Real-time predictive seasonal influenza model in Catalonia, Spain

PubMed Central

Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

2018-01-01

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

Using a Bayesian network to predict barrier island geomorphologic characteristics

USGS Publications Warehouse

Gutierrez, Ben; Plant, Nathaniel G.; Thieler, E. Robert; Turecek, Aaron

2015-01-01

Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.

Prediction of hypotension during spinal anesthesia for elective cesarean section by altered heart rate variability induced by postural change.

PubMed

Sakata, K; Yoshimura, N; Tanabe, K; Kito, K; Nagase, K; Iida, H

2017-02-01

Maternal hypotension is a common complication during cesarean section performed under spinal anesthesia. Changes in maternal heart rate with postural changes or values of heart rate variability have been reported to predict hypotension. Therefore, we hypothesized that changes in heart rate variability due to postural changes can predict hypotension. A total of 45 women scheduled to undergo cesarean section under spinal anesthesia were enrolled. A postural change test was performed the day before cesarean section. The ratio of the power of low and high frequency components contributing to heart rate variability was assessed in the order of supine, left lateral, and supine. Patients who exhibited a ⩾two-fold increase in the low-to-high frequency ratio when moving to supine from the lateral position were assigned to the postural change test-positive group. According to the findings of the postural change test, patients were assigned to the positive (n=22) and negative (n=23) groups, respectively. Hypotension occurred in 35/45 patients, of whom 21 (60%) were in the positive group and 14 (40%) were in the negative group. The incidence of hypotension was greater in the positive group (P<0.01). The total dose of ephedrine was greater in the positive group (15±11 vs. 7±7mg, P=0.005). The area under the receiver operating characteristic curve was 0.76 for the postural change test as a predictor of hypotension. The postural change test with heart rate variability analysis may be used to predict the risk of hypotension during spinal anesthesia for cesarean section. Copyright © 2016 Elsevier Ltd. All rights reserved.

Variability in case-mix adjusted in-hospital cardiac arrest rates.

PubMed

Merchant, Raina M; Yang, Lin; Becker, Lance B; Berg, Robert A; Nadkarni, Vinay; Nichol, Graham; Carr, Brendan G; Mitra, Nandita; Bradley, Steven M; Abella, Benjamin S; Groeneveld, Peter W

2012-02-01

It is unknown how in-hospital cardiac arrest (IHCA) rates vary across hospitals and predictors of variability. Measure variability in IHCA across hospitals and determine if hospital-level factors predict differences in case-mix adjusted event rates. Get with the Guidelines Resuscitation (GWTG-R) (n=433 hospitals) was used to identify IHCA events between 2003 and 2007. The American Hospital Association survey, Medicare, and US Census were used to obtain detailed information about GWTG-R hospitals. Adult patients with IHCA. Case-mix-adjusted predicted IHCA rates were calculated for each hospital and variability across hospitals was compared. A regression model was used to predict case-mix adjusted event rates using hospital measures of volume, nurse-to-bed ratio, percent intensive care unit beds, palliative care services, urban designation, volume of black patients, income, trauma designation, academic designation, cardiac surgery capability, and a patient risk score. We evaluated 103,117 adult IHCAs at 433 US hospitals. The case-mix adjusted IHCA event rate was highly variable across hospitals, median 1/1000 bed days (interquartile range: 0.7 to 1.3 events/1000 bed days). In a multivariable regression model, case-mix adjusted IHCA event rates were highest in urban hospitals [rate ratio (RR), 1.1; 95% confidence interval (CI), 1.0-1.3; P=0.03] and hospitals with higher proportions of black patients (RR, 1.2; 95% CI, 1.0-1.3; P=0.01) and lower in larger hospitals (RR, 0.54; 95% CI, 0.45-0.66; P<0.0001). Case-mix adjusted IHCA event rates varied considerably across hospitals. Several hospital factors associated with higher IHCA event rates were consistent with factors often linked with lower hospital quality of care.

THE INFLUENCE OF VARIABLE ELIMINATION RATE AND BODY FAT MASS IN A PBPK MODEL FOR TCDD IN PREDICTING THE SERUM TCDD CONCENTRATIONS FROM VETERANS OF OPERATION RANCH HAND

EPA Science Inventory

The Influence of Variable Elimination Rate and Body Fat Mass in a PBPK Model for TCDD in Predicting the Serum TCDD Concentrations from Veterans of Operation Ranch Hand.
C Emond1,2, LS Birnbaum2, JE Michalek3, MJ DeVito2
1 National Research Council, National Academy of Scien...

Predicting national suicide numbers with social media data.

PubMed

Won, Hong-Hee; Myung, Woojae; Song, Gil-Young; Lee, Won-Hee; Kim, Jong-Won; Carroll, Bernard J; Kim, Doh Kwan

2013-01-01

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors - consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

Predicting National Suicide Numbers with Social Media Data

PubMed Central

Won, Hong-Hee; Song, Gil-Young; Lee, Won-Hee; Kim, Jong-Won; Carroll, Bernard J.

2013-01-01

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention. PMID:23630615

Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction

NASA Technical Reports Server (NTRS)

Makikallio, T. H.; Seppanen, T.; Airaksinen, K. E.; Koistinen, J.; Tulppo, M. P.; Peng, C. K.; Goldberger, A. L.; Huikuri, H. V.

1997-01-01

Dynamics analysis of RR interval behavior and traditional measures of heart rate variability were compared between postinfarction patients with and without vulnerability to ventricular tachyarrhythmias in a case-control study. Short-term fractal correlation of heart rate dynamics was better than traditional measures of heart rate variability in differentiating patients with and without life-threatening arrhythmias.

Ventricular arrhythmias and changes in heart rate preceding ventricular tachycardia in patients with an implantable cardioverter defibrillator.

PubMed

Lerma, Claudia; Wessel, Niels; Schirdewan, Alexander; Kurths, Jürgen; Glass, Leon

2008-07-01

The objective was to determine the characteristics of heart rate variability and ventricular arrhythmias prior to the onset of ventricular tachycardia (VT) in patients with an implantable cardioverter defibrillator (ICD). Sixty-eight beat-to-beat time series from 13 patients with an ICD were analyzed to quantify heart rate variability and ventricular arrhythmias. The episodes of VT were classified in one of two groups depending on whether the sinus rate in the 1 min preceding the VT was greater or less than 90 beats per minute. In a subset of patients, increased heart rate and reduced heart rate variability was often observed up to 20 min prior to the VT. There was a non-significant trend to higher incidence of premature ventricular complexes (PVCs) before VT compared to control recordings. The patterns of the ventricular arrhythmias were highly heterogeneous among different patients and even within the same patient. Analysis of the changes of heart rate and heart rate variability may have predictive value about the onset of VT in selected patients. The patterns of ventricular arrhythmia could not be used to predict onset of VT in this group of patients.

Event rate and reaction time performance in ADHD: Testing predictions from the state regulation deficit hypothesis using an ex-Gaussian model.

PubMed

Metin, Baris; Wiersema, Jan R; Verguts, Tom; Gasthuys, Roos; van Der Meere, Jacob J; Roeyers, Herbert; Sonuga-Barke, Edmund

2016-01-01

According to the state regulation deficit (SRD) account, ADHD is associated with a problem using effort to maintain an optimal activation state under demanding task settings such as very fast or very slow event rates. This leads to a prediction of disrupted performance at event rate extremes reflected in higher Gaussian response variability that is a putative marker of activation during motor preparation. In the current study, we tested this hypothesis using ex-Gaussian modeling, which distinguishes Gaussian from non-Gaussian variability. Twenty-five children with ADHD and 29 typically developing controls performed a simple Go/No-Go task under four different event-rate conditions. There was an accentuated quadratic relationship between event rate and Gaussian variability in the ADHD group compared to the controls. The children with ADHD had greater Gaussian variability at very fast and very slow event rates but not at moderate event rates. The results provide evidence for the SRD account of ADHD. However, given that this effect did not explain all group differences (some of which were independent of event rate) other cognitive and/or motivational processes are also likely implicated in ADHD performance deficits.

Prenatal Antecedents of Newborn Neurological Maturation

PubMed Central

DiPietro, Janet A.; Kivlighan, Katie T.; Costigan, Kathleen A.; Rubin, Suzanne E.; Shiffler, Dorothy E.; Henderson, Janice L.; Pillion, Joseph P.

2009-01-01

Fetal neurobehavioral development was modeled longitudinally using data collected at weekly intervals from 24- to -38 weeks gestation in a sample of 112 healthy pregnancies. Predictive associations between 3 measures of fetal neurobehavioral functioning and their developmental trajectories to neurological maturation in the 1st weeks after birth were examined. Prenatal measures included fetal heart rate variability, fetal movement, and coupling between fetal motor activity and heart rate patterning; neonatal outcomes include a standard neurologic examination (n = 97) and brainstem auditory evoked potential (BAEP; n = 47). Optimality in newborn motor activity and reflexes was predicted by fetal motor activity; fetal heart rate variability and somatic-cardiac coupling predicted BAEP parameters. Maternal pregnancy-specific psychological stress was associated with accelerated neurologic maturation. PMID:20331657

Predicting the Spatial Distribution of Aspen Growth Potential in the Upper Great Lakes Region

Treesearch

Eric J. Gustafson; Sue M. Lietz; John L. Wright

2003-01-01

One way to increase aspen yields is to produce aspen on sites where aspen growth potential is highest. Aspen growth rates are typically predicted using site index, but this is impractical for landscape-level assessments. We tested the hypothesis that aspen growth can be predicted from site and climate variables and generated a model to map the spatial variability of...

Predictive Variables of Half-Marathon Performance for Male Runners

PubMed Central

Gómez-Molina, Josué; Ogueta-Alday, Ana; Camara, Jesus; Stickley, Christoper; Rodríguez-Marroyo, José A.; García-López, Juan

2017-01-01

The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO2max, speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance. Key points The present study obtained four equations involving anthropometric, training, physiological and biomechanical variables to estimate half-marathon performance. These equations were validated in a different population, demonstrating narrows ranges of prediction than previous studies and also their consistency. As a novelty, some biomechanical variables (i.e. step length and step rate at RCT, and maximal step length) have been related to half-marathon performance. PMID:28630571

Environmental stochasticity controls soil erosion variability

PubMed Central

Kim, Jongho; Ivanov, Valeriy Y.; Fatichi, Simone

2016-01-01

Understanding soil erosion by water is essential for a range of research areas but the predictive skill of prognostic models has been repeatedly questioned because of scale limitations of empirical data and the high variability of soil loss across space and time scales. Improved understanding of the underlying processes and their interactions are needed to infer scaling properties of soil loss and better inform predictive methods. This study uses data from multiple environments to highlight temporal-scale dependency of soil loss: erosion variability decreases at larger scales but the reduction rate varies with environment. The reduction of variability of the geomorphic response is attributed to a ‘compensation effect’: temporal alternation of events that exhibit either source-limited or transport-limited regimes. The rate of reduction is related to environment stochasticity and a novel index is derived to reflect the level of variability of intra- and inter-event hydrometeorologic conditions. A higher stochasticity index implies a larger reduction of soil loss variability (enhanced predictability at the aggregated temporal scales) with respect to the mean hydrologic forcing, offering a promising indicator for estimating the degree of uncertainty of erosion assessments. PMID:26925542

A Prediction Model for Community Colleges Using Graduation Rate as the Performance Indicator

ERIC Educational Resources Information Center

Moosai, Susan

2010-01-01

In this thesis a prediction model using graduation rate as the performance indicator is obtained for community colleges for three cohort years, 2003, 2004, and 2005 in the states of California, Florida, and Michigan. Multiple Regression analysis, using an aggregate of seven predictor variables, was employed in determining this prediction model.…

An Analysis on the Unemployment Rate in the Philippines: A Time Series Data Approach

NASA Astrophysics Data System (ADS)

Urrutia, J. D.; Tampis, R. L.; E Atienza, JB

2017-03-01

This study aims to formulate a mathematical model for forecasting and estimating unemployment rate in the Philippines. Also, factors which can predict the unemployment is to be determined among the considered variables namely Labor Force Rate, Population, Inflation Rate, Gross Domestic Product, and Gross National Income. Granger-causal relationship and integration among the dependent and independent variables are also examined using Pairwise Granger-causality test and Johansen Cointegration Test. The data used were acquired from the Philippine Statistics Authority, National Statistics Office, and Bangko Sentral ng Pilipinas. Following the Box-Jenkins method, the formulated model for forecasting the unemployment rate is SARIMA (6, 1, 5) × (0, 1, 1)4 with a coefficient of determination of 0.79. The actual values are 99 percent identical to the predicted values obtained through the model, and are 72 percent closely relative to the forecasted ones. According to the results of the regression analysis, Labor Force Rate and Population are the significant factors of unemployment rate. Among the independent variables, Population, GDP, and GNI showed to have a granger-causal relationship with unemployment. It is also found that there are at least four cointegrating relations between the dependent and independent variables.

The Dubious Utility of the Value-Added Concept in Higher Education: The Case of Accounting

ERIC Educational Resources Information Center

Yunker, J.A.

2005-01-01

Using data on CPA exam pass rates and various institutional variables, this research examines the potential usefulness of the value-added concept in accounting higher education. For a sample of 548 US colleges and universities, predicted pass rates were computed from regression equations relating observed pass rates to institutional variables. The…

Evaluating the Bias of Alternative Cost Progress Models: Tests Using Aerospace Industry Acquisition Programs

DTIC Science & Technology

1992-12-01

suspect :mat, -n2 extent predict:.on cas jas ccsiziveiv crrei:=e amonc e v:arious models, :he fandom *.;aik, learn ha r ur e, i;<ea- variable and Bemis...Functions, Production Rate Adjustment Model, Learning Curve Model. Random Walk Model. Bemis Model. Evaluating Model Bias, Cost Prediction Bias. Cost...of four cost progress models--a random walk model, the tradiuonai learning curve model, a production rate model Ifixed-variable model). and a model

Speed and Cardiac Recovery Variables Predict the Probability of Elimination in Equine Endurance Events

PubMed Central

Younes, Mohamed; Robert, Céline; Cottin, François; Barrey, Eric

2015-01-01

Nearly 50% of the horses participating in endurance events are eliminated at a veterinary examination (a vet gate). Detecting unfit horses before a health problem occurs and treatment is required is a challenge for veterinarians but is essential for improving equine welfare. We hypothesized that it would be possible to detect unfit horses earlier in the event by measuring heart rate recovery variables. Hence, the objective of the present study was to compute logistic regressions of heart rate, cardiac recovery time and average speed data recorded at the previous vet gate (n-1) and thus predict the probability of elimination during successive phases (n and following) in endurance events. Speed and heart rate data were extracted from an electronic database of endurance events (80–160 km in length) organized in four countries. Overall, 39% of the horses that started an event were eliminated—mostly due to lameness (64%) or metabolic disorders (15%). For each vet gate, logistic regressions of explanatory variables (average speed, cardiac recovery time and heart rate measured at the previous vet gate) and categorical variables (age and/or event distance) were computed to estimate the probability of elimination. The predictive logistic regressions for vet gates 2 to 5 correctly classified between 62% and 86% of the eliminated horses. The robustness of these results was confirmed by high areas under the receiving operating characteristic curves (0.68–0.84). Overall, a horse has a 70% chance of being eliminated at the next gate if its cardiac recovery time is longer than 11 min at vet gate 1 or 2, or longer than 13 min at vet gates 3 or 4. Heart rate recovery and average speed variables measured at the previous vet gate(s) enabled us to predict elimination at the following vet gate. These variables should be checked at each veterinary examination, in order to detect unfit horses as early as possible. Our predictive method may help to improve equine welfare and ethical considerations in endurance events. PMID:26322506

Speed and Cardiac Recovery Variables Predict the Probability of Elimination in Equine Endurance Events.

PubMed

Younes, Mohamed; Robert, Céline; Cottin, François; Barrey, Eric

2015-01-01

Nearly 50% of the horses participating in endurance events are eliminated at a veterinary examination (a vet gate). Detecting unfit horses before a health problem occurs and treatment is required is a challenge for veterinarians but is essential for improving equine welfare. We hypothesized that it would be possible to detect unfit horses earlier in the event by measuring heart rate recovery variables. Hence, the objective of the present study was to compute logistic regressions of heart rate, cardiac recovery time and average speed data recorded at the previous vet gate (n-1) and thus predict the probability of elimination during successive phases (n and following) in endurance events. Speed and heart rate data were extracted from an electronic database of endurance events (80-160 km in length) organized in four countries. Overall, 39% of the horses that started an event were eliminated--mostly due to lameness (64%) or metabolic disorders (15%). For each vet gate, logistic regressions of explanatory variables (average speed, cardiac recovery time and heart rate measured at the previous vet gate) and categorical variables (age and/or event distance) were computed to estimate the probability of elimination. The predictive logistic regressions for vet gates 2 to 5 correctly classified between 62% and 86% of the eliminated horses. The robustness of these results was confirmed by high areas under the receiving operating characteristic curves (0.68-0.84). Overall, a horse has a 70% chance of being eliminated at the next gate if its cardiac recovery time is longer than 11 min at vet gate 1 or 2, or longer than 13 min at vet gates 3 or 4. Heart rate recovery and average speed variables measured at the previous vet gate(s) enabled us to predict elimination at the following vet gate. These variables should be checked at each veterinary examination, in order to detect unfit horses as early as possible. Our predictive method may help to improve equine welfare and ethical considerations in endurance events.

Prediction by data mining, of suicide attempts in Korean adolescents: a national study

PubMed Central

Bae, Sung Man; Lee, Seung A; Lee, Seung-Hwan

2015-01-01

Objective This study aimed to develop a prediction model for suicide attempts in Korean adolescents. Methods We conducted a decision tree analysis of 2,754 middle and high school students nationwide. We fixed suicide attempt as the dependent variable and eleven sociodemographic, intrapersonal, and extrapersonal variables as independent variables. Results The rate of suicide attempts of the total sample was 9.5%, and severity of depression was the strongest variable to predict suicide attempt. The rates of suicide attempts in the depression and potential depression groups were 5.4 and 2.8 times higher than that of the non-depression group. In the depression group, the most powerful factor to predict a suicide attempt was delinquency, and the rate of suicide attempts in those in the depression group with higher delinquency was two times higher than in those in the depression group with lower delinquency. Of special note, the rate of suicide attempts in the depressed females with higher delinquency was the highest. Interestingly, in the potential depression group, the most impactful factor to predict a suicide attempt was intimacy with family, and the rate of suicide attempts of those in the potential depression group with lower intimacy with family was 2.4 times higher than that of those in the potential depression group with higher intimacy with family. And, among the potential depression group, middle school students with lower intimacy with family had a 2.5-times higher rate of suicide attempts than high school students with lower intimacy with family. Finally, in the non-depression group, stress level was the most powerful factor to predict a suicide attempt. Among the non-depression group, students who reported high levels of stress showed an 8.3-times higher rate of suicide attempts than students who reported average levels of stress. Discussion Based on the results, we especially need to pay attention to depressed females with higher delinquency and those with potential depression with lower intimacy with family to prevent suicide attempts in teenagers. PMID:26396521

Dearth by a Thousand Cuts? Accounting for Gender Differences in Top-Ranked Publication Rates in Social Psychology.

PubMed

Cikara, Mina; Rudman, Laurie; Fiske, Susan

2012-01-01

Publication in the Journal of Personality and Social Psychology , a flagship indicator of scientific prestige, shows dramatic gender disparities. A bibliometric analysis included yoked-control authors matched for Ph.D. prestige and cohort. Though women publish less, at slower annual rates, they are more cited in handbooks and textbooks per JPSP -article-published. No gender differences emerged on variables reflecting differential qualifications. Many factors explain gender discrepancy in productivity. Among top publishers, per-year rate and first authorship especially differ by gender; rate uniquely predicts top-male productivity, whereas career-length uniquely predicts top-female productivity. Among men, across top-publishers and controls, productivity correlates uniquely with editorial negotiating and being married. For women, no personal variables predict productivity. A separate inquiry shows tiny gender differences in acceptance rates per JPSP article submitted; discrimination would be a small-but-plausible contributor, absent independent indicators of manuscript quality. Recent productivity rates mirror earlier gender disparities, suggesting gender gaps will continue.

Individual laboratory-measured discount rates predict field behavior

PubMed Central

Chabris, Christopher F.; Laibson, David; Morris, Carrie L.; Schuldt, Jonathon P.; Taubinsky, Dmitry

2009-01-01

We estimate discount rates of 555 subjects using a laboratory task and find that these individual discount rates predict inter-individual variation in field behaviors (e.g., exercise, BMI, smoking). The correlation between the discount rate and each field behavior is small: none exceeds 0.28 and many are near 0. However, the discount rate has at least as much predictive power as any variable in our dataset (e.g., sex, age, education). The correlation between the discount rate and field behavior rises when field behaviors are aggregated: these correlations range from 0.09-0.38. We present a model that explains why specific intertemporal choice behaviors are only weakly correlated with discount rates, even though discount rates robustly predict aggregates of intertemporal decisions. PMID:19412359

Individual laboratory-measured discount rates predict field behavior.

PubMed

Chabris, Christopher F; Laibson, David; Morris, Carrie L; Schuldt, Jonathon P; Taubinsky, Dmitry

2008-12-01

We estimate discount rates of 555 subjects using a laboratory task and find that these individual discount rates predict inter-individual variation in field behaviors (e.g., exercise, BMI, smoking). The correlation between the discount rate and each field behavior is small: none exceeds 0.28 and many are near 0. However, the discount rate has at least as much predictive power as any variable in our dataset (e.g., sex, age, education). The correlation between the discount rate and field behavior rises when field behaviors are aggregated: these correlations range from 0.09-0.38. We present a model that explains why specific intertemporal choice behaviors are only weakly correlated with discount rates, even though discount rates robustly predict aggregates of intertemporal decisions.

Relationships between ecosystem metabolism, benthic macroinvertebrate densities, and environmental variables in a sub-arctic Alaskan river

USGS Publications Warehouse

Benson, Emily R.; Wipfli, Mark S.; Clapcott, Joanne E.; Hughes, Nicholas F.

2013-01-01

Relationships between environmental variables, ecosystem metabolism, and benthos are not well understood in sub-arctic ecosystems. The goal of this study was to investigate environmental drivers of river ecosystem metabolism and macroinvertebrate density in a sub-arctic river. We estimated primary production and respiration rates, sampled benthic macroinvertebrates, and monitored light intensity, discharge rate, and nutrient concentrations in the Chena River, interior Alaska, over two summers. We employed Random Forests models to identify predictor variables for metabolism rates and benthic macroinvertebrate density and biomass, and calculated Spearman correlations between in-stream nutrient levels and metabolism rates. Models indicated that discharge and length of time between high water events were the most important factors measured for predicting metabolism rates. Discharge was the most important variable for predicting benthic macroinvertebrate density and biomass. Primary production rate peaked at intermediate discharge, respiration rate was lowest at the greatest time since last high water event, and benthic macroinvertebrate density was lowest at high discharge rates. The ratio of dissolved inorganic nitrogen to soluble reactive phosphorus ranged from 27:1 to 172:1. We found that discharge plays a key role in regulating stream ecosystem metabolism, but that low phosphorous levels also likely limit primary production in this sub-arctic stream.

Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation

NASA Technical Reports Server (NTRS)

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.

A comparison of acoustic and observed sediment classifications as predictor variables for modelling biotope distributions in Galway Bay, Ireland

NASA Astrophysics Data System (ADS)

O'Carroll, Jack P. J.; Kennedy, Robert; Ren, Lei; Nash, Stephen; Hartnett, Michael; Brown, Colin

2017-10-01

The INFOMAR (Integrated Mapping For the Sustainable Development of Ireland's Marine Resource) initiative has acoustically mapped and classified a significant proportion of Ireland's Exclusive Economic Zone (EEZ), and is likely to be an important tool in Ireland's efforts to meet the criteria of the MSFD. In this study, open source and relic data were used in combination with new grab survey data to model EUNIS level 4 biotope distributions in Galway Bay, Ireland. The correct prediction rates of two artificial neural networks (ANNs) were compared to assess the effectiveness of acoustic sediment classifications versus sediments that were visually classified by an expert in the field as predictor variables. To test for autocorrelation between predictor variables the RELATE routine with Spearman rank correlation method was used. Optimal models were derived by iteratively removing predictor variables and comparing the correct prediction rates of each model. The models with the highest correct prediction rates were chosen as optimal. The optimal models each used a combination of salinity (binary; 0 = polyhaline and 1 = euhaline), proximity to reef (binary; 0 = within 50 m and 1 = outside 50 m), depth (continuous; metres) and a sediment descriptor (acoustic or observed) as predictor variables. As the status of benthic habitats is required to be assessed under the MSFD the Ecological Status (ES) of the subtidal sediments of Galway Bay was also assessed using the Infaunal Quality Index. The ANN that used observed sediment classes as predictor variables could correctly predict the distribution of biotopes 67% of the time, compared to 63% for the ANN using acoustic sediment classes. Acoustic sediment ANN predictions were affected by local sediment heterogeneity, and the lack of a mixed sediment class. The all-round poor performance of ANNs is likely to be a result of the temporally variable and sparsely distributed data within the study area.

Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

PubMed Central

2014-01-01

Introduction Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown. Methods We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models. Results Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation failure of 3.0 (95% confidence interval (CI) 1.2 to 5.2) and 3.5 (95% CI 1.9 to 5.4), respectively. Conclusions Altered HRV and RRV (during the SBT prior to extubation) are significantly associated with extubation failure. A predictive model using RRV during the last SBT provided optimal accuracy of prediction in all patients, with improved accuracy when combined with clinical impression or RSBI. This model requires a validation cohort to evaluate accuracy and generalizability. Trial registration ClinicalTrials.gov NCT01237886. Registered 13 October 2010. PMID:24713049

Artificial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolithotomy.

PubMed

Aminsharifi, Alireza; Irani, Dariush; Pooyesh, Shima; Parvin, Hamid; Dehghani, Sakineh; Yousofi, Khalilolah; Fazel, Ebrahim; Zibaie, Fatemeh

2017-05-01

To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome variable. During the study period, all adult patients who underwent PCNL at our institute were enrolled in the study. Preoperative and postoperative variables were recorded, and stone-free status was assessed perioperatively with computed tomography scans. MATLAB software was used to design and train the network in a feed forward back-propagation error adjustment scheme. Preoperative and postoperative data from 200 patients (training set) were used to analyze the effect and relative relevance of preoperative values on postoperative parameters. The validated adequately trained ANN was used to predict postoperative outcomes in the subsequent 254 adult patients (test set) whose preoperative values were serially fed into the system. To evaluate system accuracy in predicting each postoperative variable, predicted values were compared with actual outcomes. Two hundred fifty-four patients (155 [61%] males) were considered the test set. Mean stone burden was 6702.86 ± 381.6 mm 3 . Overall stone-free rate was 76.4%. Fifty-four out of 254 patients (21.3%) required ancillary procedures (shockwave lithotripsy 5.9%, transureteral lithotripsy 10.6%, and repeat PCNL 4.7%). The accuracy and sensitivity of the system in predicting different postoperative variables ranged from 81.0% to 98.2%. As a complex nonlinear mathematical model, our ANN system is an interconnected data mining tool, which prospectively analyzes and "learns" the relationships between variables. The accuracy and sensitivity of the system for predicting the stone-free rate, the need for blood transfusion, and post-PCNL ancillary procedures ranged from 81.0% to 98.2%.The stone burden and the stone morphometry were among the most significant preoperative characteristics that affected all postoperative outcome variables and they received the highest relative weight by the ANN system.

Deriving the Intrahepatic Arteriovenous Shunt Rate from CT Images and Biochemical Data Instead of from Arterial Perfusion Scintigraphy in Hepatic Arterial Infusion Chemotherapy

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

Ozaki, Toshiro, E-mail: ganronbun@amail.plala.or.jp; Seki, Hiroshi; Shiina, Makoto

2009-09-15

The purpose of the present study was to elucidate a method for predicting the intrahepatic arteriovenous shunt rate from computed tomography (CT) images and biochemical data, instead of from arterial perfusion scintigraphy, because adverse exacerbated systemic effects may be induced in cases where a high shunt rate exists. CT and arterial perfusion scintigraphy were performed in patients with liver metastases from gastric or colorectal cancer. Biochemical data and tumor marker levels of 33 enrolled patients were measured. The results were statistically verified by multiple regression analysis. The total metastatic hepatic tumor volume (V{sub metastasized}), residual hepatic parenchyma volume (V{sub residual};more » calculated from CT images), and biochemical data were treated as independent variables; the intrahepatic arteriovenous (IHAV) shunt rate (calculated from scintigraphy) was treated as a dependent variable. The IHAV shunt rate was 15.1 {+-} 11.9%. Based on the correlation matrixes, the best correlation coefficient of 0.84 was established between the IHAV shunt rate and V{sub metastasized} (p < 0.01). In the multiple regression analysis with the IHAV shunt rate as the dependent variable, the coefficient of determination (R{sup 2}) was 0.75, which was significant at the 0.1% level with two significant independent variables (V{sub metastasized} and V{sub residual}). The standardized regression coefficients ({beta}) of V{sub metastasized} and V{sub residual} were significant at the 0.1 and 5% levels, respectively. Based on this result, we can obtain a predicted value of IHAV shunt rate (p < 0.001) using CT images. When a high shunt rate was predicted, beneficial and consistent clinical monitoring can be initiated in, for example, hepatic arterial infusion chemotherapy.« less

Why significant variables aren't automatically good predictors.

PubMed

Lo, Adeline; Chernoff, Herman; Zheng, Tian; Lo, Shaw-Hwa

2015-11-10

Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.

The Prediction of Labor Force Status: Implications from International Adult Skill Assessments. Research Report. ETS RR-16-11

ERIC Educational Resources Information Center

Li, Tongyun; von Davier, Matthias; Hancock, Gregory R.

2016-01-01

This report investigates the prediction of labor force status using observed variables, such as gender, age, and immigrant status, and more importantly, measured skill variables, including literacy proficiency and a categorical rating of educational attainment based on the 1994 International Adult Literacy Survey (IALS), the 2003 Adult Literacy…

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

PubMed

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.

Comparison of TID Effects in Space-Like Variable Dose Rates and Constant Dose Rates

NASA Technical Reports Server (NTRS)

Harris, Richard D.; McClure, Steven S.; Rax, Bernard G.; Evans, Robin W.; Jun, Insoo

2008-01-01

The degradation of the LM193 dual voltage comparator has been studied at different TID dose rate profiles, including several different constant dose rates and a variable dose rate that simulates the behavior of a solar flare. A comparison of results following constant dose rate vs. variable dose rates is made to explore how well the constant dose rates used for typical part testing predict the performance during a simulated space-like mission. Testing at a constant dose rate equal to the lowest dose rate seen during the simulated flare provides an extremely conservative estimate of the overall amount of degradation. A constant dose rate equal to the average dose rate is also more conservative than the variable rate. It appears that, for this part, weighting the dose rates by the amount of total dose received at each rate (rather than the amount of time at each dose rate) results in an average rate that produces an amount of degradation that is a reasonable approximation to that received by the variable rate.

Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge.

PubMed

Steinman, David A; Hoi, Yiemeng; Fahy, Paul; Morris, Liam; Walsh, Michael T; Aristokleous, Nicolas; Anayiotos, Andreas S; Papaharilaou, Yannis; Arzani, Amirhossein; Shadden, Shawn C; Berg, Philipp; Janiga, Gábor; Bols, Joris; Segers, Patrick; Bressloff, Neil W; Cibis, Merih; Gijsen, Frank H; Cito, Salvatore; Pallarés, Jordi; Browne, Leonard D; Costelloe, Jennifer A; Lynch, Adrian G; Degroote, Joris; Vierendeels, Jan; Fu, Wenyu; Qiao, Aike; Hodis, Simona; Kallmes, David F; Kalsi, Hardeep; Long, Quan; Kheyfets, Vitaly O; Finol, Ender A; Kono, Kenichi; Malek, Adel M; Lauric, Alexandra; Menon, Prahlad G; Pekkan, Kerem; Esmaily Moghadam, Mahdi; Marsden, Alison L; Oshima, Marie; Katagiri, Kengo; Peiffer, Véronique; Mohamied, Yumnah; Sherwin, Spencer J; Schaller, Jens; Goubergrits, Leonid; Usera, Gabriel; Mendina, Mariana; Valen-Sendstad, Kristian; Habets, Damiaan F; Xiang, Jianping; Meng, Hui; Yu, Yue; Karniadakis, George E; Shaffer, Nicholas; Loth, Francis

2013-02-01

Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.

Rate dependent fractionation of sulfur isotopes in through-flowing systems

NASA Astrophysics Data System (ADS)

Giannetta, M.; Sanford, R. A.; Druhan, J. L.

2017-12-01

The fidelity of reactive transport models in quantifying microbial activity in the subsurface is often improved through the use stable isotopes. However, the accuracy of current predictions for microbially mediated isotope fractionations within open through-flowing systems typically depends on nutrient availability. This disparity arises from the common application of a single `effective' fractionation factor assigned to a given system, despite extensive evidence for variability in the fractionation factor between eutrophic environments and many naturally occurring, nutrient-limited environments. Here, we demonstrate a reactive transport model with the capacity to simulate a variable fractionation factor over a range of microbially mediated reduction rates and constrain the model with experimental data for nutrient limited conditions. Two coupled isotope-specific Monod rate laws for 32S and 34S, constructed to quantify microbial sulfate reduction and predict associated S isotope partitioning, were parameterized using a series of batch reactor experiments designed to minimize microbial growth. In the current study, we implement these parameterized isotope-specific rate laws within an open, through-flowing system to predict variable fractionation with distance as a function of sulfate reduction rate. These predictions are tested through a supporting laboratory experiment consisting of a flow-through column packed with homogenous porous media inoculated with the same species of sulfate reducing bacteria used in the previous batch reactors, Desulfovibrio vulgaris. The collective results of batch reactor and flow-through column experiments support a significant improvement for S isotope predictions in isotope-sensitive multi-component reactive transport models through treatment of rate-dependent fractionation. Such an update to the model will better equip reactive transport software for isotope informed characterization of microbial activity within energy and nutrient limited environments.

Alcohol use among university students: Considering a positive deviance approach.

PubMed

Tucker, Maryanne; Harris, Gregory E

2016-09-01

Harmful alcohol consumption among university students continues to be a significant issue. This study examined whether variables identified in the positive deviance literature would predict responsible alcohol consumption among university students. Surveyed students were categorized into three groups: abstainers, responsible drinkers and binge drinkers. Multinomial logistic regression modelling was significant (χ(2) = 274.49, degrees of freedom = 24, p < .001), with several variables predicting group membership. While the model classification accuracy rate (i.e. 71.2%) exceeded the proportional by chance accuracy rate (i.e. 38.4%), providing further support for the model, the model itself best predicted binge drinker membership over the other two groups. © The Author(s) 2015.

Occurrence and in-stream attenuation of wastewater-derived pharmaceuticals in Iberian rivers.

PubMed

Acuña, Vicenç; von Schiller, Daniel; García-Galán, Maria Jesús; Rodríguez-Mozaz, Sara; Corominas, Lluís; Petrovic, Mira; Poch, Manel; Barceló, Damià; Sabater, Sergi

2015-01-15

A multitude of pharmaceuticals enter surface waters via discharges of wastewater treatment plants (WWTPs), and many raise environmental and health concerns. Chemical fate models predict their concentrations using estimates of mass loading, dilution and in-stream attenuation. However, current comprehension of the attenuation rates remains a limiting factor for predictive models. We assessed in-stream attenuation of 75 pharmaceuticals in 4 river segments, aiming to characterize in-stream attenuation variability among different pharmaceutical compounds, as well as among river segments differing in environmental conditions. Our study revealed that in-stream attenuation was highly variable among pharmaceuticals and river segments and that none of the considered pharmaceutical physicochemical and molecular properties proved to be relevant in determining the mean attenuation rates. Instead, the octanol-water partition coefficient (Kow) influenced the variability of rates among river segments, likely due to its effect on sorption to sediments and suspended particles, and therefore influencing the balance between the different attenuation mechanisms (biotransformation, photolysis, sorption, and volatilization). The magnitude of the measured attenuation rates urges scientists to consider them as important as dilution when aiming to predict concentrations in freshwater ecosystems. Copyright © 2014 Elsevier B.V. All rights reserved.

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.

PubMed

Bertl, Johanna; Guo, Qianyun; Juul, Malene; Besenbacher, Søren; Nielsen, Morten Muhlig; Hornshøj, Henrik; Pedersen, Jakob Skou; Hobolth, Asger

2018-04-19

Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

Ecological impacts and management strategies for western larch in the face of climate-change

Treesearch

Gerald E. Rehfeldt; Barry C. Jaquish

2010-01-01

Approximately 185,000 forest inventory and ecological plots from both USA and Canada were used to predict the contemporary distribution of western larch (Larix occidentalis Nutt.) from climate variables. The random forests algorithm, using an 8-variable model, produced an overall error rate of about 2.9 %, nearly all of which consisted of predicting presence at...

Impacts of Austrian Climate Variability on Honey Bee Mortality

NASA Astrophysics Data System (ADS)

Switanek, Matt; Brodschneider, Robert; Crailsheim, Karl; Truhetz, Heimo

2015-04-01

Global food production, as it is today, is not possible without pollinators such as the honey bee. It is therefore alarming that honey bee populations across the world have seen increased mortality rates in the last few decades. The challenges facing the honey bee calls into question the future of our food supply. Beside various infectious diseases, Varroa destructor is one of the main culprits leading to increased rates of honey bee mortality. Varroa destructor is a parasitic mite which strongly depends on honey bee brood for reproduction and can wipe out entire colonies. However, climate variability may also importantly influence honey bee breeding cycles and bee mortality rates. Persistent weather events affects vegetation and hence foraging possibilities for honey bees. This study first defines critical statistical relationships between key climate indicators (e.g., precipitation and temperature) and bee mortality rates across Austria, using 6 consecutive years of data. Next, these leading indicators, as they vary in space and time, are used to build a statistical model to predict bee mortality rates and the respective number of colonies affected. Using leave-one-out cross validation, the model reduces the Root Mean Square Error (RMSE) by 21% with respect to predictions made with the mean mortality rate and the number of colonies. Furthermore, a Monte Carlo test is used to establish that the model's predictions are statistically significant at the 99.9% confidence level. These results highlight the influence of climate variables on honey bee populations, although variability in climate, by itself, cannot fully explain colony losses. This study was funded by the Austrian project 'Zukunft Biene'.

Predicting Positive Outcomes for Students with Emotional Disturbance

ERIC Educational Resources Information Center

Nickerson, Amanda B.; Brosof, Amy M.; Shapiro, Valerie B.

2004-01-01

This longitudinal study assessed changes in skills for students with emotional disturbance (ED) over a one-year time period in a private special education school and examined variables that predicted positive outcomes for these students. At Time 1, teachers rated 84 students with ED using standardized behavior rating scales to assess problem…

A probabilistic fatigue analysis of multiple site damage

NASA Technical Reports Server (NTRS)

Rohrbaugh, S. M.; Ruff, D.; Hillberry, B. M.; Mccabe, G.; Grandt, A. F., Jr.

1994-01-01

The variability in initial crack size and fatigue crack growth is incorporated in a probabilistic model that is used to predict the fatigue lives for unstiffened aluminum alloy panels containing multiple site damage (MSD). The uncertainty of the damage in the MSD panel is represented by a distribution of fatigue crack lengths that are analytically derived from equivalent initial flaw sizes. The variability in fatigue crack growth rate is characterized by stochastic descriptions of crack growth parameters for a modified Paris crack growth law. A Monte-Carlo simulation explicitly describes the MSD panel by randomly selecting values from the stochastic variables and then grows the MSD cracks with a deterministic fatigue model until the panel fails. Different simulations investigate the influences of the fatigue variability on the distributions of remaining fatigue lives. Six cases that consider fixed and variable conditions of initial crack size and fatigue crack growth rate are examined. The crack size distribution exhibited a dominant effect on the remaining fatigue life distribution, and the variable crack growth rate exhibited a lesser effect on the distribution. In addition, the probabilistic model predicted that only a small percentage of the life remains after a lead crack develops in the MSD panel.

Singers' phonation threshold pressure and ratings of self-perceived effort on vocal tasks.

PubMed

McHenry, Monica; Evans, Joseph; Powitzky, Eric

2013-05-01

This study was designed to determine if singers' self-ratings of vocal effort could predict phonation threshold pressure (PTP). It was hypothesized that effort ratings on the more complex task of singing "Happy Birthday" would best predict PTP. A multiple regression analysis was performed with PTP as the predicted variable and self-ratings on four phonatory tasks as the predictor variables. Participants were 48 undergraduate and graduate students majoring in vocal performance. They produced /pi/ syllable trains as softly as possible for the measurement of PTP. They then rated their self-perceived vocal effort while softly producing the following: (1) sustained "ah" (comfortable, midrange pitch); (2) "ah" glide (chest to head voice); (3) Staccato "ah" in head voice (not falsetto); and (4) Happy Birthday in head voice (not falsetto). No ratings of vocal effort predicted PTP. The lack of correlation between PTP and ratings of Happy Birthday remained when separately evaluating graduate versus undergraduate students or males versus females. Informal evaluation of repeated ratings over time suggested the potential for effective self-monitoring. Students' ratings of self-perceived vocal effort were poor predictors of PTP. This may be because of the use of "effortless" imagery during singing instruction or consistent positive feedback regarding vocal performance. It is possible that self-rating could become an effective tool to predict vocal health if task elicitation instructions were more precise, and the student and voice teacher worked collaboratively to improve self-evaluation. Copyright © 2013 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

Rates of projected climate change dramatically exceed past rates of climatic niche evolution among vertebrate species.

PubMed

Quintero, Ignacio; Wiens, John J

2013-08-01

A key question in predicting responses to anthropogenic climate change is: how quickly can species adapt to different climatic conditions? Here, we take a phylogenetic approach to this question. We use 17 time-calibrated phylogenies representing the major tetrapod clades (amphibians, birds, crocodilians, mammals, squamates, turtles) and climatic data from distributions of > 500 extant species. We estimate rates of change based on differences in climatic variables between sister species and estimated times of their splitting. We compare these rates to predicted rates of climate change from 2000 to 2100. Our results are striking: matching projected changes for 2100 would require rates of niche evolution that are > 10,000 times faster than rates typically observed among species, for most variables and clades. Despite many caveats, our results suggest that adaptation to projected changes in the next 100 years would require rates that are largely unprecedented based on observed rates among vertebrate species. © 2013 John Wiley & Sons Ltd/CNRS.

Identifying Medical Students Likely to Exhibit Poor Professionalism and Knowledge During Internship

PubMed Central

Durning, Steven J.; Cohen, Daniel L.; Cruess, David; Jackson, Jeffrey L.

2007-01-01

CONTEXT Identifying medical students who will perform poorly during residency is difficult. OBJECTIVE Determine whether commonly available data predicts low performance ratings during internship by residency program directors. DESIGN Prospective cohort involving medical school data from graduates of the Uniformed Services University (USU), surveys about experiences at USU, and ratings of their performance during internship by their program directors. SETTING Uniformed Services University. PARTICIPANTS One thousand sixty-nine graduates between 1993 and 2002. MAIN OUTCOME MEASURE(S) Residency program directors completed an 18-item survey assessing intern performance. Factor analysis of these items collapsed to 2 domains: knowledge and professionalism. These domains were scored and performance dichotomized at the 10th percentile. RESULTS Many variables showed a univariate relationship with ratings in the bottom 10% of both domains. Multivariable logistic regression modeling revealed that grades earned during the third year predicted low ratings in both knowledge (odds ratio [OR] = 4.9; 95%CI = 2.7–9.2) and professionalism (OR = 7.3; 95%CI = 4.1–13.0). USMLE step 1 scores (OR = 1.03; 95%CI = 1.01–1.05) predicted knowledge but not professionalism. The remaining variables were not independently predictive of performance ratings. The predictive ability for the knowledge and professionalism models was modest (respective area under ROC curves = 0.735 and 0.725). CONCLUSIONS A strong association exists between the third year GPA and internship ratings by program directors in professionalism and knowledge. In combination with third year grades, either the USMLE step 1 or step 2 scores predict poor knowledge ratings. Despite a wealth of available markers and a large data set, predicting poor performance during internship remains difficult. PMID:17952512

Injury versus non-injury factors as predictors of post-concussive symptoms following mild traumatic brain injury in children

PubMed Central

McNally, Kelly A.; Bangert, Barbara; Dietrich, Ann; Nuss, Kathy; Rusin, Jerome; Wright, Martha; Taylor, H. Gerry; Yeates, Keith Owen

2013-01-01

Objective To examine the relative contributions of injury characteristics and non-injury child and family factors as predictors of postconcussive symptoms (PCS) following mild traumatic brain injury (TBI) in children. Methods Participants were 8- to 15-year-old children, 186 with mild TBI and 99 with mild orthopedic injuries (OI). Parents and children rated PCS shortly after injury and at 1, 3, and 12 months post-injury. Hierarchical regression analyses were conducted to predict PCS from (1) demographic variables; (2) pre-morbid child factors (WASI IQ; WRAT-3 Reading; Child Behavior Checklist; ratings of pre-injury PCS); (3) family factors (Family Assessment Device General Functioning Scale; Brief Symptom Inventory; and Life Stressors and Social Resources Inventory); and (4) injury group (OI, mild TBI with loss of consciousness [LOC] and associated injuries [AI], mild TBI with LOC but without AI, mild TBI without LOC but with AI, and mild TBI without LOC or AI) Results Injury group predicted parent and child ratings of PCS but showed a decreasing contribution over time. Demographic variables consistently predicted symptom ratings across time. Premorbid child factors, especially retrospective ratings of premorbid symptoms, accounted for the most variance in symptom ratings. Family factors, particularly parent adjustment, consistently predicted parent, but not child, ratings of PCS. Conclusions Injury characteristics predict PCS in the first months following mild TBI but show a decreasing contribution over time. In contrast, non-injury factors are more consistently related to persistent PCS. PMID:23356592

What Process Mediates Predictions of Childhood IQ from Infant Habituation and Recognition Memory? Speculations on the Roles of Inhibition and Rate of Information Processing.

ERIC Educational Resources Information Center

McCall, Robert B.

1994-01-01

This editorial proposes that the dependent variables that predict childhood intelligence quotient (IQ) from habituation and recognition memory assessments made during infancy may primarily reflect individual differences in rate of information processing. Inhibition may be a stable thread in mental development. (Author/SLD)

Measuring Life Stress: A Comparison of the Predictive Validity of Different Scoring Systems for the Social Readjustment Rating Scale.

ERIC Educational Resources Information Center

McGrath, Robert E. V.; Burkhart, Barry R.

1983-01-01

Assessed whether accounting for variables in the scoring of the Social Readjustment Rating Scale (SRRS) would improve the predictive validity of the inventory. Results from 107 sets of questionnaires showed that income and level of education are significant predictors of the capacity to cope with stress. (JAC)

Effects of sample survey design on the accuracy of classification tree models in species distribution models

USGS Publications Warehouse

Edwards, T.C.; Cutler, D.R.; Zimmermann, N.E.; Geiser, L.; Moisen, Gretchen G.

2006-01-01

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys. ?? 2006 Elsevier B.V. All rights reserved.

Predictive Value of Beat-to-Beat QT Variability Index across the Continuum of Left Ventricular Dysfunction: Competing Risks of Non-cardiac or Cardiovascular Death, and Sudden or Non-Sudden Cardiac Death

PubMed Central

Tereshchenko, Larisa G.; Cygankiewicz, Iwona; McNitt, Scott; Vazquez, Rafael; Bayes-Genis, Antoni; Han, Lichy; Sur, Sanjoli; Couderc, Jean-Philippe; Berger, Ronald D.; de Luna, Antoni Bayes; Zareba, Wojciech

2012-01-01

Background The goal of this study was to determine the predictive value of beat-to-beat QT variability in heart failure (HF) patients across the continuum of left ventricular dysfunction. Methods and Results Beat-to-beat QT variability index (QTVI), heart rate variance (LogHRV), normalized QT variance (QTVN), and coherence between heart rate variability and QT variability have been measured at rest during sinus rhythm in 533 participants of the Muerte Subita en Insuficiencia Cardiaca (MUSIC) HF study (mean age 63.1±11.7; males 70.6%; LVEF >35% in 254 [48%]) and in 181 healthy participants from the Intercity Digital Electrocardiogram Alliance (IDEAL) database. During a median of 3.7 years of follow-up, 116 patients died, 52 from sudden cardiac death (SCD). In multivariate competing risk analyses, the highest QTVI quartile was associated with cardiovascular death [hazard ratio (HR) 1.67(95%CI 1.14-2.47), P=0.009] and in particular with non-sudden cardiac death [HR 2.91(1.69-5.01), P<0.001]. Elevated QTVI separated 97.5% of healthy individuals from subjects at risk for cardiovascular [HR 1.57(1.04-2.35), P=0.031], and non-sudden cardiac death in multivariate competing risk model [HR 2.58(1.13-3.78), P=0.001]. No interaction between QTVI and LVEF was found. QTVI predicted neither non-cardiac death (P=0.546) nor SCD (P=0.945). Decreased heart rate variability (HRV) rather than increased QT variability was the reason for increased QTVI in this study. Conclusions Increased QTVI due to depressed HRV predicts cardiovascular mortality and non-sudden cardiac death, but neither SCD nor excracardiac mortality in HF across the continuum of left ventricular dysfunction. Abnormally augmented QTVI separates 97.5% of healthy individuals from HF patients at risk. PMID:22730411

Spatiotemporal predictability of schooling and nonschooling prey of Pigeon Guillemots

USGS Publications Warehouse

Litzow, Michael A.; Piatt, John F.; Abookire, Alisa A.; Speckman, Suzann G.; Arimitsu, Mayumi L.; Figurski, Jared D.

2004-01-01

Low spatiotemporal variability in the abundance of nonschooling prey might allow Pigeon Guillemots (Cepphus columba) to maintain the high chick provisioning rates that are characteristic of the species. We tested predictions of this hypothesis with data collected with beach seines and scuba and hydroacoustic surveys in Kachemak Bay, Alaska, during 1996–1999. Coefficients of variability were 20–211% greater for schooling than nonschooling prey on day, seasonal, and km scales. However, the proportion of schooling prey in chick diets explained relatively little variability in Pigeon Guillemot meal delivery rates at the scale of hours (r2 = 0.07) and weeks (r2 = 0.19). Behavioral adaptations such as flexible time budgets likely ameliorate the negative effects of high resource variability, but we propose that these adaptations are only effective when schooling prey are available at distances well below the maximum foraging range of the species.

Understanding Disproportionate Representation in Special Education by Examining Group Differences in Behavior Ratings

ERIC Educational Resources Information Center

Peters, Christina D.; Kranzler, John H.; Algina, James; Smith, Stephen W.; Daunic, Ann P.

2014-01-01

The aim of the current study was to examine mean-group differences on behavior rating scales and variables that may predict such differences. Sixty-five teachers completed the Clinical Assessment of Behavior-Teacher Form (CAB-T) for a sample of 982 students. Four outcome variables from the CAB-T were assessed. Hierarchical linear modeling was used…

Effects of climate change and variability on population dynamics in a long-lived shorebird.

PubMed

van de Pol, Martijn; Vindenes, Yngvild; Saether, Bernt-Erik; Engen, Steinar; Ens, Bruno J; Oosterbeek, Kees; Tinbergen, Joost M

2010-04-01

Climate change affects both the mean and variability of climatic variables, but their relative impact on the dynamics of populations is still largely unexplored. Based on a long-term study of the demography of a declining Eurasian Oystercatcher (Haematopus ostralegus) population, we quantify the effect of changes in mean and variance of winter temperature on different vital rates across the life cycle. Subsequently, we quantify, using stochastic stage-structured models, how changes in the mean and variance of this environmental variable affect important characteristics of the future population dynamics, such as the time to extinction. Local mean winter temperature is predicted to strongly increase, and we show that this is likely to increase the population's persistence time via its positive effects on adult survival that outweigh the negative effects that higher temperatures have on fecundity. Interannual variation in winter temperature is predicted to decrease, which is also likely to increase persistence time via its positive effects on adult survival that outweigh the negative effects that lower temperature variability has on fecundity. Overall, a 0.1 degrees C change in mean temperature is predicted to alter median time to extinction by 1.5 times as many years as would a 0.1 degrees C change in the standard deviation in temperature, suggesting that the dynamics of oystercatchers are more sensitive to changes in the mean than in the interannual variability of this climatic variable. Moreover, as climate models predict larger changes in the mean than in the standard deviation of local winter temperature, the effects of future climatic variability on this population's time to extinction are expected to be overwhelmed by the effects of changes in climatic means. We discuss the mechanisms by which climatic variability can either increase or decrease population viability and how this might depend both on species' life histories and on the vital rates affected. This study illustrates that, for making reliable inferences about population consequences in species in which life history changes with age or stage, it is crucial to investigate the impact of climate change on vital rates across the entire life cycle. Disturbingly, such data are unavailable for most species of conservation concern.

Predicting key malaria transmission factors, biting and entomological inoculation rates, using modelled soil moisture in Kenya.

PubMed

Patz, J A; Strzepek, K; Lele, S; Hedden, M; Greene, S; Noden, B; Hay, S I; Kalkstein, L; Beier, J C

1998-10-01

While malaria transmission varies seasonally, large inter-annual heterogeneity of malaria incidence occurs. Variability in entomological parameters, biting rates and entomological inoculation rates (EIR) have been strongly associated with attack rates in children. The goal of this study was to assess the weather's impact on weekly biting and EIR in the endemic area of Kisian, Kenya. Entomological data collected by the U.S. Army from March 1986 through June 1988 at Kisian, Kenya was analysed with concurrent weather data from nearby Kisumu airport. A soil moisture model of surface-water availability was used to combine multiple weather parameters with landcover and soil features to improve disease prediction. Modelling soil moisture substantially improved prediction of biting rates compared to rainfall; soil moisture lagged two weeks explained up to 45% of An. gambiae biting variability, compared to 8% for raw precipitation. For An. funestus, soil moisture explained 32% variability, peaking after a 4-week lag. The interspecies difference in response to soil moisture was significant (P < 0.00001). A satellite normalized differential vegetation index (NDVI) of the study site yielded a similar correlation (r = 0.42 An. gambiae). Modelled soil moisture accounted for up to 56% variability of An. gambiae EIR, peaking at a lag of six weeks. The relationship between temperature and An. gambiae biting rates was less robust; maximum temperature r2 = -0.20, and minimum temperature r2 = 0.12 after lagging one week. Benefits of hydrological modelling are compared to raw weather parameters and to satellite NDVI. These findings can improve both current malaria risk assessments and those based on El Niño forecasts or global climate change model projections.

Language-independent talker-specificity in first-language and second-language speech production by bilingual talkers: L1 speaking rate predicts L2 speaking rate

PubMed Central

Bradlow, Ann R.; Kim, Midam; Blasingame, Michael

2017-01-01

Second-language (L2) speech is consistently slower than first-language (L1) speech, and L1 speaking rate varies within- and across-talkers depending on many individual, situational, linguistic, and sociolinguistic factors. It is asked whether speaking rate is also determined by a language-independent talker-specific trait such that, across a group of bilinguals, L1 speaking rate significantly predicts L2 speaking rate. Two measurements of speaking rate were automatically extracted from recordings of read and spontaneous speech by English monolinguals (n = 27) and bilinguals from ten L1 backgrounds (n = 86): speech rate (syllables/second), and articulation rate (syllables/second excluding silent pauses). Replicating prior work, L2 speaking rates were significantly slower than L1 speaking rates both across-groups (monolinguals' L1 English vs bilinguals' L2 English), and across L1 and L2 within bilinguals. Critically, within the bilingual group, L1 speaking rate significantly predicted L2 speaking rate, suggesting that a significant portion of inter-talker variation in L2 speech is derived from inter-talker variation in L1 speech, and that individual variability in L2 spoken language production may be best understood within the context of individual variability in L1 spoken language production. PMID:28253679

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

PubMed

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

2018-05-13

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

Epidemiology of Mild Traumatic Brain Injury with Intracranial Hemorrhage: Focusing Predictive Models for Neurosurgical Intervention.

PubMed

Orlando, Alessandro; Levy, A Stewart; Carrick, Matthew M; Tanner, Allen; Mains, Charles W; Bar-Or, David

2017-11-01

To outline differences in neurosurgical intervention (NI) rates between intracranial hemorrhage (ICH) types in mild traumatic brain injuries and help identify which ICH types are most likely to benefit from creation of predictive models for NI. A multicenter retrospective study of adult patients spanning 3 years at 4 U.S. trauma centers was performed. Patients were included if they presented with mild traumatic brain injury (Glasgow Coma Scale score 13-15) with head CT scan positive for ICH. Patients were excluded for skull fractures, "unspecified hemorrhage," or coagulopathy. Primary outcome was NI. Stepwise multivariable logistic regression models were built to analyze the independent association between ICH variables and outcome measures. The study comprised 1876 patients. NI rate was 6.7%. There was a significant difference in rate of NI by ICH type. Subdural hematomas had the highest rate of NI (15.5%) and accounted for 78% of all NIs. Isolated subarachnoid hemorrhages had the lowest, nonzero, NI rate (0.19%). Logistic regression models identified ICH type as the most influential independent variable when examining NI. A model predicting NI for isolated subarachnoid hemorrhages would require 26,928 patients, but a model predicting NI for isolated subdural hematomas would require only 328 patients. This study highlighted disparate NI rates among ICH types in patients with mild traumatic brain injury and identified mild, isolated subdural hematomas as most appropriate for construction of predictive NI models. Increased health care efficiency will be driven by accurate understanding of risk, which can come only from accurate predictive models. Copyright © 2017 Elsevier Inc. All rights reserved.

Bedload and Total Load Sediment Transport Equations for Rough Open-Channel Flow

NASA Astrophysics Data System (ADS)

Abrahams, A. D.; Gao, P.

2001-12-01

The total sediment load transported by an open-channel flow may be divided into bedload and suspended load. Bedload transport occurs by saltation at low shear stress and by sheetflow at high shear stress. Dimensional analysis is used to identify the dimensionless variables that control the transport rate of noncohesive sediments over a plane bed, and regression analysis is employed to isolate the significant variables and determine the values of the coefficients. In the general bedload transport equation (i.e. for saltation and sheetflow) the dimensionless bedload transport rate is a function of the dimensionless shear stress, the friction factor, and an efficiency coefficient. For sheetflow the last term approaches 1, so that the bedload transport rate becomes a function of just the dimensionless shear stress and the friction factor. The dimensional analysis indicates that the dimensionless total load transport rate is a function of the dimensionless bedload transport rate and the dimensionless settling velocity of the sediment. Predicted values of the transport rates are graphed against the computed values of these variables for 505 flume experiments reported in the literature. These graphs indicate that the equations developed in this study give good unbiased predictions of both the bedload transport rate and total load transport rate over a wide range of conditions.

Poisson-Like Spiking in Circuits with Probabilistic Synapses

PubMed Central

Moreno-Bote, Rubén

2014-01-01

Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex. PMID:25032705

Emotional exhaustion and workload predict clinician-rated and objective patient safety

PubMed Central

Welp, Annalena; Meier, Laurenz L.; Manser, Tanja

2015-01-01

Aims: To investigate the role of clinician burnout, demographic, and organizational characteristics in predicting subjective and objective indicators of patient safety. Background: Maintaining clinician health and ensuring safe patient care are important goals for hospitals. While these goals are not independent from each other, the interplay between clinician psychological health, demographic and organizational variables, and objective patient safety indicators is poorly understood. The present study addresses this gap. Method: Participants were 1425 physicians and nurses working in intensive care. Regression analysis (multilevel) was used to investigate the effect of burnout as an indicator of psychological health, demographic (e.g., professional role and experience) and organizational (e.g., workload, predictability) characteristics on standardized mortality ratios, length of stay and clinician-rated patient safety. Results: Clinician-rated patient safety was associated with burnout, trainee status, and professional role. Mortality was predicted by emotional exhaustion. Length of stay was predicted by workload. Contrary to our expectations, burnout did not predict length of stay, and workload and predictability did not predict standardized mortality ratios. Conclusion: At least in the short-term, clinicians seem to be able to maintain safety despite high workload and low predictability. Nevertheless, burnout poses a safety risk. Subjectively, burnt-out clinicians rated safety lower, and objectively, units with high emotional exhaustion had higher standardized mortality ratios. In summary, our results indicate that clinician psychological health and patient safety could be managed simultaneously. Further research needs to establish causal relationships between these variables and support to the development of managerial guidelines to ensure clinicians’ psychological health and patients’ safety. PMID:25657627

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

PubMed Central

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

2016-01-01

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

Executive functioning independently predicts self-rated health and improvement in self-rated health over time among community-dwelling older adults.

PubMed

McHugh, Joanna Edel; Lawlor, Brian A

2016-01-01

Self-rated health, as distinct from objective measures of health, is a clinically informative metric among older adults. The purpose of our study was to examine the cognitive and psychosocial factors associated with self-rated health. 624 participants over the age of 60 were assessed at baseline, and of these, 510 were contacted for a follow-up two years later. Measures of executive function and self-rated health were assessed at baseline, and self-rated health was assessed at follow-up. We employed multiple linear regression analyses to investigate the relationship between executive functioning and self-rated health, while controlling for demographic, psychosocial and biological variables. Controlling for other relevant variables, executive functioning independently and solely predicted self-rated health, both at a cross-sectional level, and also over time. Loneliness was also found to cross-sectionally predict self-rated health, although this relationship was not present at a longitudinal level. Older adults' self-rated health may be related to their executive functioning and to their loneliness. Self-rated health appeared to improve over time, and the extent of this improvement was also related to executive functioning at baseline. Self-rated health may be a judgement made of one's functioning, especially executive functioning, which changes with age and therefore may be particularly salient in the reflections of older adults.

Heart Rate Variability Moderates the Association Between Separation-Related Psychological Distress and Blood Pressure Reactivity Over Time.

PubMed

Bourassa, Kyle J; Hasselmo, Karen; Sbarra, David A

2016-08-01

Divorce is a stressor associated with long-term health risk, though the mechanisms of this effect are poorly understood. Cardiovascular reactivity is one biological pathway implicated as a predictor of poor long-term health after divorce. A sample of recently separated and divorced adults (N = 138) was assessed over an average of 7.5 months to explore whether individual differences in heart rate variability-assessed by respiratory sinus arrhythmia-operate in combination with subjective reports of separation-related distress to predict prospective changes in cardiovascular reactivity, as indexed by blood pressure reactivity. Participants with low resting respiratory sinus arrhythmia at baseline showed no association between divorce-related distress and later blood pressure reactivity, whereas participants with high respiratory sinus arrhythmia showed a positive association. In addition, within-person variation in respiratory sinus arrhythmia and between-persons variation in separation-related distress interacted to predict blood pressure reactivity at each laboratory visit. Individual differences in heart rate variability and subjective distress operate together to predict cardiovascular reactivity and may explain some of the long-term health risk associated with divorce. © The Author(s) 2016.

Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study.

PubMed

Lan, Kun-Chan; Raknim, Paweeya; Kao, Wei-Fong; Huang, Jyh-How

2018-04-21

Heart rate variability (HRV) is often used to assess the risk of cardiovascular disease, and data on this can be obtained via electrocardiography (ECG). However, collecting heart rate data via photoplethysmography (PPG) is now a lot easier. We investigate the feasibility of using the PPG-based heart rate to estimate HRV and predict diseases. We obtain three months of PPG-based heart rate data from subjects with and without hypertension, and calculate the HRV based on various forms of time and frequency domain analysis. We then apply a data mining technique to this estimated HRV data, to see if it is possible to correctly identify patients with hypertension. We use six HRV parameters to predict hypertension, and find SDNN has the best predictive power. We show that early disease prediction is possible through collecting one's PPG-based heart rate information.

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

PubMed

Torres, Leigh G; Read, Andrew J; Halpin, Patrick

2008-10-01

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

Individualized prediction of lung-function decline in chronic obstructive pulmonary disease

PubMed Central

Zafari, Zafar; Sin, Don D.; Postma, Dirkje S.; Löfdahl, Claes-Göran; Vonk, Judith; Bryan, Stirling; Lam, Stephen; Tammemagi, C. Martin; Khakban, Rahman; Man, S.F. Paul; Tashkin, Donald; Wise, Robert A.; Connett, John E.; McManus, Bruce; Ng, Raymond; Hollander, Zsuszanna; Sadatsafavi, Mohsen

2016-01-01

Background: The rate of lung-function decline in chronic obstructive pulmonary disease (COPD) varies substantially among individuals. We sought to develop and validate an individualized prediction model for forced expiratory volume at 1 second (FEV1) in current smokers with mild-to-moderate COPD. Methods: Using data from a large long-term clinical trial (the Lung Health Study), we derived mixed-effects regression models to predict future FEV1 values over 11 years according to clinical traits. We modelled heterogeneity by allowing regression coefficients to vary across individuals. Two independent cohorts with COPD were used for validating the equations. Results: We used data from 5594 patients (mean age 48.4 yr, 63% men, mean baseline FEV1 2.75 L) to create the individualized prediction equations. There was significant between-individual variability in the rate of FEV1 decline, with the interval for the annual rate of decline that contained 95% of individuals being −124 to −15 mL/yr for smokers and −83 to 15 mL/yr for sustained quitters. Clinical variables in the final model explained 88% of variation around follow-up FEV1. The C statistic for predicting severity grades was 0.90. Prediction equations performed robustly in the 2 external data sets. Interpretation: A substantial part of individual variation in FEV1 decline can be explained by easily measured clinical variables. The model developed in this work can be used for prediction of future lung health in patients with mild-to-moderate COPD. Trial registration: Lung Health Study — ClinicalTrials.gov, no. NCT00000568; Pan-Canadian Early Detection of Lung Cancer Study — ClinicalTrials.gov, no. NCT00751660 PMID:27486205

Adding HRV biofeedback to psychotherapy increases heart rate variability and improves the treatment of major depressive disorder.

PubMed

Caldwell, Yoko Tsui; Steffen, Patrick R

2018-01-05

Heart rate variability (HRV) is a significant marker of health outcomes with decreased HRV predicting increased disease risk. HRV is decreased in major depressive disorder (MDD) but existing treatments for depression do not return heart rate variability to normal levels even with successful treatment of depression. Heart rate variability biofeedback (HRVB) increases heart rate variability but no studies to date have examined whether combining HRVB with psychotherapy improves outcome in MDD treatment. The present study used a randomized controlled design to compare the effects of HRVB combined with psychotherapy on MDD relative to a psychotherapy treatment as usual group and to a non-depressed control group. The HRVB+psychotherapy group showed a larger increase in HRV and a larger decrease in depressive symptoms relative to the other groups over a six-week period, whereas the psychotherapy group only did not improve HRV. Results support the supplementation of psychotherapy with HRVB in the treatment of MDD. Copyright © 2018 Elsevier B.V. All rights reserved.

Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population.

PubMed

Sumithran, P; Purcell, K; Kuyruk, S; Proietto, J; Prendergast, L A

2018-02-01

Consistent, strong predictors of obesity treatment outcomes have not been identified. It has been suggested that broadening the range of predictor variables examined may be valuable. We explored methods to predict outcomes of a very-low-energy diet (VLED)-based programme in a clinically comparable setting, using a wide array of pre-intervention biological and psychosocial participant data. A total of 61 women and 39 men (mean ± standard deviation [SD] body mass index: 39.8 ± 7.3 kg/m 2 ) underwent an 8-week VLED and 12-month follow-up. At baseline, participants underwent a blood test and assessment of psychological, social and behavioural factors previously associated with treatment outcomes. Logistic regression, linear discriminant analysis, decision trees and random forests were used to model outcomes from baseline variables. Of the 100 participants, 88 completed the VLED and 42 attended the Week 60 visit. Overall prediction rates for weight loss of ≥10% at weeks 8 and 60, and attrition at Week 60, using combined data were between 77.8 and 87.6% for logistic regression, and lower for other methods. When logistic regression analyses included only baseline demographic and anthropometric variables, prediction rates were 76.2-86.1%. In this population, considering a wide range of biological and psychosocial data did not improve outcome prediction compared to simply-obtained baseline characteristics. © 2017 World Obesity Federation.

Behavioral Momentum Theory Fails to Account for the Effects of Reinforcement Rate on Resurgence

PubMed Central

Craig, Andrew R.; Shahan, Timothy A.

2017-01-01

The behavioral-momentum model of resurgence predicts reinforcer rates within a resurgence preparation should have three effects on target behavior. First, higher reinforcer rates in baseline (Phase 1) produce more persistent target behavior during extinction plus alternative reinforcement. Second, higher rate alternative reinforcement during Phase 2 generates greater disruption of target responding during extinction. Finally, higher rates of either reinforcement source should produce greater responding when alternative reinforcement is suspended in Phase 3. Recent empirical reports have produced mixed results in terms of these predictions. Thus, the present experiment further examined reinforcer-rate effects on persistence and resurgence. Rats pressed target levers for high-rate or low-rate variable-interval food during Phase 1. In Phase 2, target-lever pressing was extinguished, an alternative nose-poke became available, and nose-poking produced either high-rate variable-interval, low-rate variable-interval, or no (an extinction control) alternative reinforcement. Alternative reinforcement was suspended in Phase 3. For groups that received no alternative reinforcement, target-lever pressing was less persistent following high-rate than low-rate Phase-1 reinforcement. Target behavior was more persistent with low-rate alternative reinforcement than with high-rate alternative reinforcement or extinction alone. Finally, no differences in Phase-3 responding were observed for groups that received either high-rate or low-rate alternative reinforcement, and resurgence occurred only following high-rate alternative reinforcement. These findings are inconsistent with the momentum-based model of resurgence. We conclude this model mischaracterizes the effects of rein-forcer rates on persistence and resurgence of operant behavior. PMID:27193242

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.

PubMed

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.

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

PubMed

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

2010-03-01

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

Significance of vapor phase chemical reactions on CVD rates predicted by chemically frozen and local thermochemical equilibrium boundary layer theories

NASA Technical Reports Server (NTRS)

Gokoglu, Suleyman A.

1988-01-01

This paper investigates the role played by vapor-phase chemical reactions on CVD rates by comparing the results of two extreme theories developed to predict CVD mass transport rates in the absence of interfacial kinetic barrier: one based on chemically frozen boundary layer and the other based on local thermochemical equilibrium. Both theories consider laminar convective-diffusion boundary layers at high Reynolds numbers and include thermal (Soret) diffusion and variable property effects. As an example, Na2SO4 deposition was studied. It was found that gas phase reactions have no important role on Na2SO4 deposition rates and on the predictions of the theories. The implications of the predictions of the two theories to other CVD systems are discussed.

Coach/player relationships in tennis.

PubMed

Prapavessis, H; Gordon, S

1991-09-01

The present study examined the variables that predict coach/athlete compatibility. Compatibility among a sample of 52 elite tennis coach/player dyads was assessed using a sport adapted version of Schutz's (1966) Fundamental Interpersonal Relations Orientation-Behaviour (FIRO-B), a sport adapted version of Fiedler's (1967) Least Preferred Co-worker scale (LPC), and Chelladurai and Saleh's (1980) Leadership Scale for Sport (LSS). Self-ratings of the quality of the interaction were obtained from both coach and athlete. Multiple-regression analyses using self-rating scores as the dependent measure were carried out to determine which variables best predicted the degree of compatibility. The sole inventory that significantly predicted compatibility was the LSS. More specifically, the discrepancy between the athlete's preferences and perceptions on the autocratic dimension was the best predictor. Implications for tennis coaches and recommendations for future research in this area are discussed.

An examination of predictive variables toward graduation of minority students in science at a selected urban university

NASA Astrophysics Data System (ADS)

Hunter, Evelyn M. Irving

1998-12-01

The purpose of this study was to examine the relationship and predictive power of the variables gender, high school GPA, class rank, SAT scores, ACT scores, and socioeconomic status on the graduation rates of minority college students majoring in the sciences at a selected urban university. Data was examined on these variables as they related to minority students majoring in science. The population consisted of 101 minority college students who had majored in the sciences from 1986 to 1996 at an urban university in the southwestern region of Texas. A non-probability sampling procedure was used in this study. The non-probability sampling procedure in this investigation was incidental sampling technique. A profile sheet was developed to record the information regarding the variables. The composite scores from SAT and ACT testing were used in the study. The dichotomous variables gender and socioeconomic status were dummy coded for analysis. For the gender variable, zero (0) indicated male, and one (1) indicated female. Additionally, zero (0) indicated high SES, and one (1) indicated low SES. Two parametric procedures were used to analyze the data in this investigation. They were the multiple correlation and multiple regression procedures. Multiple correlation is a statistical technique that indicates the relationship between one variable and a combination of two other variables. The variables socioeconomic status and GPA were found to contribute significantly to the graduation rates of minority students majoring in all sciences when combined with chemistry (Hypotheses Two and Four). These variables accounted for 7% and 15% of the respective variance in the graduation rates of minority students in the sciences and in chemistry. Hypotheses One and Three, the predictor variables gender, high school GPA, SAT Total Scores, class rank, and socioeconomic status did not contribute significantly to the graduation rates of minority students in biology and pharmacy.

Predictive Variables of Half-Marathon Performance for Male Runners.

PubMed

Gómez-Molina, Josué; Ogueta-Alday, Ana; Camara, Jesus; Stickley, Christoper; Rodríguez-Marroyo, José A; García-López, Juan

2017-06-01

The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO 2max , speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance.

Use of Admission Criteria to Predict Performance of Students in an Entry-Level Master's Program on Fieldwork Placements and in Academic Courses.

PubMed

Kirchner, G L; Stone, R G; Holm, M B

2001-01-01

The relationships among clinical outcomes, academic success, and predictors used to screen applicants for entrance into a Master in Occupational Therapy Program (MOT) were examined. The dependent variables were grade point average in occupational therapy courses (OT-GPA), client therapy outcomes at the clinic, and ratings of MOT students by Level II Fieldwork supervisors. Predictor variables included undergraduate GPA, scores on the Graduate Record Examination (GRE), and an essay. Both undergraduate GPA and scores on the GRE were found to predict OT-GPA. The analytical section of the GRE was also positively correlated with fieldwork supervisors' ratings of students.

Derivation and Internal Validation of a Clinical Prediction Tool for 30-Day Mortality in Lower Gastrointestinal Bleeding.

PubMed

Sengupta, Neil; Tapper, Elliot B

2017-05-01

There are limited data to predict which patients with lower gastrointestinal bleeding are at risk for adverse outcomes. We aimed to develop a clinical tool based on admission variables to predict 30-day mortality in lower gastrointestinal bleeding. We used a validated machine learning algorithm to identify adult patients hospitalized with lower gastrointestinal bleeding at an academic medical center between 2008 and 2015. The cohort was split randomly into derivation and validation cohorts. In the derivation cohort, we used multiple logistic regression on all candidate admission variables to create a prediction model for 30-day mortality, using area under the receiving operator characteristic curve and misclassification rate to estimate prediction accuracy. Regression coefficients were used to derive an integer score, and mortality risk associated with point totals was assessed. In the derivation cohort (n = 4044), 8 variables were most associated with 30-day mortality: age, dementia, metastatic cancer, chronic kidney disease, chronic pulmonary disease, anticoagulant use, admission hematocrit, and albumin. The model yielded a misclassification rate of 0.06 and area under the curve of 0.81. The integer score ranged from -10 to 26 in the derivation cohort, with a misclassification rate of 0.11 and area under the curve of 0.74. In the validation cohort (n = 2060), the score had an area under the curve of 0.72 with a misclassification rate of 0.12. After dividing the score into 4 quartiles of risk, 30-day mortality in the derivation and validation sets was 3.6% and 4.4% in quartile 1, 4.9% and 7.3% in quartile 2, 9.9% and 9.1% in quartile 3, and 24% and 26% in quartile 4, respectively. A clinical tool can be used to predict 30-day mortality in patients hospitalized with lower gastrointestinal bleeding. Copyright © 2017 Elsevier Inc. All rights reserved.

Improving rational thermal comfort prediction by using subpopulation characteristics: A case study at Hermitage Amsterdam

PubMed Central

Kramer, Rick; Schellen, Lisje; Schellen, Henk; Kingma, Boris

2017-01-01

ABSTRACT This study aims to improve the prediction accuracy of the rational standard thermal comfort model, known as the Predicted Mean Vote (PMV) model, by (1) calibrating one of its input variables “metabolic rate,” and (2) extending it by explicitly incorporating the variable running mean outdoor temperature (RMOT) that relates to adaptive thermal comfort. The analysis was performed with survey data (n = 1121) and climate measurements of the indoor and outdoor environment from a one year-long case study undertaken at Hermitage Amsterdam museum in the Netherlands. The PMVs were calculated for 35 survey days using (1) an a priori assumed metabolic rate, (2) a calibrated metabolic rate found by fitting the PMVs to the thermal sensation votes (TSVs) of each respondent using an optimization routine, and (3) extending the PMV model by including the RMOT. The results show that the calibrated metabolic rate is estimated to be 1.5 Met for this case study that was predominantly visited by elderly females. However, significant differences in metabolic rates have been revealed between adults and elderly showing the importance of differentiating between subpopulations. Hence, the standard tabular values, which only differentiate between various activities, may be oversimplified for many cases. Moreover, extending the PMV model with the RMOT substantially improves the thermal sensation prediction, but thermal sensation toward extreme cool and warm sensations remains partly underestimated. PMID:28680934

Improving rational thermal comfort prediction by using subpopulation characteristics: A case study at Hermitage Amsterdam.

PubMed

Kramer, Rick; Schellen, Lisje; Schellen, Henk; Kingma, Boris

2017-01-01

This study aims to improve the prediction accuracy of the rational standard thermal comfort model, known as the Predicted Mean Vote (PMV) model, by (1) calibrating one of its input variables "metabolic rate," and (2) extending it by explicitly incorporating the variable running mean outdoor temperature (RMOT) that relates to adaptive thermal comfort. The analysis was performed with survey data ( n = 1121) and climate measurements of the indoor and outdoor environment from a one year-long case study undertaken at Hermitage Amsterdam museum in the Netherlands. The PMVs were calculated for 35 survey days using (1) an a priori assumed metabolic rate, (2) a calibrated metabolic rate found by fitting the PMVs to the thermal sensation votes (TSVs) of each respondent using an optimization routine, and (3) extending the PMV model by including the RMOT. The results show that the calibrated metabolic rate is estimated to be 1.5 Met for this case study that was predominantly visited by elderly females. However, significant differences in metabolic rates have been revealed between adults and elderly showing the importance of differentiating between subpopulations. Hence, the standard tabular values, which only differentiate between various activities, may be oversimplified for many cases. Moreover, extending the PMV model with the RMOT substantially improves the thermal sensation prediction, but thermal sensation toward extreme cool and warm sensations remains partly underestimated.

Prediction and Computation of Corrosion Rates of A36 Mild Steel in Oilfield Seawater

NASA Astrophysics Data System (ADS)

Paul, Subir; Mondal, Rajdeep

2018-04-01

The parameters which primarily control the corrosion rate and life of steel structures are several and they vary across the different ocean and seawater as well as along the depth. While the effect of single parameter on corrosion behavior is known, the conjoint effects of multiple parameters and the interrelationship among the variables are complex. Millions sets of experiments are required to understand the mechanism of corrosion failure. Statistical modeling such as ANN is one solution that can reduce the number of experimentation. ANN model was developed using 170 sets of experimental data of A35 mild steel in simulated seawater, varying the corrosion influencing parameters SO4 2-, Cl-, HCO3 -,CO3 2-, CO2, O2, pH and temperature as input and the corrosion current as output. About 60% of experimental data were used to train the model, 20% for testing and 20% for validation. The model was developed by programming in Matlab. 80% of the validated data could predict the corrosion rate correctly. Corrosion rates predicted by the ANN model are displayed in 3D graphics which show many interesting phenomenon of the conjoint effects of multiple variables that might throw new ideas of mitigation of corrosion by simply modifying the chemistry of the constituents. The model could predict the corrosion rates of some real systems.

Calibration between Undergraduate Students' Prediction of and Actual Performance: The Role of Gender and Performance Attributions

ERIC Educational Resources Information Center

Gutierrez, Antonio P.; Price, Addison F.

2017-01-01

This study investigated changes in male and female students' prediction and postdiction calibration accuracy and bias scores, and the predictive effects of explanatory styles on these variables beyond gender. Seventy undergraduate students rated their confidence in performance before and after a 40-item exam. There was an improvement in students'…

Earthquake Prediction in Large-scale Faulting Experiments

NASA Astrophysics Data System (ADS)

Junger, J.; Kilgore, B.; Beeler, N.; Dieterich, J.

2004-12-01

We study repeated earthquake slip of a 2 m long laboratory granite fault surface with approximately homogenous frictional properties. In this apparatus earthquakes follow a period of controlled, constant rate shear stress increase, analogous to tectonic loading. Slip initiates and accumulates within a limited area of the fault surface while the surrounding fault remains locked. Dynamic rupture propagation and slip of the entire fault surface is induced when slip in the nucleating zone becomes sufficiently large. We report on the event to event reproducibility of loading time (recurrence interval), failure stress, stress drop, and precursory activity. We tentatively interpret these variations as indications of the intrinsic variability of small earthquake occurrence and source physics in this controlled setting. We use the results to produce measures of earthquake predictability based on the probability density of repeating occurrence and the reproducibility of near-field precursory strain. At 4 MPa normal stress and a loading rate of 0.0001 MPa/s, the loading time is ˜25 min, with a coefficient of variation of around 10%. Static stress drop has a similar variability which results almost entirely from variability of the final (rather than initial) stress. Thus, the initial stress has low variability and event times are slip-predictable. The variability of loading time to failure is comparable to the lowest variability of recurrence time of small repeating earthquakes at Parkfield (Nadeau et al., 1998) and our result may be a good estimate of the intrinsic variability of recurrence. Distributions of loading time can be adequately represented by a log-normal or Weibel distribution but long term prediction of the next event time based on probabilistic representation of previous occurrence is not dramatically better than for field-observed small- or large-magnitude earthquake datasets. The gradually accelerating precursory aseismic slip observed in the region of nucleation in these experiments is consistent with observations and theory of Dieterich and Kilgore (1996). Precursory strains can be detected typically after 50% of the total loading time. The Dieterich and Kilgore approach implies an alternative method of earthquake prediction based on comparing real-time strain monitoring with previous precursory strain records or with physically-based models of accelerating slip. Near failure, time to failure t is approximately inversely proportional to precursory slip rate V. Based on a least squares fit to accelerating slip velocity from ten or more events, the standard deviation of the residual between predicted and observed log t is typically 0.14. Scaling these results to natural recurrence suggests that a year prior to an earthquake, failure time can be predicted from measured fault slip rate with a typical error of 140 days, and a day prior to the earthquake with a typical error of 9 hours. However, such predictions require detecting aseismic nucleating strains, which have not yet been found in the field, and on distinguishing earthquake precursors from other strain transients. There is some field evidence of precursory seismic strain for large earthquakes (Bufe and Varnes, 1993) which may be related to our observations. In instances where precursory activity is spatially variable during the interseismic period, as in our experiments, distinguishing precursory activity might be best accomplished with deep arrays of near fault instruments and pattern recognition algorithms such as principle component analysis (Rundle et al., 2000).

Intercenter Differences in Bronchopulmonary Dysplasia or Death Among Very Low Birth Weight Infants

PubMed Central

Walsh, Michele; Bobashev, Georgiy; Das, Abhik; Levine, Burton; Carlo, Waldemar A.; Higgins, Rosemary D.

2011-01-01

OBJECTIVES: To determine (1) the magnitude of clustering of bronchopulmonary dysplasia (36 weeks) or death (the outcome) across centers of the Eunice Kennedy Shriver National Institute of Child and Human Development National Research Network, (2) the infant-level variables associated with the outcome and estimate their clustering, and (3) the center-specific practices associated with the differences and build predictive models. METHODS: Data on neonates with a birth weight of <1250 g from the cluster-randomized benchmarking trial were used to determine the magnitude of clustering of the outcome according to alternating logistic regression by using pairwise odds ratio and predictive modeling. Clinical variables associated with the outcome were identified by using multivariate analysis. The magnitude of clustering was then evaluated after correction for infant-level variables. Predictive models were developed by using center-specific and infant-level variables for data from 2001 2004 and projected to 2006. RESULTS: In 2001–2004, clustering of bronchopulmonary dysplasia/death was significant (pairwise odds ratio: 1.3; P < .001) and increased in 2006 (pairwise odds ratio: 1.6; overall incidence: 52%; range across centers: 32%–74%); center rates were relatively stable over time. Variables that varied according to center and were associated with increased risk of outcome included lower body temperature at NICU admission, use of prophylactic indomethacin, specific drug therapy on day 1, and lack of endotracheal intubation. Center differences remained significant even after correction for clustered variables. CONCLUSION: Bronchopulmonary dysplasia/death rates demonstrated moderate clustering according to center. Clinical variables associated with the outcome were also clustered. Center differences after correction of clustered variables indicate presence of as-yet unmeasured center variables. PMID:21149431

Computational Simulation of the High Strain Rate Tensile Response of Polymer Matrix Composites

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.

2002-01-01

A research program is underway to develop strain rate dependent deformation and failure models for the analysis of polymer matrix composites subject to high strain rate impact loads. Under these types of loading conditions, the material response can be highly strain rate dependent and nonlinear. State variable constitutive equations based on a viscoplasticity approach have been developed to model the deformation of the polymer matrix. The constitutive equations are then combined with a mechanics of materials based micromechanics model which utilizes fiber substructuring to predict the effective mechanical and thermal response of the composite. To verify the analytical model, tensile stress-strain curves are predicted for a representative composite over strain rates ranging from around 1 x 10(exp -5)/sec to approximately 400/sec. The analytical predictions compare favorably to experimentally obtained values both qualitatively and quantitatively. Effective elastic and thermal constants are predicted for another composite, and compared to finite element results.

OPTIMIZING MODEL PERFORMANCE: VARIABLE SIZE RESOLUTION IN CLOUD CHEMISTRY MODELING. (R826371C005)

EPA Science Inventory

Under many conditions size-resolved aqueous-phase chemistry models predict higher sulfate production rates than comparable bulk aqueous-phase models. However, there are special circumstances under which bulk and size-resolved models offer similar predictions. These special con...

Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation

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

Meehl, Gerald A.; Hu, Aixue; Teng, Haiyan

The negative phase of the Interdecadal Pacific Oscillation (IPO), a dominant mode of multi-decadal variability of sea surface temperatures (SSTs) in the Pacific, contributed to the reduced rate of global surface temperature warming in the early 2000s. Here, a proposed mechanism for IPO multidecadal variability indicates that the presence of decadal timescale upper ocean heat content in the off-equatorial western tropical Pacific can provide conditions for an interannual El Nino/Southern Oscillation event to trigger a transition of tropical Pacific SSTs to the opposite IPO phase. Here we show that a decadal prediction initialized in 2013 simulates predicted Nino3.4 SSTs thatmore » have qualitatively tracked the observations through 2015. The year three to seven average prediction (2015-2019) from the 2013 initial state shows a transition to the positive phase of the IPO from the previous negative phase and a resumption of larger rates of global warming over the 2013-2022 period consistent with a positive IPO phase.« less

Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation

DOE PAGES

Meehl, Gerald A.; Hu, Aixue; Teng, Haiyan

2016-06-02

The negative phase of the Interdecadal Pacific Oscillation (IPO), a dominant mode of multi-decadal variability of sea surface temperatures (SSTs) in the Pacific, contributed to the reduced rate of global surface temperature warming in the early 2000s. Here, a proposed mechanism for IPO multidecadal variability indicates that the presence of decadal timescale upper ocean heat content in the off-equatorial western tropical Pacific can provide conditions for an interannual El Nino/Southern Oscillation event to trigger a transition of tropical Pacific SSTs to the opposite IPO phase. Here we show that a decadal prediction initialized in 2013 simulates predicted Nino3.4 SSTs thatmore » have qualitatively tracked the observations through 2015. The year three to seven average prediction (2015-2019) from the 2013 initial state shows a transition to the positive phase of the IPO from the previous negative phase and a resumption of larger rates of global warming over the 2013-2022 period consistent with a positive IPO phase.« less

Forced Expiratory Volume in 1 Second Variability Helps Identify Patients with Cystic Fibrosis at Risk of Greater Loss of Lung Function.

PubMed

Morgan, Wayne J; VanDevanter, Donald R; Pasta, David J; Foreman, Aimee J; Wagener, Jeffrey S; Konstan, Michael W

2016-02-01

To evaluate several alternative measures of forced expiratory volume in 1 second percent predicted (FEV1 %pred) variability as potential predictors of future FEV1 %pred decline in patients with cystic fibrosis. We included 13,827 patients age ≥6 years from the Epidemiologic Study of Cystic Fibrosis 1994-2002 with ≥4 FEV1 %pred measurements spanning ≥366 days in both a 2-year baseline period and a 2-year follow-up period. We predicted change from best baseline FEV1 %pred to best follow-up FEV1 %pred and change from baseline to best in the second follow-up year by using multivariable regression stratified by 4 lung-disease stages. We assessed 5 measures of variability (some as deviations from the best and some as deviations from the trend line) both alone and after controlling for demographic and clinical factors and for the slope and level of FEV1 %pred. All 5 measures of FEV1 %pred variability were predictive, but the strongest predictor was median deviation from the best FEV1 %pred in the baseline period. The contribution to explanatory power (R(2)) was substantial and exceeded the total contribution of all other factors excluding the FEV1 %pred rate of decline. Adding the other variability measures provided minimal additional value. Median deviation from the best FEV1 %pred is a simple metric that markedly improves prediction of FEV1 %pred decline even after the inclusion of demographic and clinical characteristics and the FEV1 %pred rate of decline. The routine calculation of this variability measure could allow clinicians to better identify patients at risk and therefore in need of increased intervention. Copyright © 2016 Elsevier Inc. All rights reserved.

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

PubMed

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.

Can the biomass-ratio hypothesis predict mixed-species litter decomposition along a climatic gradient?

PubMed Central

Tardif, Antoine; Shipley, Bill; Bloor, Juliette M. G.; Soussana, Jean-François

2014-01-01

Background and Aims The biomass-ratio hypothesis states that ecosystem properties are driven by the characteristics of dominant species in the community. In this study, the hypothesis was operationalized as community-weighted means (CWMs) of monoculture values and tested for predicting the decomposition of multispecies litter mixtures along an abiotic gradient in the field. Methods Decomposition rates (mg g−1 d−1) of litter from four herb species were measured using litter-bed experiments with the same soil at three sites in central France along a correlated climatic gradient of temperature and precipitation. All possible combinations from one to four species mixtures were tested over 28 weeks of incubation. Observed mixture decomposition rates were compared with those predicted by the biomass-ratio hypothesis. Variability of the prediction errors was compared with the species richness of the mixtures, across sites, and within sites over time. Key Results Both positive and negative prediction errors occurred. Despite this, the biomass-ratio hypothesis was true as an average claim for all sites (r = 0·91) and for each site separately, except for the climatically intermediate site, which showed mainly synergistic deviations. Variability decreased with increasing species richness and in less favourable climatic conditions for decomposition. Conclusions Community-weighted mean values provided good predictions of mixed-species litter decomposition, converging to the predicted values with increasing species richness and in climates less favourable to decomposition. Under a context of climate change, abiotic variability would be important to take into account when predicting ecosystem processes. PMID:24482152

Sensory experience ratings (SERs) for 1,659 French words: Relationships with other psycholinguistic variables and visual word recognition.

PubMed

Bonin, Patrick; Méot, Alain; Ferrand, Ludovic; Bugaïska, Aurélia

2015-09-01

We collected sensory experience ratings (SERs) for 1,659 French words in adults. Sensory experience for words is a recently introduced variable that corresponds to the degree to which words elicit sensory and perceptual experiences (Juhasz & Yap Behavior Research Methods, 45, 160-168, 2013; Juhasz, Yap, Dicke, Taylor, & Gullick Quarterly Journal of Experimental Psychology, 64, 1683-1691, 2011). The relationships of the sensory experience norms with other psycholinguistic variables (e.g., imageability and age of acquisition) were analyzed. We also investigated the degree to which SER predicted performance in visual word recognition tasks (lexical decision, word naming, and progressive demasking). The analyses indicated that SER reliably predicted response times in lexical decision, but not in word naming or progressive demasking. The findings are discussed in relation to the status of SER, the role of semantic code activation in visual word recognition, and the embodied view of cognition.

A STATE-VARIABLE APPROACH FOR PREDICTING THE TIME REQUIRED FOR 50% RECRYSTALLIZATION

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

M. STOUT; ET AL

2000-08-01

It is important to be able to model the recrystallization kinetics in aluminum alloys during hot deformation. The industrial relevant process of hot rolling is an example of where the knowledge of whether or not a material recrystallizes is critical to making a product with the correct properties. Classically, the equations that describe the kinetics of recrystallization predict the time to 50% recrystallization. These equations are largely empirical; they are based on the free energy for recrystallization, a Zener-Holloman parameter, and have several adjustable exponents to fit the equation to engineering data. We have modified this form of classical theorymore » replacing the Zener-Hollomon parameter with a deformation energy increment, a free energy available to drive recrystallization. The advantage of this formulation is that the deformation energy increment is calculated based on the previously determined temperature and strain-rate sensitivity of the constitutive response. We modeled the constitutive response of the AA5182 aluminum using a state variable approach, the value of the state variable is a function of the temperature and strain-rate history of deformation. Thus, the recrystallization kinetics is a function of only the state variable and free energy for recrystallization. There are no adjustable exponents as in classical theory. Using this approach combined with engineering recrystallization data we have been able to predict the kinetics of recrystallization in AA5182 as a function of deformation strain rate and temperature.« less

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques.

PubMed

Goo, Yeung-Ja James; Chi, Der-Jang; Shen, Zong-De

2016-01-01

The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

PREDICT: Pattern Representation and Evaluation of Data through Integration, Correlation, and Transformation

DTIC Science & Technology

2015-02-01

than 36ºC; 2) heart rate > 90 beats/min; 3) respiratory rate > 20 breaths/min; and 4) white blood cell count > 12,000/mm3 or < 4,000/mm3 which more than...4) white blood cell count 5) heart rate variability 6) blood pressure The challenge is that once these criteria are met, it is often the case that...Figure 7) was not actually meant to be read (no individual variables or numbers). We believed that showing an increasing size (and color and

A diffusion decision model analysis of evidence variability in the lexical decision task.

PubMed

Tillman, Gabriel; Osth, Adam F; van Ravenzwaaij, Don; Heathcote, Andrew

2017-12-01

The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decision Model (DDM; Ratcliff, Gomez, & McKoon, Psychological Review, 111, 159-182, 2004) frameworks, lexical-decisions are based on a continuous source of word-likeness evidence for both words and non-words. The Retrieving Effectively from Memory model of Lexical-Decision (REM-LD; Wagenmakers et al., Cognitive Psychology, 48(3), 332-367, 2004) provides a comprehensive explanation of lexical-decision data and makes the prediction that word-likeness evidence is more variable for words than non-words and that higher frequency words are more variable than lower frequency words. To test these predictions, we analyzed five lexical-decision data sets with the DDM. For all data sets, drift-rate variability changed across word frequency and non-word conditions. For the most part, REM-LD's predictions about the ordering of evidence variability across stimuli in the lexical-decision task were confirmed.

Predicting ecosystem stability from community composition and biodiversity.

PubMed

de Mazancourt, Claire; Isbell, Forest; Larocque, Allen; Berendse, Frank; De Luca, Enrica; Grace, James B; Haegeman, Bart; Wayne Polley, H; Roscher, Christiane; Schmid, Bernhard; Tilman, David; van Ruijven, Jasper; Weigelt, Alexandra; Wilsey, Brian J; Loreau, Michel

2013-05-01

As biodiversity is declining at an unprecedented rate, an important current scientific challenge is to understand and predict the consequences of biodiversity loss. Here, we develop a theory that predicts the temporal variability of community biomass from the properties of individual component species in monoculture. Our theory shows that biodiversity stabilises ecosystems through three main mechanisms: (1) asynchrony in species' responses to environmental fluctuations, (2) reduced demographic stochasticity due to overyielding in species mixtures and (3) reduced observation error (including spatial and sampling variability). Parameterised with empirical data from four long-term grassland biodiversity experiments, our prediction explained 22-75% of the observed variability, and captured much of the effect of species richness. Richness stabilised communities mainly by increasing community biomass and reducing the strength of demographic stochasticity. Our approach calls for a re-evaluation of the mechanisms explaining the effects of biodiversity on ecosystem stability. © 2013 Blackwell Publishing Ltd/CNRS.

Predicting ecosystem stability from community composition and biodiversity

USGS Publications Warehouse

Mazancourt, Claire de; Isbell, Forest; Larocque, Allen; Berendse, Frank; De Luca, Enrica; Grace, James B.; Haegeman, Bart; Polley, H. Wayne; Roscher, Christiane; Schmid, Bernhard; Tilman, David; van Ruijven, Jasper; Weigelt, Alexandra; Wilsey, Brian J.; Loreau, Michel

2013-01-01

As biodiversity is declining at an unprecedented rate, an important current scientific challenge is to understand and predict the consequences of biodiversity loss. Here, we develop a theory that predicts the temporal variability of community biomass from the properties of individual component species in monoculture. Our theory shows that biodiversity stabilises ecosystems through three main mechanisms: (1) asynchrony in species’ responses to environmental fluctuations, (2) reduced demographic stochasticity due to overyielding in species mixtures and (3) reduced observation error (including spatial and sampling variability). Parameterised with empirical data from four long-term grassland biodiversity experiments, our prediction explained 22–75% of the observed variability, and captured much of the effect of species richness. Richness stabilised communities mainly by increasing community biomass and reducing the strength of demographic stochasticity. Our approach calls for a re-evaluation of the mechanisms explaining the effects of biodiversity on ecosystem stability.

The contribution of coping related variables and cardiac vagal activity on the performance of a dart throwing task under pressure.

PubMed

Mosley, Emma; Laborde, Sylvain; Kavanagh, Emma

2017-10-01

The aims of this study were 1) to assess the predictive role of coping related variables (CRV) on cardiac vagal activity (derived from heart rate variability), and 2) to investigate the influence of CRV (including cardiac vagal activity) on a dart throwing task under low pressure (LP) and high pressure (HP) conditions. Participants (n=51) completed trait CRV questionnaires: Decision Specific Reinvestment Scale, Movement Specific Reinvestment Scale and Trait Emotional Intelligence Questionnaire. They competed in a dart throwing task under LP and HP conditions. Cardiac vagal activity measurements were taken at resting, task and during recovery for 5min. Self-reported ratings of stress were recorded at three time points via a visual analogue scale. Upon completion of the task, self-report measures of motivation, stress appraisal, attention, perceived pressure and dart throwing experience were completed. Results indicated that resting cardiac vagal activity had no predictors. Task cardiac vagal activity was predicted by resting cardiac vagal activity in both pressure conditions with the addition of a trait CRV in HP. Post task cardiac vagal activity was predicted by resting cardiac vagal activity in both conditions with the addition of a trait CRV in HP. Cardiac vagal reactivity (difference from resting to task) was predicted by a trait CRV in HP conditions. Cardiac vagal recovery (difference from task to post task) was predicted by a state CRV only in LP. Dart throwing task performance was predicted by a combination of both CRV and cardiac vagal activity. The current research suggests that coping related variables and cardiac vagal activity influence dart throwing task performance differently dependent on pressure condition. Copyright © 2017 Elsevier Inc. All rights reserved.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Variability in personality expression across contexts: a social network approach.

PubMed

Clifton, Allan

2014-04-01

The current research investigated how the contextual expression of personality differs across interpersonal relationships. Two related studies were conducted with college samples (Study 1: N = 52, 38 female; Study 2: N = 111, 72 female). Participants in each study completed a five-factor measure of personality and constructed a social network detailing their 30 most important relationships. Participants used a brief Five-Factor Model scale to rate their personality as they experience it when with each person in their social network. Multiple informants selected from each social network then rated the target participant's personality (Study 1: N = 227, Study 2: N = 777). Contextual personality ratings demonstrated incremental validity beyond standard global self-report in predicting specific informants' perceptions. Variability in these contextualized personality ratings was predicted by the position of the other individuals within the social network. Across both studies, participants reported being more extraverted and neurotic, and less conscientious, with more central members of their social networks. Dyadic social network-based assessments of personality provide incremental validity in understanding personality, revealing dynamic patterns of personality variability unobservable with standard assessment techniques. © 2013 Wiley Periodicals, Inc.

Teacher and Student Variables Affecting Special Education Evaluation and Referral

ERIC Educational Resources Information Center

Woodson, Lorenzo Adrian

2017-01-01

Past research has revealed that African American/Black boys are referred for special education evaluation at disproportionately higher rates than boys of other racial/ethnic groups. This correlational study used survey methodology to examine whether student and teacher demographic variables predicted how likely a teacher would refer boy students…

Application of neural networks and sensitivity analysis to improved prediction of trauma survival.

PubMed

Hunter, A; Kennedy, L; Henry, J; Ferguson, I

2000-05-01

The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.

Individual differences in the day-to-day variability of pain, fatigue, and well-being in patients with rheumatic disease: Associations with psychological variables

PubMed Central

Schneider, Stefan; Junghaenel, Doerte U.; Keefe, Francis J.; Schwartz, Joseph E.; Stone, Arthur A.; Broderick, Joan E.

2012-01-01

This paper examines day-to-day variability in rheumatology patients' ratings of pain and related quality-of-life variables as well as predictors of that variability. Data from two studies were used. The hypothesis was that greater psychological distress (i.e., depression and anxiety) and poorer coping appraisals (i.e., higher pain catastrophizing and lower self-efficacy) are associated with more variability. Electronic daily diary ratings were collected from 106 patients from a community rheumatology practice across 28 days (Study 1), and from 194 osteoarthritis patients across 7 days (Study 2). In multilevel modeling analyses, substantial day-to-day variability was evident for all variables in both studies, andindividual patients differed considerably and somewhat reliably in the magnitude of their variability. Higher levels of depression significantly predicted greater variability in pain, as well as in happiness and frustration (Study 1). Lower self-efficacy was associated with more variability in patients' daily satisfaction with accomplishments and in the quality of their day (Study 2). Greater pain catastrophizing and higher depression predicted more variability in interference with social relationships (Study 2). Anxiety was not significantly associated with day-to-day variability. The results of these studies suggest that individual differences in the magnitude of symptom fluctuation may play a vital role in understanding patients' adjustment to pain. Future research will be needed to examine the clinical utility of measuring variability in patients' pain and well being, and to understand whether reducing variability may be an important treatment target. PMID:22349917

Short-term predictions in forex trading

NASA Astrophysics Data System (ADS)

Muriel, A.

2004-12-01

Using a kinetic equation that is used to model turbulence (Physica A, 1985-1988, Physica D, 2001-2003), we redefine variables to model the time evolution of the foreign exchange rates of three major currencies. We display live and predicted data for one period of trading in October, 2003.

Single neuropsychological test scores associated with rate of cognitive decline in early Alzheimer disease.

PubMed

Parikh, Mili; Hynan, Linda S; Weiner, Myron F; Lacritz, Laura; Ringe, Wendy; Cullum, C Munro

2014-01-01

Alzheimer disease (AD) characteristically begins with episodic memory impairment followed by other cognitive deficits; however, the course of illness varies, with substantial differences in the rate of cognitive decline. For research and clinical purposes it would be useful to distinguish between persons who will progress slowly from persons who will progress at an average or faster rate. Our objective was to use neurocognitive performance features and disease-specific and health information to determine a predictive model for the rate of cognitive decline in participants with mild AD. We reviewed the records of a series of 96 consecutive participants with mild AD from 1995 to 2011 who had been administered selected neurocognitive tests and clinical measures. Based on Clinical Dementia Rating (CDR) of functional and cognitive decline over 2 years, participants were classified as Faster (n = 45) or Slower (n = 51) Progressors. Stepwise logistic regression analyses using neurocognitive performance features, disease-specific, health, and demographic variables were performed. Neuropsychological scores that distinguished Faster from Slower Progressors included Trail Making Test - A, Digit Symbol, and California Verbal Learning Test (CVLT) Total Learned and Primacy Recall. No disease-specific, health, or demographic variable predicted rate of progression; however, history of heart disease showed a trend. Among the neuropsychological variables, Trail Making Test - A best distinguished Faster from Slower Progressors, with an overall accuracy of 68%. In an omnibus model including neuropsychological, disease-specific, health, and demographic variables, only Trail Making Test - A distinguished between groups. Several neuropsychological performance features were associated with the rate of cognitive decline in mild AD, with baseline Trail Making Test - A performance best separating those who declined at an average or faster rate from those who showed slower progression.

Prediction of surface roughness in turning of Ti-6Al-4V using cutting parameters, forces and tool vibration

NASA Astrophysics Data System (ADS)

Sahu, Neelesh Kumar; Andhare, Atul B.; Andhale, Sandip; Raju Abraham, Roja

2018-04-01

Present work deals with prediction of surface roughness using cutting parameters along with in-process measured cutting force and tool vibration (acceleration) during turning of Ti-6Al-4V with cubic boron nitride (CBN) inserts. Full factorial design is used for design of experiments using cutting speed, feed rate and depth of cut as design variables. Prediction model for surface roughness is developed using response surface methodology with cutting speed, feed rate, depth of cut, resultant cutting force and acceleration as control variables. Analysis of variance (ANOVA) is performed to find out significant terms in the model. Insignificant terms are removed after performing statistical test using backward elimination approach. Effect of each control variables on surface roughness is also studied. Correlation coefficient (R2 pred) of 99.4% shows that model correctly explains the experiment results and it behaves well even when adjustment is made in factors or new factors are added or eliminated. Validation of model is done with five fresh experiments and measured forces and acceleration values. Average absolute error between RSM model and experimental measured surface roughness is found to be 10.2%. Additionally, an artificial neural network model is also developed for prediction of surface roughness. The prediction results of modified regression model are compared with ANN. It is found that RSM model and ANN (average absolute error 7.5%) are predicting roughness with more than 90% accuracy. From the results obtained it is found that including cutting force and vibration for prediction of surface roughness gives better prediction than considering only cutting parameters. Also, ANN gives better prediction over RSM models.

Development of uniform and predictable battery materials for nickel-cadmium aerospace cells

NASA Technical Reports Server (NTRS)

1971-01-01

Battery materials and manufacturing methods were analyzed with the aim of developing uniform and predictable battery plates for nickel cadmium aerospace cells. A study is presented for the high temperature electrochemical impregnation process for the preparation of nickel cadmium battery plates. This comparative study is set up as a factorially designed experiment to examine both manufacturing and operational variables and any interaction that might exist between them. The manufacturing variables in the factorial design include plaque preparative method, plaque porosity and thickness, impregnation method, and loading, The operational variables are type of duty cycle, charge and discharge rate, extent of overcharge, and depth of discharge.

Violence in inpatients with schizophrenia: a prospective study.

PubMed

Arango, C; Calcedo Barba, A; González-Salvador; Calcedo Ordóñez, A

1999-01-01

Accurate evaluations of the dangers posed by psychiatric inpatients are necessary, although a number of studies have questioned the accuracy of violence prediction. In this prospective study, we evaluated several variables in the prediction of violence in 63 inpatients with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder. Nurses rated violent incidents with the Overt Aggression Scale. During hospitalization, sociodemographic variables, clinical history, neurological soft signs, community alcohol or drug abuse, and electroencephalographic abnormalities did not differ between violent and nonviolent groups. Violent patients had significantly more positive symptoms as measured by the Positive and Negative Syndrome Scale (PANSS), higher scores on the PANSS general psychopathology scale, and less insight in the different constructs assessed. A logistic regression was performed to discriminate between violent and nonviolent patients. Three variables entered the model: insight into symptoms, PANSS general psychopathology score, and violence in the previous week. The actuarial model correctly classified 84.13 percent of the sample; this result is significantly better than chance for the base rate of violence in this study. At hospital admission, clinical rather than sociodemographic variables were more predictive of violence. This finding has practical importance because clinical symptoms are amenable to therapeutic approaches. This study is the first to demonstrate that insight into psychotic symptoms is a predictor of violence in inpatients with schizophrenia.

Investigation of the Process Conditions for Hydrogen Production by Steam Reforming of Glycerol over Ni/Al₂O₃ Catalyst Using Response Surface Methodology (RSM).

PubMed

Ebshish, Ali; Yaakob, Zahira; Taufiq-Yap, Yun Hin; Bshish, Ahmed

2014-03-19

In this work; a response surface methodology (RSM) was implemented to investigate the process variables in a hydrogen production system. The effects of five independent variables; namely the temperature (X₁); the flow rate (X₂); the catalyst weight (X₃); the catalyst loading (X₄) and the glycerol-water molar ratio (X₅) on the H₂ yield (Y₁) and the conversion of glycerol to gaseous products (Y₂) were explored. Using multiple regression analysis; the experimental results of the H₂ yield and the glycerol conversion to gases were fit to quadratic polynomial models. The proposed mathematical models have correlated the dependent factors well within the limits that were being examined. The best values of the process variables were a temperature of approximately 600 °C; a feed flow rate of 0.05 mL/min; a catalyst weight of 0.2 g; a catalyst loading of 20% and a glycerol-water molar ratio of approximately 12; where the H₂ yield was predicted to be 57.6% and the conversion of glycerol was predicted to be 75%. To validate the proposed models; statistical analysis using a two-sample t -test was performed; and the results showed that the models could predict the responses satisfactorily within the limits of the variables that were studied.

Testing a Multi-Stage Screening System: Predicting Performance on Australia's National Achievement Test Using Teachers' Ratings of Academic and Social Behaviors

ERIC Educational Resources Information Center

Kettler, Ryan J.; Elliott, Stephen N.; Davies, Michael; Griffin, Patrick

2012-01-01

This study addresses the predictive validity of results from a screening system of academic enablers, with a sample of Australian elementary school students, when the criterion variable is end-of-year achievement. The investigation included (a) comparing the predictive validity of a brief criterion-referenced nomination system with more…

Predawn respiration rates during flowering are highly predictive of yield response in Gossypium hirsutum when yield variability is water-induced

USDA-ARS?s Scientific Manuscript database

Respiratory carbon evolution by leaves under abiotic stress is implicated as a major limitation to crop productivity; however, respiration rates of fully expanded leaves are positively associated with plant growth rates. Given the substantial sensitivity of plant growth to drought, it was hypothesiz...

Assessment of general movements and heart rate variability in prediction of neurodevelopmental outcome in preterm infants.

PubMed

Dimitrijević, Lidija; Bjelaković, Bojko; Čolović, Hristina; Mikov, Aleksandra; Živković, Vesna; Kocić, Mirjana; Lukić, Stevo

2016-08-01

Adverse neurologic outcome in preterm infants could be associated with abnormal heart rate (HR) characteristics as well as with abnormal general movements (GMs) in the 1st month of life. To demonstrate to what extent GMs assessment can predict neurological outcome in preterm infants in our clinical setting; and to assess the clinical usefulness of time-domain indices of heart rate variability (HRV) in improving predictive value of poor repertoire (PR) GMs in writhing period. Qualitative assessment of GMs at 1 and 3 months corrected age; 24h electrocardiography (ECG) recordings and analyzing HRV at 1 month corrected age. Seventy nine premature infants at risk of neurodevelopmental impairments were included prospectively. Neurodevelopmental outcome was assessed at the age of 2 years corrected. Children were classified as having normal neurodevelopmental status, minor neurologic dysfunction (MND), or cerebral palsy (CP). We found that GMs in writhing period (1 month corrected age) predicted CP at 2 years with sensitivity of 100%, and specificity of 72.1%. Our results demonstrated the excellent predictive value of cramped synchronized (CS) GMs, but not of PR pattern. Analyzing separately a group of infants with PR GMs we found significantly lower values of HRV parameters in infants who later developed CP or MND vs. infants with PR GMs who had normal outcome. The quality of GMs was predictive for neurodevelopmental outcome at 2 years. Prediction of PR GMs was significantly enhanced with analyzing HRV parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Continuously Variable Rating: a new, simple and logical procedure to evaluate original scientific publications

PubMed Central

Silva, Mauricio Rocha e

2011-01-01

OBJECTIVE: Impact Factors (IF) are widely used surrogates to evaluate single articles, in spite of known shortcomings imposed by cite distribution skewness. We quantify this asymmetry and propose a simple computer-based procedure for evaluating individual articles. METHOD: (a) Analysis of symmetry. Journals clustered around nine Impact Factor points were selected from the medical “Subject Categories” in Journal Citation Reports 2010. Citable items published in 2008 were retrieved and ranked by granted citations over the Jan/2008 - Jun/2011 period. Frequency distribution of cites, normalized cumulative cites and absolute cites/decile were determined for each journal cluster. (b) Positive Predictive Value. Three arbitrarily established evaluation classes were generated: LOW (1.3≤IF<2.6); MID: (2.6≤IF<3.9); HIGH: (IF≥3.9). Positive Predictive Value for journal clusters within each class range was estimated. (c) Continuously Variable Rating. An alternative evaluation procedure is proposed to allow the rating of individually published articles in comparison to all articles published in the same journal within the same year of publication. The general guiding lines for the construction of a totally dedicated software program are delineated. RESULTS AND CONCLUSIONS: Skewness followed the Pareto Distribution for (1

Impacts of Considering Climate Variability on Investment Decisions in Ethiopia

NASA Astrophysics Data System (ADS)

Strzepek, K.; Block, P.; Rosegrant, M.; Diao, X.

2005-12-01

In Ethiopia, climate extremes, inducing droughts or floods, are not unusual. Monitoring the effects of these extremes, and climate variability in general, is critical for economic prediction and assessment of the country's future welfare. The focus of this study involves adding climate variability to a deterministic, mean climate-driven agro-economic model, in an attempt to understand its effects and degree of influence on general economic prediction indicators for Ethiopia. Four simulations are examined, including a baseline simulation and three investment strategies: simulations of irrigation investment, roads investment, and a combination investment of both irrigation and roads. The deterministic model is transformed into a stochastic model by dynamically adding year-to-year climate variability through climate-yield factors. Nine sets of actual, historic, variable climate data are individually assembled and implemented into the 12-year stochastic model simulation, producing an ensemble of economic prediction indicators. This ensemble allows for a probabilistic approach to planning and policy making, allowing decision makers to consider risk. The economic indicators from the deterministic and stochastic approaches, including rates of return to investments, are significantly different. The predictions of the deterministic model appreciably overestimate the future welfare of Ethiopia; the predictions of the stochastic model, utilizing actual climate data, tend to give a better semblance of what may be expected. Inclusion of climate variability is vital for proper analysis of the predictor values from this agro-economic model.

Discrepancies between self and observer ratings of depression. The relationship to demographic, clinical and personality variables.

PubMed

Enns, M W; Larsen, D K; Cox, B J

2000-10-01

The observer-rated Hamilton depression scale (HamD) and the self-report Beck Depression Inventory (BDI) are among the most commonly used rating scales for depression, and both have well demonstrated reliability and validity. However, many depressed subjects have discrepant scores on these two assessment methods. The present study evaluated the ability of demographic, clinical and personality factors to account for the discrepancies observed between BDI and HamD ratings. The study group consisted of 94 SCID-diagnosed outpatients with a current major depressive disorder. Subjects were rated with the 21-item HamD and completed the BDI and the NEO-Five Factor Inventory. Younger age, higher educational attainment, and depressive subtype (atypical, non-melancholic) were predictive of higher BDI scores relative to HamD observer ratings. In addition, high neuroticism, low extraversion and low agreeableness were associated with higher endorsement of depressive symptoms on the BDI relative to the HamD. In general, these predictive variables showed a greater ability to explain discrepancies between self and observer ratings of psychological symptoms of depression compared to somatic symptoms of depression. The study does not determine which aspects of neuroticism and extraversion contribute to the observed BDI/HamD discrepancies. Depression ratings obtained with the BDI and HamD are frequently discordant and a number of patient characteristics robustly predict the discrepancy between these two rating methods. The value of multi-modal assessment in the conduct of research on depressive disorders is re-affirmed.

Predawn respiration rates during flowering are highly predictive of yield response in Gossypium hirsutum when yield variability is water-induced.

PubMed

Snider, John L; Chastain, Daryl R; Meeks, Calvin D; Collins, Guy D; Sorensen, Ronald B; Byrd, Seth A; Perry, Calvin D

2015-07-01

Respiratory carbon evolution by leaves under abiotic stress is implicated as a major limitation to crop productivity; however, respiration rates of fully expanded leaves are positively associated with plant growth rates. Given the substantial sensitivity of plant growth to drought, it was hypothesized that predawn respiration rates (RPD) would be (1) more sensitive to drought than photosynthetic processes and (2) highly predictive of water-induced yield variability in Gossypium hirsutum. Two studies (at Tifton and Camilla Georgia) addressed these hypotheses. At Tifton, drought was imposed beginning at the onset of flowering (first flower) and continuing for three weeks (peak bloom) followed by a recovery period, and predawn water potential (ΨPD), RPD, net photosynthesis (AN) and maximum quantum yield of photosystem II (Fv/Fm) were measured throughout the study period. At Camilla, plants were exposed to five different irrigation regimes throughout the growing season, and average ΨPD and RPD were determined between first flower and peak bloom for all treatments. For both sites, fiber yield was assessed at crop maturity. The relationships between ΨPD, RPD and yield were assessed via non-linear regression. It was concluded for field-grown G. hirsutum that (1) RPD is exceptionally sensitive to progressive drought (more so than AN or Fv/Fm) and (2) average RPD from first flower to peak bloom is highly predictive of water-induced yield variability. Copyright © 2015 Elsevier GmbH. All rights reserved.

Impaired heart rate variability and altered cardiac sympathovagal balance after antidepressant overdose.

PubMed

Waring, W S; Rhee, J Y; Bateman, D N; Leggett, G E; Jamie, H

2008-11-01

Antidepressant overdose may be associated with significant cardiotoxicity, and recent data have shown that acute toxic effects are associated with impaired heart rate variability. This study was designed to examine the feasibility of non-invasive heart rate variability recording in patients that present to hospital after deliberate antidepressant ingestion. This was a prospective study of 72 consecutive patients attending the Emergency Department after deliberate antidepressant overdose and 72 age-matched patients that ingested paracetamol, as a control group. Single time-point continuous electrocardiographic recordings were used to allow spectral analyses of heart rate variability determined in low-frequency (LF) and high-frequency (HF) domains. The LF:HF ratio was used to represent overall sympathovagal cardiac activity. Antidepressant overdose was associated with reduced overall heart rate variability: 1329 vs. 2018 ms(2) (P = 0.0239 by Mann-Whitney test). Variability in the LF domain was higher (64.8 vs. 49.8, P = 0.0006), whereas that in the HF domain was lower (24.3 vs. 36.4, P = 0.0001), and the LF:HF ratio was higher in the antidepressant group (2.4 vs. 1.2, P = 0.0003). Antidepressant overdose is associated with impaired heart rate variability in a pattern consistent with excess cardiac sympathetic activity. Further work is required to establish the significance of these findings and to explore whether the impairment of heart rate variability may be used to predict the development of arrhythmia in this patient group.

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data.

PubMed

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J; Kim, Doh Kwan

2018-04-01

Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.

Weighted partial least squares based on the error and variance of the recovery rate in calibration set.

PubMed

Yu, Shaohui; Xiao, Xue; Ding, Hong; Xu, Ge; Li, Haixia; Liu, Jing

2017-08-05

The quantitative analysis is very difficult for the emission-excitation fluorescence spectroscopy of multi-component mixtures whose fluorescence peaks are serious overlapping. As an effective method for the quantitative analysis, partial least squares can extract the latent variables from both the independent variables and the dependent variables, so it can model for multiple correlations between variables. However, there are some factors that usually affect the prediction results of partial least squares, such as the noise, the distribution and amount of the samples in calibration set etc. This work focuses on the problems in the calibration set that are mentioned above. Firstly, the outliers in the calibration set are removed by leave-one-out cross-validation. Then, according to two different prediction requirements, the EWPLS method and the VWPLS method are proposed. The independent variables and dependent variables are weighted in the EWPLS method by the maximum error of the recovery rate and weighted in the VWPLS method by the maximum variance of the recovery rate. Three organic matters with serious overlapping excitation-emission fluorescence spectroscopy are selected for the experiments. The step adjustment parameter, the iteration number and the sample amount in the calibration set are discussed. The results show the EWPLS method and the VWPLS method are superior to the PLS method especially for the case of small samples in the calibration set. Copyright © 2017 Elsevier B.V. All rights reserved.

Changes in monthly unemployment rates may predict changes in the number of psychiatric presentations to emergency services in South Australia.

PubMed

Bidargaddi, Niranjan; Bastiampillai, Tarun; Schrader, Geoffrey; Adams, Robert; Piantadosi, Cynthia; Strobel, Jörg; Tucker, Graeme; Allison, Stephen

2015-07-24

To determine the extent to which variations in monthly Mental Health Emergency Department (MHED) presentations in South Australian Public Hospitals are associated with the Australian Bureau of Statistics (ABS) monthly unemployment rates. Times series modelling of relationships between monthly MHED presentations to South Australian Public Hospitals derived from the Integrated South Australian Activity Collection (ISAAC) data base and the ABS monthly unemployment rates in South Australia between January 2004-June 2011. Time series modelling using monthly unemployment rates from ABS as a predictor variable explains 69% of the variation in monthly MHED presentations across public hospitals in South Australia. Thirty-two percent of the variation in current month's male MHED presentations can be predicted by using the 2 months' prior male unemployment rate. Over 63% of the variation in monthly female MHED presentations can be predicted by either male or female prior monthly unemployment rates. The findings of this study highlight that even with the relatively favourable economic conditions, small shifts in monthly unemployment rates can predict variations in monthly MHED presentations, particularly for women. Monthly ABS unemployment rates may be a useful metric for predicting demand for emergency mental health services.

Early prediction of extreme stratospheric polar vortex states based on causal precursors

NASA Astrophysics Data System (ADS)

Kretschmer, Marlene; Runge, Jakob; Coumou, Dim

2017-08-01

Variability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead time because they poorly capture low-frequency processes. Empirical models often suffer from overfitting problems as the relevant physical processes and time lags are often not well understood. Here we introduce a novel empirical prediction method by uniting a response-guided community detection scheme with a causal discovery algorithm. This way, we objectively identify causal precursors of the SPV at subseasonal lead times and find them to be in good agreement with known physical drivers. A linear regression prediction model based on the causal precursors can explain most SPV variability (r2 = 0.58), and our scheme correctly predicts 58% (46%) of extremely weak SPV states for lead times of 1-15 (16-30) days with false-alarm rates of only approximately 5%. Our method can be applied to any variable relevant for (sub)seasonal weather forecasts and could thus help improving long-lead predictions.

Estimating degradation in real time and accelerated stability tests with random lot-to-lot variation: a simulation study.

PubMed

Magari, Robert T

2002-03-01

The effect of different lot-to-lot variability levels on the prediction of stability are studied based on two statistical models for estimating degradation in real time and accelerated stability tests. Lot-to-lot variability is considered as random in both models, and is attributed to two sources-variability at time zero, and variability of degradation rate. Real-time stability tests are modeled as a function of time while accelerated stability tests as a function of time and temperatures. Several data sets were simulated, and a maximum likelihood approach was used for estimation. The 95% confidence intervals for the degradation rate depend on the amount of lot-to-lot variability. When lot-to-lot degradation rate variability is relatively large (CV > or = 8%) the estimated confidence intervals do not represent the trend for individual lots. In such cases it is recommended to analyze each lot individually. Copyright 2002 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 91: 893-899, 2002

Heart rate variability reactivity and new romance: Cause or consequence?

PubMed

Bailey, Laura K; Davis, Ron

2017-09-01

There are documented physiological differences between single and coupled individuals during the "honeymoon period" of nascent romantic relationships. One such difference is in autonomic reactivity, specifically heart rate variability (HRV) reactivity. This finding had previously been interpreted as evidence of a stress buffering effect of relationship formation. The present study explored among university women two competing longitudinal hypotheses conceptualizing differences in HRV reactivity as either a cause or a consequence of romantic relationship formation. Results did not support the hypothesis that HRV reactivity changes as a consequence of beginning a new romantic relationship. Instead, lower HRV reactivity predicted greater relationship formation amongst women with low BMI and higher resting HRV. The functioning of the heart therefore predicted the likelihood that an individual would find love. These interactions may be the result of differing success rates of various mating strategies for women with low and high BMI and HRV. Copyright © 2017 Elsevier B.V. All rights reserved.

Intrapartum fetal heart rate monitoring: evaluation of a standardized system of interpretation for prediction of metabolic acidosis at delivery and neonatal neurological morbidity.

PubMed

Soncini, Emanuele; Paganelli, Simone; Vezzani, Cristina; Gargano, Giancarlo; Giovanni Battista, La Sala

2014-09-01

To assess the ability of the intrapartum fetal heart rate interpretation system developed in 2008 by the National Institute of Child Health and Human Development (NICHD) to predict fetal metabolic acidosis at delivery and neonatal neurological morbidity. We analyzed the intrapartum fetal heart rate tracings of 314 singleton fetuses at ≥ 37 weeks using the NICHD three-tier system of interpretation: Category I (normal), Category II (indeterminate) and Category III (abnormal). Category II was further divided into Category IIA, with moderate fetal heart rate variability or accelerations, and Category IIB, with minimal/absent fetal heart rate variability and no accelerations. The presence and duration of the different patterns were compared with several clinical neonatal outcomes and with umbilical artery acid-base balance at birth. The mean values of pH and base excess decreased proportionally as tracings worsened (p < 0.001). The duration of at least 30 min for Category III tracings was highly predictive of a pH <7.00 and a base excess ≤-12 mmol/L. The same was true for the duration of Category IIB tracings that lasted for at least 50 min. Our study demonstrates that the interpretation of fetal heart rate tracings based on a strictly standardized system is closely associated with umbilical artery acid-base status at delivery.

Exploiting temporal variability to understand tree recruitment response to climate change

Treesearch

Ines Ibanez; James S. Clark; Shannon LaDeau; Janneke Hill Ris Lambers

2007-01-01

Predicting vegetation shifts under climate change is a challenging endeavor, given the complex interactions between biotic and abiotic variables that influence demographic rates. To determine how current trends and variation in climate change affect seedling establishment, we analyzed demographic responses to spatiotemporal variation to temperature and soil moisture in...

Predicting the Health of a Natural Water System

ERIC Educational Resources Information Center

Graves, Gregory H.

2010-01-01

This project was developed as an interdisciplinary application of the optimization of a single-variable function. It was used in a freshman-level single-variable calculus course. After the first month of the course, students had been exposed to the concepts of the derivative as a rate of change, average and instantaneous velocities, derivatives of…

An empirical model for prediction of household solid waste generation rate - A case study of Dhanbad, India.

PubMed

Kumar, Atul; Samadder, S R

2017-10-01

Accurate prediction of the quantity of household solid waste generation is very much essential for effective management of municipal solid waste (MSW). In actual practice, modelling methods are often found useful for precise prediction of MSW generation rate. In this study, two models have been proposed that established the relationships between the household solid waste generation rate and the socioeconomic parameters, such as household size, total family income, education, occupation and fuel used in the kitchen. Multiple linear regression technique was applied to develop the two models, one for the prediction of biodegradable MSW generation rate and the other for non-biodegradable MSW generation rate for individual households of the city Dhanbad, India. The results of the two models showed that the coefficient of determinations (R 2 ) were 0.782 for biodegradable waste generation rate and 0.676 for non-biodegradable waste generation rate using the selected independent variables. The accuracy tests of the developed models showed convincing results, as the predicted values were very close to the observed values. Validation of the developed models with a new set of data indicated a good fit for actual prediction purpose with predicted R 2 values of 0.76 and 0.64 for biodegradable and non-biodegradable MSW generation rate respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

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

Approximate convective heating equations for hypersonic flows

NASA Technical Reports Server (NTRS)

Zoby, E. V.; Moss, J. N.; Sutton, K.

1979-01-01

Laminar and turbulent heating-rate equations appropriate for engineering predictions of the convective heating rates about blunt reentry spacecraft at hypersonic conditions are developed. The approximate methods are applicable to both nonreacting and reacting gas mixtures for either constant or variable-entropy edge conditions. A procedure which accounts for variable-entropy effects and is not based on mass balancing is presented. Results of the approximate heating methods are in good agreement with existing experimental results as well as boundary-layer and viscous-shock-layer solutions.

Biobehavioral Triggers of Cardiac Arrhythmia during Daily Life: The Role of Emotion, Physical Activity, and Heart Rate Variability

DTIC Science & Technology

2004-01-01

chronically reduced HRV (e.g., Kawachi, et al, 1995). Heart rate variability among individuals without psychopathology, but with high levels of trait anxiety ...Figure 1.) After controlling for ejection fraction, anxiety scores remained predictive of all of the aforementioned measures of HRV (VLF r = -.49, p...associated with any measure of HRV (r = .07 to -.30, p = .80 to .26). Results regarding anxiety and HRV were unchanged after analyzing participants with

Variability and predictors of negative mood intensity in patients with borderline personality disorder and recurrent suicidal behavior: multilevel analyses applied to experience sampling methodology.

PubMed

Nisenbaum, Rosane; Links, Paul S; Eynan, Rahel; Heisel, Marnin J

2010-05-01

Variability in mood swings is a characteristic of borderline personality disorder (BPD) and is associated with suicidal behavior. This study investigated patterns of mood variability and whether such patterns could be predicted from demographic and suicide-related psychological risk factors. Eighty-two adults with BPD and histories of recurrent suicidal behavior were recruited from 3 outpatient psychiatric programs in Canada. Experience sampling methodology (ESM) was used to assess negative mood intensity ratings on a visual analogue scale, 6 random times daily, for 21 days. Three-level models estimated variability between times (52.8%), days (22.2%), and patients (25.1%) and supported a quadratic pattern of daily mood variability. Depression scores predicted variability between patients' initial rating of the day. Average daily mood patterns depended on levels of hopelessness, suicide ideation, and sexual abuse history. Patients reporting moderate to severe sexual abuse and elevated suicide ideation were characterized by worsening moods from early morning up through evening, with little or no relief; patients reporting mild sexual abuse and low suicide ideation reported improved mood throughout the day. These patterns, if replicated in larger ESM studies, may potentially assist the clinician in determining which patients require close monitoring.

Increases in Parent Attendance to Behavioral Parent Training Due to Concurrent Child Treatment Groups

ERIC Educational Resources Information Center

Jensen, Scott A.; Grimes, Lisa K.

2010-01-01

Though behavioral parent training has been demonstrated to be an effective intervention for child behavior problems, it continues to suffer from high attrition rates. Few variables have been found to predict or decrease high attrition rates from parent training classes. The present study found 43-52% increases in attendance rates for parents whose…

A Reconceptualization of the Adaptability Rating for Military Aviation

DTIC Science & Technology

2017-01-01

7 LIST OF TABLES Page Table 1. Flying Adaptability Rating System in 1931...colleagues, and one that satisfies the needs of the system by fostering cooperation with Line leadership. 2 DISTRIBUTION STATEMENT A. Approved for...came the Flying Adaptability Rating. Assigning value to certain variables, it used a grading system in an attempt to predict whether the candidate

A database of 629 English compound words: ratings of familiarity, lexeme meaning dominance, semantic transparency, age of acquisition, imageability, and sensory experience.

PubMed

Juhasz, Barbara J; Lai, Yun-Hsuan; Woodcock, Michelle L

2015-12-01

Since the work of Taft and Forster (1976), a growing literature has examined how English compound words are recognized and organized in the mental lexicon. Much of this research has focused on whether compound words are decomposed during recognition by manipulating the word frequencies of their lexemes. However, many variables may impact morphological processing, including relational semantic variables such as semantic transparency, as well as additional form-related and semantic variables. In the present study, ratings were collected on 629 English compound words for six variables [familiarity, age of acquisition (AoA), semantic transparency, lexeme meaning dominance (LMD), imageability, and sensory experience ratings (SER)]. All of the compound words selected for this study are contained within the English Lexicon Project (Balota et al., 2007), which made it possible to use a regression approach to examine the predictive power of these variables for lexical decision and word naming performance. Analyses indicated that familiarity, AoA, imageability, and SER were all significant predictors of both lexical decision and word naming performance when they were added separately to a model containing the length and frequency of the compounds, as well as the lexeme frequencies. In addition, rated semantic transparency also predicted lexical decision performance. The database of English compound words should be beneficial to word recognition researchers who are interested in selecting items for experiments on compound words, and it will also allow researchers to conduct further analyses using the available data combined with word recognition times included in the English Lexicon Project.

Effect of predictive sign of acceleration on heart rate variability in passive translation situation: preliminary evidence using visual and vestibular stimuli in VR environment

PubMed Central

Watanabe, Hiroshi; Teramoto, Wataru; Umemura, Hiroyuki

2007-01-01

Objective We studied the effects of the presentation of a visual sign that warned subjects of acceleration around the yaw and pitch axes in virtual reality (VR) on their heart rate variability. Methods Synchronization of the immersive virtual reality equipment (CAVE) and motion base system generated a driving scene and provided subjects with dynamic and wide-ranging depth information and vestibular input. The heart rate variability of 21 subjects was measured while the subjects observed a simulated driving scene for 16 minutes under three different conditions. Results When the predictive sign of the acceleration appeared 3500 ms before the acceleration, the index of the activity of the autonomic nervous system (low/high frequency ratio; LF/HF ratio) of subjects did not change much, whereas when no sign appeared the LF/HF ratio increased over the observation time. When the predictive sign of the acceleration appeared 750 ms before the acceleration, no systematic change occurred. Conclusion The visual sign which informed subjects of the acceleration affected the activity of the autonomic nervous system when it appeared long enough before the acceleration. Also, our results showed the importance of the interval between the sign and the event and the relationship between the gradual representation of events and their quantity. PMID:17903267

Smoker Characteristics and Smoking-Cessation Milestones

PubMed Central

Japuntich, Sandra J.; Leventhal, Adam M.; Piper, Megan E.; Bolt, Daniel M.; Roberts, Linda J.; Fiore, Michael C.; Baker, Timothy B.

2011-01-01

Background Contextual variables often predict long-term abstinence, but little is known about how these variables exert their effects. These variables could influence abstinence by affecting the ability to quit at all, or by altering risk of lapsing, or progressing from a lapse to relapse. Purpose To examine the effect of common predictors of smoking-cessation failure on smoking-cessation processes. Methods The current study (N = 1504, 58% female, 84% Caucasian; recruited from January 2005 to June 2007; data analyzed in 2009) uses the approach advocated by Shiffman et al., (2006), which measures cessation outcomes on three different cessation milestones (achieving initial abstinence, lapse risk, and the lapse-relapse transition) to examine relationships of smoker characteristics (dependence, contextual and demographic factors) with smoking-cessation process. Results High nicotine dependence strongly predicted all milestones: not achieving initial abstinence, and a higher risk of both lapse and transitioning from lapse to complete relapse. Numerous contextual and demographic variables were associated with higher initial cessation rates and/or decreased lapse risk at 6 months post-quit (e.g., ethnicity, gender, marital status, education, smoking in the workplace, number of smokers in the social network, and number of supportive others). However, aside from nicotine dependence, only gender significantly predicted the risk of transition from lapse to relapse. Conclusions These findings demonstrate that: (1) higher nicotine dependence predicted worse outcomes across every cessation milestone; (2) demographic and contextual variables are generally associated with initial abstinence rates and lapse risk and not the lapse-relapse transition. These results identify groups who are at risk for failure at specific stages of the smoking-cessation process, and this may have implications for treatment. PMID:21335259

Noise, stress, and annoyance in a pediatric intensive care unit.

PubMed

Morrison, Wynne E; Haas, Ellen C; Shaffner, Donald H; Garrett, Elizabeth S; Fackler, James C

2003-01-01

To measure and describe hospital noise and determine whether noise can be correlated with nursing stress measured by questionnaire, salivary amylase, and heart rate. Cohort observational study. Tertiary care center pediatric intensive care unit. Registered nurses working in the unit. None. Eleven nurse volunteers were recruited. An audiogram, questionnaire data, salivary amylase, and heart rate were collected in a quiet room. Each nurse was observed for a 3-hr period during patient care. Heart rate and sound level were recorded continuously; saliva samples and stress/annoyance ratings were collected every 30 mins. Variables assessed as potential confounders were years of nursing experience, caffeine intake, patients' Pediatric Risk of Mortality Score, shift assignment, and room assignment. Data were analyzed by random effects multiple linear regression using Stata 6.0. The average daytime sound level was 61 dB(A), nighttime 59 dB(A). Higher average sound levels significantly predicted higher heart rates (p =.014). Other significant predictors of tachycardia were higher caffeine intake, less nursing experience, and daytime shift. Ninety percent of the variability in heart rate was explained by the regression equation. Amylase measurements showed a large variability and were not significantly affected by noise levels. Higher average sound levels were also predictive of greater subjective stress (p =.021) and annoyance (p =.016). In this small study, noise was shown to correlate with several measures of stress including tachycardia and annoyance ratings. Further studies of interventions to reduce noise are essential.

Liver Transplantation for Fulminant Hepatic Failure

PubMed Central

Farmer, Douglas G.; Anselmo, Dean M.; Ghobrial, R. Mark; Yersiz, Hasan; McDiarmid, Suzanne V.; Cao, Carlos; Weaver, Michael; Figueroa, Jesus; Khan, Khurram; Vargas, Jorge; Saab, Sammy; Han, Steven; Durazo, Francisco; Goldstein, Leonard; Holt, Curtis; Busuttil, Ronald W.

2003-01-01

Objective To analyze outcomes after liver transplantation (LT) in patients with fulminant hepatic failure (FHF) with emphasis on pretransplant variables that can potentially help predict posttransplant outcome. Summary Background Data FHF is a formidable clinical problem associated with a high mortality rate. While LT is the treatment of choice for irreversible FHF, few investigations have examined pretransplant variables that can potentially predict outcome after LT. Methods A retrospective review was undertaken of all patients undergoing LT for FHF at a single transplant center. The median follow-up was 41 months. Thirty-five variables were analyzed by univariate and multivariate analysis to determine their impact on patient and graft survival. Results Two hundred four patients (60% female, median age 20.2 years) required urgent LT for FHF. Before LT, the majority of patients were comatose (76%), on hemodialysis (16%), and ICU-bound. The 1- and 5-year survival rates were 73% and 67% (patient) and 63% and 57% (graft). The primary cause of patient death was sepsis, and the primary cause of graft failure was primary graft nonfunction. Univariate analysis of pre-LT variables revealed that 19 variables predicted survival. From these results, multivariate analysis determined that the serum creatinine was the single most important prognosticator of patient survival. Conclusions This study, representing one of the largest published series on LT for FHF, demonstrates a long-term survival of nearly 70% and develops a clinically applicable and readily measurable set of pretransplant factors that determine posttransplant outcome. PMID:12724633

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.

PubMed

Samad, Manar D; Ulloa, Alvaro; Wehner, Gregory J; Jing, Linyuan; Hartzel, Dustin; Good, Christopher W; Williams, Brent A; Haggerty, Christopher M; Fornwalt, Brandon K

2018-06-09

The goal of this study was to use machine learning to more accurately predict survival after echocardiography. Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, there may be significant predictive information within additional echocardiography-derived measurements combined with clinical electronic health record data. Mortality was studied in 171,510 unselected patients who underwent 331,317 echocardiograms in a large regional health system. We investigated the predictive performance of nonlinear machine learning models compared with that of linear logistic regression models using 3 different inputs: 1) clinical variables, including 90 cardiovascular-relevant International Classification of Diseases, Tenth Revision, codes, and age, sex, height, weight, heart rate, blood pressures, low-density lipoprotein, high-density lipoprotein, and smoking; 2) clinical variables plus physician-reported EF; and 3) clinical variables and EF, plus 57 additional echocardiographic measurements. Missing data were imputed with a multivariate imputation by using a chained equations algorithm (MICE). We compared models versus each other and baseline clinical scoring systems by using a mean area under the curve (AUC) over 10 cross-validation folds and across 10 survival durations (6 to 60 months). Machine learning models achieved significantly higher prediction accuracy (all AUC >0.82) over common clinical risk scores (AUC = 0.61 to 0.79), with the nonlinear random forest models outperforming logistic regression (p < 0.01). The random forest model including all echocardiographic measurements yielded the highest prediction accuracy (p < 0.01 across all models and survival durations). Only 10 variables were needed to achieve 96% of the maximum prediction accuracy, with 6 of these variables being derived from echocardiography. Tricuspid regurgitation velocity was more predictive of survival than LVEF. In a subset of studies with complete data for the top 10 variables, multivariate imputation by chained equations yielded slightly reduced predictive accuracies (difference in AUC of 0.003) compared with the original data. Machine learning can fully utilize large combinations of disparate input variables to predict survival after echocardiography with superior accuracy. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations.

PubMed

Tekin, Yücel; Kuang, Boyan; Mouazen, Abdul M

2013-08-08

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate.

Potential of On-Line Visible and Near Infrared Spectroscopy for Measurement of pH for Deriving Variable Rate Lime Recommendations

PubMed Central

Tekin, Yücel; Kuang, Boyan; Mouazen, Abdul M.

2013-01-01

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate. PMID:23966186

Player's success prediction in rugby union: From youth performance to senior level placing.

PubMed

Fontana, Federico Y; Colosio, Alessandro L; Da Lozzo, Giorgio; Pogliaghi, Silvia

2017-04-01

The study questioned if and to what extent specific anthropometric and functional characteristics measured in youth draft camps, can accurately predict subsequent career progression in rugby union. Original research. Anthropometric and functional characteristics of 531 male players (U16) were retrospectively analysed in relation to senior level team representation at age 21-24. Players were classified as International (Int: National team and international clubs) or National (Nat: 1st, 2nd and other divisions and dropout). Multivariate analysis of variance (one-way MANOVA) tested differences between Int and Nat, along a combination of anthropometric (body mass, height, body fat, fat-free mass) and functional variables (SJ, CMJ, t 15m , t 30m , VO 2max ). A discriminant function (DF) was determined to predict group assignment based on the linear combination of variables that best discriminate groups. Correct level assignment was expressed as % hit rate. A combination of anthropometric and functional characteristics reflects future level assignment (Int vs. Nat). Players' success can be accurately predicted (hit rate=81% and 77% for Int and Nat respectively) by a DF that combines anthropometric and functional variables as measured at ∼15 years of age, percent body fat and speed being the most influential predictors of group stratification. Within a group of 15 year-olds with exceptional physical characteristics, future players' success can be predicted using a linear combination of anthropometric and functional variables, among which a lower percent body fat and higher speed over a 15m sprint provide the most important predictors of the highest career success. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

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

PubMed

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.

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

PubMed Central

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

Anxiety symptoms in amnestic mild cognitive impairment are associated with medial temporal atrophy and predict conversion to Alzheimer disease.

PubMed

Mah, Linda; Binns, Malcolm A; Steffens, David C

2015-05-01

To test the hypothesis that anxiety in amnestic mild cognitive impairment (aMCI) increases rates of conversion to Alzheimer disease (AD) and to identify potential neural mechanisms underlying such an association. Participants (N = 376) with aMCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were studied over a median period of 36 months. A Cox proportional-hazards model was used to assess the association between anxiety severity ratings on the Neuropsychiatric Inventory Questionnaire and AD risk. Other variables were depression, memory loss, and MRI-derived AD-related regions of interest (ROIs), including hippocampal, amygdalar, entorhinal cortical (EC) volumes, and EC thickness, In addition, a linear regression model was used to determine the effect of anxiety in aMCI on rates of atrophy within ROIs. Anxiety severity increased rate of aMCI conversion to AD, after controlling for depression and cognitive decline. The association between anxiety and AD remained significant even with inclusion of ROI baseline values or atrophy rates as explanatory variables. Further, anxiety status predicted greater rates of decrease in EC volume. An association between anxiety and EC thickness missed significance. Anxiety symptoms in aMCI predict conversion to AD, over and beyond the effects of depression, memory loss, or atrophy within AD neuroimaging biomarkers. These findings, together with the greater EC atrophy rate predicted by anxiety, are compatible with the hypothesis that anxiety is not a prodromal noncognitive feature of AD but may accelerate decline toward AD through direct or indirect effects on EC. Copyright © 2015 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Behavioral and Physiological Correlates of Children's Reactions to Others in Distress.

ERIC Educational Resources Information Center

Fabes, Richard A.; And Others

1993-01-01

Measured the facial expressions, heart rate variability (HRV), and skin conductance of 37 third graders and 29 sixth graders while they viewed a film about a distressed child. Found that high HRV was predictive of children's sympathetic rather than distressed reactions and that, for boys, sympathetic responsiveness positively predicted a…

Predicting fire spread in Arizona's oak chaparral

Treesearch

A. W. Lindenmuth; James R. Davis

1973-01-01

Five existing fire models, both experimental and theoretical, did not adequately predict rate-of-spread (ROS) when tested on single- and multiclump fires in oak chaparral in Arizona. A statistical model developed using essentially the same input variables but weighted differently accounted for 81 percent ofthe variation in ROS. A chemical coefficient that accounts for...

Autonomic Regulation on the Stroop Predicts Reading Achievement in School Age Children

ERIC Educational Resources Information Center

Becker, Derek R.; Carrere, Sybil; Siler, Chelsea; Jones, Stephanie; Bowie, Bonnie; Cooke, Cheryl

2012-01-01

In this study we examined high frequency heart rate variability (HF-HRV, a parasympathetic index) both at rest and during challenge, to assess if variations in cardiovascular activity measured during a Stroop task could be used to predict reading achievement in typically developing children. Reading achievement was examined using the Peabody…

Prediction of sand transport over immobile gravel from supply limited to capacity conditions

USDA-ARS?s Scientific Manuscript database

The prediction of the transport of sand in armored gravel reaches downstream of dams is complicated by variable bed conditions ranging from sand transported through gravel to sand in transport over buried gravel. Knowledge of the rate of sand transport in these conditions, however, is necessary for...

Six-minute walk test and heart rate variability: lack of association in advanced stages of heart failure.

PubMed

Woo, M A; Moser, D K; Stevenson, L W; Stevenson, W G

1997-09-01

The 6-minute walk and heart rate variability have been used to assess mortality risk in patients with heart failure, but their relationship to each other and their usefulness for predicting mortality at 1 year are unknown. To assess the relationships between the 6-minute walk test, heart rate variability, and 1-year mortality. A sample of 113 patients in advanced stages of heart failure (New York Heart Association Functional Class III-IV, left ventricular ejection < 0.25) were studied. All 6-minute walks took place in an enclosed, level, measured corridor and were supervised by the same nurse. Heart rate variability was measured by using (1) a standard-deviation method and (2) Poincaré plots. Data on RR intervals obtained by using 24-hour Holter monitoring were analyzed. Survival was determined at 1 year after the Holter recording. The results showed no significant associations between the results of the 6-minute walk and the two measures of heart rate variability. The results of the walk were related to 1-year mortality but not to the risk of sudden death. Both measures of heart rate variability had significant associations with 1-year mortality and with sudden death. However, only heart rate variability measured by using Poincaré plots was a predictor of total mortality and risk of sudden death, independent of left ventricular ejection fraction, serum levels of sodium, results of the 6-minute walk test, and the standard-deviation measure of heart rate variability. Results of the 6-minute walk have poor association with mortality and the two measures of heart rate variability in patients with advanced-stage heart failure and a low ejection fraction. Further studies are needed to determine the optimal clinical usefulness of the 6-minute walk and heart rate variability in patients with advanced-stage heart failure.

Dynamical predictors of an imminent phenotypic switch in bacteria

NASA Astrophysics Data System (ADS)

Wang, Huijing; Ray, J. Christian J.

2017-08-01

Single cells can stochastically switch across thresholds imposed by regulatory networks. Such thresholds can act as a tipping point, drastically changing global phenotypic states. In ecology and economics, imminent transitions across such tipping points can be predicted using dynamical early warning indicators. A typical example is ‘flickering’ of a fast variable, predicting a longer-lasting switch from a low to a high state or vice versa. Considering the different timescales between metabolite and protein fluctuations in bacteria, we hypothesized that metabolic early warning indicators predict imminent transitions across a network threshold caused by enzyme saturation. We used stochastic simulations to determine if flickering predicts phenotypic transitions, accounting for a variety of molecular physiological parameters, including enzyme affinity, burstiness of enzyme gene expression, homeostatic feedback, and rates of metabolic precursor influx. In most cases, we found that metabolic flickering rates are robustly peaked near the enzyme saturation threshold. The degree of fluctuation was amplified by product inhibition of the enzyme. We conclude that sensitivity to flickering in fast variables may be a possible natural or synthetic strategy to prepare physiological states for an imminent transition.

Can outcome of pancreatic pseudocysts be predicted? Proposal for a new scoring system.

PubMed

Şenol, Kazım; Akgül, Özgür; Gündoğdu, Salih Burak; Aydoğan, İhsan; Tez, Mesut; Coşkun, Faruk; Tihan, Deniz Necdet

2016-03-01

The spontaneous resolution rate of pancreatic pseudocysts (PPs) is 86%, and the serious complication rate is 3-9%. The aim of the present study was to develop a scoring system that would predict spontaneous resolution of PPs. Medical records of 70 patients were retrospectively reviewed. Two patients were excluded. Demographic data and laboratory measurements were obtained from patient records. Mean age of the 68 patients included was 56.6 years. Female:male ratio was 1.34:1. Causes of pancreatitis were stones (48.5%), alcohol consumption (26.5%), and unknown etiology (25%). Mean size of PP was 71 mm. Pseudocysts disappeared in 32 patients (47.1%). With univariate analysis, serum direct bilirubin level (>0.95 mg/dL), cyst carcinoembryonic antigen (CEA) level (>1.5), and cyst diameter (>55 mm) were found to be significantly different between patients with and without spontaneous resolution. In multivariate analysis, these variables were statistically significant. Scores were calculated with points assigned to each variable. Final scores predicted spontaneous resolution in approximately 80% of patients. The scoring system developed to predict resolution of PPs is simple and useful, but requires validation.

Pulse Rate and Transit Time Analysis to Predict Hypotension Events After Spinal Anesthesia During Programmed Cesarean Labor.

PubMed

Bolea, Juan; Lázaro, Jesús; Gil, Eduardo; Rovira, Eva; Remartínez, José M; Laguna, Pablo; Pueyo, Esther; Navarro, Augusto; Bailón, Raquel

2017-09-01

Prophylactic treatment has been proved to reduce hypotension incidence after spinal anesthesia during cesarean labor. However, the use of pharmacological prophylaxis could carry out undesirable side-effects on mother and fetus. Thus, the prediction of hypotension becomes an important challenge. Hypotension events are hypothesized to be related to a malfunctioning of autonomic nervous system (ANS) regulation of blood pressure. In this work, ANS responses to positional changes of 51 pregnant women programmed for a cesarean labor were explored for hypotension prediction. Lateral and supine decubitus, and sitting position were considered while electrocardiographic and pulse photoplethysmographic signals were recorded. Features based on heart rate variability, pulse rate variability (PRV) and pulse transit time (PTT) analysis were used in a logistic regression classifier. The results showed that PRV irregularity changes, assessed by approximate entropy, from supine to lateral decubitus, and standard deviation of PTT in supine decubitus were found as the combination of features that achieved the best classification results sensitivity of 76%, specificity of 70% and accuracy of 72%, being normotensive the positive class. Peripheral regulation and blood pressure changes, measured by PRV and PTT analysis, could help to predict hypotension events reducing prophylactic side-effects in the low-risk population.

Fire danger index efficiency as a function of fuel moisture and fire behavior.

PubMed

Torres, Fillipe Tamiozzo Pereira; Romeiro, Joyce Machado Nunes; Santos, Ana Carolina de Albuquerque; de Oliveira Neto, Ricardo Rodrigues; Lima, Gumercindo Souza; Zanuncio, José Cola

2018-08-01

Assessment of the performance of forest fire hazard indices is important for prevention and management strategies, such as planning prescribed burnings, public notifications and firefighting resource allocation. The objective of this study was to evaluate the performance of fire hazard indices considering fire behavior variables and susceptibility expressed by the moisture of combustible material. Controlled burns were carried out at different times and information related to meteorological conditions, characteristics of combustible material and fire behavior variables were recorded. All variables analyzed (fire behavior and fuel moisture content) can be explained by the prediction indices. The Brazilian EVAP/P showed the best performance, both at predicting moisture content of the fuel material and fire behavior variables, and the Canadian system showed the best performance to predicting the rate of spread. The coherence of the correlations between the indices and the variables analyzed makes the methodology, which can be applied anywhere, important for decision-making in regions with no records or with only unreliable forest fire data. Copyright © 2018 Elsevier B.V. All rights reserved.

Modeling the dynamics of choice.

PubMed

Baum, William M; Davison, Michael

2009-06-01

A simple linear-operator model both describes and predicts the dynamics of choice that may underlie the matching relation. We measured inter-food choice within components of a schedule that presented seven different pairs of concurrent variable-interval schedules for 12 food deliveries each with no signals indicating which pair was in force. This measure of local choice was accurately described and predicted as obtained reinforcer sequences shifted it to favor one alternative or the other. The effect of a changeover delay was reflected in one parameter, the asymptote, whereas the effect of a difference in overall rate of food delivery was reflected in the other parameter, rate of approach to the asymptote. The model takes choice as a primary dependent variable, not derived by comparison between alternatives-an approach that agrees with the molar view of behaviour.

Heart rate variability: Pre-deployment predictor of post-deployment PTSD symptoms

PubMed Central

Pyne, Jeffrey M.; Constans, Joseph I.; Wiederhold, Mark D.; Gibson, Douglas P.; Kimbrell, Timothy; Kramer, Teresa L.; Pitcock, Jeffery A.; Han, Xiaotong; Williams, D. Keith; Chartrand, Don; Gevirtz, Richard N.; Spira, James; Wiederhold, Brenda K.; McCraty, Rollin; McCune, Thomas R.

2017-01-01

Heart rate variability is a physiological measure associated with autonomic nervous system activity. This study hypothesized that lower pre-deployment HRV would be associated with higher post-deployment post-traumatic stress disorder (PTSD) symptoms. Three-hundred-forty-three Army National Guard soldiers enrolled in the Warriors Achieving Resilience (WAR) study were analyzed. The primary outcome was PTSD symptom severity using the PTSD Checklist – Military version (PCL) measured at baseline, 3- and 12-month post-deployment. Heart rate variability predictor variables included: high frequency power (HF) and standard deviation of the normal cardiac inter-beat interval (SDNN). Generalized linear mixed models revealed that the pre-deployment PCL*ln(HF) interaction term was significant (p < 0.0001). Pre-deployment SDNN was not a significant predictor of post-deployment PCL. Covariates included age, pre-deployment PCL, race/ethnicity, marital status, tobacco use, childhood abuse, pre-deployment traumatic brain injury, and previous combat zone deployment. Pre-deployment heart rate variability predicts post-deployment PTSD symptoms in the context of higher pre-deployment PCL scores. PMID:27773678

Estimating energy expenditure from heart rate in older adults: a case for calibration.

PubMed

Schrack, Jennifer A; Zipunnikov, Vadim; Goldsmith, Jeff; Bandeen-Roche, Karen; Crainiceanu, Ciprian M; Ferrucci, Luigi

2014-01-01

Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear. To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration. Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance. Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min). These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

Analysis of model development strategies: predicting ventral hernia recurrence.

PubMed

Holihan, Julie L; Li, Linda T; Askenasy, Erik P; Greenberg, Jacob A; Keith, Jerrod N; Martindale, Robert G; Roth, J Scott; Liang, Mike K

2016-11-01

There have been many attempts to identify variables associated with ventral hernia recurrence; however, it is unclear which statistical modeling approach results in models with greatest internal and external validity. We aim to assess the predictive accuracy of models developed using five common variable selection strategies to determine variables associated with hernia recurrence. Two multicenter ventral hernia databases were used. Database 1 was randomly split into "development" and "internal validation" cohorts. Database 2 was designated "external validation". The dependent variable for model development was hernia recurrence. Five variable selection strategies were used: (1) "clinical"-variables considered clinically relevant, (2) "selective stepwise"-all variables with a P value <0.20 were assessed in a step-backward model, (3) "liberal stepwise"-all variables were included and step-backward regression was performed, (4) "restrictive internal resampling," and (5) "liberal internal resampling." Variables were included with P < 0.05 for the Restrictive model and P < 0.10 for the Liberal model. A time-to-event analysis using Cox regression was performed using these strategies. The predictive accuracy of the developed models was tested on the internal and external validation cohorts using Harrell's C-statistic where C > 0.70 was considered "reasonable". The recurrence rate was 32.9% (n = 173/526; median/range follow-up, 20/1-58 mo) for the development cohort, 36.0% (n = 95/264, median/range follow-up 20/1-61 mo) for the internal validation cohort, and 12.7% (n = 155/1224, median/range follow-up 9/1-50 mo) for the external validation cohort. Internal validation demonstrated reasonable predictive accuracy (C-statistics = 0.772, 0.760, 0.767, 0.757, 0.763), while on external validation, predictive accuracy dipped precipitously (C-statistic = 0.561, 0.557, 0.562, 0.553, 0.560). Predictive accuracy was equally adequate on internal validation among models; however, on external validation, all five models failed to demonstrate utility. Future studies should report multiple variable selection techniques and demonstrate predictive accuracy on external data sets for model validation. Copyright © 2016 Elsevier Inc. All rights reserved.

Intelligence, General Knowledge and Personality as Predictors of Creativity

ERIC Educational Resources Information Center

Batey, Mark; Furnham, Adrian; Safiullina, Xeniya

2010-01-01

This study sought to examine the contribution of fluid intelligence, general knowledge and Big Five personality traits in predicting four indices of creativity: Divergent Thinking (DT) fluency, Rated DT, Creative Achievement and Self-Rated creativity and a combined Total Creativity variable. When creativity was assessed by DT test, the consistent…

Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia

USGS Publications Warehouse

Jennings, Cecil A.; Sundmark, Aaron P.

2017-01-01

The relationships between environmental variables and the growth rates of fishes are important and rapidly expanding topics in fisheries ecology. We used an informationtheoretic approach to evaluate the influence of lake surface area and total phosphorus on the age-specific growth rates of Lepomis macrochirus (Bluegill) in 6 small impoundments in central Georgia. We used model averaging to create composite models and determine the relative importance of the variables within each model. Results indicated that surface area was the most important factor in the models predicting growth of Bluegills aged 1–4 years; total phosphorus was also an important predictor for the same age-classes. These results suggest that managers can use water quality and lake morphometry variables to create predictive models specific to their waterbody or region to help develop lake-specific management plans that select for and optimize local-level habitat factors for enhancing Bluegill growth.

Mortality determinants and prediction of outcome in high risk newborns.

PubMed

Dalvi, R; Dalvi, B V; Birewar, N; Chari, G; Fernandez, A R

1990-06-01

The aim of this study was to determine independent patient-related predictors of mortality in high risk newborns admitted at our centre. The study population comprised 100 consecutive newborns each, from the premature unit (PU) and sick baby care unit (SBCU), respectively. Thirteen high risk factors (variables) for each of the two units, were entered into a multivariate regression analysis. Variables with independent predictive value for poor outcome (i.e., death) in PU were, weight less than 1 kg, hyaline membrane disease, neurologic problems, and intravenous therapy. High risk factors in SBCU included, blood gas abnormality, bleeding phenomena, recurrent convulsions, apnea, and congenital anomalies. Identification of these factors guided us in defining priority areas for improvement in our system of neonatal care. Also, based on these variables a simple predictive score for outcome was constructed. The prediction equation and the score were cross-validated by applying them to a 'test-set' of 100 newborns each for PU and SBCU. Results showed a comparable sensitivity, specificity and error rate.

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

PubMed

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.

Longitudinal association of short-term, metronome-paced heart rate variability and echocardiographically assessed cardiac structure at a 4-year follow-up: results from the prospective, population-based CARLA cohort.

PubMed

Medenwald, Daniel; Swenne, Cees A; Frantz, Stefan; Nuding, Sebastian; Kors, Jan A; Pietzner, Diana; Tiller, Daniel; Greiser, Karin H; Kluttig, Alexander; Haerting, Johannes

2017-12-01

To assess the value of cardiac structure/function in predicting heart rate variability (HRV) and the possibly predictive value of HRV on cardiac parameters. Baseline and 4-year follow-up data from the population-based CARLA cohort were used (790 men, 646 women, aged 45-83 years at baseline and 50-87 years at follow-up). Echocardiographic and HRV recordings were performed at baseline and at follow-up. Linear regression models with a quadratic term were used. Crude and covariate adjusted estimates were calculated. Missing values were imputed by means of multiple imputation. Heart rate variability measures taken into account consisted of linear time and frequency domain [standard deviation of normal-to-normal intervals (SDNN), high-frequency power (HF), low-frequency power (LF), LF/HF ratio] and non-linear measures [detrended fluctuation analysis (DFA1), SD1, SD2, SD1/SD2 ratio]. Echocardiographic parameters considered were ventricular mass index, diastolic interventricular septum thickness, left ventricular diastolic dimension, left atrial dimension systolic (LADS), and ejection fraction (Teichholz). A negative quadratic relation between baseline LADS and change in SDNN and HF was observed. The maximum HF and SDNN change (an increase of roughly 0.02%) was predicted at LADS of 3.72 and 3.57 cm, respectively, while the majority of subjects experienced a decrease in HRV. There was no association between further echocardiographic parameters and change in HRV, and there was no evidence of a predictive value of HRV in the prediction of changes in cardiac structure. In the general population, LADS predicts 4-year alteration in SDNN and HF non-linearly. Because of the novelty of the result, analyses should be replicated in other populations. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions please email: journals.permissions@oup.com.

Maximal exercise testing variables and 10-year survival: fitness risk score derivation from the FIT Project.

PubMed

Ahmed, Haitham M; Al-Mallah, Mouaz H; McEvoy, John W; Nasir, Khurram; Blumenthal, Roger S; Jones, Steven R; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J

2015-03-01

To determine which routinely collected exercise test variables most strongly correlate with survival and to derive a fitness risk score that can be used to predict 10-year survival. This was a retrospective cohort study of 58,020 adults aged 18 to 96 years who were free of established heart disease and were referred for an exercise stress test from January 1, 1991, through May 31, 2009. Demographic, clinical, exercise, and mortality data were collected on all patients as part of the Henry Ford ExercIse Testing (FIT) Project. Cox proportional hazards models were used to identify exercise test variables most predictive of survival. A "FIT Treadmill Score" was then derived from the β coefficients of the model with the highest survival discrimination. The median age of the 58,020 participants was 53 years (interquartile range, 45-62 years), and 28,201 (49%) were female. Over a median of 10 years (interquartile range, 8-14 years), 6456 patients (11%) died. After age and sex, peak metabolic equivalents of task and percentage of maximum predicted heart rate achieved were most highly predictive of survival (P<.001). Subsequent addition of baseline blood pressure and heart rate, change in vital signs, double product, and risk factor data did not further improve survival discrimination. The FIT Treadmill Score, calculated as [percentage of maximum predicted heart rate + 12(metabolic equivalents of task) - 4(age) + 43 if female], ranged from -200 to 200 across the cohort, was near normally distributed, and was found to be highly predictive of 10-year survival (Harrell C statistic, 0.811). The FIT Treadmill Score is easily attainable from any standard exercise test and translates basic treadmill performance measures into a fitness-related mortality risk score. The FIT Treadmill Score should be validated in external populations. Copyright © 2015 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

Does mobility explain variation in colonisation and population recovery among stream fishes?

USGS Publications Warehouse

Angermeier, Paul L.; Albanese, Brett; Peterson, James T.

2009-01-01

1. Colonisation and population recovery are crucial to species persistence in environmentally variable ecosystems, but are poorly understood processes. After documenting movement rates for several species of stream fish, we predicted that this variable would influence colonisation rates more strongly than local abundance, per cent occupancy, body size and taxonomic family. We also predicted that populations of species with higher movement rates would recover more rapidly than species with lower movement rates and that assemblage structure would change accordingly. 2. To test these predictions, we removed fishes from a headwater and a mainstem creek in southwest Virginia and monitored colonisation over a 2-year period. Using an information–theoretic approach, we evaluated the relative plausibility of 15 alternative models containing different combinations of our predictor variables. Our best-supported model contained movement rate and abundance and was 41 times more likely to account for observed patterns in colonisation rates than the next-best model. Movement rate and abundance were both positively related to colonisation rates and explained 88% of the variation in colonisation rates among species. 3. Population recovery, measured as the per cent of initial abundance restored, was also positively associated with movement rate. One species recovered within 3 months, most recovered within 2 years, but two species still had not recovered after 2 years. Despite high variation in recovery, the removal had only a slight impact on assemblage structure because species that were abundant in pre-removal samples were also abundant in post-removal samples. 4. The significance of interspecific variation in colonisation and recovery rates has been underappreciated because of the widely documented recovery of stream fish assemblages following fish kills and small-scale experimental defaunations. Our results indicate that recovery of the overall assemblage does not imply recovery of each component species. Populations of species that are rare and less mobile will recover more slowly and will be more vulnerable to extinction in systems where chemical spills, hydrological alteration, extreme droughts and other impacts are frequent.

Investigation of the Process Conditions for Hydrogen Production by Steam Reforming of Glycerol over Ni/Al2O3 Catalyst Using Response Surface Methodology (RSM)

PubMed Central

Ebshish, Ali; Yaakob, Zahira; Taufiq-Yap, Yun Hin; Bshish, Ahmed

2014-01-01

In this work; a response surface methodology (RSM) was implemented to investigate the process variables in a hydrogen production system. The effects of five independent variables; namely the temperature (X1); the flow rate (X2); the catalyst weight (X3); the catalyst loading (X4) and the glycerol-water molar ratio (X5) on the H2 yield (Y1) and the conversion of glycerol to gaseous products (Y2) were explored. Using multiple regression analysis; the experimental results of the H2 yield and the glycerol conversion to gases were fit to quadratic polynomial models. The proposed mathematical models have correlated the dependent factors well within the limits that were being examined. The best values of the process variables were a temperature of approximately 600 °C; a feed flow rate of 0.05 mL/min; a catalyst weight of 0.2 g; a catalyst loading of 20% and a glycerol-water molar ratio of approximately 12; where the H2 yield was predicted to be 57.6% and the conversion of glycerol was predicted to be 75%. To validate the proposed models; statistical analysis using a two-sample t-test was performed; and the results showed that the models could predict the responses satisfactorily within the limits of the variables that were studied. PMID:28788567

Mortality factors in geriatric blunt trauma patients.

PubMed

Knudson, M M; Lieberman, J; Morris, J A; Cushing, B M; Stubbs, H A

1994-04-01

To examine various clinical factors for their ability to predict mortality in geriatric patients following blunt trauma. In this retrospective study, trauma registries and medical records from three trauma centers were reviewed for patients 65 years and older who had sustained blunt trauma. The following variables were extracted and examined independently and in combination for their ability to predict death: age, gender, mechanism of injury, admission blood pressure, and Glasgow Coma Scale score, respiratory status, Trauma Score, Revised Trauma Score, and Injury Severity Score. Three urban trauma centers. Geriatric trauma patients entering three trauma centers (Stanford [Calif] University Hospital, Vanderbilt University Medical Center, Nashville, Tenn, and Maryland Institute for Emergency Medical Services Systems, Baltimore) following blunt trauma during a 7-year period (1982 to 1989). The Injury Severity Score was the single variable that correlated most significantly with mortality. Mortality rates were higher for men than for women and were significantly higher in patients 75 years and older. Admission variables associated with the highest relative risks of death included a Trauma Score less than 7; hypotension (systolic blood pressure, < 90 mm Hg); hypoventilation (respiratory rate, < 10 breaths per minute); or a Glasgow Coma Scale score equal to 3. Admission variables in geriatric trauma patients can be used to predict outcome and may also be useful in making decisions about triage, quality assurance, and use of intensive care unit beds.

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Prediction of Response to Medication and Cognitive Therapy in the Treatment of Moderate to Severe Depression

ERIC Educational Resources Information Center

Fournier, Jay C.; DeRubeis, Robert J.; Shelton, Richard C.; Hollon, Steven D.; Amsterdam, Jay D.; Gallop, Robert

2009-01-01

A recent randomized controlled trial found nearly equivalent response rates for antidepressant medications and cognitive therapy in a sample of moderate to severely depressed outpatients. In this article, the authors seek to identify the variables that were associated with response across both treatments as well as variables that predicted…

Predicting Mother/Father-Child Interactions: Parental Personality and Well-Being, Socioeconomic Variables and Child Disability Status

ERIC Educational Resources Information Center

Glidden, Laraine M.; Bamberger, Katharine T.; Turek, Kevin C.; Hill, Kelli L.

2010-01-01

Background: Child and parent characteristics as well as socioeconomic family variables can influence the quality of parent-child interactions. Methods: Coders rated parent behaviour from a video-taped 30-min family interaction in 91 families rearing children who were either typically developing or had intellectual/developmental disabilities. In…

Understanding which parameters control shallow ascent of silicic effusive magma

NASA Astrophysics Data System (ADS)

Thomas, Mark E.; Neuberg, Jurgen W.

2014-11-01

The estimation of the magma ascent rate is key to predicting volcanic activity and relies on the understanding of how strongly the ascent rate is controlled by different magmatic parameters. Linking potential changes of such parameters to monitoring data is an essential step to be able to use these data as a predictive tool. We present the results of a suite of conduit flow models Soufrière that assess the influence of individual model parameters such as the magmatic water content, temperature or bulk magma composition on the magma flow in the conduit during an extrusive dome eruption. By systematically varying these parameters we assess their relative importance to changes in ascent rate. We show that variability in the rate of low frequency seismicity, assumed to correlate directly with the rate of magma movement, can be used as an indicator for changes in ascent rate and, therefore, eruptive activity. The results indicate that conduit diameter and excess pressure in the magma chamber are amongst the dominant controlling variables, but the single most important parameter is the volatile content (assumed as only water). Modeling this parameter in the range of reported values causes changes in the calculated ascent velocities of up to 800%.

Respiratory effects of diesel exhaust in salt miners

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

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

1983-09-01

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

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data

PubMed Central

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J.; Kim, Doh Kwan

2018-01-01

Objective Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. Methods The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Results Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. Conclusion These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events. PMID:29614852

The pharmacist Aggregate Demand Index to explain changing pharmacist demand over a ten-year period.

PubMed

Knapp, Katherine K; Shah, Bijal M; Barnett, Mitchell J

2010-12-15

To describe Aggregate Demand Index (ADI) trends from 1999-2010; to compare ADI time trends to concurrent data for US unemployment levels, US entry-level pharmacy graduates, and US retail prescription growth rate; and to determine which variables were significant predictors of ADI. Annual ADI data (dependent variable) were analyzed against annual unemployment rates, annual number of pharmacy graduates, and annual prescription growth rate (independent variables). ADI data trended toward lower demand levels for pharmacists since late 2006, paralleling the US economic downturn. National ADI data were most highly correlated with unemployment (p < 0.001), then graduates (p < 0.006), then prescription growth rate (p < 0.093). A hierarchical model with the 3 variables was significant (p = 0.019), but only unemployment was a significant ADI predictor. Unemployment and ADI also were significantly related at the regional, division, and state levels. The ADI is strongly linked to US unemployment rates. The relationship suggests that an improving economy might coincide with increased pharmacist demand. Predictable increases in future graduates and other factors support revisiting the modeling process as new data accumulate.

Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. TRACE Investigators. TRAndolapril Cardiac Evaluation

NASA Technical Reports Server (NTRS)

Makikallio, T. H.; Hoiber, S.; Kober, L.; Torp-Pedersen, C.; Peng, C. K.; Goldberger, A. L.; Huikuri, H. V.

1999-01-01

A number of new methods have been recently developed to quantify complex heart rate (HR) dynamics based on nonlinear and fractal analysis, but their value in risk stratification has not been evaluated. This study was designed to determine whether selected new dynamic analysis methods of HR variability predict mortality in patients with depressed left ventricular (LV) function after acute myocardial infarction (AMI). Traditional time- and frequency-domain HR variability indexes along with short-term fractal-like correlation properties of RR intervals (exponent alpha) and power-law scaling (exponent beta) were studied in 159 patients with depressed LV function (ejection fraction <35%) after an AMI. By the end of 4-year follow-up, 72 patients (45%) had died and 87 (55%) were still alive. Short-term scaling exponent alpha (1.07 +/- 0.26 vs 0.90 +/- 0.26, p <0.001) and power-law slope beta (-1.35 +/- 0.23 vs -1.44 +/- 0.25, p <0.05) differed between survivors and those who died, but none of the traditional HR variability measures differed between these groups. Among all analyzed variables, reduced scaling exponent alpha (<0.85) was the best univariable predictor of mortality (relative risk 3.17, 95% confidence interval 1.96 to 5.15, p <0.0001), with positive and negative predictive accuracies of 65% and 86%, respectively. In the multivariable Cox proportional hazards analysis, mortality was independently predicted by the reduced exponent alpha (p <0.001) after adjustment for several clinical variables and LV function. A short-term fractal-like scaling exponent was the most powerful HR variability index in predicting mortality in patients with depressed LV function. Reduction in fractal correlation properties implies more random short-term HR dynamics in patients with increased risk of death after AMI.

A multilevel perspective to explain recycling behaviour in communities.

PubMed

Tabernero, Carmen; Hernández, Bernardo; Cuadrado, Esther; Luque, Bárbara; Pereira, Cícero R

2015-08-15

Previous research on the motivation for environmentally responsible behaviour has focused mainly on individual variables, rather than organizational or collective variables. Therefore, the results of those studies are hardly applicable to environmental management. This study considers individual, collective, and organizational variables together that contribute to the management of environmental waste. The main aim is to identify, through the development of a multilevel model, those predictive variables of recycling behaviour that help organizations to increase the recycling rates in their communities. Individual (age, gender, educational level, self-efficacy with respect to residential recycling, individual recycling behaviour), organizational (satisfaction with the quality of the service provided by a recycling company), and collective (community recycling rates, number of inhabitants, community efficacy beliefs) motivational factors relevant to recycling behaviour were analysed. A sample of 1501 residents from 55 localities was surveyed. The results of multilevel analyses indicated that there was significant variability within and between localities. Interactions between variables at the level of the individual (e.g. satisfaction with service quality) and variables at the level of the collective (e.g. community efficacy) predicted recycling behaviour in localities with low and high community recycling rates and large and small populations. The interactions showed that the relationship between self-efficacy and recycling is stronger in localities with weak community efficacy beliefs than in communities with strong beliefs. The findings show that the relationship between satisfaction with service quality and recycling behaviour is stronger in localities with strong community efficacy beliefs than in communities with weaker beliefs and a smaller population. The results are discussed accordingly in relation to theory and possible contribution to waste management. Those findings may be incorporated in national and international environmental policies in order to promote environmentally responsible behaviour in citizenship. Copyright © 2015 Elsevier Ltd. All rights reserved.

Power Relative to Body Mass Best Predicts Change in Core Temperature During Exercise-Heat Stress.

PubMed

Gibson, Oliver R; Willmott, Ashley G B; James, Carl A; Hayes, Mark; Maxwell, Neil S

2017-02-01

Gibson, OR, Willmott, AGB, James, CA, Hayes, M, and Maxwell, NS. Power relative to body mass best predicts change in core temperature during exercise-heat stress. J Strength Cond Res 31(2): 403-414, 2017-Controlling internal temperature is crucial when prescribing exercise-heat stress, particularly during interventions designed to induce thermoregulatory adaptations. This study aimed to determine the relationship between the rate of rectal temperature (Trec) increase, and various methods for prescribing exercise-heat stress, to identify the most efficient method of prescribing isothermic heat acclimation (HA) training. Thirty-five men cycled in hot conditions (40° C, 39% R.H.) for 29 ± 2 minutes. Subjects exercised at 60 ± 9% V[Combining Dot Above]O2peak, with methods for prescribing exercise retrospectively observed for each participant. Pearson product moment correlations were calculated for each prescriptive variable against the rate of change in Trec (° C·h), with stepwise multiple regressions performed on statistically significant variables (p ≤ 0.05). Linear regression identified the predicted intensity required to increase Trec by 1.0-2.0° C between 20- and 45-minute periods and the duration taken to increase Trec by 1.5° C in response to incremental intensities to guide prescription. Significant (p ≤ 0.05) relationships with the rate of change in Trec were observed for prescriptions based on relative power (W·kg; r = 0.764), power (%Powermax; r = 0.679), rating of perceived exertion (RPE) (r = 0.577), V[Combining Dot Above]O2 (%V[Combining Dot Above]O2peak; r = 0.562), heart rate (HR) (%HRmax; r = 0.534), and thermal sensation (r = 0.311). Stepwise multiple regressions observed relative power and RPE as variables to improve the model (r = 0.791), with no improvement after inclusion of any anthropometric variable. Prescription of exercise under heat stress using power (W·kg or %Powermax) has the strongest relationship with the rate of change in Trec with no additional requirement to correct for body composition within a normal range. Practitioners should therefore prescribe exercise intensity using relative power during isothermic HA training to increase Trec efficiently and maximize adaptation.

Asymmetric patch size distribution leads to disruptive selection on dispersal.

PubMed

Massol, François; Duputié, Anne; David, Patrice; Jarne, Philippe

2011-02-01

Numerous models have been designed to understand how dispersal ability evolves when organisms live in a fragmented landscape. Most of them predict a single dispersal rate at evolutionary equilibrium, and when diversification of dispersal rates has been predicted, it occurs as a response to perturbation or environmental fluctuation regimes. Yet abundant variation in dispersal ability is observed in natural populations and communities, even in relatively stable environments. We show that this diversification can operate in a simple island model without temporal variability: disruptive selection on dispersal occurs when the environment consists of many small and few large patches, a common feature in natural spatial systems. This heterogeneity in patch size results in a high variability in the number of related patch mates by individual, which, in turn, triggers disruptive selection through a high per capita variance of inclusive fitness. Our study provides a likely, parsimonious and testable explanation for the diversity of dispersal rates encountered in nature. It also suggests that biological conservation policies aiming at preserving ecological communities should strive to keep the distribution of patch size sufficiently asymmetric and variable. © 2010 The Author(s). Evolution© 2010 The Society for the Study of Evolution.

Rain attenuation measurements: Variability and data quality assessment

NASA Technical Reports Server (NTRS)

Crane, Robert K.

1989-01-01

Year to year variations in the cumulative distributions of rain rate or rain attenuation are evident in any of the published measurements for a single propagation path that span a period of several years of observation. These variations must be described by models for the prediction of rain attenuation statistics. Now that a large measurement data base has been assembled by the International Radio Consultative Committee, the information needed to assess variability is available. On the basis of 252 sample cumulative distribution functions for the occurrence of attenuation by rain, the expected year to year variation in attenuation at a fixed probability level in the 0.1 to 0.001 percent of a year range is estimated to be 27 percent. The expected deviation from an attenuation model prediction for a single year of observations is estimated to exceed 33 percent when any of the available global rain climate model are employed to estimate the rain rate statistics. The probability distribution for the variation in attenuation or rain rate at a fixed fraction of a year is lognormal. The lognormal behavior of the variate was used to compile the statistics for variability.

Temperature variability is a key component in accurately forecasting the effects of climate change on pest phenology.

PubMed

Merrill, Scott C; Peairs, Frank B

2017-02-01

Models describing the effects of climate change on arthropod pest ecology are needed to help mitigate and adapt to forthcoming changes. Challenges arise because climate data are at resolutions that do not readily synchronize with arthropod biology. Here we explain how multiple sources of climate and weather data can be synthesized to quantify the effects of climate change on pest phenology. Predictions of phenological events differ substantially between models that incorporate scale-appropriate temperature variability and models that do not. As an illustrative example, we predicted adult emergence of a pest of sunflower, the sunflower stem weevil Cylindrocopturus adspersus (LeConte). Predictions of the timing of phenological events differed by an average of 11 days between models with different temperature variability inputs. Moreover, as temperature variability increases, developmental rates accelerate. Our work details a phenological modeling approach intended to help develop tools to plan for and mitigate the effects of climate change. Results show that selection of scale-appropriate temperature data is of more importance than selecting a climate change emission scenario. Predictions derived without appropriate temperature variability inputs will likely result in substantial phenological event miscalculations. Additionally, results suggest that increased temperature instability will lead to accelerated pest development. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Predicting Health Care Utilization in Marginalized Populations: Black, Female, Street-based Sex Workers

PubMed Central

Varga, Leah M.; Surratt, Hilary L.

2014-01-01

Background Patterns of social and structural factors experienced by vulnerable populations may negatively affect willingness and ability to seek out health care services, and ultimately, their health. Methods The outcome variable was utilization of health care services in the previous 12 months. Using Andersen’s Behavioral Model for Vulnerable Populations, we examined self-reported data on utilization of health care services among a sample of 546 Black, street-based female sex workers in Miami, Florida. To evaluate the impact of each domain of the model on predicting health care utilization, domains were included in the logistic regression analysis by blocks using the traditional variables first and then adding the vulnerable domain variables. Findings The most consistent variables predicting health care utilization were having a regular source of care and self-rated health. The model that included only enabling variables was the most efficient model in predicting health care utilization. Conclusions Any type of resource, link, or connection to or with an institution, or any consistent point of care contributes significantly to health care utilization behaviors. A consistent and reliable source for health care may increase health care utilization and subsequently decrease health disparities among vulnerable and marginalized populations, as well as contribute to public health efforts that encourage preventive health. PMID:24657047

Ecological niche modeling for a cultivated plant species: a case study on taro (Colocasia esculenta) in Hawaii.

PubMed

Kodis, Mali'o; Galante, Peter; Sterling, Eleanor J; Blair, Mary E

2018-04-26

Under the threat of ongoing and projected climate change, communities in the Pacific Islands face challenges of adapting culture and lifestyle to accommodate a changing landscape. Few models can effectively predict how biocultural livelihoods might be impacted. Here, we examine how environmental and anthropogenic factors influence an ecological niche model (ENM) for the realized niche of cultivated taro (Colocasia esculenta) in Hawaii. We created and tuned two sets of ENMs: one using only environmental variables, and one using both environmental and cultural characteristics of Hawaii. These models were projected under two different Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) for 2070. Models were selected and evaluated using average omission rate and area under the receiver operating characteristic curve (AUC). We compared optimal model predictions by comparing the percentage of taro plots predicted present and measured ENM overlap using Schoener's D statistic. The model including only environmental variables consisted of 19 Worldclim bioclimatic variables, in addition to slope, altitude, distance to perennial streams, soil evaporation, and soil moisture. The optimal model with environmental variables plus anthropogenic features also included a road density variable (which we assumed as a proxy for urbanization) and a variable indicating agricultural lands of importance to the state of Hawaii. The model including anthropogenic features performed better than the environment-only model based on omission rate, AUC, and review of spatial projections. The two models also differed in spatial projections for taro under anticipated future climate change. Our results demonstrate how ENMs including anthropogenic features can predict which areas might be best suited to plant cultivated species in the future, and how these areas could change under various climate projections. These predictions might inform biocultural conservation priorities and initiatives. In addition, we discuss the incongruences that arise when traditional ENM theory is applied to species whose distribution has been significantly impacted by human intervention, particularly at a fine scale relevant to biocultural conservation initiatives. © 2018 by the Ecological Society of America.

Validation of the close-to-delivery prediction model for vaginal birth after cesarean delivery in a Middle Eastern cohort.

PubMed

Abdel Aziz, Ahmed; Abd Rabbo, Amal; Sayed Ahmed, Waleed A; Khamees, Rasha E; Atwa, Khaled A

2016-07-01

To validate a prediction model for vaginal birth after cesarean (VBAC) that incorporates variables available at admission for delivery among Middle Eastern women. The present prospective cohort study enrolled women at 37weeks of pregnancy or more with cephalic presentation who were willing to attempt a trial of labor (TOL) after a single prior low transverse cesarean delivery at Al-Jahra Hospital, Kuwait, between June 2013 and June 2014. The predicted success rate of VBAC determined via the close-to-delivery prediction model of Grobman et al. was compared between participants whose TOL was and was not successful. Among 203 enrolled women, 140 (69.0%) had successful VBAC. The predicted VBAC success rate was higher among women with successful TOL (82.4%±13.1%) than among those with failed TOL (67.7%±18.3%; P<0.001). There was a high positive correlation between actual and predicted success rates. For deciles of predicted success rate increasing from >30%-40% to >90%-100%, the actual success rate was 20%, 30.7%, 38.5%, 59.1%, 71.4%, 76%, and 84.5%, respectively (r=0.98, P=0.013). The close-to-delivery prediction model was found to be applicable to Middle Eastern women and might predict VBAC success rates, thereby decreasing morbidities associated with failed TOL. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Dopamine Modulates Adaptive Prediction Error Coding in the Human Midbrain and Striatum.

PubMed

Diederen, Kelly M J; Ziauddeen, Hisham; Vestergaard, Martin D; Spencer, Tom; Schultz, Wolfram; Fletcher, Paul C

2017-02-15

Learning to optimally predict rewards requires agents to account for fluctuations in reward value. Recent work suggests that individuals can efficiently learn about variable rewards through adaptation of the learning rate, and coding of prediction errors relative to reward variability. Such adaptive coding has been linked to midbrain dopamine neurons in nonhuman primates, and evidence in support for a similar role of the dopaminergic system in humans is emerging from fMRI data. Here, we sought to investigate the effect of dopaminergic perturbations on adaptive prediction error coding in humans, using a between-subject, placebo-controlled pharmacological fMRI study with a dopaminergic agonist (bromocriptine) and antagonist (sulpiride). Participants performed a previously validated task in which they predicted the magnitude of upcoming rewards drawn from distributions with varying SDs. After each prediction, participants received a reward, yielding trial-by-trial prediction errors. Under placebo, we replicated previous observations of adaptive coding in the midbrain and ventral striatum. Treatment with sulpiride attenuated adaptive coding in both midbrain and ventral striatum, and was associated with a decrease in performance, whereas bromocriptine did not have a significant impact. Although we observed no differential effect of SD on performance between the groups, computational modeling suggested decreased behavioral adaptation in the sulpiride group. These results suggest that normal dopaminergic function is critical for adaptive prediction error coding, a key property of the brain thought to facilitate efficient learning in variable environments. Crucially, these results also offer potential insights for understanding the impact of disrupted dopamine function in mental illness. SIGNIFICANCE STATEMENT To choose optimally, we have to learn what to expect. Humans dampen learning when there is a great deal of variability in reward outcome, and two brain regions that are modulated by the brain chemical dopamine are sensitive to reward variability. Here, we aimed to directly relate dopamine to learning about variable rewards, and the neural encoding of associated teaching signals. We perturbed dopamine in healthy individuals using dopaminergic medication and asked them to predict variable rewards while we made brain scans. Dopamine perturbations impaired learning and the neural encoding of reward variability, thus establishing a direct link between dopamine and adaptation to reward variability. These results aid our understanding of clinical conditions associated with dopaminergic dysfunction, such as psychosis. Copyright © 2017 Diederen et al.

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050.

PubMed

Song, Yongze; Ge, Yong; Wang, Jinfeng; Ren, Zhoupeng; Liao, Yilan; Peng, Junhuan

2016-07-07

Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. Average malaria incidence was 0.107 ‰ per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R(2) = 0.825) and 17.102 % for test data (R(2) = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas.

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Predictors of Dropout in Psychodynamic Psychotherapy of Borderline Personality Disorder

PubMed Central

SMITH, THOMAS E.; KOENIGSBERG, HAROLD W.; YEOMANS, FRANK E.; CLARKIN, JOHN F.; SELZER, MICHAEL A.

1995-01-01

This study aimed to identify patient factors that predict early dropout from psychodynamic psychotherapy for borderline personality disorder (BPD). Thirty-six BPD patients began an open-ended course of twice per week psychodynamic psychotherapy that was defined in a treatment manual and supervised. Dropout rates were 31% and 36% at 3 and 6 months of therapy, respectively. Survival analysis techniques demonstrated that age and hostility ratings predicted early dropout, with continuers more likely to be older and expressing lower levels of hostility than dropouts. Many variables hypothesized to predict dropout failed to do so. Both the positive and negative findings are discussed relative to the literature. PMID:22700251

A Predictive Model of Student Loan Default at a Two-Year Community College

ERIC Educational Resources Information Center

Brown, Chanda Denea

2015-01-01

This study explored whether a predictive model of student loan default could be developed with data from an institution's three-year cohort default rate report. The study used borrower data provided by a large two-year community college. Independent variables under investigation included total undergraduate Stafford student loan debt, total number…

Predicting fire behavior in palmetto-gallberry fuel complexes

Treesearch

W A. Hough; F. A. Albini

1978-01-01

Rate of spread, fireline intensity, and flame length can be predicted with reasonable accuracy for backfires and low-intensity head fires in the palmetto-gallberry fuel complex of the South. This fuel complex was characterized and variables were adjusted for use in Rothermel's (1972) spread model. Age of rough, height of understory, percent of area covered by...

Background or Experience? Using Logistic Regression to Predict College Retention

ERIC Educational Resources Information Center

Synco, Tracee M.

2012-01-01

Tinto, Astin and countless others have researched the retention and attrition of students from college for more than thirty years. However, the six year graduation rate for all first-time full-time freshmen for the 2002 cohort was 57%. This study sought to determine the retention variables that predicted continued enrollment of entering freshmen…

Predicting duff and woody fuel consumed by prescribed fire in the Northern Rocky Mountains

Treesearch

James K. Brown; Michael A. Marsden; Kevin C. Ryan; Elizabeth D. Reinhardt

1985-01-01

Relationships for predicting duff reduction, mineral soil exposure, and consumption of downed woody fuel were determined to assist in planning prescribed fires. Independent variables included lower and entire duff moisture contents, loadings of downed woody fuels, duff depth, National Fire-Danger Rating System 1,000-hour moisture content, and Canadian Duff Moisture...

An evaluation of the effect of recent temperature variability on the prediction of coral bleaching events.

PubMed

Donner, Simon D

2011-07-01

Over the past 30 years, warm thermal disturbances have become commonplace on coral reefs worldwide. These periods of anomalous sea surface temperature (SST) can lead to coral bleaching, a breakdown of the symbiosis between the host coral and symbiotic dinoflagellates which reside in coral tissue. The onset of bleaching is typically predicted to occur when the SST exceeds a local climatological maximum by 1 degrees C for a month or more. However, recent evidence suggests that the threshold at which bleaching occurs may depend on thermal history. This study uses global SST data sets (HadISST and NOAA AVHRR) and mass coral bleaching reports (from Reefbase) to examine the effect of historical SST variability on the accuracy of bleaching prediction. Two variability-based bleaching prediction methods are developed from global analysis of seasonal and interannual SST variability. The first method employs a local bleaching threshold derived from the historical variability in maximum annual SST to account for spatial variability in past thermal disturbance frequency. The second method uses a different formula to estimate the local climatological maximum to account for the low seasonality of SST in the tropics. The new prediction methods are tested against the common globally fixed threshold method using the observed bleaching reports. The results find that estimating the bleaching threshold from local historical SST variability delivers the highest predictive power, but also a higher rate of Type I errors. The second method has the lowest predictive power globally, though regional analysis suggests that it may be applicable in equatorial regions. The historical data analysis suggests that the bleaching threshold may have appeared to be constant globally because the magnitude of interannual variability in maximum SST is similar for many of the world's coral reef ecosystems. For example, the results show that a SST anomaly of 1 degrees C is equivalent to 1.73-2.94 standard deviations of the maximum monthly SST for two-thirds of the world's coral reefs. Coral reefs in the few regions that experience anomalously high interannual SST variability like the equatorial Pacific could prove critical to understanding how coral communities acclimate or adapt to frequent and/or severe thermal disturbances.

Long-Term Coarse Particulate Matter Exposure and Heart Rate Variability in the Multi-Ethnic Study of Atherosclerosis (MESA)

PubMed Central

Adhikari, Richa; D’Souza, Jennifer; Solimon, Elsayed Z.; Burke, Gregory L.; Daviglus, Martha; Jacobs, David R.; Park, Sung Kyun; Sheppard, Lianne; Thorne, Peter S.; Kaufman, Joel D.; Larson, Timothy V.; Adar, Sara D.

2017-01-01

Background Reduced heart rate variability, a marker of impaired cardiac autonomic function, has been linked to short-term exposure to airborne particles. This research adds to the literature by examining associations with long-term exposures to coarse particles (PM10-2.5). Methods Using electrocardiogram recordings from 2,780 participants (45-84 years) from three Multi-Ethnic Study of Atherosclerosis sites, we assessed the standard deviation of normal-to-normal intervals (SDNN) and root-mean square differences of successive normal-to-normal intervals (rMSSD) at a baseline (2000-2002) and follow-up (2010-2012) examination (mean visits/person=1.5). Annual average concentrations of PM10-2.5 mass, copper, zinc, phosphorus, silicon, and endotoxin were estimated using site-specific spatial prediction models. We assessed associations for baseline heart rate variability and rate of change in heart rate variability over time using multivariable mixed models adjusted for time, sociodemographic, lifestyle, health, and neighborhood confounders, including co-pollutants. Results In our primary models adjusted for demographic and lifestyle factors and site, PM10-2.5 mass was associated with 1.0% (95% CI: -4.1, 2.1%) lower SDNN levels per interquartile range of 2 μg/m3. Stronger associations, however, were observed prior to site adjustment and with increasing residential stablity. Similar patterns were found for rMSSD. We found little evidence for associations with other chemical species and with with the rate of change in heart rate variability, though endotoxin was associated with increasing heart rate variability over time. Conclusion We found only weak evidence that long-term PM10-2.5 exposures are associated with lowered heart rate variability. Stronger associations among residentially stable individuals suggest that confirmatory studies are needed. PMID:27035690

Attributions or Retributions: Student Ratings and the Perceived Causes of Performance.

ERIC Educational Resources Information Center

Theall, Michael; And Others

The nature and extent of variations in student attributions about performance in their courses were studied, and the relationships between the attributions and responses on certain items of a student ratings questionnaire were determined. Causal or predictive relationships among these variables were also investigated. Data were collected using:…

The interactive effects of press/pulse intensity and duration on regime shifts at multiple scales

USDA-ARS?s Scientific Manuscript database

Regime shifts are difficult-to-reverse transitions that occur when an ecosystem reorganizes around a new set of self-reinforcing feedbacks. Regime shifts are predicted to occur when the intensity of some exogenous driver variable, such as temperature, annual harvest rate, or nutrient addition rate, ...

The Impact of Soil Sampling Errors on Variable Rate Fertilization

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

R. L. Hoskinson; R C. Rope; L G. Blackwood

2004-07-01

Variable rate fertilization of an agricultural field is done taking into account spatial variability in the soil’s characteristics. Most often, spatial variability in the soil’s fertility is the primary characteristic used to determine the differences in fertilizers applied from one point to the next. For several years the Idaho National Engineering and Environmental Laboratory (INEEL) has been developing a Decision Support System for Agriculture (DSS4Ag) to determine the economically optimum recipe of various fertilizers to apply at each site in a field, based on existing soil fertility at the site, predicted yield of the crop that would result (and amore » predicted harvest-time market price), and the current costs and compositions of the fertilizers to be applied. Typically, soil is sampled at selected points within a field, the soil samples are analyzed in a lab, and the lab-measured soil fertility of the point samples is used for spatial interpolation, in some statistical manner, to determine the soil fertility at all other points in the field. Then a decision tool determines the fertilizers to apply at each point. Our research was conducted to measure the impact on the variable rate fertilization recipe caused by variability in the measurement of the soil’s fertility at the sampling points. The variability could be laboratory analytical errors or errors from variation in the sample collection method. The results show that for many of the fertility parameters, laboratory measurement error variance exceeds the estimated variability of the fertility measure across grid locations. These errors resulted in DSS4Ag fertilizer recipe recommended application rates that differed by up to 138 pounds of urea per acre, with half the field differing by more than 57 pounds of urea per acre. For potash the difference in application rate was up to 895 pounds per acre and over half the field differed by more than 242 pounds of potash per acre. Urea and potash differences accounted for almost 87% of the cost difference. The sum of these differences could result in a $34 per acre cost difference for the fertilization. Because of these differences, better analysis or better sampling methods may need to be done, or more samples collected, to ensure that the soil measurements are truly representative of the field’s spatial variability.« less

Factors predicting Behavior Management Problems during Initial Dental Examination in Children Aged 2 to 8 Years

PubMed Central

Kumar, Dipanshu; Anand, Ashish; Mittal, Vipula; Singh, Aparna; Aggarwal, Nidhi

2017-01-01

Aim The aim of the present study was to identify the various background variables and its influence on behavior management problems (BMP) in children. Materials and methods The study included 165 children aged 2 to 8 years. During the initial dental visit, an experienced operator obtained each child’s background variables from accompanying guardians using a standardized questionnaire. Children’s dental behavior was rated by Frankel behavior rating scale. The behavior was then analyzed in relation to the answers of the questionnaire, and a logistic regression model was used to determine the power of the variables, separately or combined, to predict BMP. Results The logistic regression analysis considering differences in background variables between children with negative or positive behavior. Four variables turned out to be as predictors: Age, the guardian’s expectation of the child’s behavior at the dental examination, the child’s anxiety when meeting unfamiliar people, and the presence and absence of toothache. Conclusion The present study concluded that by means of simple questionnaire BMP in children may be expected if one of these attributes is found. Clinical significance Information on the origin of dental fear and uncooperative behavior in a child patient prior to treatment process may help the pediatric dentist plan appropriate behavior management and treatment strategy. How to cite this article Sharma A, Kumar D, Anand A, Mittal V, Singh A, Aggarwal N. Factors predicting Behavior Management Problems during Initial Dental Examination in Children Aged 2 to 8 Years. Int J Clin Pediatr Dent 2017;10(1):5-9. PMID:28377646

Mathematical Model Of Variable-Polarity Plasma Arc Welding

NASA Technical Reports Server (NTRS)

Hung, R. J.

1996-01-01

Mathematical model of variable-polarity plasma arc (VPPA) welding process developed for use in predicting characteristics of welds and thus serves as guide for selection of process parameters. Parameters include welding electric currents in, and durations of, straight and reverse polarities; rates of flow of plasma and shielding gases; and sizes and relative positions of welding electrode, welding orifice, and workpiece.

The Role of Early Symptom Trajectories and Pretreatment Variables in Predicting Treatment Response to Cognitive Behavioral Therapy

ERIC Educational Resources Information Center

Lewis, Cara C.; Simons, Anne D.; Kim, Hyoun K.

2012-01-01

Objective: Research has focused on 2 different approaches to answering the question, "Which clients will respond to cognitive behavioral therapy (CBT) for depression?" One approach focuses on rates of symptom change within the 1st few weeks of treatment, whereas the 2nd approach looks to pretreatment client variables (e.g., hopelessness) to…

Resting-state qEEG predicts rate of second language learning in adults.

PubMed

Prat, Chantel S; Yamasaki, Brianna L; Kluender, Reina A; Stocco, Andrea

2016-01-01

Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner. Published by Elsevier Inc.

The Impact of Accounting Methods on Cost Reduction Rates in Defense Aerospace Weapons System Programs

DTIC Science & Technology

1988-12-01

and adhered to in U.S. industry, allow some flexibility in accounting. Under GAAP , accounting areas such as depreciation , inventory, investment tax... depreciation , inventory and investment tax credit) in predicting cost reduction rates are studied. Of the three accounting variables, only inventory...RATES .. ................. ........... 5 1. Depreciation ........ ............... 6 2. Capitalizing or Expensing of Costs . . .. 6 3. Material Costs

Testing life history predictions in a long-lived seabird: A population matrix approach with improved parameter estimation

USGS Publications Warehouse

Doherty, P.F.; Schreiber, E.A.; Nichols, J.D.; Hines, J.E.; Link, W.A.; Schenk, G.A.; Schreiber, R.W.

2004-01-01

Life history theory and associated empirical generalizations predict that population growth rate (λ) in long-lived animals should be most sensitive to adult survival; the rates to which λ is most sensitive should be those with the smallest temporal variances; and stochastic environmental events should most affect the rates to which λ is least sensitive. To date, most analyses attempting to examine these predictions have been inadequate, their validity being called into question by problems in estimating parameters, problems in estimating the variability of parameters, and problems in measuring population sensitivities to parameters. We use improved methodologies in these three areas and test these life-history predictions in a population of red-tailed tropicbirds (Phaethon rubricauda). We support our first prediction that λ is most sensitive to survival rates. However the support for the second prediction that these rates have the smallest temporal variance was equivocal. Previous support for the second prediction may be an artifact of a high survival estimate near the upper boundary of 1 and not a result of natural selection canalizing variances alone. We did not support our third prediction that effects of environmental stochasticity (El Niño) would most likely be detected in vital rates to which λ was least sensitive and which are thought to have high temporal variances. Comparative data-sets on other seabirds, within and among orders, and in other locations, are needed to understand these environmental effects.

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

PubMed

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.

A predictive model for diagnosing bipolar disorder based on the clinical characteristics of major depressive episodes in Chinese population.

PubMed

Gan, Zhaoyu; Diao, Feici; Wei, Qinling; Wu, Xiaoli; Cheng, Minfeng; Guan, Nianhong; Zhang, Ming; Zhang, Jinbei

2011-11-01

A correct timely diagnosis of bipolar depression remains a big challenge for clinicians. This study aimed to develop a clinical characteristic based model to predict the diagnosis of bipolar disorder among patients with current major depressive episodes. A prospective study was carried out on 344 patients with current major depressive episodes, with 268 completing 1-year follow-up. Data were collected through structured interviews. Univariate binary logistic regression was conducted to select potential predictive variables among 19 initial variables, and then multivariate binary logistic regression was performed to analyze the combination of risk factors and build a predictive model. Receiver operating characteristic (ROC) curve was plotted. Of 19 initial variables, 13 variables were preliminarily selected, and then forward stepwise exercise produced a final model consisting of 6 variables: age at first onset, maximum duration of depressive episodes, somatalgia, hypersomnia, diurnal variation of mood, irritability. The correct prediction rate of this model was 78% (95%CI: 75%-86%) and the area under the ROC curve was 0.85 (95%CI: 0.80-0.90). The cut-off point for age at first onset was 28.5 years old, while the cut-off point for maximum duration of depressive episode was 7.5 months. The limitations of this study include small sample size, relatively short follow-up period and lack of treatment information. Our predictive models based on six clinical characteristics of major depressive episodes prove to be robust and can help differentiate bipolar depression from unipolar depression. Copyright © 2011 Elsevier B.V. All rights reserved.

A hybrid constructed wetland for organic-material and nutrient removal from sewage: Process performance and multi-kinetic models.

PubMed

Nguyen, X Cuong; Chang, S Woong; Nguyen, Thi Loan; Ngo, H Hao; Kumar, Gopalakrishnan; Banu, J Rajesh; Vu, M Cuong; Le, H Sinh; Nguyen, D Duc

2018-09-15

A pilot-scale hybrid constructed wetland with vertical flow and horizontal flow in series was constructed and used to investigate organic material and nutrient removal rate constants for wastewater treatment and establish a practical predictive model for use. For this purpose, the performance of multiple parameters was statistically evaluated during the process and predictive models were suggested. The measurement of the kinetic rate constant was based on the use of the first-order derivation and Monod kinetic derivation (Monod) paired with a plug flow reactor (PFR) and a continuously stirred tank reactor (CSTR). Both the Lindeman, Merenda, and Gold (LMG) analysis and Bayesian model averaging (BMA) method were employed for identifying the relative importance of variables and their optimal multiple regression (MR). The results showed that the first-order-PFR (M 2 ) model did not fit the data (P > 0.05, and R 2  < 0.5), whereas the first-order-CSTR (M 1 ) model for the chemical oxygen demand (COD Cr ) and Monod-CSTR (M 3 ) model for the COD Cr and ammonium nitrogen (NH 4 -N) showed a high correlation with the experimental data (R 2  > 0.5). The pollutant removal rates in the case of M 1 were 0.19 m/d (COD Cr ) and those for M 3 were 25.2 g/m 2 ∙d for COD Cr and 2.63 g/m 2 ∙d for NH 4 -N. By applying a multi-variable linear regression method, the optimal empirical models were established for predicting the final effluent concentration of five days' biochemical oxygen demand (BOD 5 ) and NH 4 -N. In general, the hydraulic loading rate was considered an important variable having a high value of relative importance, which appeared in all the optimal predictive models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Does body fat percentage predict post-exercise heart rate response in non-obese children and adolescents?

PubMed

Jezdimirovic, Tatjana; Stajer, Valdemar; Semeredi, Sasa; Calleja-Gonzalez, Julio; Ostojic, Sergej M

2017-05-24

A correlation between adiposity and post-exercise autonomic regulation has been established in overweight and obese children. However, little information exists about this link in non-obese youth. The main purpose of this cross-sectional study was to describe the relationship between body fat percentage (BFP) and heart rate recovery after exercise [post-exercise heart rate (PEHR)], a marker of autonomic regulation, in normal-weight children and adolescents. We evaluated the body composition of 183 children and adolescents (age 15.0±2.3 years; 132 boys and 51 girls) who performed a maximal graded exercise test on a treadmill, with the heart rate monitored during and immediately after exercise. A strong positive trend was observed in the association between BFP and PEHR (r=0.14; p=0.06). Hierarchical multiple regression revealed that our model explained 18.3% of the variance in PEHR (p=0.00), yet BFP accounted for only 0.9% of the variability in PEHR (p=0.16). The evaluation of the contribution of each independent variable revealed that only two variables made a unique statistically significant contribution to our model (p<0.01), with age contributing 38.7% to our model (p=0.00) while gender accounted for an additional 25.5% (p=0.01). Neither BFP (14.4%; p=0.16) nor cardiorespiratory endurance (5.0%, p=0.60) made a significant unique contribution to the model. Body fatness seems to poorly predict PEHR in our sample of non-obese children and adolescents, while non-modifiable variables (age and gender) were demonstrated as strong predictors of heart rate recovery. The low amount of body fat reported in non-obese young participants was perhaps too small to cause disturbances in autonomic nervous system regulation.

Patterns and controls of mercury accumulation in sediments from three thermokarst lakes on the Arctic Coastal Plain of Alaska

USGS Publications Warehouse

Burke, Samantha M.; Zimmerman, Christian E.; Branfireun, Brian A.; Koch, Joshua C.; Swanson, Heidi K.

2018-01-01

The biogeochemical cycle of mercury will be influenced by climate change, particularly at higher latitudes. Investigations of historical mercury accumulation in lake sediments inform future predictions as to how climate change might affect mercury biogeochemistry; however, in regions with a paucity of data, such as the thermokarst-rich Arctic Coastal Plain of Alaska (ACP), the trajectory of mercury accumulation in lake sediments is particularly uncertain. Sediment cores from three thermokarst lakes on the ACP were analyzed to understand changes in, and drivers of, Hg accumulation over the past ~ 100 years. Mercury accumulation in two of the three lakes was variable and high over the past century (91.96 and 78.6 µg/m2/year), and largely controlled by sedimentation rate. Mercury accumulation in the third lake was lower (14.2 µg/m2/year), more temporally uniform, and was more strongly related to sediment Hg concentration than sedimentation rate. Sediment mercury concentrations were quantitatively related to measures of sediment composition and VRS-inferred chlorophyll a, and sedimentation rates were related to various catchment characteristics. These results were compared to data from 37 previously studied Arctic and Alaskan lakes. Results from the meta-analysis indicate that thermokarst lakes have significantly higher and more variable Hg accumulation rates than non-thermokarst lakes, suggesting that certain properties (e.g., thermal erosion, thaw slumping, low hydraulic conductivity) likely make lakes prone to high and variable Hg accumulation rates. Differences and high variability in Hg accumulation among high latitude lakes highlight the complexity of predicting future climate-related change impacts on mercury cycling in these environments.

A meta-analytic study of event rate effects on Go/No-Go performance in attention-deficit/hyperactivity disorder.

PubMed

Metin, Baris; Roeyers, Herbert; Wiersema, Jan R; van der Meere, Jaap; Sonuga-Barke, Edmund

2012-12-15

According to the state regulation deficit model, event rate (ER) is an important determinant of performance of children with attention-deficit/hyperactivity disorder (ADHD). Fast ER is predicted to create overactivation and produce errors of commission, whereas slow ER is thought to create underactivation marked by slow and variable reaction times (RT) and errors of omission. To test these predictions, we conducted a systematic search of the literature to identify all reports of comparisons of ADHD and control individuals' performance on Go/No-Go tasks published between 2000 and 2011. In one analysis, we included all trials with at least two event rates and calculated the difference between ER conditions. In a second analysis, we used metaregression to test for the moderating role of ER on ADHD versus control differences seen across Go/No-Go studies. There was a significant and disproportionate slowing of reaction time in ADHD relative to controls on trials with slow event rates in both meta-analyses. For commission errors, the effect sizes were larger on trials with fast event rates. No ER effects were seen for RT variability. There were also general effects of ADHD on performance for all variables that persisted after effects of ER were taken into account. The results provide support for the state regulation deficit model of ADHD by showing the differential effects of fast and slow ER. The lack of an effect of ER on RT variability suggests that this behavioral characteristic may not be a marker of cognitive energetic effects in ADHD. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Bullet trajectory predicts the need for damage control: an artificial neural network model.

PubMed

Hirshberg, Asher; Wall, Matthew J; Mattox, Kenneth L

2002-05-01

Effective use of damage control in trauma hinges on an early decision to use it. Bullet trajectory has never been studied as a marker for damage control. We hypothesize that this decision can be predicted by an artificial neural network (ANN) model based on the bullet trajectory and the patient's blood pressure. A multilayer perceptron ANN predictive model was developed from a data set of 312 patients with single abdominal gunshot injuries. Input variables were the bullet path, trajectory patterns, and admission systolic pressure. The output variable was either a damage control laparotomy or intraoperative death. The best performing ANN was implemented on prospectively collected data from 34 patients. The model achieved a correct classification rate of 0.96 and area under the receiver operating characteristic curve of 0.94. External validation showed the model to have a sensitivity of 88% and specificity of 96%. Model implementation on the prospectively collected data had a correct classification rate of 0.91. Sensitivity analysis showed that systolic pressure, bullet path across the midline, and trajectory involving the right upper quadrant were the three most important input variables. Bullet trajectory is an important, hitherto unrecognized, factor that should be incorporated into the decision to use damage control.

Prediction of space sickness in astronauts from preflight fluid, electrolyte, and cardiovascular variables and Weightless Environmental Training Facility (WETF) training

NASA Technical Reports Server (NTRS)

Simanonok, K.; Mosely, E.; Charles, J.

1992-01-01

Nine preflight variables related to fluid, electrolyte, and cardiovascular status from 64 first-time Shuttle crewmembers were differentially weighted by discrimination analysis to predict the incidence and severity of each crewmember's space sickness as rated by NASA flight surgeons. The nine variables are serum uric acid, red cell count, environmental temperature at the launch site, serum phosphate, urine osmolality, serum thyroxine, sitting systolic blood pressure, calculated blood volume, and serum chloride. Using two methods of cross-validation on the original samples (jackknife and a stratefied random subsample), these variables enable the prediction of space sickness incidence (NONE or SICK) with 80 percent sickness and space severity (NONE, MILD, MODERATE, of SEVERE) with 59 percent success by one method of cross-validation and 67 percent by another method. Addition of a tenth variable, hours spent in the Weightlessness Environment Training Facility (WETF) did not improve the prediction of space sickness incidences but did improve the prediction of space sickness severity to 66 percent success by the first method of cross-validation of original samples and to 71 percent by the second method. Results to date suggest the presence of predisposing physiologic factors to space sickness that implicate fluid shift etiology. The data also suggest that prior exposure to fluid shift during WETF training may produce some circulatory pre-adaption to fluid shifts in weightlessness that results in a reduction of space sickness severity.

Maternal risk factors predicting child physical characteristics and dysmorphology in fetal alcohol syndrome and partial fetal alcohol syndrome.

PubMed

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

2011-12-01

Previous research in South Africa revealed very high rates of fetal alcohol syndrome (FAS), of 46-89 per 1000 among young children. Maternal and child data from studies in this community summarize the multiple predictors of FAS and partial fetal alcohol syndrome (PFAS). Sequential regression was employed to examine influences on child physical characteristics and dysmorphology from four categories of maternal traits: physical, demographic, childbearing, and drinking. Then, a structural equation model (SEM) was constructed to predict influences on child physical characteristics. Individual sequential regressions revealed that maternal drinking measures were the most powerful predictors of a child's physical anomalies (R² = .30, p < .001), followed by maternal demographics (R² = .24, p < .001), maternal physical characteristics (R²=.15, p < .001), and childbearing variables (R² = .06, p < .001). The SEM utilized both individual variables and the four composite categories of maternal traits to predict a set of child physical characteristics, including a total dysmorphology score. As predicted, drinking behavior is a relatively strong predictor of child physical characteristics (β = 0.61, p < .001), even when all other maternal risk variables are included; higher levels of drinking predict child physical anomalies. Overall, the SEM model explains 62% of the variance in child physical anomalies. As expected, drinking variables explain the most variance. But this highly controlled estimation of multiple effects also reveals a significant contribution played by maternal demographics and, to a lesser degree, maternal physical and childbearing variables. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Fatigue life and crack growth prediction methodology

NASA Technical Reports Server (NTRS)

Newman, J. C., Jr.; Phillips, E. P.; Everett, R. A., Jr.

1993-01-01

The capabilities of a plasticity-induced crack-closure model and life-prediction code to predict fatigue crack growth and fatigue lives of metallic materials are reviewed. Crack-tip constraint factors, to account for three-dimensional effects, were selected to correlate large-crack growth rate data as a function of the effective-stress-intensity factor range (delta(K(sub eff))) under constant-amplitude loading. Some modifications to the delta(K(sub eff))-rate relations were needed in the near threshold regime to fit small-crack growth rate behavior and endurance limits. The model was then used to calculate small- and large-crack growth rates, and in some cases total fatigue lives, for several aluminum and titanium alloys under constant-amplitude, variable-amplitude, and spectrum loading. Fatigue lives were calculated using the crack growth relations and microstructural features like those that initiated cracks. Results from the tests and analyses agreed well.

The Cancer of the Prostate Risk Assessment (CAPRA) score predicts biochemical recurrence in intermediate-risk prostate cancer treated with external beam radiotherapy (EBRT) dose escalation or low-dose rate (LDR) brachytherapy.

PubMed

Krishnan, Vimal; Delouya, Guila; Bahary, Jean-Paul; Larrivée, Sandra; Taussky, Daniel

2014-12-01

To study the prognostic value of the University of California, San Francisco Cancer of the Prostate Risk Assessment (CAPRA) score to predict biochemical failure (bF) after various doses of external beam radiotherapy (EBRT) and/or permanent seed low-dose rate (LDR) prostate brachytherapy (PB). We retrospectively analysed 345 patients with intermediate-risk prostate cancer, with PSA levels of 10-20 ng/mL and/or Gleason 7 including 244 EBRT patients (70.2-79.2 Gy) and 101 patients treated with LDR PB. The minimum follow-up was 3 years. No patient received primary androgen-deprivation therapy. bF was defined according to the Phoenix definition. Cox regression analysis was used to estimate the differences between CAPRA groups. The overall bF rate was 13% (45/345). The CAPRA score, as a continuous variable, was statistically significant in multivariate analysis for predicting bF (hazard ratio [HR] 1.37, 95% confidence interval [CI] 1.10-1.72, P = 0.006). There was a trend for a lower bF rate in patients treated with LDR PB when compared with those treated by EBRT ≤ 74 Gy (HR 0.234, 95% CI 0.05-1.03, P = 0.055) in multivariate analysis. In the subgroup of patients with a CAPRA score of 3-5, CAPRA remained predictive of bF as a continuous variable (HR 1.51, 95% CI 1.01-2.27, P = 0.047) in multivariate analysis. The CAPRA score is useful for predicting biochemical recurrence in patients treated for intermediate-risk prostate cancer with EBRT or LDR PB. It could help in treatment decisions. © 2013 The Authors. BJU International © 2013 BJU International.

Cardiovascular mortality prediction in veterans with arm exercise vs pharmacologic myocardial perfusion imaging.

PubMed

Martin, Wade H; Xian, Hong; Chandiramani, Pooja; Bainter, Emily; Klein, Andrew J P

2015-08-01

No data exist comparing outcome prediction from arm exercise vs pharmacologic myocardial perfusion imaging (MPI) stress test variables in patients unable to perform treadmill exercise. In this retrospective study, 2,173 consecutive lower extremity disabled veterans aged 65.4 ± 11.0years (mean ± SD) underwent either pharmacologic MPI (1730 patients) or arm exercise stress tests (443 patients) with MPI (n = 253) or electrocardiography alone (n = 190) between 1997 and 2002. Cox multivariate regression models and reclassification analysis by integrated discrimination improvement (IDI) were used to characterize stress test and MPI predictors of cardiovascular mortality at ≥10-year follow-up after inclusion of significant demographic, clinical, and other variables. Cardiovascular death occurred in 561 pharmacologic MPI and 102 arm exercise participants. Multivariate-adjusted cardiovascular mortality was predicted by arm exercise resting metabolic equivalents (hazard ratio [HR] 0.52, 95% CI 0.39-0.69, P < .001), 1-minute heart rate recovery (HR 0.61, 95% CI 0.44-0.86, P < .001), and pharmacologic and arm exercise delta (peak-rest) heart rate (both P < .001). Only an abnormal arm exercise MPI prognosticated cardiovascular death by multivariate Cox analysis (HR 1.98, 95% CI 1.04-3.77, P < .05). Arm exercise MPI defect number, type, and size provided IDI over covariates for prediction of cardiovascular mortality (IDI = 0.074-0.097). Only pharmacologic defect size prognosticated cardiovascular mortality (IDI = 0.022). Arm exercise capacity, heart rate recovery, and pharmacologic and arm exercise heart rate responses are robust predictors of cardiovascular mortality. Arm exercise MPI results are equivalent and possibly superior to pharmacologic MPI for cardiovascular mortality prediction in patients unable to perform treadmill exercise. Published by Elsevier Inc.

Beyond the conventional understanding of water-rock reactivity

NASA Astrophysics Data System (ADS)

Fischer, Cornelius; Luttge, Andreas

2017-01-01

A common assumption is that water-rock reaction rates should converge to a mean value. There is, however, an emerging consensus on the genuine nature of reaction rate variations under identical chemical conditions. Thus, the further use of mean reaction rates for the prediction of material fluxes is environmentally and economically risky, manifest for example in the management of nuclear waste or the evolution of reservoir rocks. Surface-sensitive methods and resulting information about heterogeneous surface reactivity illustrate the inherent rate variability. Consequently, a statistical analysis was developed in order to quantify the heterogeneity of surface rates. We show how key components of the rate combine to give an overall rate and how the identification of those individual rate contributors provide mechanistic insight into complex heterogeneous reactions. This generates a paradigm change by proposing a new pathway to reaction model parameterization and for the prediction of reaction rates.

Evaluating predictive modeling’s potential to improve teleretinal screening participation in urban safety net clinics

PubMed Central

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Introduction Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Methods Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. Results The predictive models were modestly predictive with the best model having an AUC of 0.71. Discussion Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics. PMID:23920536

Evaluating predictive modeling's potential to improve teleretinal screening participation in urban safety net clinics.

PubMed

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. The predictive models were modestly predictive with the best model having an AUC of 0.71. Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics.

Emotion Regulation Predicts Pain and Functioning in Children With Juvenile Idiopathic Arthritis: An Electronic Diary Study

PubMed Central

Bromberg, Maggie H.; Anthony, Kelly K.; Gil, Karen M.; Franks, Lindsey; Schanberg, Laura E.

2012-01-01

Objectives This study utilized e-diaries to evaluate whether components of emotion regulation predict daily pain and function in children with juvenile idiopathic arthritis (JIA). Methods 43 children ages 8–17 years and their caregivers provided baseline reports of child emotion regulation. Children then completed thrice daily e-diary assessments of emotion, pain, and activity involvement for 28 days. E-diary ratings of negative and positive emotions were used to calculate emotion variability and to infer adaptive emotion modulation following periods of high or low emotion intensity. Hierarchical linear models were used to evaluate how emotion regulation related to pain and function. Results The attenuation of negative emotion following a period of high negative emotion predicted reduced pain; greater variability of negative emotion predicted higher pain and increased activity limitation. Indices of positive emotion regulation also significantly predicted pain. Conclusions Components of emotion regulation as captured by e-diaries predict important health outcomes in children with JIA. PMID:22037006

Optimization and formulation design of gels of Diclofenac and Curcumin for transdermal drug delivery by Box-Behnken statistical design.

PubMed

Chaudhary, Hema; Kohli, Kanchan; Amin, Saima; Rathee, Permender; Kumar, Vikash

2011-02-01

The aim of this study was to develop and optimize a transdermal gel formulation for Diclofenac diethylamine (DDEA) and Curcumin (CRM). A 3-factor, 3-level Box-Behnken design was used to derive a second-order polynomial equation to construct contour plots for prediction of responses. Independent variables studied were the polymer concentration (X(1)), ethanol (X(2)) and propylene glycol (X(3)) and the levels of each factor were low, medium, and high. The dependent variables studied were the skin permeation rate of DDEA (Y(1)), skin permeation rate of CRM (Y(2)), and viscosity of the gels (Y(3)). Response surface plots were drawn, statistical validity of the polynomials was established to find the compositions of optimized formulation which was evaluated using the Franz-type diffusion cell. The permeation rate of DDEA increased proportionally with ethanol concentration but decreased with polymer concentration, whereas the permeation rate of CRM increased proportionally with polymer concentration. Gels showed a non-Fickian super case II (typical zero order) and non-Fickian diffusion release mechanism for DDEA and CRM, respectively. The design demonstrated the role of the derived polynomial equation and contour plots in predicting the values of dependent variables for the preparation and optimization of gel formulation for transdermal drug release. Copyright © 2010 Wiley-Liss, Inc.

A method for predicting optimized processing parameters for surfacing

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

Dupont, J.N.; Marder, A.R.

1994-12-31

Welding is used extensively for surfacing applications. To operate a surfacing process efficiently, the variables must be optimized to produce low levels of dilution with the substrate while maintaining high deposition rates. An equation for dilution in terms of the welding variables, thermal efficiency factors, and thermophysical properties of the overlay and substrate was developed by balancing energy and mass terms across the welding arc. To test the validity of the resultant dilution equation, the PAW, GTAW, GMAW, and SAW processes were used to deposit austenitic stainless steel onto carbon steel over a wide range of parameters. Arc efficiency measurementsmore » were conducted using a Seebeck arc welding calorimeter. Melting efficiency was determined based on knowledge of the arc efficiency. Dilution was determined for each set of processing parameters using a quantitative image analysis system. The pertinent equations indicate dilution is a function of arc power (corrected for arc efficiency), filler metal feed rate, melting efficiency, and thermophysical properties of the overlay and substrate. With the aid of the dilution equation, the effect of processing parameters on dilution is presented by a new processing diagram. A new method is proposed for determining dilution from welding variables. Dilution is shown to depend on the arc power, filler metal feed rate, arc and melting efficiency, and the thermophysical properties of the overlay and substrate. Calculated dilution levels were compared with measured values over a large range of processing parameters and good agreement was obtained. The results have been applied to generate a processing diagram which can be used to: (1) predict the maximum deposition rate for a given arc power while maintaining adequate fusion with the substrate, and (2) predict the resultant level of dilution with the substrate.« less

Modeling porosity reductions caused by mineral fouling in continuous-wall permeable reactive barriers.

PubMed

Li, Lin; Benson, Craig H; Lawson, Elizabeth M

2006-02-01

A study was conducted to assess key factors to include when modeling porosity reductions caused by mineral fouling in permeable reactive barriers (PRBs) containing granular zero valent iron. The public domain codes MODFLOW and RT3D were used and a geochemical algorithm was developed for RT3D to simulate geochemical reactions occurring in PRBs. Results of simulations conducted with the model show that the largest porosity reductions occur between the entrance and mid-plane of the PRB as a result of precipitation of carbonate minerals and that smaller porosity reductions occur between the mid-plane and exit face due to precipitation of ferrous hydroxide. These findings are consistent with field and laboratory observations, as well as modeling predictions made by others. Parametric studies were conducted to identify the most important variables to include in a model evaluating porosity reduction. These studies showed that three minerals (CaCO3, FeCO3, and Fe(OH)2 (am)) account for more than 99% of the porosity reductions that were predicted. The porosity reduction is sensitive to influent concentrations of HCO3-, Ca2+, CO3(2-), and dissolved oxygen, the anaerobic iron corrosion rate, and the rates of CaCO3 and FeCO3 formation. The predictions also show that porosity reductions in PRBs can be spatially variable and mineral forming ions penetrate deeper into the PRB as a result of flow heterogeneities, which reflects the balance between the rate of mass transport and geochemical reaction rates. Level of aquifer heterogeneity and the contrast in hydraulic conductivity between the aquifer and PRB are the most important hydraulic variables affecting porosity reduction. Spatial continuity of aquifer hydraulic conductivity is less significant.

Poor environmental tracking can make extinction risk insensitive to the colour of environmental noise

PubMed Central

van de Pol, Martijn; Vindenes, Yngvild; Sæther, Bernt-Erik; Engen, Steinar; Ens, Bruno J.; Oosterbeek, Kees; Tinbergen, Joost M.

2011-01-01

The relative importance of environmental colour for extinction risk compared with other aspects of environmental noise (mean and interannual variability) is poorly understood. Such knowledge is currently relevant, as climate change can cause the mean, variability and temporal autocorrelation of environmental variables to change. Here, we predict that the extinction risk of a shorebird population increases with the colour of a key environmental variable: winter temperature. However, the effect is weak compared with the impact of changes in the mean and interannual variability of temperature. Extinction risk was largely insensitive to noise colour, because demographic rates are poor in tracking the colour of the environment. We show that three mechanisms—which probably act in many species—can cause poor environmental tracking: (i) demographic rates that depend nonlinearly on environmental variables filter the noise colour, (ii) demographic rates typically depend on several environmental signals that do not change colour synchronously, and (iii) demographic stochasticity whitens the colour of demographic rates at low population size. We argue that the common practice of assuming perfect environmental tracking may result in overemphasizing the importance of noise colour for extinction risk. Consequently, ignoring environmental autocorrelation in population viability analysis could be less problematic than generally thought. PMID:21561978

Spatial Variability in Biodegradation Rates as Evidenced by Methane Production from an Aquifer

PubMed Central

Adrian, Neal R.; Robinson, Joseph A.; Suflita, Joseph M.

1994-01-01

Accurate predictions of carbon and energy cycling rates in the environment depend on sampling frequencies and on the spatial variability associated with biological activities. We examined the variability associated with anaerobic biodegradation rates at two sites in an alluvial sand aquifer polluted by municipal landfill leachate. In situ rates of methane production were measured for almost a year, using anaerobic wells installed at two sites. Methane production ranged from 0 to 560 μmol · m-2 · day-1 at one site (A), while a range of 0 to 120,000 μmol · m-2 · day-1 was measured at site B. The mean and standard deviations associated with methane production at site A were 17 and 57 μmol · m-2 · day-1, respectively. The comparable summary statistics for site B were 2,000 and 9,900 μmol · m-2 · day-1. The coefficients of variation at sites A and B were 340 and 490%, respectively. Despite these differences, the two sites had similar seasonal trends, with the maximal rate of methane production occurring in summer. However, the relative variability associated with the seasonal rates changed very little. Our results suggest that (i) two spatially distinct sites exist in the aquifer, (ii) methanogenesis is a highly variable process, (iii) the coefficient of variation varied little with the rate of methane production, and (iv) in situ anaerobic biodegradation rates are lognormally distributed. PMID:16349410

What Does Eye-Blink Rate Variability Dynamics Tell Us About Cognitive Performance?

PubMed Central

Paprocki, Rafal; Lenskiy, Artem

2017-01-01

Cognitive performance is defined as the ability to utilize knowledge, attention, memory, and working memory. In this study, we briefly discuss various markers that have been proposed to predict cognitive performance. Next, we develop a novel approach to characterize cognitive performance by analyzing eye-blink rate variability dynamics. Our findings are based on a sample of 24 subjects. The subjects were given a 5-min resting period prior to a 10-min IQ test. During both stages, eye blinks were recorded from Fp1 and Fp2 electrodes. We found that scale exponents estimated for blink rate variability during rest were correlated with subjects' performance on the subsequent IQ test. This surprising phenomenon could be explained by the person to person variation in concentrations of dopamine in PFC and accumulation of GABA in the visual cortex, as both neurotransmitters play a key role in cognitive processes and affect blinking. This study demonstrates the possibility that blink rate variability dynamics at rest carry information about cognitive performance and can be employed in the assessment of cognitive abilities without taking a test. PMID:29311876

Predicting Bobsled Pushing Ability from Various Combine Testing Events.

PubMed

Tomasevicz, Curtis L; Ransone, Jack W; Bach, Christopher W

2018-03-12

The requisite combination of speed, power, and strength necessary for a bobsled push athlete coupled with the difficulty in directly measuring pushing ability makes selecting effective push crews challenging. Current practices by USA Bobsled and Skeleton (USABS) utilize field combine testing to assess and identify specifically selected performance variables in an attempt to best predict push performance abilities. Combine data consisting of 11 physical performance variables were collected from 75 subjects across two winter Olympic qualification years (2009 and 2013). These variables were sprints of 15-, 30-, and 60 m, a flying 30 m sprint, a standing broad jump, a shot toss, squat, power clean, body mass, and dry-land brake and side bobsled pushes. Discriminant Analysis (DA) in addition to Principle Component Analysis (PCA) was used to investigate two cases (Case 1: Olympians vs. non-Olympians; Case 2: National Team vs. non-National Team). Using these 11 variables, DA led to a classification rule that proved capable of identifying Olympians from non-Olympians and National Team members from non-National Team members with 9.33% and 14.67% misclassification rates, respectively. The PCA was used to find similar test variables within the combine that provided redundant or useless data. After eliminating the unnecessary variables, DA on the new combinations showed that 8 (Case 1) and 20 (Case 2) other combinations with fewer performance variables yielded misclassification rates as low as 6.67% and 13.33% respectively. Utilizing fewer performance variables can allow governing bodies in many other sports to create more appropriate combine testing that maximize accuracy, while minimizing irrelevant and redundant strategies.

The development and evaluation of accident predictive models

NASA Astrophysics Data System (ADS)

Maleck, T. L.

1980-12-01

A mathematical model that will predict the incremental change in the dependent variables (accident types) resulting from changes in the independent variables is developed. The end product is a tool for estimating the expected number and type of accidents for a given highway segment. The data segments (accidents) are separated in exclusive groups via a branching process and variance is further reduced using stepwise multiple regression. The standard error of the estimate is calculated for each model. The dependent variables are the frequency, density, and rate of 18 types of accidents among the independent variables are: district, county, highway geometry, land use, type of zone, speed limit, signal code, type of intersection, number of intersection legs, number of turn lanes, left-turn control, all-red interval, average daily traffic, and outlier code. Models for nonintersectional accidents did not fit nor validate as well as models for intersectional accidents.

Prediction of Malaysian monthly GDP

NASA Astrophysics Data System (ADS)

Hin, Pooi Ah; Ching, Soo Huei; Yeing, Pan Wei

2015-12-01

The paper attempts to use a method based on multivariate power-normal distribution to predict the Malaysian Gross Domestic Product next month. Letting r(t) be the vector consisting of the month-t values on m selected macroeconomic variables, and GDP, we model the month-(t+1) GDP to be dependent on the present and l-1 past values r(t), r(t-1),…,r(t-l+1) via a conditional distribution which is derived from a [(m+1)l+1]-dimensional power-normal distribution. The 100(α/2)% and 100(1-α/2)% points of the conditional distribution may be used to form an out-of sample prediction interval. This interval together with the mean of the conditional distribution may be used to predict the month-(t+1) GDP. The mean absolute percentage error (MAPE), estimated coverage probability and average length of the prediction interval are used as the criterions for selecting the suitable lag value l-1 and the subset from a pool of 17 macroeconomic variables. It is found that the relatively better models would be those of which 2 ≤ l ≤ 3, and involving one or two of the macroeconomic variables given by Market Indicative Yield, Oil Prices, Exchange Rate and Import Trade.

Treatment of scalp psoriasis with clobetasol-17 propionate 0.05% shampoo: a study on daily clinical practice.

PubMed

Bovenschen, H J; Van de Kerkhof, P C M

2010-04-01

Safety and clinical effectiveness of clobetasol-17 propionate 0.05% shampoo have been shown in patients with scalp psoriasis. First, to evaluate treatment satisfaction, user convenience safety and effectiveness of clobetasol-17 propionate 0.05% shampoo treatment in daily clinical practice. Second, to identify subgroup variables that may predict treatment success or failure. A total of 56 patients with scalp psoriasis were treated with short-contact clobetasol-17 propionate 0.05% shampoo once daily for 4 weeks. Data on treatment satisfaction, user convenience, safety and effectiveness were assessed on a 7-point Likert scale using postal questionnaires. Subgroup analyses were performed to identify variables that may predict treatment outcome. A total of 41 patients returned both questionnaires (73%). Positive treatment satisfaction and user convenience were reported by 66% and 79% of patients respectively. Patient-rated indicators for disease severity improved by 39-46% (P < 0.05%). No major side-effects were reported. Subgroup analyses did not reveal any statistically significant patient variable that may predict treatment outcome. However, a tendency towards improved treatment satisfaction was observed in patients who had received fewer topical antipsoriatic treatments previously (P > 0.05). Short-contact treatment with clobetasol-17 propionate 0.05% shampoo has high user convenience and patient satisfaction rates. Moreover, the treatment is well-tolerated and efficacious from patients' perspective. Subgroup analyses did not reveal factors predicting treatment outcome, although treatment success tended to be more evident in patients who had received fewer treatments previously.

Assessment of heart rate, acidosis, consciousness, oxygenation, and respiratory rate to predict noninvasive ventilation failure in hypoxemic patients.

PubMed

Duan, Jun; Han, Xiaoli; Bai, Linfu; Zhou, Lintong; Huang, Shicong

2017-02-01

To develop and validate a scale using variables easily obtained at the bedside for prediction of failure of noninvasive ventilation (NIV) in hypoxemic patients. The test cohort comprised 449 patients with hypoxemia who were receiving NIV. This cohort was used to develop a scale that considers heart rate, acidosis, consciousness, oxygenation, and respiratory rate (referred to as the HACOR scale) to predict NIV failure, defined as need for intubation after NIV intervention. The highest possible score was 25 points. To validate the scale, a separate group of 358 hypoxemic patients were enrolled in the validation cohort. The failure rate of NIV was 47.8 and 39.4% in the test and validation cohorts, respectively. In the test cohort, patients with NIV failure had higher HACOR scores at initiation and after 1, 12, 24, and 48 h of NIV than those with successful NIV. At 1 h of NIV the area under the receiver operating characteristic curve was 0.88, showing good predictive power for NIV failure. Using 5 points as the cutoff value, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for NIV failure were 72.6, 90.2, 87.2, 78.1, and 81.8%, respectively. These results were confirmed in the validation cohort. Moreover, the diagnostic accuracy for NIV failure exceeded 80% in subgroups classified by diagnosis, age, or disease severity and also at 1, 12, 24, and 48 h of NIV. Among patients with NIV failure with a HACOR score of >5 at 1 h of NIV, hospital mortality was lower in those who received intubation at ≤12 h of NIV than in those intubated later [58/88 (66%) vs. 138/175 (79%); p = 0.03). The HACOR scale variables are easily obtained at the bedside. The scale appears to be an effective way of predicting NIV failure in hypoxemic patients. Early intubation in high-risk patients may reduce hospital mortality.

Responses of plant growth and metabolism to environmental variables predicted from laboratory measurements

Treesearch

Lee D. Hansen; Bruce N. Smith; Richard S. Criddle; J. N. Church

2001-01-01

The Arrhenius activation energies, and therefore temperature coefficients, for rates of catabolic production of ATP and for anabolic use of ATP differ. Because the intracellular concentration of ATP and the phosphorylation potential must be controlled within a narrow range for cell survival, a mechanism must exist to balance these rates during temperature variation in...

Variable pleiotropic effects from mutations at the same locus hamper prediction of fitness from a fitness component.

PubMed

Pepin, Kim M; Samuel, Melanie A; Wichman, Holly A

2006-04-01

The relationship of genotype, fitness components, and fitness can be complicated by genetic effects such as pleiotropy and epistasis and by heterogeneous environments. However, because it is often difficult to measure genotype and fitness directly, fitness components are commonly used to estimate fitness without regard to genetic architecture. The small bacteriophage X174 enables direct evaluation of genetic and environmental effects on fitness components and fitness. We used 15 mutants to study mutation effects on attachment rate and fitness in six hosts. The mutants differed from our lab strain of X174 by only one or two amino acids in the major capsid protein (gpF, sites 101 and 102). The sites are variable in natural and experimentally evolved X174 populations and affect phage attachment rate. Within the limits of detection of our assays, all mutations were neutral or deleterious relative to the wild type; 11 mutants had decreased host range. While fitness was predictable from attachment rate in most cases, 3 mutants had rapid attachment but low fitness on most hosts. Thus, some mutations had a pleiotropic effect on a fitness component other than attachment rate. In addition, on one host most mutants had high attachment rate but decreased fitness, suggesting that pleiotropic effects also depended on host. The data highlight that even in this simple, well-characterized system, prediction of fitness from a fitness component depends on genetic architecture and environment.

A non-modal analytical method to predict turbulent properties applied to the Hasegawa-Wakatani model

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

Friedman, B., E-mail: friedman11@llnl.gov; Lawrence Livermore National Laboratory, Livermore, California 94550; Carter, T. A.

2015-01-15

Linear eigenmode analysis often fails to describe turbulence in model systems that have non-normal linear operators and thus nonorthogonal eigenmodes, which can cause fluctuations to transiently grow faster than expected from eigenmode analysis. When combined with energetically conservative nonlinear mode mixing, transient growth can lead to sustained turbulence even in the absence of eigenmode instability. Since linear operators ultimately provide the turbulent fluctuations with energy, it is useful to define a growth rate that takes into account non-modal effects, allowing for prediction of energy injection, transport levels, and possibly even turbulent onset in the subcritical regime. We define such amore » non-modal growth rate using a relatively simple model of the statistical effect that the nonlinearities have on cross-phases and amplitude ratios of the system state variables. In particular, we model the nonlinearities as delta-function-like, periodic forces that randomize the state variables once every eddy turnover time. Furthermore, we estimate the eddy turnover time to be the inverse of the least stable eigenmode frequency or growth rate, which allows for prediction without nonlinear numerical simulation. We test this procedure on the 2D and 3D Hasegawa-Wakatani model [A. Hasegawa and M. Wakatani, Phys. Rev. Lett. 50, 682 (1983)] and find that the non-modal growth rate is a good predictor of energy injection rates, especially in the strongly non-normal, fully developed turbulence regime.« less

A non-modal analytical method to predict turbulent properties applied to the Hasegawa-Wakatani model

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

Friedman, B.; Carter, T. A.

2015-01-15

Linear eigenmode analysis often fails to describe turbulence in model systems that have non-normal linear operators and thus nonorthogonal eigenmodes, which can cause fluctuations to transiently grow faster than expected from eigenmode analysis. When combined with energetically conservative nonlinear mode mixing, transient growth can lead to sustained turbulence even in the absence of eigenmode instability. Since linear operators ultimately provide the turbulent fluctuations with energy, it is useful to define a growth rate that takes into account non-modal effects, allowing for prediction of energy injection, transport levels, and possibly even turbulent onset in the subcritical regime. Here, we define suchmore » a non-modal growth rate using a relatively simple model of the statistical effect that the nonlinearities have on cross-phases and amplitude ratios of the system state variables. In particular, we model the nonlinearities as delta-function-like, periodic forces that randomize the state variables once every eddy turnover time. Furthermore, we estimate the eddy turnover time to be the inverse of the least stable eigenmode frequency or growth rate, which allows for prediction without nonlinear numerical simulation. Also, we test this procedure on the 2D and 3D Hasegawa-Wakatani model [A. Hasegawa and M. Wakatani, Phys. Rev. Lett. 50, 682 (1983)] and find that the non-modal growth rate is a good predictor of energy injection rates, especially in the strongly non-normal, fully developed turbulence regime.« less

Job compensable factors and factor weights derived from job analysis data.

PubMed

Chi, Chia-Fen; Chang, Tin-Chang; Hsia, Ping-Ling; Song, Jen-Chieh

2007-06-01

Government data on 1,039 job titles in Taiwan were analyzed to assess possible relationships between job attributes and compensation. For each job title, 79 specific variables in six major classes (required education and experience, aptitude, interest, work temperament, physical demands, task environment) were coded to derive the statistical predictors of wage for managers, professionals, technical, clerical, service, farm, craft, operatives, and other workers. Of the 79 variables, only 23 significantly related to pay rate were subjected to a factor and multiple regression analysis for predicting monthly wages. Given the heterogeneous nature of collected job titles, a 4-factor solution (occupational knowledge and skills, human relations skills, work schedule hardships, physical hardships) explaining 43.8% of the total variance but predicting only 23.7% of the monthly pay rate was derived. On the other hand, multiple regression with 9 job analysis items (required education, professional training, professional certificate, professional experience, coordinating, leadership and directing, demand on hearing, proportion of shift working indoors, outdoors and others, rotating shift) better predicted pay and explained 32.5% of the variance. A direct comparison of factors and subfactors of job evaluation plans indicated mental effort and responsibility (accountability) had not been measured with the current job analysis data. Cross-validation of job evaluation factors and ratings with the wage rates is required to calibrate both.

Predicting health care utilization in marginalized populations: Black, female, street-based sex workers.

PubMed

Varga, Leah M; Surratt, Hilary L

2014-01-01

Patterns of social and structural factors experienced by vulnerable populations may negatively affect willingness and ability to seek out health care services, and ultimately, their health. The outcome variable was utilization of health care services in the previous 12 months. Using Andersen's Behavioral Model for Vulnerable Populations, we examined self-reported data on utilization of health care services among a sample of 546 Black, street-based, female sex workers in Miami, Florida. To evaluate the impact of each domain of the model on predicting health care utilization, domains were included in the logistic regression analysis by blocks using the traditional variables first and then adding the vulnerable domain variables. The most consistent variables predicting health care utilization were having a regular source of care and self-rated health. The model that included only enabling variables was the most efficient model in predicting health care utilization. Any type of resource, link, or connection to or with an institution, or any consistent point of care, contributes significantly to health care utilization behaviors. A consistent and reliable source for health care may increase health care utilization and subsequently decrease health disparities among vulnerable and marginalized populations, as well as contribute to public health efforts that encourage preventive health. Copyright © 2014 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Characterization and prediction of monomer-based dose rate effects in electron-beam polymerization

NASA Astrophysics Data System (ADS)

Schissel, Sage M.; Lapin, Stephen C.; Jessop, Julie L. P.

2017-12-01

Properties of some materials produced by electron-beam (EB) induced polymerization appear dependent upon the rate at which the initiating dose was delivered. However, the magnitude of these dose rate effects (DREs) can vary greatly with different monomer formulations, suggesting DREs are dependent on chemical structure. The relationship among dose, dose rate, conversion, and the glass transition temperature (Tg) of the cured material was explored for an acrylate monomer series. A strong correlation was determined between the DRE magnitude and monomer size, and this correlation may be attributed to chain transfer. Using the Tg shift caused by changes in dose, a preliminary predictive relationship was developed to estimate the magnitude of the Tg DRE, enabling scale-up of process variables for polymers prone to dose rate effects.

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

PubMed

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 < or = 5 cm was present in 40% of patients (27/67), and an amniotic fluid index of < or = 8 cm was present in 72% of patients (48/67). The probability of success 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.

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

PubMed

Mehra, Lucky K; Cowger, Christina; Gross, Kevin; Ojiambo, Peter S

2016-01-01

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of the risk of SNB, facilitating sound disease management decisions prior to planting of wheat.

Physicochemical properties influencing denitrification rate and microbial activity in denitrification bioreactors

NASA Astrophysics Data System (ADS)

Schmidt, C. A.

2012-12-01

The use of N-based fertilizer will need to increase to meet future demands, yet existing applications have been implicated as the main source of coastal eutrophication and hypoxic zones. Producing sufficient crops to feed a growing planet will require efficient production in combination with sustainable treatment solutions. The long-term success of denitrification bioreactors to effectively remove nitrate (NO¬3), indicates this technology is a feasible treatment option. Assessing and quantifying the media properties that affect NO¬3 removal rate and microbial activity can improve predictions on bioreactor performance. It was hypothesized that denitrification rates and microbial biomass would be correlated with total C, NO¬3 concentration, metrics of organic matter quality, media surface area and laboratory measures of potential denitrification rate. NO¬3 removal rates and microbial biomass were evaluated in mesocosms filled with different wood treatments and the unique influence of these predictor variables was determined using a multiple linear regression analysis. NO3 reduction rates were independent of NO¬3 concentration indicating zero order reaction kinetics. Temperature was strongly correlated with denitrification rate (r2=0.87; Q10=4.7), indicating the variability of bioreactor performance in differing climates. Fiber quality, and media surface area were strong (R>0.50), unique predictors of rates and microbial biomass, although C:N ratio and potential denitrification rate did not predict actual denitrification rate or microbial biomass. Utilizing a stepwise multiple linear regression, indicates that the denitrification rate can be effectively (r2=0.56;p<0.0001) predicted if the groundwater temperature, neutral detergent fiber and surface area alone are quantified. These results will assist with the widespread implementation of denitrification bioreactors to achieve significant N load reductions in large watersheds. The nitrate reduction rate as a function of groundwater temperature for all treatments. Correlations between nitrate reduction rate and properties of carbon media;

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?

DOE PAGES

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas; ...

2016-02-24

In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of processmore » rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.« less

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?

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

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas

In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of processmore » rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.« less

Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

PubMed Central

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J. M.; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C.; Glanville, Helen C.; Jones, Davey L.; Angel, Roey; Salminen, Janne; Newton, Ryan J.; Bürgmann, Helmut; Ingram, Lachlan J.; Hamer, Ute; Siljanen, Henri M. P.; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C.; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C.; Lopes, Ana R.; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S.; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S.; Basiliko, Nathan; Nemergut, Diana R.

2016-01-01

Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology. PMID:26941732

Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

PubMed

Graham, Emily B; Knelman, Joseph E; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J M; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C; Glanville, Helen C; Jones, Davey L; Angel, Roey; Salminen, Janne; Newton, Ryan J; Bürgmann, Helmut; Ingram, Lachlan J; Hamer, Ute; Siljanen, Henri M P; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C; Lopes, Ana R; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S; Basiliko, Nathan; Nemergut, Diana R

2016-01-01

Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

Predicting Vasovagal Syncope from Heart Rate and Blood Pressure: A Prospective Study in 140 Subjects.

PubMed

Virag, Nathalie; Erickson, Mark; Taraborrelli, Patricia; Vetter, Rolf; Lim, Phang Boon; Sutton, Richard

2018-04-28

We developed a vasovagal syncope (VVS) prediction algorithm for use during head-up tilt with simultaneous analysis of heart rate (HR) and systolic blood pressure (SBP). We previously tested this algorithm retrospectively in 1155 subjects, showing sensitivity 95%, specificity 93% and median prediction time of 59s. This study was prospective, single center, on 140 subjects to evaluate this VVS prediction algorithm and assess if retrospective results were reproduced and clinically relevant. Primary endpoint was VVS prediction: sensitivity and specificity >80%. In subjects, referred for 60° head-up tilt (Italian protocol), non-invasive HR and SBP were supplied to the VVS prediction algorithm: simultaneous analysis of RR intervals, SBP trends and their variability represented by low-frequency power generated cumulative risk which was compared with a predetermined VVS risk threshold. When cumulative risk exceeded threshold, an alert was generated. Prediction time was duration between first alert and syncope. Of 140 subjects enrolled, data was usable for 134. Of 83 tilt+ve (61.9%), 81 VVS events were correctly predicted and of 51 tilt-ve subjects (38.1%), 45 were correctly identified as negative by the algorithm. Resulting algorithm performance was sensitivity 97.6%, specificity 88.2%, meeting primary endpoint. Mean VVS prediction time was 2min 26s±3min16s with median 1min 25s. Using only HR and HR variability (without SBP) the mean prediction time reduced to 1min34s±1min45s with median 1min13s. The VVS prediction algorithm, is clinically-relevant tool and could offer applications including providing a patient alarm, shortening tilt-test time, or triggering pacing intervention in implantable devices. Copyright © 2018. Published by Elsevier Inc.

The identification of depression and the coverage of antidepressant drug prescriptions in Italian general practice.

PubMed

Bellantuono, Cesario; Mazzi, Maria Angela; Tansella, Michele; Rizzo, Raffaella; Goldberg, David

2002-10-01

Studies on antidepressant prescriptions in general practice need to assess the level of prescriptions relative to the need for them ('coverage'), and the variability among doctors. Two different cut-off scores on a screening test for depression (the Personal Health Questionnaire, PHQ) are used to predict rates for depression, and rates for depressive patients thought likely to benefit from antidepressants (according to a severity criterion) in primary care patients. These two rates are compared with assessments by 11 GPs of recognised depression, as well as with rates of drug prescribed. The rate for depression thought likely to be treated with antidepressants estimated with the PHQ is broadly comparable with the rate for conspicuous depressive illness, and much lower than that predicted by the PHQ for depression. There was great variability between GPs in their ability to detect depression, and their preparedness to prescribe antidepressants. Antidepressants were only prescribed for 3.5% of the patients, compared to the 8.9% thought to need them. However, antidepressants, mostly SSRIs, are much more likely to be prescribed than tranquillisers. The limitations of the study are that the PHQ is able to estimate 'coverage' but not 'focusing' (the proportion of those receiving antidepressants who needed them). Although the rate for conspicuous depression is similar to that for depressions thought to be treated with antidepressants, the 'coverage' of antidepressants was only 39.3%. The variability between physicians confirm the need of good practice guidelines and training packages for the identification and management of depression. Large epidemiological studies are needed to overcome the current lack of clinically relevant data on the quality of antidepressant prescriptions in general practice.

Improving the Prediction of Mortality and the Need for Life-Saving Interventions in Trauma Patients Using Standard Vital Signs With Heart-Rate Variability and Complexity

DTIC Science & Technology

2015-06-01

Trauma 69:S10YS13, 2010. 2. Liu NT, Holcomb JB, Wade CE, Darrah MI, Salinas J: Utility of vital signs, heart-rate variability and complexity, and machine ... learning for identifying the need for life-saving interventions in trauma patients. Shock 42:108Y114, 2014. 3. Pickering TG, Shimbo D, Hass D...Ann Emerg Med 45:68Y76, 2005. 8. Liu NT, Holcomb JB, Wade CE, Batchinsky AI, Cancio LC, Darrah MI, Salinas J: Development and validation of a machine

Remote sensing and urban public health

NASA Technical Reports Server (NTRS)

Rush, M.; Vernon, S.

1975-01-01

The applicability of remote sensing in the form of aerial photography to urban public health problems is examined. Environmental characteristics are analyzed to determine if health differences among areas could be predicted from the visual expression of remote sensing data. The analysis is carried out on a socioeconomic cross-sectional sample of census block groups. Six morbidity and mortality rates are the independent variables while environmental measures from aerial photographs and from the census constitute the two independent variable sets. It is found that environmental data collected by remote sensing are as good as census data in evaluating rates of health outcomes.

Client Predictors of Short-term Psychotherapy Outcomes among Asian and White American Outpatients

PubMed Central

Kim, Jin E.; Zane, Nolan W.; Blozis, Shelley A.

2015-01-01

Purpose To examine predictors of psychotherapy outcomes, focusing on client characteristics that are especially salient for culturally diverse clients. Method Sixty clients (31 women; 27 White Americans, 33 Asian Americans) participated in this treatment study. Client characteristics were measured at pre-treatment, and outcomes were measured post-fourth session via therapist ratings of functioning and symptomatology. Regression analyses were utilized to test for predictors of outcomes, and bootstrap analyses were utilized to test for mediators. Results Higher levels of somatic symptoms predicted lower psychosocial functioning at post-treatment. Avoidant coping style predicted more negative symptoms and more psychological discomfort. Non-English language preference predicted worse outcomes; this effect was mediated by an avoidant coping style. Conclusions Language preference, avoidant coping style, and somatic symptoms predicted treatment outcome in a culturally diverse sample. Findings suggest that race/ethnicity-related variables may function through mediating proximal variables to affect outcomes. PMID:22836681

Why abundant tropical tree species are phylogenetically old.

PubMed

Wang, Shaopeng; Chen, Anping; Fang, Jingyun; Pacala, Stephen W

2013-10-01

Neutral models of species diversity predict patterns of abundance for communities in which all individuals are ecologically equivalent. These models were originally developed for Panamanian trees and successfully reproduce observed distributions of abundance. Neutral models also make macroevolutionary predictions that have rarely been evaluated or tested. Here we show that neutral models predict a humped or flat relationship between species age and population size. In contrast, ages and abundances of tree species in the Panamanian Canal watershed are found to be positively correlated, which falsifies the models. Speciation rates vary among phylogenetic lineages and are partially heritable from mother to daughter species. Variable speciation rates in an otherwise neutral model lead to a demographic advantage for species with low speciation rate. This demographic advantage results in a positive correlation between species age and abundance, as found in the Panamanian tropical forest community.

Variability in heart rate recovery measurements over 1 year in healthy, middle-aged adults.

PubMed

Mellis, M G; Ingle, L; Carroll, S

2014-02-01

This study assessed the longer-term (12-month) variability in post-exercise heart rate recovery following a submaximal exercise test. Longitudinal data was analysed for 97 healthy middle-aged adults (74 male, 23 female) from 2 occasions, 12 months apart. Participants were retrospectively selected if they had stable physical activity habits, submaximal treadmill fitness and anthropometric measurements between the 2 assessment visits. A submaximal Bruce treadmill test was performed to at least 85% age-predicted maximum heart rate. Absolute heart rate and Δ heart rate recovery (change from peak exercise heart rate) were recorded for 1 and 2 min post-exercise in an immediate supine position. Heart rate recovery at both time-points was shown to be reliable with intra-class correlation coefficient values ≥ 0.714. Absolute heart rate 1-min post-exercise showed the strongest agreement between repeat tests (r = 0.867, P < 0.001). Lower coefficient of variation (≤ 10.2%) and narrower limits of agreement were found for actual heart rate values rather than Δ heart rate recovery, and for 1-min rather than 2-min post-exercise recovery time points. Log-transformed values generated better variability with acceptable coefficient of variation for all measures (2.2-10%). Overall, 1 min post-exercise heart rate recovery data had least variability over the 12-month period in apparently healthy middle-aged adults. © Georg Thieme Verlag KG Stuttgart · New York.

The intrinsic growth rate as a predictor of population viability under climate warming.

PubMed

Amarasekare, Priyanga; Coutinho, Renato M

2013-11-01

1. Lately, there has been interest in using the intrinsic growth rate (rm) to predict the effects of climate warming on ectotherm population viability. However, because rm is calculated using the Euler-Lotka equation, its reliability in predicting population persistence depends on whether ectotherm populations can achieve a stable age/stage distribution in thermally variable environments. Here, we investigate this issue using a mathematical framework that incorporates mechanistic descriptions of temperature effects on vital rates into a stage-structured population model that realistically captures the temperature-induced variability in developmental delays that characterize ectotherm life cycles. 2. We find that populations experiencing seasonal temperature variation converge to a stage distribution whose intra-annual pattern remains invariant across years. As a result, the mean annual per capita growth rate also remains constant between years. The key insight is the mechanism that allows populations converge to a stationary stage distribution. Temperature effects on the biochemical processes (e.g. enzyme kinetics, hormonal regulation) that underlie life-history traits (reproduction, development and mortality) exhibit well-defined thermodynamical properties (e.g. changes in entropy and enthalpy) that lead to predictable outcomes (e.g. reduction in reaction rates or hormonal action at temperature extremes). As a result, life-history traits exhibit a systematic and predictable response to seasonal temperature variation. This in turn leads to temporally predictable temperature responses of the stage distribution and the per capita growth rate. 3. When climate warming causes an increase in the mean annual temperature and/or the amplitude of seasonal fluctuations, the population model predicts the mean annual per capita growth rate to decline to zero within 100 years when warming is slow relative to the developmental period of the organism (0.03-0.05°C per year) and to become negative, causing population extinction, well before 100 years when warming is fast (e.g. 0.1°C per year). The Euler-Lotka equation predicts a slower decrease in rm when warming is slow and a longer persistence time when warming is fast, with the deviation between the two metrics increasing with increasing developmental period. These results suggest that predictions of ectotherm population viability based on rm may be valid only for species with short developmental delays, and even then, only over short time-scales and under slow warming regimes. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients.

PubMed

Mbah, Chamberlain; De Ruyck, Kim; De Schrijver, Silke; De Sutter, Charlotte; Schiettecatte, Kimberly; Monten, Chris; Paelinck, Leen; De Neve, Wilfried; Thierens, Hubert; West, Catharine; Amorim, Gustavo; Thas, Olivier; Veldeman, Liv

2018-05-01

Evaluation of patient characteristics inducing toxicity in breast radiotherapy, using simultaneous modeling of multiple endpoints. In 269 early-stage breast cancer patients treated with whole-breast irradiation (WBI) after breast-conserving surgery, toxicity was scored, based on five dichotomized endpoints. Five logistic regression models were fitted, one for each endpoint and the effect sizes of all variables were estimated using maximum likelihood (MLE). The MLEs are improved with James-Stein estimates (JSEs). The method combines all the MLEs, obtained for the same variable but from different endpoints. Misclassification errors were computed using MLE- and JSE-based prediction models. For associations, p-values from the sum of squares of MLEs were compared with p-values from the Standardized Total Average Toxicity (STAT) Score. With JSEs, 19 highest ranked variables were predictive of the five different endpoints. Important variables increasing radiation-induced toxicity were chemotherapy, age, SATB2 rs2881208 SNP and nodal irradiation. Treatment position (prone position) was most protective and ranked eighth. Overall, the misclassification errors were 45% and 34% for the MLE- and JSE-based models, respectively. p-Values from the sum of squares of MLEs and p-values from STAT score led to very similar conclusions, except for the variables nodal irradiation and treatment position, for which STAT p-values suggested an association with radiosensitivity, whereas p-values from the sum of squares indicated no association. Breast volume was ranked as the most significant variable in both strategies. The James-Stein estimator was used for selecting variables that are predictive for multiple toxicity endpoints. With this estimator, 19 variables were predictive for all toxicities of which four were significantly associated with overall radiosensitivity. JSEs led to almost 25% reduction in the misclassification error rate compared to conventional MLEs. Finally, patient characteristics that are associated with radiosensitivity were identified without explicitly quantifying radiosensitivity.

Mathematical modeling to predict residential solid waste generation.

PubMed

Benítez, Sara Ojeda; Lozano-Olvera, Gabriela; Morelos, Raúl Adalberto; Vega, Carolina Armijo de

2008-01-01

One of the challenges faced by waste management authorities is determining the amount of waste generated by households in order to establish waste management systems, as well as trying to charge rates compatible with the principle applied worldwide, and design a fair payment system for households according to the amount of residential solid waste (RSW) they generate. The goal of this research work was to establish mathematical models that correlate the generation of RSW per capita to the following variables: education, income per household, and number of residents. This work was based on data from a study on generation, quantification and composition of residential waste in a Mexican city in three stages. In order to define prediction models, five variables were identified and included in the model. For each waste sampling stage a different mathematical model was developed, in order to find the model that showed the best linear relation to predict residential solid waste generation. Later on, models to explore the combination of included variables and select those which showed a higher R(2) were established. The tests applied were normality, multicolinearity and heteroskedasticity. Another model, formulated with four variables, was generated and the Durban-Watson test was applied to it. Finally, a general mathematical model is proposed to predict residential waste generation, which accounts for 51% of the total.

Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

PubMed

Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

2013-02-01

Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

Limnology of nine small lakes, Matanuska-Susitna Borough, Alaska, and the survival and growth rates of rainbow trout

USGS Publications Warehouse

Woods, P.F.

1985-01-01

The survival and growth rates of rainbow trout (Salmo gairdnieri) were concurrently measured with selected limnological characteristics in nine small (surface area < 25 sq hectometers) lakes in the Matanuska-Susitna Borough. The project goal was to develop empirical models for predicting rainbow trout growth rates from the following variables: total phosphorus concentration, chlorophyll a concentration, Secchi disc transparency, or the morphoedaphic index--a means of characterizing potential biological productivity. No suitable model could be developed from the data collected during 1982 and 1983. The lack of significant correlation was attributed in part to the wide variation in survival of rainbow trout. Winterkills, caused by severe depletion of dissolved oxygen, were suspected in four of the lakes. Varied levels of fishing pressure and competition with threespine stickleback (Gasterosteus aculeatus) also influenced survival of rainbow trout but their effects were overshadowed by winterkill. Predictive capability was also reduced because of inconsistencies in rankings generated by each of the four limnological variables chosen as indicators of potential biological productivity. A lake ranked low in productivity by one variable was commonly ranked high in productivity by another variable. The survivability of rainbow trout stocked in lakes such as these nine may be a more important indicator of potential biomass production than are indicators of lake fertility. Assessments of a lake 's susceptibility to winterkill and the degree of competition with threespine stickleback are suggested as important topics for additional research. (Author 's abstract)

Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby

2014-01-01

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...

Differential Validities for Shop Courses: Proposal B: Follow-Up of Subjects' Work Experiences. Final Report. Vol I: Procedures and Results.

ERIC Educational Resources Information Center

Isabelle, L. A.; Lokan, J. J.

Follow-up information was collected on 1500 students who attended a two-year occupational high school, in order to relate predictor measures to success during training and subsequent job success. Although not predictive of dropouts, variables in the pre-test battery did predict performance in academic and shop courses; ratings of job success were…

Prediction of municipal solid waste generation using nonlinear autoregressive network.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A

2015-12-01

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Speaking-rate-induced variability in F2 trajectories.

PubMed

Tjaden, K; Weismer, G

1998-10-01

This study examined speaking-rate-induced spectral and temporal variability of F2 formant trajectories for target words produced in a carrier phrase at speaking rates ranging from fast to slow. F2 onset frequency measured at the first glottal pulse following the stop consonant release in target words was used to quantify the extent to which adjacent consonantal and vocalic gestures overlapped; F2 target frequency was operationally defined as the first occurrence of a frequency minimum or maximum following F2 onset frequency. Regression analyses indicated 70% of functions relating F2 onset and vowel duration were statistically significant. The strength of the effect was variable, however, and the direction of significant functions often differed from that predicted by a simple model of overlapping, sliding gestures. Results of a partial correlation analysis examining interrelationships among F2 onset, F2 target frequency, and vowel duration across the speaking rate range indicated that covariation of F2 target with vowel duration may obscure the relationship between F2 onset and vowel duration across rate. The results further suggested that a sliding based model of acoustic variability associated with speaking rate change only partially accounts for the present data, and that such a view accounts for some speakers' data better than others.

Asymptomatic Alzheimer disease: Defining resilience.

PubMed

Hohman, Timothy J; McLaren, Donald G; Mormino, Elizabeth C; Gifford, Katherine A; Libon, David J; Jefferson, Angela L

2016-12-06

To define robust resilience metrics by leveraging CSF biomarkers of Alzheimer disease (AD) pathology within a latent variable framework and to demonstrate the ability of such metrics to predict slower rates of cognitive decline and protection against diagnostic conversion. Participants with normal cognition (n = 297) and mild cognitive impairment (n = 432) were drawn from the Alzheimer's Disease Neuroimaging Initiative. Resilience metrics were defined at baseline by examining the residuals when regressing brain aging outcomes (hippocampal volume and cognition) on CSF biomarkers. A positive residual reflected better outcomes than expected for a given level of pathology (high resilience). Residuals were integrated into a latent variable model of resilience and validated by testing their ability to independently predict diagnostic conversion, cognitive decline, and the rate of ventricular dilation. Latent variables of resilience predicted a decreased risk of conversion (hazard ratio < 0.54, p < 0.0001), slower cognitive decline (β > 0.02, p < 0.001), and slower rates of ventricular dilation (β < -4.7, p < 2 × 10 -15 ). These results were significant even when analyses were restricted to clinically normal individuals. Furthermore, resilience metrics interacted with biomarker status such that biomarker-positive individuals with low resilience showed the greatest risk of subsequent decline. Robust phenotypes of resilience calculated by leveraging AD biomarkers and baseline brain aging outcomes provide insight into which individuals are at greatest risk of short-term decline. Such comprehensive definitions of resilience are needed to further our understanding of the mechanisms that protect individuals from the clinical manifestation of AD dementia, especially among biomarker-positive individuals. © 2016 American Academy of Neurology.

The "killing zone" revisited: serial nonlinearities predict general aviation accident rates from pilot total flight hours.

PubMed

Knecht, William R

2013-11-01

Is there a "killing zone" (Craig, 2001)-a range of pilot flight time over which general aviation (GA) pilots are at greatest risk? More broadly, can we predict accident rates, given a pilot's total flight hours (TFH)? These questions interest pilots, aviation policy makers, insurance underwriters, and researchers alike. Most GA research studies implicitly assume that accident rates are linearly related to TFH, but that relation may actually be multiply nonlinear. This work explores the ability of serial nonlinear modeling functions to predict GA accident rates from noisy rate data binned by TFH. Two sets of National Transportation Safety Board (NTSB)/Federal Aviation Administration (FAA) data were log-transformed, then curve-fit to a gamma-pdf-based function. Despite high rate-noise, this produced weighted goodness-of-fit (Rw(2)) estimates of .654 and .775 for non-instrument-rated (non-IR) and instrument-rated pilots (IR) respectively. Serial-nonlinear models could be useful to directly predict GA accident rates from TFH, and as an independent variable or covariate to control for flight risk during data analysis. Applied to FAA data, these models imply that the "killing zone" may be broader than imagined. Relatively high risk for an individual pilot may extend well beyond the 2000-h mark before leveling off to a baseline rate. Published by Elsevier Ltd.

Developing predictive models for return to work using the Military Power, Performance and Prevention (MP3) musculoskeletal injury risk algorithm: a study protocol for an injury risk assessment programme.

PubMed

Rhon, Daniel I; Teyhen, Deydre S; Shaffer, Scott W; Goffar, Stephen L; Kiesel, Kyle; Plisky, Phil P

2018-02-01

Musculoskeletal injuries are a primary source of disability in the US Military, and low back pain and lower extremity injuries account for over 44% of limited work days annually. History of prior musculoskeletal injury increases the risk for future injury. This study aims to determine the risk of injury after returning to work from a previous injury. The objective is to identify criteria that can help predict likelihood for future injury or re-injury. There will be 480 active duty soldiers recruited from across four medical centres. These will be patients who have sustained a musculoskeletal injury in the lower extremity or lumbar/thoracic spine, and have now been cleared to return back to work without any limitations. Subjects will undergo a battery of physical performance tests and fill out sociodemographic surveys. They will be followed for a year to identify any musculoskeletal injuries that occur. Prediction algorithms will be derived using regression analysis from performance and sociodemographic variables found to be significantly different between injured and non-injured subjects. Due to the high rates of injuries, injury prevention and prediction initiatives are growing. This is the first study looking at predicting re-injury rates after an initial musculoskeletal injury. In addition, multivariate prediction models appear to have move value than models based on only one variable. This approach aims to validate a multivariate model used in healthy non-injured individuals to help improve variables that best predict the ability to return to work with lower risk of injury, after a recent musculoskeletal injury. NCT02776930. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Predicting variability of aquatic concentrations of human pharmaceuticals

EPA Science Inventory

Potential exposure to active pharmaceutical ingredients (APIs) in the aquatic environment is a subject of ongoing concern. We recently estimated maximum likely potency-normalized exposure rates at the national level for several hundred commonly used human prescription pharmaceut...

Puppy Temperament Assessments Predict Breed and American Kennel Club Group but Not Adult Temperament.

PubMed

Robinson, Lauren M; Skiver Thompson, Rebekah; Ha, James C

2016-01-01

Puppy assessments for companion dogs have shown mixed long-term reliability. Temperament is cited among the reasons for surrendering dogs to shelters. A puppy temperament test that reliably predicts adult behavior is one potential way to lower the number of dogs given to shelters. This study used a longitudinal design to assess temperament in puppies from 8 different breeds at 7 weeks old (n = 52) and 6 years old (n = 34) using modified temperament tests, physiological measures, and a follow-up questionnaire. For 7-week-old puppies, results revealed (a) puppy breed was predictable using 3 variables, (b) 4 American Kennel Club breed groups had some validity based on temperament, (c) temperament was variable within litters of puppies, and (d) certain measures of temperament were related to physiological measures (heart rate). Finally, puppy temperament assessments were reliable in predicting the scores of 2 of the 8 adult dog temperament measures. However, overall, the puppy temperament scores were unreliable in predicting adult temperament.

A needs index for mental health care.

PubMed

Glover, G R; Robin, E; Emami, J; Arabscheibani, G R

1998-02-01

The study aimed to develop a mental illness needs index to help local managers, district purchasers and national policy makers in allocating resources. Formulae were developed by regression analysis using 1991 census data to predict the period prevalence of acute psychiatric admission from electoral wards. Census variables used were chosen on the basis of an established association with mental illness rates. Data from one English Health Service region were analysed for patterns common to wards at hospital catchment area level and patterns common to district health authorities at regional level. The North East Thames region was chosen as the setting for the study, with 7096 patients being admitted during 1991. In most, but not all, catchment areas reasonable prediction of the pattern of admission prevalence was possible using the variables chosen. However, different population characteristics predicted admission prevalence in rural and urban areas. Prediction methods based on one or two variables are thus unlikely to work in both settings. A Mental Illness Needs Index (MINI) based on social isolation, poverty, unemployment, permanent sickness and temporary and insecure housing predicted differences in admission prevalence between wards at catchment area level better than Jarman's Underprivileged Area (UPA) score [1] and between districts at regional level better than the UPA score and comparably to the York Psychiatric Index [2] (adjusted r2 at regional level (MINI 0.82, UPA 0.53, York index 0.70). District admission prevalence rates vary by a factor of three between rural and inner city areas; this difference may not fully reflect the variation in the cost of providing care. It did not prove possible to incorporate factors related to bed availability in the models used; reasons for this are discussed. Data covering other aspects of mental health care in addition to hospital admission are needed for more satisfactory modelling.

A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes.

PubMed

Ponitz, Claire Cameron; McClelland, Megan M; Matthews, J S; Morrison, Frederick J

2009-05-01

The authors examined a new assessment of behavioral regulation and contributions to achievement and teacher-rated classroom functioning in a sample (N = 343) of kindergarteners from 2 geographical sites in the United States. Behavioral regulation was measured with the Head-Toes-Knees-Shoulders (HTKS) task, a structured observation requiring children to perform the opposite of a dominant response to 4 different oral commands. Results revealed considerable variability in HTKS scores. Evidence for construct validity was found in positive correlations with parent ratings of attentional focusing and inhibitory control and teacher ratings of classroom behavioral regulation. Hierarchical linear modeling indicated that higher levels of behavioral regulation in the fall predicted stronger levels of achievement in the spring and better teacher-rated classroom self-regulation (all ps < .01) but not interpersonal skills. Evidence for domain specificity emerged, in which gains in behavioral regulation predicted gains in mathematics but not in language and literacy over the kindergarten year (p < .01) after site, child gender, and other background variables were controlled. Discussion focuses on the importance of behavioral regulation for successful adjustment to the demands of kindergarten. Copyright 2009 APA, all rights reserved

Combustion-acoustic stability analysis for premixed gas turbine combustors

NASA Technical Reports Server (NTRS)

Darling, Douglas; Radhakrishnan, Krishnan; Oyediran, Ayo; Cowan, Lizabeth

1995-01-01

Lean, prevaporized, premixed combustors are susceptible to combustion-acoustic instabilities. A model was developed to predict eigenvalues of axial modes for combustion-acoustic interactions in a premixed combustor. This work extends previous work by including variable area and detailed chemical kinetics mechanisms, using the code LSENS. Thus the acoustic equations could be integrated through the flame zone. Linear perturbations were made of the continuity, momentum, energy, chemical species, and state equations. The qualitative accuracy of our approach was checked by examining its predictions for various unsteady heat release rate models. Perturbations in fuel flow rate are currently being added to the model.

Prediction of situational awareness in F-15 pilots.

PubMed

Carretta, T R; Perry, D C; Ree, M J

1996-01-01

Situational awareness (SA) is a skill often deemed essential to pilot performance in both combat and noncombat flying. A study was conducted to determine if SA in U.S. Air Force F-15 pilots could be predicted. The participants were 171 active duty F-15 A/C pilots who completed a test battery representative of various psychological constructs proposed or demonstrated to be valid for the prediction of performance in a wide variety of military and civilian jobs. These predictors encompassed measures of cognitive ability, psychomotor ability, and personality. Supervisor and peer ratings of SA were collected. Supervisors and peers showed substantial agreement on the SA ratings of the pilots. The first unrotated principle component extracted from the supervisor and peer ratings accounted for 92.5% of the variability of ratings. The unrotated first principal component served as the SA criterion. Flying experience measured in number of F-15 hours was the best predictor of SA. After controlling for the effects of F-15 flying hours, the measures of general cognitive ability based on working memory, spatial reasoning, and divided attention were found to be predictive of SA. Psychomotor and personality measures were not predictive. With additional F-15 flying hours it is expected that pilots would improve their ratings of SA.

Survival Model for Foot and Leg High Rate Axial Impact Injury Data.

PubMed

Bailey, Ann M; McMurry, Timothy L; Poplin, Gerald S; Salzar, Robert S; Crandall, Jeff R

2015-01-01

Understanding how lower extremity injuries from automotive intrusion and underbody blast (UBB) differ is of key importance when determining whether automotive injury criteria can be applied to blast rate scenarios. This article provides a review of existing injury risk analyses and outlines an approach to improve injury prediction for an expanded range of loading rates. This analysis will address issues with existing injury risk functions including inaccuracies due to inertial and potential viscous resistance at higher loading rates. This survival analysis attempts to minimize these errors by considering injury location statistics and a predictor variable selection process dependent upon failure mechanisms of bone. Distribution of foot/ankle/leg injuries induced by axial impact loading at rates characteristic of UBB as well as automotive intrusion was studied and calcaneus injuries were found to be the most common injury; thus, footplate force was chosen as the main predictor variable because of its proximity to injury location to prevent inaccuracies associated with inertial differences due to loading rate. A survival analysis was then performed with age, sex, dorsiflexion angle, and mass as covariates. This statistical analysis uses data from previous axial postmortem human surrogate (PMHS) component leg tests to provide perspectives on how proximal boundary conditions and loading rate affect injury probability in the foot/ankle/leg (n = 82). Tibia force-at-fracture proved to be up to 20% inaccurate in previous analyses because of viscous resistance and inertial effects within the data set used, suggesting that previous injury criteria are accurate only for specific rates of loading and boundary conditions. The statistical model presented in this article predicts 50% probability of injury for a plantar force of 10.2 kN for a 50th percentile male with a neutral ankle position. Force rate was found to be an insignificant covariate because of the limited range of loading rate differences within the data set; however, compensation for inertial effects caused by measuring the force-at-fracture in a location closer to expected injury location improved the model's predictive capabilities for the entire data set. This study provides better injury prediction capabilities for both automotive and blast rates because of reduced sensitivity to inertial effects and tibia-fibula load sharing. Further, a framework is provided for future injury criteria generation for high rate loading scenarios. This analysis also suggests key improvements to be made to existing anthropomorphic test device (ATD) lower extremities to provide accurate injury prediction for high rate applications such as UBB.

Therapist Use of Socratic Questioning Predicts Session-to-Session Symptom Change in Cognitive Therapy for Depression

PubMed Central

Braun, Justin D.; Strunk, Daniel R.; Sasso, Katherine E.; Cooper, Andrew A.

2015-01-01

Socratic questioning is a key therapeutic strategy in cognitive therapy (CT) for depression. However, little is known regarding its relation to outcome. In this study, we examine therapist use of Socratic questioning as a predictor of session-to-session symptom change. Participants were 55 depressed adults who participated in a 16-week course of CT (see Adler, Strunk, & Fazio, 2015). Socratic questioning was assessed through observer ratings of the first three sessions. Socratic ratings were disaggregated into scores reflecting within-patient and between-patient variability to facilitate an examination of the relation of within-patient Socratic questioning and session-to-session symptom change. Because we examined within-patient variability in Socratic questioning, the identification of such a relation cannot be attributed to any stable patient characteristics that might otherwise introduce a spurious relation. Within-patient Socratic questioning significantly predicted session-to-session symptom change across the early sessions, with a one standard deviation increase in Socratic-Within predicting a 1.51-point decrease in BDI-II scores in the following session. Within-patient Socratic questioning continued to predict symptom change after controlling for within-patient ratings of the therapeutic alliance (i.e., Relationship and Agreement), suggesting that the relation of Socratic questioning and symptom change was not only independent of stable characteristics, but also within-patient variation in the alliance. Our results provide the first empirical support for a relation of therapist use of Socratic questioning and symptom change in CT for depression. PMID:25965026

Spatial variation in population growth rate and community structure affects local and regional dynamics.

PubMed

Trzcinski, M Kurtis; Walde, Sandra J; Taylor, Philip D

2008-11-01

1. Theory predicting that populations with high maximum rates of increase (r(max)) will be less stable, and that metapopulations with high average r(max) will be less synchronous, was tested using a small protist, Bodo, that inhabits pitcher plant leaves (Sarracenia purpurea L.). The effects of predators and resources on these relationships were also determined. 2. Abundance data collected for a total of 60 populations of Bodo, over a period of 3 months, at six sites in three bogs in eastern Canada, were used to test these predictions. Mosquitoes were manipulated in half the leaves partway through the season to increase the range of predation rates. 3. Dynamics differed greatly among leaves and sites, but most populations exhibited one or more episodes of rapid increase followed by a population crash. Estimates of r(max) obtained using a linear mixed-effects model, ranged from 1 x 5 to 2 x 7 per day. Resource levels (captured insect) and midge abundances affected r(max). 4. Higher r(max) was associated with greater temporal variability and lower synchrony as predicted. However, in contrast to expectations, populations with higher r(max) also had lower mean abundance and were more suppressed by predators. 5. This study demonstrates that the link between r(max) and temporal variability is key to understanding the dynamics of populations that spend little time near equilibrium, and to predicting and interpreting the effects of community structure on the dynamics of such populations.

Modeling the Flow Behavior, Recrystallization, and Crystallographic Texture in Hot-Deformed Fe-30 Wt Pct Ni Austenite

NASA Astrophysics Data System (ADS)

Abbod, M. F.; Sellars, C. M.; Cizek, P.; Linkens, D. A.; Mahfouf, M.

2007-10-01

The present work describes a hybrid modeling approach developed for predicting the flow behavior, recrystallization characteristics, and crystallographic texture evolution in a Fe-30 wt pct Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 °C and 1050 °C and strain rates between 0.1 and 10 s-1. The evolution of grain structure, crystallographic texture, and dislocation substructure was characterized in detail for a deformation temperature of 950 °C and strain rates of 0.1 and 10 s-1, using electron backscatter diffraction and transmission electron microscopy. The hybrid modeling method utilizes a combination of empirical, physically-based, and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallization behavior is predicted from the measured microstructural state variables of internal dislocation density, subgrain size, and misorientation between subgrains using a physically-based model. The texture evolution is modeled using artificial neural networks.

The Dynamics of Self-Esteem in Cognitive Therapy for Avoidant and Obsessive-Compulsive Personality Disorders: An Adaptive Role of Self-Esteem Variability?

PubMed Central

Cummings, Jorden A.; Hayes, Adele M.; Cardaciotto, LeeAnn; Newman, Cory F.

2011-01-01

Self-esteem variability is often associated with poor functioning. However, in disorders with entrenched negative views of self and in a context designed to challenge those views, variable self-esteem might represent a marker of change. We examined self-esteem variability in a sample of 27 patients with Avoidant and Obsessive-Compulsive Personality Disorders who received Cognitive Therapy (CT). A therapy coding system was used to rate patients’ positive and negative views of self expressed in the first ten sessions of a 52-week treatment. Ratings of negative (reverse scored) and positive view of self were summed to create a composite score for each session. Self-esteem variability was calculated as the standard deviation of self-esteem scores across sessions. More self-esteem variability predicted more improvement in personality disorder and depression symptoms at the end of treatment, beyond baseline and average self-esteem. Early variability in self-esteem, in this population and context, appeared to be a marker of therapeutic change. PMID:22923855

[The theory of the demographic transition as a reference for demo-economic models].

PubMed

Genne, M

1981-01-01

The aim of the theory of demographic transition (TTD) is to better understand the behavior and interrelationship of economic and demographic variables. There are 2 types of demo-economic models: 1) the malthusian models, which consider demographic variables as pure exogenous variables, and 2) the neoclassical models, which consider demographic variables as strictly endogenous. If TTD can explore the behavior of exogenous and endogenous demographic variables, it cannot demonstrate neither the relation nor the order of causality among the various demographic and economic variables, but it is simply the theoretical framework of a complex social and economic phenomenon which started in Europe in the 19th Century, and which today can be extended to developing countries. There are 4 stages in the TTD; the 1st stage is characterized by high levels of fecundity and mortality; the 2nd stage is characterized by high fecundity levels and declining mortality levels; the 3rd stage is characterized by declining fecundity levels and low mortality levels; the 4th stage is characterized by low fertility and mortality levels. The impact of economic variables over mortality and birth rates is evident for mortality rates, which decline earlier and at a greater speed than birth rates. According to reliable mathematical predictions, around the year 1987 mortality rates in developing countries will have reached the low level of European countries, and growth rate will be only 1.5%. If the validity of demo-economic models has not yet been established, TTD has clearly shown that social and economic development is the factor which influences demographic expansion.

PREDICTION OF VO2PEAK USING OMNI RATINGS OF PERCEIVED EXERTION FROM A SUBMAXIMAL CYCLE EXERCISE TEST

PubMed Central

Mays, Ryan J.; Goss, Fredric L.; Nagle-Stilley, Elizabeth F.; Gallagher, Michael; Schafer, Mark A.; Kim, Kevin H.; Robertson, Robert J.

2015-01-01

Summary The primary aim of this study was to develop statistical models to predict peak oxygen consumption (VO2peak) using OMNI Ratings of Perceived Exertion measured during submaximal cycle ergometry. Men (mean ± standard error: 20.90 ± 0.42 yrs) and women (21.59 ± 0.49 yrs) participants (n = 81) completed a load-incremented maximal cycle ergometer exercise test. Simultaneous multiple linear regression was used to develop separate VO2peak statistical models using submaximal ratings of perceived exertion for the overall body, legs, and chest/breathing as predictor variables. VO2peak (L·min−1) predicted for men and women from ratings of perceived exertion for the overall body (3.02 ± 0.06; 2.03 ± 0.04), legs (3.02 ± 0.06; 2.04 ± 0.04) and chest/breathing (3.02 ± 0.05; 2.03 ± 0.03) were similar with measured VO2peak (3.02 ± 0.10; 2.03 ± 0.06, ps > .05). Statistical models based on submaximal OMNI Ratings of Perceived Exertion provide an easily administered and accurate method to predict VO2peak. PMID:25068750

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

PubMed

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

2007-01-01

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

Predicting dropout in outpatient dialectical behavior therapy with patients with borderline personality disorder receiving psychiatric disability.

PubMed

Landes, Sara J; Chalker, Samantha A; Comtois, Katherine Anne

2016-01-01

Rates of treatment dropout in outpatient Dialectical Behavior Therapy (DBT) in the community can be as high as 24 % to 58 %, making dropout a great concern. The primary purpose of this article was to examine predictors of dropout from DBT in a community mental health setting. Participants were 56 consumers with borderline personality disorder (BPD) who were psychiatrically disabled participating in a larger feasibility trial of Dialectical Behavior Therapy- Accepting the Challenges of Exiting the System. The following variables were examined to see whether they predicted dropout in DBT: age, education level, baseline level of distress, baseline level of non-acceptance of emotional responses, and skills module in which a consumer started DBT skills group. These variables were chosen based on known predictors of dropout in consumers with BPD and in DBT, as well as an interest in what naturally occurring variables might impact dropout. The dropout rate in this sample was 51.8 %. Results of the logistic regression show that younger age, higher levels of baseline distress, and a higher level of baseline non-acceptance of emotional responses were significantly associated with dropout. The DBT skills module in which an individual started group did not predict dropout. The implications of these findings are that knowledge of consumer age and pretreatment levels of distress and non-acceptance of emotional responses can impact providers' choice of commitment and treatment strategies to reduce dropout. Future research should examine these strategies, as well as the impact of predictor variables on outcome and reasons for dropout.

Scaling laws for perturbations in the ocean-atmosphere system following large CO2 emissions

NASA Astrophysics Data System (ADS)

Towles, N.; Olson, P.; Gnanadesikan, A.

2015-01-01

Scaling relationships are derived for the perturbations to atmosphere and ocean variables from large transient CO2 emissions. Using the carbon cycle model LOSCAR (Zeebe et al., 2009; Zeebe, 2012b) we calculate perturbations to atmosphere temperature and total carbon, ocean temperature, total ocean carbon, pH, and alkalinity, marine sediment carbon, plus carbon-13 isotope anomalies in the ocean and atmosphere resulting from idealized CO2 emission events. The peak perturbations in the atmosphere and ocean variables are then fit to power law functions of the form γDαEbeta, where D is the event duration, E is its total carbon emission, and γ is a coefficient. Good power law fits are obtained for most system variables for E up to 50 000 PgC and D up to 100 kyr. However, these power laws deviate substantially from predictions based on simplified equilibrium considerations. For example, although all of the peak perturbations increase with emission rate E/D, we find no evidence of emission rate-only scaling α + β =0, a prediction of the long-term equilibrium between CO2 input by volcanism and CO2 removal by silicate weathering. Instead, our scaling yields α + β ≃ 1 for total ocean and atmosphere carbon and 0< α + β < 1 for most of the other system variables. The deviations in these scaling laws from equilibrium predictions are mainly due to the multitude and diversity of time scales that govern the exchange of carbon between marine sediments, the ocean, and the atmosphere.

Simulation of Crack Propagation in Engine Rotating Components under Variable Amplitude Loading

NASA Technical Reports Server (NTRS)

Bonacuse, P. J.; Ghosn, L. J.; Telesman, J.; Calomino, A. M.; Kantzos, P.

1998-01-01

The crack propagation life of tested specimens has been repeatedly shown to strongly depend on the loading history. Overloads and extended stress holds at temperature can either retard or accelerate the crack growth rate. Therefore, to accurately predict the crack propagation life of an actual component, it is essential to approximate the true loading history. In military rotorcraft engine applications, the loading profile (stress amplitudes, temperature, and number of excursions) can vary significantly depending on the type of mission flown. To accurately assess the durability of a fleet of engines, the crack propagation life distribution of a specific component should account for the variability in the missions performed (proportion of missions flown and sequence). In this report, analytical and experimental studies are described that calibrate/validate the crack propagation prediction capability ]or a disk alloy under variable amplitude loading. A crack closure based model was adopted to analytically predict the load interaction effects. Furthermore, a methodology has been developed to realistically simulate the actual mission mix loading on a fleet of engines over their lifetime. A sequence of missions is randomly selected and the number of repeats of each mission in the sequence is determined assuming a Poisson distributed random variable with a given mean occurrence rate. Multiple realizations of random mission histories are generated in this manner and are used to produce stress, temperature, and time points for fracture mechanics calculations. The result is a cumulative distribution of crack propagation lives for a given, life limiting, component location. This information can be used to determine a safe retirement life or inspection interval for the given location.

Dose-Response Calculator for ArcGIS

USGS Publications Warehouse

Hanser, Steven E.; Aldridge, Cameron L.; Leu, Matthias; Nielsen, Scott E.

2011-01-01

The Dose-Response Calculator for ArcGIS is a tool that extends the Environmental Systems Research Institute (ESRI) ArcGIS 10 Desktop application to aid with the visualization of relationships between two raster GIS datasets. A dose-response curve is a line graph commonly used in medical research to examine the effects of different dosage rates of a drug or chemical (for example, carcinogen) on an outcome of interest (for example, cell mutations) (Russell and others, 1982). Dose-response curves have recently been used in ecological studies to examine the influence of an explanatory dose variable (for example, percentage of habitat cover, distance to disturbance) on a predicted response (for example, survival, probability of occurrence, abundance) (Aldridge and others, 2008). These dose curves have been created by calculating the predicted response value from a statistical model at different levels of the explanatory dose variable while holding values of other explanatory variables constant. Curves (plots) developed using the Dose-Response Calculator overcome the need to hold variables constant by using values extracted from the predicted response surface of a spatially explicit statistical model fit in a GIS, which include the variation of all explanatory variables, to visualize the univariate response to the dose variable. Application of the Dose-Response Calculator can be extended beyond the assessment of statistical model predictions and may be used to visualize the relationship between any two raster GIS datasets (see example in tool instructions). This tool generates tabular data for use in further exploration of dose-response relationships and a graph of the dose-response curve.

Modeling the effects of free-living marine bacterial community composition on heterotrophic remineralization rates and biogeochemical carbon cycling

NASA Astrophysics Data System (ADS)

Teel, E.; Liu, X.; Cram, J. A.; Sachdeva, R.; Fuhrman, J. A.; Levine, N. M.

2016-12-01

Global oceanic ecosystem models either disregard fluctuations in heterotrophic bacterial remineralization or vary remineralization as a simple function of temperature, available carbon, and nutrient limitation. Most of these models were developed before molecular techniques allowed for the description of microbial community composition and functional diversity. Here we investigate the impact of a dynamic heterotrophic community and variable remineralization rates on biogeochemical cycling. Specifically, we integrated variable microbial remineralization into an ecosystem model by utilizing molecular community composition data, association network analysis, and biogeochemical rate data from the San Pedro Ocean Time-series (SPOT) station. Fluctuations in free-living bacterial community function and composition were examined using monthly environmental and biological data collected at SPOT between 2000 and 2011. On average, the bacterial community showed predictable seasonal changes in community composition and peaked in abundance in the spring with a one-month lag from peak chlorophyll concentrations. Bacterial growth efficiency (BGE), estimated from bacterial production, was found to vary widely at the site (5% to 40%). In a multivariate analysis, 47.6% of BGE variability was predicted using primary production, bacterial community composition, and temperature. A classic Nutrient-Phytoplankton-Zooplankton-Detritus model was expanded to include a heterotroph module that captured the observed relationships at the SPOT site. Results show that the inclusion of dynamic bacterial remineralization into larger oceanic ecosystem models can significantly impact microzooplankton grazing, the duration of surface phytoplankton blooms, and picophytoplankton primary production rates.

Adolescents and their parents' perceptions about parenting characteristics. Who can better predict the adolescent's academic competence?

PubMed

Pelegrina, Santiago; García-Linares, M Cruz; Casanova, Pedro F

2003-12-01

This study examined family factors reported by parents and their children in relation to children's academic competence. Adolescents and their parents (N=323) reported about the same family characteristics: parental acceptance and involvement in the children's education. Measures related to children's academic competence were: academic competence rated by the teacher, self-reported grades, perceived academic competence and motivational orientation. The results revealed low interrater agreement in family measures. Moreover, ratings by children about parenting characteristics seem higher than those of their parents in predicting academic-related measures. This was true especially in the case of children's reports on acceptance. However, in the case of involvement, parent's reports contributed towards predicting a higher number of variables.

Analysis of cardiovascular oscillations: A new approach to the early prediction of pre-eclampsia

NASA Astrophysics Data System (ADS)

Malberg, H.; Bauernschmitt, R.; Voss, A.; Walther, T.; Faber, R.; Stepan, H.; Wessel, N.

2007-03-01

Pre-eclampsia (PE) is a serious disorder with high morbidity and mortality occurring during pregnancy; 3%-5% of all pregnant women are affected. Early prediction is still insufficient in clinical practice. Although most pre-eclamptic patients show pathological uterine perfusion in the second trimester, this parameter has a positive predictive accuracy of only 30%, which makes it unsuitable for early, reliable prediction. The study is based on the hypothesis that alterations in cardiovascular regulatory behavior can be used to predict PE. Ninety-six pregnant women in whom Doppler investigation detected perfusion disorders of the uterine arteries were included in the study. Twenty-four of these pregnant women developed PE after the 30th week of gestation. During pregnancy, additional several noninvasive continuous blood pressure recordings were made over 30 min under resting conditions by means of a finger cuff. The time series extracted of systolic as well as diastolic beat-to-beat pressures and the heart rate were studied by variability and coupling analysis to find predictive factors preceding genesis of the disease. In the period between the 18th and 26th weeks of pregnancy, three special variability and baroreflex parameters were able to predict PE several weeks before clinical manifestation. Discriminant function analysis of these parameters was able to predict PE with a sensitivity and specificity of 87.5% and a positive predictive value of 70%. The combined clinical assessment of uterine perfusion and cardiovascular variability demonstrates the best current prediction several weeks before clinical manifestation of PE.

Soil erosion assessment - Mind the gap

NASA Astrophysics Data System (ADS)

Kim, Jongho; Ivanov, Valeriy Y.; Fatichi, Simone

2016-12-01

Accurate assessment of erosion rates remains an elusive problem because soil loss is strongly nonunique with respect to the main drivers. In addressing the mechanistic causes of erosion responses, we discriminate between macroscale effects of external factors - long studied and referred to as "geomorphic external variability", and microscale effects, introduced as "geomorphic internal variability." The latter source of erosion variations represents the knowledge gap, an overlooked but vital element of geomorphic response, significantly impacting the low predictability skill of deterministic models at field-catchment scales. This is corroborated with experiments using a comprehensive physical model that dynamically updates the soil mass and particle composition. As complete knowledge of microscale conditions for arbitrary location and time is infeasible, we propose that new predictive frameworks of soil erosion should embed stochastic components in deterministic assessments of external and internal types of geomorphic variability.

Student Success: An Investigation of the Role of the Pre-Admission Variables of Academic Preparation, Personal Attributes, and Demographic Characteristics Contribute in Predicting Graduation

ERIC Educational Resources Information Center

Briggs, Lianne

2012-01-01

Despite retention being a significant focus of higher education research, graduation rates remain of concern. Increased numbers of students are advancing to college bringing with them a wider range of abilities, attributes, and characteristics. There is much we know about what predicts success for these students but our knowledge is far from…

Meta-Analysis of Integrity Tests: A Critical Examination of Validity Generalization and Moderator Variables.

DTIC Science & Technology

1992-06-01

predicting both job performance and counterproductive behaviors on the job such as theft, disciplinary problems, and absenteeism . Validities were found to...DECLASSIFICATION/DOWNGRADING SCHEDULE 4 PERFORMING ORGANIZATION REPORT NUMBER(S) 92-1 6a NAME OF PERFORMING ORGANIZATION Universi+y of Iowa...be generalizable. The estimated mean operational predictive validity of integrity tests for supervisory ratings of job performance is .41. For the

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

PubMed

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.

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

NASA Technical Reports Server (NTRS)

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.

Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability.

PubMed

Armbruster-Genç, Diana J N; Ueltzhöffer, Kai; Fiebach, Christian J

2016-04-06

Recent research yielded the intriguing conclusion that, in healthy adults, higher levels of variability in neuronal processes are beneficial for cognitive functioning. Beneficial effects of variability in neuronal processing can also be inferred from neurocomputational theories of working memory, albeit this holds only for tasks requiring cognitive flexibility. However, cognitive stability, i.e., the ability to maintain a task goal in the face of irrelevant distractors, should suffer under high levels of brain signal variability. To directly test this prediction, we studied both behavioral and brain signal variability during cognitive flexibility (i.e., task switching) and cognitive stability (i.e., distractor inhibition) in a sample of healthy human subjects and developed an efficient and easy-to-implement analysis approach to assess BOLD-signal variability in event-related fMRI task paradigms. Results show a general positive effect of neural variability on task performance as assessed by accuracy measures. However, higher levels of BOLD-signal variability in the left inferior frontal junction area result in reduced error rate costs during task switching and thus facilitate cognitive flexibility. In contrast, variability in the same area has a detrimental effect on cognitive stability, as shown in a negative effect of variability on response time costs during distractor inhibition. This pattern was mirrored at the behavioral level, with higher behavioral variability predicting better task switching but worse distractor inhibition performance. Our data extend previous results on brain signal variability by showing a differential effect of brain signal variability that depends on task context, in line with predictions from computational theories. Recent neuroscientific research showed that the human brain signal is intrinsically variable and suggested that this variability improves performance. Computational models of prefrontal neural networks predict differential effects of variability for different behavioral situations requiring either cognitive flexibility or stability. However, this hypothesis has so far not been put to an empirical test. In this study, we assessed cognitive flexibility and cognitive stability, and, besides a generally positive effect of neural variability on accuracy measures, we show that neural variability in a prefrontal brain area at the inferior frontal junction is differentially associated with performance: higher levels of variability are beneficial for the effectiveness of task switching (cognitive flexibility) but detrimental for the efficiency of distractor inhibition (cognitive stability). Copyright © 2016 the authors 0270-6474/16/363978-10$15.00/0.

The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

NASA Astrophysics Data System (ADS)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, A. P. P. Abdul; Razali Abdullah, Mohamad; Aizzat Zakaria, Muhammad; Muaz Alim, Muhammad; Arif Mat Jizat, Jessnor; Fauzi Ibrahim, Mohamad

2018-03-01

Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models, i.e. linear and fine radial basis function (RBF) kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

Predictors of participation in sports after hip and knee arthroplasty.

PubMed

Williams, Daniel H; Greidanus, Nelson V; Masri, Bassam A; Duncan, Clive P; Garbuz, Donald S

2012-02-01

While the primary objective of joint arthroplasty is to improve patient quality of life, pain, and function, younger active patients often demand a return to higher function that includes sporting activity. Knowledge of rates and predictors of return to sports will help inform expectations in patients anticipating return to sports after joint arthroplasty. We measured the rate of sports participation at 1 year using the UCLA activity score and explored 11 variables, including choice of procedure/prosthesis, that might predict return to a high level of sporting activity, when controlling for potential confounding variables. We retrospectively evaluated 736 patients who underwent primary metal-on-polyethylene THA, metal-on-metal THA, hip resurfacing arthroplasty, revision THA, primary TKA, unicompartmental knee arthroplasty, and revision TKA between May 2005 and June 2007. We obtained UCLA activity scores on all patients; we defined high activity as a UCLA score of 7 or more. We evaluated patient demographics (age, sex, BMI, comorbidity), quality of life (WOMAC score, Oxford Hip Score, SF-12 score), and surgeon- and procedural/implant-specific variables to identify factors associated with postoperative activity score. Minimum followup was 11 months (mean, 12.1 months; range, 11-13 months). Preoperative UCLA activity score, age, male sex, and BMI predicted high activity scores. The type of operation and implant characteristics did not predict return to high activity sports. Our data suggest patient-specific factors predict postoperative activity rather than factors specific to type of surgery, implant, or surgeon factors. Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.

An aerial sightability model for estimating ferruginous hawk population size

USGS Publications Warehouse

Ayers, L.W.; Anderson, S.H.

1999-01-01

Most raptor aerial survey projects have focused on numeric description of visibility bias without identifying the contributing factors or developing predictive models to account for imperfect detection rates. Our goal was to develop a sightability model for nesting ferruginous hawks (Buteo regalis) that could account for nests missed during aerial surveys and provide more accurate population estimates. Eighteen observers, all unfamiliar with nest locations in a known population, searched for nests within 300 m of flight transects via a Maule fixed-wing aircraft. Flight variables tested for their influence on nest-detection rates included aircraft speed, height, direction of travel, time of day, light condition, distance to nest, and observer experience level. Nest variables included status (active vs. inactive), condition (i.e., excellent, good, fair, poor, bad), substrate type, topography, and tree density. A multiple logistic regression model identified nest substrate type, distance to nest, and observer experience level as significant predictors of detection rates (P < 0.05). The overall model was significant (??26 = 124.4, P < 0.001, n = 255 nest observations), and the correct classification rate was 78.4%. During 2 validation surveys, observers saw 23.7% (14/59) and 36.5% (23/63) of the actual population. Sightability model predictions, with 90% confidence intervals, captured the true population in both tests. Our results indicate standardized aerial surveys, when used in conjunction with the predictive sightability model, can provide unbiased population estimates for nesting ferruginous hawks.

Effects of Variable Inflationary Conditions on AN Inventory Model with Inflation-Proportional Demand Rate

NASA Astrophysics Data System (ADS)

Mirzazadeh, Abolfazl

2009-08-01

The inflation rate in the most of the previous researches has been considered constant and well-known over the time horizon, although the future rate of inflation is inherently uncertain and unstable, and is difficult to predict it accurately. Therefore, A time varying inventory model for deteriorating items with allowable shortages is developed in this paper. The inflation rates (internal and external) are time-dependent and demand rate is inflation-proportional. The inventory level is described by differential equations over the time horizon and present value method is used. The numerical example is given to explain the results. Some particular cases, which follow the main problem, will discuss and the results will compare with the main model by using the numerical examples. It has been achieved which shortages increases considerably in comparison with the case of without variable inflationary conditions.

A Hierarchical Analysis of Tree Growth and Environmental Drivers Across Eastern US Temperate Forests

NASA Astrophysics Data System (ADS)

Mantooth, J.; Dietze, M.

2014-12-01

Improving predictions of how forests in the eastern United States will respond to future global change requires a better understanding of the drivers of variability in tree growth rates. Current inventory data lack the temporal resolution to characterize interannual variability, while existing growth records lack the extent required to assess spatial scales of variability. Therefore, we established a network of forest inventory plots across ten sites across the eastern US, and measured growth in adult trees using increment cores. Sites were chosen to maximize climate space explored, while within sites, plots were spread across primary environmental gradients to explore landscape-level variability in growth. Using the annual growth record available from tree cores, we explored the responses of trees to multiple environmental covariates over multiple spatial and temporal scales. We hypothesized that within and across sites growth rates vary among species, and that intraspecific growth rates increase with temperature along a species' range. We also hypothesized that trees show synchrony in growth responses to landscape-scale climatic changes. Initial analyses of growth increments indicate that across sites, trees with intermediate shade tolerance, e.g. Red Oak (Quercus rubra), tend to have the highest growth rates. At the site level, there is evidence for synchrony in response to large-scale climatic events (e.g. prolonged drought and above average temperatures). However, growth responses to climate at the landscape scale have yet to be detected. Our current analysis utilizes hierarchical Bayesian state-space modeling to focus on growth responses of adult trees to environmental covariates at multiple spatial and temporal scales. This predictive model of tree growth currently incorporates observed effects at the individual, plot, site, and landscape scale. Current analysis using this model shows a potential slowing of growth in the past decade for two sites in the northeastern US (Harvard Forest and Bartlett Experimental Forest), however more work is required to determine the robustness of this trend. Finally, these observations are being incorporated into ecosystem models using the Brown Dog informatics tools and the Predictive Ecosystem Analyzer (PEcAn) data assimilation workflow.

Oral Appliance Treatment Response and Polysomnographic Phenotypes of Obstructive Sleep Apnea

PubMed Central

Sutherland, Kate; Takaya, Hisashi; Qian, Jin; Petocz, Peter; Ng, Andrew T.; Cistulli, Peter A.

2015-01-01

Study Objectives: Mandibular advancement splints (MAS) are an effective treatment for obstructive sleep apnea (OSA); however, therapeutic response is variable. Younger age, female gender, less obesity, and milder and supine-dependent OSA have variably been associated with treatment success in relatively small samples. Our objective was to utilize a large cohort of MAS treated patients (1) to compare efficacy across patients with different phenotypes of OSA and (2) to assess demographic, anthropometric, and polysomnography variables as treatment response predictors. Methods: Retrospective analysis of MAS-treated patients participating in clinical trials in sleep centers in Sydney, Australia between years 2000–2013. All studies used equivalent customized two-piece MAS devices and treatment protocols. Treatment response was defined as (1) apnea-hypopnea index (AHI) < 5/h, (2) AHI < 10/h and ≥ 50% reduction, and (3) ≥ 50% AHI reduction. Results: A total of 425 patients (109 female) were included (age 51.2 ± 10.9 years, BMI 29.2 ± 5.0 kg/m2). MAS reduced AHI by 50.3% ± 50.7% across the group. Supine-predominant OSA patients had lower treatment response rates than non-positional OSA (e.g., 36% vs. 59% for AHI < 10/h). REM-predominant OSA showed a lower response rate than either NREM or non-stage dependent OSA. In prediction modelling, age, baseline AHI, and anthropometric variables were predictive of MAS treatment outcome but not OSA phenotype. Gender was not associated with treatment outcome. Conclusions: Lower MAS treatment response rates were observed in supine and REM sleep. In a large sample, we confirm that demographic, anthropometric, and polysomnographic data only weakly inform about MAS efficacy, supporting the need for alternative objective prediction methods to reliably select patients for MAS treatment. Citation: Sutherland K, Takaya H, Qian J, Petocz P, Ng AT, Cistulli PA. Oral appliance treatment response and polysomnographic phenotypes of obstructive sleep apnea. J Clin Sleep Med 2015;11(8):861–868. PMID:25845897

The development and initial validation of a virtual dripping sweat rate and a clothing wetness ratio for use in predictive heat strain models.

PubMed

Kubota, H; Kuwabara, K; Hamada, Y

2014-08-01

This paper applies the heat balance equation (HBE) for clothed subjects as a linear function of mean skin temperature (t sk ) by a new sweating efficiency (η sw ) and an approximation for the thermoregulatory sweat rate. The equation predicting t sk in steady state conditions was derived as the solution of the HBE and used for a predictive heat strain scale. The heat loss from the wet clothing (WCL) area was identified with a new variable of 'virtual dripping sweat rate VDSR' (S wdr ). This is a subject's un-evaporated sweat rate in dry clothing from the regional sweat rate exceeding the maximum evaporative capacity, and adds the moisture to the clothing, reducing the intrinsic clothing insulation. The S wdr allowed a mass balance analysis of the wet clothing area identified as clothing wetness (w cl ). The w cl was derived by combining the HBE at the WCL surface from which the evaporation rate and skin heat loss from WCL region are given. Experimental results on eight young male subjects wearing typical summer clothing, T-shirt and trousers verified the model for predicting t sk with WCL thermal resistance (R cl,w ) identified as 25 % of dry clothing (R cl,d ).

The development and initial validation of a virtual dripping sweat rate and a clothing wetness ratio for use in predictive heat strain models

NASA Astrophysics Data System (ADS)

Kubota, H.; Kuwabara, K.; Hamada, Y.

2014-08-01

This paper applies the heat balance equation (HBE) for clothed subjects as a linear function of mean skin temperature ( t sk ) by a new sweating efficiency ( η sw ) and an approximation for the thermoregulatory sweat rate. The equation predicting t sk in steady state conditions was derived as the solution of the HBE and used for a predictive heat strain scale. The heat loss from the wet clothing (WCL) area was identified with a new variable of `virtual dripping sweat rate VDSR' ( S wdr ). This is a subject's un-evaporated sweat rate in dry clothing from the regional sweat rate exceeding the maximum evaporative capacity, and adds the moisture to the clothing, reducing the intrinsic clothing insulation. The S wdr allowed a mass balance analysis of the wet clothing area identified as clothing wetness ( w cl ). The w cl was derived by combining the HBE at the WCL surface from which the evaporation rate and skin heat loss from WCL region are given. Experimental results on eight young male subjects wearing typical summer clothing, T-shirt and trousers verified the model for predicting t sk with WCL thermal resistance ( R cl,w ) identified as 25 % of dry clothing ( R cl,d ).

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

PubMed

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.

Rates and predictors of depression in adoptive mothers: moving toward theory.

PubMed

Foli, Karen J; South, Susan C; Lim, Eunjung

2012-01-01

There are approximately 1.8 million adopted children living in the United States. Adoptive parents may experience depressive symptoms and put their children at risk for negative outcomes. The results of this study describe the rates of depression in 300 adoptive mothers and associations with hypothesized explanatory variables, which predict approximately half of the variance in maternal depressive symptoms: expectations of themselves as mothers, the child, and family and friends; feeling of rest; past and present psychiatric difficulties (self-esteem, history of depression); and interpersonal variables (bonding, marital satisfaction, perceived support). These findings are useful in planning effective interventions to mitigate depressive symptoms.

School Location, School Section and Students' Gender as Predictors to Secondary School Dropout Rate in Rivers State, Nigeria

ERIC Educational Resources Information Center

Christian, Mathew

2015-01-01

This study was undertaken to underscore the extent the variables of school location, students' gender and school section can predict the rate of drop out of secondary school students. Ex post facto design was adopted and all data on students' enrollment, retention and completion were collected from available schools' records for two cohorts of…

Robotic laparoscopic radical prostatectomy for biopsy Gleason 8 to 10: prediction of favorable pathologic outcome with preoperative parameters.

PubMed

Shikanov, Sergey A; Thong, Alan; Gofrit, Ofer N; Zagaja, Gregory P; Steinberg, Gary D; Shalhav, Arieh L; Zorn, Kevin C

2008-07-01

We sought to evaluate the pathologic results and postoperative outcomes for men undergoing robot-assisted laparoscopic radical prostatectomy (RLRP) for biopsy Gleason score (GS) 8 to 10 disease. Stratification of these patients according to preoperative variables was also performed in an attempt to predict organ-confined cancer. A prospective RLRP database identified all patients with preoperative biopsy GS 8 to 10. Variables, including prostate-specific antigen (PSA), percent positive biopsy cores (%PBC), maximal percentage of cancer in biopsy core (%MCB), clinical stage, pathologic stage, pathologic GS, surgical margins status, lymph node status, time to biochemical recurrence, and recurrence rate, were evaluated. Preoperative variables were treated as continuous and categorical using PSA, %PBC and %MCB cutoffs of 10 ng/mL, 50%, and 30%, respectively. Between February 2003 and September 2007, a total of 1225 RLRPs were performed at the University of Chicago Medical Center. Seventy-two (5.9%) patients had preoperative biopsy GS 8 to 10. Two patients received neoadjuvant hormonal therapy and were excluded. Among 70 patients evaluated, 33 (47%) had organconfined (pT(2)N0) disease. Forty (60.6%) patients had pathologic downgrading to GS

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

PubMed

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

2013-12-01

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

Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology.

PubMed

Oztekin, Asil; Delen, Dursun; Kong, Zhenyu James

2009-12-01

Predicting the survival of heart-lung transplant patients has the potential to play a critical role in understanding and improving the matching procedure between the recipient and graft. Although voluminous data related to the transplantation procedures is being collected and stored, only a small subset of the predictive factors has been used in modeling heart-lung transplantation outcomes. The previous studies have mainly focused on applying statistical techniques to a small set of factors selected by the domain-experts in order to reveal the simple linear relationships between the factors and survival. The collection of methods known as 'data mining' offers significant advantages over conventional statistical techniques in dealing with the latter's limitations such as normality assumption of observations, independence of observations from each other, and linearity of the relationship between the observations and the output measure(s). There are statistical methods that overcome these limitations. Yet, they are computationally more expensive and do not provide fast and flexible solutions as do data mining techniques in large datasets. The main objective of this study is to improve the prediction of outcomes following combined heart-lung transplantation by proposing an integrated data-mining methodology. A large and feature-rich dataset (16,604 cases with 283 variables) is used to (1) develop machine learning based predictive models and (2) extract the most important predictive factors. Then, using three different variable selection methods, namely, (i) machine learning methods driven variables-using decision trees, neural networks, logistic regression, (ii) the literature review-based expert-defined variables, and (iii) common sense-based interaction variables, a consolidated set of factors is generated and used to develop Cox regression models for heart-lung graft survival. The predictive models' performance in terms of 10-fold cross-validation accuracy rates for two multi-imputed datasets ranged from 79% to 86% for neural networks, from 78% to 86% for logistic regression, and from 71% to 79% for decision trees. The results indicate that the proposed integrated data mining methodology using Cox hazard models better predicted the graft survival with different variables than the conventional approaches commonly used in the literature. This result is validated by the comparison of the corresponding Gains charts for our proposed methodology and the literature review based Cox results, and by the comparison of Akaike information criteria (AIC) values received from each. Data mining-based methodology proposed in this study reveals that there are undiscovered relationships (i.e. interactions of the existing variables) among the survival-related variables, which helps better predict the survival of the heart-lung transplants. It also brings a different set of variables into the scene to be evaluated by the domain-experts and be considered prior to the organ transplantation.

A MELD-based model to determine risk of mortality among patients with acute variceal bleeding.

PubMed

Reverter, Enric; Tandon, Puneeta; Augustin, Salvador; Turon, Fanny; Casu, Stefania; Bastiampillai, Ravin; Keough, Adam; Llop, Elba; González, Antonio; Seijo, Susana; Berzigotti, Annalisa; Ma, Mang; Genescà, Joan; Bosch, Jaume; García-Pagán, Joan Carles; Abraldes, Juan G

2014-02-01

Patients with cirrhosis with acute variceal bleeding (AVB) have high mortality rates (15%-20%). Previously described models are seldom used to determine prognoses of these patients, partially because they have not been validated externally and because they include subjective variables, such as bleeding during endoscopy and Child-Pugh score, which are evaluated inconsistently. We aimed to improve determination of risk for patients with AVB. We analyzed data collected from 178 patients with cirrhosis (Child-Pugh scores of A, B, and C: 15%, 57%, and 28%, respectively) and esophageal AVB who received standard therapy from 2007 through 2010. We tested the performance (discrimination and calibration) of previously described models, including the model for end-stage liver disease (MELD), and developed a new MELD calibration to predict the mortality of patients within 6 weeks of presentation with AVB. MELD-based predictions were validated in cohorts of patients from Canada (n = 240) and Spain (n = 221). Among study subjects, the 6-week mortality rate was 16%. MELD was the best model in terms of discrimination; it was recalibrated to predict the 6-week mortality rate with logistic regression (logit, -5.312 + 0.207 • MELD; bootstrapped R(2), 0.3295). MELD values of 19 or greater predicted 20% or greater mortality, whereas MELD scores less than 11 predicted less than 5% mortality. The model performed well for patients from Canada at all risk levels. In the Spanish validation set, in which all patients were treated with banding ligation, MELD predictions were accurate up to the 20% risk threshold. We developed a MELD-based model that accurately predicts mortality among patients with AVB, based on objective variables available at admission. This model could be useful to evaluate the efficacy of new therapies and stratify patients in randomized trials. Copyright © 2014 AGA Institute. Published by Elsevier Inc. All rights reserved.

Modeling and forecasting US presidential election using learning algorithms

NASA Astrophysics Data System (ADS)

Zolghadr, Mohammad; Niaki, Seyed Armin Akhavan; Niaki, S. T. A.

2017-09-01

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president's approval rate, and others are considered in a stepwise regression to identify significant variables. The president's approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method's calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast.

Gender, TIMI risk score and in-hospital mortality in STEMI patients undergoing primary PCI: results from the Belgian STEMI registry.

PubMed

Gevaert, Sofie A; De Bacquer, Dirk; Evrard, Patrick; Convens, Carl; Dubois, Philippe; Boland, Jean; Renard, Marc; Beauloye, Christophe; Coussement, Patrick; De Raedt, Herbert; de Meester, Antoine; Vandecasteele, Els; Vranckx, Pascal; Sinnaeve, Peter R; Claeys, Marc J

2014-01-22

The relationship between the predictive performance of the TIMI risk score for STEMI and gender has not been evaluated in the setting of primary PCI (pPCI). Here, we compared in-hospital mortality and predictive performance of the TIMI risk score between Belgian women and men undergoing pPCI. In-hospital mortality was analysed in 8,073 (1,920 [23.8%] female and 6,153 [76.2%] male patients) consecutive pPCI-treated STEMI patients, included in the prospective, observational Belgian STEMI registry (January 2007 to February 2011). A multivariable logistic regression model, including TIMI risk score variables and gender, evaluated differences in in-hospital mortality between men and women. The predictive performance of the TIMI risk score according to gender was evaluated in terms of discrimination and calibration. Mortality rates for TIMI scores in women and men were compared. Female patients were older, had more comorbidities and longer ischaemic times. Crude in-hospital mortality was 10.1% in women vs. 4.9% in men (OR 2.2; 95% CI: 1.82-2.66, p<0.001). When adjusting for TIMI risk score variables, mortality remained higher in women (OR 1.47, 95% CI: 1.15-1.87, p=0.002). The TIMI risk score provided a good predictive discrimination and calibration in women as well as in men (c-statistic=0.84 [95% CI: 0.809-0.866], goodness-of-fit p=0.53 and c-statistic=0.89 [95% CI: 0.873-0.907], goodness-of-fit p=0.13, respectively), but mortality prediction for TIMI scores was better in men (p=0.02 for TIMI score x gender interaction). In the Belgian STEMI registry, pPCI-treated women had a higher in-hospital mortality rate even after correcting for TIMI risk score variables. The TIMI risk score was effective in predicting in-hospital mortality but performed slightly better in men. The database was registered with clinicaltrials.gov (NCT00727623).

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

PubMed Central

Batterman, Stuart

2015-01-01

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21, 33, 24 and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than noncommercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity. PMID:26688671

The no-show patient in the model family practice unit.

PubMed

Dervin, J V; Stone, D L; Beck, C H

1978-12-01

Appointment breaking by patients causes problems for the physician's office. Patients who neither keep nor cancel their appointments are often referred to as "no shows." Twenty variables were identified as potential predictors of no-show behavior. These predictors were applied to 291 Family Practice Center patients during a one-month study in April 1977. A discriminant function and multiple regression procedure were utilized ascertain the predictability of the selected variables. Predictive accuracy of the variables was 67.4 percent compared to the presently utilized constant predictor technique, which is 73 percent accurate. Modification of appointment schedules based upon utilization of the variables studies as predictors of show/no-show behavior does not appear to be an effective strategy in the Family Practice Center of the Community Hospital of Sonoma County, Santa Rosa, due to the high proportion of patients who do, in fact, show. In clinics with lower show rates, the technique may prove to be an effective strategy.

Simulating and explaining passive air sampling rates for semi-volatile compounds on polyurethane foam passive samplers

PubMed Central

Petrich, Nicholas T.; Spak, Scott N.; Carmichael, Gregory R.; Hu, Dingfei; Martinez, Andres; Hornbuckle, Keri C.

2013-01-01

Passive air samplers (PAS) including polyurethane foam (PUF) are widely deployed as an inexpensive and practical way to sample semi-volatile pollutants. However, concentration estimates from PAS rely on constant empirical mass transfer rates, which add unquantified uncertainties to concentrations. Here we present a method for modeling hourly sampling rates for semi-volatile compounds from hourly meteorology using first-principle chemistry, physics, and fluid dynamics, calibrated from depuration experiments. This approach quantifies and explains observed effects of meteorology on variability in compound-specific sampling rates and analyte concentrations; simulates nonlinear PUF uptake; and recovers synthetic hourly concentrations at a reference temperature. Sampling rates are evaluated for polychlorinated biphenyl congeners at a network of Harner model samplers in Chicago, Illinois during 2008, finding simulated average sampling rates within analytical uncertainty of those determined from loss of depuration compounds, and confirming quasi-linear uptake. Results indicate hourly, daily and interannual variability in sampling rates, sensitivity to temporal resolution in meteorology, and predictable volatility-based relationships between congeners. We quantify importance of each simulated process to sampling rates and mass transfer and assess uncertainty contributed by advection, molecular diffusion, volatilization, and flow regime within the PAS, finding PAS chamber temperature contributes the greatest variability to total process uncertainty (7.3%). PMID:23837599

Depression and anxiety as predictors of heart rate variability after myocardial infarction.

PubMed

Martens, E J; Nyklícek, I; Szabó, B M; Kupper, N

2008-03-01

Reduced heart rate variability (HRV) is a prognostic factor for cardiac mortality. Both depression and anxiety have been associated with increased risk for mortality in cardiac patients. Low HRV may act as an intermediary in this association. The present study examined to what extent depression and anxiety differently predict 24-h HRV indices recorded post-myocardial infarction (MI). Ninety-three patients were recruited during hospitalization for MI and assessed on self-reported symptoms of depression and anxiety. Two months post-MI, patients were assessed on clinical diagnoses of lifetime depressive and anxiety disorder. Adequate 24-h ambulatory electrocardiography data were obtained from 82 patients on average 78 days post-MI. In unadjusted analyses, lifetime diagnoses of major depressive disorder was predictive of lower SDNN [standard deviation of all normal-to-normal (NN) intervals; beta=-0.26, p=0.022] and SDANN (standard deviation of all 5-min mean NN intervals; beta=0.25, p=0.023), and lifetime anxiety disorder of lower RMSSD (root mean square of successive differences; beta=-0.23, p=0.039). Depression and anxiety symptoms did not significantly predict HRV. After adjustment for age, sex, cardiac history and multi-vessel disease, lifetime depressive disorder was no longer predictive of HRV. Lifetime anxiety disorder predicted reduced high-frequency spectral power (beta=-0.22, p=0.039) and RMSSD (beta=-0.25, p=0.019), even after additional adjustment of anxiety symptoms. Clinical anxiety, but not depression, negatively influenced parasympathetic modulation of heart rate in post-MI patients. These findings elucidate the physiological mechanisms underlying anxiety as a risk factor for adverse outcomes, but also raise questions about the potential role of HRV as an intermediary between depression and post-MI prognosis.

Forecasting High-Priority Infectious Disease Surveillance Regions: A Socioeconomic Model

PubMed Central

Chan, Emily H.; Scales, David A.; Brewer, Timothy F.; Madoff, Lawrence C.; Pollack, Marjorie P.; Hoen, Anne G.; Choden, Tenzin; Brownstein, John S.

2013-01-01

Background. Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. Methods. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Results. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996–2008. Conclusions. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions. PMID:23118271

Agrometeorological parameters for prediction of the maturation period of Arabica coffee cultivars

NASA Astrophysics Data System (ADS)

Pezzopane, José Ricardo Macedo; Salva, Terezinha de Jesus Garcia; de Lima, Valéria Bittencourt; Fazuoli, Luiz Carlos

2012-09-01

The objective of this study was to determine the harvest period of coffee fruits based on the relationship between agrometeorological parameters and sucrose accumulation in the seeds. Over the crop years 2004/2005 and 2006/2007, from 150 days after flowering (DAF) onwards, samples of 50 fruits of cultivars Mundo Novo IAC 376-4, Obatã IAC 1669-20 and Catuaí Vermelho IAC 144 were collected from coffee trees located in Campinas, Brazil. The endosperm of the fruits was freeze-dried, ground and analyzed for sucrose content by high-performance liquid chromatography. A weather station provided data to calculate the accumulated growing degree-day (GDD) units, and the reference (ETo) and actual (ETr) evapotranspiration rates. The results showed that the highest rates of sucrose accumulation occurred at the transition from the cane-green to the cherry phenological stage. Models for the estimation of sucrose content during maturation based on meteorological variables exhibited similar or better performance than the DAF variable, with better results for the variables GDD and ETo. The Mundo Novo cultivar reached the highest sucrose level in the endosperm after 2,790 GDD, while cultivar Catuaí attained its maximum sucrose concentration after the accumulated evapotranspiration rate has reached a value of 870 mm. As for cultivar Obatã, the maximum sucrose concentration was predicted with the same degree of accuracy using any of the parameters investigated. For the Obatã cultivar, the values of the variables calculated for the maximum sucrose concentration to be reached were 249 DAF, 3,090 GDD, 1,020 ETo and 900 ETr.

KRAS Genomic Status Predicts the Sensitivity of Ovarian Cancer Cells to Decitabine | Office of Cancer Genomics

Cancer.gov

Decitabine, a cancer therapeutic that inhibits DNA methylation, produces variable antitumor response rates in patients with solid tumors that might be leveraged clinically with identification of a predictive biomarker. In this study, we profiled the response of human ovarian, melanoma, and breast cancer cells treated with decitabine, finding that RAS/MEK/ERK pathway activation and DNMT1 expression correlated with cytotoxic activity. Further, we showed that KRAS genomic status predicted decitabine sensitivity in low-grade and high-grade serous ovarian cancer cells.

Regional peak mucosal cooling predicts the perception of nasal patency.

PubMed

Zhao, Kai; Jiang, Jianbo; Blacker, Kara; Lyman, Brian; Dalton, Pamela; Cowart, Beverly J; Pribitkin, Edmund A

2014-03-01

Nasal obstruction is the principal symptom that drives patients with rhinosinus disease to seek medical treatment. However, patient perception of obstruction often bears little relationship to actual measured physical obstruction of airflow. This lack of an objective clinical tool hinders effective diagnosis and treatment. Previous work has suggested that the perception of nasal patency may involve nasal trigeminal activation by cool inspiratory airflow; we attempt to derive clinically relevant variables following this phenomenon. Prospective healthy cohort. Twenty-two healthy subjects rated unilateral nasal patency in controlled room air using a visual analog scale, followed by rhinomanometry, acoustic rhinometry, and butanol lateralization thresholds (BLTs). Each subject then immediately underwent a computed tomography scan, enabling the construction of a real-time computational fluid dynamics (CFD) nasal airway model, which was used to simulate nasal mucosa heat loss during steady resting breathing. Among all measured and computed variables, only CFD-simulated peak heat loss posterior to the nasal vestibule significantly correlated with patency ratings (r = -0.46, P < .01). Linear discriminant analysis predicted patency categories with 89% success rate, with BLT and rhinomanometric nasal resistance being two additional significant variables. As validation, CFD simulated nasal resistance significantly correlated with rhinomanometrically measured resistance (r = 0.41, P < .01). These results reveal that our noses are sensing patency via a mechanism involving localized peak nasal mucosal cooling. The analysis provides a strong rationale for combining the individualized CFD with other objective and neurologic measures to create a novel clinical tool to diagnose nasal obstruction and to predict and evaluate treatment outcomes. © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

Homework compliance counts in cognitive-behavioral therapy.

PubMed

Lebeau, Richard T; Davies, Carolyn D; Culver, Najwa C; Craske, Michelle G

2013-01-01

Prior research has demonstrated that there is some association between treatment engagement and treatment outcome in behavioral therapy for anxiety disorders. However, many of these investigations have been limited by weak measurement of treatment engagement variables, failure to control for potentially important baseline variables, and failure to consider various treatment engagement variables simultaneously. The purpose of the present study is to examine the relationship between two treatment engagement variables (treatment expectancy and homework compliance) and the extent to which they predict improvement from cognitive-behavioral therapy (CBT) for anxiety disorders. 84 adults with a DSM-IV-defined principal anxiety disorder took part in up to 12 sessions of CBT or acceptance and commitment therapy. Pre- and post-treatment disorder severity was assessed using clinical severity ratings from a semi-structured diagnostic interview. Participants made ratings of treatment expectancy after the first session. Homework compliance was assessed each session by the treating clinician. Contrary to hypotheses, treatment expectancy and homework compliance were poorly correlated. Regression analyses revealed that homework compliance, but not treatment expectancy, predicted a significant portion of the variance in treatment outcome (10%). The present research suggests that although treatment expectation and homework compliance likely represent unique constructs of treatment engagement, homework compliance may be the more important treatment engagement variable for outcomes. The present research suggests that improvement of homework compliance has the potential to be a highly practical and effective way to improve clinical outcomes in CBT targeting anxiety disorders.

Comment on Hall et al. (2017), "How to Choose Between Measures of Tinnitus Loudness for Clinical Research? A Report on the Reliability and Validity of an Investigator-Administered Test and a Patient-Reported Measure Using Baseline Data Collected in a Phase IIa Drug Trial".

PubMed

Sabour, Siamak

2018-03-08

The purpose of this letter, in response to Hall, Mehta, and Fackrell (2017), is to provide important knowledge about methodology and statistical issues in assessing the reliability and validity of an audiologist-administered tinnitus loudness matching test and a patient-reported tinnitus loudness rating. The author uses reference textbooks and published articles regarding scientific assessment of the validity and reliability of a clinical test to discuss the statistical test and the methodological approach in assessing validity and reliability in clinical research. Depending on the type of the variable (qualitative or quantitative), well-known statistical tests can be applied to assess reliability and validity. The qualitative variables of sensitivity, specificity, positive predictive value, negative predictive value, false positive and false negative rates, likelihood ratio positive and likelihood ratio negative, as well as odds ratio (i.e., ratio of true to false results), are the most appropriate estimates to evaluate validity of a test compared to a gold standard. In the case of quantitative variables, depending on distribution of the variable, Pearson r or Spearman rho can be applied. Diagnostic accuracy (validity) and diagnostic precision (reliability or agreement) are two completely different methodological issues. Depending on the type of the variable (qualitative or quantitative), well-known statistical tests can be applied to assess validity.

Improving Global Vascular Risk Prediction with Behavioral and Anthropometric Factors: The Multi-ethnic Northern Manhattan Cohort Study

PubMed Central

Sacco, Ralph L.; Khatri, Minesh; Rundek, Tatjana; Xu, Qiang; Gardener, Hannah; Boden-Albala, Bernadette; Di Tullio, Marco R.; Homma, Shunichi; Elkind, Mitchell SV; Paik, Myunghee C

2010-01-01

Objective To improve global vascular risk prediction with behavioral and anthropometric factors. Background Few cardiovascular risk models are designed to predict the global vascular risk of MI, stroke, or vascular death in multi-ethnic individuals, and existing schemes do not fully include behavioral risk factors. Methods A randomly-derived, population-based, prospective cohort of 2737 community participants free of stroke and coronary artery disease were followed annually for a median of 9.0 years in the Northern Manhattan Study (mean age 69 years; 63.2% women; 52.7% Hispanic, 24.9% African-American, 19.9% white). A global vascular risk score (GVRS) predictive of stroke, myocardial infarction, or vascular death was developed by adding variables to the traditional Framingham cardiovascular variables based on the likelihood ratio criterion. Model utility was assessed through receiver operating characteristics, calibration, and effect on reclassification of subjects. Results Variables which significantly added to the traditional Framingham profile included waist circumference, alcohol consumption, and physical activity. Continuous measures for blood pressure and fasting blood sugar were used instead of hypertension and diabetes. Ten -year event-free probabilities were 0.95 for the first quartile of GVRS, 0.89 for the second quartile, 0.79 for the third quartile, and 0.56 for the fourth quartile. The addition of behavioral factors in our model improved prediction of 10 -year event rates compared to a model restricted to the traditional variables. Conclusion A global vascular risk score that combines both traditional, behavioral, and anthropometric risk factors, uses continuous variables for physiological parameters, and is applicable to non-white subjects could improve primary prevention strategies. PMID:19958966

An experimental test of the role of environmental temperature variability on ectotherm molecular, physiological and life-history traits: implications for global warming.

PubMed

Folguera, Guillermo; Bastías, Daniel A; Caers, Jelle; Rojas, José M; Piulachs, Maria-Dolors; Bellés, Xavier; Bozinovic, Francisco

2011-07-01

Global climate change is one of the greatest threats to biodiversity; one of the most important effects is the increase in the mean earth surface temperature. However, another but poorly studied main characteristic of global change appears to be an increase in temperature variability. Most of the current analyses of global change have focused on mean values, paying less attention to the role of the fluctuations of environmental variables. We experimentally tested the effects of environmental temperature variability on characteristics associated to the fitness (body mass balance, growth rate, and survival), metabolic rate (VCO(2)) and molecular traits (heat shock protein expression, Hsp70), in an ectotherm, the terrestrial woodlouse Porcellio laevis. Our general hypotheses are that higher values of thermal amplitude may directly affect life-history traits, increasing metabolic cost and stress responses. At first, results supported our hypotheses showing a diversity of responses among characters to the experimental thermal treatments. We emphasize that knowledge about the cellular and physiological mechanisms by which animals cope with environmental changes is essential to understand the impact of mean climatic change and variability. Also, we consider that the studies that only incorporate only mean temperatures to predict the life-history, ecological and evolutionary impact of global temperature changes present important problems to predict the diversity of responses of the organism. This is because the analysis ignores the complexity and details of the molecular and physiological processes by which animals cope with environmental variability, as well as the life-history and demographic consequences of such variability. Copyright © 2011 Elsevier Inc. All rights reserved.

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

PubMed Central

Arab, Mohammad M.; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

2017-01-01

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation. PMID:29163583

Individual differences in emotion word processing: A diffusion model analysis.

PubMed

Mueller, Christina J; Kuchinke, Lars

2016-06-01

The exploratory study investigated individual differences in implicit processing of emotional words in a lexical decision task. A processing advantage for positive words was observed, and differences between happy and fear-related words in response times were predicted by individual differences in specific variables of emotion processing: Whereas more pronounced goal-directed behavior was related to a specific slowdown in processing of fear-related words, the rate of spontaneous eye blinks (indexing brain dopamine levels) was associated with a processing advantage of happy words. Estimating diffusion model parameters revealed that the drift rate (rate of information accumulation) captures unique variance of processing differences between happy and fear-related words, with highest drift rates observed for happy words. Overall emotion recognition ability predicted individual differences in drift rates between happy and fear-related words. The findings emphasize that a significant amount of variance in emotion processing is explained by individual differences in behavioral data.

Building and validation of a prognostic model for predicting extracorporeal circuit clotting in patients with continuous renal replacement therapy.

PubMed

Fu, Xia; Liang, Xinling; Song, Li; Huang, Huigen; Wang, Jing; Chen, Yuanhan; Zhang, Li; Quan, Zilin; Shi, Wei

2014-04-01

To develop a predictive model for circuit clotting in patients with continuous renal replacement therapy (CRRT). A total of 425 cases were selected. 302 cases were used to develop a predictive model of extracorporeal circuit life span during CRRT without citrate anticoagulation in 24 h, and 123 cases were used to validate the model. The prediction formula was developed using multivariate Cox proportional-hazards regression analysis, from which a risk score was assigned. The mean survival time of the circuit was 15.0 ± 1.3 h, and the rate of circuit clotting was 66.6 % during 24 h of CRRT. Five significant variables were assigned a predicting score according to the regression coefficient: insufficient blood flow, no anticoagulation, hematocrit ≥0.37, lactic acid of arterial blood gas analysis ≤3 mmol/L and APTT < 44.2 s. The Hosmer-Lemeshow test showed no significant difference between the predicted and actual circuit clotting (R (2) = 0.232; P = 0.301). A risk score that includes the five above-mentioned variables can be used to predict the likelihood of extracorporeal circuit clotting in patients undergoing CRRT.

ROC curves predicted by a model of visual search.

PubMed

Chakraborty, D P

2006-07-21

In imaging tasks where the observer is uncertain whether lesions are present, and where they could be present, the image is searched for lesions. In the free-response paradigm, which closely reflects this task, the observer provides data in the form of a variable number of mark-rating pairs per image. In a companion paper a statistical model of visual search has been proposed that has parameters characterizing the perceived lesion signal-to-noise ratio, the ability of the observer to avoid marking non-lesion locations, and the ability of the observer to find lesions. The aim of this work is to relate the search model parameters to receiver operating characteristic (ROC) curves that would result if the observer reported the rating of the most suspicious finding on an image as the overall rating. Also presented are the probability density functions (pdfs) of the underlying latent decision variables corresponding to the highest rating for normal and abnormal images. The search-model-predicted ROC curves are 'proper' in the sense of never crossing the chance diagonal and the slope is monotonically changing. They also have the interesting property of not allowing the observer to move the operating point continuously from the origin to (1, 1). For certain choices of parameters the operating points are predicted to be clustered near the initial steep region of the curve, as has been observed by other investigators. The pdfs are non-Gaussians, markedly so for the abnormal images and for certain choices of parameter values, and provide an explanation for the well-known observation that experimental ROC data generally imply a wider pdf for abnormal images than for normal images. Some features of search-model-predicted ROC curves and pdfs resemble those predicted by the contaminated binormal model, but there are significant differences. The search model appears to provide physical explanations for several aspects of experimental ROC curves.

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

DTIC Science & Technology

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model , is able to model the rate of occurrence of...which adds specificity to the model and can make nonlinear data more manageable. Early results show that the 1. REPORT DATE (DD-MM-YYYY) 4. TITLE

Prediction of intraventricular haemorrhage in preterm infants using time series analysis of blood pressure and respiratory signals

NASA Astrophysics Data System (ADS)

Huvanandana, Jacqueline; Nguyen, Chinh; Thamrin, Cindy; Tracy, Mark; Hinder, Murray; McEwan, Alistair L.

2017-04-01

Despite the decline in mortality rates of extremely preterm infants, intraventricular haemorrhage (IVH) remains common in survivors. The need for resuscitation and cardiorespiratory management, particularly within the first 24 hours of life, are important factors in the incidence and timing of IVH. Variability analyses of heart rate and blood pressure data has demonstrated potential approaches to predictive monitoring. In this study, we investigated the early identification of infants at a high risk of developing IVH, using time series analysis of blood pressure and respiratory data. We also explore approaches to improving model performance, such as the inclusion of multiple variables and signal pre-processing to enhance the results from detrended fluctuation analysis. Of the models we evaluated, the highest area under receiver-operator characteristic curve (5th, 95th percentile) achieved was 0.921 (0.82, 1.00) by mean diastolic blood pressure and the long-term scaling exponent of pulse interval (PI α2), exhibiting a sensitivity of >90% at a specificity of 75%. Following evaluation in a larger population, our approach may be useful in predictive monitoring to identify infants at high risk of developing IVH, offering caregivers more time to adjust intensive care treatment.

The BioMedical Admissions Test for medical student selection: issues of fairness and bias.

PubMed

Emery, Joanne L; Bell, John F; Vidal Rodeiro, Carmen L

2011-01-01

The BioMedical Admissions Test (BMAT) forms part of the undergraduate medical admission process at the University of Cambridge. The fairness of admissions tests is an important issue. Aims were to investigate the relationships between applicants' background variables and BMAT scores, whether they were offered a place or rejected and, for those admitted, performance on the first year course examinations. Multilevel regression models were employed with data from three combined applicant cohorts. Admission rates for different groups were investigated with and without controlling for BMAT performance. The fairness of the BMAT was investigated by determining, for those admitted, whether scores predicted examination performance equitably. Despite some differences in applicants' BMAT performance (e.g. by school type and gender), BMAT scores predicted mean examination marks equitably for all background variables considered. The probability of achieving a 1st class examination result, however, was slightly under-predicted for those admitted from schools and colleges entering relatively few applicants. Not all differences in admission rates were accounted for by BMAT performance. However, the test constitutes only one part of a compensatory admission system in which other factors, such as interview performance, are important considerations. Results are in support of the equity of the BMAT.

Kinetic Risk Factors of Running-Related Injuries in Female Recreational Runners.

PubMed

Napier, Christopher; MacLean, Christopher L; Maurer, Jessica; Taunton, Jack E; Hunt, Michael A

2018-05-30

Our objective was to prospectively investigate the association of kinetic variables with running-related injury (RRI) risk. Seventy-four healthy female recreational runners ran on an instrumented treadmill while 3D kinetic and kinematic data were collected. Kinetic outcomes were vertical impact transient, average vertical loading rate, instantaneous vertical loading rate, active peak, vertical impulse, and peak braking force (PBF). Participants followed a 15-week half-marathon training program. Exposure time (hours of running) was calculated from start of program until onset of injury, loss to follow-up, or end of program. After converting kinetic variables from continuous to ordinal variables based on tertiles, Cox proportional hazard models with competing risks were fit for each variable independently, before analysis in a forward stepwise multivariable model. Sixty-five participants were included in the final analysis, with a 33.8% injury rate. PBF was the only kinetic variable that was a significant predictor of RRI. Runners in the highest tertile (PBF <-0.27 BW) were injured at 5.08 times the rate of those in the middle tertile and 7.98 times the rate of those in the lowest tertile. When analyzed in the multivariable model, no kinetic variables made a significant contribution to predicting injury beyond what had already been accounted for by PBF alone. Findings from this study suggest PBF is associated with a significantly higher injury hazard ratio in female recreational runners and should be considered as a target for gait retraining interventions. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Thermal Modeling of Resistance Spot Welding and Prediction of Weld Microstructure

NASA Astrophysics Data System (ADS)

Sheikhi, M.; Valaee Tale, M.; Usefifar, GH. R.; Fattah-Alhosseini, Arash

2017-11-01

The microstructure of nuggets in resistance spot welding can be influenced by the many variables involved. This study aimed at examining such a relationship and, consequently, put forward an analytical model to predict the thermal history and microstructure of the nugget zone. Accordingly, a number of numerical simulations and experiments were conducted and the accuracy of the model was assessed. The results of this assessment revealed that the proposed analytical model could accurately predict the cooling rate in the nugget and heat-affected zones. Moreover, both analytical and numerical models confirmed that sheet thickness and electrode-sheet interface temperature were the most important factors influencing the cooling rate at temperatures lower than about T l/2. Decomposition of austenite is one of the most important transformations in steels occurring over this temperature range. Therefore, an easy-to-use map was designed against these parameters to predict the weld microstructure.

Divergent Effects of Beliefs in Heaven and Hell on National Crime Rates

PubMed Central

Shariff, Azim F.; Rhemtulla, Mijke

2012-01-01

Though religion has been shown to have generally positive effects on normative ‘prosocial’ behavior, recent laboratory research suggests that these effects may be driven primarily by supernatural punishment. Supernatural benevolence, on the other hand, may actually be associated with less prosocial behavior. Here, we investigate these effects at the societal level, showing that the proportion of people who believe in hell negatively predicts national crime rates whereas belief in heaven predicts higher crime rates. These effects remain after accounting for a host of covariates, and ultimately prove stronger predictors of national crime rates than economic variables such as GDP and income inequality. Expanding on laboratory research on religious prosociality, this is the first study to tie religious beliefs to large-scale cross-national trends in pro- and anti-social behavior. PMID:22723927

STOCHASTIC SIMULATION OF FIELD-SCALE PESTICIDE TRANSPORT USING OPUS AND GLEAMS

EPA Science Inventory

Incorporating variability in soil and chemical properties into root zone leaching models should provide a better representation of pollutant distribution in natural field conditions. Our objective was to determine if a more mechanistic rate-based model (Opus) would predict soil w...

Disorganized attachment and inhibitory capacity: predicting externalizing problem behaviors.

PubMed

Bohlin, Gunilla; Eninger, Lilianne; Brocki, Karin Cecilia; Thorell, Lisa B

2012-04-01

The aim of the present study was to investigate whether attachment insecurity, focusing on disorganized attachment, and the executive function (EF) component of inhibition, assessed at age 5, were longitudinally related to general externalizing problem behaviors as well as to specific symptoms of ADHD and Autism spectrum disorder (ASD), and callous-unemotional (CU) traits. General externalizing problem behaviors were also measured at age 5 to allow for a developmental analysis. Outcome variables were rated by parents and teachers. The sample consisted of 65 children with an oversampling of children with high levels of externalizing behaviors. Attachment was evaluated using a story stem attachment doll play procedure. Inhibition was measured using four different tasks. The results showed that both disorganized attachment and poor inhibition were longitudinally related to all outcome variables. Controlling for initial level of externalizing problem behavior, poor inhibition predicted ADHD symptoms and externalizing problem behaviors, independent of disorganized attachment, whereas for ASD symptoms no predictive relations remained. Disorganized attachment independently predicted CU traits.

Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

PubMed Central

Pagán, Josué; Irene De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco-Martín, José L.; Vivancos Mora, J.; Moya, José M.; Ayala, José L.

2015-01-01

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives. PMID:26134103

No cataclysmic variables missing: higher merger rate brings into agreement observed and predicted space densities

NASA Astrophysics Data System (ADS)

Belloni, Diogo; Schreiber, Matthias R.; Zorotovic, Mónica; Iłkiewicz, Krystian; Hurley, Jarrod R.; Giersz, Mirek; Lagos, Felipe

2018-06-01

The predicted and observed space density of cataclysmic variables (CVs) have been for a long time discrepant by at least an order of magnitude. The standard model of CV evolution predicts that the vast majority of CVs should be period bouncers, whose space density has been recently measured to be ρ ≲ 2 × 10-5 pc-3. We performed population synthesis of CVs using an updated version of the Binary Stellar Evolution (BSE) code for single and binary star evolution. We find that the recently suggested empirical prescription of consequential angular momentum loss (CAML) brings into agreement predicted and observed space densities of CVs and period bouncers. To progress with our understanding of CV evolution it is crucial to understand the physical mechanism behind empirical CAML. Our changes to the BSE code are also provided in details, which will allow the community to accurately model mass transfer in interacting binaries in which degenerate objects accrete from low-mass main-sequence donor stars.

PREDICTING RECIDIVISM FOR RELEASED STATE PRISON OFFENDERS

PubMed Central

Stahler, Gerald J.; Mennis, Jeremy; Belenko, Steven; Welsh, Wayne N.; Hiller, Matthew L.; Zajac, Gary

2013-01-01

We examined the influence of individual and neighborhood characteristics and spatial contagion in predicting reincarceration on a sample of 5,354 released Pennsylvania state prisoners. Independent variables included demographic characteristics, offense type, drug involvement, various neighborhood variables (e.g., concentrated disadvantage, residential mobility), and spatial contagion (i.e., proximity to others who become reincarcerated). Using geographic information systems (GIS) and logistic regression modeling, our results showed that the likelihood of reincarceration was increased with male gender, drug involvement, offense type, and living in areas with high rates of recidivism. Older offenders and those convicted of violent or drug offenses were less likely to be reincarcerated. For violent offenders, drug involvement, age, and spatial contagion were particular risk factors for reincarceration. None of the neighborhood environment variables were associated with increased risk of reincarceration. Reentry programs need to particularly address substance abuse issues of ex-offenders as well as take into consideration their residential locations. PMID:24443612

A multivariate model of parent-adolescent relationship variables in early adolescence.

PubMed

McKinney, Cliff; Renk, Kimberly

2011-08-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle school in a Southeastern state. The parents of a subset of these adolescents (i.e., 487 mother-father pairs) participated in this study as well. Correlational analyses indicate that authoritative and authoritarian parenting, family cohesion and adaptability, and conflict are significant predictors of early adolescents' internalizing and externalizing problems. Structural equation modeling analyses indicate that fathers' parenting may not predict directly externalizing problems in male and female adolescents but instead may act through conflict. More direct relationships exist when examining mothers' parenting. The impact of parenting, family environment, and conflict on early adolescents' internalizing and externalizing problems and the importance of both gender and cross-informant ratings are emphasized.

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning

NASA Astrophysics Data System (ADS)

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-04-01

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning

PubMed Central

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-01-01

Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate. PMID:28418043

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

PubMed

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-04-18

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Assessing the prediction accuracy of a cure model for censored survival data with long-term survivors: Application to breast cancer data.

PubMed

Asano, Junichi; Hirakawa, Akihiro

2017-01-01

The Cox proportional hazards cure model is a survival model incorporating a cure rate with the assumption that the population contains both uncured and cured individuals. It contains a logistic regression for the cure rate, and a Cox regression to estimate the hazard for uncured patients. A single predictive model for both the cure and hazard can be developed by using a cure model that simultaneously predicts the cure rate and hazards for uncured patients; however, model selection is a challenge because of the lack of a measure for quantifying the predictive accuracy of a cure model. Recently, we developed an area under the receiver operating characteristic curve (AUC) for determining the cure rate in a cure model (Asano et al., 2014), but the hazards measure for uncured patients was not resolved. In this article, we propose novel C-statistics that are weighted by the patients' cure status (i.e., cured, uncured, or censored cases) for the cure model. The operating characteristics of the proposed C-statistics and their confidence interval were examined by simulation analyses. We also illustrate methods for predictive model selection and for further interpretation of variables using the proposed AUCs and C-statistics via application to breast cancer data.

Time budgets of Snow Geese Chen caerulescens and Ross's Geese Chen rossii in mixed flocks: Implications of body size, ambient temperature and family associations

USGS Publications Warehouse

Jonsson, J.E.; Afton, A.D.

2009-01-01

Body size affects foraging and forage intake rates directly via energetic processes and indirectly through interactions with social status and social behaviour. Ambient temperature has a relatively greater effect on the energetics of smaller species, which also generally are more vulnerable to predator attacks than are larger species. We examined variability in an index of intake rates and an index of alertness in Lesser Snow Geese Chen caerulescens caerulescens and Ross's Geese Chen rossii wintering in southwest Louisiana. Specifically we examined variation in these response variables that could be attributed to species, age, family size and ambient temperature. We hypothesized that the smaller Ross's Geese would spend relatively more time feeding, exhibit relatively higher peck rates, spend more time alert or raise their heads up from feeding more frequently, and would respond to declining temperatures by increasing their proportion of time spent feeding. As predicted, we found that Ross's Geese spent more time feeding than did Snow Geese and had slightly higher peck rates than Snow Geese in one of two winters. Ross's Geese spent more time alert than did Snow Geese in one winter, but alert rates differed by family size, independent of species, in contrast to our prediction. In one winter, time spent foraging and walking was inversely related to average daily temperature, but both varied independently of species. Effects of age and family size on time budgets were generally independent of species and in accordance with previous studies. We conclude that body size is a key variable influencing time spent feeding in Ross's Geese, which may require a high time spent feeding at the expense of other activities. ?? 2008 The Authors.

The Ultrasensitivity of Living Polymers

NASA Astrophysics Data System (ADS)

O'Shaughnessy, Ben; Vavylonis, Dimitrios

2003-03-01

Synthetic and biological living polymers are self-assembling chains whose chain length distributions (CLDs) are dynamic. We show these dynamics are ultrasensitive: Even a small perturbation (e.g., temperature jump) nonlinearly distorts the CLD, eliminating or massively augmenting short chains. The origin is fast relaxation of mass variables (mean chain length, monomer concentration) which perturbs CLD shape variables before these can relax via slow chain growth rate fluctuations. Viscosity relaxation predictions agree with experiments on the best-studied synthetic system, α-methylstyrene.

A Cross-sectional Study Assessing Predictors of Essential Medicines Prescribing Behavior Based on Information-motivation-behavioral Skills Model among County Hospitals in Anhui, China

PubMed Central

Zhao, Yun-Wu; Wu, Jing-Ya; Wang, Heng; Li, Nian-Nian; Bian, Cheng; Xu, Shu-Man; Li, Peng; Lu, Hua; Xu, Lei

2015-01-01

Background: The self-consciousness and practicality of preferentially prescribed essential medicines (EMs) are not high enough in county hospitals. The purposes of this study were to use the information-motivation-behavioral skills (IMB) model to identify the predictors of essential medicines prescribing behavior (EMPB) among doctors and to examine the association between demographic variables, IMB, and EMPB. Methods: A cross-sectional study was carried out to assess predictive relationships among demographic variables and IMB model variables using an anonymous questionnaire administered in nine county hospitals of Anhui province. A structural equation model was constructed for the IMB model to test the instruments using analysis of moment structures 17.0. Results: A total of 732 participants completed the survey. The average age of the participants was 37.7 ± 8.9 years old (range: 22–67 years old). The correct rate of information was 90.64%. The average scores of the motivation and behavioral skills were 45.46 ± 7.34 (hundred mark system: 75.77) and 19.92 ± 3.44 (hundred mark system: 79.68), respectively. Approximately half (50.8%) of respondents reported that the proportion of EM prescription was below 60%. The final revised model indicated a good fit to the data (χ2/df = 4.146, goodness of fit index = 0.948, comparative fit index = 0.938, root mean square error of approximation = 0.066). More work experience (β = 0.153, P < 0.001) and behavioral skills (β = 0.449, P < 0.001) predicted more EMPB. Higher income predicted less information (β = −0.197, P < 0.001) and motivation (β = −0.204, P < 0.001). Behavioral skills were positively predicted by information (β = 0.135, P < 0.001) and motivation (β = 0.742, P < 0.001). Conclusion: The present study predicted some factors of EMPB, and specified the relationships among the model variables. The utilization rate of EM was not high enough. Motivation and behavior skills were crucial factors affecting EMPB. The influence of demographic variables, such as income and work experience, on EMPB should be fully appreciated. Comprehensive intervention measures should be implemented from multiple perspectives. PMID:26521786

Therapist use of Socratic questioning predicts session-to-session symptom change in cognitive therapy for depression.

PubMed

Braun, Justin D; Strunk, Daniel R; Sasso, Katherine E; Cooper, Andrew A

2015-07-01

Socratic questioning is a key therapeutic strategy in cognitive therapy (CT) for depression. However, little is known regarding its relation to outcome. In this study, we examine therapist use of Socratic questioning as a predictor of session-to-session symptom change. Participants were 55 depressed adults who participated in a 16-week course of CT (see Adler, Strunk, & Fazio, 2015). Socratic questioning was assessed through observer ratings of the first three sessions. Socratic ratings were disaggregated into scores reflecting within-patient and between-patient variability to facilitate an examination of the relation of within-patient Socratic questioning and session-to-session symptom change. Because we examined within-patient variability in Socratic questioning, the identification of such a relation cannot be attributed to any stable patient characteristics that might otherwise introduce a spurious relation. Within-patient Socratic questioning significantly predicted session-to-session symptom change across the early sessions, with a one standard deviation increase in Socratic-Within predicting a 1.51-point decrease in BDI-II scores in the following session. Within-patient Socratic questioning continued to predict symptom change after controlling for within-patient ratings of the therapeutic alliance (i.e., Relationship and Agreement), suggesting that the relation of Socratic questioning and symptom change was not only independent of stable characteristics, but also within-patient variation in the alliance. Our results provide the first empirical support for a relation of therapist use of Socratic questioning and symptom change in CT for depression. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012

PubMed Central

Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O.

2018-01-01

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping. PMID:29617333

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999-2012.

PubMed

Stefanoff, Pawel; Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O; Rosinska, Magdalena

2018-04-04

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping.

Quality by Design approach for studying the impact of formulation and process variables on product quality of oral disintegrating films.

PubMed

Mazumder, Sonal; Pavurala, Naresh; Manda, Prashanth; Xu, Xiaoming; Cruz, Celia N; Krishnaiah, Yellela S R

2017-07-15

The present investigation was carried out to understand the impact of formulation and process variables on the quality of oral disintegrating films (ODF) using Quality by Design (QbD) approach. Lamotrigine (LMT) was used as a model drug. Formulation variable was plasticizer to film former ratio and process variables were drying temperature, air flow rate in the drying chamber, drying time and wet coat thickness of the film. A Definitive Screening Design of Experiments (DoE) was used to identify and classify the critical formulation and process variables impacting critical quality attributes (CQA). A total of 14 laboratory-scale DoE formulations were prepared and evaluated for mechanical properties (%elongation at break, yield stress, Young's modulus, folding endurance) and other CQA (dry thickness, disintegration time, dissolution rate, moisture content, moisture uptake, drug assay and drug content uniformity). The main factors affecting mechanical properties were plasticizer to film former ratio and drying temperature. Dissolution rate was found to be sensitive to air flow rate during drying and plasticizer to film former ratio. Data were analyzed for elucidating interactions between different variables, rank ordering the critical materials attributes (CMA) and critical process parameters (CPP), and for providing a predictive model for the process. Results suggested that plasticizer to film former ratio and process controls on drying are critical to manufacture LMT ODF with the desired CQA. Published by Elsevier B.V.

Evaluation of a Mysis bioenergetics model

USGS Publications Warehouse

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

2002-01-01

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

TI-59 helps predict IPRs for gravel-packed gas wells

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

Capdevielle, W.C.

The inflow performance relationship (IPR) is an important tool for reservoir and production engineers. It helps optimize completion, tubing, gas lift, and storm choke design. It facilitates accurate rate predictions that can be used to evaluate field development decisions. The IPR is the first step of the systems analysis that translates reservoir rock and fluid parameters into predictable flow rates. Use of gravel packing for sand control complicates the calculation that predicts a well's IPR curve, particularly in gas wells where high velocities in the formation and through gravel-filled perforation tunnels can cause turbulent flow. The program presented in thismore » article calculates the pressure drop and the flowing bottomhole pressures at varying flow rates for gravel-packed gas wells. The program was written for a Texas Instruments TI-59 programmable calculator with a PC-100 printer. Program features include: Calculations for in-casing gravel packs, open-hole gravel packs, or ungravel packed wells. Program prompts for the required data variables. Easy change of data values to run new cases. Calculates pressures for an unlimited number of flow rates. Results show the total pressure drop and the relative magnitude of its components.« less

Quality of care and investment in property, plant, and equipment in hospitals.

PubMed

Levitt, S W

1994-02-01

This study explores the relationship between quality of care and investment in property, plant, and equipment (PPE) in hospitals. Hospitals' investment in PPE was derived from audited financial statements for the fiscal years 1984-1989. Peer Review Organization (PRO) Generic Quality Screen (GQS) reviews and confirmed failures between April 1989 and September 1990 were obtained from the Massachusetts PRO. Weighted least squares regression models used PRO GQS confirmed failure rates as the dependent variable, and investment in PPE as the key explanatory variable. Investment in PPE was standardized, summed by the hospital over the six years, and divided by the hospital's average number of beds in that period. The number of PRO reviewed cases with one or more GQS confirmed failures was divided by the total number of cases reviewed to create confirmed failure rates. Investment in PPE in Massachusetts hospitals is correlated with GQS confirmed failure rates. A financial variable, investment in PPE, predicts certain dimensions of quality of care in hospitals.

Recurrence-plot-based measures of complexity and their application to heart-rate-variability data.

PubMed

Marwan, Norbert; Wessel, Niels; Meyerfeldt, Udo; Schirdewan, Alexander; Kurths, Jürgen

2002-08-01

The knowledge of transitions between regular, laminar or chaotic behaviors is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods that, however, require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart-rate-variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e., chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our measures to the heart-rate-variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias.

Climate-driven unsteady denudation and sediment flux in a high-relief unglaciated catchment-fan using 26Al and 10Be: Panamint Valley, California

NASA Astrophysics Data System (ADS)

Mason, Cody C.; Romans, Brian W.

2018-06-01

Environmental changes within erosional catchments of sediment routing systems are predicted to modulate sediment transfer dynamics. However, empirical and numerical models that predict such phenomena are difficult to test in natural systems over multi-millennial timescales. Tectonic boundary conditions and climate history in the Panamint Range, California, are relatively well-constrained by existing low-temperature thermochronology and regional multi-proxy paleoclimate studies, respectively. Catchment-fan systems present there minimize sediment storage and recycling, offering an excellent natural laboratory to test models of climate-sedimentary dynamics. We used stratigraphic characterization and cosmogenic radionuclides (CRNs; 26Al and 10Be) in the Pleasant Canyon complex (PCC), a linked catchment-fan system, to examine the effects of Pleistocene high-magnitude, high-frequency climate change on CRN-derived denudation rates and sediment flux in a high-relief, unglaciated catchment-fan system. Calculated 26Al/10Be burial ages from 13 samples collected in an ∼180 m thick outcropping stratigraphic succession range from ca. 1.55 ± 0.22 Ma in basal strata, to ca. 0.36 ± 0.18-0.52 ± 0.20 Ma within the uppermost part of the succession. The mean long-term CRN-derived paleodenudation rate, 36 ± 8 mm/kyr (1σ), is higher than the modern rate of 24 ± 0.6 mm/kyr from Pleasant Canyon, and paleodenudation rates during the middle Pleistocene display some high-frequency variability in the high end (up to 54 ± 10 mm/kyr). The highest CRN-derived denudation rates are associated with stratigraphic evidence for increased precipitation during glacial-pluvial events after the middle Pleistocene transition (post ca. 0.75 Ma), suggesting 100 kyr Milankovitch periodicity could drive the observed variability. We investigated the potential for non-equilibrium sedimentary processes, i.e. increased landslides or sediment storage/recycling, to influence apparent paleodenudation rates; end-member mixing models suggest that a mixture of >50% low-CRN-concentration sediment from landslides is required to produce the largest observed increase in paleodenudation rate. The overall pattern of CRN-derived burial ages, paleodenudation rates, and stratigraphic facies suggests Milankovitch timescale climate transitions drive variability in catchment denudation rates and sediment flux, or alternatively that climate transitions affect sedimentary process regimes that result in measurable variability of CRN concentrations in unglaciated catchment-fan systems.

Simple predictions of maximum transport rate in unsaturated soil and rock

USGS Publications Warehouse

Nimmo, John R.

2007-01-01

In contrast with the extreme variability expected for water and contaminant fluxes in the unsaturated zone, evidence from 64 field tests of preferential flow indicates that the maximum transport speed Vmax, adjusted for episodicity of infiltration, deviates little from a geometric mean of 13 m/d. A model based on constant‐speed travel during infiltration pulses of actual or estimated duration can predict Vmax with approximate order‐of‐magnitude accuracy, irrespective of medium or travel distance, thereby facilitating such problems as the prediction of worst‐case contaminant traveltimes. The lesser variability suggests that preferential flow is subject to rate‐limiting mechanisms analogous to those that impose a terminal velocity on objects in free fall and to rate‐compensating mechanisms analogous to Le Chatlier's principle. A critical feature allowing such mechanisms to dominate may be the presence of interfacial boundaries confined by neither solid material nor capillary forces.

Development of a dynamic growth-death model for Escherichia coli O157:H7 in minimally processed leafy green vegetables.

PubMed

McKellar, Robin C; Delaquis, Pascal

2011-11-15

Escherichia coli O157:H7, an occasional contaminant of fresh produce, can present a serious health risk in minimally processed leafy green vegetables. A good predictive model is needed for Quantitative Risk Assessment (QRA) purposes, which adequately describes the growth or die-off of this pathogen under variable temperature conditions experienced during processing, storage and shipping. Literature data on behaviour of this pathogen on fresh-cut lettuce and spinach was taken from published graphs by digitization, published tables or from personal communications. A three-phase growth function was fitted to the data from 13 studies, and a square root model for growth rate (μ) as a function of temperature was derived: μ=(0.023*(Temperature-1.20))(2). Variability in the published data was incorporated into the growth model by the use of weighted regression and the 95% prediction limits. A log-linear die-off function was fitted to the data from 13 studies, and the resulting rate constants were fitted to a shifted lognormal distribution (Mean: 0.013; Standard Deviation, 0.010; Shift, 0.001). The combined growth-death model successfully predicted pathogen behaviour under both isothermal and non-isothermal conditions when compared to new published data. By incorporating variability, the resulting model is an improvement over existing ones, and is suitable for QRA applications. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Is a specific oncological scoring system better at predicting the prognosis of cancer patients admitted for an acute medical complication in an intensive care unit than general gravity scores?

PubMed

Berghmans, T; Paesmans, M; Sculier, J P

2004-04-01

To evaluate the effectiveness of a specific oncologic scoring system-the ICU Cancer Mortality model (ICM)-in predicting hospital mortality in comparison to two general severity scores-the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Simplified Acute Physiology Score (SAPS II). All 247 patients admitted for a medical acute complication over an 18-month period in an oncological medical intensive care unit were prospectively registered. Their data, including type of complication, vital status at discharge and cancer characteristics as well as other variables necessary to calculate the three scoring systems were retrospectively assessed. Observed in-hospital mortality was 34%. The predicted in-hospital mortality rate for APACHE II was 32%; SAPS II, 24%; and ICM, 28%. The goodness of fit was inadequate except for the ICM score. Comparison of the area under the ROC curves revealed a better fit for ICM (area 0.79). The maximum correct classification rate was 72% for APACHE II, 74% for SAPS II and 77% for ICM. APACHE II and SAPS II were better at predicting outcome for survivors to hospital discharge, although ICM was better for non-survivors. Two variables were independently predicting the risk of death during hospitalisation: ICM (OR=2.31) and SAPS II (OR=1.05). Gravity scores were the single independent predictors for hospital mortality, and ICM was equivalent to APACHE II and SAPS II.

Reducing Dropout in Treatment for Depression: Translating Dropout Predictors Into Individualized Treatment Recommendations.

PubMed

Zilcha-Mano, Sigal; Keefe, John R; Chui, Harold; Rubin, Avinadav; Barrett, Marna S; Barber, Jacques P

2016-12-01

Premature discontinuation of therapy is a widespread problem that hampers the delivery of mental health treatment. A high degree of variability has been found among rates of premature treatment discontinuation, suggesting that rates may differ depending on potential moderators. In the current study, our aim was to identify demographic and interpersonal variables that moderate the association between treatment assignment and dropout. Data from a randomized controlled trial conducted from November 2001 through June 2007 (N = 156) comparing supportive-expressive therapy, antidepressant medication, and placebo for the treatment of depression (based on DSM-IV criteria) were used. Twenty prerandomization variables were chosen based on previous literature. These variables were subjected to exploratory bootstrapped variable selection and included in the logistic regression models if they passed variable selection. Three variables were found to moderate the association between treatment assignment and dropout: age, pretreatment therapeutic alliance expectations, and the presence of vindictive tendencies in interpersonal relationships. When patients were divided into those randomly assigned to their optimal treatment and those assigned to their least optimal treatment, dropout rates in the optimal treatment group (24.4%) were significantly lower than those in the least optimal treatment group (47.4%; P = .03). Present findings suggest that a patient's age and pretreatment interpersonal characteristics predict the association between common depression treatments and dropout rate. If validated by further studies, these characteristics can assist in reducing dropout through targeted treatment assignment. Secondary analysis of data from ClinicalTrials.gov identifier: NCT00043550. © Copyright 2016 Physicians Postgraduate Press, Inc.

A Bayesian network approach for modeling local failure in lung cancer

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Craft, Jeffrey; Lozi, Rawan Al; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O.; Bradley, Jeffrey D.; El Naqa, Issam

2011-03-01

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

Predicting functional ability in mild cognitive impairment with the Dementia Rating Scale-2.

PubMed

Greenaway, Melanie C; Duncan, Noah L; Hanna, Sherrie; Smith, Glenn E

2012-06-01

We examined the utility of cognitive evaluation to predict instrumental activities of daily living (IADLs) and decisional ability in Mild Cognitive Impairment (MCI). Sixty-seven individuals with single-domain amnestic MCI were administered the Dementia Rating Scale-2 (DRS-2) as well as the Everyday Cognition assessment form to assess functional ability. The DRS-2 Total Scores and Initiation/Perseveration and Memory subscales were found to be predictive of IADLs, with Total Scores accounting for 19% of the variance in IADL performance on average. In addition, the DRS-2 Initiation/Perseveration and Total Scores were predictive of ability to understand information, and the DRS-2 Conceptualization helped predict ability to communicate with others, both key variables in decision-making ability. These findings suggest that performance on the DRS-2, and specific subscales related to executive function and memory, is significantly related to IADLs in individuals with MCI. These cognitive measures are also associated with decision-making-related abilities in MCI.

Empirical Leucine-to-Carbon Conversion Factors for Estimating Heterotrophic Bacterial Production: Seasonality and Predictability in a Temperate Coastal Ecosystem▿

PubMed Central

Calvo-Díaz, Alejandra; Morán, Xosé Anxelu G.

2009-01-01

Leucine-to-carbon conversion factors (CFs) are needed for converting substrate incorporation into biomass production of heterotrophic bacteria. During 2006 we performed 20 dilution experiments for determining the spatiotemporal variability of empirical CFs in temperate Atlantic coastal waters. Values (0.49 to 1.92 kg C mol Leu−1) showed maxima in autumn to early winter and minima in summer. Spatially averaged CFs were significantly negatively correlated with in situ leucine incorporation rates (r = −0.91) and positively correlated with phosphate concentrations (r = 0.76). These relationships, together with a strong positive covariation between cell-specific leucine incorporation rates and carbon contents (r = 0.85), were interpreted as a strategy to maximize survival through protein synthesis and low growth rates under nutrient limitation (low CFs) until favorable conditions stimulate cell division relative to protein synthesis (high CFs). A multiple regression with in situ leucine incorporation rates and cellular carbon contents explained 96% of CF variance in our ecosystem, suggesting their potential prediction from more easily measurable routine variables. The use of the theoretical CF of 1.55 kg C mol Leu−1 would have resulted in a serious overestimation (73%) of annual bacterial production rates. Our results emphasize the need for considering the temporal scale in CFs for bacterial production studies. PMID:19304821

Predictors of long-term compliance in attending a worksite hypertension programme.

PubMed

Landers, R; Riccobene, A; Beyreuther, M; Neusy, A J

1993-12-01

Variables such as patient's anxiety, knowledge, number of medication changes, medication-induced side-effects and programme-derived benefits and conveniences have been reported or theorised to be important determinants of patient's attendance at worksite hypertension programmes. This study investigates whether these variables have predictive value in differentiating compliers from noncompliers attending a union-sponsored worksite hypertension programme for at least five years. Scores were created from a questionnaire distributed to 243 patients with a response rate of 98%. Compliance was defined as missing < or = 25% of scheduled clinic appointments. By discriminant statistical analysis scores for patient's anxiety, knowledge, number of medication changes, medication side-effects, perceived benefits and conveniences failed to show any predictive value for patient's compliance with appointment keeping.

Energy expenditure during barbiturate coma.

PubMed

Ashcraft, Christine M; Frankenfield, David C

2013-10-01

Barbiturate coma may have a significant effect on metabolic rate, but the phenomenon is not extensively studied. The primary purpose of the current study was to compare the metabolic rate of general critical care patients with those requiring barbiturate coma. A secondary purpose was to evaluate the accuracy of the Penn State prediction equation between these 2 groups of patients. Indirect calorimetry was used to measure the resting metabolic rate of mechanically ventilated, critically ill patients in a barbiturate coma and those of similar height, weight, and age but not in a barbiturate coma. Measurements of resting metabolic rate were compared with predictions using the Penn State equation accounting for body size, body temperature, and minute ventilation. The barbiturate coma group had a lower resting metabolic rate than the control group that remained lower even after adjustment for predicted healthy metabolic rate and maximum body temperature (1859 ± 290 vs 2037 ± 289 kcal/d, P = .020). When minute ventilation was also included in the analysis, the resting metabolic rate between the groups became statistically insignificant (1929 ± 229 vs 2023 ± 226 kcal/d, P = .142). The Penn State equation, which uses these variables, was accurate in 73% of the control patients and also the barbiturate coma patients. Resting metabolic rate is moderately reduced in barbiturate coma, but the decrease is out of proportion with changes in body temperature. However, if both body temperature and minute ventilation are considered, then the change is predictable.

Rotary engine performance limits predicted by a zero-dimensional model

NASA Technical Reports Server (NTRS)

Bartrand, Timothy A.; Willis, Edward A.

1992-01-01

A parametric study was performed to determine the performance limits of a rotary combustion engine. This study shows how well increasing the combustion rate, insulating, and turbocharging increase brake power and decrease fuel consumption. Several generalizations can be made from the findings. First, it was shown that the fastest combustion rate is not necessarily the best combustion rate. Second, several engine insulation schemes were employed for a turbocharged engine. Performance improved only for a highly insulated engine. Finally, the variability of turbocompounding and the influence of exhaust port shape were calculated. Rotary engines performance was predicted by an improved zero-dimensional computer model based on a model developed at the Massachusetts Institute of Technology in the 1980's. Independent variables in the study include turbocharging, manifold pressures, wall thermal properties, leakage area, and exhaust port geometry. Additions to the computer programs since its results were last published include turbocharging, manifold modeling, and improved friction power loss calculation. The baseline engine for this study is a single rotor 650 cc direct-injection stratified-charge engine with aluminum housings and a stainless steel rotor. Engine maps are provided for the baseline and turbocharged versions of the engine.

A simulation study of capacity utilization to predict future capacity for manufacturing system sustainability

NASA Astrophysics Data System (ADS)

Rimo, Tan Hauw Sen; Chai Tin, Ong

2017-12-01

Capacity utilization (CU) measurement is an important task in a manufacturing system, especially in make-to-order (MTO) type manufacturing system with product customization, in predicting capacity to meet future demand. A stochastic discrete-event simulation is developed using ARENA software to determine CU and capacity gap (CG) in short run production function. This study focused on machinery breakdown and product defective rate as random variables in the simulation. The study found that the manufacturing system run in 68.01% CU and 31.99% CG. It is revealed that machinery breakdown and product defective rate have a direct relationship with CU. By improving product defective rate into zero defect, manufacturing system can improve CU up to 73.56% and CG decrease to 26.44%. While improving machinery breakdown into zero breakdowns will improve CU up to 93.99% and the CG decrease to 6.01%. This study helps operation level to study CU using “what-if” analysis in order to meet future demand in more practical and easier method by using simulation approach. Further study is recommended by including other random variables that affect CU to make the simulation closer with the real-life situation for a better decision.

Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate.

PubMed

Switanek, Matthew; Crailsheim, Karl; Truhetz, Heimo; Brodschneider, Robert

2017-02-01

Insect pollinators are essential to global food production. For this reason, it is alarming that honey bee (Apis mellifera) populations across the world have recently seen increased rates of mortality. These changes in colony mortality are often ascribed to one or more factors including parasites, diseases, pesticides, nutrition, habitat dynamics, weather and/or climate. However, the effect of climate on colony mortality has never been demonstrated. Therefore, in this study, we focus on longer-term weather conditions and/or climate's influence on honey bee winter mortality rates across Austria. Statistical correlations between monthly climate variables and winter mortality rates were investigated. Our results indicate that warmer and drier weather conditions in the preceding year were accompanied by increased winter mortality. We subsequently built a statistical model to predict colony mortality using temperature and precipitation data as predictors. Our model reduces the mean absolute error between predicted and observed colony mortalities by 9% and is statistically significant at the 99.9% confidence level. This is the first study to show clear evidence of a link between climate variability and honey bee winter mortality. Copyright © 2016 British Geological Survey, NERC. Published by Elsevier B.V. All rights reserved.

Risk-adjusted capitation based on the Diagnostic Cost Group Model: an empirical evaluation with health survey information.

PubMed Central

Lamers, L M

1999-01-01

OBJECTIVE: To evaluate the predictive accuracy of the Diagnostic Cost Group (DCG) model using health survey information. DATA SOURCES/STUDY SETTING: Longitudinal data collected for a sample of members of a Dutch sickness fund. In the Netherlands the sickness funds provide compulsory health insurance coverage for the 60 percent of the population in the lowest income brackets. STUDY DESIGN: A demographic model and DCG capitation models are estimated by means of ordinary least squares, with an individual's annual healthcare expenditures in 1994 as the dependent variable. For subgroups based on health survey information, costs predicted by the models are compared with actual costs. Using stepwise regression procedures a subset of relevant survey variables that could improve the predictive accuracy of the three-year DCG model was identified. Capitation models were extended with these variables. DATA COLLECTION/EXTRACTION METHODS: For the empirical analysis, panel data of sickness fund members were used that contained demographic information, annual healthcare expenditures, and diagnostic information from hospitalizations for each member. In 1993, a mailed health survey was conducted among a random sample of 15,000 persons in the panel data set, with a 70 percent response rate. PRINCIPAL FINDINGS: The predictive accuracy of the demographic model improves when it is extended with diagnostic information from prior hospitalizations (DCGs). A subset of survey variables further improves the predictive accuracy of the DCG capitation models. The predictable profits and losses based on survey information for the DCG models are smaller than for the demographic model. Most persons with predictable losses based on health survey information were not hospitalized in the preceding year. CONCLUSIONS: The use of diagnostic information from prior hospitalizations is a promising option for improving the demographic capitation payment formula. This study suggests that diagnostic information from outpatient utilization is complementary to DCGs in predicting future costs. PMID:10029506

Growth and food consumption by tiger muskellunge: Effects of temperature and ration level on bioenergetic model predictions

USGS Publications Warehouse

Chipps, S.R.; Einfalt, L.M.; Wahl, David H.

2000-01-01

We measured growth of age-0 tiger muskellunge as a function of ration size (25, 50, 75, and 100% C(max))and water temperature (7.5-25??C) and compared experimental results with those predicted from a bioenergetic model. Discrepancies between actual and predicted values varied appreciably with water temperature and growth rate. On average, model output overestimated winter consumption rates at 10 and 7.5??C by 113 to 328%, respectively, whereas model predictions in summer and autumn (20-25??C) were in better agreement with actual values (4 to 58%). We postulate that variation in model performance was related to seasonal changes in esocid metabolic rate, which were not accounted for in the bioenergetic model. Moreover, accuracy of model output varied with feeding and growth rate of tiger muskellunge. The model performed poorly for fish fed low rations compared with estimates based on fish fed ad libitum rations and was attributed, in part, to the influence of growth rate on the accuracy of bioenergetic predictions. Based on modeling simulations, we found that errors associated with bioenergetic parameters had more influence on model output when growth rate was low, which is consistent with our observations. In addition, reduced conversion efficiency at high ration levels may contribute to variable model performance, thereby implying that waste losses should be modeled as a function of ration size for esocids. Our findings support earlier field tests of the esocid bioenergetic model and indicate that food consumption is generally overestimated by the model, particularly in winter months and for fish exhibiting low feeding and growth rates.

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study.

PubMed

Escarela, Gabriel

2012-06-01

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.

The Role of Social-Cognitive and Emotional Factors on Exclusive Breastfeeding Duration.

PubMed

Shepherd, Lee; Walbey, Cherokee; Lovell, Brian

2017-08-01

Previous research has suggested that exclusive breastfeeding is likely to be predicted by social-cognitive variables and fear. However, there is little research assessing the role of regret and self-conscious emotions (e.g., pride and guilt) in promoting exclusive breastfeeding. Research aim: The primary aim of this research was to determine whether social-cognitive variables, fear, regret, and self-conscious emotions predict exclusive breastfeeding duration. The secondary aim of this research was to assess whether these factors predict infant-feeding choice (i.e., exclusively breastfed, combination fed, or generally formula fed). In this nonexperimental one-group self-report survey, 375 mothers rated social-cognitive variables toward breastfeeding (attitude, subjective norm, perceived control, and self-efficacy), their fear toward inadequate nutrition from breastfeeding and breastfeeding damaging their physical appearance, and the extent to which mothers may feel pride toward breastfeeding and negative self-conscious emotions (guilt and shame) and regret for not breastfeeding their infant. Exclusive breastfeeding duration was positively predicted by self-efficacy, pride, and regret but negatively predicted by the fear toward inadequate nutrition. We also found that in contrast with exclusive breastfeeding, generally formula feeding an infant was associated with lower self-efficacy, pride, and regret but higher subjective norm and fear toward inadequate nutrition through breastfeeding. The authors argue that it is important to consider the role of self-conscious emotions and regret on exclusive breastfeeding.

Improved prediction of biochemical recurrence after radical prostatectomy by genetic polymorphisms.

PubMed

Morote, Juan; Del Amo, Jokin; Borque, Angel; Ars, Elisabet; Hernández, Carlos; Herranz, Felipe; Arruza, Antonio; Llarena, Roberto; Planas, Jacques; Viso, María J; Palou, Joan; Raventós, Carles X; Tejedor, Diego; Artieda, Marta; Simón, Laureano; Martínez, Antonio; Rioja, Luis A

2010-08-01

Single nucleotide polymorphisms are inherited genetic variations that can predispose or protect individuals against clinical events. We hypothesized that single nucleotide polymorphism profiling may improve the prediction of biochemical recurrence after radical prostatectomy. We performed a retrospective, multi-institutional study of 703 patients treated with radical prostatectomy for clinically localized prostate cancer who had at least 5 years of followup after surgery. All patients were genotyped for 83 prostate cancer related single nucleotide polymorphisms using a low density oligonucleotide microarray. Baseline clinicopathological variables and single nucleotide polymorphisms were analyzed to predict biochemical recurrence within 5 years using stepwise logistic regression. Discrimination was measured by ROC curve AUC, specificity, sensitivity, predictive values, net reclassification improvement and integrated discrimination index. The overall biochemical recurrence rate was 35%. The model with the best fit combined 8 covariates, including the 5 clinicopathological variables prostate specific antigen, Gleason score, pathological stage, lymph node involvement and margin status, and 3 single nucleotide polymorphisms at the KLK2, SULT1A1 and TLR4 genes. Model predictive power was defined by 80% positive predictive value, 74% negative predictive value and an AUC of 0.78. The model based on clinicopathological variables plus single nucleotide polymorphisms showed significant improvement over the model without single nucleotide polymorphisms, as indicated by 23.3% net reclassification improvement (p = 0.003), integrated discrimination index (p <0.001) and likelihood ratio test (p <0.001). Internal validation proved model robustness (bootstrap corrected AUC 0.78, range 0.74 to 0.82). The calibration plot showed close agreement between biochemical recurrence observed and predicted probabilities. Predicting biochemical recurrence after radical prostatectomy based on clinicopathological data can be significantly improved by including patient genetic information. Copyright (c) 2010 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Numerical Analysis of an Impinging Jet Reactor for the CVD and Gas-Phase Nucleation of Titania

NASA Technical Reports Server (NTRS)

Gokoglu, Suleyman A.; Stewart, Gregory D.; Collins, Joshua; Rosner, Daniel E.

1994-01-01

We model a cold-wall atmospheric pressure impinging jet reactor to study the CVD and gas-phase nucleation of TiO2 from a titanium tetra-iso-propoxide (TTIP)/oxygen dilute source gas mixture in nitrogen. The mathematical model uses the computational code FIDAP and complements our recent asymptotic theory for high activation energy gas-phase reactions in thin chemically reacting sublayers. The numerical predictions highlight deviations from ideality in various regions inside the experimental reactor. Model predictions of deposition rates and the onset of gas-phase nucleation compare favorably with experiments. Although variable property effects on deposition rates are not significant (approximately 11 percent at 1000 K), the reduction rates due to Soret transport is substantial (approximately 75 percent at 1000 K).

Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates

NASA Astrophysics Data System (ADS)

Teneva, Lida; Karnauskas, Mandy; Logan, Cheryl A.; Bianucci, Laura; Currie, Jock C.; Kleypas, Joan A.

2012-03-01

Sea surface temperature fields (1870-2100) forced by CO2-induced climate change under the IPCC SRES A1B CO2 scenario, from three World Climate Research Programme Coupled Model Intercomparison Project Phase 3 (WCRP CMIP3) models (CCSM3, CSIRO MK 3.5, and GFDL CM 2.1), were used to examine how coral sensitivity to thermal stress and rates of adaption affect global projections of coral-reef bleaching. The focus of this study was two-fold, to: (1) assess how the impact of Degree-Heating-Month (DHM) thermal stress threshold choice affects potential bleaching predictions and (2) examine the effect of hypothetical adaptation rates of corals to rising temperature. DHM values were estimated using a conventional threshold of 1°C and a variability-based threshold of 2σ above the climatological maximum Coral adaptation rates were simulated as a function of historical 100-year exposure to maximum annual SSTs with a dynamic rather than static climatological maximum based on the previous 100 years, for a given reef cell. Within CCSM3 simulations, the 1°C threshold predicted later onset of mild bleaching every 5 years for the fraction of reef grid cells where 1°C > 2σ of the climatology time series of annual SST maxima (1961-1990). Alternatively, DHM values using both thresholds, with CSIRO MK 3.5 and GFDL CM 2.1 SSTs, did not produce drastically different onset timing for bleaching every 5 years. Across models, DHMs based on 1°C thermal stress threshold show the most threatened reefs by 2100 could be in the Central and Western Equatorial Pacific, whereas use of the variability-based threshold for DHMs yields the Coral Triangle and parts of Micronesia and Melanesia as bleaching hotspots. Simulations that allow corals to adapt to increases in maximum SST drastically reduce the rates of bleaching. These findings highlight the importance of considering the thermal stress threshold in DHM estimates as well as potential adaptation models in future coral bleaching projections.

Research of Water Level Prediction for a Continuous Flood due to Typhoons Based on a Machine Learning Method

NASA Astrophysics Data System (ADS)

Nakatsugawa, M.; Kobayashi, Y.; Okazaki, R.; Taniguchi, Y.

2017-12-01

This research aims to improve accuracy of water level prediction calculations for more effective river management. In August 2016, Hokkaido was visited by four typhoons, whose heavy rainfall caused severe flooding. In the Tokoro river basin of Eastern Hokkaido, the water level (WL) at the Kamikawazoe gauging station, which is at the lower reaches exceeded the design high-water level and the water rose to the highest level on record. To predict such flood conditions and mitigate disaster damage, it is necessary to improve the accuracy of prediction as well as to prolong the lead time (LT) required for disaster mitigation measures such as flood-fighting activities and evacuation actions by residents. There is the need to predict the river water level around the peak stage earlier and more accurately. Previous research dealing with WL prediction had proposed a method in which the WL at the lower reaches is estimated by the correlation with the WL at the upper reaches (hereinafter: "the water level correlation method"). Additionally, a runoff model-based method has been generally used in which the discharge is estimated by giving rainfall prediction data to a runoff model such as a storage function model and then the WL is estimated from that discharge by using a WL discharge rating curve (H-Q curve). In this research, an attempt was made to predict WL by applying the Random Forest (RF) method, which is a machine learning method that can estimate the contribution of explanatory variables. Furthermore, from the practical point of view, we investigated the prediction of WL based on a multiple correlation (MC) method involving factors using explanatory variables with high contribution in the RF method, and we examined the proper selection of explanatory variables and the extension of LT. The following results were found: 1) Based on the RF method tuned up by learning from previous floods, the WL for the abnormal flood case of August 2016 was properly predicted with a lead time of 6 h. 2) Based on the contribution of explanatory variables, factors were selected for the MC method. In this way, plausible prediction results were obtained.

Emotion dysregulation in alexithymia: Startle reactivity to fearful affective imagery and its relation to heart rate variability.

PubMed

Panayiotou, Georgia; Constantinou, Elena

2017-09-01

Alexithymia is associated with deficiencies in recognizing and expressing emotions and impaired emotion regulation, though few studies have verified the latter assertion using objective measures. This study examined startle reflex modulation by fearful imagery and its associations with heart rate variability in alexithymia. Fifty-four adults (27 alexithymic) imagined previously normed fear scripts. Startle responses were assessed during baseline, first exposure, and reexposure. During first exposure, participants, in separate trials, engaged in either shallow or deep emotion processing, giving emphasis on descriptive or affective aspects of imagery, respectively. Resting heart rate variability was assessed during 2 min of rest prior to the experiment, with high alexithymic participants demonstrating significantly higher LF/HF (low frequency/high frequency) ratio than controls. Deep processing was associated with nonsignificantly larger and faster startle responses at first exposure for alexithymic participants. Lower LF/HF ratio, reflecting higher parasympathetic cardiac activity, predicted greater startle amplitude habituation for alexithymia but lower habituation for controls. Results suggest that, when exposed to prolonged threat, alexithymics may adjust poorly, showing a smaller initial defensive response but slower habituation. This pattern seems related to their low emotion regulation ability as indexed by heart rate variability. © 2017 Society for Psychophysiological Research.

Climate controls photosynthetic capacity more than leaf nitrogen contents

NASA Astrophysics Data System (ADS)

Ali, A. A.; Xu, C.; McDowell, N. G.

2013-12-01

Global vegetation models continue to lack the ability to make reliable predictions because the photosynthetic capacity varies a lot with growth conditions, season and among species. It is likely that vegetation models link photosynthetic capacity to concurrent changes in leaf nitrogen content only. To improve the predictions of the vegetation models, there is an urgent need to review species growth conditions and their seasonal response to changing climate. We sampled the global distribution of the Vcmax (maximum carboxylation rates) data of various species across different environmental gradients from the literature and standardized its value to 25 degree Celcius. We found that species explained the largest variation in (1) the photosynthetic capacity and (2) the proportion of nitrogen allocated for rubisco (PNcb). Surprisingly, climate variables explained more variations in photosynthetic capacity as well as PNcb than leaf nitrogen content and/or specific leaf area. The chief climate variables that explain variation in photosynthesis and PNcb were radiation, temperature and daylength. Our analysis suggests that species have the greatest control over photosynthesis and PNcb. Further, compared to leaf nitrogen content and/or specific leaf area, climate variables have more control over photosynthesis and PNcb. Therefore, climate variables should be incorporated in the global vegetation models when making predictions about the photosynthetic capacity.

Day-to-Day Variability in Self-Reported Cigarettes Per Day.

PubMed

Hughes, John R; Shiffman, Saul; Naud, Shelly; Peters, Erica N

2017-09-01

Nicotine addiction theory predicts small day-to-day variability in cigarettes/day (CPD) whereas social learning theory predicts large variability. A description of the variability in CPD over multiple days is not available. We conducted secondary analyses of two natural history studies with daily smokers-one of smokers not intending to quit, and one of smokers intending to quit sometime in the next 3 months. In the former, smokers recorded their smoking during the day by Ecological Momentary Assessment, using a palm-top computer. In the latter, participants reported CPD nightly via a phone Interactive Voice Response system. Analyses were based on smokers who reported averaging ≥10 CPD, and on days in which there was no attempt to stop or reduce smoking. Across the two studies, on average, smokers had small changes in day-to-day CPD (mean changes were 2.2 and 2.9 CPD). However a minority averaged changing by ≥5 CPD from one day to the next (7% and 11%), and many changed by ≥5 CPD on at least 10 of the 90 days (8% and 31%). Neither smoking restrictions, dependence, stereotypy ratings, nor interest in quitting predicted variability. Although on average, smokers have little change day-to-day CPD, a substantial minority of smokers often change by 5 CPD from day-to-day. We did not find potential causes of this variability. Across day variability in CPD is larger than implied in prior studies. Determining causes of day-to-day variability should increase our understanding of the determinants of smoking. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Effects of Corporate Social Responsibility and Governance on Its Credit Ratings

PubMed Central

Kim, Dong-young

2014-01-01

This study reviews the impact of corporate social responsibility (CSR) and corporate governance on its credit rating. The result of regression analysis to credit ratings with relevant primary independent variables shows that both factors have significant effects on it. As we have predicted, the signs of both regression coefficients have a positive sign (+) proving that corporates with excellent CSR and governance index (CGI) scores have higher credit ratings and vice versa. The results show nonfinancial information also may have effects on corporate credit rating. The investment on personal data protection could be an example of CSR/CGI activities which have positive effects on corporate credit ratings. PMID:25401134

Effects of corporate social responsibility and governance on its credit ratings.

PubMed

Kim, Dong-young; Kim, JeongYeon

2014-01-01

This study reviews the impact of corporate social responsibility (CSR) and corporate governance on its credit rating. The result of regression analysis to credit ratings with relevant primary independent variables shows that both factors have significant effects on it. As we have predicted, the signs of both regression coefficients have a positive sign (+) proving that corporates with excellent CSR and governance index (CGI) scores have higher credit ratings and vice versa. The results show nonfinancial information also may have effects on corporate credit rating. The investment on personal data protection could be an example of CSR/CGI activities which have positive effects on corporate credit ratings.

A Kinetic Model Describing Injury-Burden in Team Sports.

PubMed

Fuller, Colin W

2017-12-01

Injuries in team sports are normally characterised by the incidence, severity, and location and type of injuries sustained: these measures, however, do not provide an insight into the variable injury-burden experienced during a season. Injury burden varies according to the team's match and training loads, the rate at which injuries are sustained and the time taken for these injuries to resolve. At the present time, this time-based variation of injury burden has not been modelled. To develop a kinetic model describing the time-based injury burden experienced by teams in elite team sports and to demonstrate the model's utility. Rates of injury were quantified using a large eight-season database of rugby injuries (5253) and exposure (60,085 player-match-hours) in English professional rugby. Rates of recovery from injury were quantified using time-to-recovery analysis of the injuries. The kinetic model proposed for predicting a team's time-based injury burden is based on a composite rate equation developed from the incidence of injury, a first-order rate of recovery from injury and the team's playing load. The utility of the model was demonstrated by examining common scenarios encountered in elite rugby. The kinetic model developed describes and predicts the variable injury-burden arising from match play during a season of rugby union based on the incidence of match injuries, the rate of recovery from injury and the playing load. The model is equally applicable to other team sports and other scenarios.

Trial-to-trial fluctuations in attentional state and their relation to intelligence.

PubMed

Unsworth, Nash; McMillan, Brittany D

2014-05-01

Trial-to-trial fluctuations in attentional state while performing measures of intelligence were examined in the current study. Participants performed various measures of fluid and crystallized intelligence while also providing attentional state ratings prior to each trial. It was found that pre-trial attentional state ratings strongly predicted subsequent trial performance on the fluid intelligence measures, such that when participants rated their current attentional state as highly focused on the current task, performance tended to be high compared to when participants reported their current attentional state as being low and unfocused on the current task. Furthermore, overall attentional state ratings and variability in attentional state ratings were moderately correlated with overall levels of performance on the fluid intelligence measures. However, attentional state ratings did not predict performance on the measure of crystallized intelligence. These results suggest a strong link between variation in attention state and variation in fluid intelligence as postulated by a number of recent theories. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Predicting umbilical artery pH during labour: Development and validation of a nomogram using fetal heart rate patterns.

PubMed

Ramanah, Rajeev; Omar, Sikiyah; Guillien, Alicia; Pugin, Aurore; Martin, Alain; Riethmuller, Didier; Mottet, Nicolas

2018-06-01

Nomograms are statistical models that combine variables to obtain the most accurate and reliable prediction for a particular risk. Fetal heart rate (FHR) interpretation alone has been found to be poorly predictive for fetal acidosis while other clinical risk factors exist. The aim of this study was to create and validate a nomogram based on FHR patterns and relevant clinical parameters to provide a non-invasive individualized prediction of umbilical artery pH during labour. A retrospective observational study was conducted on 4071 patients in labour presenting singleton pregnancies at >34 gestational weeks and delivering vaginally. Clinical characteristics, FHR patterns and umbilical cord gas of 1913 patients were used to construct a nomogram predicting an umbilical artery (Ua) pH <7.18 (10th centile of the study population) after an univariate and multivariate stepwise logistic regression analysis. External validation was obtained from an independent cohort of 2158 patients. Area under the receiver operating characteristics (ROC) curve, sensitivity, specificity, positive and negative predictive values of the nomogram were determined. Upon multivariate analysis, parity (p < 0.01), induction of labour (p = 0.01), a prior uterine scar (p = 0.02), maternal fever (p = 0.02) and the type of FHR (p < 0.01) were significantly associated with an Ua pH <7.18 (p < 0.05). Apgar score at 1, 5 and 10 min were significantly lower in the group with an Ua pH <7.18 (p < 0.01). The nomogram constructed had a Concordance Index of 0.75 (area under the curve) with a sensitivity of 57%, a specificity of 91%, a negative predictive value of 5% and a positive predictive value of 99%. Calibration found no difference between the predicted probabilities and the observed rate of Ua pH <7.18 (p = 0.63). The validation set had a Concordance Index of 0.72 and calibration with a p < 0.77. We successfully developed and validated a nomogram to predict Ua pH by combining easily available clinical variables and FHR. Discrimination and calibration of the model were statistically good. This mathematical tool can help clinicians in the management of labour by predicting umbilical artery pH based on FHR tracings. Copyright © 2018 Elsevier B.V. All rights reserved.

Spectral multigrid methods for the solution of homogeneous turbulence problems

NASA Technical Reports Server (NTRS)

Erlebacher, G.; Zang, T. A.; Hussaini, M. Y.

1987-01-01

New three-dimensional spectral multigrid algorithms are analyzed and implemented to solve the variable coefficient Helmholtz equation. Periodicity is assumed in all three directions which leads to a Fourier collocation representation. Convergence rates are theoretically predicted and confirmed through numerical tests. Residual averaging results in a spectral radius of 0.2 for the variable coefficient Poisson equation. In general, non-stationary Richardson must be used for the Helmholtz equation. The algorithms developed are applied to the large-eddy simulation of incompressible isotropic turbulence.

Application of Statistic Experimental Design to Assess the Effect of Gammairradiation Pre-Treatment on the Drying Characteristics and Qualities of Wheat

NASA Astrophysics Data System (ADS)

Yu, Yong; Wang, Jun

Wheat, pretreated by 60Co gamma irradiation, was dried by hot-air with irradiation dosage 0-3 kGy, drying temperature 40-60 °C, and initial moisture contents 19-25% (drying basis). The drying characteristics and dried qualities of wheat were evaluated based on drying time, average dehydration rate, wet gluten content (WGC), moisture content of wet gluten (MCWG)and titratable acidity (TA). A quadratic rotation-orthogonal composite experimental design, with three variables (at five levels) and five response functions, and analysis method were employed to study the effect of three variables on the individual response functions. The five response functions (drying time, average dehydration rate, WGC, MCWG, TA) correlated with these variables by second order polynomials consisting of linear, quadratic and interaction terms. A high correlation coefficient indicated the suitability of the second order polynomial to predict these response functions. The linear, interaction and quadratic effects of three variables on the five response functions were all studied.

Use of the partial farm budget technique to predict the economic impact of the flock management decision to use B-mode ultrasonographic pregnancy diagnosis.

PubMed

Sprecher, D J; Ley, W B; Whittier, W D; Bowen, J M; Thatcher, C D; Pelzer, K D; Moore, J M

1989-07-15

A computer spreadsheet was developed to predict the economic impact of a management decision to use B-mode ultrasonographic ovine pregnancy diagnosis. The spreadsheet design and spreadsheet cell formulas are provided. The program used the partial farm budget technique to calculate net return (NR) or cash flow changes that resulted from the decision to use ultrasonography. Using the program, either simple pregnancy diagnosis or pregnancy diagnosis with the ability to determine singleton or multiple pregnancies may be compared with no flock ultrasonographic pregnancy diagnosis. A wide range of user-selected regional variables are used to calculate the cash flow changes associated with the ultrasonography decisions. A variable may be altered through a range of values to conduct a sensitivity analysis of predicted NR. Example sensitivity analyses are included for flock conception rate, veterinary ultrasound fee, and the price of corn. Variables that influence the number of cull animals and the cost of ultrasonography have the greatest impact on predicted NR. Because the determination of singleton or multiple pregnancies is more time consuming, its economic practicality in comparison with simple pregnancy diagnosis is questionable. The value of feed saved by identifying and separately feeding ewes with singleton pregnancies is not offset by the increased ultrasonography cost.

Identifying black swans in NextGen: predicting human performance in off-nominal conditions.

PubMed

Wickens, Christopher D; Hooey, Becky L; Gore, Brian F; Sebok, Angelia; Koenicke, Corey S

2009-10-01

The objective is to validate a computational model of visual attention against empirical data--derived from a meta-analysis--of pilots' failure to notice safety-critical unexpected events. Many aircraft accidents have resulted, in part, because of failure to notice nonsalient unexpected events outside of foveal vision, illustrating the phenomenon of change blindness. A model of visual noticing, N-SEEV (noticing-salience, expectancy, effort, and value), was developed to predict these failures. First, 25 studies that reported objective data on miss rate for unexpected events in high-fidelity cockpit simulations were identified, and their miss rate data pooled across five variables (phase of flight, event expectancy, event location, presence of a head-up display, and presence of a highway-in-the-sky display). Second, the parameters of the N-SEEV model were tailored to mimic these dichotomies. The N-SEEV model output predicted variance in the obtained miss rate (r = .73). The individual miss rates of all six dichotomous conditions were predicted within 14%, and four of these were predicted within 7%. The N-SEEV model, developed on the basis of an independent data set, was able to successfully predict variance in this safety-critical measure of pilot response to abnormal circumstances, as collected from the literature. As new technology and procedures are envisioned for the future airspace, it is important to predict if these may compromise safety in terms of pilots' failing to notice unexpected events. Computational models such as N-SEEV support cost-effective means of making such predictions.

Longitudinal follow-up of fibrosing interstitial pneumonia: relationship between physiologic testing, computed tomography changes, and survival rate.

PubMed

Hwang, Jeong-Hwa; Misumi, Shigeki; Curran-Everett, Douglas; Brown, Kevin K; Sahin, Hakan; Lynch, David A

2011-08-01

The aim of this study was to evaluate the prognostic implications of computed tomography (CT) and physiologic variables at baseline and on sequential evaluation in patients with fibrosing interstitial pneumonia. We identified 72 patients with fibrosing interstitial pneumonia (42 with idiopathic disease, 30 with collagen vascular disease). Pulmonary function tests and CT were performed at the time of diagnosis and at a median follow-up of 12 months, respectively. Two chest radiologists scored the extent of specific abnormalities and overall disease on baseline and follow-up CT. Rate of survival was estimated using the Kaplan-Meier method. Three Cox proportional hazards models were constructed to evaluate the relationship between CT and physiologic variables and rate of survival: model 1 included only baseline variables, model 2 included only serial change variables, and model 3 included both baseline and serial change variables. On follow-up CT, the extent of mixed ground-glass and reticular opacities (P<0.001), pure reticular opacity (P=0.04), honeycombing (P=0.02), and overall extent of disease (P<0.001) was increased in the idiopathic group, whereas these variables remained unchanged in the collagen vascular disease group. Patients with idiopathic disease had a shorter rate of survival than those with collagen vascular disease (P=0.03). In model 1, the extent of honeycombing on baseline CT was the only independent predictor of mortality (P=0.02). In model 2, progression in honeycombing was the only predictor of mortality (P=0.005). In model 3, baseline extent of honeycombing and progression of honeycombing were the only independent predictors of mortality (P=0.001 and 0.002, respectively). Neither baseline nor serial change physiologic variables, nor the presence of collagen vascular disease, was predictive of rate of survival. The extent of honeycombing at baseline and its progression on follow-up CT are important determinants of rate of survival in patients with fibrosing interstitial pneumonia.

Logistic Risk Model for the Unique Effects of Inherent Aerobic Capacity on (+)G(sub z) Tolerance Before and After Simulated Weightlessness

NASA Technical Reports Server (NTRS)

Ludwig, David A.; Convertino, Victor A.; Goldwater, Danielle J.; Sandler, Harold

1987-01-01

Small sample size (n less than 1O) and inappropriate analysis of multivariate data have hindered previous attempts to describe which physiologic and demographic variables are most important in determining how long humans can tolerate acceleration. Data from previous centrifuge studies conducted at NASA/Ames Research Center, utilizing a 7-14 d bed rest protocol to simulate weightlessness, were included in the current investigation. After review, data on 25 women and 22 men were available for analysis. Study variables included gender, age, weight, height, percent body fat, resting heart rate, mean arterial pressure, Vo(sub 2)max and plasma volume. Since the dependent variable was time to greyout (failure), two contemporary biostatistical modeling procedures (proportional hazard and logistic discriminant function) were used to estimate risk, given a particular subject's profile. After adjusting for pro-bed-rest tolerance time, none of the profile variables remained in the risk equation for post-bed-rest tolerance greyout. However, prior to bed rest, risk of greyout could be predicted with 91% accuracy. All of the profile variables except weight, MAP, and those related to inherent aerobic capacity (Vo(sub 2)max, percent body fat, resting heart rate) entered the risk equation for pro-bed-rest greyout. A cross-validation using 24 new subjects indicated a very stable model for risk prediction, accurate within 5% of the original equation. The result for the inherent fitness variables is significant in that a consensus as to whether an increased aerobic capacity is beneficial or detrimental has not been satisfactorily established. We conclude that tolerance to +Gz acceleration before and after simulated weightlessness is independent of inherent aerobic fitness.

Risk stratification in middle-aged patients with congestive heart failure: prospective comparison of the Heart Failure Survival Score (HFSS) and a simplified two-variable model.

PubMed

Zugck, C; Krüger, C; Kell, R; Körber, S; Schellberg, D; Kübler, W; Haass, M

2001-10-01

The performance of a US-American scoring system (Heart Failure Survival Score, HFSS) was prospectively evaluated in a sample of ambulatory patients with congestive heart failure (CHF). Additionally, it was investigated whether the HFSS might be simplified by assessment of the distance ambulated during a 6-min walk test (6'WT) instead of determination of peak oxygen uptake (peak VO(2)). In 208 middle-aged CHF patients (age 54+/-10 years, 82% male, NYHA class 2.3+/-0.7; follow-up 28+/-14 months) the seven variables of the HFSS: CHF aetiology; heart rate; mean arterial pressure; serum sodium concentration; intraventricular conduction time; left ventricular ejection fraction (LVEF); and peak VO(2), were determined. Additionally, a 6'WT was performed. The HFSS allowed discrimination between patients at low, medium and high risk, with mortality rates of 16, 39 and 50%, respectively. However, the prognostic power of the HFSS was not superior to a two-variable model consisting only of LVEF and peak VO(2). The areas under the receiver operating curves (AUC) for prediction of 1-year survival were even higher for the two-variable model (0.84 vs. 0.74, P<0.05). Replacing peak VO(2) with 6'WT resulted in a similar AUC (0.83). The HFSS continued to predict survival when applied to this patient sample. However, the HFSS was inferior to a two-variable model containing only LVEF and either peak VO(2) or 6'WT. As the 6'WT requires no sophisticated equipment, a simplified two-variable model containing only LVEF and 6'WT may be more widely applicable, and is therefore recommended.

Changes in patellofemoral pain resulting from repetitive impact landings are associated with the magnitude and rate of patellofemoral joint loading.

PubMed

Atkins, Lee T; James, C Roger; Yang, Hyung Suk; Sizer, Phillip S; Brismée, Jean-Michel; Sawyer, Steven F; Powers, Christopher M

2018-03-01

Although a relationship between elevated patellofemoral forces and pain has been proposed, it is unknown which joint loading variable (magnitude, rate) is best associated with pain changes. The purpose of this study was to examine associations among patellofemoral joint loading variables and changes in patellofemoral pain across repeated single limb landings. Thirty-one females (age: 23.5(2.8) year; height: 166.8(5.8) cm; mass: 59.6(8.1) kg) with PFP performed 5 landing trials from 0.25 m. The dependent variable was rate of change in pain obtained from self-reported pain scores following each trial. Independent variables included 5-trial averages of peak, time-integral, and average and maximum development rates of the patellofemoral joint reaction force obtained using a previously described model. Pearson correlation coefficients were calculated to evaluate individual associations between rate of change in pain and each independent variable (α = 0.05). Stepwise linear multiple regression (α enter  = 0.05; α exit  = 0.10) was used to identify the best predictor of rate of change in pain. Subjects reported an average increase of 0.38 pain points with each landing trial. Although, rate of change in pain was positively correlated with peak force (r = 0.44, p = 0.01), and average (r = 0.41, p = 0.02) and maximum force development rates (r = 0.39, p = 0.03), only the peak force entered the predictive model explaining 19% of variance in rate of change in pain (r 2  = 0.19, p = 0.01). Peak patellofemoral joint reaction force was the best predictor of the rate of change in pain following repetitive singe limb landings. The current study supports the theory that patellofemoral joint loading contributes to changes in patellofemoral pain. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fatigue Analyses Under Constant- and Variable-Amplitude Loading Using Small-Crack Theory

NASA Technical Reports Server (NTRS)

Newman, J. C., Jr.; Phillips, E. P.; Everett, R. A., Jr.

1999-01-01

Studies on the growth of small cracks have led to the observation that fatigue life of many engineering materials is primarily "crack growth" from micro-structural features, such as inclusion particles, voids, slip-bands or from manufacturing defects. This paper reviews the capabilities of a plasticity-induced crack-closure model to predict fatigue lives of metallic materials using "small-crack theory" under various loading conditions. Constraint factors, to account for three-dimensional effects, were selected to correlate large-crack growth rate data as a function of the effective stress-intensity factor range (delta-Keff) under constant-amplitude loading. Modifications to the delta-Keff-rate relations in the near-threshold regime were needed to fit measured small-crack growth rate behavior. The model was then used to calculate small-and large-crack growth rates, and to predict total fatigue lives, for notched and un-notched specimens under constant-amplitude and spectrum loading. Fatigue lives were predicted using crack-growth relations and micro-structural features like those that initiated cracks in the fatigue specimens for most of the materials analyzed. Results from the tests and analyses agreed well.

Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers.

PubMed

Vaegter, Katarina Kebbon; Lakic, Tatevik Ghukasyan; Olovsson, Matts; Berglund, Lars; Brodin, Thomas; Holte, Jan

2017-03-01

To construct a prediction model for live birth after in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment and single-embryo transfer (SET) after 2 days of embryo culture. Prospective observational cohort study. University-affiliated private infertility center. SET in 8,451 IVF/ICSI treatments in 5,699 unselected consecutive couples during 1999-2014. A total of 100 basal patient characteristics and treatment data were analyzed for associations with live birth after IVF/ICSI (adjusted for repeated treatments) and subsequently combined for prediction model construction. Live birth rate (LBR) and performance of live birth prediction model. Embryo score, treatment history, ovarian sensitivity index (OSI; number of oocytes/total dose of FSH administered), female age, infertility cause, endometrial thickness, and female height were all independent predictors of live birth. A prediction model (training data set; n = 5,722) based on these variables showed moderate discrimination, but predicted LBR with high accuracy in subgroups of patients, with LBR estimates ranging from <10% to >40%. Outcomes were similar in an internal validation data set (n = 2,460). Based on 100 variables prospectively recorded during a 15-year period, a model for live birth prediction after strict SET was constructed and showed excellent calibration in internal validation. For the first time, female height qualified as a predictor of live birth after IVF/ICSI. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

LES study of microphysical variability bias in shallow cumulus

NASA Astrophysics Data System (ADS)

Kogan, Yefim

2017-05-01

Subgrid-scale (SGS) variability of cloud microphysical variables over the mesoscale numerical weather prediction (NWP) model has been evaluated by means of joint probability distribution functions (JPDFs). The latter were obtained using dynamically balanced Large Eddy Simulation (LES) model dataset from a case of marine trade cumulus initialized with soundings from Rain in Cumulus Over the Ocean (RICO) field project. Bias in autoconversion and accretion rates from different formulations of the JPDFs was analyzed. Approximating the 2-D PDF using a generic (fixed-in-time), but variable-in-height JPDFs give an acceptable level of accuracy, whereas neglecting the SGS variability altogether results in a substantial underestimate of the grid-mean total conversion rate and producing negative bias in rain water. Nevertheless the total effect on rain formation may be uncertain in the long run due to the fact that the negative bias in rain water may be counterbalanced by the positive bias in cloud water. Consequently, the overall effect of SGS neglect needs to be investigated in direct simulations with a NWP model.

Predicting Intelligibility Gains in Dysarthria through Automated Speech Feature Analysis

ERIC Educational Resources Information Center

Fletcher, Annalise R.; Wisler, Alan A.; McAuliffe, Megan J.; Lansford, Kaitlin L.; Liss, Julie M.

2017-01-01

Purpose: Behavioral speech modifications have variable effects on the intelligibility of speakers with dysarthria. In the companion article, a significant relationship was found between measures of speakers' baseline speech and their intelligibility gains following cues to speak louder and reduce rate (Fletcher, McAuliffe, Lansford, Sinex, &…

Relation between Video Game Addiction and Interfamily Relationships on Primary School Students

ERIC Educational Resources Information Center

Zorbaz, Selen Demirtas; Ulas, Ozlem; Kizildag, Seval

2015-01-01

This study seeks to analyze whether or not the following three variables of "Discouraging Family Relations," "Supportive Family Relations," "Total Time Spent on the Computer," and "Grade Point Average (GPA)" predict elementary school students' video game addiction rates, and whether or not there exists a…

On Correlations, Distances and Error Rates.

ERIC Educational Resources Information Center

Dorans, Neil J.

The nature of the criterion (dependent) variable may play a useful role in structuring a list of classification/prediction problems. Such criteria are continuous in nature, binary dichotomous, or multichotomous. In this paper, discussion is limited to the continuous normally distributed criterion scenarios. For both cases, it is assumed that the…

T-Wave Morphology Restitution Predicts Sudden Cardiac Death in Patients With Chronic Heart Failure.

PubMed

Ramírez, Julia; Orini, Michele; Mincholé, Ana; Monasterio, Violeta; Cygankiewicz, Iwona; Bayés de Luna, Antonio; Martínez, Juan Pablo; Pueyo, Esther; Laguna, Pablo

2017-05-19

Patients with chronic heart failure are at high risk of sudden cardiac death (SCD). Increased dispersion of repolarization restitution has been associated with SCD, and we hypothesize that this should be reflected in the morphology of the T-wave and its variations with heart rate. The aim of this study is to propose an electrocardiogram (ECG)-based index characterizing T-wave morphology restitution (TMR), and to assess its association with SCD risk in a population of chronic heart failure patients. Holter ECGs from 651 ambulatory patients with chronic heart failure from the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study were available for the analysis. TMR was quantified by measuring the morphological variation of the T-wave per RR increment using time-warping metrics, and its predictive power was compared to that of clinical variables such as the left ventricular ejection fraction and other ECG-derived indices, such as T-wave alternans and heart rate variability. TMR was significantly higher in SCD victims than in the rest of patients (median 0.046 versus 0.039, P <0.001). When TMR was dichotomized at TMR=0.040, the SCD rate was significantly higher in the TMR≥0.040 group ( P <0.001). Cox analysis revealed that TMR≥0.040 was strongly associated with SCD, with a hazard ratio of 3.27 ( P <0.001), independently of clinical and ECG-derived variables. No association was found between TMR and pump failure death. This study shows that TMR is specifically associated with SCD in a population of chronic heart failure patients, and it is a better predictor than clinical and ECG-derived variables. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Different slopes for different folks: alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks.

PubMed

Mathewson, Kyle E; Basak, Chandramallika; Maclin, Edward L; Low, Kathy A; Boot, Walter R; Kramer, Arthur F; Fabiani, Monica; Gratton, Gabriele

2012-12-01

We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes. Copyright © 2012 Society for Psychophysiological Research.

Modeling of electron cyclotron resonance discharges

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

Meyyappan, M.; Govindan, T.R.

The current trend in plasma processing is the development of high density plasma sources to achieve high deposition and etch rates, uniformity over large ares, and low wafer damage. Here, is a simple model to predict the spatially-averaged plasma characteristics of electron cyclotron resonance (ECR) reactors is presented. The model consists of global conservation equations for species concentration, electron density and energy. A gas energy balance is used to predict the neutral temperature self-consistently. The model is demonstrated for an ECR argon discharge. The predicted behavior of the discharge as a function of system variables agrees well with experimental observations.

Using individual patient anatomy to predict protocol compliance for prostate intensity-modulated radiotherapy

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

Caine, Hannah; Whalley, Deborah; Kneebone, Andrew

If a prostate intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) plan has protocol violations, it is often a challenge knowing whether this is due to unfavorable anatomy or suboptimal planning. This study aimed to create a model to predict protocol violations based on patient anatomical variables and their potential relationship to target and organ at risk (OAR) end points in the setting of definitive, dose-escalated IMRT/VMAT prostate planning. Radiotherapy plans from 200 consecutive patients treated with definitive radiation for prostate cancer using IMRT or VMAT were analyzed. The first 100 patient plans (hypothesis-generating cohort) were examined to identifymore » anatomical variables that predict for dosimetric outcome, in particular OAR end points. Variables that scored significance were further assessed for their ability to predict protocol violations using a Classification and Regression Tree (CART) analysis. These results were then validated in a second group of 100 patients (validation cohort). In the initial analysis of the hypothesis-generating cohort, percentage of rectum overlap in the planning target volume (PTV) (%OR) and percentage of bladder overlap in the PTV (%OB) were highlighted as significant predictors of rectal and bladder dosimetry. Lymph node treatment was also significant for bladder outcomes. For the validation cohort, CART analysis showed that %OR of < 6%, 6% to 9% and > 9% predicted a 13%, 63%, and 100% rate of rectal protocol violations respectively. For the bladder, %OB of < 9% vs > 9% is associated with 13% vs 88% rate of bladder constraint violations when lymph nodes were not treated. If nodal irradiation was delivered, plans with a %OB of < 9% had a 59% risk of violations. Percentage of rectum and bladder within the PTV can be used to identify individual plan potential to achieve dose-volume histogram (DVH) constraints. A model based on these factors could be used to reduce planning time, improve work flow, and strengthen plan quality and consistency.« less

GYM score: 30-day mortality predictive model in elderly patients attended in the emergency department with infection.

PubMed

González Del Castillo, Juan; Escobar-Curbelo, Luis; Martínez-Ortíz de Zárate, Mikel; Llopis-Roca, Ferrán; García-Lamberechts, Jorge; Moreno-Cuervo, Álvaro; Fernández, Cristina; Martín-Sánchez, Francisco Javier

2017-06-01

To determine the validity of the classic sepsis criteria or systemic inflammatory response syndrome (heart rate, respiratory rate, temperature, and leukocyte count) and the modified sepsis criteria (systemic inflammatory response syndrome criteria plus glycemia and altered mental status), and the validity of each of these variables individually to predict 30-day mortality, as well as develop a predictive model of 30-day mortality in elderly patients attended for infection in emergency departments (ED). A prospective cohort study including patients at least 75 years old attended in three Spanish university ED for infection during 2013 was carried out. Demographic variables and data on comorbidities, functional status, hemodynamic sepsis diagnosis variables, site of infection, and 30-day mortality were collected. A total of 293 patients were finally included, mean age 84.0 (SD 5.5) years, and 158 (53.9%) were men. Overall, 185 patients (64%) fulfilled the classic sepsis criteria and 224 patients (76.5%) fulfilled the modified sepsis criteria. The all-cause 30-day mortality was 13.0%. The area under the curve of the classic sepsis criteria was 0.585 [95% confidence interval (CI) 0.488-0.681; P=0.106], 0.594 for modified sepsis criteria (95% CI: 0.502-0.685; P=0.075), and 0.751 (95% CI: 0.660-0.841; P<0.001) for the GYM score (Glasgow <15; tachYpnea>20 bpm; Morbidity-Charlson index ≥3) to predict 30-day mortality, with statistically significant differences (P=0.004 and P<0.001, respectively). The GYM score showed good calibration after bootstrap correction, with an area under the curve of 0.710 (95% CI: 0.605-0.815). The GYM score showed better capacity than the classic and the modified sepsis criteria to predict 30-day mortality in elderly patients attended for infection in the ED.

Simultaneous Optimization of Multiple Response Variables for the Gelatin-chitosan Microcapsules Containing Angelica Essential Oil.

PubMed

Li, Qiang; Sun, Li-Jian; Gong, Xian-Feng; Wang, Yang; Zhao, Xue-Ling

2017-01-01

Angelica essential oil (AO), a major pharmacologically active component of Angelica sinensis (Oliv.) Diels, possesses hemogenesis, analgesic activities, and sedative effect. The application of AO in pharmaceutical systems had been limited because of its low oxidative stability. The AO-loaded gelatin-chitosan microcapsules with prevention from oxidation were developed and optimized using response surface methodology. The effects of formulation variables (pH at complex coacervation, gelatin concentration, and core/wall ratio) on multiple response variables (yield, encapsulation efficiency, antioxidation rate, percent of drug released in 1 h, and time to 85% drug release) were systemically investigated. A desirability function that combined these five response variables was constructed. All response variables investigated were found to be highly dependent on the formulation variables, with strong interactions observed between the formulation variables. It was found that optimum overall desirability of AO microcapsules could be obtained at pH 6.20, gelatin concentration 25.00%, and core/wall ratio 40.40%. The experimental values of the response variables highly agreed with the predicted values. The antioxidation rate of optimum formulation was approximately 8 times higher than that of AO. The in-vitro drug release from microcapsules was followed Higuchi model with super case-II transport mechanism.

Fuel droplet burning rates at high pressures

NASA Technical Reports Server (NTRS)

Canada, G. S.; Faeth, G. M.

1972-01-01

Combustion of methanol, ethanol, propanol -1, n - pentane, n - heptane and n - decane was observed in air under natural convection conditions at pressures up to 100 atm. The droplets were simulated by porous spheres with diameters in the range 0.63 - 1.90 cm. The pressure levels of the tests were high enough so that near critical combustion was observed for methanol and ethanol. Measurements were made of the burning rate and liquid surface temperatures of the fuels. The data were compared with variable property analysis of the combustion process, including a correction for natural convection. The burning rate predictions of the various theories were similar and in fair agreement with the data. The high pressure theory gave the best prediction for the liquid surface temperatures of ethanol and propanol -1 at high pressure. The experiments indicated the approach of critical burning conditions for methanol and ethanol at pressures on the order of 80 - 100 atm, which was in good agreement with the predictions of both the low and high pressure analysis.

Does climate variability influence the demography of wild primates? Evidence from long-term life-history data in seven species.

PubMed

Campos, Fernando A; Morris, William F; Alberts, Susan C; Altmann, Jeanne; Brockman, Diane K; Cords, Marina; Pusey, Anne; Stoinski, Tara S; Strier, Karen B; Fedigan, Linda M

2017-11-01

Earth's rapidly changing climate creates a growing need to understand how demographic processes in natural populations are affected by climate variability, particularly among organisms threatened by extinction. Long-term, large-scale, and cross-taxon studies of vital rate variation in relation to climate variability can be particularly valuable because they can reveal environmental drivers that affect multiple species over extensive regions. Few such data exist for animals with slow life histories, particularly in the tropics, where climate variation over large-scale space is asynchronous. As our closest relatives, nonhuman primates are especially valuable as a resource to understand the roles of climate variability and climate change in human evolutionary history. Here, we provide the first comprehensive investigation of vital rate variation in relation to climate variability among wild primates. We ask whether primates are sensitive to global changes that are universal (e.g., higher temperature, large-scale climate oscillations) or whether they are more sensitive to global change effects that are local (e.g., more rain in some places), which would complicate predictions of how primates in general will respond to climate change. To address these questions, we use a database of long-term life-history data for natural populations of seven primate species that have been studied for 29-52 years to investigate associations between vital rate variation, local climate variability, and global climate oscillations. Associations between vital rates and climate variability varied among species and depended on the time windows considered, highlighting the importance of temporal scale in detection of such effects. We found strong climate signals in the fertility rates of three species. However, survival, which has a greater impact on population growth, was little affected by climate variability. Thus, we found evidence for demographic buffering of life histories, but also evidence of mechanisms by which climate change could affect the fates of wild primates. © 2017 John Wiley & Sons Ltd.

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

PubMed Central

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 new seed bank sources for species that are affected by the distance to natural lands and roads. PMID:29360857

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

PubMed

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 new seed bank sources for species that are affected by the distance to natural lands and roads.

Leptin levels in patients with anorexia nervosa following day/inpatient treatment do not predict weight 1 year post-referral.

PubMed

Seitz, Jochen; Bühren, Katharina; Biemann, Ronald; Timmesfeld, Nina; Dempfle, Astrid; Winter, Sibylle Maria; Egberts, Karin; Fleischhaker, Christian; Wewetzer, Christoph; Herpertz-Dahlmann, Beate; Hebebrand, Johannes; Föcker, Manuel

2016-09-01

Elevated serum leptin levels following rapid therapeutically induced weight gain in anorexia nervosa (AN) patients are discussed as a potential biomarker for renewed weight loss as a result of leptin-related suppression of appetite and increased energy expenditure. This study aims to analyze the predictive value of leptin levels at discharge as well as the average rate of weight gain during inpatient or day patient treatment for body weight at 1-year follow-up. 121 patients were recruited from the longitudinal Anorexia Nervosa Day patient versus Inpatient (ANDI) trial. Serum leptin levels were analyzed at referral and discharge. A multiple linear regression analysis to predict age-adjusted body mass index (BMI-SDS) at 1-year follow-up was performed. Leptin levels, the average rate of weight gain, premorbid BMI-SDS, BMI-SDS at referral, age and illness duration were included as independent variables. Neither leptin levels at discharge nor rate of weight gain significantly predicted BMI-SDS at 1-year follow-up explaining only 1.8 and 0.4 % of the variance, respectively. According to our results, leptin levels at discharge and average rate of weight gain did not exhibit any value in predicting weight at 1-year follow-up in our longitudinal observation study of adolescent patients with AN. Thus, research should focus on other potential factors to predict weight at follow-up. As elevated leptin levels and average rate of weight gain did not pose a risk for reduced weight, we found no evidence for the beneficial effect of slow refeeding in patients with acute AN.

Children's Sleep and Autonomic Function: Low Sleep Quality Has an Impact on Heart Rate Variability

PubMed Central

Michels, Nathalie; Clays, Els; De Buyzere, Marc; Vanaelst, Barbara; De Henauw, Stefaan; Sioen, Isabelle

2013-01-01

Objectives: Short sleep duration and poor sleep quality in children have been associated with concentration, problem behavior, and emotional instability, but recently also with disrupted autonomic nervous function, which predicts cardiovascular health. Heart rate variability (HRV) was used as noninvasive indicator of autonomic function to examine the influence of sleep. Design: Cross-sectional and longitudinal observational study on the effect of sleep on HRV Participants: Belgian children (5-11 years) of the ChiBS study in 2010 (N = 334) and 2011 (N = 293). Interventions: N/A. Methods: Sleep duration was reported and in a subgroup sleep quality (efficiency, latency, awakenings) was measured with accelerometry. High-frequency (HF) power and autonomic balance (LF/HF) were calculated on supine 5-minute HRV measurements. Stress was measured by emotion and problem behavior questionnaires. Sleep duration and quality were used as HRV predictors in corrected cross-sectional and longitudinal regressions. Stress was tested as mediator (intermediate pathway) or moderator (interaction) in sleep-HRV associations. Results: In both cross-sectional and longitudinal analyses, long sleep latency could predict lower HF (parasympathetic activity), while nocturnal awakenings, sleep latency, low sleep efficiency, and low corrected sleep duration were related to higher LF/HF (sympathetic/parasympathetic balance). Parental reported sleep duration was not associated with HRV. The significances remained after correction for stress. Stress was not a mediator, but a moderator (enhancer) in the relationship between sleep quality and HRV. Conclusions: Low sleep quality but not parent-reported low sleep duration leads to an unhealthier heart rate variability pattern (sympathetic over parasympathetic dominance). This stresses the importance of good sleep quality for cardiovascular health in children. Citation: Michels N; Clays E; De Buyzere M; Vanaelst B; De Henauw S; Sioen I. Children's sleep and autonomic function: low sleep quality has an impact on heart rate variability. SLEEP 2013;36(12):1939-1946. PMID:24293769

Predictors of stroke in patients with impaired glucose tolerance: results from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research trial.

PubMed

Preiss, David; Giles, Thomas D; Thomas, Laine E; Sun, Jie-Lena; Haffner, Steven M; Holman, Rury R; Standl, Eberhard; Mazzone, Theodore; Rutten, Guy E; Tognoni, Gianni; Chiang, Fu-Tien; McMurray, John J V; Califf, Robert M

2013-09-01

Risk factors for stroke are well-established in general populations but sparsely studied in individuals with impaired glucose tolerance. We identified predictors of stroke among participants with impaired glucose tolerance in the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial. Cox proportional-hazard regression models were constructed using baseline variables, including the 2 medications studied, valsartan and nateglinide. Among 9306 participants, 237 experienced a stroke over 6.4 years. Predictors of stroke included classical risk factors such as existing cerebrovascular and coronary heart disease, higher pulse pressure, higher low-density lipoprotein cholesterol, older age, and atrial fibrillation. Other factors, including previous venous thromboembolism, higher waist circumference, lower estimated glomerular filtration rate, lower heart rate, and lower body mass index, provided additional important predictive information, yielding a C-index of 0.72. Glycemic measures were not predictive of stroke. Variables associated with stroke were similar in participants with no prior history of cerebrovascular disease at baseline. The most powerful predictors of stroke in patients with impaired glucose tolerance included a combination of established risk factors and novel variables, such as previous venous thromboembolism and elevated waist circumference, allowing moderately effective identification of high-risk individuals.

Emotionally excited eyeblink-rate variability predicts an experience of transportation into the narrative world

PubMed Central

Nomura, Ryota; Hino, Kojun; Shimazu, Makoto; Liang, Yingzong; Okada, Takeshi

2015-01-01

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 years, SD = 13.18 years, range 18–63 years) 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 and 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 SD) 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. PMID:26029123

Functional Traits and Water Transport Strategies in Lowland Tropical Rainforest Trees.

PubMed

Apgaua, Deborah M G; Ishida, Françoise Y; Tng, David Y P; Laidlaw, Melinda J; Santos, Rubens M; Rumman, Rizwana; Eamus, Derek; Holtum, Joseph A M; Laurance, Susan G W

2015-01-01

Understanding how tropical rainforest trees may respond to the precipitation extremes predicted in future climate change scenarios is paramount for their conservation and management. Tree species clearly differ in drought susceptibility, suggesting that variable water transport strategies exist. Using a multi-disciplinary approach, we examined the hydraulic variability in trees in a lowland tropical rainforest in north-eastern Australia. We studied eight tree species representing broad plant functional groups (one palm and seven eudicot mature-phase, and early-successional trees). We characterised the species' hydraulic system through maximum rates of volumetric sap flow and velocities using the heat ratio method, and measured rates of tree growth and several stem, vessel, and leaf traits. Sap flow measures exhibited limited variability across species, although early-successional species and palms had high mean sap velocities relative to most mature-phase species. Stem, vessel, and leaf traits were poor predictors of sap flow measures. However, these traits exhibited different associations in multivariate analysis, revealing gradients in some traits across species and alternative hydraulic strategies in others. Trait differences across and within tree functional groups reflect variation in water transport and drought resistance strategies. These varying strategies will help in our understanding of changing species distributions under predicted drought scenarios.

Uncertainty and Sensitivity Analysis of Afterbody Radiative Heating Predictions for Earth Entry

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Johnston, Christopher O.; Hosder, Serhat

2016-01-01

The objective of this work was to perform sensitivity analysis and uncertainty quantification for afterbody radiative heating predictions of Stardust capsule during Earth entry at peak afterbody radiation conditions. The radiation environment in the afterbody region poses significant challenges for accurate uncertainty quantification and sensitivity analysis due to the complexity of the flow physics, computational cost, and large number of un-certain variables. In this study, first a sparse collocation non-intrusive polynomial chaos approach along with global non-linear sensitivity analysis was used to identify the most significant uncertain variables and reduce the dimensions of the stochastic problem. Then, a total order stochastic expansion was constructed over only the important parameters for an efficient and accurate estimate of the uncertainty in radiation. Based on previous work, 388 uncertain parameters were considered in the radiation model, which came from the thermodynamics, flow field chemistry, and radiation modeling. The sensitivity analysis showed that only four of these variables contributed significantly to afterbody radiation uncertainty, accounting for almost 95% of the uncertainty. These included the electronic- impact excitation rate for N between level 2 and level 5 and rates of three chemical reactions in uencing N, N(+), O, and O(+) number densities in the flow field.

Functional Traits and Water Transport Strategies in Lowland Tropical Rainforest Trees

PubMed Central

Apgaua, Deborah M. G.; Ishida, Françoise Y.; Tng, David Y. P.; Laidlaw, Melinda J.; Santos, Rubens M.; Rumman, Rizwana; Eamus, Derek; Holtum, Joseph A. M.; Laurance, Susan G. W.

2015-01-01

Understanding how tropical rainforest trees may respond to the precipitation extremes predicted in future climate change scenarios is paramount for their conservation and management. Tree species clearly differ in drought susceptibility, suggesting that variable water transport strategies exist. Using a multi-disciplinary approach, we examined the hydraulic variability in trees in a lowland tropical rainforest in north-eastern Australia. We studied eight tree species representing broad plant functional groups (one palm and seven eudicot mature-phase, and early-successional trees). We characterised the species’ hydraulic system through maximum rates of volumetric sap flow and velocities using the heat ratio method, and measured rates of tree growth and several stem, vessel, and leaf traits. Sap flow measures exhibited limited variability across species, although early-successional species and palms had high mean sap velocities relative to most mature-phase species. Stem, vessel, and leaf traits were poor predictors of sap flow measures. However, these traits exhibited different associations in multivariate analysis, revealing gradients in some traits across species and alternative hydraulic strategies in others. Trait differences across and within tree functional groups reflect variation in water transport and drought resistance strategies. These varying strategies will help in our understanding of changing species distributions under predicted drought scenarios. PMID:26087009

Association Between Air Temperature and Cancer Death Rates in Florida: An Ecological Study.

PubMed

Hart, John

2015-01-01

Proponents of global warming predict adverse events due to a slight warming of the planet in the last 100 years. This ecological study tests one of the possible arguments that might support the global warming theory - that it may increase cancer death rates. Thus, average daily air temperature is compared to cancer death rates at the county level in a U.S. state, while controlling for variables of smoking, race, and land elevation. The study revealed that lower cancer death rates were associated with warmer temperatures. Further study is indicated to verify these findings.

Association Between Air Temperature and Cancer Death Rates in Florida

PubMed Central

2015-01-01

Proponents of global warming predict adverse events due to a slight warming of the planet in the last 100 years. This ecological study tests one of the possible arguments that might support the global warming theory – that it may increase cancer death rates. Thus, average daily air temperature is compared to cancer death rates at the county level in a U.S. state, while controlling for variables of smoking, race, and land elevation. The study revealed that lower cancer death rates were associated with warmer temperatures. Further study is indicated to verify these findings. PMID:26674418

Development of constitutive models for cyclic plasticity and creep behavior of super alloys at high temperature

NASA Technical Reports Server (NTRS)

Haisler, W. E.

1983-01-01

An uncoupled constitutive model for predicting the transient response of thermal and rate dependent, inelastic material behavior was developed. The uncoupled model assumes that there is a temperature below which the total strain consists essentially of elastic and rate insensitive inelastic strains only. Above this temperature, the rate dependent inelastic strain (creep) dominates. The rate insensitive inelastic strain component is modelled in an incremental form with a yield function, blow rule and hardening law. Revisions to the hardening rule permit the model to predict temperature-dependent kinematic-isotropic hardening behavior, cyclic saturation, asymmetric stress-strain response upon stress reversal, and variable Bauschinger effect. The rate dependent inelastic strain component is modelled using a rate equation in terms of back stress, drag stress and exponent n as functions of temperature and strain. A sequence of hysteresis loops and relaxation tests are utilized to define the rate dependent inelastic strain rate. Evaluation of the model has been performed by comparison with experiments involving various thermal and mechanical load histories on 5086 aluminum alloy, 304 stainless steel and Hastelloy X.

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Classification tree analysis to enhance targeting for follow-up exam of colorectal cancer screening

PubMed Central

2013-01-01

Background Follow-up rate after a fecal occult blood test (FOBT) is low worldwide. In order to increase the follow-up rate, segmentation of the target population has been proposed as a promising strategy, because an intervention can then be tailored toward specific subgroups of the population rather than using one type of intervention for all groups. The aim of this study is to identify subgroups that share the same patterns of characteristics related to follow-up exams after FOBT. Methods The study sample consisted of 143 patients aged 50–69 years who were requested to undergo follow-up exams after FOBT. A classification tree analysis was performed, using the follow-up rate as a dependent variable and sociodemographic variables, psychological variables, past FOBT and follow-up exam, family history of colorectal cancer (CRC), and history of bowel disease as predictive variables. Results The follow-up rate in 143 participants was 74.1% (n = 106). A classification tree analysis identified four subgroups as follows; (1) subgroup with a high degree of fear of CRC, unemployed and with a history of bowel disease (n = 24, 100.0% follow-up rate), (2) subgroup with a high degree of fear of CRC, unemployed and with no history of bowel disease (n = 17, 82.4% follow-up rate), (3) subgroup with a high degree of fear of CRC and employed (n = 24, 66.7% follow-up rate), and (4) subgroup with a low degree of fear of CRC (n = 78, 66.7% follow-up rate). Conclusion The identification of four subgroups with a diverse range of follow-up rates for CRC screening indicates the direction to take in future development of an effective tailored intervention strategy. PMID:24112563

Prediction of sea ice thickness cluster in the Northern Hemisphere

NASA Astrophysics Data System (ADS)

Fuckar, Neven-Stjepan; Guemas, Virginie; Johnson, Nathaniel; Doblas-Reyes, Francisco

2016-04-01

Sea ice thickness (SIT) has a potential to contain substantial climate memory and predictability in the northern hemisphere (NH) sea ice system. We use 5-member NH SIT, reconstructed with an ocean-sea-ice general circulation model (NEMOv3.3 with LIM2) with a simple data assimilation routine, to determine NH SIT modes of variability disentangled from the long-term climate change. Specifically, we apply the K-means cluster analysis - one of nonhierarchical clustering methods that partition data into modes or clusters based on their distances in the physical - to determine optimal number of NH SIT clusters (K=3) and their historical variability. To examine prediction skill of NH SIT clusters in EC-Earth2.3, a state-of-the-art coupled climate forecast system, we use 5-member ocean and sea ice initial conditions (IC) from the same ocean-sea-ice historical reconstruction and atmospheric IC from ERA-Interim reanalysis. We focus on May 1st and Nov 1st start dates from 1979 to 2010. Common skill metrics of probability forecast, such as rank probability skill core and ROC (relative operating characteristics - hit rate versus false alarm rate) and reliability diagrams show that our dynamical model predominately perform better than the 1st order Marko chain forecast (that beats climatological forecast) over the first forecast year. On average May 1st start dates initially have lower skill than Nov 1st start dates, but their skill is degraded at slower rate than skill of forecast started on Nov 1st.

Predicting the natural flow regime: Models for assessing hydrological alteration in streams

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Wolock, D.M.; Meador, M.R.; Norris, R.H.

2009-01-01

Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national- and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also demonstrate how the models can be applied to predict expected natural flow characteristics at ungaged sites. ?? 2009 John Wiley & Sons, Ltd.

An analysis of the determinants of maternal mortality in sub-Saharan Africa.

PubMed

Buor, Daniel; Bream, Kent

2004-10-01

To establish what population characteristics affect the high maternal mortality rate in the sub-Saharan Africa region and to propose possible solutions to reduce this rate. This study is a secondary analysis of existing data sources from the World Bank, the World Health Organization (WHO), as well as direct and indirect sources from UNAIDS, the United Nations, Demographic and Health Surveys (DHS), Macro International, and national statistical offices. Instead of looking at continentwide or individual nation models, it develops a regional model. Sociodemographic population variables are used as independent variables to predict the dependent variable, maternal mortality. Additionally, a new country-specific political stability independent variable is introduced into the model. Data from 28 sub-Saharan African countries are used. Bivariate correlations are used to establish associations among the variables, whereas cross-tabulations, using Kendall's tau-c values, and regression lines are used to establish impacts. In the sub-Saharan Africa region, births attended by skilled health personnel and life expectancy at birth strongly correlate with maternal mortality. Gross national product (GNP) per capita and health expenditure per capita also have strong association with maternal mortality. The availability of skilled delivery personnel, life expectancy, national economic wealth, and health expenditure per capita predict the maternal mortality rate of a country. Based on these findings, it is recommended that structural arrangements be made to train skilled health personnel to take care of maternal health problems. In view of the high cost of training physicians, middle-level health personnel may offer an affordable alternative to handle emergency obstetrical cases to address the shortage of physicians. In addition, the allocation of adequate resources to the health sector could improve maternal mortality. The economic wealth of a country and life expectancy at birth are less modifiable through short-term specific interventions. Additionally, it is recommended that country-specific interventions are needed to correct the problem of lack of critical data for analysis.

Stable isotope records of convection variability in the West Pacific Warm Pool from fast-growing stalagmites

NASA Astrophysics Data System (ADS)

Maupin, C. R.; Partin, J. W.; Quinn, T. M.; Shen, C.; Lin, K.; Taylor, F. W.; Sinclair, D. J.; Banner, J. L.

2010-12-01

The potential response of the tropical Pacific to ongoing anthropogenic global warming conditions is informed by instrumental data, model predictions and climate proxy evidence. However, these distinct lines of evidence lead to opposing predictions in terms of the nature of interannual (ENSO) variability in a warming world. Interpreted in an ENSO framework, warming in the tropical Pacific may elicit a zonally asymmetrical response and lead to an intensified Walker Circulation (more ‘La Niña - like’). Alternatively, discrepancies in the increasing rates of latent heat flux and rainfall due to warming conditions may in fact reduce Walker Circulation (more ‘El Niño - like’). However, in order for such a framework to be useful in the context of future climate change, some knowledge of the natural variability in the strength of Walker Circulation components is required. The extant instrumental data are not of sufficient temporal length to fully assess the spectrum of natural variability in such climate components. Oxygen isotope records from tropical speleothems have been successfully used to document the nature of precessional forcing on precipitation and atmospheric circulation patterns throughout the tropics. Typical stalagmite growth rates of 10-100 μm yr-1 allow decadally resolved records of δ18O variability on time scales of centuries to millennia and beyond. Here we present the initial results from calcite stalagmites of heretofore unprecedented growth rates (~1-4 mm yr-1) in a cave in northwest Guadalcanal, Solomon Islands (~9°S, 160°E). These stalagmites have been absolutely dated by U-Th techniques and indicate stalagmite growth spanning ~1650 to 2010 CE. The δ18O records from stalagmites provide evidence for changes in convection in the equatorial WPWP region of the SPCZ: the rising limb of the Pacific Walker Circulation, and therefore provide critical insight into changes in zonal atmospheric circulation across the Pacific.

Modeling of Gallium Nitride Hydride Vapor Phase Epitaxy

NASA Technical Reports Server (NTRS)

Meyyappan, Meyya; Arnold, James O. (Technical Monitor)

1997-01-01

A reactor model for the hydride vapor phase epitaxy of GaN is presented. The governing flow, energy, and species conservation equations are solved in two dimensions to examine the growth characteristics as a function of process variables and reactor geometry. The growth rate varies with GaCl composition but independent of NH3 and H2 flow rates. A change in carrier gas for Ga source from H2 to N2 affects the growth rate and uniformity for a fixed reactor configuration. The model predictions are in general agreement with observed experimental behavior.

The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio

USGS Publications Warehouse

Walter, Donald A.; Starn, J. Jeffrey

2013-01-01

Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.

Personality Subtypes of Suicidal Adults

PubMed Central

Westen, Drew; Bradley, Rebekah

2009-01-01

Research into personality factors related to suicidality suggests substantial variability among suicide attempters. A potentially useful approach that accounts for this complexity is personality subtyping. As part of a large sample looking at personality pathology, this study used Q-factor analysis to identify subtypes of 311 adult suicide attempters using SWAP-II personality profiles. Identified subtypes included Internalizing, Emotionally Dysregulated, Dependent, Hostile-Isolated, Psychopathic, and Anxious-Somatizing. Subtypes differed in hypothesized ways on criterion variables that address their construct validity, including adaptive functioning, Axis I and II comorbidity, and etiology-related variables (e.g., history of abuse). Furthermore, dimensional ratings of the subtypes predicted adaptive functioning above DSM-based diagnoses and symptoms. PMID:19752649

Autonomic Nervous System and Stress to Predict Secondary Ischemic Events after Transient Ischemic Attack or Minor Stroke: Possible Implications of Heart Rate Variability.

PubMed

Guan, Ling; Collet, Jean-Paul; Mazowita, Garey; Claydon, Victoria E

2018-01-01

Transient ischemic attack (TIA) and minor stroke have high risks of recurrence and deterioration into severe ischemic strokes. Risk stratification of TIA and minor stroke is essential for early effective treatment. Traditional tools have only moderate predictive value, likely due to their inclusion of the limited number of stroke risk factors. Our review follows Hans Selye's fundamental work on stress theory and the progressive shift of the autonomic nervous system (ANS) from adaptation to disease when stress becomes chronic. We will first show that traditional risk factors and acute triggers of ischemic stroke are chronic and acute stress factors or "stressors," respectively. Our first review shows solid evidence of the relationship between chronic stress and stroke occurrence. The stress response is tightly regulated by the ANS whose function can be assessed with heart rate variability (HRV). Our second review demonstrates that stress-related risk factors of ischemic stroke are correlated with ANS dysfunction and impaired HRV. Our conclusions support the idea that HRV parameters may represent the combined effects of all body stressors that are risk factors for ischemic stroke and, thus, may be of important predictive value for the risk of subsequent ischemic events after TIA or minor stroke.

Modelling hard and soft states of Cygnus X-1 with propagating mass accretion rate fluctuations

NASA Astrophysics Data System (ADS)

Rapisarda, S.; Ingram, A.; van der Klis, M.

2017-12-01

We present a timing analysis of three Rossi X-ray Timing Explorer observations of the black hole binary Cygnus X-1 with the propagating mass accretion rate fluctuations model PROPFLUC. The model simultaneously predicts power spectra, time lags and coherence of the variability as a function of energy. The observations cover the soft and hard states of the source, and the transition between the two. We find good agreement between model predictions and data in the hard and soft states. Our analysis suggests that in the soft state the fluctuations propagate in an optically thin hot flow extending up to large radii above and below a stable optically thick disc. In the hard state, our results are consistent with a truncated disc geometry, where the hot flow extends radially inside the inner radius of the disc. In the transition from soft to hard state, the characteristics of the rapid variability are too complex to be successfully described with PROPFLUC. The surface density profile of the hot flow predicted by our model and the lack of quasi-periodic oscillations in the soft and hard states suggest that the spin of the black hole is aligned with the inner accretion disc and therefore probably with the rotational axis of the binary system.

Autonomic Nervous System and Stress to Predict Secondary Ischemic Events after Transient Ischemic Attack or Minor Stroke: Possible Implications of Heart Rate Variability

PubMed Central

Guan, Ling; Collet, Jean-Paul; Mazowita, Garey; Claydon, Victoria E.

2018-01-01

Transient ischemic attack (TIA) and minor stroke have high risks of recurrence and deterioration into severe ischemic strokes. Risk stratification of TIA and minor stroke is essential for early effective treatment. Traditional tools have only moderate predictive value, likely due to their inclusion of the limited number of stroke risk factors. Our review follows Hans Selye’s fundamental work on stress theory and the progressive shift of the autonomic nervous system (ANS) from adaptation to disease when stress becomes chronic. We will first show that traditional risk factors and acute triggers of ischemic stroke are chronic and acute stress factors or “stressors,” respectively. Our first review shows solid evidence of the relationship between chronic stress and stroke occurrence. The stress response is tightly regulated by the ANS whose function can be assessed with heart rate variability (HRV). Our second review demonstrates that stress-related risk factors of ischemic stroke are correlated with ANS dysfunction and impaired HRV. Our conclusions support the idea that HRV parameters may represent the combined effects of all body stressors that are risk factors for ischemic stroke and, thus, may be of important predictive value for the risk of subsequent ischemic events after TIA or minor stroke. PMID:29556209

Meditation-induced changes in high-frequency heart rate variability predict smoking outcomes

PubMed Central

Libby, Daniel J.; Worhunsky, Patrick D.; Pilver, Corey E.; Brewer, Judson A.

2012-01-01

Background: High-frequency heart rate variability (HF-HRV) is a measure of parasympathetic nervous system (PNS) output that has been associated with enhanced self-regulation. Low resting levels of HF-HRV are associated with nicotine dependence and blunted stress-related changes in HF-HRV are associated with decreased ability to resist smoking. Meditation has been shown to increase HF-HRV. However, it is unknown whether tonic levels of HF-HRV or acute changes in HF-HRV during meditation predict treatment responses in addictive behaviors such as smoking cessation. Purpose: To investigate the relationship between HF-HRV and subsequent smoking outcomes. Methods: HF-HRV during resting baseline and during mindfulness meditation was measured within two weeks of completing a 4-week smoking cessation intervention in a sample of 31 community participants. Self-report measures of smoking were obtained at a follow up 17-weeks after the initiation of treatment. Results: Regression analyses indicated that individuals exhibiting acute increases in HF-HRV from resting baseline to meditation smoked fewer cigarettes at follow-up than those who exhibited acute decreases in HF-HRV (b = −4.89, p = 0.008). Conclusion: Acute changes in HF-HRV in response to meditation may be a useful tool to predict smoking cessation treatment response. PMID:22457646

Predictive Models of the Hydrological Regime of Unregulated Streams in Arizona

USGS Publications Warehouse

Anning, David W.; Parker, John T.C.

2009-01-01

Three statistical models were developed by the U.S. Geological Survey in cooperation with the Arizona Department of Environmental Quality to improve the predictability of flow occurrence in unregulated streams throughout Arizona. The models can be used to predict the probabilities of the hydrological regime being one of four categories developed by this investigation: perennial, which has streamflow year-round; nearly perennial, which has streamflow 90 to 99.9 percent of the year; weakly perennial, which has streamflow 80 to 90 percent of the year; or nonperennial, which has streamflow less than 80 percent of the year. The models were developed to assist the Arizona Department of Environmental Quality in selecting sites for participation in the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program. One model was developed for each of the three hydrologic provinces in Arizona - the Plateau Uplands, the Central Highlands, and the Basin and Range Lowlands. The models for predicting the hydrological regime were calibrated using statistical methods and explanatory variables of discharge, drainage-area, altitude, and location data for selected U.S. Geological Survey streamflow-gaging stations and a climate index derived from annual precipitation data. Models were calibrated on the basis of streamflow data from 46 stations for the Plateau Uplands province, 82 stations for the Central Highlands province, and 90 stations for the Basin and Range Lowlands province. The models were developed using classification trees that facilitated the analysis of mixed numeric and factor variables. In all three models, a threshold stream discharge was the initial variable to be considered within the classification tree and was the single most important explanatory variable. If a stream discharge value at a station was below the threshold, then the station record was determined as being nonperennial. If, however, the stream discharge was above the threshold, subsequent decisions were made according to the classification tree and explanatory variables to determine the hydrological regime of the reach as being perennial, nearly perennial, weakly perennial, or nonperennial. Using model calibration data, misclassification rates for each model were 17 percent for the Plateau Uplands, 15 percent for the Central Highlands, and 14 percent for the Basin and Range Lowlands models. The actual misclassification rate may be higher; however, the model has not been field verified for a full error assessment. The calibrated models were used to classify stream reaches for which the Arizona Department of Environmental Quality had collected miscellaneous discharge measurements. A total of 5,080 measurements at 696 sites were routed through the appropriate classification tree to predict the hydrological regime of the reaches in which the measurements were made. The predictions resulted in classification of all stream reaches as perennial or nonperennial; no reaches were predicted as nearly perennial or weakly perennial. The percentages of sites predicted as being perennial and nonperennial, respectively, were 77 and 23 for the Plateau Uplands, 87 and 13 for the Central Highlands, and 76 and 24 for the Basin and Range Lowlands.

The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach.

PubMed

Taha, Zahari; Musa, Rabiu Muazu; P P Abdul Majeed, Anwar; Alim, Muhammad Muaz; Abdullah, Mohamad Razali

2018-02-01

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme. Copyright © 2017 Elsevier B.V. All rights reserved.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Predictors of body appearance cognitive distraction during sexual activity in men and women.

PubMed

Pascoal, Patrícia; Narciso, Isabel; Pereira, Nuno Monteiro

2012-11-01

Cognitive distraction is a core concept in cognitive models of sexual dysfunction. Body appearance cognitive distraction during sexual activity (BACDSA) has been mainly studied among female college samples. However, the relative contribution of different indicators of body dissatisfaction among men and women from community samples, including the contribution of relationship variables to BACDSA, has yet to be examined. The aim of this study was to examine the extent to which aspects of body dissatisfaction and relationship variables predict BACDSA. A total of 669 cohabitating, heterosexual, Portuguese participants (390 women and 279 men) with no sexual problems completed an anonymous online survey. The survey included a sociodemographic questionnaire and a set of questionnaires assessing body- and relationship-related variables. We used a single item measure of the participant's satisfaction with the opinion that they perceive their partner has about the participant's body (PPO); the Global Body Dissatisfaction Subscale of the Body Attitudes Test (GBD); a version of the Contour Drawing Rating Scale; the Global Measure of Relationship Satisfaction; and the Inclusion of Other in Self Scale. Focus on specific body parts during sexual activity (FBP) and relationship length were assessed with an open-ended question. Hierarchical multiple regression indicated that GBD and FBP were the only body dissatisfaction variables that significantly predicted BACDSA in both men and women. The relationship variables significantly increased the amount of variance explained in BACDSA for both men and women. However, PPO was the only significant relationship variable that predicted BACDSA and only in women. Body and relationship variables are significant factors in body appearance cognitive distraction. They require further research and assessment, particularly for clinical intervention. © 2012 International Society for Sexual Medicine.

Thirty-Three Years of Ocean Benthic Warming Along the U.S. Northeast Continental Shelf and Slope: Patterns, Drivers, and Ecological Consequences

NASA Astrophysics Data System (ADS)

Kavanaugh, Maria T.; Rheuban, Jennie E.; Luis, Kelly M. A.; Doney, Scott C.

2017-12-01

The U.S. Northeast Continental Shelf is experiencing rapid warming, with potentially profound consequences to marine ecosystems. While satellites document multiple scales of spatial and temporal variability on the surface, our understanding of the status, trends, and drivers of the benthic environmental change remains limited. We interpolated sparse benthic temperature data along the New England Shelf and upper Slope using a seasonally dynamic, regionally specific multiple linear regression model that merged in situ and remote sensing data. The statistical model predicted nearly 90% of the variability of the data, resulting in a synoptic time series spanning over three decades from 1982 to 2014. Benthic temperatures increased throughout the domain, including in the Gulf of Maine. Rates of benthic warming ranged from 0.1 to 0.4°C per decade, with fastest rates occurring in shallow, nearshore regions and on Georges Bank, the latter exceeding rates observed in the surface. Rates of benthic warming were up to 1.6 times faster in winter than the rest of the year in many regions, with important implications for disease occurrence and energetics of overwintering species. Drivers of warming varied over the domain. In southern New England and the mid-Atlantic shallow Shelf regions, benthic warming was tightly coupled to changes in SST, whereas both regional and basin-scale changes in ocean circulation affect temperatures in the Gulf of Maine, the Continental Shelf, and Georges Banks. These results highlight data gaps, the current feasibility of prediction from remotely sensed variables, and the need for improved understanding on how climate may affect seasonally specific ecological processes.

Thirty-Three Years of Ocean Benthic Warming Along the U.S. Northeast Continental Shelf and Slope: Patterns, Drivers, and Ecological Consequences.

PubMed

Kavanaugh, Maria T; Rheuban, Jennie E; Luis, Kelly M A; Doney, Scott C

2017-12-01

The U.S. Northeast Continental Shelf is experiencing rapid warming, with potentially profound consequences to marine ecosystems. While satellites document multiple scales of spatial and temporal variability on the surface, our understanding of the status, trends, and drivers of the benthic environmental change remains limited. We interpolated sparse benthic temperature data along the New England Shelf and upper Slope using a seasonally dynamic, regionally specific multiple linear regression model that merged in situ and remote sensing data. The statistical model predicted nearly 90% of the variability of the data, resulting in a synoptic time series spanning over three decades from 1982 to 2014. Benthic temperatures increased throughout the domain, including in the Gulf of Maine. Rates of benthic warming ranged from 0.1 to 0.4°C per decade, with fastest rates occurring in shallow, nearshore regions and on Georges Bank, the latter exceeding rates observed in the surface. Rates of benthic warming were up to 1.6 times faster in winter than the rest of the year in many regions, with important implications for disease occurrence and energetics of overwintering species. Drivers of warming varied over the domain. In southern New England and the mid-Atlantic shallow Shelf regions, benthic warming was tightly coupled to changes in SST, whereas both regional and basin-scale changes in ocean circulation affect temperatures in the Gulf of Maine, the Continental Shelf, and Georges Banks. These results highlight data gaps, the current feasibility of prediction from remotely sensed variables, and the need for improved understanding on how climate may affect seasonally specific ecological processes.

Enviromental influences on the {sup 137}Cs kinetics of the yellow-bellied turtle (Trachemys Scripta)

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

Peters, E.L.; Brisbin, L.I. Jr.

1996-02-01

Assessments of ecological risk require accurate predictions of contaminant dynamics in natural populations. However, simple deterministic models that assume constant uptake rates and elimination fractions may compromise both their ecological realism and their general application to animals with variable metabolism or diets. In particular, the temperature-dependent model of metabolic rates characteristic of ectotherms may lead to significant differences between observed and predicted contaminant kinetics. We examined the influence of a seasonally variable thermal environment on predicting the uptake and annual cycling of contaminants by ectotherms, using a temperature-dependent model of {sup 137}Cs kinetics in free-living yellow-bellied turtles, Trachemys scripta. Wemore » compared predictions from this model with those of deterministics negative exponential and flexibly shaped Richards sigmoidal models. Concentrations of {sup 137}Cs in a population if this species in Pond B, a radionuclide-contaminated nuclear reactor cooling reservoir, and {sup 137}Cs uptake by the uncontaminated turtles held captive in Pond B for 4 yr confirmed both the pattern of uptake and the equilibrium concentrations predicted by the temperature-dependent model. Almost 90% of the variance on the predicted time-integrated {sup 137}Cs concentration was explainable by linear relationships with model paramaters. The model was also relatively insensitive to uncertainties in the estimates of ambient temperature, suggesting that adequate estimates of temperature-dependent ingestion and elimination may require relatively few measurements of ambient conditions at sites of interest. Analyses of Richards sigmoidal models of {sup 137}Cs uptake indicated significant differences from a negative exponential trajectory in the 1st yr after the turtles` release into Pond B. 76 refs., 7 figs., 5 tabs.« less

Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

PubMed

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation.

PubMed

Mohebbi, Maryam; Ghassemian, Hassan; Asl, Babak Mohammadzadeh

2011-05-01

This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. This method consists of four steps: Preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features, but also increases the classification accuracy by selecting the most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively.

MADRS symptom subtypes in ECT-treated depressed patients: relationship to response and subsequent ECT.

PubMed

Spashett, Renee; Fernie, Gordon; Reid, Ian C; Cameron, Isobel M

2014-09-01

This study aimed to explore the relationship of Montgomery-Åsberg Depression Rating Scale (MADRS) symptom subtypes with response to electroconvulsive therapy (ECT) and subsequent ECT treatment within 12 months. A consecutive sample of 414 patients with depression receiving ECT in the North East of Scotland was assessed by retrospective chart review. Response rate was defined as greater than or equal to 50% decrease in pretreatment total MADRS score or a posttreatment total MADRS less than or equal to 10. Principal component analyses were conducted on a sample with psychotic features (n = 124) and a sample without psychotic features (n = 290). Scores on extracted factor subscales, clinical and demographic characteristics were assessed for association with response and subsequent ECT treatment within 12 months. Where more than 1 variable was associated with response or subsequent ECT, logistic regression analysis was applied. MADRS symptom subtypes formed 3 separate factors in both samples. Logistic regression revealed older age and high "Despondency" subscale score predicted response in the nonpsychotic group. Older age alone predicted response in the group with psychotic features. Nonpsychotic patients subsequently re-treated with ECT were older than those not prescribed subsequent ECT. No association of variables emerged with subsequent ECT treatment in the group with psychotic features. Being of older age and the presence of psychotic features predicted response. Presence of psychotic features alone predicted subsequent retreatment. Subscale scores of the MADRS are of limited use in predicting which patients with depression will respond to ECT, with the exception of "Despondency" subscale scores in patients without psychotic features.

Performance of diagnosis-based risk adjustment measures in a population of sick Australians.

PubMed

Duckett, S J; Agius, P A

2002-12-01

Australia is beginning to explore 'managed competition' as an organising framework for the health care system. This requires setting fair capitation rates, i.e. rates that adjust for the risk profile of covered lives. This paper tests two US-developed risk adjustment approaches using Australian data. Data from the 'co-ordinated care' dataset (which incorporates all service costs of 16,538 participants in a large health service research project conducted in 1996-99) were grouped into homogenous risk categories using risk adjustment 'grouper software'. The grouper products yielded three sets of homogenous categories: Diagnostic Groups and Diagnostic cost Groups. A two-stage analysis of predictive power was used: probability of any service use in the concurrent year, next year and the year after (logistic regression) and, for service users, a regression of logged cost of service use. The independent variables were diagnosis gender, a SES variable and the Age, gender and diagnosis-based risk adjustment measures explain around 40-45% of variation in costs of service use in the current year for untrimmed data (compared with around 15% for age and gender alone). Prediction of subsequent use is much poorer (around 20%). Using more information to assign people to risk categories generally improves prediction. Predictive power of diagnosis-base risk adjusters on this Australian dataset is similar to that found in Low predictive power carries policy risks of cream skimming rather than managing population health and care. Competitive funding models with risk adjustment on prior year experience could reduce system efficiency if implemented with current risk adjustment technology.

An Overview of Depression among Transgender Women

PubMed Central

2014-01-01

Rates of depression are higher in transgender women than in the general population, warranting an understanding of the variables related to depression in this group. Results of the literature review of depression in transgender women reveal several variables influencing depression, including social support, violence, sex work, and gender identity. The theoretical constructs of minority stress, coping, and identity control theory are explored in terms of how they may predict depression in transgender women. Depression and depressive symptoms have been used to predict high-risk sexual behaviors with mixed results. The implications of the findings on treating depression in transgender women include taking into account the stress of transition and the importance of supportive peers and family. Future studies should explore a model of depression and high-risk behaviors in transgender women. PMID:24744918

Social Cognitive Theory Predictors of Exercise Behavior in Endometrial Cancer Survivors

PubMed Central

Basen-Engquist, Karen; Carmack, Cindy L.; Li, Yisheng; Brown, Jubilee; Jhingran, Anuja; Hughes, Daniel C.; Perkins, Heidi Y.; Scruggs, Stacie; Harrison, Carol; Baum, George; Bodurka, Diane C.; Waters, Andrew

2014-01-01

Objective This study evaluated whether social cognitive theory (SCT) variables, as measured by questionnaire and ecological momentary assessment (EMA), predicted exercise in endometrial cancer survivors. Methods One hundred post-treatment endometrial cancer survivors received a 6-month home-based exercise intervention. EMAs were conducted using hand-held computers for 10- to 12-day periods every 2 months. Participants rated morning self-efficacy and positive and negative outcome expectations using the computer, recorded exercise information in real time and at night, and wore accelerometers. At the midpoint of each assessment period participants completed SCT questionnaires. Using linear mixed-effects models, we tested whether morning SCT variables predicted minutes of exercise that day (Question 1) and whether exercise minutes at time point Tj could be predicted by questionnaire measures of SCT variables from time point Tj-1 (Question 2). Results Morning self-efficacy significantly predicted that day’s exercise minutes (p<.0001). Morning positive outcome expectations was also associated with exercise minutes (p=0.0003), but the relationship was attenuated when self-efficacy was included in the model (p=0.4032). Morning negative outcome expectations was not associated with exercise minutes. Of the questionnaire measures of SCT variables, only exercise self-efficacy predicted exercise at the next time point (p=0.003). Conclusions The consistency of the relationship between self-efficacy and exercise minutes over short (same day) and longer (Tj to Tj-1) time periods provides support for a causal relationship. The strength of the relationship between morning self-efficacy and exercise minutes suggest that real-time interventions that target daily variation in self-efficacy may benefit endometrial cancer survivors’ exercise adherence. PMID:23437853

Diamond Tool Specific Wear Rate Assessment in Granite Machining by Means of Knoop Micro-Hardness and Process Parameters

NASA Astrophysics Data System (ADS)

Goktan, R. M.; Gunes Yılmaz, N.

2017-09-01

The present study was undertaken to investigate the potential usability of Knoop micro-hardness, both as a single parameter and in combination with operational parameters, for sawblade specific wear rate (SWR) assessment in the machining of ornamental granites. The sawing tests were performed on different commercially available granite varieties by using a fully instrumented side-cutting machine. During the sawing tests, two fundamental productivity parameters, namely the workpiece feed rate and cutting depth, were varied at different levels. The good correspondence observed between the measured Knoop hardness and SWR values for different operational conditions indicates that it has the potential to be used as a rock material property that can be employed in preliminary wear estimations of diamond sawblades. Also, a multiple regression model directed to SWR prediction was developed which takes into account the Knoop hardness, cutting depth and workpiece feed rate. The relative contribution of each independent variable in the prediction of SWR was determined by using test statistics. The prediction accuracy of the established model was checked against new observations. The strong prediction performance of the model suggests that its framework may be applied to other granites and operational conditions for quantifying or differentiating the relative wear performance of diamond sawblades.

Evaluation of a pilot workload metric for simulated VTOL landing tasks

NASA Technical Reports Server (NTRS)

North, R. A.; Graffunder, K.

1979-01-01

A methodological approach to measuring workload was investigated for evaluation of new concepts in VTOL aircraft displays. Multivariate discriminant functions were formed from conventional flight performance and/or visual response variables to maximize detection of experimental differences. The flight performance variable discriminant showed maximum differentiation between crosswind conditions. The visual response measure discriminant maximized differences between fixed vs. motion base conditions and experimental displays. Physiological variables were used to attempt to predict the discriminant function values for each subject/condition/trial. The weights of the physiological variables in these equations showed agreement with previous studies. High muscle tension, light but irregular breathing patterns, and higher heart rate with low amplitude all produced higher scores on this scale and thus, represented higher workload levels.

Stigma predicts residential treatment length for substance use disorder

PubMed Central

Luoma, Jason B.; Kulesza, Magdalena; Hayes, Steven C.; Kohlenberg, Barbara; Larimer, Mary

2016-01-01

Background Stigma has been suggested as a possible contributor to the high rates of treatment attrition in substance-dependent individuals, but no published empirical studies have examined this association. Objectives The present paper assessed the relationship between baseline stigma variables and length of treatment stay in a sample of patients in a residential addictions treatment unit. Methods The relationship between baseline stigma variables (self-stigma, enacted stigma, and shame) and length of stay for participants (n = 103) in a residential addictions treatment unit was examined. Results Higher self-stigma predicted longer stay in residential addictions treatment, even after controlling for age, marital status, race, overall mental health, social support, enacted stigma, and internalized shame. However, other stigma variables (i.e. internalized shame, stigma-related rejection) did not reliably predict length of treatment stay. Conclusion These results are consistent with other findings suggesting that people with higher self-stigma may have a lowered sense of self-efficacy and heightened fear of being stigmatized and therefore retreat into more protected settings such as residential treatment, potentially resulting in higher treatment costs. Specialized clinical interventions may be necessary to help participants cope with reduced self-efficacy and fear of being stigmatized. PMID:24766087

The role of gender and sexual experience in predicting adolescent condom use intentions using the theory of planned behaviour.

PubMed

Rich, Antonia; Mullan, Barbara A; Sainsbury, Kirby; Kuczmierczyk, Andrzej R

2014-08-01

To examine how the prediction of condom-related cognitions, intentions, and behaviour amongst adolescents may differ according to gender and sexual experience within a theory of planned behaviour (TPB) framework. Adolescents (N = 306) completed questionnaires about sexual experience, condom use, TPB variables, perceived risk, and safe sex knowledge. Significant differences in TPB variables, perceived risk, and knowledge were found; males and sexually experienced participants were generally less positive about condom use. Twenty percent of the variance in attitudes was accounted for by four variables; specifically, female gender, no previous sexual experience, better safe sex knowledge, and greater risk perceptions were associated with more positive attitudes. The prediction of intentions separately amongst sexually experienced (R(2) = 0.468) and inexperienced (R(2) = 0.436) participants revealed that, for the former group, attitudes and subjective norms were the most important considerations. In contrast, among the inexperienced participants, attitudes and the gender-by-perceived risk interaction term represented significant influences. The results suggest that interventions designed to improve adolescents' intentions to use condoms and rates of actual condom use should consider differences in gender and sexual experience.

A simple validated method for predicting the risk of hospitalization for worsening of heart failure in ambulatory patients: the Redin-SCORE.

PubMed

Álvarez-García, Jesús; Ferrero-Gregori, Andreu; Puig, Teresa; Vázquez, Rafael; Delgado, Juan; Pascual-Figal, Domingo; Alonso-Pulpón, Luis; González-Juanatey, José R; Rivera, Miguel; Worner, Fernando; Bardají, Alfredo; Cinca, Juan

2015-08-01

Prevention of hospital readmissions is one of the main objectives in the management of patients with heart failure (HF). Most of the models predicting readmissions are based on data extracted from hospitalized patients rather than from outpatients. Our objective was to develop a validated score predicting 1-month and 1-year risk of readmission for worsening of HF in ambulatory patients. A cohort of 2507 ambulatory patients with chronic HF was prospectively followed for a median of 3.3 years. Clinical, echocardiographic, ECG, and biochemical variables were used in a competing risk regression analysis to construct a risk score for readmissions due to worsening of HF. Thereafter, the score was externally validated using a different cohort of 992 patients with chronic HF (MUSIC registry). Predictors of 1-month readmission were the presence of elevated natriuretic peptides, left ventricular (LV) HF signs, and estimated glomerular filtration rate (eGFR) <60 mL/min/m(2) . Predictors of 1-year readmission were elevated natriuretic peptides, anaemia, left atrial size >26 mm/m(2) , heart rate >70 b.p.m., LV HF signs, and eGFR <60 mL/min/m(2) . The C-statistics for the models were 0.72 and 0.66, respectively. The cumulative incidence function distinguished low-risk (<1% event rate) and high-risk groups (>5% event rate) for 1-month HF readmission. Likewise, low-risk (7.8%), intermediate-risk (15.6%) and high-risk groups (26.1%) were identified for 1-year HF readmission risk. The C-statistics remained consistent after the external validation (<5% loss of discrimination). The Redin-SCORE predicts early and late readmission for worsening of HF using proven prognostic variables that are routinely collected in outpatient management of chronic HF. © 2015 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

Role of vegetation in interplay of climate, soil and groundwater recharge in a global dataset

NASA Astrophysics Data System (ADS)

Kim, J. H.; Jackson, R. B.

2010-12-01

Groundwater is an essential resource for people and ecosystems worldwide. Our capacity to ameliorate predicted global water shortages and to maintain sustainable water supplies depend on a better understanding of the controls of recharge and how vegetation change may affect recharge mechanisms. The goals of this study are to quantify the importance of vegetation as a dominant control on recharge globally and to compare the importance of vegetation with other hydrologically important variables, including climate and soil. We based our global analysis on > 500 recharge estimates from the literature that contained information on vegetation, soil and climate or location. Plant functional types significantly affected groundwater recharge rates substantially. After climatic factors (water inputs, PET, and seasonality), vegetation types explained about 15% of the residuals in the dataset. Across all climatic factors, croplands had the highest recharge rates, followed by grasslands, scrublands and woodlands (average recharge: 75, 63, 30, 22 mm/yr respectively). Recharge under woodlands showed the most nonlinear response to water inputs. Differences in recharge between the vegetation types were more exaggerated at arid climates and in clay soils, indicating greater biological control on soil water fluxes in these conditions. Our results shows that vegetation greatly affects recharge rates globally and alters relationship between recharge and physical variables allowing us to better predict recharge rates globally.

Thermal history regulates methylbutenol basal emission rate in Pinus ponderosa.

PubMed

Gray, Dennis W; Goldstein, Allen H; Lerdau, Manuel T

2006-07-01

Methylbutenol (MBO) is a 5-carbon alcohol that is emitted by many pines in western North America, which may have important impacts on the tropospheric chemistry of this region. In this study, we document seasonal changes in basal MBO emission rates and test several models predicting these changes based on thermal history. These models represent extensions of the ISO G93 model that add a correction factor C(basal), allowing MBO basal emission rates to change as a function of thermal history. These models also allow the calculation of a new emission parameter E(standard30), which represents the inherent capacity of a plant to produce MBO, independent of current or past environmental conditions. Most single-component models exhibited large departures in early and late season, and predicted day-to-day changes in basal emission rate with temporal offsets of up to 3 d relative to measured basal emission rates. Adding a second variable describing thermal history at a longer time scale improved early and late season model performance while retaining the day-to-day performance of the parent single-component model. Out of the models tested, the T(amb),T(max7) model exhibited the best combination of day-to-day and seasonal predictions of basal MBO emission rates.

Hydrograph Predictions of Glacial Lake Outburst Floods From an Ice-Dammed Lake

NASA Astrophysics Data System (ADS)

McCoy, S. W.; Jacquet, J.; McGrath, D.; Koschitzki, R.; Okuinghttons, J.

2017-12-01

Understanding the time evolution of glacial lake outburst floods (GLOFs), and ultimately predicting peak discharge, is crucial to mitigating the impacts of GLOFs on downstream communities and understanding concomitant surface change. The dearth of in situ measurements taken during GLOFs has left many GLOF models currently in use untested. Here we present a dataset of 13 GLOFs from Lago Cachet Dos, Aysen Region, Chile in which we detail measurements of key environmental variables (total volume drained, lake temperature, and lake inflow rate) and high temporal resolution discharge measurements at the source lake, in addition to well-constrained ice thickness and bedrock topography. Using this dataset we test two common empirical equations as well as the physically-based model of Spring-Hutter-Clarke. We find that the commonly used empirical relationships based solely on a dataset of lake volume drained fail to predict the large variability in observed peak discharges from Lago Cachet Dos. This disagreement is likely because these equations do not consider additional environmental variables that we show also control peak discharge, primarily, lake water temperature and the rate of meltwater inflow to the source lake. We find that the Spring-Hutter-Clarke model can accurately simulate the exponentially rising hydrographs that are characteristic of ice-dammed GLOFs, as well as the order of magnitude variation in peak discharge between events if the hydraulic roughness parameter is allowed to be a free fitting parameter. However, the Spring-Hutter-Clarke model over predicts peak discharge in all cases by 10 to 35%. The systematic over prediction of peak discharge by the model is related to its abrupt flood termination that misses the observed steep falling limb of the flood hydrograph. Although satisfactory model fits are produced, the range in hydraulic roughness required to obtain these fits across all events was large, which suggests that current models do not completely capture the physics of these systems, thus limiting their ability to truly predict peak discharges using only independently constrained parameters. We suggest what some of these missing physics might be.

Predicting Fog in the Nocturnal Boundary Layer

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Risk factors for accelerated polyethylene wear and osteolysis in ABG I total hip arthroplasty

PubMed Central

Havranek, Vitezslav; Zapletalova, Jana

2009-01-01

We analysed data from 155 revisions of identical cementless hip prostheses to determine the influence of patient-, implant- and surgery-related factors on the polyethylene wear rate and size of periprosthetic osteolysis (OL). This was calculated by logistic regression analysis. Factors associated with an increased/decreased wear rate included position of the cup relative to Kohler’s line, increase in abduction angle of the cup, traumatic and inflammatory arthritis as a primary diagnosis, and patient height. Severe acetabular bone defects were predicted by an increased wear rate (odds ratio, OR = 5.782 for wear rate above 200 mm3/y), and increased height of the patient (OR = 0.905 per each centimetre). Predictors of severe bone defects in the femur were the increased wear rate (OR = 3.479 for wear rate above 200 mm3/y) and placement of the cup outside of the true acetabulum (OR = 3.292). Variables related to surgical technique were the most predictive of polyethylene wear rate. PMID:19214506

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization

NASA Astrophysics Data System (ADS)

Chiu, Hung-Chih; Lin, Yen-Hung; Lo, Men-Tzung; Tang, Sung-Chun; Wang, Tzung-Dau; Lu, Hung-Chun; Ho, Yi-Lwun; Ma, Hsi-Pin; Peng, Chung-Kang

2015-08-01

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization

PubMed Central

Chiu, Hung-Chih; Lin, Yen-Hung; Lo, Men-Tzung; Tang, Sung-Chun; Wang, Tzung-Dau; Lu, Hung-Chun; Ho, Yi-Lwun; Ma, Hsi-Pin; Peng, Chung-Kang

2015-01-01

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress. PMID:26286628

The incremental value of self-reported mental health measures in predicting functional outcomes of veterans.

PubMed

Eisen, Susan V; Bottonari, Kathryn A; Glickman, Mark E; Spiro, Avron; Schultz, Mark R; Herz, Lawrence; Rosenheck, Robert; Rofman, Ethan S

2011-04-01

Research on patient-centered care supports use of patient/consumer self-report measures in monitoring health outcomes. This study examined the incremental value of self-report mental health measures relative to a clinician-rated measure in predicting functional outcomes among mental health service recipients. Participants (n = 446) completed the Behavior and Symptom Identification Scale, the Brief Symptom Inventory, and the Veterans/Rand Short Form-36 at enrollment in the study (T1) and 3 months later (T2). Global Assessment of Functioning (GAF) ratings, mental health service utilization, and psychiatric diagnoses were obtained from administrative data files. Controlling for demographic and clinical variables, results indicated that improvement based on the self-report measures significantly predicted one or more functional outcomes (i.e., decreased likelihood of post-enrollment psychiatric hospitalization and increased likelihood of paid employment), above and beyond the predictive value of the GAF. Inclusion of self-report measures may be a useful addition to performance measurement efforts.

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

Effects of treatment on respiratory rate, serum natriuretic peptide concentration, and Doppler echocardiographic indices of left ventricular filling pressure in dogs with congestive heart failure secondary to degenerative mitral valve disease and dilated cardiomyopathy.

PubMed

Schober, Karsten E; Hart, Taye M; Stern, Joshua A; Li, Xiaobai; Samii, Valerie F; Zekas, Lisa J; Scansen, Brian A; Bonagura, John D

2011-08-15

To evaluate the effects of treatment on respiratory rate, serum natriuretic peptide concentrations, and Doppler echocardiographic indices of left ventricular filling pressure in dogs with congestive heart failure (CHF) secondary to degenerative mitral valve disease (MVD) and dilated cardiomyopathy (DCM). Prospective cohort study. 63 client-owned dogs. Physical examination, thoracic radiography, analysis of natriuretic peptide concentrations, and Doppler echocardiography were performed twice, at baseline (examination 1) and 5 to 14 days later (examination 2). Home monitoring of respiratory rate was performed by the owners between examinations. In dogs with MVD, resolution of CHF was associated with a decrease in respiratory rate, serum N-terminal probrain natriuretic peptide (NT-proBNP) concentration, and diastolic functional class and an increase of the ratio of peak velocity of early diastolic transmitral flow to peak velocity of early diastolic lateral mitral annulus motion (E:Ea Lat). In dogs with DCM, resolution of CHF was associated with a decrease in respiratory rate and serum NT-proBNP concentration and significant changes in 7 Doppler echocardiographic variables, including a decrease of E:Ea Lat and the ratio of peak velocity of early diastolic transmitral flow to isovolumic relaxation time. Only respiratory rate predicted the presence of CHF at examination 2 with high accuracy. Resolution of CHF was associated with predictable changes in respiratory rate, serum NT-proBNP concentration, and selected Doppler echocardiographic variables in dogs with DCM and MVD. Home monitoring of respiratory rate was simple and was the most useful in the assessment of successful treatment of CHF.

Reliability analysis of C-130 turboprop engine components using artificial neural network

NASA Astrophysics Data System (ADS)

Qattan, Nizar A.

In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer.

Variable Demographic Rates in an Invasive Plant Species: Differences Among Populations and Management Implications

USDA-ARS?s Scientific Manuscript database

Population managers are frequently faced with the challenge of selecting the most effective management strategy from a set of available strategies. In the case of classical weed biological control, this requires predicting a priori which of a group of candidate biocontrol agent species has the great...

Exploring the Impact of Structured Learning Assistance (SLA) on College Writing

ERIC Educational Resources Information Center

Giraldo-García, Regina J.; Magiste, Edward J.

2018-01-01

This study determined that the addition of Structured Learning Assistance (SLA) attendance increased passage rates (from 66.5% to 82%) of first year students in English 101 courses. The model predicts first year students' performance in college writing, controlling for variables such as American College Test scores, and gender. A…

The Validity of Physical Aggression in Predicting Adolescent Academic Performance

ERIC Educational Resources Information Center

Loveland, James M.; Lounsbury, John W.; Welsh, Deborah; Buboltz, Walter C.

2007-01-01

Background: Aggression has a long history in academic research as both a criterion and a predictor variable and it is well documented that aggression is related to a variety of poor academic outcomes such as: lowered academic performance, absenteeism and lower graduation rates. However, recent research has implicated physical aggression as being…

Drinking and Smoking Habits of Students at Northern Territory University.

ERIC Educational Resources Information Center

Roberts, Kathryn L.; Jackson, Adrian S.

Persons in the Northern Territory who drink have the highest per capita daily consumption of alcohol and the highest rate of tobacco smoking in Australia. This study identifies the drinking patterns and demographic and personal variables that might predict risk levels for Northern Territory University (NTU) students and therefore give direction to…

Long-Term Speech Results of Cleft Palate Speakers with Marginal Velopharyngeal Competence.

ERIC Educational Resources Information Center

Hardin, Mary A.; And Others

1990-01-01

This study of the longitudinal speech performance of 48 cleft palate speakers with marginal velopharyngeal competence, from age 6 to adolescence, found that the adolescent subjects' velopharyngeal status could be predicted based on 2 variables at age 6: the severity ratings of articulation defectiveness and nasality. (Author/JDD)

Organizational justice is related to heart rate variability in white-collar workers, but not in blue-collar workers-findings from a cross-sectional study.

PubMed

Herr, Raphael M; Bosch, Jos A; van Vianen, Annelies E M; Jarczok, Marc N; Thayer, Julian F; Li, Jian; Schmidt, Burkhard; Fischer, Joachim E; Loerbroks, Adrian

2015-06-01

Perceived injustice at work predicts coronary heart disease. Vagal dysregulation represents a potential psychobiological pathway. We examined associations between organizational justice and heart rate variability (HRV) indicators. Grounded in social exchange and psychological contract theory, we tested predictions that these associations are more pronounced among white-collar than among blue-collar workers. Cross-sectional data from 222 blue-collar and 179 white-collar men were used. Interactional and procedural justice were measured by questionnaire. Ambulatory HRV was assessed across 24 h. Standardized regression coefficients (β) were calculated. Among white-collar workers, interactional justice showed positive relationships with 24-h HRV, which were strongest during sleeping time (adjusted βs≥0.26; p values≤0.01). No associations were found for blue-collar workers. A comparable but attenuated pattern was observed for procedural justice. Both dimensions of organizational injustice were associated with lowered HRV among white-collar workers. The impact of justice and possibly its association with health seems to differ by occupational groups.

Sociodemographic and Psychiatric Diagnostic Predictors of 3-Year Incidence of DSM-IV Substance Use Disorders among Men and Women in the National Epidemiologic Survey on Alcohol and Related Conditions

PubMed Central

Goldstein, Risë B.; Smith, Sharon M.; Dawson, Deborah A.; Grant, Bridget F.

2016-01-01

Incidence rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women, but information on whether sociodemographic and psychiatric diagnostic predictors of AUD and DUD incidence differ by sex is limited. Using data from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions, sex-specific 3-year incidence rates of AUDs and DUDs among United States adults were compared by sociodemographic variables and baseline psychiatric disorders. Sex-specific logistic regression models estimated odds ratios for prediction of incident AUDs and DUDs, adjusting for potentially confounding baseline sociodemographic and diagnostic variables. Few statistically significant sex differences in predictive relationships were identified and those observed were generally modest. Prospective research is needed to identify predictors of incident DSM-5 AUDs and DUDs and their underlying mechanisms, including whether there is sex specificity by developmental phase, in the role of additional comorbidity in etiology and course, and in outcomes of prevention and treatment. PMID:26727008

Termites promote resistance of decomposition to spatiotemporal variability in rainfall.

PubMed

Veldhuis, Michiel P; Laso, Francisco J; Olff, Han; Berg, Matty P

2017-02-01

The ecological impact of rapid environmental change will depend on the resistance of key ecosystems processes, which may be promoted by species that exert strong control over local environmental conditions. Recent theoretical work suggests that macrodetritivores increase the resistance of African savanna ecosystems to changing climatic conditions, but experimental evidence is lacking. We examined the effect of large fungus-growing termites and other non-fungus-growing macrodetritivores on decomposition rates empirically with strong spatiotemporal variability in rainfall and temperature. Non-fungus-growing larger macrodetritivores (earthworms, woodlice, millipedes) promoted decomposition rates relative to microbes and small soil fauna (+34%) but both groups reduced their activities with decreasing rainfall. However, fungus-growing termites increased decomposition rates strongest (+123%) under the most water-limited conditions, making overall decomposition rates mostly independent from rainfall. We conclude that fungus-growing termites are of special importance in decoupling decomposition rates from spatiotemporal variability in rainfall due to the buffered environment they create within their extended phenotype (mounds), that allows decomposition to continue when abiotic conditions outside are less favorable. This points at a wider class of possibly important ecological processes, where soil-plant-animal interactions decouple ecosystem processes from large-scale climatic gradients. This may strongly alter predictions from current climate change models. © 2016 by the Ecological Society of America.

The Effect of Sex on Heart Rate Variability at High Altitude.

PubMed

Boos, Christopher John; Vincent, Emma; Mellor, Adrian; O'Hara, John; Newman, Caroline; Cruttenden, Richard; Scott, Phylip; Cooke, Mark; Matu, Jamie; Woods, David Richard

2017-12-01

There is evidence suggesting that high altitude (HA) exposure leads to a fall in heart rate variability (HRV) that is linked to the development of acute mountain sickness (AMS). The effects of sex on changes in HRV at HA and its relationship to AMS are unknown. HRV (5-min single-lead ECG) was measured in 63 healthy adults (41 men and 22 women) 18-56 yr of age at sea level (SL) and during a HA trek at 3619, 4600, and 5140 m, respectively. The main effects of altitude (SL, 3619 m, 4600 m, and 5140 m) and sex (men vs women) and their potential interaction were assessed using a factorial repeated-measures ANOVA. Logistic regression analyses were performed to assess the ability of HRV to predict AMS. Men and women were of similar age (31.2 ± 9.3 vs 31.7 ± 7.5 yr), ethnicity, and body and mass index. There was main effect for altitude on heart rate, SD of normal-to-normal (NN) intervals (SDNN), root mean square of successive differences (RMSSD), number of pairs of successive NN differing by >50 ms (NN50), NN50/total number of NN, very low-frequency power, low-frequency (LF) power, high-frequency (HF) power, and total power (TP). The most consistent effect on post hoc analysis was reduction in these HRV measures between 3619 and 5140 m at HA. Heart rate was significantly lower and SDNN, RMSSD, LF power, HF power, and TP were higher in men compared with women at HA. There was no interaction between sex and altitude for any of the HRV indices measured. HRV was not predictive of AMS development. Increasing HA leads to a reduction in HRV. Significant differences between men and women emerge at HA. HRV was not predictive of AMS.

Analysis of factors that influence rates of carbon monoxide uptake, distribution, and washout from blood and extravascular tissues using a multicompartment model.

PubMed

Bruce, Margaret C; Bruce, Eugene N

2006-04-01

To better understand factors that influence carbon monoxide (CO) washout rates, we utilized a multicompartment mathematical model to predict rates of CO uptake, distribution in vascular and extravascular (muscle vs. other soft tissue) compartments, and washout over a range of exposure and washout conditions with varied subject-specific parameters. We fitted this model to experimental data from 15 human subjects, for whom subject-specific parameters were known, multiple washout carboxyhemoglobin (COHb) levels were available, and CO exposure conditions were identical, to investigate the contributions of exposure conditions and individual variability to CO washout from blood. We found that CO washout from venous blood was biphasic and that postexposure times at which COHb samples were obtained significantly influenced the calculated CO half times (P < 0.0001). The first, more rapid, phase of CO washout from the blood reflected the loss of CO to the expired air and to a slow uptake by the muscle compartment, whereas the second, slower washout phase was attributable to CO flow from the muscle compartment back to the blood and removal from blood via the expired air. When the model was used to predict the effects of varying exposure conditions for these subjects, the CO exposure duration, concentration, peak COHb levels, and subject-specific parameters each influenced washout half times. Blood volume divided by ventilation correlated better with half-time predictions than did cardiac output, muscle mass, or ventilation, but it explained only approximately 50% of half-time variability. Thus exposure conditions, COHb sampling times, and individual parameters should be considered when estimating CO washout rates for poisoning victims.

Extracting information on the spatial variability in erosion rate stored in detrital cooling age distributions in river sands

NASA Astrophysics Data System (ADS)

Braun, Jean; Gemignani, Lorenzo; van der Beek, Peter

2018-03-01

One of the main purposes of detrital thermochronology is to provide constraints on the regional-scale exhumation rate and its spatial variability in actively eroding mountain ranges. Procedures that use cooling age distributions coupled with hypsometry and thermal models have been developed in order to extract quantitative estimates of erosion rate and its spatial distribution, assuming steady state between tectonic uplift and erosion. This hypothesis precludes the use of these procedures to assess the likely transient response of mountain belts to changes in tectonic or climatic forcing. Other methods are based on an a priori knowledge of the in situ distribution of ages to interpret the detrital age distributions. In this paper, we describe a simple method that, using the observed detrital mineral age distributions collected along a river, allows us to extract information about the relative distribution of erosion rates in an eroding catchment without relying on a steady-state assumption, the value of thermal parameters or an a priori knowledge of in situ age distributions. The model is based on a relatively low number of parameters describing lithological variability among the various sub-catchments and their sizes and only uses the raw ages. The method we propose is tested against synthetic age distributions to demonstrate its accuracy and the optimum conditions for it use. In order to illustrate the method, we invert age distributions collected along the main trunk of the Tsangpo-Siang-Brahmaputra river system in the eastern Himalaya. From the inversion of the cooling age distributions we predict present-day erosion rates of the catchments along the Tsangpo-Siang-Brahmaputra river system, as well as some of its tributaries. We show that detrital age distributions contain dual information about present-day erosion rate, i.e., from the predicted distribution of surface ages within each catchment and from the relative contribution of any given catchment to the river distribution. The method additionally allows comparing modern erosion rates to long-term exhumation rates. We provide a simple implementation of the method in Python code within a Jupyter Notebook that includes the data used in this paper for illustration purposes.

Daily variability in working memory is coupled with negative affect: the role of attention and motivation.

PubMed

Brose, Annette; Schmiedek, Florian; Lövdén, Martin; Lindenberger, Ulman

2012-06-01

Across days, individuals experience varying levels of negative affect, control of attention, and motivation. We investigated whether this intraindividual variability was coupled with daily fluctuations in working memory (WM) performance. In 100 days, 101 younger individuals worked on a spatial N-back task and rated negative affect, control of attention, and motivation. Results showed that individuals differed in how reliably WM performance fluctuated across days, and that subjective experiences were primarily linked to performance accuracy. WM performance was lower on days with higher levels of negative affect, reduced control of attention, and reduced task-related motivation. Thus, variables that were found to predict WM in between-subjects designs showed important relationships to WM at the within-person level. In addition, there was shared predictive variance among predictors of WM. Days with increased negative affect and reduced performance were also days with reduced control of attention and reduced motivation to work on tasks. These findings are in line with proposed mechanisms linking negative affect and cognitive performance.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time‐to‐Event Analysis

PubMed Central

Gong, Xiajing; Hu, Meng

2018-01-01

Abstract Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time‐to‐event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high‐dimensional data featured by a large number of predictor variables. Our results showed that ML‐based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high‐dimensional data. The prediction performances of ML‐based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML‐based methods provide a powerful tool for time‐to‐event analysis, with a built‐in capacity for high‐dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. PMID:29536640

Community change and evidence for variable warm-water temperature adaptation of corals in Northern Male Atoll, Maldives.

PubMed

McClanahan, T R; Muthiga, N A

2014-03-15

This study provides a descriptive analysis of the North Male, Maldives seven years after the 1998 bleaching disturbance to determine the state of the coral community composition, the recruitment community, evidence for recovery, and adaptation to thermal stress. Overall, hard coral cover recovered at a rate commonly reported in the literature but with high spatial variability and shifts in taxonomic composition. Massive Porites, Pavona, Synarea, and Goniopora were unusually common in both the recruit and adult communities. Coral recruitment was low and some coral taxa, namely Tubipora, Seriatopora, and Stylophora, were rarer than expected. A study of the bleaching response to a thermal anomaly in 2005 indicated that some taxa, including Leptoria, Platygyra, Favites, Fungia, Hydnophora, and Galaxea astreata, bleached as predicted while others, including Acropora, Pocillopora, branching Porites, Montipora, Stylophora, and Alveopora, bleached less than predicted. This indicates variable-adaptation potentials among the taxa and considerable potential for ecological reorganization of the coral community. Copyright © 2014 Elsevier Ltd. All rights reserved.

Profiles of neurological outcome prediction among intensivists.

PubMed

Racine, Eric; Dion, Marie-Josée; Wijman, Christine A C; Illes, Judy; Lansberg, Maarten G

2009-12-01

Advances in intensive care medicine have increased survival rates of patients with critical neurological conditions. The focus of prognostication for such patients is therefore shifting from predicting chances of survival to meaningful neurological recovery. This study assessed the variability in long-term outcome predictions among physicians and aimed to identify factors that may account for this variability. Based on a clinical vignette describing a comatose patient suffering from post-anoxic brain injury intensivists were asked in a semi-structured interview about the patient's specific neurological prognosis and about prognostication in general. Qualitative research methods were used to identify areas of variability in prognostication and to classify physicians according to specific prognostication profiles. Quantitative statistics were used to assess for associations between prognostication profiles and physicians' demographic and practice characteristics. Eighteen intensivists participated. Functional outcome predictions varied along an evaluative dimension (fair/good-poor) and a confidence dimension (certain-uncertain). More experienced physicians tended to be more pessimistic about the patient's functional outcome and more certain of their prognosis. Attitudes toward quality of life varied along an evaluative dimension (good-poor) and a "style" dimension (objective-subjective). Older and more experienced physicians were more likely to express objective judgments of quality of life and to predict a worse quality of life for the patient than their younger and less experienced counterparts. Various prognostication profiles exist among intensivists. These may be dictated by factors such as physicians' age and clinical experience. Awareness of these associations may be a first step to more uniform prognostication.

Quality of care and investment in property, plant, and equipment in hospitals.

PubMed Central

Levitt, S W

1994-01-01

OBJECTIVE. This study explores the relationship between quality of care and investment in property, plant, and equipment (PPE) in hospitals. DATA SOURCES. Hospitals' investment in PPE was derived from audited financial statements for the fiscal years 1984-1989. Peer Review Organization (PRO) Generic Quality Screen (GQS) reviews and confirmed failures between April 1989 and September 1990 were obtained from the Massachusetts PRO. STUDY DESIGN. Weighted least squares regression models used PRO GQS confirmed failure rates as the dependent variable, and investment in PPE as the key explanatory variable. DATA EXTRACTION. Investment in PPE was standardized, summed by the hospital over the six years, and divided by the hospital's average number of beds in that period. The number of PRO reviewed cases with one or more GQS confirmed failures was divided by the total number of cases reviewed to create confirmed failure rates. PRINCIPAL FINDINGS. Investment in PPE in Massachusetts hospitals is correlated with GQS confirmed failure rates. CONCLUSIONS. A financial variable, investment in PPE, predicts certain dimensions of quality of care in hospitals. PMID:8113054

Evaluating within-population variability in behavior and demography for the adaptive potential of a dispersal-limited species to climate change

USGS Publications Warehouse

Muñoz, David J.; Miller Hesed, Kyle; Grant, Evan H. Campbell; Miller, David A.W.

2016-01-01

Multiple pathways exist for species to respond to changing climates. However, responses of dispersal-limited species will be more strongly tied to ability to adapt within existing populations as rates of environmental change will likely exceed movement rates. Here, we assess adaptive capacity in Plethodon cinereus, a dispersal-limited woodland salamander. We quantify plasticity in behavior and variation in demography to observed variation in environmental variables over a 5-year period. We found strong evidence that temperature and rainfall influence P. cinereus surface presence, indicating changes in climate are likely to affect seasonal activity patterns. We also found that warmer summer temperatures reduced individual growth rates into the autumn, which is likely to have negative demographic consequences. Reduced growth rates may delay reproductive maturity and lead to reductions in size-specific fecundity, potentially reducing population-level persistence. To better understand within-population variability in responses, we examined differences between two common color morphs. Previous evidence suggests that the color polymorphism may be linked to physiological differences in heat and moisture tolerance. We found only moderate support for morph-specific differences for the relationship between individual growth and temperature. Measuring environmental sensitivity to climatic variability is the first step in predicting species' responses to climate change. Our results suggest phenological shifts and changes in growth rates are likely responses under scenarios where further warming occurs, and we discuss possible adaptive strategies for resulting selective pressures.

Analysis of clinically important factors on the performance of advanced hydraulic, microprocessor-controlled exo-prosthetic knee joints based on 899 trial fittings

PubMed Central

Hahn, Andreas; Lang, Michael; Stuckart, Claudia

2016-01-01

Abstract The objective of this work is to evaluate whether clinically important factors may predict an individual's capability to utilize the functional benefits provided by an advanced hydraulic, microprocessor-controlled exo-prosthetic knee component. This retrospective cross-sectional cohort analysis investigated the data of above knee amputees captured during routine trial fittings. Prosthetists rated the performance indicators showing the functional benefits of the advanced maneuvering capabilities of the device. Subjects were asked to rate their perception. Simple and multiple linear and logistic regression was applied. Data from 899 subjects with demographics typical for the population were evaluated. Ability to vary gait speed, perform toileting, and ascend stairs were identified as the most sensitive performance predictors. Prior C-Leg users showed benefits during advanced maneuvering. Variables showed plausible and meaningful effects, however, could not claim predictive power. Mobility grade showed the largest effect but also failed to be predictive. Clinical parameters such as etiology, age, mobility grade, and others analyzed here do not suffice to predict individual potential. Daily walking distance may pose a threshold value and be part of a predictive instrument. Decisions based solely on single parameters such as mobility grade rating or walking distance seem to be questionable. PMID:27828871

Analysis of clinically important factors on the performance of advanced hydraulic, microprocessor-controlled exo-prosthetic knee joints based on 899 trial fittings.

PubMed

Hahn, Andreas; Lang, Michael; Stuckart, Claudia

2016-11-01

The objective of this work is to evaluate whether clinically important factors may predict an individual's capability to utilize the functional benefits provided by an advanced hydraulic, microprocessor-controlled exo-prosthetic knee component.This retrospective cross-sectional cohort analysis investigated the data of above knee amputees captured during routine trial fittings. Prosthetists rated the performance indicators showing the functional benefits of the advanced maneuvering capabilities of the device. Subjects were asked to rate their perception. Simple and multiple linear and logistic regression was applied.Data from 899 subjects with demographics typical for the population were evaluated. Ability to vary gait speed, perform toileting, and ascend stairs were identified as the most sensitive performance predictors. Prior C-Leg users showed benefits during advanced maneuvering. Variables showed plausible and meaningful effects, however, could not claim predictive power. Mobility grade showed the largest effect but also failed to be predictive.Clinical parameters such as etiology, age, mobility grade, and others analyzed here do not suffice to predict individual potential. Daily walking distance may pose a threshold value and be part of a predictive instrument. Decisions based solely on single parameters such as mobility grade rating or walking distance seem to be questionable.

Modeling the formation of iron sulfide scales using thermodynamic simulation software

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

Anderko, A.; Shuler, P.J.

1998-12-31

A program has been developed for generating stability diagrams that concisely represent the thermodynamic state of multicomponent, multiphase aqueous systems in wide ranges of temperature and component concentrations. The diagrams are based on a thermodynamic model that combines the Helgeson-Kirkham-Flowers equation of state for standard-state properties with a solutions nonideality model based on the activity coefficient expressions developed by Bromley and Pitzer. The diagrams offer a flexible choice of independent variables, which include component concentrations in addition to the potential and pH. The stability diagrams are used to predict the conditions that favor the formation of stable and metastable ironmore » sulfide species, which are commonly deposited under oil field-related conditions. First, the diagrams have been applied to establish a sequence of transformations that iron sulfides undergo as they age. The predicted transformation sequences take into account environmental variables (e.g., hydrogen sulfide concentration, oxygen availability, etc.). The predictions are in agreement with experimental data on iron sulfide formation at the iron/solution interface and in bulk solution. The understanding of iron sulfide transformation sequences makes it possible to simulate experimental studies of H{sub 2}S/CO{sub 2} corrosion in the presence or absence of oxygen. A comparison with laboratory corrosion rate data under gas pipeline conditions indicates that the magnitude of corrosion rates can be correlated with the predicted stability of metastable iron sulfide phases.« less

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

PubMed

Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.