTS: Both sCD163 and the ELF components increased in a stepwise manner...

  8. Specific psychological variables predict quality of diet in women of lower, but not higher, educational attainment.

    Science.gov (United States)

    Lawrence, Wendy; Schlotz, Wolff; Crozier, Sarah; Skinner, Timothy C; Haslam, Cheryl; Robinson, Sian; Inskip, Hazel; Cooper, Cyrus; Barker, Mary

    2011-02-01

    Our previous work found that perceived control over life was a significant predictor of the quality of diet of women of lower educational attainment. In this paper, we explore the influence on quality of diet of a range of psychological and social factors identified during focus group discussions, and specify the way this differs in women of lower and higher educational attainment. We assessed educational attainment, quality of diet, and psycho-social factors in 378 women attending Sure Start Children's Centres and baby clinics in Southampton, UK. Multiple-group path analysis showed that in women of lower educational attainment, the effect of general self-efficacy on quality of diet was mediated through perceptions of control and through food involvement, but that there were also direct effects of social support for healthy eating and having positive outcome expectancies. There was no effect of self-efficacy, perceived control or outcome expectancies on the quality of diet of women of higher educational attainment, though having more social support and food involvement were associated with improved quality of diet in these women. Our analysis confirms our hypothesis that control-related factors are more important in determining dietary quality in women of lower educational attainment than in women of higher educational attainment. Copyright © 2010 Elsevier Ltd. All rights reserved.

  9. Prostate cancer volume adds significantly to prostate-specific antigen in the prediction of early biochemical failure after external beam radiation therapy

    International Nuclear Information System (INIS)

    D'Amico, Anthony V.; Propert, Kathleen J.

    1996-01-01

    Purpose: A new clinical pretreatment quantity that closely approximates the true prostate cancer volume is defined. Methods and Materials: The cancer-specific prostate-specific antigen (PSA), PSA density, prostate cancer volume (V Ca ), and the volume fraction of the gland involved with carcinoma (V Ca fx) were calculated for 227 prostate cancer patients managed definitively with external beam radiation therapy. 1. PSA density PSA/ultrasound prostate gland volume 2. Cancer-specific PSA = PSA - [PSA from benign epithelial tissue] 3. V Ca = Cancer-specific PSA/[PSA in serum per cm 3 of cancer] 4. V Ca fx = V Ca /ultrasound prostate gland volume A Cox multiple regression analysis was used to test whether any of these-clinical pretreatment parameters added significantly to PSA in predicting early postradiation PSA failure. Results: The prostate cancer volume (p = 0.039) and the volume fraction of the gland involved by carcinoma (p = 0.035) significantly added to the PSA in predicting postradiation PSA failure. Conversely, the PSA density and the cancer-specific PSA did not add significantly (p > 0.05) to PSA in predicting postradiation PSA failure. The 20-month actuarial PSA failure-free rates for patients with calculated tumor volumes of ≤0.5 cm 3 , 0.5-4.0 cm 3 , and >4.0 cm 3 were 92, 80, and 47%, respectively (p = 0.00004). Conclusion: The volume of prostate cancer (V Ca ) and the resulting volume fraction of cancer both added significantly to PSA in their ability to predict for early postradiation PSA failure. These new parameters may be used to select patients in prospective randomized trials that examine the efficacy of combining radiation and androgen ablative therapy in patients with clinically localized disease, who are at high risk for early postradiation PSA failure

  10. Validity of Miles Equation in Predicting Propellant Slosh Damping in Baffled Tanks at Variable Slosh Amplitude

    Science.gov (United States)

    Yang, H. Q.; West, Jeff

    2018-01-01

    Determination of slosh damping is a very challenging task as there is no analytical solution. The damping physics involves the vorticity dissipation which requires the full solution of the nonlinear Navier-Stokes equations. As a result, previous investigations were mainly carried out by extensive experiments. A systematical study is needed to understand the damping physics of baffled tanks, to identify the difference between the empirical Miles equation and experimental measurements, and to develop new semi-empirical relations to better represent the real damping physics. The approach of this study is to use Computational Fluid Dynamics (CFD) technology to shed light on the damping mechanisms of a baffled tank. First, a 1-D Navier-Stokes equation representing different length scales and time scales in the baffle damping physics is developed and analyzed. Loci-STREAM-VOF, a well validated CFD solver developed at NASA MSFC, is applied to study the vorticity field around a baffle and around the fluid-gas interface to highlight the dissipation mechanisms at different slosh amplitudes. Previous measurement data is then used to validate the CFD damping results. The study found several critical parameters controlling fluid damping from a baffle: local slosh amplitude to baffle thickness (A/t), surface liquid depth to tank radius (d/R), local slosh amplitude to baffle width (A/W); and non-dimensional slosh frequency. The simulation highlights three significant damping regimes where different mechanisms dominate. The study proves that the previously found discrepancies between Miles equation and experimental measurement are not due to the measurement scatter, but rather due to different damping mechanisms at various slosh amplitudes. The limitations on the use of Miles equation are discussed based on the flow regime.

  11. Predicting superdeformed rotational band-head spin in A ∼ 190 mass region using variable moment of inertia model

    International Nuclear Information System (INIS)

    Uma, V.S.; Goel, Alpana; Yadav, Archana; Jain, A.K.

    2016-01-01

    The band-head spin (I 0 ) of superdeformed (SD) rotational bands in A ∼ 190 mass region is predicted using the variable moment of inertia (VMI) model for 66 SD rotational bands. The superdeformed rotational bands exhibited considerably good rotational property and rigid behaviour. The transition energies were dependent on the prescribed band-head spins. The ratio of transition energies over spin Eγ/ 2 I (RTEOS) vs. angular momentum (I) have confirmed the rigid behaviour, provided the band-head spin value is assigned correctly. There is a good agreement between the calculated and the observed transition energies. This method gives a very comprehensive interpretation for spin assignment of SD rotational bands which could help in designing future experiments for SD bands. (author)

  12. Using life-histories to predict and interpret variability in yolk hormones.

    Science.gov (United States)

    Love, O P; Gilchrist, H G; Bêty, J; Wynne-Edwards, K E; Berzins, L; Williams, T D

    2009-09-01

    Variation in yolk hormones is assumed to provide the plasticity necessary for mothers to individually optimize reproductive decisions via changes in offspring phenotype, the benefit being to maximise fitness. However, rather than routinely expecting adaptive variation within all species, the pattern and magnitude of yolk hormone deposition should theoretically relate to variation in life-histories. Here we present data on intra-clutch variation in yolk corticosterone in three species along a developmental continuum (European starling (Sturnus vulgaris): fully altricial; black guillemot (Cepphus grylle): semi-precocial; common eider (Somateria mollissima): fully precocial) to examine how and why variation in life-histories might relate to the evolution of variation in yolk steroids. Starlings and guillemots showed a significant increase in yolk corticosterone across the laying sequence; however, we found no pattern within eider clutches. Moreover, starlings showed the largest difference (94.6%) in yolk corticosterone between first- and last-laid eggs, whereas guillemots showed a moderate difference (58.9%). Despite these general species-specific patterns, individuals showed marked variation in the intra-clutch patterns of yolk corticosterone within each species indicating potential differences in intra-clutch flexibility among females. It is well documented that exposure to elevated yolk glucocorticoids reduces offspring quality at birth/hatching in many taxa and it has therefore been proposed that elevated yolk levels may modulate offspring competition and/or facilitate brood reduction under harsh conditions in birds. Our data suggests that intra-clutch variation in yolk corticosterone has the potential to act as an adaptive maternal effect in species where modulation of competition between nest-bound offspring would benefit mothers (starlings and guillemots). However, in precocial species where mothers would not benefit from a modulation of offspring quality

  13. Sub-seasonal prediction of significant wave heights over the Western Pacific and Indian Oceans, part II: The impact of ENSO and MJO

    Science.gov (United States)

    Shukla, Ravi P.; Kinter, James L.; Shin, Chul-Su

    2018-03-01

    This study evaluates the effect of El Niño and the Southern Oscillation (ENSO) and Madden Julian Oscillation (MJO) events on 14-day mean significant wave height (SWH) at 3 weeks lead time (Wk34) over the Western Pacific and Indian Oceans using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). The WAVEWATCH-3 (WW3) model is forced with daily 10m-winds predicted by a modified version of CFSv2 that is initialized with multiple ocean analyses in both January and May for 1979-2008. A significant anomaly correlation of predicted and observed SWH anomalies (SWHA) at Wk34 lead-time is found over portions of the domain, including the central western Pacific, South China Sea (SCS), Bay of Bengal (BOB) and southern Indian Ocean (IO) in January cases, and over BOB, equatorial western Pacific, the Maritime Continent and southern IO in May cases. The model successfully predicts almost all the important features of the observed composite SWHA during El Niño events in January, including negative SWHA in the central IO where westerly wind anomalies act on an easterly mean state, and positive SWHA over the southern Ocean (SO) where westerly wind anomalies act on a westerly mean state. The model successfully predicts the sign and magnitude of SWHA at Wk34 lead-time in May over the BOB and SCS in composites of combined phases-2-3 and phases-6-7 of MJO. The observed leading mode of SWHA in May and the third mode of SWHA in January are influenced by the combined effects of ENSO and MJO. Based on spatial and temporal correlations, the spatial patterns of SWHA in the model at Wk34 in both January and May are in good agreement with the observations over the equatorial western Pacific, equatorial and southern IO, and SO.

  14. Prediction of the Dimensions of the Spiritual Well-Being of Students at Kermanshah University of Medical Sciences, Iran: The Roles of Demographic Variables.

    Science.gov (United States)

    Ziapour, Arash; Khatony, Alireza; Jafari, Faranak; Kianipour, Neda

    2017-07-01

    Spiritual well-being is one of the aspects of well-being which organize the physical, psychological, and social aspects. Given the outstanding and unique roles of students in society, providing spiritual well-being as well as identifying and eliminating the negative factors affecting their mental well-being are of the essence. The present study aimed to predict the dimensions of the spiritual well-being of students at Kermanshah University of Medical Sciences and to investigate the roles of demographic variables in this respect. In this descriptive and correlational study, the statistical population was comprised of 346 doctoral students in the for-profit Schools of Medicine, Dentistry and Pharmaceuticals in Kermanshah University of Medical Sciences in 2016. For data collection, an instrument comprising the demographic questions and the 20-item spiritual well-being scale by Paloutzian and Ellison (1982) was utilized. To analyze data, the descriptive (frequency distribution, mean, and standard deviation) and inferential statistics (independent t-test, one-way ANOVA, and chi-squared test) were employed in the SPSS Statistics Software Version 21.0. The results of the present study demonstrated that the spiritual well-being of students was average (71.86±4.84), and of all demographic variables under study, only the variable of gender significantly correlated with the mean score of spiritual well-being. Also, the results revealed that the students' score of religious well-being measured higher than that of their existential well-being. However, a significant correlation was found between spiritual well-being and its dimensions. Also, the religious and existential well-being were found to be significantly related (pspirituality among the students of the for-profit Schools at Kermanshah University of Medical Sciences. Therefore, it is recommended that appropriate plans be laid by the culture and education policy makers to promote the spiritual well-being of university

  15. Only sociodemographic variables predict quality of life after radiography in patients with head-and-neck cancer

    International Nuclear Information System (INIS)

    Sehlen, Susanne; Hollenhorst, Helmuth; Lenk, Markus; Schymura, Beatrice; Herschbach, Peter; Aydemir, Uelker; Duehmke, Eckhart

    2002-01-01

    Purpose: Psychosocial factors influence patient compliance and have an effect on survival. Identifying patients at risk of decreased quality of life (QOL) with no extra expenditure in terms of hospital staff time or resources is mandatory to plan psychosocial support. Methods and Materials: Between 1997 and 2000, 242 patients with head-and-neck cancer (30% pharyngeal, 29% oropharyngeal, and 13% laryngeal cancer) were screened. Of these, 28.5% refused to participate and 19.0% were excluded (Karnofsky performance score <50, language and cognitive deficits, death, or noncompliance). A total of 124 patients were assessed with the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire at ti1 (beginning of radiotherapy [RT]). Eighty-three patients from this group were examined, with complete data from ti1 to ti3 (6 weeks after RT). Results: The QOL did not change during RT. In logistic regression analysis, medical information, in contrast to sociodemographic variables, turned out to have no influence on the ability to predict low QOL (sensitivity 80% vs. 32%). Four sociodemographic variables were entered in the regression model (children, currently employment, ethanol abuse, level of secondary education) and accounted for 26% of variance in QOL at ti3. Conclusion: By routinely obtaining clinical information from the patient's history, patients at risk of low QOL after RT can be identified and could benefit from early psychosocial support

  16. Climate variability and predictability associated with the Indo-Pacific Oceanic Channel Dynamics in the CCSM4 Coupled System Model

    Science.gov (United States)

    Yuan, Dongliang; Xu, Peng; Xu, Tengfei

    2017-01-01

    An experiment using the Community Climate System Model (CCSM4), a participant of the Coupled Model Intercomparison Project phase-5 (CMIP5), is analyzed to assess the skills of this model in simulating and predicting the climate variabilities associated with the oceanic channel dynamics across the Indo-Pacific Oceans. The results of these analyses suggest that the model is able to reproduce the observed lag correlation between the oceanic anomalies in the southeastern tropical Indian Ocean and those in the cold tongue in the eastern equatorial Pacific Ocean at a time lag of 1 year. This success may be largely attributed to the successful simulation of the interannual variations of the Indonesian Throughflow, which carries the anomalies of the Indian Ocean Dipole (IOD) into the western equatorial Pacific Ocean to produce subsurface temperature anomalies, which in turn propagate to the eastern equatorial Pacific to generate ENSO. This connection is termed the "oceanic channel dynamics" and is shown to be consistent with the observational analyses. However, the model simulates a weaker connection between the IOD and the interannual variability of the Indonesian Throughflow transport than found in the observations. In addition, the model overestimates the westerly wind anomalies in the western-central equatorial Pacific in the year following the IOD, which forces unrealistic upwelling Rossby waves in the western equatorial Pacific and downwelling Kelvin waves in the east. This assessment suggests that the CCSM4 coupled climate system has underestimated the oceanic channel dynamics and overestimated the atmospheric bridge processes.

  17. Age at disease onset and peak ammonium level rather than interventional variables predict the neurological outcome in urea cycle disorders.

    Science.gov (United States)

    Posset, Roland; Garcia-Cazorla, Angeles; Valayannopoulos, Vassili; Teles, Elisa Leão; Dionisi-Vici, Carlo; Brassier, Anaïs; Burlina, Alberto B; Burgard, Peter; Cortès-Saladelafont, Elisenda; Dobbelaere, Dries; Couce, Maria L; Sykut-Cegielska, Jolanta; Häberle, Johannes; Lund, Allan M; Chakrapani, Anupam; Schiff, Manuel; Walter, John H; Zeman, Jiri; Vara, Roshni; Kölker, Stefan

    2016-09-01

    Patients with urea cycle disorders (UCDs) have an increased risk of neurological disease manifestation. Determining the effect of diagnostic and therapeutic interventions on the neurological outcome. Evaluation of baseline, regular follow-up and emergency visits of 456 UCD patients prospectively followed between 2011 and 2015 by the E-IMD patient registry. About two-thirds of UCD patients remained asymptomatic until age 12 days [i.e. the median age at diagnosis of patients identified by newborn screening (NBS)] suggesting a potential benefit of NBS. In fact, NBS lowered the age at diagnosis in patients with late onset of symptoms (>28 days), and a trend towards improved long-term neurological outcome was found for patients with argininosuccinate synthetase and lyase deficiency as well as argininemia identified by NBS. Three to 17 different drug combinations were used for maintenance therapy, but superiority of any single drug or specific drug combination above other combinations was not demonstrated. Importantly, non-interventional variables of disease severity, such as age at disease onset and peak ammonium level of the initial hyperammonemic crisis (cut-off level: 500 μmol/L) best predicted the neurological outcome. Promising results of NBS for late onset UCD patients are reported and should be re-evaluated in a larger and more advanced age group. However, non-interventional variables affect the neurological outcome of UCD patients. Available evidence-based guideline recommendations are currently heterogeneously implemented into practice, leading to a high variability of drug combinations that hamper our understanding of optimised long-term and emergency treatment.

  18. Uncertainty in model predictions of Vibrio vulnificus response to climate variability and change: a Chesapeake Bay case study.

    Directory of Open Access Journals (Sweden)

    Erin A Urquhart

    Full Text Available The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4°C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

  19. Edaphic history over seedling characters predicts integration and plasticity of integration across geologically variable populations of Arabidopsis thaliana.

    Science.gov (United States)

    Cousins, Elsa A; Murren, Courtney J

    2017-12-01

    Studies on phenotypic plasticity and plasticity of integration have uncovered functionally linked modules of aboveground traits and seedlings of Arabidopsis thaliana , but we lack details about belowground variation in adult plants. Functional modules can be comprised of additional suites of traits that respond to environmental variation. We assessed whether shoot and root responses to nutrient environments in adult A. thaliana were predictable from seedling traits or population-specific geologic soil characteristics at the site of origin. We compared 17 natural accessions from across the native range of A. thaliana using 14-day-old seedlings grown on agar or sand and plants grown to maturity across nutrient treatments in sand. We measured aboveground size, reproduction, timing traits, root length, and root diameter. Edaphic characteristics were obtained from a global-scale dataset and related to field data. We detected significant among-population variation in root traits of seedlings and adults and in plasticity in aboveground and belowground traits of adult plants. Phenotypic integration of roots and shoots varied by population and environment. Relative integration was greater in roots than in shoots, and integration was predicted by edaphic soil history, particularly organic carbon content, whereas seedling traits did not predict later ontogenetic stages. Soil environment of origin has significant effects on phenotypic plasticity in response to nutrients, and on phenotypic integration of root modules and shoot modules. Root traits varied among populations in reproductively mature individuals, indicating potential for adaptive and integrated functional responses of root systems in annuals. © 2017 Botanical Society of America.

  20. Using a topographic index to distribute variable source area runoff predicted with the SCS curve-number equation

    Science.gov (United States)

    Lyon, Steve W.; Walter, M. Todd; Gérard-Marchant, Pierre; Steenhuis, Tammo S.

    2004-10-01

    Because the traditional Soil Conservation Service curve-number (SCS-CN) approach continues to be used ubiquitously in water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed and tested a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Predicting the location of source areas is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point-source pollution. The method presented here used the traditional SCS-CN approach to predict runoff volume and spatial extent of saturated areas and a topographic index, like that used in TOPMODEL, to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was applied to two subwatersheds of the Delaware basin in the Catskill Mountains region of New York State and one watershed in south-eastern Australia to produce runoff-probability maps. Observed saturated area locations in the watersheds agreed with the distributed CN-VSA method. Results showed good agreement with those obtained from the previously validated soil moisture routing (SMR) model. When compared with the traditional SCS-CN method, the distributed CN-VSA method predicted a similar total volume of runoff, but vastly different locations of runoff generation. Thus, the distributed CN-VSA approach provides a physically based method that is simple enough to be incorporated into water quality models, and other tools that currently use the traditional SCS-CN method, while still adhering to the principles of VSA hydrology.

  1. Developing models to predict 8th grade students' achievement levels on timss science based on opportunity-to-learn variables

    Science.gov (United States)

    Whitford, Melinda M.

    Science educational reforms have placed major emphasis on improving science classroom instruction and it is therefore vital to study opportunity-to-learn (OTL) variables related to student science learning experiences and teacher teaching practices. This study will identify relationships between OTL and student science achievement and will identify OTL predictors of students' attainment at various distinct achievement levels (low/intermediate/high/advanced). Specifically, the study (a) address limitations of previous studies by examining a large number of independent and control variables that may impact students' science achievement and (b) it will test hypotheses of structural relations to how the identified predictors and mediating factors impact on student achievement levels. The study will follow a multi-stage and integrated bottom-up and top-down approach to identify predictors of students' achievement levels on standardized tests using TIMSS 2011 dataset. Data mining or pattern recognition, a bottom-up approach will identify the most prevalent association patterns between different student achievement levels and variables related to student science learning experiences, teacher teaching practices and home and school environments. The second stage is a top-down approach, testing structural equation models of relations between the significant predictors and students' achievement levels according.

  2. Clinical Significance of the Prognostic Nutritional Index for Predicting Short- and Long-Term Surgical Outcomes After Gastrectomy: A Retrospective Analysis of 7781 Gastric Cancer Patients.

    Science.gov (United States)

    Lee, Jee Youn; Kim, Hyoung-Il; Kim, You-Na; Hong, Jung Hwa; Alshomimi, Saeed; An, Ji Yeong; Cheong, Jae-Ho; Hyung, Woo Jin; Noh, Sung Hoon; Kim, Choong-Bai

    2016-05-01

    To evaluate the predictive and prognostic significance of the prognostic nutritional index (PNI) in a large cohort of gastric cancer patients who underwent gastrectomy.Assessing a patient's immune and nutritional status, PNI has been reported as a predictive marker for surgical outcomes in various types of cancer.We retrospectively reviewed data from a prospectively maintained database of 7781 gastric cancer patients who underwent gastrectomy from January 2001 to December 2010 at a single center. From this data, we analyzed clinicopathologic characteristics, PNI, and short- and long-term surgical outcomes for each patient. We used the PNI value for the 10th percentile (46.70) of the study cohort as a cut-off for dividing patients into low and high PNI groups.Regarding short-term outcomes, multivariate analysis showed a low PNI (odds ratio [OR] = 1.505, 95% CI = 1.212-1.869, P cancer recurrence.

  3. Ventricular short-axis measurements in patients with pulmonary embolism: Effect of ECG-gating on variability, accuracy, and risk prediction

    International Nuclear Information System (INIS)

    Scheffel, Hans; Stolzmann, Paul; Leschka, Sebastian; Desbiolles, Lotus; Seifert, Burkhardt; Marincek, Borut; Alkadhi, Hatem

    2012-01-01

    Objective: To assess prospectively the intra- and interobserver variability, accuracy, and prognostic value of right and left ventricular short-axis diameter (RVd and LVd) measurements for risk stratification in patients with pulmonary embolism (PE) using ECG-gated compared to non-gated CT. Materials and methods: Sixty consecutive patients (33 women; mean age 58.7 ± 10.3 years) with suspicion of PE underwent both non-gated and ECG-gated chest CT. RVd and LVd on four-chamber views and intra- and interobserver agreements were calculated for both protocols. RVd/LVd ratios were calculated and were related to 30-days adverse clinical events using receiver operating characteristics with area-under-the-curve (AUC) analyses. Results: Both inter- and intraobserver variability showed narrower limits of agreement for all measurements with ECG-gated as compared to non-gated CT. Diameter measurements were significantly lower using non-ECG-gated CT as compared to ECG-gated CT for RVd and LVd (both p < .05). The AUC for the RVd/LVd ratio from ECG-gated CT was significantly larger than that from non-gated CT (0.956, 95% CI: 0.768–0.999 versus 0.675, 95% CI: 0.439–0.860; p = .048). Conclusion: RVd and LVd measurements from ECG-gated chest CT show less intra- and interobserver variability and more accurately reflect ventricular function. In our patient cohort ECG-gated chest CT allows better prediction of short-term outcome of patients with acute PE that needs to be validated in a larger outcome study

  4. Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of Preterm Birth

    Science.gov (United States)

    Pearce, B.D.; Grove, J.; Bonney, E.A.; Bliwise, N.; Dudley, D.J.; Schendel, D.E.; Thorsen, P.

    2010-01-01

    Background/Aims To examine the relationship of biological mediators (cytokines, stress hormones), psychosocial, obstetric history, and demographic factors in the early prediction of preterm birth (PTB) using a comprehensive logistic regression model incorporating diverse risk factors. Methods In this prospective case-control study, maternal serum biomarkers were quantified at 9–23 weeks’ gestation in 60 women delivering at order interactions. Results Among individual biomarkers, we found that macrophage migration inhibitory factor (MIF), interleukin-10, C-reactive protein (CRP), and tumor necrosis factor-α were statistically significant predictors of PTB at all cutoff levels tested (75th, 85th, and 90th percentiles). We fit multifactor models for PTB prediction at each biomarker cutoff. Our best models revealed that MIF, CRP, risk-taking behavior, and low educational attainment were consistent predictors of PTB at all biomarker cutoffs. The 75th percentile cutoff yielded the best predicting model with an area under the ROC curve of 0.808 (95% CI 0.743–0.874). Conclusion Our comprehensive models highlight the prominence of behavioral risk factors for PTB and point to MIF as a possible psychobiological mediator. PMID:20160447

  5. Diagnostic value of thallium-201 myocardial perfusion IQ-SPECT without and with computed tomography-based attenuation correction to predict clinically significant and insignificant fractional flow reserve

    Science.gov (United States)

    Tanaka, Haruki; Takahashi, Teruyuki; Ohashi, Norihiko; Tanaka, Koichi; Okada, Takenori; Kihara, Yasuki

    2017-01-01

    Abstract The aim of this study was to clarify the predictive value of fractional flow reserve (FFR) determined by myocardial perfusion imaging (MPI) using thallium (Tl)-201 IQ-SPECT without and with computed tomography-based attenuation correction (CT-AC) for patients with stable coronary artery disease (CAD). We assessed 212 angiographically identified diseased vessels using adenosine-stress Tl-201 MPI-IQ-SPECT/CT in 84 consecutive, prospectively identified patients with stable CAD. We compared the FFR in 136 of the 212 diseased vessels using visual semiquantitative interpretations of corresponding territories on MPI-IQ-SPECT images without and with CT-AC. FFR inversely correlated most accurately with regional summed difference scores (rSDS) in images without and with CT-AC (r = −0.584 and r = −0.568, respectively, both P system can predict FFR at an optimal cut-off of <0.80, and we propose a novel application of CT-AC to MPI-IQ-SPECT for predicting clinically significant and insignificant FFR even in nonobese patients. PMID:29390486

  6. Variability and Predictability of West African Droughts. A Review in the Role of Sea Surface Temperature Anomalies

    Science.gov (United States)

    Rodriguez-Fonseca, Belen; Mohino, Elsa; Mechoso, Carlos R.; Caminade, Cyril; Biasutti, Michela; Gaetani, Marco; Garcia-Serrano, J.; Vizy, Edward K.; Cook, Kerry; Xue, Yongkang; hide

    2015-01-01

    The Sahel experienced a severe drought during the 1970s and 1980s after wet periods in the 1950s and 1960s. Although rainfall partially recovered since the 1990s, the drought had devastating impacts on society. Most studies agree that this dry period resulted primarily from remote effects of sea surface temperature (SST) anomalies amplified by local land surface-atmosphere interactions. This paper reviews advances made during the last decade to better understand the impact of global SST variability on West African rainfall at interannual to decadal time scales. At interannual time scales, a warming of the equatorial Atlantic and Pacific/Indian Oceans results in rainfall reduction over the Sahel, and positive SST anomalies over the Mediterranean Sea tend to be associated with increased rainfall. At decadal time scales, warming over the tropics leads to drought over the Sahel, whereas warming over the North Atlantic promotes increased rainfall. Prediction systems have evolved from seasonal to decadal forecasting. The agreement among future projections has improved from CMIP3 to CMIP5, with a general tendency for slightly wetter conditions over the central part of the Sahel, drier conditions over the western part, and a delay in the monsoon onset. The role of the Indian Ocean, the stationarity of teleconnections, the determination of the leader ocean basin in driving decadal variability, the anthropogenic role, the reduction of the model rainfall spread, and the improvement of some model components are among the most important remaining questions that continue to be the focus of current international projects.

  7. Do resettlement variables predict psychiatric treatment outcomes in a sample of asylum-seeking survivors of torture?

    Science.gov (United States)

    Whitsett, David; Sherman, Martin F

    2017-12-01

    Mental health clinicians who work with asylum seekers provide services to patients who face stressful everyday living conditions. However, little is known about how these problems potentially impact psychiatric treatment within these populations. The purpose of this study was thus to examine whether resettlement factors predict outcomes of a mental health intervention for a sample of asylum-seeking survivors of torture. The study included data from a US outpatient clinic that specialized in treating asylum-seeking survivors of torture. Patients (primarily from Iraq, Afghanistan and African Countries) were evaluated on demographic factors at intake and psychiatric symptoms throughout the course of treatment. Patients experienced significant reductions in depression, anxiety and trauma symptoms, although symptoms still remained near or above clinical thresholds. Stable, uncrowded housing conditions significantly predicted lower depression, anxiety and trauma symptoms at follow-up. These findings support the hypotheses that individuals seeking asylum within the United States who have survived torture can benefit from psychiatric treatment and emphasize the importance of stable living conditions in improving treatment effectiveness. This suggests the need for further research on social predictors of treatment outcomes, as well as the need for clinicians and policymakers to target improved housing as a potentially important tool to reduce psychiatric problems related to torture and forced migration.

  8. Mid-Treatment Sleep Duration Predicts Clinically Significant Knee Osteoarthritis Pain reduction at 6 months: Effects From a Behavioral Sleep Medicine Clinical Trial.

    Science.gov (United States)

    Salwen, Jessica K; Smith, Michael T; Finan, Patrick H

    2017-02-01

    To determine the relative influence of sleep continuity (sleep efficiency, sleep onset latency, total sleep time [TST], and wake after sleep onset) on clinical pain outcomes within a trial of cognitive behavioral therapy for insomnia (CBT-I) for patients with comorbid knee osteoarthritis and insomnia. Secondary analyses were performed on data from 74 patients with comorbid insomnia and knee osteoarthritis who completed a randomized clinical trial of 8-session multicomponent CBT-I versus an active behavioral desensitization control condition (BD), including a 6-month follow-up assessment. Data used herein include daily diaries of sleep parameters, actigraphy data, and self-report questionnaires administered at specific time points. Patients who reported at least 30% improvement in self-reported pain from baseline to 6-month follow-up were considered responders (N = 31). Pain responders and nonresponders did not differ significantly at baseline across any sleep continuity measures. At mid-treatment, only TST predicted pain response via t tests and logistic regression, whereas other measures of sleep continuity were nonsignificant. Recursive partitioning analyses identified a minimum cut-point of 382 min of TST achieved at mid-treatment in order to best predict pain improvements 6-month posttreatment. Actigraphy results followed the same pattern as daily diary-based results. Clinically significant pain reductions in response to both CBT-I and BD were optimally predicted by achieving approximately 6.5 hr sleep duration by mid-treatment. Thus, tailoring interventions to increase TST early in treatment may be an effective strategy to promote long-term pain reductions. More comprehensive research on components of behavioral sleep medicine treatments that contribute to pain response is warranted. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  9. Linking Compositional and Functional Predictions to Decipher the Biogeochemical Significance in DFAA Turnover of Abundant Bacterioplankton Lineages in the North Sea

    Directory of Open Access Journals (Sweden)

    Bernd Wemheuer

    2017-11-01

    Full Text Available Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria. Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.

  10. Linking Compositional and Functional Predictions to Decipher the Biogeochemical Significance in DFAA Turnover of Abundant Bacterioplankton Lineages in the North Sea.

    Science.gov (United States)

    Wemheuer, Bernd; Wemheuer, Franziska; Meier, Dimitri; Billerbeck, Sara; Giebel, Helge-Ansgar; Simon, Meinhard; Scherber, Christoph; Daniel, Rolf

    2017-11-05

    Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria . Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA) showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.

  11. Prediction and optimization of process variables to maximize the Young's modulus of plasma sprayed alumina coatings on AZ31B magnesium alloy

    Directory of Open Access Journals (Sweden)

    D. Thirumalaikumarasamy

    2017-03-01

    Full Text Available Like other manufacturing techniques, plasma spraying has also a non-linear behavior because of the contribution of many coating variables. This characteristic results in finding optimal factor combination difficult. Subsequently, the issue can be solved through effective and strategic statistical procedures integrated with systematic experimental data. Plasma spray parameters such as power, stand-off distance and powder feed rate have significant influence on coating characteristics like Young's modulus. This paper presents the use of statistical techniques in specifically response surface methodology (RSM, analysis of variance, and regression analysis to develop empirical relationship to predict Young's modulus of plasma-sprayed alumina coatings. The developed empirical relationships can be effectively used to predict Young's modulus of plasma-sprayed alumina coatings at 95% confidence level. Response graphs and contour plots were constructed to identify the optimum plasma spray parameters to attain maximum Young's modulus in alumina coatings. A linear regression relationship was established between porosity and Young's modulus of the alumina coatings.

  12. Predictive ability of visit-to-visit variability in HbA1c and systolic blood pressure for the development of microalbuminuria and retinopathy in people with type 2 diabetes.

    Science.gov (United States)

    Takao, Toshiko; Suka, Machi; Yanagisawa, Hiroyuki; Matsuyama, Yutaka; Iwamoto, Yasuhiko

    2017-06-01

    We explored whether visit-to-visit variability in both glycated hemoglobin (HbA1c) and systolic blood pressure (SBP) simultaneously predicted the development of microalbuminuria and retinopathy, and whether the predictive ability of these measurements changed according to mean HbA1c and SBP levels in people with type 2 diabetes. A retrospective observational cohort study was conducted on 243 type 2 diabetes patients with normoalbuminuria and 486 without retinopathy at the first visit and within 1year thereafter. The two cohorts were followed up from 1995 until 2012. Multivariate and stratified analyses were performed using Cox proportional hazard models. Microalbuminuria developed in 84 patients and retinopathy in 108. Hazard ratios (HRs) for the development of microalbuminuria associated with the coefficient of variation (CV) and variation independent of mean (VIM) of both HbA1c and SBP significantly increased. In participants with a mean SBP HbA1c were abruptly elevated and significant compared with those with a mean SBP ≥130mmHg. Visit-to-visit variability in both HbA1c and SBP simultaneously predict the development of microalbuminuria. HbA1c variability may predict the development of retinopathy when the mean SBP is normal (<130mmHg). Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Real Time Hybrid Model Predictive Control for the Current Profile of the Tokamak à Configuration Variable (TCV

    Directory of Open Access Journals (Sweden)

    Izaskun Garrido

    2016-08-01

    Full Text Available Plasma stability is one of the obstacles in the path to the successful operation of fusion devices. Numerical control-oriented codes as it is the case of the widely accepted RZIp may be used within Tokamak simulations. The novelty of this article relies in the hierarchical development of a dynamic control loop. It is based on a current profile Model Predictive Control (MPC algorithm within a multiloop structure, where a MPC is developed at each step so as to improve the Proportional Integral Derivative (PID global scheme. The inner control loop is composed of a PID-based controller that acts over the Multiple Input Multiple Output (MIMO system resulting from the RZIp plasma model of the Tokamak à Configuration Variable (TCV. The coefficients of this PID controller are initially tuned using an eigenmode reduction over the passive structure model. The control action corresponding to the state of interest is then optimized in the outer MPC loop. For the sake of comparison, both the traditionally used PID global controller as well as the multiloop enhanced MPC are applied to the same TCV shot. The results show that the proposed control algorithm presents a superior performance over the conventional PID algorithm in terms of convergence. Furthermore, this enhanced MPC algorithm contributes to extend the discharge length and to overcome the limited power availability restrictions that hinder the performance of advanced tokamaks.

  14. Variable temperature ion trap studies of CH4+ + H2, HD and D2: negative temperature dependence and significant isotope effect

    International Nuclear Information System (INIS)

    Asvany, O.; Savic, I.; Schlemmer, S.; Gerlich, D.

    2004-01-01

    Reactions of methane cations, CH 4 + , with H 2 , HD and D 2 have been studied in a variable temperature 22-pole ion trap from room temperature down to 15 K. The formation of CH 5 + in collisions with H 2 is slow at 300 K, but it becomes faster by at least one order of magnitude when the temperature is lowered to 15 K. This behavior is tentatively explained with a longer complex lifetime at low temperatures. However, since tunneling is most probably not responsible for product formation, other dynamical or statistical restrictions must be responsible for the negative temperature dependence. In collisions of CH 4 + with HD, the CH 5 + product ion (68% at 15 K) prevails over CH 4 D + (32%). Reaction of CH 4 + with D 2 is found to be much slower than with H 2 or HD. The rate coefficient for converting CH 4 + into CH 3 D + by H-D exchange has been determined to be smaller than 10 -12 cm 3 /s, indicating that scrambling in the CH 6 + complex is very unlikely

  15. Variable temperature ion trap studies of CH{sub 4}{sup +} + H{sub 2}, HD and D{sub 2}: negative temperature dependence and significant isotope effect

    Energy Technology Data Exchange (ETDEWEB)

    Asvany, O.; Savic, I.; Schlemmer, S.; Gerlich, D

    2004-03-08

    Reactions of methane cations, CH{sub 4}{sup +}, with H{sub 2}, HD and D{sub 2} have been studied in a variable temperature 22-pole ion trap from room temperature down to 15 K. The formation of CH{sub 5}{sup +} in collisions with H{sub 2} is slow at 300 K, but it becomes faster by at least one order of magnitude when the temperature is lowered to 15 K. This behavior is tentatively explained with a longer complex lifetime at low temperatures. However, since tunneling is most probably not responsible for product formation, other dynamical or statistical restrictions must be responsible for the negative temperature dependence. In collisions of CH{sub 4}{sup +} with HD, the CH{sub 5}{sup +} product ion (68% at 15 K) prevails over CH{sub 4}D{sup +} (32%). Reaction of CH{sub 4}{sup +} with D{sub 2} is found to be much slower than with H{sub 2} or HD. The rate coefficient for converting CH{sub 4}{sup +} into CH{sub 3}D{sup +} by H-D exchange has been determined to be smaller than 10{sup -12} cm{sup 3}/s, indicating that scrambling in the CH{sub 6}{sup +} complex is very unlikely.

  16. Predictive value of serum ALT and T-cell receptor beta variable chain for HBeAg seroconversion in chronic hepatitis B patients during tenofovir treatment.

    Science.gov (United States)

    Yang, Jiezuan; Yan, Dong; Guo, Renyong; Chen, Jiajia; Li, Yongtao; Fan, Jun; Fu, Xuyan; Yao, Xinsheng; Diao, Hongyan; Li, Lanjuan

    2017-03-01

    Effective antiviral therapy plays a key role in slowing the progression of chronic hepatitis B (CHB). Identification of serum indices, including hepatitis B e antigen (HBeAg) expression and seroconversion, will facilitate evaluation of the efficacy of antiviral therapy in HBeAg-positive CHB patients. The biochemical, serological, virological parameters, and the frequency of circulating CD4CD25 regulatory T cell (Treg) in 32 patients were measured at baseline and every 12 weeks during 96 weeks of tenofovir disoproxil fumarate (TDF) treatment. The relationship between the hepatitis B virus (HBV) deoxyribonucleic acid (DNA) and Treg and alanine aminotransferase (ALT) levels was analyzed, respectively. The molecular profiles of T-cell receptor beta variable chain (TRBV) were determined using gene melting spectral pattern. For the seroconverted 12 patients, ALT declined to normal levels by week 24 and remained at this level in subsequent treatment; moreover, the predictive cutoff value of ALT for HBeAg seroconversion (SC) was 41.5 U/L at week 24. The positive correlation between HBV DNA and Treg and ALT was significant in SC patients, but not in non-SC patients. Six TRBV families (BV3, BV11, BV12, BV14, BV20, and BV24) were predominantly expressed in SC patients at baseline. The decline of ALT could be used to predict HBeAg seroconversion for CHB patients during TDF treatment. In addition, the profile of Tregs and TRBVs may be associated with HBeAg seroconversion and could also be a potential indicator for predicting HBeAg SC and treatment outcome for CHB patients.

  17. Comparison of the significance of the RENAL, PADUA, and C-index nephrometric scales for the prediction of the complexity of laparoscopic nephrectomy

    Directory of Open Access Journals (Sweden)

    Yu. G. Alyaev

    2018-01-01

    time, extent of intraoperative blood loss and possibility of development after complications (p = 0.049; 0.028; 0.046. None of indices were significant for multivariant analysis of prognosis the duration of laparoscopic partial nephrectomy. The indices  of the RENAL (p = 0.032 and C-index (p = 0.040 nephrometry score systems were significant for univariate analysis of prognosis the duration of the laparoscopic partial nephrectomy.Conclusion. The usage of RENAL, PADUA, C-index nephrometry score systems is useful for the prediction of warm ischaemic time, extent  of blood loss, duration of operative measure and possibility of rate of postoperative complications at laparoscopic partial nephrectomy. According to our data the index of RENAL nephrometry scoring system has the highest predictive value. Applications of 3D modelling for counting nephrometry indices in preoperative period makes the process of counting balls easier on all three nephrometry score systems.

  18. Prediction of bull fertility.

    Science.gov (United States)

    Utt, Matthew D

    2016-06-01

    Prediction of male fertility is an often sought-after endeavor for many species of domestic animals. This review will primarily focus on providing some examples of dependent and independent variables to stimulate thought about the approach and methodology of identifying the most appropriate of those variables to predict bull (bovine) fertility. Although the list of variables will continue to grow with advancements in science, the principles behind making predictions will likely not change significantly. The basic principle of prediction requires identifying a dependent variable that is an estimate of fertility and an independent variable or variables that may be useful in predicting the fertility estimate. Fertility estimates vary in which parts of the process leading to conception that they infer about and the amount of variation that influences the estimate and the uncertainty thereof. The list of potential independent variables can be divided into competence of sperm based on their performance in bioassays or direct measurement of sperm attributes. A good prediction will use a sample population of bulls that is representative of the population to which an inference will be made. Both dependent and independent variables should have a dynamic range in their values. Careful selection of independent variables includes reasonable measurement repeatability and minimal correlation among variables. Proper estimation and having an appreciation of the degree of uncertainty of dependent and independent variables are crucial for using predictions to make decisions regarding bull fertility. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Spatial variability of phytoplankton pigment distributions in the Subtropical South Pacific Ocean: comparison between in situ and predicted data

    Directory of Open Access Journals (Sweden)

    J. Ras

    2008-03-01

    Full Text Available In the frame of the BIOSOPE cruise in 2004, the spatial distribution and structure of phytoplankton pigments was investigated along a transect crossing the ultra-oligotrophic South Pacific Subtropical Gyre (SPSG between the Marquesas Archipelago (141° W–8° S and the Chilean upwelling (73° W–34° S. A High Performance Liquid Chromatography (HPLC method was improved in order to be able to accurately quantify pigments over such a large range of trophic levels, and especially from strongly oligotrophic conditions. Seven diagnostic pigments were associated to three phytoplankton size classes (pico-, nano and microphytoplankton. The total chlorophyll-α concentrations [TChlα] in surface waters were the lowest measured in the centre of the gyre, reaching 0.017 mg m−3. Pigment concentrations at the Deep Chlorophyll Maximum (DCM were generally 10 fold the surface values. Results were compared to predictions from a global parameterisation based on remotely sensed surface [TChlα]. The agreement between the in situ and predicted data for such contrasting phytoplankton assemblages was generally good: throughout the oligotrophic gyre system, picophytoplankton (prochlorophytes and cyanophytes and nanophytoplankton were the dominant classes. Relative bacteriochlorophyll-α concentrations varied around 2%. The transition zone between the Marquesas and the SPSG was also well predicted by the model. However, some regional characteristics have been observed where measured and modelled data differ. Amongst these features is the extreme depth of the DCM (180 m towards the centre of the gyre, the presence of a deep nanoflagellate population beneath the DCM or the presence of a prochlorophyte-enriched population in the formation area of the high salinity South Pacific Tropical Water. A coastal site sampled in the eutrophic upwelling zone, characterised by recently upwelled water, was significantly and unusually enriched in picoeucaryotes, in

  20. A proposal for a pharmacokinetic interaction significance classification system (PISCS) based on predicted drug exposure changes and its potential application to alert classifications in product labelling.

    Science.gov (United States)

    Hisaka, Akihiro; Kusama, Makiko; Ohno, Yoshiyuki; Sugiyama, Yuichi; Suzuki, Hiroshi

    2009-01-01

    Pharmacokinetic drug-drug interactions (DDIs) are one of the major causes of adverse events in pharmacotherapy, and systematic prediction of the clinical relevance of DDIs is an issue of significant clinical importance. In a previous study, total exposure changes of many substrate drugs of cytochrome P450 (CYP) 3A4 caused by coadministration of inhibitor drugs were successfully predicted by using in vivo information. In order to exploit these predictions in daily pharmacotherapy, the clinical significance of the pharmacokinetic changes needs to be carefully evaluated. The aim of the present study was to construct a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered with pharmacokinetic changes in a systematic manner. Furthermore, the classifications proposed by PISCS were compared in a detailed manner with current alert classifications in the product labelling or the summary of product characteristics used in Japan, the US and the UK. A matrix table was composed by stratifying two basic parameters of the prediction: the contribution ratio of CYP3A4 to the oral clearance of substrates (CR), and the inhibition ratio of inhibitors (IR). The total exposure increase was estimated for each cell in the table by associating CR and IR values, and the cells were categorized into nine zones according to the magnitude of the exposure increase. Then, correspondences between the DDI significance and the zones were determined for each drug group considering the observed exposure changes and the current classification in the product labelling. Substrate drugs of CYP3A4 selected from three therapeutic groups, i.e. HMG-CoA reductase inhibitors (statins), calcium-channel antagonists/blockers (CCBs) and benzodiazepines (BZPs), were analysed as representative examples. The product labelling descriptions of drugs in Japan, US and UK were obtained from the websites of each regulatory body. Among 220

  1. Predicting weight status stability and change from fifth grade to eighth grade: the significant role of adolescents' social-emotional well-being.

    Science.gov (United States)

    Chang, Yiting; Gable, Sara

    2013-04-01

    The primary objective of this study was to predict weight status stability and change across the transition to adolescence using parent reports of child and household routines and teacher and child self-reports of social-emotional development. Data were from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K), a nationally representative sample of children who entered kindergarten during 1998-1999 and were followed through eighth grade. At fifth grade, parents reported on child and household routines and the study child and his/her primary classroom teacher reported on the child's social-emotional functioning. At fifth and eighth grade, children were directly weighed and measured at school. Nine mutually-exclusive weight trajectory groups were created to capture stability or change in weight status from fifth to eighth grade: (1) stable obese (ObeSta); (2) obese to overweight (ObePos1); (3) obese to healthy (ObePos2); (4) stable overweight (OverSta); (5) overweight to healthy (OverPos); (6) overweight to obese (OverNeg); (7) stable healthy (HelSta); (8) healthy to overweight (HelNeg1); and (9) healthy to obese (HelNeg2). Except for breakfast consumption at home, school-provided lunches, nighttime sleep duration, household and child routines did not predict stability or change in weight status. Instead, weight status trajectory across the transition to adolescence was significantly predicted by measures of social-emotional functioning at fifth grade. Assessing children's social-emotional well-being in addition to their lifestyle routines during the transition to adolescence is a noteworthy direction for adolescent obesity prevention and intervention. Copyright © 2013 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  2. Blastocystis Isolates from Patients with Irritable Bowel Syndrome and from Asymptomatic Carriers Exhibit Similar Parasitological Loads, but Significantly Different Generation Times and Genetic Variability across Multiple Subtypes.

    Directory of Open Access Journals (Sweden)

    Gie-Bele Vargas-Sanchez

    selected, reducing their genetic variability.

  3. A new general dynamic model predicting radionuclide concentrations and fluxes in coastal areas from readily accessible driving variables

    International Nuclear Information System (INIS)

    Haakanson, Lars

    2004-01-01

    This paper presents a general, process-based dynamic model for coastal areas for radionuclides (metals, organics and nutrients) from both single pulse fallout and continuous deposition. The model gives radionuclide concentrations in water (total, dissolved and particulate phases and concentrations in sediments and fish) for entire defined coastal areas. The model gives monthly variations. It accounts for inflow from tributaries, direct fallout to the coastal area, internal fluxes (sedimentation, resuspension, diffusion, burial, mixing and biouptake and retention in fish) and fluxes to and from the sea outside the defined coastal area and/or adjacent coastal areas. The fluxes of water and substances between the sea and the coastal area are differentiated into three categories of coast types: (i) areas where the water exchange is regulated by tidal effects; (ii) open coastal areas where the water exchange is regulated by coastal currents; and (iii) semi-enclosed archipelago coasts. The coastal model gives the fluxes to and from the following four abiotic compartments: surface water, deep water, ET areas (i.e., areas where fine sediment erosion and transport processes dominate the bottom dynamic conditions and resuspension appears) and A-areas (i.e., areas of continuous fine sediment accumulation). Criteria to define the boundaries for the given coastal area towards the sea, and to define whether a coastal area is open or closed are given in operational terms. The model is simple to apply since all driving variables may be readily accessed from maps and standard monitoring programs. The driving variables are: latitude, catchment area, mean annual precipitation, fallout and month of fallout and parameters expressing coastal size and form as determined from, e.g., digitized bathymetric maps using a GIS program. Selected results: the predictions of radionuclide concentrations in water and fish largely depend on two factors, the concentration in the sea outside the given

  4. Clinical significance of the neutrophil-lymphocyte ratio as an early predictive marker for adverse outcomes in patients with acute pancreatitis.

    Science.gov (United States)

    Jeon, Tae Joo; Park, Ji Young

    2017-06-07

    To investigated the prognostic value of the neutrophil-lymphocyte ratio (NLR) in patients with acute pancreatitis and determined an optimal cut-off value for the prediction of adverse outcomes in these patients. We retrospectively analyzed 490 patients with acute pancreatitis diagnosed between March 2007 and December 2012. NLRs were calculated at admission and 24, 48, and 72 h after admission. Patients were grouped according to acute pancreatitis severity and organ failure occurrence, and a comparative analysis was performed to compare the NLR between groups. Among the 490 patients, 70 had severe acute pancreatitis with 31 experiencing organ failure. The severe acute pancreatitis group had a significantly higher NLR than the mild acute pancreatitis group on all 4 d (median, 6.14, 6.71, 5.70, and 4.00 vs 4.74, 4.47, 3.20, and 3.30, respectively, P pancreatitis. Elevated baseline NLR correlates with severe acute pancreatitis and organ failure.

  5. Comparison of FLOWTRAN predictions of onset of significant voiding (OSV) to Savannah River Heat Transfer Laboratory subcooled boiling flow instability measurements, Part 1

    International Nuclear Information System (INIS)

    Laurinat, J.E.

    1988-10-01

    The onset of flow instability (OFI) was measured in the first of a scheduled series of subcooled boiling tests at the Savannah River Heat Transfer Laboratory (HTL). This report summarizes the benchmarking of predictions of the onset of significant voiding (OSV) using Version 16 of the FLOWTRANΩ reactor limits code against the HTL measurements. This study confirms that, for this series of HTL subcooled boiling tests, the Saha-Zuber OSV correlation was a conservative indicator of OFI for Peclet numbers between 30,000 and 80,000. The Saha-Zuber correlation was not a conservative indicator of OFI for Peclet numbers below 30,000. A conservative bound to the Saha-Zuber correlation (the Saha-Zuber constant Stanton number criterion -- 30%) was agreed to at a meeting of SRL, DOE, and the DOE EH and DP review panels. This bound was a conservative indicator of OFI for all measurements in this study

  6. Variable RBE in proton therapy: comparison of different model predictions and their influence on clinical-like scenarios

    International Nuclear Information System (INIS)

    Giovannini, Giulia; Böhlen, Till; Cabal, Gonzalo; Bauer, Julia; Tessonnier, Thomas; Frey, Kathrin; Debus, Jürgen; Mairani, Andrea; Parodi, Katia

    2016-01-01

    In proton radiation therapy a constant relative biological effectiveness (RBE) of 1.1 is usually assumed. However, biological experiments have evidenced RBE dependencies on dose level, proton linear energy transfer (LET) and tissue type. This work compares the predictions of three of the main radio-biological models proposed in the literature by Carabe-Fernandez, Wedenberg, Scholz and coworkers. Using the chosen models, a spread-out Bragg peak (SOBP) as well as two exemplary clinical cases (single field and two fields) for cranial proton irradiation, all delivered with state-of-the-art pencil-beam scanning, have been analyzed in terms of absorbed dose, dose-averaged LET (LET D ), RBE-weighted dose (D RBE ) and biological range shift distributions. In the systematic comparison of RBE predictions by the three models we could show different levels of agreement depending on (α/β) x and LET values. The SOBP study emphasizes the variation of LET D and RBE not only as a function of depth but also of lateral distance from the central beam axis. Application to clinical-like scenario shows consistent discrepancies from the values obtained for a constant RBE of 1.1, when using a variable RBE scheme for proton irradiation in tissues with low (α/β) x , regardless of the model. Biological range shifts of 0.6– 2.4 mm (for high (α/β) x ) and 3.0 – 5.4 mm (for low (α/β) x ) were found from the fall-off analysis of individual profiles of RBE-weighted fraction dose along the beam penetration depth. Although more experimental evidence is needed to validate the accuracy of the investigated models and their input parameters, their consistent trend suggests that their main RBE dependencies (dose, LET and (α/β) x ) should be included in treatment planning systems. In particular, our results suggest that simpler models based on the linear-quadratic formalism and LET D might already be sufficient to reproduce important RBE dependencies for re-evaluation of plans optimized with

  7. [Predictive factors of clinically significant drug-drug interactions among regimens based on protease inhibitors, non-nucleoside reverse transcriptase inhibitors and raltegravir].

    Science.gov (United States)

    Cervero, Miguel; Torres, Rafael; Jusdado, Juan José; Pastor, Susana; Agud, Jose Luis

    2016-04-15

    To determine the prevalence and types of clinically significant drug-drug interactions (CSDI) in the drug regimens of HIV-infected patients receiving antiretroviral treatment. retrospective review of database. Centre: Hospital Universitario Severo Ochoa, Infectious Unit. one hundred and forty-two participants followed by one of the authors were selected from January 1985 to December 2014. from their outpatient medical records we reviewed information from the last available visit of the participants, in relation to HIV infection, comorbidities, demographics and the drugs that they were receiving; both antiretroviral drugs and drugs not related to HIV infection. We defined CSDI from the information sheet and/or database on antiretroviral drug interactions of the University of Liverpool (http://www.hiv-druginteractions.org) and we developed a diagnostic tool to predict the possibility of CSDI. By multivariate logistic regression analysis and by estimating the diagnostic performance curve obtained, we identified a quick tool to predict the existence of drug interactions. Of 142 patients, 39 (29.11%) had some type of CSDI and in 11.2% 2 or more interactions were detected. In only one patient the combination of drugs was contraindicated (this patient was receiving darunavir/r and quetiapine). In multivariate analyses, predictors of CSDI were regimen type (PI or NNRTI) and the use of 3 or more non-antiretroviral drugs (AUC 0.886, 95% CI 0.828 to 0.944; P=.0001). The risk was 18.55 times in those receiving NNRTI and 27,95 times in those receiving IP compared to those taking raltegravir. Drug interactions, including those defined as clinically significant, are common in HIV-infected patients treated with antiretroviral drugs, and the risk is greater in IP-based regimens. Raltegravir-based prescribing, especially in patients who receive at least 3 non-HIV drugs could avoid interactions. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  8. Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils.

    Science.gov (United States)

    Terra, Luciana A; Filgueiras, Paulo R; Tose, Lílian V; Romão, Wanderson; de Souza, Douglas D; de Castro, Eustáquio V R; de Oliveira, Mirela S L; Dias, Júlio C M; Poppi, Ronei J

    2014-10-07

    Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

  9. V S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response

    Science.gov (United States)

    Derras, Boumédiène; Bard, Pierre-Yves; Cotton, Fabrice

    2017-09-01

    The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m ( V S30), the topographical slope (slope), the fundamental resonance frequency ( f 0) and the depth beyond which V s exceeds 800 m/s ( H 800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [ V S30- f 0], [ V S30- H 800], [ f 0-slope], [ H 800-slope], [ V S30-slope] and [ f 0- H 800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA ( T), and M w, R JB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median ground-motion prediction, it does impact the level of aleatory uncertainty. V S30 is found to perform the best of single proxies at short periods ( T < 0.6 s), while f 0 and H 800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [ V S30- H 800] and [ f 0-slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the "stiff" spectral ordinate at the considered period.[Figure not available: see fulltext.

  10. Predicting dissolution patterns in variable aperture fractures: 1. Development and evaluation of an enhanced depth-averaged computational model

    Energy Technology Data Exchange (ETDEWEB)

    Detwiler, R L; Rajaram, H

    2006-04-21

    Water-rock interactions within variable-aperture fractures can lead to dissolution of fracture surfaces and local alteration of fracture apertures, potentially transforming the transport properties of the fracture over time. Because fractures often provide dominant pathways for subsurface flow and transport, developing models that effectively quantify the role of dissolution on changing transport properties over a range of scales is critical to understanding potential impacts of natural and anthropogenic processes. Dissolution of fracture surfaces is controlled by surface-reaction kinetics and transport of reactants and products to and from the fracture surfaces. We present development and evaluation of a depth-averaged model of fracture flow and reactive transport that explicitly calculates local dissolution-induced alterations in fracture apertures. The model incorporates an effective mass transfer relationship that implicitly represents the transition from reaction-limited dissolution to transport-limited dissolution. We evaluate the model through direct comparison to previously reported physical experiments in transparent analog fractures fabricated by mating an inert, transparent rough surface with a smooth single crystal of potassium dihydrogen phosphate (KDP), which allowed direct measurement of fracture aperture during dissolution experiments using well-established light transmission techniques [Detwiler, et al., 2003]. Comparison of experiments and simulations at different flow rates demonstrate the relative impact of the dimensionless Peclet and Damkohler numbers on fracture dissolution and the ability of the computational model to simulate dissolution. Despite some discrepancies in the small-scale details of dissolution patterns, the simulations predict the evolution of large-scale features quite well for the different experimental conditions. This suggests that our depth-averaged approach to simulating fracture dissolution provides a useful approach for

  11. Latitudinal and seasonal variability of the micrometeor input function: A study using model predictions and observations from Arecibo and PFISR

    Science.gov (United States)

    Fentzke, J. T.; Janches, D.; Sparks, J. J.

    2009-05-01

    In this work, we use a semi-empirical model of the micrometeor input function (MIF) together with meteor head-echo observations obtained with two high power and large aperture (HPLA) radars, the 430 MHz Arecibo Observatory (AO) radar in Puerto Rico (18°N, 67°W) and the 450 MHz Poker flat incoherent scatter radar (PFISR) in Alaska (65°N, 147°W), to study the seasonal and geographical dependence of the meteoric flux in the upper atmosphere. The model, recently developed by Janches et al. [2006a. Modeling the global micrometeor input function in the upper atmosphere observed by high power and large aperture radars. Journal of Geophysical Research 111] and Fentzke and Janches [2008. A semi-empirical model of the contribution from sporadic meteoroid sources on the meteor input function observed at arecibo. Journal of Geophysical Research (Space Physics) 113 (A03304)], includes an initial mass flux that is provided by the six known meteor sources (i.e. orbital families of dust) as well as detailed modeling of meteoroid atmospheric entry and ablation physics. In addition, we use a simple ionization model to treat radar sensitivity issues by defining minimum electron volume density production thresholds required in the meteor head-echo plasma for detection. This simplified approach works well because we use observations from two radars with similar frequencies, but different sensitivities and locations. This methodology allows us to explore the initial input of particles and how it manifests in different parts of the MLT as observed by these instruments without the need to invoke more sophisticated plasma models, which are under current development. The comparisons between model predictions and radar observations show excellent agreement between diurnal, seasonal, and latitudinal variability of the detected meteor rate and radial velocity distributions, allowing us to understand how individual meteoroid populations contribute to the overall flux at a particular

  12. Is the biparietal diameter of fetuses in late gestation too variable to predict readiness for cesarean section in dogs?

    Science.gov (United States)

    De Cramer, K G M; Nöthling, J O

    2018-06-01

    Correct assessment of readiness for cesarean section is essential for timing elective cesarean section during late pregnancy in the bitch. In humans, biparietal diameter is sufficiently precise and accurate and used in a clinical setting daily. The objectives of this study were to determine whether fetal biparietal diameter in late gestation in the dog could be used to predict readiness for cesarean section by having reached a minimum cut-off value and to correlate the biparietal diameter to birth weight. The biparietal diameter of 208 puppies in 34 litters from 31 English bulldog bitches and 660 puppies in 78 litters from 70 Boerboel bitches were measured immediately after delivery by cesarean section, performed at full term, using digital calipers. At the same time the birth weight of the same 208 English bulldog puppies and 494 of the same Boerboel puppies in 59 litters from 54 bitches was measured by means of an electronic scale. With a cesarean section, all the puppies in a litter are delivered simultaneously and readiness for cesarean section must be determined for a litter. The minimum, median and maximum biparietal diameter varied from 21.1 to 47.8, 32.9 to 50.0 and 34.2-58.2 mm, respectively, among English bulldog litters and from 18.4 to 48.7, 35.5 to 49.7 and 39.8-54.3 mm among Boerboel litters. This large variation suggests that biparietal diameter is too variable within and among litters to be useful as a means of determining readiness for cesarean section. Copyright © 2018 Elsevier Inc. All rights reserved.

  13. Independent predictive factors for significant liver histological changes in patients with HBeAg-positive high-viral-load chronic HBV infection and a normal alanine aminotransferase level

    Directory of Open Access Journals (Sweden)

    LI Qiang

    2016-07-01

    Full Text Available Objective To investigate the independent predictive factors for significant liver histological changes (SLHCs in patients with HBeAg-positive high-viral-load chronic hepatitis B virus (HBV infection and a normal alanine aminotransferase (ALT level. MethodsA retrospective analysis was performed on the clinical data of 116 previously untreated patients with HBeAg-positive high-viral-load (HBV DNA≥105 copies/ml chronic HBV infection and a normal ALT level (<50 U/L who were hospitalized in Shanghai Public Health Clinical Center Affiliated to Fudan University from June 2013 to August 2015. The definition of SLHCs was inflammation ≥G2 and/or fibrosis≥S2. The t-test or Mann-Whitney U rank sum test was used for comparison of continuous data between groups, and the chi-square test was used for comparison of categorical data between groups. Univariate and multivariate regression analyses were used to determine independent predictive factors for SLHCs. ResultsOf all the 116 patients, 47(40.5% had SLHCs. The multivariate analysis showed that age (OR=2.828, P<0.05, ALT (OR=1.011, P<0.05, and gamma-glutamyl transpeptidase (GGT (OR=1.089, P<0.05 were independent predictors for SLHCs in patients with HBeAg-positive high-viral-load chronic HBV infection and a normal ALT level. The patients aged ≤30 years had a significantly lower incidence rate of SLHCs than those aged>30 years (21.6% vs 49.4%, χ2=6.42, P=0.015, the patients with ALT ≤30 U/L had a significantly lower incidence rate of SLHCs than those with 30 U/L<ALT≤50 U/L (17.6% vs 50.0%, χ2=19.86, P<0.001, and the patients with GGT≤40 U/L had a significantly lower incidence rate of SLHCs than those with GGT>40 U/L (28.8% vs 66.7%, χ2=28.63, P<0.001. ConclusionIn patients with HBeAg-positive high-viral-load chronic HBV infection and a normal ALT level, those with an age of>30 years, ALT>30 U/L, and GGT>40 U/L tend to develop SLHCs and need liver biopsy.

  14. Predicting Performance in Art College: How Useful Are the Entry Portfolio and Other Variables in Explaining Variance in First Year Marks?

    Science.gov (United States)

    O'Donoghue, Donal

    2009-01-01

    This article examines if and to what extent a set of pre-enrolment variables and background characteristics predict first year performance in art college. The article comes from a four-year longitudinal study that followed a cohort of tertiary art entrants in Ireland from their time of entry in 2002 to their time of exit in 2006 (or before, for…

  15. AN INVESTIGATION ON RELATION AND PREDICTION OF PERCEIVED ORGANIZATIONAL SUPPORT (POS ACCORDING TO 15 FOLD ORGANIZATIONAL VARIABLES

    Directory of Open Access Journals (Sweden)

    Hassan DARVISH

    2012-01-01

    Full Text Available The aim of this article is to study the relation between perceived organizational support(POS with 15 fold variables including cooperation in decision making, servicing the public, job vision, trust to supervisor, satisfaction with salary, promotion opportunity, inner provocation, quality of supervising, desire to remain, leaving the job, organizational trust, job interest, satisfaction with supervisor and satisfaction with colleagues. In order to achieve the foregoing aim, there were 198 people selected from all employed personnel in Rahpooyan Company and answered the questionnaires. The document related to validity and reliability of this investigation means were in an acceptable level. The data collected from these questionnaires was analysed via coefficient of Pierson correlation, analysis of step by step regression, analysis of structural equation (path analysis. The results indicate there was a significant correlation between perceived organizational support and including cooperation in decision making, servicing the public, job vision, trust to supervisor, satisfaction with salary, promotion opportunity, quality of supervising, desire to remain, leaving the job, organizational trust, satisfaction with supervisor and satisfaction with colleagues. But there was no significant correlation between inner provocation and job interest with perceived organizational support. In analysis of step by step regression, it was also indicated that cooperation in decision making, promotion opportunity, trust to supervisor, job interest and organizational trust can specify about 56% of the perceived organizational support variance. The results of the path analysis also indicated that cooperation in decision making, promotion opportunity, trust to supervisor, job interest have a coefficient of direct path on perceived organizational support.

  16. Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ou, Yu-Yen

    2016-07-30

    Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. We used an independent data set to evaluate the performance of the proposed method, which had an accuracy of 69.84 %. We compared the performance of the proposed method in analyzing two newly discovered electron transport protein sequences with that of the general FAD binding predictor presented by Mishra and Raghava and determined that the accuracy of the proposed method improved by 9-45 % and its Matthew's correlation coefficient was 0.14-0.5. Furthermore, the proposed method enabled reducing the number of false positives significantly and can provide useful information for biologists. We developed a method that is based on PSSM profiles and SAAPs for identifying FAD binding sites in newly discovered electron transport protein sequences. This approach achieved a significant improvement after we added SAAPs to PSSM features to analyze FAD binding proteins in the electron transport chain. The proposed method can serve as an effective tool for predicting FAD binding sites in electron

  17. Diagnosis of the Asian summer monsoon variability and the climate prediction of monsoon precipitation via physical decomposition

    Science.gov (United States)

    Lim, Young-Kwon

    This study investigates the space-time evolution of the dominant modes that constitute the Asian summer monsoon (ASM), and, as an ultimate goal, the climate prediction of the ASM rainfall. Precipitation and other synoptic variables during the prominent life cycle of the ASM (May 21 to September 17) are used to show the detailed features of dominant modes, which are identified as the seasonal cycle, the ISO defined by the 40--50 day intraseasonal oscillation including the Madden-Julian oscillation, and the El Nino mode. The present study reveals that the ISO is the second largest component of the ASM rainfall variation. Correlation analysis indicates that ISO explains a larger fraction of the variance of the observed precipitation (without climatology) than the ENSO mode. The dominant ISO signal faithfully explains the northward propagation of the ISO toward the Asian continent causing intraseasonal active/break periods. The interannual variation of the ISO strength suggests that the ENSO exerts some influence on the ISO. The composite convective ISO anomaly and Kelvin-Rossby wave response over the Indian Ocean shows that the ISO tends to be stronger during the early stage of the ASM than normal in El Nino (La Nina) years, indicating greater (smaller) possibility of ISO-related extreme rainfall over India, Bangladesh, and the Bay of Bengal. The ENSO mode reveals that the following factors affect the evolution of the ASM system in El Nino (La Nina) years. (1) The anomalous sea surface temperature and sea level pressure over the Indian Ocean during the early stage of the ASM weaken (enhance) the meridional pressure gradient. (2) As a result, the westerly jet and the ensuing moisture transport toward India and the Bay of Bengal become weak (strong) and delayed (expedited), providing a less (more) favorable condition for regional monsoon onsets. (3) The Walker circulation anomaly results in an enhanced subsidence (ascent) and drought (flood) over the Maritime continent