Advanced techniques for mitigating the effects of temporal distortions in human in the loop control systems

NASA Astrophysics Data System (ADS)

Guo, Liwen

The desire to create more complex visual scenes in modern flight simulators outpaces recent increases in processor speed. As a result, the simulation transport delay remains a problem. Because of the limitations shown in the three prominent existing delay compensators---the lead/lag filter, the McFarland compensator and the Sobiski/Cardullo predictor---new approaches of compensating the transport delay in a flight simulator have been developed. The first novel compensator is the adaptive predictor making use of the Kalman filter algorithm in a unique manner so that the predictor can provide accurately the desired amount of prediction, significantly reducing the large spikes caused by the McFarland predictor. Among several simplified online adaptive predictors it illustrates mathematically why the stochastic approximation algorithm achieves the best compensation results. A second novel approach employed a reference aircraft dynamics model to implement a state space predictor on a flight simulator. The practical implementation formed the filter state vector from the operator's control input and the aircraft states. The relationship between the reference model and the compensator performance was investigated in great detail, and the best performing reference model was selected for implementation in the final tests. Piloted simulation tests were conducted for assessing the effectiveness of the two novel compensators in comparison to the McFarland predictor and no compensation. Thirteen pilots with heterogeneous flight experience executed straight-in and offset approaches, at various delay configurations, on a flight simulator where different predictors were applied to compensate for transport delay. Four metrics---the glide slope and touchdown errors, power spectral density of the pilot control inputs, NASA Task Load Index, and Cooper-Harper rating on the handling qualities---were employed for the analyses. The overall analyses show that while the adaptive predictor results in slightly poorer compensation for short added delay (up to 48 ms) and better compensation for long added delay (up to 192 ms) than the McFarland compensator, the state space predictor is fairly superior for short delay and significantly superior for long delay to the McFarland compensator. The state space predictor also achieves better compensation than the adaptive predictor. The results of the evaluation on the effectiveness of these predictors in the piloted tests agree with those in the theoretical offline tests conducted with the recorded simulation aircraft states.

Optimization techniques applied to passive measures for in-orbit spacecraft survivability

NASA Technical Reports Server (NTRS)

Mog, Robert A.; Price, D. Marvin

1991-01-01

Spacecraft designers have always been concerned about the effects of meteoroid impacts on mission safety. The engineering solution to this problem has generally been to erect a bumper or shield placed outboard from the spacecraft wall to disrupt/deflect the incoming projectiles. Spacecraft designers have a number of tools at their disposal to aid in the design process. These include hypervelocity impact testing, analytic impact predictors, and hydrodynamic codes. Analytic impact predictors generally provide the best quick-look estimate of design tradeoffs. The most complete way to determine the characteristics of an analytic impact predictor is through optimization of the protective structures design problem formulated with the predictor of interest. Space Station Freedom protective structures design insight is provided through the coupling of design/material requirements, hypervelocity impact phenomenology, meteoroid and space debris environment sensitivities, optimization techniques and operations research strategies, and mission scenarios. Major results are presented.

Can psychological well-being scales and hormone levels be used to predict acute performance of anaerobic training tasks in elite female volleyball players?

PubMed

Mielgo-Ayuso, Juan; Zourdos, Michael C; Clemente-Suárez, Vicente J; Calleja-González, Julio; Shipherd, Amber M

2017-10-15

The purpose of this study was to examine relationships between pre-training psychological well-being assessment scales (General Health Questionnaire-28-GHQ-28, Competitive State Anxiety Inventory-2-CSAI-2, Sport Competition Anxiety Test-SCAT, State-Trait Anxiety Inventory-S-STAI-S, Oviedo Sleep Questionnaire-OSQ and Psychological Characteristics Related to Sport Performance-PCSP), and pre-training stress hormone concentrations (cortisol-C, total testosterone-TT, free testosterone-FT, adrenocorticotropic hormone-ACTH and testosterone/cortisol-T/C ratios), on acute neuromuscular performance (ANP) in female volleyballers. Forty elite female volleyballers (27±4yrs.; 178.3±8.5cm; 67.9±7.2kg) participated. Bivariate correlations were performed between psychological assessments and hormone levels with ANP. All psychological scales presented at least one significant (p<0.05) relationship or prediction of ANP. Contrastingly, among hormones, the only significant relationship was between TT/C ratio and Overhead Medicine Ball Throw (r=0.34; p<0.05). Therefore, our data shows that results of general and sport-specific psychological well-being scales prior to training are more consistently related to performance in elite female volleyballers than pre-training stress hormone concentrations. Copyright © 2017 Elsevier Inc. All rights reserved.

Urban Green Space Perception and Its Contribution to Well-Being.

PubMed

Kothencz, Gyula; Kolcsár, Ronald; Cabrera-Barona, Pablo; Szilassi, Péter

2017-07-12

Individual perceptions are essential when evaluating the well-being benefits from urban green spaces. This study predicted the influence of perceived green space characteristics in the city of Szeged, Hungary, on two well-being variables: the green space visitors' level of satisfaction and the self-reported quality of life. The applied logistic regression analysis used nine predictors: seven perceived green space characteristics from a questionnaire survey among visitors of five urban green spaces of Szeged; and the frequency of green space visitors' crowd-sourced recreational running paths and photographs picturing green space aesthetics. Results revealed that perceived green space characteristics with direct well-being benefits were strong predictors of both dependent variables. Perceived green space characteristics with indirect, yet fundamental, well-being benefits, namely, regulating ecosystem services had minor influence on the dependent variables. The crowd-sourced geo-tagged data predicted only the perceived quality of life contributions; but revealed spatial patterns of recreational green space use and aesthetics. This study recommends that regulating ecosystem services should be planned with a focus on residents' aesthetic and recreational needs. Further research on the combination of green space visitors´ perceptions and crowd-sourced geo-tagged data is suggested to promote planning for well-being and health benefits of urban green spaces.

Urban Green Space Perception and Its Contribution to Well-Being

PubMed Central

Kolcsár, Ronald; Cabrera-Barona, Pablo; Szilassi, Péter

2017-01-01

Individual perceptions are essential when evaluating the well-being benefits from urban green spaces. This study predicted the influence of perceived green space characteristics in the city of Szeged, Hungary, on two well-being variables: the green space visitors’ level of satisfaction and the self-reported quality of life. The applied logistic regression analysis used nine predictors: seven perceived green space characteristics from a questionnaire survey among visitors of five urban green spaces of Szeged; and the frequency of green space visitors’ crowd-sourced recreational running paths and photographs picturing green space aesthetics. Results revealed that perceived green space characteristics with direct well-being benefits were strong predictors of both dependent variables. Perceived green space characteristics with indirect, yet fundamental, well-being benefits, namely, regulating ecosystem services had minor influence on the dependent variables. The crowd-sourced geo-tagged data predicted only the perceived quality of life contributions; but revealed spatial patterns of recreational green space use and aesthetics. This study recommends that regulating ecosystem services should be planned with a focus on residents’ aesthetic and recreational needs. Further research on the combination of green space visitors´ perceptions and crowd-sourced geo-tagged data is suggested to promote planning for well-being and health benefits of urban green spaces. PMID:28704969

Regional Distribution Models with Lack of Proximate Predictors: Africanized Honeybees Expanding North

NASA Technical Reports Server (NTRS)

Jarnevich, Catherine S.; Esaias, Wayne E.; Ma, Peter L. A.; Morisette, Jeffery T.; Nickeson, Jaime E.; Stohlgren, Thomas J.; Holcombe, Tracy R.; Nightingale, Joanne M.; Wolfe, Robert E.; Tan, Bin

2014-01-01

Species distribution models have often been hampered by poor local species data, reliance on coarse-scale climate predictors and the assumption that species-environment relationships, even with non-proximate predictors, are consistent across geographical space. Yet locally accurate maps of invasive species, such as the Africanized honeybee (AHB) in North America, are needed to support conservation efforts. Current AHB range maps are relatively coarse and are inconsistent with observed data. Our aim was to improve distribution maps using more proximate predictors (phenology) and using regional models rather than one across the entire range of interest to explore potential differences in drivers.

Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north

USGS Publications Warehouse

Jarnevich, Catherine S.; Esaias, Wayne E.; Ma, Peter L.A.; Morisette, Jeffery T.; Nickeson, Jaime E.; Stohlgren, Thomas J.; Holcombe, Tracy R.; Nightingale, Joanne M.; Wolfe, Robert E.; Tan, Bin

2014-01-01

Species distribution models have often been hampered by poor local species data, reliance on coarse-scale climate predictors and the assumption that species–environment relationships, even with non-proximate predictors, are consistent across geographical space. Yet locally accurate maps of invasive species, such as the Africanized honeybee (AHB) in North America, are needed to support conservation efforts. Current AHB range maps are relatively coarse and are inconsistent with observed data. Our aim was to improve distribution maps using more proximate predictors (phenology) and using regional models rather than one across the entire range of interest to explore potential differences in drivers.

Supervised embedding of textual predictors with applications in clinical diagnostics for pediatric cardiology.

PubMed

Perry, Thomas Ernest; Zha, Hongyuan; Zhou, Ke; Frias, Patricio; Zeng, Dadan; Braunstein, Mark

2014-02-01

Electronic health records possess critical predictive information for machine-learning-based diagnostic aids. However, many traditional machine learning methods fail to simultaneously integrate textual data into the prediction process because of its high dimensionality. In this paper, we present a supervised method using Laplacian Eigenmaps to enable existing machine learning methods to estimate both low-dimensional representations of textual data and accurate predictors based on these low-dimensional representations at the same time. We present a supervised Laplacian Eigenmap method to enhance predictive models by embedding textual predictors into a low-dimensional latent space, which preserves the local similarities among textual data in high-dimensional space. The proposed implementation performs alternating optimization using gradient descent. For the evaluation, we applied our method to over 2000 patient records from a large single-center pediatric cardiology practice to predict if patients were diagnosed with cardiac disease. In our experiments, we consider relatively short textual descriptions because of data availability. We compared our method with latent semantic indexing, latent Dirichlet allocation, and local Fisher discriminant analysis. The results were assessed using four metrics: the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), specificity, and sensitivity. The results indicate that supervised Laplacian Eigenmaps was the highest performing method in our study, achieving 0.782 and 0.374 for AUC and MCC, respectively. Supervised Laplacian Eigenmaps showed an increase of 8.16% in AUC and 20.6% in MCC over the baseline that excluded textual data and a 2.69% and 5.35% increase in AUC and MCC, respectively, over unsupervised Laplacian Eigenmaps. As a solution, we present a supervised Laplacian Eigenmap method to embed textual predictors into a low-dimensional Euclidean space. This method allows many existing machine learning predictors to effectively and efficiently capture the potential of textual predictors, especially those based on short texts.

Predicting Efficiency of Travel in Young, Visually Impaired Children from Their Other Spatial Skills.

ERIC Educational Resources Information Center

Hill, Anita; And Others

1985-01-01

To test ways of predicting how efficiently visually impaired children learn travel skills, a criteria checklist of spatial skills was developed for close-body space, local space, and geographical/travel space. Comparison was made between predictors of efficient learning including subjective ratings of teachers, personal qualities and factors of…

Development of equations to predict the influence of floor space on average daily gain, average daily feed intake and gain : feed ratio of finishing pigs.

PubMed

Flohr, J R; Dritz, S S; Tokach, M D; Woodworth, J C; DeRouchey, J M; Goodband, R D

2018-05-01

Floor space allowance for pigs has substantial effects on pig growth and welfare. Data from 30 papers examining the influence of floor space allowance on the growth of finishing pigs was used in a meta-analysis to develop alternative prediction equations for average daily gain (ADG), average daily feed intake (ADFI) and gain : feed ratio (G : F). Treatment means were compiled in a database that contained 30 papers for ADG and 28 papers for ADFI and G : F. The predictor variables evaluated were floor space (m2/pig), k (floor space/final BW0.67), Initial BW, Final BW, feed space (pigs per feeder hole), water space (pigs per waterer), group size (pigs per pen), gender, floor type and study length (d). Multivariable general linear mixed model regression equations were used. Floor space treatments within each experiment were the observational and experimental unit. The optimum equations to predict ADG, ADFI and G : F were: ADG, g=337.57+(16 468×k)-(237 350×k 2)-(3.1209×initial BW (kg))+(2.569×final BW (kg))+(71.6918×k×initial BW (kg)); ADFI, g=833.41+(24 785×k)-(388 998×k 2)-(3.0027×initial BW (kg))+(11.246×final BW (kg))+(187.61×k×initial BW (kg)); G : F=predicted ADG/predicted ADFI. Overall, the meta-analysis indicates that BW is an important predictor of ADG and ADFI even after computing the constant coefficient k, which utilizes final BW in its calculation. This suggests including initial and final BW improves the prediction over using k as a predictor alone. In addition, the analysis also indicated that G : F of finishing pigs is influenced by floor space allowance, whereas individual studies have concluded variable results.

Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction

ERIC Educational Resources Information Center

Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T.

2012-01-01

Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…

Predictors of HIV Testing and Intention to Test Among Hispanic Farmworkers in South Florida

ERIC Educational Resources Information Center

Fernandez, M. Isabel; Collazo, Jose B.; Bowen, G. Stephen; Varga, Leah M.; Hernandez, Nilda; Perrino, Tatiana

2005-01-01

Context and Purpose: This study examined the predictors of HIV testing and factors associated with intention to accept a free HIV test among 244 Hispanic migrant/seasonal farmworkers in South Florida. Methods: Time and space sampling procedures were used to recruit participants in public venues. Bilingual staff interviewed eligible respondents in…

Advanced Transport Delay Compensation Algorithms: Results of Delay Measurement and Piloted Performance Tests

NASA Technical Reports Server (NTRS)

Guo, Liwen; Cardullo, Frank M.; Kelly, Lon C.

2007-01-01

This report summarizes the results of delay measurement and piloted performance tests that were conducted to assess the effectiveness of the adaptive compensator and the state space compensator for alleviating the phase distortion of transport delay in the visual system in the VMS at the NASA Langley Research Center. Piloted simulation tests were conducted to assess the effectiveness of two novel compensators in comparison to the McFarland predictor and the baseline system with no compensation. Thirteen pilots with heterogeneous flight experience executed straight-in and offset approaches, at various delay configurations, on a flight simulator where different predictors were applied to compensate for transport delay. The glideslope and touchdown errors, power spectral density of the pilot control inputs, NASA Task Load Index, and Cooper-Harper rating of the handling qualities were employed for the analyses. The overall analyses show that the adaptive predictor results in slightly poorer compensation for short added delay (up to 48 ms) and better compensation for long added delay (up to 192 ms) than the McFarland compensator. The analyses also show that the state space predictor is fairly superior for short delay and significantly superior for long delay than the McFarland compensator.

Combining climatic and soil properties better predicts covers of Brazilian biomes.

PubMed

Arruda, Daniel M; Fernandes-Filho, Elpídio I; Solar, Ricardo R C; Schaefer, Carlos E G R

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km 2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Combining climatic and soil properties better predicts covers of Brazilian biomes

NASA Astrophysics Data System (ADS)

Arruda, Daniel M.; Fernandes-Filho, Elpídio I.; Solar, Ricardo R. C.; Schaefer, Carlos E. G. R.

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Posterior consistency in conditional distribution estimation

PubMed Central

Pati, Debdeep; Dunson, David B.; Tokdar, Surya T.

2014-01-01

A wide variety of priors have been proposed for nonparametric Bayesian estimation of conditional distributions, and there is a clear need for theorems providing conditions on the prior for large support, as well as posterior consistency. Estimation of an uncountable collection of conditional distributions across different regions of the predictor space is a challenging problem, which differs in some important ways from density and mean regression estimation problems. Defining various topologies on the space of conditional distributions, we provide sufficient conditions for posterior consistency focusing on a broad class of priors formulated as predictor-dependent mixtures of Gaussian kernels. This theory is illustrated by showing that the conditions are satisfied for a class of generalized stick-breaking process mixtures in which the stick-breaking lengths are monotone, differentiable functions of a continuous stochastic process. We also provide a set of sufficient conditions for the case where stick-breaking lengths are predictor independent, such as those arising from a fixed Dirichlet process prior. PMID:25067858

Radiographic assessment of lower third molar eruption in different anteroposterior skeletal patterns and age-related groups.

PubMed

Jakovljevic, Aleksandar; Lazic, Emira; Soldatovic, Ivan; Nedeljkovic, Nenad; Andric, Miroslav

2015-07-01

To analyze radiographic predictors for lower third molar eruption among subjects with different anteroposterior skeletal relations and of different age groups. In total, 300 lower third molars were recorded on diagnostic digital orthopantomograms (DPTs) and lateral cephalograms (LCs). The radiographs were grouped according to sagittal intermaxillary angle (ANB), subject age, and level of lower third molar eruption. The DPT was used to analyze retromolar space, mesiodistal crown width, space/width ratio, third and second molar angulation (α, γ), third molar inclination (β), and gonion angle. The LC was used to determine ANB, angles of maxillar and mandibular prognathism (SNA, SNB), mandibular plane angle (SN/MP), and mandibular lengths. A logistic regression model was created using the statistically significant predictors. The logistic regression analysis revealed a statistically significant impact of β angle and distance between gonion and gnathion (Go-Gn) on the level of lower third molar eruption (P < .001 and P < .015, respectively). The retromolar space was significantly increased in the adult subgroup for all skeletal classes. The lower third molar impaction rate was significantly higher in the adult subgroup with the Class II (62.3%) compared with Class III subjects (31.7%; P < .013). The most favorable values of linear and angular predictors of mandibular third molar eruption were measured in Class III subjects. For valid estimation of mandibular third molar eruption, certain linear and angular measures (β angle, Go-Gn), as well as the size of the retromolar space, need to be considered.

Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors

ERIC Educational Resources Information Center

Waller, Niels; Jones, Jeff

2011-01-01

We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…

RBSURFpred: Modeling protein accessible surface area in real and binary space using regularized and optimized regression.

PubMed

Tarafder, Sumit; Toukir Ahmed, Md; Iqbal, Sumaiya; Tamjidul Hoque, Md; Sohel Rahman, M

2018-03-14

Accessible surface area (ASA) of a protein residue is an effective feature for protein structure prediction, binding region identification, fold recognition problems etc. Improving the prediction of ASA by the application of effective feature variables is a challenging but explorable task to consider, specially in the field of machine learning. Among the existing predictors of ASA, REGAd 3 p is a highly accurate ASA predictor which is based on regularized exact regression with polynomial kernel of degree 3. In this work, we present a new predictor RBSURFpred, which extends REGAd 3 p on several dimensions by incorporating 58 physicochemical, evolutionary and structural properties into 9-tuple peptides via Chou's general PseAAC, which allowed us to obtain higher accuracies in predicting both real-valued and binary ASA. We have compared RBSURFpred for both real and binary space predictions with state-of-the-art predictors, such as REGAd 3 p and SPIDER2. We also have carried out a rigorous analysis of the performance of RBSURFpred in terms of different amino acids and their properties, and also with biologically relevant case-studies. The performance of RBSURFpred establishes itself as a useful tool for the community. Copyright © 2018 Elsevier Ltd. All rights reserved.

A new finite element formulation for computational fluid dynamics. IX - Fourier analysis of space-time Galerkin/least-squares algorithms

NASA Technical Reports Server (NTRS)

Shakib, Farzin; Hughes, Thomas J. R.

1991-01-01

A Fourier stability and accuracy analysis of the space-time Galerkin/least-squares method as applied to a time-dependent advective-diffusive model problem is presented. Two time discretizations are studied: a constant-in-time approximation and a linear-in-time approximation. Corresponding space-time predictor multi-corrector algorithms are also derived and studied. The behavior of the space-time algorithms is compared to algorithms based on semidiscrete formulations.

Life-Space Mobility Change Predicts 6-Month Mortality.

PubMed

Kennedy, Richard E; Sawyer, Patricia; Williams, Courtney P; Lo, Alexander X; Ritchie, Christine S; Roth, David L; Allman, Richard M; Brown, Cynthia J

2017-04-01

To examine 6-month change in life-space mobility as a predictor of subsequent 6-month mortality in community-dwelling older adults. Prospective cohort study. Community-dwelling older adults from five Alabama counties in the University of Alabama at Birmingham (UAB) Study of Aging. A random sample of 1,000 Medicare beneficiaries, stratified according to sex, race, and rural or urban residence, recruited between November 1999 and February 2001, followed by a telephone interview every 6 months for the subsequent 8.5 years. Mortality data were determined from informant contacts and confirmed using the National Death Index and Social Security Death Index. Life-space was measured at each interview using the UAB Life-Space Assessment, a validated instrument for assessing community mobility. Eleven thousand eight hundred seventeen 6-month life-space change scores were calculated over 8.5 years of follow-up. Generalized linear mixed models were used to test predictors of mortality at subsequent 6-month intervals. Three hundred fifty-four deaths occurred within 6 months of two sequential life-space assessments. Controlling for age, sex, race, rural or urban residence, and comorbidity, life-space score and life-space decline over the preceding 6-month interval predicted mortality. A 10-point decrease in life-space resulted in a 72% increase in odds of dying over the subsequent 6 months (odds ratio = 1.723, P < .001). Life-space score at the beginning of a 6-month interval and change in life-space over 6 months were each associated with significant differences in subsequent 6-month mortality. Life-space assessment may assist clinicians in identifying older adults at risk of short-term mortality. © 2017, Copyright the Authors Journal compilation © 2017, The American Geriatrics Society.

Statistical Evaluation of Causal Factors Associated with Astronaut Shoulder Injury in Space Suits.

PubMed

Anderson, Allison P; Newman, Dava J; Welsch, Roy E

2015-07-01

Shoulder injuries due to working inside the space suit are some of the most serious and debilitating injuries astronauts encounter. Space suit injuries occur primarily in the Neutral Buoyancy Laboratory (NBL) underwater training facility due to accumulated musculoskeletal stress. We quantitatively explored the underlying causal mechanisms of injury. Logistic regression was used to identify relevant space suit components, training environment variables, and anthropometric dimensions related to an increased propensity for space-suited injury. Two groups of subjects were analyzed: those whose reported shoulder incident is attributable to the NBL or working in the space suit, and those whose shoulder incidence began in active duty, meaning working in the suit could be a contributing factor. For both groups, percent of training performed in the space suit planar hard upper torso (HUT) was the most important predictor variable for injury. Frequency of training and recovery between training were also significant metrics. The most relevant anthropometric dimensions were bideltoid breadth, expanded chest depth, and shoulder circumference. Finally, record of previous injury was found to be a relevant predictor for subsequent injury. The first statistical model correctly identifies 39% of injured subjects, while the second model correctly identifies 68% of injured subjects. A review of the literature suggests this is the first work to quantitatively evaluate the hypothesized causal mechanisms of all space-suited shoulder injuries. Although limited in predictive capability, each of the identified variables can be monitored and modified operationally to reduce future impacts on an astronaut's health.

A Deep Machine Learning Algorithm to Optimize the Forecast of Atmospherics

NASA Astrophysics Data System (ADS)

Russell, A. M.; Alliss, R. J.; Felton, B. D.

Space-based applications from imaging to optical communications are significantly impacted by the atmosphere. Specifically, the occurrence of clouds and optical turbulence can determine whether a mission is a success or a failure. In the case of space-based imaging applications, clouds produce atmospheric transmission losses that can make it impossible for an electro-optical platform to image its target. Hence, accurate predictions of negative atmospheric effects are a high priority in order to facilitate the efficient scheduling of resources. This study seeks to revolutionize our understanding of and our ability to predict such atmospheric events through the mining of data from a high-resolution Numerical Weather Prediction (NWP) model. Specifically, output from the Weather Research and Forecasting (WRF) model is mined using a Random Forest (RF) ensemble classification and regression approach in order to improve the prediction of low cloud cover over the Haleakala summit of the Hawaiian island of Maui. RF techniques have a number of advantages including the ability to capture non-linear associations between the predictors (in this case physical variables from WRF such as temperature, relative humidity, wind speed and pressure) and the predictand (clouds), which becomes critical when dealing with the complex non-linear occurrence of clouds. In addition, RF techniques are capable of representing complex spatial-temporal dynamics to some extent. Input predictors to the WRF-based RF model are strategically selected based on expert knowledge and a series of sensitivity tests. Ultimately, three types of WRF predictors are chosen: local surface predictors, regional 3D moisture predictors and regional inversion predictors. A suite of RF experiments is performed using these predictors in order to evaluate the performance of the hybrid RF-WRF technique. The RF model is trained and tuned on approximately half of the input dataset and evaluated on the other half. The RF approach is validated using in-situ observations of clouds. All of the hybrid RF-WRF experiments demonstrated here significantly outperform the base WRF local low cloud cover forecasts in terms of the probability of detection and the overall bias. In particular, RF experiments that use only regional three-dimensional moisture predictors from the WRF model produce the highest accuracy when compared to RF experiments that use local surface predictors only or regional inversion predictors only. Furthermore, adding multiple types of WRF predictors and additional WRF predictors to the RF algorithm does not necessarily add more value in the resulting forecasts, indicating that it is better to have a small set of meaningful predictors than to have a vast set of indiscriminately-chosen predictors. This work also reveals that the WRF-based RF approach is highly sensitive to the time period over which the algorithm is trained and evaluated. Future work will focus on developing a similar WRF-based RF model for high cloud prediction and expanding the algorithm to two-dimensions horizontally.

Child's and parents' catastrophizing about pain is associated with procedural fear in children: a study in children with diabetes and their mothers.

PubMed

Vervoort, T; Goubert, L; Vandenbossche, H; Van Aken, S; Matthys, D; Crombez, G

2011-12-01

The contribution of the child's and parents' catastrophizing about pain was explored in explaining procedural pain and fear in children. Procedural fear and pain were investigated in 44 children with Type I diabetes undergoing a finger prick. The relationships between parents' catastrophizing and parents' own fear and estimates of their child's pain were also investigated. The children and their mothers completed questionnaires prior to a routine consultation with the diabetes physician. Children completed a situation-specific measure of the Pain Catastrophizing Scale for Children (PCS-C) and provided ratings of their experienced pain and fear on a 0-10 numerical rating scale (NRS). Parents completed a situation-specific measure of the Pain Catastrophizing Scale For Parents (PCS-P) d provided estimates of their child's pain and their own experienced fear on a 0-10 NRS. Analyses indicated that higher catastrophizing by children was associated with more fear and pain during the finger prick. Scores for parents' catastrophzing about their children's pain were positively related to parents' scores for their own fear, estimates of their children's pain, and child-reported fear, but not the amount of pain reported by the child. The findings attest to the importance of assessing for and targeting child and parents' catastrophizing about pain. Addressing catastrophizing and related fears and concerns of both parents and children may be necessary to assure appropriate self-management. Further investigation of the mechanisms relating catastrophizing to deleterious outcomes is warranted.

Vestibular response to pseudorandom angular velocity input: progress report.

PubMed

Lessard, C S; Wong, W C

1987-09-01

Space motion sickness was not reported during the first Apollo missions; however, since Apollo 8 through the current Shuttle and Skylab missions, approximately 50% of the crewmembers have experienced instances of space motion sickness. One of NASA's efforts to resolve the space adaptation syndrome is to model the vestibular response for both basic knowledge and as a possible predictor of an individual's susceptibility to the disorder. This report describes a method to analyze the vestibular system when subjected to a pseudorandom angular velocity input.

A neuroanatomical model of space-based and object-centered processing in spatial neglect.

PubMed

Pedrazzini, Elena; Schnider, Armin; Ptak, Radek

2017-11-01

Visual attention can be deployed in space-based or object-centered reference frames. Right-hemisphere damage may lead to distinct deficits of space- or object-based processing, and such dissociations are thought to underlie the heterogeneous nature of spatial neglect. Previous studies have suggested that object-centered processing deficits (such as in copying, reading or line bisection) result from damage to retro-rolandic regions while impaired spatial exploration reflects damage to more anterior regions. However, this evidence is based on small samples and heterogeneous tasks. Here, we tested a theoretical model of neglect that takes in account the space- and object-based processing and relates them to neuroanatomical predictors. One hundred and one right-hemisphere-damaged patients were examined with classic neuropsychological tests and structural brain imaging. Relations between neglect measures and damage to the temporal-parietal junction, intraparietal cortex, insula and middle frontal gyrus were examined with two structural equation models by assuming that object-centered processing (involved in line bisection and single-word reading) and space-based processing (involved in cancelation tasks) either represented a unique latent variable or two distinct variables. Of these two models the latter had better explanatory power. Damage to the intraparietal sulcus was a significant predictor of object-centered, but not space-based processing, while damage to the temporal-parietal junction predicted space-based, but not object-centered processing. Space-based processing and object-centered processing were strongly intercorrelated, indicating that they rely on similar, albeit partly dissociated processes. These findings indicate that object-centered and space-based deficits in neglect are partly independent and result from superior parietal and inferior parietal damage, respectively.

Flood regionalization: A hybrid geographic and predictor-variable region-of-influence regression method

USGS Publications Warehouse

Eng, K.; Milly, P.C.D.; Tasker, Gary D.

2007-01-01

To facilitate estimation of streamflow characteristics at an ungauged site, hydrologists often define a region of influence containing gauged sites hydrologically similar to the estimation site. This region can be defined either in geographic space or in the space of the variables that are used to predict streamflow (predictor variables). These approaches are complementary, and a combination of the two may be superior to either. Here we propose a hybrid region-of-influence (HRoI) regression method that combines the two approaches. The new method was applied with streamflow records from 1,091 gauges in the southeastern United States to estimate the 50-year peak flow (Q50). The HRoI approach yielded lower root-mean-square estimation errors and produced fewer extreme errors than either the predictor-variable or geographic region-of-influence approaches. It is concluded, for Q50 in the study region, that similarity with respect to the basin characteristics considered (area, slope, and annual precipitation) is important, but incomplete, and that the consideration of geographic proximity of stations provides a useful surrogate for characteristics that are not included in the analysis. ?? 2007 ASCE.

Encke-Beta Predictor for Orion Burn Targeting and Guidance

NASA Technical Reports Server (NTRS)

Robinson, Shane; Scarritt, Sara; Goodman, John L.

2016-01-01

The state vector prediction algorithm selected for Orion on-board targeting and guidance is known as the Encke-Beta method. Encke-Beta uses a universal anomaly (beta) as the independent variable, valid for circular, elliptical, parabolic, and hyperbolic orbits. The variable, related to the change in eccentric anomaly, results in integration steps that cover smaller arcs of the trajectory at or near perigee, when velocity is higher. Some burns in the EM-1 and EM-2 mission plans are much longer than burns executed with the Apollo and Space Shuttle vehicles. Burn length, as well as hyperbolic trajectories, has driven the use of the Encke-Beta numerical predictor by the predictor/corrector guidance algorithm in place of legacy analytic thrust and gravity integrals.

The impact of subjective memory complaints on quality of life in community-dwelling older adults.

PubMed

Maki, Yohko; Yamaguchi, Tomoharu; Yamagami, Tetsuya; Murai, Tatsuhiko; Hachisuka, Kenji; Miyamae, Fumiko; Ito, Kae; Awata, Shuichi; Ura, Chiaki; Takahashi, Ryutaro; Yamaguchi, Haruyasu

2014-09-01

The aim of this study was to evaluate the impact of memory complaints on quality of life (QOL) in elderly community dwellers with or without mild cognitive impairment (MCI). Participants included 120 normal controls (NC) and 37 with MCI aged 65 and over. QOL was measured using the Japanese version of Satisfaction in Daily Life, and memory complaints were measured using a questionnaire consisting of four items. The relevance of QOL was evaluated with psychological factors of personality traits, sense of self-efficacy, depressive mood, self-evaluation of daily functioning, range of social activities (Life-Space Assessment), social network size, and cognitive functions including memory. The predictors of QOL were analyzed by multiple linear regression analysis. QOL was not significantly different between the NC and MCI groups. In both groups, QOL was positively correlated with self-efficacy, daily functioning, social network size, Life-Space Assessment, and the personality traits of extraversion and agreeableness; QOL was negatively correlated with memory complaints, depressive mood, and the personality trait of neuroticism. In regression analysis, memory complaints were a negative predictor of QOL in the MCI group, but not in the NC group. The partial correlation coefficient between QOL and memory complaints was -0.623 (P < 0.05), after scores of depressive mood and self-efficacy were controlled. Depressive mood was a common negative predictor in both groups. Positive predictors were Life-Space Assessment in the NC group and sense of self-efficacy in the MCI group. Memory complaints exerted a negative impact on self-rated QOL in the MCI group, whereas a negative correlation was weak in the NC group. Memory training has been widely practised in individuals with MCI to prevent the development of dementia. However, such approaches inevitably identify their memory deficits and could aggravate their awareness of memory decline. Thus, it is critical to give sufficient consideration not to reduce QOL in the intervention for those with MCI. © 2014 The Authors. Psychogeriatrics © 2014 Japanese Psychogeriatric Society.

Estimating the effect of selected predictors on agricultural confined-space hazard perceptions of Utah farm owner/operators.

PubMed

Pate, M L; Dai, X

2014-04-01

The purpose of this study was to assess how selected variables affect the confined-space hazard perceptions of farmers in Utah. A confined space was defined as "any space found in an agricultural workplace that was not designed or intended as a regular workstation, has limited or restricted means of entry or exit, and contains potential physical and toxic hazards to workers who intentionally or unintentionally enter the space" (proposed by NCERA-197, 18 May 2011, draft copy). A total of 303 out of 327 farm owner/operators provided complete surveys that were used in the analysis. The state of Utah was grouped into five regions in this study: central, east, northeast, northwest, and southwest. Grain and dairy production comprised 48.7% of the operations responding to the survey. The general linear modeling (GLM) procedure in SAS 9.3 was used to select the models on hazard perception scores for the five studied regions. Interested predictors included response type, production type, safety planning, and injury concerns. Animal production operations had the highest average number of confined spaces (micro = 4, SD = 2.7). Regionally, the northwest region had the highest average number of confined spaces (micro = 4, SD = 2.5). The variables contributing most to confined-space hazard perceptions were injury and death concerns while working alone in confined spaces. Three factors were generated using principle factor analysis (PFA) with orthogonal varimax rotation. Results suggested that factors affect hazard perceptions differently by region. We conclude that outreach and educational efforts to change safety behaviors regarding confined-space hazards should be strategically targeted for each region based on predicting factors. The result can assist agricultural safety and health professionals in targeting agricultural producers' social networks to address human factors such as worker attitudes and/or lack of skills or knowledge that effect hazard perceptions of confined spaces in agriculture.

Predictor Development and Pilot Testing of a Prototype Selection Instrument for Army Flight Training

DTIC Science & Technology

2007-02-01

called the Automated Pilot Examination System, or "APEX") during the preliminary validation reserach . The current version of the ASTB includes subtests...of objects in three-dimensional space . Aviation & Nautical Information: items assess an examinee’s familiarity with aviation history, nautical...proficiency. Aviation, Space and Environmental Medicine, 46, 309-311. Daryanian, B. (1980). Subjective scaling of mental workload in a multi-task environment

Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces.

PubMed

Gentry, Amanda Elswick; Jackson-Cook, Colleen K; Lyon, Debra E; Archer, Kellie J

2015-01-01

The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.

An estimator-predictor approach to PLL loop filter design

NASA Technical Reports Server (NTRS)

Statman, J. I.; Hurd, W. J.

1986-01-01

An approach to the design of digital phase locked loops (DPLLs), using estimation theory concepts in the selection of a loop filter, is presented. The key concept is that the DPLL closed-loop transfer function is decomposed into an estimator and a predictor. The estimator provides recursive estimates of phase, frequency, and higher order derivatives, while the predictor compensates for the transport lag inherent in the loop. This decomposition results in a straightforward loop filter design procedure, enabling use of techniques from optimal and sub-optimal estimation theory. A design example for a particular choice of estimator is presented, followed by analysis of the associated bandwidth, gain margin, and steady state errors caused by unmodeled dynamics. This approach is under consideration for the design of the Deep Space Network (DSN) Advanced Receiver Carrier DPLL.

Hybrid Smith predictor and phase lead based divergence compensation for hardware-in-the-loop contact simulation with measurement delay

NASA Astrophysics Data System (ADS)

Qi, Chenkun; Gao, Feng; Zhao, Xianchao; Wang, Qian; Ren, Anye

2018-06-01

On the ground the hardware-in-the-loop (HIL) simulation is a good approach to test the contact dynamics of spacecraft docking process in space. Unfortunately, due to the time delay in the system the HIL contact simulation becomes divergent. However, the traditional first-order phase lead compensation approach still result in a small divergence for the pure time delay. The serial Smith predictor and phase lead compensation approach proposed by the authors recently will lead to an over-compensation and an obvious convergence. In this study, a hybrid Smith predictor and phase lead compensation approach is proposed. The hybrid Smith predictor and phase lead compensation can achieve a higher simulation fidelity with a little convergence. The phase angle of the compensator is analyzed and the stability condition of the HIL simulation system is given. The effectiveness of the proposed compensation approach is tested by simulations on an undamped elastic contact process.

Improved Survival Endpoints With Adjuvant Radiation Treatment in Patients With High-Risk Early-Stage Endometrial Carcinoma

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

Elshaikh, Mohamed A., E-mail: melshai1@hfhs.org; Vance, Sean; Suri, Jaipreet S.

2014-02-01

Purpose/Objective(s): To determine the impact of adjuvant radiation treatment (RT) on recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) in patients with high-risk 2009 International Federation of Gynecology and Obstetrics stage I-II endometrial carcinoma. Methods and Materials: We identified 382 patients with high-risk EC who underwent hysterectomy. RFS, DSS, and OS were calculated from the date of hysterectomy by use of the Kaplan-Meier method. Cox regression modeling was used to explore the risks associated with various factors on survival endpoints. Results: The median follow-up time for the study cohort was 5.4 years. The median age was 71 years.more » All patients underwent hysterectomy and salpingo-oophorectomy, 93% had peritoneal cytology, and 85% underwent lymphadenectomy. Patients with endometrioid histology constituted 72% of the study cohort, serous in 16%, clear cell in 7%, and mixed histology in 4%. Twenty-three percent of patients had stage II disease. Adjuvant management included RT alone in 220 patients (57%), chemotherapy alone in 25 patients (7%), and chemoradiation therapy in 27 patients (7%); 110 patients (29%) were treated with close surveillance. The 5-year RFS, DSS, and OS were 76%, 88%, and 73%, respectively. On multivariate analysis, adjuvant RT was a significant predictor of RFS (P<.001) DSS (P<.001), and OS (P=.017). Lymphovascular space involvement was a significant predictor of RFS and DSS (P<.001). High tumor grade was a significant predictor for RFS (P=.038) and DSS (P=.025). Involvement of the lower uterine segment was also a predictor of RFS (P=.049). Age at diagnosis and lymphovascular space involvement were significant predictors of OS: P<.001 and P=.002, respectively. Conclusion: In the treatment of patients with high-risk features, our study suggests that adjuvant RT significantly improves recurrence-free, disease-specific, and overall survival in patients with early-stage endometrial carcinoma. Furthermore, adjuvant RT is an independent predictor for RFS, DSS, and OS in this group of patients. These findings need validation from a prospective randomized study.« less

Does initial spacing influence crown and hydraulic architecture of Eucalyptus marginata?

PubMed

Grigg, A H; Macfarlane, C; Evangelista, C; Eamus, D; Adams, M A

2008-05-01

Long-term declines in rainfall in south-western Australia have resulted in increased interest in the hydraulic characteristics of jarrah (Eucalyptus marginata Donn ex Smith) forest established in the region's drinking water catchments on rehabilitated bauxite mining sites. We hypothesized that in jarrah forest established on rehabilitated mine sites: (1) leaf area index (L) is independent of initial tree spacing; and (2) more densely planted trees have less leaf area for the same leaf mass, or the same sapwood area, and have denser sapwood. Initial stand densities ranged from about 600 to 9000 stems ha(-1), and trees were 18 years old at the time of sampling. Leaf area index was unaffected by initial stand density, except in the most sparsely stocked stands where L was 1.2 compared with 2.0-2.5 in stands at other spacings. The ratio of leaf area to sapwood area (A(l):A(s)) was unaffected by tree spacing or tree size and was 0.2 at 1.3 m height and 0.25 at the crown base. There were small increases in sapwood density and decreases in leaf specific area with increased spacing. Tree diameter or basal area was a better predictor of leaf area than sapwood area. At the stand scale, basal area was a good predictor of L (r(2) = 0.98, n = 15) except in the densest stands. We conclude that the hydraulic attributes of this forest type are largely independent of initial tree spacing, thus simplifying parameterization of stand and catchment water balance models.

ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

PubMed

Kayala, Matthew A; Baldi, Pierre

2012-10-22

Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have historically used obfuscating representations and manually encoded patterns or rules. Here we present ReactionPredictor, a machine learning approach to reaction prediction that models elementary, mechanistic reactions as interactions between approximate molecular orbitals (MOs). A training data set of productive reactions known to occur at reasonable rates and yields and verified by inclusion in the literature or textbooks is derived from an existing rule-based system and expanded upon with manual curation from graduate level textbooks. Using this training data set of complex polar, hypervalent, radical, and pericyclic reactions, a two-stage machine learning prediction framework is trained and validated. In the first stage, filtering models trained at the level of individual MOs are used to reduce the space of possible reactions to consider. In the second stage, ranking models over the filtered space of possible reactions are used to order the reactions such that the productive reactions are the top ranked. The resulting model, ReactionPredictor, perfectly ranks polar reactions 78.1% of the time and recovers all productive reactions 95.7% of the time when allowing for small numbers of errors. Pericyclic and radical reactions are perfectly ranked 85.8% and 77.0% of the time, respectively, rising to >93% recovery for both reaction types with a small number of allowed errors. Decisions about which of the polar, pericyclic, or radical reaction type ranking models to use can be made with >99% accuracy. Finally, for multistep reaction pathways, we implement the first mechanistic pathway predictor using constrained tree-search to discover a set of reasonable mechanistic steps from given reactants to given products. Webserver implementations of both the single step and pathway versions of ReactionPredictor are available via the chemoinformatics portal http://cdb.ics.uci.edu/.

The complex roles of space and environment in structuring functional, taxonomic and phylogenetic beta diversity of frogs in the Atlantic Forest

PubMed Central

Luiz, Amom Mendes; Sawaya, Ricardo J.

2018-01-01

Ecological communities are complex entities that can be maintained and structured by niche-based processes such as environmental conditions, and spatial processes such as dispersal. Thus, diversity patterns may be shaped simultaneously at different spatial scales by very distinct processes. Herein we assess whether and how functional, taxonomic, and phylogenetic beta diversities of frog tadpoles are explained by environmental and/or spatial predictors. We implemented a distance–based redundancy analysis to explore variation in components of beta diversity explained by pure environmental and pure spatial predictors, as well as their interactions, at both fine and broad spatial scales. Our results indicated important but complex roles of spatial and environmental predictors in structuring phylogenetic, taxonomic and functional beta diversities. The pure fine-scales spatial fraction was more important in structuring all beta diversity components, especially to functional and taxonomical spatial turnover. Environmental variables such as canopy cover and vegetation structure were important predictors of all components, but especially to functional and taxonomic beta diversity. We emphasize that distinct factors related to environment and space are affecting distinct components of beta diversity in different ways. Although weaker, phylogenetic beta diversity, which is structured more on biogeographical scales, and thus can be represented by spatially structured processes, was more related to broad spatial processes than other components. However, selected fine-scale spatial predictors denoted negative autocorrelation, which may be revealing the existence of differences in unmeasured habitat variables among samples. Although overall important, local environmental-based processes explained better functional and taxonomic beta diversity, as these diversity components carry an important ecological value. We highlight the importance of assessing different components of diversity patterns at different scales by spatially explicit models in order to improve our understanding of community structure and help to unravel the complex nature of biodiversity. PMID:29672575

Bayesian isotonic density regression

PubMed Central

Wang, Lianming; Dunson, David B.

2011-01-01

Density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space. Such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions, and are well supported in many applications. A rich variety of Bayesian methods have been proposed for density regression, but it is not clear whether such priors have full support so that any true data-generating model can be accurately approximated. This article develops a new class of density regression models that incorporate stochastic-ordering constraints which are natural when a response tends to increase or decrease monotonely with a predictor. Theory is developed showing large support. Methods are developed for hypothesis testing, with posterior computation relying on a simple Gibbs sampler. Frequentist properties are illustrated in a simulation study, and an epidemiology application is considered. PMID:22822259

Intergroup conflict: Ecological predictors of winning and consequences of defeat in a wild primate population

PubMed Central

MARKHAM, A. CATHERINE; ALBERTS, SUSAN C.; ALTMANN, JEANNE

2012-01-01

In many social species, competition between groups is a major factor proximately affecting group-level movement patterns and space use and ultimately shaping the evolution of group living and complex sociality. Here we evaluated the factors influencing group-level dominance among 5 social groups of wild baboons (Papio cynocephalus), in particular focusing on the spatial determinants of dominance and the consequences of defeat. When direct conflict occurred between conspecific baboon groups, the winning group was predicted by differences in the number of adult males in each group and/or groups that had used the areas surrounding the encounter location more intensively than their opponent in the preceding 9 or 12 months. Relative intensity of space use over shorter timescales examined (3 and 6 months) was a poor predictor of the interaction’s outcome. Losing groups but not winning groups experienced clear short-term costs. Losing groups used the area surrounding the interaction less following an agonistic encounter (relative to their intensity of use of the area prior to the interaction). These findings offer insight into the influences and consequences of intergroup competition on group-level patterns of space use. PMID:22837555

Anomaly Detection Using an Ensemble of Feature Models

PubMed Central

Noto, Keith; Brodley, Carla; Slonim, Donna

2011-01-01

We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of “normal” training data points in a chosen representation of the feature space. For some data sets, the normal data may not have discernible positions in feature space, but do have consistent relationships among some features that fail to appear in the anomalous examples. Our approach learns to predict the values of training set features from the values of other features. After we have formed an ensemble of predictors, we apply this ensemble to new data points. To combine the contribution of each predictor in our ensemble, we have developed a novel, information-theoretic anomaly measure that our experimental results show selects against noisy and irrelevant features. Our results on 47 data sets show that for most data sets, this approach significantly improves performance over current state-of-the-art feature space distance and density-based approaches. PMID:22020249

Mandibular bone structure, bone mineral density, and clinical variables as fracture predictors: a 15-year follow-up of female patients in a dental clinic.

PubMed

Jonasson, Grethe; Billhult, Annika

2013-09-01

To compare three mandibular trabeculation evaluation methods, clinical variables, and osteoporosis as fracture predictors in women. One hundred and thirty-six female dental patients (35-94 years) answered a questionnaire in 1996 and 2011. Using intra-oral radiographs from 1996, five methods were compared as fracture predictors: (1) mandibular bone structure evaluated with a visual radiographic index, (2) bone texture, (3) size and number of intertrabecular spaces calculated with Jaw-X software, (4) fracture probability calculated with a fracture risk assessment tool (FRAX), and (5) osteoporosis diagnosis based on dual-energy-X-ray absorptiometry. Differences were assessed with the Mann-Whitney test and relative risk calculated. Previous fracture, gluco-corticoid medication, and bone texture were significant indicators of future and total (previous plus future) fracture. Osteoporosis diagnosis, sparse trabeculation, Jaw-X, and FRAX were significant predictors of total but not future fracture. Clinical and oral bone variables may identify individuals at greatest risk of fracture. Copyright © 2013 Elsevier Inc. All rights reserved.

Micro-CT evaluation of bone defects: applications to osteolytic bone metastases, bone cysts, and fracture.

PubMed

Buie, Helen R; Bosma, Nick A; Downey, Charlene M; Jirik, Frank R; Boyd, Steven K

2013-11-01

Bone defects can occur in various forms and present challenges to performing a standard micro-CT evaluation of bone quality because most measures are suited to homogeneous structures rather than ones with spatially focal abnormalities. Such defects are commonly associated with pain and fragility. Research involving bone defects requires quantitative approaches to be developed if micro-CT is to be employed. In this study, we demonstrate that measures of inter-microarchitectural bone spacing are sensitive to the presence of focal defects in the proximal tibia of two distinctly different mouse models: a burr-hole model for fracture healing research, and a model of osteolytic bone metastases. In these models, the cortical and trabecular bone compartments were both affected by the defect and were, therefore, evaluated as a single unit to avoid splitting the defects into multiple analysis regions. The burr-hole defect increased mean spacing (Sp) by 27.6%, spacing standard deviation (SpSD) by 113%, and maximum spacing (Spmax) by 72.8%. Regression modeling revealed SpSD (β=0.974, p<0.0001) to be a significant predictor of the defect volume (R(2)=0.949) and Spmax (β=0.712, p<0.0001) and SpSD (β=0.271, p=0.022) to be significant predictors of the defect diameter (R(2)=0.954). In the mice with osteolytic bone metastases, spacing parameters followed similar patterns of change as reflected by other imaging technologies, specifically bioluminescence data which is indicative of tumor burden. These data highlight the sensitivity of spacing measurements to bone architectural abnormalities from 3D micro-CT data and provide a tool for quantitative evaluation of defects within a bone. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Predictors of School Garden Integration: Factors Critical to Gardening Success in New York City.

PubMed

Burt, Kate Gardner; Burgermaster, Marissa; Jacquez, Raquel

2018-03-01

The purpose of this study was to determine the level of integration of school gardens and identify factors that predict integration. 211 New York City schools completed a survey that collected demographic information and utilized the School Garden Integration Scale. A mean garden integration score was calculated, and multiple regression analysis was conducted to determine independent predictors of integration and assess relationships between individual integration characteristics and budget. The average integration score was 34.1 (of 57 points) and ranged from 8 to 53. Operating budget had significant influence on integration score, controlling for all other factors ( p < .0001). Partner organizations, evaluation/feedback, planning the physical space, and characteristics of the physical space were positively and significantly related to budget. The results of this study indicate that any garden can become well integrated, as budget is a modifiable factor. When adequate funding is secured, a well-integrated garden may be established with proper planning and sound implementation.

Dietary and Urinary Sulfur can Predict Changes in Bone Metabolism During Space Flight

NASA Technical Reports Server (NTRS)

Zwart, Sara R.; Heer, Martina; Shackelford, Linda; Smith, Scott M.

2015-01-01

Mitigating space flight-induced bone loss is critical for space exploration, and diet can play a major role in this effort. Previous ground-based studies provide evidence that dietary composition can influence bone resorption during bed rest. In this study we examined the role of dietary intake patterns as one factor that can influence bone mineral loss in astronauts during space flight. Crew members were asked to consume, for 4 days at a time, prescribed menus with either a low (0.3-0.6 g/mEq) or high (1.0-1.3 g/mEq) ratio of animal protein to potassium (APro:K). Menus were developed for each crewmember, and were designed to meet both crew preferences and study constraints. Intakes of energy, total protein, calcium, and sodium were held relatively constant between the two diets. The order of the menus was randomized, and crews completed each set (low and high) once before and twice during space flight, for a total of 6 controlled diet sessions. One inflight session and three postflight sessions (R+30, R+180, R+365) monitored typical dietary intake. As of this writing, data are available from 14 crew members. The final three subjects' inflight samples are awaiting return from the International Space Station via Space-X. On the last day of each of the 4-d controlled diet sessions, 24-h urine samples were collected, along with a fasting blood sample on the morning of the 5th day. Preliminary analyses show that urinary excretion of sulfate (normalized to lean body mass) is a significant predictor of urinary n-telopeptide (NTX). Dietary sulfate (normalized to lean body mass) is also a significant predictor of urinary NTX. The results from this study, will be important to better understand diet and bone interrelationships during space flight as well as on Earth. This study was funded by the Human Health Countermeasures Element of the NASA Human Research Program.

Timing, predictors, and progress of third space fluid accumulation during preliminary phase fluid resuscitation in adult patients with dengue.

PubMed

Premaratna, R; Ragupathy, A; Miththinda, J K N D; de Silva, H J

2013-07-01

Fluid leakage remains the hallmark of dengue hemorrhagic fever (DHF). The applicability of currently recommended predictors of DHF for adults with dengue is questionable as these are based on studies conducted in children. One hundred and two adults with dengue were prospectively followed up to investigate whether home-based or hospital-based early phase fluid resuscitation has an impact on clinical and hematological parameters used for the diagnosis of early or critical phase fluid leakage. In the majority of subjects, third space fluid accumulation (TSFA) was detected on the fifth and sixth days of infection. The quantity and quality of fluids administered played no role in TSFA. A reduction in systolic blood pressure appeared to be more helpful than a reduction in pulse pressure in predicting fluid leakage. TSFA occurred with lower percentage rises in packed cell volume (PCV) than stated in the current recommendations. A rapid reduction in platelets, progressive reduction in white blood cells, percentage rises in Haemoglobin (Hb), and PCV, and rises in aspartate aminotransferase and alanine aminotransferase were observed in patients with TSFA and therefore with the development of severe illness. Clinicians should be aware of the limitations of currently recommended predictors of DHF in adult patients who are receiving fluid resuscitation. Copyright © 2013 International Society for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Change in joint space width: hyaline articular cartilage loss or alteration in meniscus?

PubMed

Hunter, D J; Zhang, Y Q; Tu, X; Lavalley, M; Niu, J B; Amin, S; Guermazi, A; Genant, H; Gale, D; Felson, D T

2006-08-01

To explore the relative contribution of hyaline cartilage morphologic features and the meniscus to the radiographic joint space. The Boston Osteoarthritis of the Knee Study is a natural history study of symptomatic knee osteoarthritis (OA). Baseline and 30-month followup assessments included knee magnetic resonance imaging (MRI) and fluoroscopically positioned weight-bearing knee radiographs. Cartilage and meniscal degeneration were scored on MRI in the medial and lateral tibiofemoral joints using a semiquantitative grading system. Meniscal position was measured to the nearest millimeter. The dependent variable was joint space narrowing (JSN) on the plain radiograph (possible range 0-3). The predictor variables were MRI cartilage score, meniscal degeneration, and meniscal position measures. We first conducted a cross-sectional analysis using multivariate regression to determine the relative contribution of meniscal factors and cartilage morphologic features to JSN, adjusting for body mass index (BMI), age, and sex. The same approach was used for change in JSN and change in predictor variables. We evaluated 264 study participants with knee OA (mean age 66.7 years, 59% men, mean BMI 31.4 kg/m(2)). The results from the models demonstrated that meniscal position and meniscal degeneration each contributed to prediction of JSN, in addition to the contribution by cartilage morphologic features. For change in medial joint space, both change in meniscal position and change in articular cartilage score contributed substantially to narrowing of the joint space. The meniscus (both its position and degeneration) accounts for a substantial proportion of the variance explained in JSN, and the change in meniscal position accounts for a substantial proportion of change in JSN.

Space versus Place in Complex Human-Natural Systems: Spatial and Multi-level Models of Tropical Land Use and Cover Change (LUCC) in Guatemala

PubMed Central

López-Carr, David; Davis, Jason; Jankowska, Marta; Grant, Laura; López-Carr, Anna Carla; Clark, Matthew

2013-01-01

The relative role of space and place has long been debated in geography. Yet modeling efforts applied to coupled human-natural systems seemingly favor models assuming continuous spatial relationships. We examine the relative importance of placebased hierarchical versus spatial clustering influences in tropical land use/cover change (LUCC). Guatemala was chosen as our study site given its high rural population growth and deforestation in recent decades. We test predictors of 2009 forest cover and forest cover change from 2001-2009 across Guatemala's 331 municipalities and 22 departments using spatial and multi-level statistical models. Our results indicate the emergence of several socio-economic predictors of LUCC regardless of model choice. Hierarchical model results suggest that significant differences exist at the municipal and departmental levels but largely maintain the magnitude and direction of single-level model coefficient estimates. They are also intervention-relevant since policies tend to be applicable to distinct political units rather than to continuous space. Spatial models complement hierarchical approaches by indicating where and to what magnitude significant negative and positive clustering associations emerge. Appreciating the comparative advantages and limitations of spatial and nested models enhances a holistic approach to geographical analysis of tropical LUCC and human-environment interactions. PMID:24013908

Gay-Straight Alliances in High Schools: Social Predictors of Early Adoption

ERIC Educational Resources Information Center

Fetner, Tina; Kush, Kristin

2008-01-01

This article examines the patterns of emergence of gay-straight alliances (GSAs) in public high schools in the United States. These extracurricular student groups offer safe spaces, social support, and opportunities for activism to lesbian, gay, bisexual, transgender, queer, and straight students. Combining data on various characteristics of…

The Campus Spiritual Climate: Predictors of Satisfaction among Students with Diverse Worldviews

ERIC Educational Resources Information Center

Rockenbach, Alyssa Bryant; Mayhew, Matthew J.

2014-01-01

Using data collected via the Campus Religious and Spiritual Climate Survey (CRSCS), we examined how dimensions of the campus spiritual climate shape student satisfaction. The findings reveal that structural worldview diversity, space for support and spiritual expression, and provocative experiences with worldview diversity positively relate to…

Student-directed retrieval practice is a predictor of medical licensing examination performance.

PubMed

Deng, Francis; Gluckstein, Jeffrey A; Larsen, Douglas P

2015-12-01

A large body of evidence indicates that retrieval practice (test-enhanced learning) and spaced repetition increase long-term information retention. Implementation of these strategies in medical curricula is unfortunately limited. However, students may choose to apply them autonomously when preparing for high-stakes, cumulative assessments, such as the United States Medical Licensing Examination Step 1. We examined the prevalence of specific self-directed methods of testing, with or without spaced repetition, among preclinical students and assessed the relationship between these methods and licensing examination performance. Seventy-two medical students at one institution completed a survey concerning their use of user-generated (Anki) or commercially-available (Firecracker) flashcards intended for spaced repetition and of boards-style multiple-choice questions (MCQs). Other information collected included Step 1 score, past academic performance (Medical College Admission Test [MCAT] score, preclinical grades), and psychological factors that may have affected exam preparation or performance (feelings of depression, burnout, and test anxiety). All students reported using practice MCQs (mean 3870, SD 1472). Anki and Firecracker users comprised 31 and 49 % of respondents, respectively. In a multivariate regression model, significant independent predictors of Step 1 score included MCQs completed (unstandardized beta coefficient [B] = 2.2 × 10 - 3 , p < 0.001), unique Anki flashcards seen (B = 5.9 × 10 - 4 , p = 0.024), second-year honours (B = 1.198, p = 0.002), and MCAT score (B = 1.078, p = 0.003). Test anxiety was a significant negative predictor (B= - 1.986, p < 0.001). Unique Firecracker flashcards seen did not predict Step 1 score. Each additional 445 boards-style practice questions or 1700 unique Anki flashcards was associated with an additional point on Step 1 when controlling for other academic and psychological factors. Medical students engage extensively in self-initiated retrieval practice, often with spaced repetition. These practices are associated with superior performance on a medical licensing examination and should be considered for formal support by educators.

Long-term distribution and habitat changes of protected wildlife: giant pandas in Wolong Nature Reserve, China.

PubMed

Bai, Wenke; Connor, Thomas; Zhang, Jindong; Yang, Hongbo; Dong, Xin; Gu, Xiaodong; Zhou, Caiquan

2018-04-01

Changes in wildlife habitat across space and time, and corresponding changes in wildlife space use, are increasingly common phenomenon. It is critical to study and understand these spatio-temporal changes to accurately inform conservation strategy and manage wildlife populations. These changes can be particularly large and complex in areas that face pressure from human development and disturbance but are also under protection and/or restoration regimes. We analyzed changes in space use and habitat suitability of giant pandas in Wolong Nature Reserve, China, over three decades using kernel density, spatio-temporal analysis of moving polygons (STAMP), and MaxEnt methods, and data from three national censuses. Between 2001 and 2012, there was a slight retraction in total range, and more area of significant space use decreases than increases. Habitat suitability varied spatially and temporally, with a 4.1% decrease in average suitability between 1987 and 2001 and a 3.5% increase in average suitability in between 2001 and 2012. Elevation and bamboo were the most important habitat predictors across the three censuses. Human and natural disturbance variables such as distance to household and the distance to landslide variable in the 4th census were also important predictors, and likely also negatively influenced important habitat variables such as bamboo and forest cover. We were able to measure changes in space utilization and habitat suitability over a large time scale, highlighting the achievements and challenges of giant panda conservation. Long-term monitoring of the changes in distribution and habitat of threatened species, and an analysis of the drivers behind these changes such as undergone here, are important to inform the management and conservation of the world's remaining wildlife populations.

The Ratio of Angiopoietin-2 to Angiopoietin-1 as a Predictor of Mortality in Acute Lung Injury Patients

PubMed Central

Ong, Thida; McClintock, Dana E.; Kallet, Richard H.; Ware, Lorraine B.; Matthay, Michael A.; Liu, Kathleen D.

2014-01-01

Objective To test the hypothesis that the concentration of angiopoietin-2 relative to angiopoietin-1 (Ang-2/Ang-1) may be a useful biologic marker of mortality in acute lung injury (ALI) patients. We also tested the association of Ang-2/Ang-1 with physiologic and biologic markers of activated endothelium. Design Prospective observational cohort study. Setting Intensive care units in a tertiary care university hospital and a university-affiliated city hospital. Patients Fifty-six mechanically ventilated patients with ALI. Interventions Baseline plasma samples and pulmonary dead space fraction measurements were collected within 48 hours of ALI diagnosis. Measurements and Main Results Plasma levels of Ang-1 and Ang-2 and of biomarkers of endothelial activation were measured by ELISA. Baseline Ang-2/Ang-1 was significantly higher in patients who died [median 58 (IQR 17–117) vs. 14 (IQR 6–35), p=0.01]. In a multivariable analysis stratified by dead space fraction, Ang-2/Ang-1 was an independent predictor of death with an adjusted odds ratio of 4.3 (95% CI 1.3–13.5, p=0.01) in those with an elevated pulmonary dead space fraction (p=0.03 for interaction between pulmonary dead space fraction and Ang-2/Ang-1). Moderate to weak correlation was found with biologic markers of endothelial activation. Conclusions The ratio of Ang-2/Ang-1 may be a prognostic biomarker of endothelial activation in ALI patients and, along with pulmonary dead space fraction, may be useful for risk stratification of ALI patients, particularly in identifying subgroups for future research and therapeutic trials. PMID:20581666

Calibration of Predictor Models Using Multiple Validation Experiments

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.

Regression calibration for models with two predictor variables measured with error and their interaction, using instrumental variables and longitudinal data.

PubMed

Strand, Matthew; Sillau, Stefan; Grunwald, Gary K; Rabinovitch, Nathan

2014-02-10

Regression calibration provides a way to obtain unbiased estimators of fixed effects in regression models when one or more predictors are measured with error. Recent development of measurement error methods has focused on models that include interaction terms between measured-with-error predictors, and separately, methods for estimation in models that account for correlated data. In this work, we derive explicit and novel forms of regression calibration estimators and associated asymptotic variances for longitudinal models that include interaction terms, when data from instrumental and unbiased surrogate variables are available but not the actual predictors of interest. The longitudinal data are fit using linear mixed models that contain random intercepts and account for serial correlation and unequally spaced observations. The motivating application involves a longitudinal study of exposure to two pollutants (predictors) - outdoor fine particulate matter and cigarette smoke - and their association in interactive form with levels of a biomarker of inflammation, leukotriene E4 (LTE 4 , outcome) in asthmatic children. Because the exposure concentrations could not be directly observed, we used measurements from a fixed outdoor monitor and urinary cotinine concentrations as instrumental variables, and we used concentrations of fine ambient particulate matter and cigarette smoke measured with error by personal monitors as unbiased surrogate variables. We applied the derived regression calibration methods to estimate coefficients of the unobserved predictors and their interaction, allowing for direct comparison of toxicity of the different pollutants. We used simulations to verify accuracy of inferential methods based on asymptotic theory. Copyright © 2013 John Wiley & Sons, Ltd.

A Cross-Language Study of Acoustic Predictors of Speech Intelligibility in Individuals With Parkinson's Disease

PubMed Central

Choi, Yaelin

2017-01-01

Purpose The present study aimed to compare acoustic models of speech intelligibility in individuals with the same disease (Parkinson's disease [PD]) and presumably similar underlying neuropathologies but with different native languages (American English [AE] and Korean). Method A total of 48 speakers from the 4 speaker groups (AE speakers with PD, Korean speakers with PD, healthy English speakers, and healthy Korean speakers) were asked to read a paragraph in their native languages. Four acoustic variables were analyzed: acoustic vowel space, voice onset time contrast scores, normalized pairwise variability index, and articulation rate. Speech intelligibility scores were obtained from scaled estimates of sentences extracted from the paragraph. Results The findings indicated that the multiple regression models of speech intelligibility were different in Korean and AE, even with the same set of predictor variables and with speakers matched on speech intelligibility across languages. Analysis of the descriptive data for the acoustic variables showed the expected compression of the vowel space in speakers with PD in both languages, lower normalized pairwise variability index scores in Korean compared with AE, and no differences within or across language in articulation rate. Conclusions The results indicate that the basis of an intelligibility deficit in dysarthria is likely to depend on the native language of the speaker and listener. Additional research is required to explore other potential predictor variables, as well as additional language comparisons to pursue cross-linguistic considerations in classification and diagnosis of dysarthria types. PMID:28821018

An Occupational Performance Test Validation Program for Fire Fighters at the Kennedy Space Center

NASA Technical Reports Server (NTRS)

Schonfeld, Brian R.; Doerr, Donald F.; Convertino, Victor A.

1990-01-01

We evaluated performance of a modified Combat Task Test (CTT) and of standard fitness tests in 20 male subjects to assess the prediction of occupational performance standards for Kennedy Space Center fire fighters. The CTT consisted of stair-climbing, a chopping simulation, and a victim rescue simulation. Average CTT performance time was 3.61 +/- 0.25 min (SEM) and all CTT tasks required 93% to 97% maximal heart rate. By using scores from the standard fitness tests, a multiple linear regression model was fitted to each parameter: the stairclimb (r(exp 2) = .905, P less than .05), the chopping performance time (r(exp 2) = .582, P less than .05), the victim rescue time (r(exp 2) = .218, P = not significant), and the total performance time (r(exp 2) = .769, P less than .05). Treadmill time was the predominant variable, being the major predictor in two of four models. These results indicated that standardized fitness tests can predict performance on some CTT tasks and that test predictors were amenable to exercise training.

Development Roadmap of an Evolvable and Extensible Multi-Mission Telecom Planning and Analysis Framework

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Tung, Ramona H.; Lee, Charles H.

2003-01-01

In this paper, we describe the development roadmap and discuss the various challenges of an evolvable and extensible multi-mission telecom planning and analysis framework. Our long-term goal is to develop a set of powerful flexible telecommunications analysis tools that can be easily adapted to different missions while maintain the common Deep Space Communication requirements. The ability of re-using the DSN ground models and the common software utilities in our adaptations has contributed significantly to our development efforts measured in terms of consistency, accuracy, and minimal effort redundancy, which can translate into shorter development time and major cost savings for the individual missions. In our roadmap, we will address the design principles, technical achievements and the associated challenges for following telecom analysis tools (i) Telecom Forecaster Predictor - TFP (ii) Unified Telecom Predictor - UTP (iii) Generalized Telecom Predictor - GTP (iv) Generic TFP (v) Web-based TFP (vi) Application Program Interface - API (vii) Mars Relay Network Planning Tool - MRNPT.

A direct Arbitrary-Lagrangian-Eulerian ADER-WENO finite volume scheme on unstructured tetrahedral meshes for conservative and non-conservative hyperbolic systems in 3D

NASA Astrophysics Data System (ADS)

Boscheri, Walter; Dumbser, Michael

2014-10-01

In this paper we present a new family of high order accurate Arbitrary-Lagrangian-Eulerian (ALE) one-step ADER-WENO finite volume schemes for the solution of nonlinear systems of conservative and non-conservative hyperbolic partial differential equations with stiff source terms on moving tetrahedral meshes in three space dimensions. A WENO reconstruction technique is used to achieve high order of accuracy in space, while an element-local space-time Discontinuous Galerkin finite element predictor on moving curved meshes is used to obtain a high order accurate one-step time discretization. Within the space-time predictor the physical element is mapped onto a reference element using a high order isoparametric approach, where the space-time basis and test functions are given by the Lagrange interpolation polynomials passing through a predefined set of space-time nodes. Since our algorithm is cell-centered, the final mesh motion is computed by using a suitable node solver algorithm. A rezoning step as well as a flattener strategy are used in some of the test problems to avoid mesh tangling or excessive element deformations that may occur when the computation involves strong shocks or shear waves. The ALE algorithm presented in this article belongs to the so-called direct ALE methods because the final Lagrangian finite volume scheme is based directly on a space-time conservation formulation of the governing PDE system, with the rezoned geometry taken already into account during the computation of the fluxes. We apply our new high order unstructured ALE schemes to the 3D Euler equations of compressible gas dynamics, for which a set of classical numerical test problems has been solved and for which convergence rates up to sixth order of accuracy in space and time have been obtained. We furthermore consider the equations of classical ideal magnetohydrodynamics (MHD) as well as the non-conservative seven-equation Baer-Nunziato model of compressible multi-phase flows with stiff relaxation source terms.

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

PubMed

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Predictors of immune function in space flight

NASA Astrophysics Data System (ADS)

Shearer, William T.; Zhang, Shaojie; Reuben, James M.; Lee, Bang-Ning; Butel, Janet S.

2007-02-01

Of all of the environmental conditions of space flight that might have an adverse effect upon human immunity and the incidence of infection, space radiation stands out as the single-most important threat. As important as this would be on humans engaged in long and deep space flight, it obviously is not possible to plan Earth-bound radiation and infection studies in humans. Therefore, we propose to develop a murine model that could predict the adverse effects of space flight radiation and reactivation of latent virus infection for humans. Recent observations on the effects of gamma and latent virus infection demonstrate latent virus reactivation and loss of T cell mediated immune responses in a murine model. We conclude that using this small animal method of quantitating the amounts of radiation and latent virus infection and resulting alterations in immune responses, it may be possible to predict the degree of immunosuppression in interplanetary space travel for humans. Moreover, this model could be extended to include other space flight conditions, such as microgravity, sleep deprivation, and isolation, to obtain a more complete assessment of space flight risks for humans.

Mapping the determinants of health inequalities in social space: can Bourdieu help us?

PubMed

Gatrell, Anthony C; Popay, Jennie; Thomas, Carol

2004-09-01

Considerable research effort has been devoted to describing and explaining, at a variety of spatial scales, geographical inequalities in health outcomes within the developed world. Following Bourdieu, we argue that structures of the social world may be revealed in different kinds of 'social' space. We outline the relational thinking that underlies these ideas. We then 'map', using correspondence analysis (on which Bourdieu himself drew), the structure of social space according to the differential availability of some forms of capital, across four study areas in north-west England. We use logistic regression analysis to explain variation in psychological morbidity (GHQ-score) and then portray the significant predictors of morbidity using multiple correspondence analysis. The area of residence of the survey respondents is used to associate them with particular locations in these social spaces.

Stay out of My Space! Territoriality and Nonverbal Immediacy as Predictors of Roommate Satisfaction

ERIC Educational Resources Information Center

Erlandson, Karen

2012-01-01

This study utilize d direct observation to explore the relationship between nonverbal communication variables (immediacy and territoriality) and roommate satisfaction. Data were collected from 51 roommate pairs (N = 102) at a small liberal arts college. Participants were asked to engage in a discussion about a time they had to negotiate activities…

Predictors of NCLEX-PN Success for Practical Nursing Students

ERIC Educational Resources Information Center

Eickhoff, Mary Ann

2016-01-01

There is currently a nursing shortage in the United States. By 2022, the Bureau of Labor Statistics (BLS) expects, the number of job openings for Practical Nurses (PN) will be 168,500, an increase of 25% over 2012 (BLS, 2014). Nursing education does not currently meet present, much less future needs. Nursing programs have limited space; according…

A comparison of mangrove canopy height using multiple independent measurements from land, air, and space

Treesearch

David Lagomasino; Temilola Fatoyinbo; SeungKuk Lee; Emanuelle Feliciano; Carl Trettin; Marc Simard

2016-01-01

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest...

Cognitive components of a mathematical processing network in 9-year-old children.

PubMed

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-07-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular 'number sense'. We suggest an 'executive memory function centric' model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors.

The Geography of Mental Health and General Wellness in Galveston Bay After Hurricane Ike: A Spatial Epidemiologic Study With Longitudinal Data.

PubMed

Gruebner, Oliver; Lowe, Sarah R; Tracy, Melissa; Cerdá, Magdalena; Joshi, Spruha; Norris, Fran H; Galea, Sandro

2016-04-01

To demonstrate a spatial epidemiologic approach that could be used in the aftermath of disasters to (1) detect spatial clusters and (2) explore geographic heterogeneity in predictors for mental health and general wellness. We used a cohort study of Hurricane Ike survivors (n=508) to assess the spatial distribution of postdisaster mental health wellness (most likely resilience trajectory for posttraumatic stress symptoms [PTSS] and depression) and general wellness (most likely resilience trajectory for PTSS, depression, functional impairment, and days of poor health) in Galveston, Texas. We applied the spatial scan statistic (SaTScan) and geographically weighted regression. We found spatial clusters of high likelihood wellness in areas north of Texas City and spatial concentrations of low likelihood wellness in Galveston Island. Geographic variation was found in predictors of wellness, showing increasing associations with both forms of wellness the closer respondents were located to Galveston City in Galveston Island. Predictors for postdisaster wellness may manifest differently across geographic space with concentrations of lower likelihood wellness and increased associations with predictors in areas of higher exposure. Our approach could be used to inform geographically targeted interventions to promote mental health and general wellness in disaster-affected communities.

Cognitive components of a mathematical processing network in 9-year-old children

PubMed Central

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-01-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular ‘number sense’. We suggest an ‘executive memory function centric’ model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors. PMID:25089322

Effective dimension reduction for sparse functional data

PubMed Central

YAO, F.; LEI, E.; WU, Y.

2015-01-01

Summary We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures of the predictor process and the effective dimension reduction space. A simulation study and an application illustrate the superior finite-sample performance of the method. PMID:26566293

Efficient conservative ADER schemes based on WENO reconstruction and space-time predictor in primitive variables

NASA Astrophysics Data System (ADS)

Zanotti, Olindo; Dumbser, Michael

2016-01-01

We present a new version of conservative ADER-WENO finite volume schemes, in which both the high order spatial reconstruction as well as the time evolution of the reconstruction polynomials in the local space-time predictor stage are performed in primitive variables, rather than in conserved ones. To obtain a conservative method, the underlying finite volume scheme is still written in terms of the cell averages of the conserved quantities. Therefore, our new approach performs the spatial WENO reconstruction twice: the first WENO reconstruction is carried out on the known cell averages of the conservative variables. The WENO polynomials are then used at the cell centers to compute point values of the conserved variables, which are subsequently converted into point values of the primitive variables. This is the only place where the conversion from conservative to primitive variables is needed in the new scheme. Then, a second WENO reconstruction is performed on the point values of the primitive variables to obtain piecewise high order reconstruction polynomials of the primitive variables. The reconstruction polynomials are subsequently evolved in time with a novel space-time finite element predictor that is directly applied to the governing PDE written in primitive form. The resulting space-time polynomials of the primitive variables can then be directly used as input for the numerical fluxes at the cell boundaries in the underlying conservative finite volume scheme. Hence, the number of necessary conversions from the conserved to the primitive variables is reduced to just one single conversion at each cell center. We have verified the validity of the new approach over a wide range of hyperbolic systems, including the classical Euler equations of gas dynamics, the special relativistic hydrodynamics (RHD) and ideal magnetohydrodynamics (RMHD) equations, as well as the Baer-Nunziato model for compressible two-phase flows. In all cases we have noticed that the new ADER schemes provide less oscillatory solutions when compared to ADER finite volume schemes based on the reconstruction in conserved variables, especially for the RMHD and the Baer-Nunziato equations. For the RHD and RMHD equations, the overall accuracy is improved and the CPU time is reduced by about 25 %. Because of its increased accuracy and due to the reduced computational cost, we recommend to use this version of ADER as the standard one in the relativistic framework. At the end of the paper, the new approach has also been extended to ADER-DG schemes on space-time adaptive grids (AMR).

Recurrence patterns and survival endpoints in women with stage II uterine endometrioid carcinoma: a multi-institution study.

PubMed

Elshaikh, Mohamed A; Al-Wahab, Zaid; Mahdi, Haider; Albuquerque, Kevin; Mahan, Meredith; Kehoe, Siobhan M; Ali-Fehmi, Rouba; Rose, Peter G; Munkarah, Adnan R

2015-02-01

There is paucity of data in regard to prognostic factors and outcome of women with 2009 FIGO stage II disease. The objective of this study was to investigate prognostic factors, recurrence patterns and survival endpoints in this group of patients. Data from four academic institutions were analyzed. 130 women were identified with 2009 FIGO stage II. All patients underwent hysterectomy, oophorectomy and lymph node evaluation with or without pelvic and paraaortic lymph node dissections and peritoneal cytology. The Kaplan-Meier approach and Cox regression analysis were used to estimate recurrence-free (RFS), disease-specific (DSS) and overall survival (OS). Median follow-up was 44months. 120 patients (92%) underwent simple hysterectomy, 78% had lymph node dissection and 95% had peritoneal cytology examination. 99 patients (76%) received adjuvant radiation treatment (RT). 5-year RFS, DSS and OS were 77%, 90%, and 72%, respectively. On multivariate analysis of RFS, adjuvant RT, the presence of lymphovascular space invasion (LVSI) and high tumor grades were significant predictors. For DSS, LVSI and high tumor grades were significant predictors while older age and high tumor grade were the only predictors of OS. In this multi-institutional study, disease-specific survival for women with FIGO stage II uterine endometrioid carcinoma is excellent. High tumor grade, lymphovascular space invasion, adjuvant radiation treatment and old age are important prognostic factors. There was no significant difference in the outcome between patients who received vaginal cuff brachytherapy compared to those who received pelvic external beam radiation treatment. Copyright © 2014 Elsevier Inc. All rights reserved.

Space sickness predictors suggest fluid shift involvement and possible countermeasures

NASA Technical Reports Server (NTRS)

Simanonok, K. E.; Moseley, E. C.; Charles, J. B.

1992-01-01

Preflight data from 64 first time Shuttle crew members were examined retrospectively to predict space sickness severity (NONE, MILD, MODERATE, or SEVERE) by discriminant analysis. From 9 input variables relating to fluid, electrolyte, and cardiovascular status, 8 variables were chosen by discriminant analysis that correctly predicted space sickness severity with 59 pct. success by one method of cross validation on the original sample and 67 pct. by another method. The 8 variables in order of their importance for predicting space sickness severity are sitting systolic blood pressure, serum uric acid, calculated blood volume, serum phosphate, urine osmolality, environmental temperature at the launch site, red cell count, and serum chloride. These results suggest the presence of predisposing physiologic factors to space sickness that implicate a fluid shift etiology. Addition of a 10th input variable, hours spent in the Weightless Environment Training Facility (WETF), improved the prediction of space sickness severity to 66 pct. success by the first method of cross validation on the original sample and to 71 pct. by the second method. The data suggest that WETF training may reduce space sickness severity.

Factors affecting female space use in ten populations of prairie chickens

USGS Publications Warehouse

Winder, Virginia L.; Carrlson, Kaylan M.; Gregory, Andrew J.; Hagen, Christian A.; Haukos, David A.; Kesler, Dylan C.; Larsson, Lena C.; Matthews, Ty W.; McNew, Lance B.; Patten, Michael; Pitman, Jim C.; Powell, Larkin A.; Smith, Jennifer A.; Thompson, Tom; Wolfe, Donald H.; Sandercock, Brett K.

2015-01-01

Conservation of wildlife depends on an understanding of the interactions between animal movements and key landscape factors. Habitat requirements of wide-ranging species often vary spatially, but quantitative assessment of variation among replicated studies at multiple sites is rare. We investigated patterns of space use for 10 populations of two closely related species of prairie grouse: Greater Prairie-Chickens (Tympanuchus cupido) and Lesser Prairie-Chickens (T. pallidicinctus). Prairie chickens require large, intact tracts of native grasslands, and are umbrella species for conservation of prairie ecosystems in North America. We used resource utilization functions to investigate space use by female prairie chickens during the 6-month breeding season from March through August in relation to lek sites, habitat conditions, and anthropogenic development. Our analysis included data from 382 radio-marked individuals across a major portion of the extant range. Our project is a unique opportunity to study comparative space use of prairie chickens, and we employed standardized methods that facilitated direct comparisons across an ecological gradient of study sites. Median home range size of females varied ~10-fold across 10 sites (3.6–36.7 km2), and home ranges tended to be larger at sites with higher annual precipitation. Proximity to lek sites was a strong and consistent predictor of space use for female prairie chickens at all 10 sites. The relative importance of other predictors of space use varied among sites, indicating that generalized habitat management guidelines may not be appropriate for these two species. Prairie chickens actively selected for prairie habitats, even at sites where ~90% of the land cover within the study area was prairie. A majority of the females monitored in our study (>95%) had activity centers within 5 km of leks, suggesting that conservation efforts can be effectively concentrated near active lek sites. Our data on female space use suggest that lek surveys of male prairie chickens can indirectly assess habitat suitability for females during the breeding season. Lek monitoring and surveys for new leks provide information on population trends, but can also guide management actions aimed at improving nesting and brood-rearing habitats.

Evaluating a Space-Based Indicator of Surface Ozone-NOx-VOC Sensitivity Over Midlatitude Source Regions and Application to Decadal Trends

NASA Astrophysics Data System (ADS)

Jin, Xiaomeng; Fiore, Arlene M.; Murray, Lee T.; Valin, Lukas C.; Lamsal, Lok N.; Duncan, Bryan; Folkert Boersma, K.; De Smedt, Isabelle; Abad, Gonzalo Gonzalez; Chance, Kelly; Tonnesen, Gail S.

2017-10-01

Determining effective strategies for mitigating surface ozone (O3) pollution requires knowledge of the relative ambient concentrations of its precursors, NOx, and VOCs. The space-based tropospheric column ratio of formaldehyde to NO2 (FNR) has been used as an indicator to identify NOx-limited versus NOx-saturated O3 formation regimes. Quantitative use of this indicator ratio is subject to three major uncertainties: (1) the split between NOx-limited and NOx-saturated conditions may shift in space and time, (2) the ratio of the vertically integrated column may not represent the near-surface environment, and (3) satellite products contain errors. We use the GEOS-Chem global chemical transport model to evaluate the quantitative utility of FNR observed from the Ozone Monitoring Instrument over three northern midlatitude source regions. We find that FNR in the model surface layer is a robust predictor of the simulated near-surface O3 production regime. Extending this surface-based predictor to a column-based FNR requires accounting for differences in the HCHO and NO2 vertical profiles. We compare four combinations of two OMI HCHO and NO2 retrievals with modeled FNR. The spatial and temporal correlations between the modeled and satellite-derived FNR vary with the choice of NO2 product, while the mean offset depends on the choice of HCHO product. Space-based FNR indicates that the spring transition to NOx-limited regimes has shifted at least a month earlier over major cities (e.g., New York, London, and Seoul) between 2005 and 2015. This increase in NOx sensitivity implies that NOx emission controls will improve O3 air quality more now than it would have a decade ago.

Evaluating a Space-Based Indicator of Surface Ozone-NO x -VOC Sensitivity Over Midlatitude Source Regions and Application to Decadal Trends.

PubMed

Jin, Xiaomeng; Fiore, Arlene M; Murray, Lee T; Valin, Lukas C; Lamsal, Lok N; Duncan, Bryan; Boersma, K Folkert; De Smedt, Isabelle; Abad, Gonzalo Gonzalez; Chance, Kelly; Tonnesen, Gail S

2017-10-16

Determining effective strategies for mitigating surface ozone (O 3 ) pollution requires knowledge of the relative ambient concentrations of its precursors, NO x , and VOCs. The space-based tropospheric column ratio of formaldehyde to NO 2 (FNR) has been used as an indicator to identify NO x -limited versus NO x -saturated O 3 formation regimes. Quantitative use of this indicator ratio is subject to three major uncertainties: (1) the split between NO x -limited and NO x -saturated conditions may shift in space and time, (2) the ratio of the vertically integrated column may not represent the near-surface environment, and (3) satellite products contain errors. We use the GEOS-Chem global chemical transport model to evaluate the quantitative utility of FNR observed from the Ozone Monitoring Instrument over three northern midlatitude source regions. We find that FNR in the model surface layer is a robust predictor of the simulated near-surface O 3 production regime. Extending this surface-based predictor to a column-based FNR requires accounting for differences in the HCHO and NO 2 vertical profiles. We compare four combinations of two OMI HCHO and NO 2 retrievals with modeled FNR. The spatial and temporal correlations between the modeled and satellite-derived FNR vary with the choice of NO 2 product, while the mean offset depends on the choice of HCHO product. Space-based FNR indicates that the spring transition to NO x -limited regimes has shifted at least a month earlier over major cities (e.g., New York, London, and Seoul) between 2005 and 2015. This increase in NO x sensitivity implies that NO x emission controls will improve O 3 air quality more now than it would have a decade ago.

Analysis of nystagmus response to a pseudorandom velocity input

NASA Technical Reports Server (NTRS)

Lessard, C. S.

1986-01-01

Space motion sickness was not reported during the first Apollo missions; however, since Apollo 8 through the current Shuttle and Skylab missions, approximately 50% of the crewmembers have experienced instances of space motion sickness. Space motion sickness, renamed space adaptation syndrome, occurs primarily during the initial period of a mission until habilation takes place. One of NASA's efforts to resolve the space adaptation syndrome is to model the individual's vestibular response for basis knowledge and as a possible predictor of an individual's susceptibility to the disorder. This report describes a method to analyse the vestibular system when subjected to a pseudorandom angular velocity input. A sum of sinusoids (pseudorandom) input lends itself to analysis by linear frequency methods. Resultant horizontal ocular movements were digitized, filtered and transformed into the frequency domain. Programs were developed and evaluated to obtain the (1) auto spectra of input stimulus and resultant ocular resonse, (2) cross spectra, (3) the estimated vestibular-ocular system transfer function gain and phase, and (4) coherence function between stimulus and response functions.

Context dependent prediction and category encoding for DPCM image compression

NASA Technical Reports Server (NTRS)

Beaudet, Paul R.

1989-01-01

Efficient compression of image data requires the understanding of the noise characteristics of sensors as well as the redundancy expected in imagery. Herein, the techniques of Differential Pulse Code Modulation (DPCM) are reviewed and modified for information-preserving data compression. The modifications include: mapping from intensity to an equal variance space; context dependent one and two dimensional predictors; rationale for nonlinear DPCM encoding based upon an image quality model; context dependent variable length encoding of 2x2 data blocks; and feedback control for constant output rate systems. Examples are presented at compression rates between 1.3 and 2.8 bits per pixel. The need for larger block sizes, 2D context dependent predictors, and the hope for sub-bits-per-pixel compression which maintains spacial resolution (information preserving) are discussed.

Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor

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

Gentili, Pier Luigi, E-mail: pierluigi.gentili@unipg.it; Gotoda, Hiroshi; Dolnik, Milos

Forecasting of aperiodic time series is a compelling challenge for science. In this work, we analyze aperiodic spectrophotometric data, proportional to the concentrations of two forms of a thermoreversible photochromic spiro-oxazine, that are generated when a cuvette containing a solution of the spiro-oxazine undergoes photoreaction and convection due to localized ultraviolet illumination. We construct the phase space for the system using Takens' theorem and we calculate the Lyapunov exponents and the correlation dimensions to ascertain the chaotic character of the time series. Finally, we predict the time series using three distinct methods: a feed-forward neural network, fuzzy logic, and amore » local nonlinear predictor. We compare the performances of these three methods.« less

Automated Knowledge Discovery From Simulators

NASA Technical Reports Server (NTRS)

Burl, Michael; DeCoste, Dennis; Mazzoni, Dominic; Scharenbroich, Lucas; Enke, Brian; Merline, William

2007-01-01

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

NASA Astrophysics Data System (ADS)

Murari, A.; Lungaroni, M.; Peluso, E.; Gaudio, P.; Vega, J.; Dormido-Canto, S.; Baruzzo, M.; Gelfusa, M.; Contributors, JET

2018-05-01

Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy ‘from scratch’ has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.

Tumour front inflammation and necrosis are independent prognostic predictors in high-grade urothelial carcinoma of the bladder.

PubMed

Hodgson, Anjelica; Xu, Bin; Satkunasivam, Raj; Downes, Michelle R

2018-02-01

Inflammation and necrosis have been associated with prognosis in multiple epithelial malignancies. Our objective was to evaluate inflammation and necrosis in a cohort of patients with high-grade urothelial carcinomas of the bladder to determine their association with pathological parameters and their prognostic effect on relapse-free and disease-specific survival. A retrospective cohort that underwent radical cystectomy for urothelial carcinomas (n=235) was evaluated for invasive front and central inflammation using the Klintrup-Makinen assessment method. Necrosis was scored using a four-point scale. The relationship of inflammation and necrosis with stage, nodal status, carcinoma in situ, tumour size, margin status and vascular space invasion and the impact on relapse-free and disease-specific survival were calculated using appropriate statistical tests. On multivariate analysis, invasive front inflammation (p=0.003) and necrosis (p=0.000) were independent predictors of relapse-free survival. Both invasive front inflammation (p=0.009) and necrosis (p=0.002) again were independent predictors of disease-specific survival. For pathological features, low invasive front inflammation was associated with lymphovascular space invasion (p=0.008), a positive soft tissue margin (p=0.028) and carcinoma in situ (p=0.042). Necrosis was statistically associated with tumours >3 cm in size (p=0.013) and carcinoma in situ (p<0.001). Necrosis and invasive front inflammation are additional histological variables with independent prognostic relevance in high-grade urothelial carcinoma of the bladder. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Effects of Using Dynamic Mathematics Software on Preservice Mathematics Teachers' Spatial Visualization Skills: The Case of Spatial Analytic Geometry

ERIC Educational Resources Information Center

Kösa, Temel

2016-01-01

The purpose of this study was to investigate the effects of using dynamic geometry software on preservice mathematics teachers' spatial visualization skills and to determine whether spatial visualization skills can be a predictor of success in learning analytic geometry of space. The study used a quasi-experimental design with a control group.…

Identification, prevalence, and treatment of painful diabetic neuropathy in patients from a rural area in South Carolina.

PubMed

Pruitt, Jimmy; Moracho-Vilrriales, Carolina; Threatt, Tiffaney; Wagner, Sarah; Wu, Jun; Romero-Sandoval, E Alfonso

2017-01-01

Diabetic peripheral neuropathy (DPN) represents significant burdens to many patients and the public health-care system. Patients with diabetes in rural areas have higher risk of developing complications and having less access to proper treatment. We studied a rural population of patients with diabetes who attended a pharmacist-led free clinic for a diabetic education program. Our objectives were to 1) determine the prevalence of DPN and painful diabetic neuropathy (p-DN) in patients with type 2 diabetes; 2) assess the proportion of patients with DPN and p-DN left undocumented upon physician referral to a pharmacist-led free clinic; and 3) determine the appropriateness of pain medication regimen. We performed a retrospective analysis of clinical records of patients from the Presbyterian College School of Pharmacy (PCSP) Wellness Center located in Clinton, SC. Diagnoses of DPN and/or p-DN were obtained from referral notes in the clinical records and compared with results from foot examinations performed in the free clinic and clinical features. Medication regimens were also obtained and compared using American Academy of Neurology (AAN) treatment guidelines. Within our study population (n=111), the prevalence of DPN was 62.2% (national average of 28%-45%) and that of p-DN was 23.4% (national average of 11%-24%). In p-DN patients (n=26), 53.8% (n=14) had a documented diagnosis of p-DN by the referring physician, and 46.2% (n=12) were identified by the pharmacists. A total of 95% (19 of 20) of the patients treated for p-DN received adequate pharmacological agents, though suboptimal as per clinical guidelines. More than 50% of the patients used subtherapeutic doses of their medications. Gabapentin was the most frequently used medication in our population (65.4%). Patients in rural South Carolina had a higher prevalence of DPN and p-DN with >60% undocumented cases of p-DN. More than 95% of treated patients did not receive optimum therapy according to AAN guidelines.

On the interest of combining an analog model to a regression model for the adaptation of the downscaling link. Application to probabilistic prediction of precipitation over France.

NASA Astrophysics Data System (ADS)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2016-04-01

Scenarios of surface weather required for the impact studies have to be unbiased and adapted to the space and time scales of the considered hydro-systems. Hence, surface weather scenarios obtained from global climate models and/or numerical weather prediction models are not really appropriated. Outputs of these models have to be post-processed, which is often carried out thanks to Statistical Downscaling Methods (SDMs). Among those SDMs, approaches based on regression are often applied. For a given station, a regression link can be established between a set of large scale atmospheric predictors and the surface weather variable. These links are then used for the prediction of the latter. However, physical processes generating surface weather vary in time. This is well known for precipitation for instance. The most relevant predictors and the regression link are also likely to vary in time. A better prediction skill is thus classically obtained with a seasonal stratification of the data. Another strategy is to identify the most relevant predictor set and establish the regression link from dates that are similar - or analog - to the target date. In practice, these dates can be selected thanks to an analog model. In this study, we explore the possibility of improving the local performance of an analog model - where the analogy is applied to the geopotential heights 1000 and 500 hPa - using additional local scale predictors for the probabilistic prediction of the Safran precipitation over France. For each prediction day, the prediction is obtained from two GLM regression models - for both the occurrence and the quantity of precipitation - for which predictors and parameters are estimated from the analog dates. Firstly, the resulting combined model noticeably allows increasing the prediction performance by adapting the downscaling link for each prediction day. Secondly, the selected predictors for a given prediction depend on the large scale situation and on the considered region. Finally, even with such an adaptive predictor identification, the downscaling link appears to be robust: for a same prediction day, predictors selected for different locations of a given region are similar and the regression parameters are consistent within the region of interest.

Real-time validation of the Dst Predictor model

USGS Publications Warehouse

McCollough, James P.; Young, Shawn L.; Rigler, E. Joshua; Simpson, Hal A.

2015-01-01

The Dst Predictor model, which has been running real-time in the Space Weather Analysis and Forecast System (SWAFS), provides 1-hour and 4-hour forecasts of the Dst index. This is useful for awareness of impending geomagnetic activity, as well as driving other real-time models that use Dst as an input. In this report, we examine the performance of this forecast model in detail. When validating indices it should be noted that performance is only with respect to a reference index as they are derived quantities assumed to reflect a state of the magnetosphere that cannot be directly measured. In this case U.S. Geological Survey (USGS) Definitive Dst is the reference index (Section 3). Whether or not the model better reflects the actual activity level is nearly impossible to discern and is outside the scope of this report. We evaluate the performance of the model by computing continuous predictant skill scores against USGS Definitive Dst values as “observations” (Section 4.2). The two sets of data are not well-correlated for both 1-hour and 4-hour forecasts. The Dst Predictor Prediction Efficiency for both the 1- and 4-hour forecasts suggests poor performance versus the climatological mean. However, the skill score against a nowcast persistence model is positive, suggesting value added by the Dst Predictor model. We further examine statistics for storm times (Section 4.3) with similar results: nowcast persistence performs worse than Dst Predictor.  Dst Predictor is superior to the nowcast persistence model for the metric used in this study. We recommend continued use of the DstPredictor model for 1-and4-hour Dst predictions along with active study of other Dst forecast models that do not rely on nowcast inputs (Section 6). The lack of certified requirements makes further recommendations difficult. A study of how the error in Dst translates to error in models and a better understanding of operational needs for magnetic storm warning are needed to determine such requirements. Nowcast persistence is often hard to beat for short term forecasts and specification and Dst Predictor clearly performs well against that standard (with 1-hour and 4-hour skill-scores of 0.233 and 0.485 respectively), although poor in absolute terms (with1-hourand4-hour prediction efficiencies of-64.6and-43.1, respectively).

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

2001-01-01

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

Presence and consequence of tooth periapical radiolucency in patients with cirrhosis

PubMed Central

Grønkjær, Lea Ladegaard; Holmstrup, Palle; Schou, Søren; Schwartz, Kristoffer; Kongstad, Johanne; Jepsen, Peter; Vilstrup, Hendrik

2016-01-01

Background Periapical radiolucency is the radiographic sign of inflammatory bone lesions around the apex of the tooth. We determined the prevalence and predictors of periapical radiolucency in patients with cirrhosis and the association with systemic inflammation status and cirrhosis-related complications. Methods A total of 110 cirrhosis patients were consecutively enrolled. Periapical radiolucency was defined as the presence of radiolucency or widening of the periapical periodontal ligament space to more than twice the normal width. Predictors of periapical radiolucency and the association with systemic inflammation markers and cirrhosis-related complications were explored by univariable and multivariable logistic regression analyses. Results Periapical radiolucency was present in one or more teeth in 46% of the patients. Strong predictors were gross caries (odds ratio [OR] 3.12, 95% confidence interval [CI] 1.43–6.79) and severe periodontitis (OR 3.98, 95% CI 1.04–15.20). Also old age (OR 1.10, 95% CI 1.01–1.19) and smoking (OR 3.24, 95% CI 1.02–17.62) were predictors. However, cirrhosis etiology (alcoholic vs nonalcoholic) or severity (Model of End-Stage Liver Disease score) were not predictors. The patients with periapical radiolucency had higher C-reactive protein (15.8 mg/L vs 8.1 mg/L, P=0.02) and lower albumin contents (25 g/L vs 28 g/L, P=0.04) than those without. Furthermore, the patients with periapical radiolucency had a higher prevalence of cirrhosis-related complications such as ascites, hepatic encephalopathy, and/or variceal bleeding (46% vs 27%, P=0.05). Conclusion Periapical radiolucency is often present as an element of poor oral health status and likely has an adverse clinical significance, which should motivate diagnostic and clinical attention to the findings. PMID:27695370

Space Mission Human Reliability Analysis (HRA) Project

NASA Technical Reports Server (NTRS)

Boyer, Roger

2014-01-01

The purpose of the Space Mission Human Reliability Analysis (HRA) Project is to extend current ground-based HRA risk prediction techniques to a long-duration, space-based tool. Ground-based HRA methodology has been shown to be a reasonable tool for short-duration space missions, such as Space Shuttle and lunar fly-bys. However, longer-duration deep-space missions, such as asteroid and Mars missions, will require the crew to be in space for as long as 400 to 900 day missions with periods of extended autonomy and self-sufficiency. Current indications show higher risk due to fatigue, physiological effects due to extended low gravity environments, and others, may impact HRA predictions. For this project, Safety & Mission Assurance (S&MA) will work with Human Health & Performance (HH&P) to establish what is currently used to assess human reliabiilty for human space programs, identify human performance factors that may be sensitive to long duration space flight, collect available historical data, and update current tools to account for performance shaping factors believed to be important to such missions. This effort will also contribute data to the Human Performance Data Repository and influence the Space Human Factors Engineering research risks and gaps (part of the HRP Program). An accurate risk predictor mitigates Loss of Crew (LOC) and Loss of Mission (LOM).The end result will be an updated HRA model that can effectively predict risk on long-duration missions.

Predictors of Daily Mobility of Adults in Peri-Urban South India.

PubMed

Sanchez, Margaux; Ambros, Albert; Salmon, Maëlle; Bhogadi, Santhi; Wilson, Robin T; Kinra, Sanjay; Marshall, Julian D; Tonne, Cathryn

2017-07-14

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women's activity spaces were smaller and more circular than men's. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health.

Application of Interval Predictor Models to Space Radiation Shielding

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy,Daniel P.; Norman, Ryan B.; Blattnig, Steve R.

2016-01-01

This paper develops techniques for predicting the uncertainty range of an output variable given input-output data. These models are called Interval Predictor Models (IPM) because they yield an interval valued function of the input. This paper develops IPMs having a radial basis structure. This structure enables the formal description of (i) the uncertainty in the models parameters, (ii) the predicted output interval, and (iii) the probability that a future observation would fall in such an interval. In contrast to other metamodeling techniques, this probabilistic certi cate of correctness does not require making any assumptions on the structure of the mechanism from which data are drawn. Optimization-based strategies for calculating IPMs having minimal spread while containing all the data are developed. Constraints for bounding the minimum interval spread over the continuum of inputs, regulating the IPMs variation/oscillation, and centering its spread about a target point, are used to prevent data over tting. Furthermore, we develop an approach for using expert opinion during extrapolation. This metamodeling technique is illustrated using a radiation shielding application for space exploration. In this application, we use IPMs to describe the error incurred in predicting the ux of particles resulting from the interaction between a high-energy incident beam and a target.

Bayesian Analysis of High Dimensional Classification

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Subhadeep; Liang, Faming

2009-12-01

Modern data mining and bioinformatics have presented an important playground for statistical learning techniques, where the number of input variables is possibly much larger than the sample size of the training data. In supervised learning, logistic regression or probit regression can be used to model a binary output and form perceptron classification rules based on Bayesian inference. In these cases , there is a lot of interest in searching for sparse model in High Dimensional regression(/classification) setup. we first discuss two common challenges for analyzing high dimensional data. The first one is the curse of dimensionality. The complexity of many existing algorithms scale exponentially with the dimensionality of the space and by virtue of that algorithms soon become computationally intractable and therefore inapplicable in many real applications. secondly, multicollinearities among the predictors which severely slowdown the algorithm. In order to make Bayesian analysis operational in high dimension we propose a novel 'Hierarchical stochastic approximation monte carlo algorithm' (HSAMC), which overcomes the curse of dimensionality, multicollinearity of predictors in high dimension and also it possesses the self-adjusting mechanism to avoid the local minima separated by high energy barriers. Models and methods are illustrated by simulation inspired from from the feild of genomics. Numerical results indicate that HSAMC can work as a general model selection sampler in high dimensional complex model space.

Predictors of Daily Mobility of Adults in Peri-Urban South India

PubMed Central

Kinra, Sanjay; Marshall, Julian D.; Tonne, Cathryn

2017-01-01

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women’s activity spaces were smaller and more circular than men’s. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health. PMID:28708095

Segmentation of the glottal space from laryngeal images using the watershed transform.

PubMed

Osma-Ruiz, Víctor; Godino-Llorente, Juan I; Sáenz-Lechón, Nicolás; Fraile, Rubén

2008-04-01

The present work describes a new method for the automatic detection of the glottal space from laryngeal images obtained either with high speed or with conventional video cameras attached to a laryngoscope. The detection is based on the combination of several relevant techniques in the field of digital image processing. The image is segmented with a watershed transform followed by a region merging, while the final decision is taken using a simple linear predictor. This scheme has successfully segmented the glottal space in all the test images used. The method presented can be considered a generalist approach for the segmentation of the glottal space because, in contrast with other methods found in literature, this approach does not need either initialization or finding strict environmental conditions extracted from the images to be processed. Therefore, the main advantage is that the user does not have to outline the region of interest with a mouse click. In any case, some a priori knowledge about the glottal space is needed, but this a priori knowledge can be considered weak compared to the environmental conditions fixed in former works.

A Sensitivity Study of the Aircraft Vortex Spacing System (AVOSS) Wake Predictor Algorithm to the Resolution of Input Meteorological Profiles

NASA Technical Reports Server (NTRS)

Rutishauser, David K.; Butler, Patrick; Riggins, Jamie

2004-01-01

The AVOSS project demonstrated the feasibility of applying aircraft wake vortex sensing and prediction technologies to safe aircraft spacing for single runway arrivals. On average, AVOSS provided spacing recommendations that were less than the current FAA prescribed spacing rules, resulting in a potential airport efficiency gain. Subsequent efforts have included quantifying the operational specifications for future Wake Vortex Advisory Systems (WakeVAS). In support of these efforts, each of the candidate subsystems for a WakeVAS must be specified. The specifications represent a consensus between the high-level requirements and the capabilities of the candidate technologies. This report documents the beginnings of an effort to quantify the capabilities of the AVOSS Prediction Algorithm (APA). Specifically, the APA horizontal position and circulation strength output sensitivity to the resolution of its wind and turbulence inputs is examined. The results of this analysis have implications for the requirements of the meteorological sensing and prediction systems comprising a WakeVAS implementation.

Using experienced activity spaces to measure foodscape exposure.

PubMed

Kestens, Yan; Lebel, Alexandre; Daniel, Mark; Thériault, Marius; Pampalon, Robert

2010-11-01

Researchers are increasingly interested in understanding how food environments influence eating behavior and weight-related health outcomes. Little is known about the dose-response relationship between foodscapes and behavior or weight, with measures of food exposure having mainly focused on fixed anchor points including residential neighborhoods, schools, or workplaces. Recent calls have been made to extend the consideration of environmental influences beyond local neighborhoods and also to shift away from place-based, to people-based, measures of exposure. This report presents analyses of novel activity-space measures of exposure to foodscapes, combining travel survey data with food store locations in Montreal and Quebec City, Canada. The resulting individual activity-space experienced foodscape exposure measures differ from traditional residential-based measures, and show variations by age and income levels. Furthermore, these activity-space exposure measures once modeled, can be used as predictors of health outcomes. Hence, travel surveys can be used to estimate environmental exposure for health survey participants. Copyright © 2010 Elsevier Ltd. All rights reserved.

Objective Lightning Probability Forecasting for Kennedy Space Center and Cape Canaveral Air Force Station, Phase II

NASA Technical Reports Server (NTRS)

Lambert, Winifred; Wheeler, Mark

2007-01-01

This report describes the work done by the Applied Meteorology Unit (AMU) to update the lightning probability forecast equations developed in Phase I. In the time since the Phase I equations were developed, new ideas regarding certain predictors were formulated and a desire to make the tool more automated was expressed by 45 WS forecasters. Five modifications were made to the data: 1) increased the period of record from 15 to 17 years, 2) modified the valid area to match the lighting warning areas, 3) added the 1000 UTC CCAFS sounding to the other soundings in determining the flow regime, 4) used a different smoothing function for the daily climatology, and 5) determined the optimal relative humidity (RH) layer to use as a predictor. The new equations outperformed the Phase I equations in several tests, and improved the skill of the forecast over the Phase I equations by 8%. A graphical user interface (GUI) was created in the Meteorological Interactive Data Display System (MIDDS) that gathers the predictor values for the equations automatically. The GUI was transitioned to operations in May 2007 for the 2007 warm season.

Fluid reasoning predicts future mathematics among children and adolescents

PubMed Central

Green, Chloe T.; Bunge, Silvia A.; Chiongbian, Victoria Briones; Barrow, Maia; Ferrer, Emilio

2017-01-01

The aim of this longitudinal study was to determine whether fluid reasoning (FR) plays a significant role in the acquisition of mathematics skills, above and beyond the effects of other cognitive and numerical abilities. Using a longitudinal cohort sequential design, we examined how FR measured at three assessment occasions, spaced approximately 1.5 years apart, predicted math outcomes for a group of 69 participants between ages 6 and 21 across all three assessment occasions. We used structural equation modeling (SEM) to examine the direct and indirect relations between children's prior cognitive abilities and their future math achievement. A model including age, FR, vocabulary, and spatial skills accounted for 90% of the variance in future math achievement. In this model, FR was the only significant predictor of future math achievement; neither age, vocabulary, nor spatial skills were significant predictors. Thus, FR was the only predictor of future math achievement across a wide age range that spanned primary and secondary school. These findings build on Cattell's conceptualization of FR (Cattell, 1987) as a scaffold for learning, showing that this domain-general ability supports the acquisition of rudimentary math skills as well as the ability to solve more complex mathematical problems. PMID:28152390

Demographic Predictors of Event-Level Associations between Alcohol Consumption and Sexual Behavior.

PubMed

Wells, Brooke E; Rendina, H Jonathon; Kelly, Brian C; Golub, Sarit A; Parsons, Jeffrey T

2016-02-01

Alcohol consumption is associated with sexual behavior and outcomes, though research indicates a variety of moderating factors, including demographic characteristics. To better target interventions aimed at alcohol-related sexual risk behavior, our analyses simultaneously examine demographic predictors of both day- and event-level associations between alcohol consumption and sexual behavior in a sample of young adults (N = 301) who are sexually active and consume alcohol. Young adults (aged 18-29) recruited using time-space sampling and incentivized snowball sampling completed a survey and a timeline follow-back calendar reporting alcohol consumption and sexual behavior in the past 30 days. On a given day, a greater number of drinks consumed was associated with higher likelihood of sex occurring, particularly for women and single participants. During a given sexual event, number of drinks consumed was not associated with condom use, nor did any demographic predictors predict that association. Findings highlight associations between alcohol and sexual behavior, though not between alcohol and sexual risk behavior, highlighting the need for additional research exploring the complex role of alcohol in sexual risk behavior and the need to develop prevention efforts to minimize the role of alcohol in the initiation of sexual encounters.

Determinants of individual and group performance

NASA Technical Reports Server (NTRS)

Helmreich, Robert L.

1986-01-01

A broad exploration of individual and group/organizational factors that influence performance in demanding environments such as space and air transport was undertaken. Primary efforts were directed toward defining critical issues, developing new methodologies for the assessment of performance in such environments, and developing new measures of personality and attitudes as predictors of performance. Substantial clarification of relevant issues for research and validation was achieved. A reliable instrument to assess crewmembers' attitudes regarding crew coordination and flightdeck management was validated. Major efforts in data collection to validate concepts were initiated. The results suggest that substantial improvements can be made in the prediction of performance and in the selection of crewmembers for aviation and space.

Correction of defective pixels for medical and space imagers based on Ising Theory

NASA Astrophysics Data System (ADS)

Cohen, Eliahu; Shnitser, Moriel; Avraham, Tsvika; Hadar, Ofer

2014-09-01

We propose novel models for image restoration based on statistical physics. We investigate the affinity between these fields and describe a framework from which interesting denoising algorithms can be derived: Ising-like models and simulated annealing techniques. When combined with known predictors such as Median and LOCO-I, these models become even more effective. In order to further examine the proposed models we apply them to two important problems: (i) Digital Cameras in space damaged from cosmic radiation. (ii) Ultrasonic medical devices damaged from speckle noise. The results, as well as benchmark and comparisons, suggest in most of the cases a significant gain in PSNR and SSIM in comparison to other filters.

Non-neutralized Electric Currents in Solar Active Regions and Flare Productivity

NASA Astrophysics Data System (ADS)

Kontogiannis, Ioannis; Georgoulis, Manolis K.; Park, Sung-Hong; Guerra, Jordan A.

2017-11-01

We explore the association of non-neutralized currents with solar flare occurrence in a sizable sample of observations, aiming to show the potential of such currents in solar flare prediction. We used the high-quality vector magnetograms that are regularly produced by the Helioseismic Magnetic Imager, and more specifically, the Space weather HMI Active Region Patches (SHARP). Through a newly established method that incorporates detailed error analysis, we calculated the non-neutralized currents contained in active regions (AR). Two predictors were produced, namely the total and the maximum unsigned non-neutralized current. Both were tested in AR time-series and a representative sample of point-in-time observations during the interval 2012 - 2016. The average values of non-neutralized currents in flaring active regions are higher by more than an order of magnitude than in non-flaring regions and correlate very well with the corresponding flare index. The temporal evolution of these parameters appears to be connected to physical processes, such as flux emergence and/or magnetic polarity inversion line formation, that are associated with increased solar flare activity. Using Bayesian inference of flaring probabilities, we show that the total unsigned non-neutralized current significantly outperforms the total unsigned magnetic flux and other well-established current-related predictors. It therefore shows good prospects for inclusion in an operational flare-forecasting service. We plan to use the new predictor in the framework of the FLARECAST project along with other highly performing predictors.

Comparison of ground-based and space flight energy expenditure and water turnover in middle-aged healthy male US astronauts

NASA Technical Reports Server (NTRS)

Lane, H. W.; Gretebeck, R. J.; Schoeller, D. A.; Davis-Street, J.; Socki, R. A.; Gibson, E. K.

1997-01-01

Energy requirements during space flight are poorly defined because they depend on metabolic-balance studies, food disappearance, and dietary records. Water turnover has been estimated by balance methods only. The purpose of this study was to determine energy requirements and water turnover for short-term space flights (8-14 d). Subjects were 13 male astronauts aged 36-51 y with normal body mass indexes (BMIs). Total energy expenditure (TEE) was determined during both a ground-based period and space flight and compared with the World Health Organization (WHO) calculations of energy requirements and dietary intake. TEE was not different for the ground-based and the space-flight periods (12.40 +/- 2.83 and 11.70 +/- 1.89 MJ/d, respectively), and the WHO calculation using the moderate activity correction was a good predictor of TEE during space flight. During the ground-based period, energy intake and TEE did not differ, but during space flight energy intake was significantly lower than TEE; body weight was also less at landing than before flight. Water turnover was lower during space flight than during the ground-based period (2.7 +/- 0.6 compared with 3.8 +/- 0.5 L/d), probably because of lower fluid intakes and perspiration loss during flight. This study confirmed that the WHO calculation can be used for male crew members' energy requirements during short space flights.

Ghettoizing outdoor advertising: disadvantage and ad panel density in black neighborhoods.

PubMed

Kwate, Naa Oyo A; Lee, Tammy H

2007-01-01

This study investigated correlates of outdoor advertising panel density in predominantly African American neighborhoods in New York City. Research shows that black neighborhoods have more outdoor advertising space than white neighborhoods, and these spaces disproportionately market alcohol and tobacco advertisements. Thus, understanding the factors associated with outdoor advertising panel density has important implications for public health. We linked 2000 census data with property data at the census block group level to investigate two neighborhood-level determinants of ad density: income level and physical decay. Results showed that block groups were exposed to an average of four ad spaces per 1,000 residents and that vacant lot square footage was a significant positive predictor of ad density. An inverse relationship between median household income and ad density did not reach significance, suggesting that relative affluence did not protect black neighborhoods from being targeted for outdoor advertisements.

Ghettoizing Outdoor Advertising: Disadvantage and Ad Panel Density in Black Neighborhoods

PubMed Central

Lee, Tammy H.

2006-01-01

This study investigated correlates of outdoor advertising panel density in predominantly African American neighborhoods in New York City. Research shows that black neighborhoods have more outdoor advertising space than white neighborhoods, and these spaces disproportionately market alcohol and tobacco advertisements. Thus, understanding the factors associated with outdoor advertising panel density has important implications for public health. We linked 2000 census data with property data at the census block group level to investigate two neighborhood-level determinants of ad density: income level and physical decay. Results showed that block groups were exposed to an average of four ad spaces per 1,000 residents and that vacant lot square footage was a significant positive predictor of ad density. An inverse relationship between median household income and ad density did not reach significance, suggesting that relative affluence did not protect black neighborhoods from being targeted for outdoor advertisements. PMID:17146710

Association between activity space exposure to food establishments and individual risk of overweight.

PubMed

Kestens, Yan; Lebel, Alexandre; Chaix, Basile; Clary, Christelle; Daniel, Mark; Pampalon, Robert; Theriault, Marius; P Subramanian, S V

2012-01-01

Environmental exposure to food sources may underpin area level differences in individual risk for overweight. Place of residence is generally used to assess neighbourhood exposure. Yet, because people are mobile, multiple exposures should be accounted for to assess the relation between food environments and overweight. Unfortunately, mobility data is often missing from health surveys. We hereby test the feasibility of linking travel survey data with food listings to derive food store exposure predictors of overweight among health survey participants. Food environment exposure measures accounting for non-residential activity places (activity spaces) were computed and modelled in Montreal and Quebec City, Canada, using travel surveys and food store listings. Models were then used to predict activity space food exposures for 5,578 participants of the Canadian Community Health Survey. These food exposure estimates, accounting for daily mobility, were used to model self-reported overweight in a multilevel framework. Median Odd Ratios were used to assess the proportion of between-neighborhood variance explained by such food exposure predictors. Estimates of food environment exposure accounting for both residential and non-residential destinations were significantly and more strongly associated with overweight than residential-only measures of exposure for men. For women, residential exposures were more strongly associated with overweight than non-residential exposures. In Montreal, adjusted models showed men in the highest quartile of exposure to food stores were at lesser risk of being overweight considering exposure to restaurants (OR = 0.36 [0.21-0.62]), fast food outlets (0.48 [0.30-0.79]), or corner stores (0.52 [0.35-0.78]). Conversely, men experiencing the highest proportion of restaurants being fast-food outlets were at higher risk of being overweight (2.07 [1.25-3.42]). Women experiencing higher residential exposures were at lower risk of overweight. Using residential neighbourhood food exposure measures may underestimate true exposure and observed associations. Using mobility data offers potential for deriving activity space exposure estimates in epidemiological models.

Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure.

PubMed

Lustgarten, Jonathan Lyle; Balasubramanian, Jeya Balaji; Visweswaran, Shyam; Gopalakrishnan, Vanathi

2017-03-01

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

Interval Predictor Models for Data with Measurement Uncertainty

NASA Technical Reports Server (NTRS)

Lacerda, Marcio J.; Crespo, Luis G.

2017-01-01

An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are considered. The first one is applicable to situations where the data is measured precisely whereas the second one is applicable to data subject to known biases and measurement error. In the latter case, the IPMs are designed to fully contain regions in the input-output space where the data is expected to fall. Moreover, we propose a strategy for reducing the computational cost associated with generating IPMs as well as means to simulate them. Numerical examples illustrate the usage and performance of the proposed formulations.

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.

Theoretical predictor for candidate structure assignment from IMS data of biomolecule-related conformational space.

PubMed

Schenk, Emily R; Nau, Frederic; Fernandez-Lima, Francisco

2015-06-01

The ability to correlate experimental ion mobility data with candidate structures from theoretical modeling provides a powerful analytical and structural tool for the characterization of biomolecules. In the present paper, a theoretical workflow is described to generate and assign candidate structures for experimental trapped ion mobility and H/D exchange (HDX-TIMS-MS) data following molecular dynamics simulations and statistical filtering. The applicability of the theoretical predictor is illustrated for a peptide and protein example with multiple conformations and kinetic intermediates. The described methodology yields a low computational cost and a simple workflow by incorporating statistical filtering and molecular dynamics simulations. The workflow can be adapted to different IMS scenarios and CCS calculators for a more accurate description of the IMS experimental conditions. For the case of the HDX-TIMS-MS experiments, molecular dynamics in the "TIMS box" accounts for a better sampling of the molecular intermediates and local energy minima.

A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong

2001-01-01

This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

Space-time patterns in ignimbrite compositions revealed by GIS and R based statistical analysis

NASA Astrophysics Data System (ADS)

Brandmeier, Melanie; Wörner, Gerhard

2017-04-01

GIS-based multivariate statistical and geospatial analysis of a compilation of 890 geochemical and ca. 1,200 geochronological data for 194 mapped ignimbrites from Central Andes documents the compositional and temporal pattern of large volume ignimbrites (so-called "ignimbrite flare-ups") during Neogene times. Rapid advances in computational sciences during the past decade lead to a growing pool of algorithms for multivariate statistics on big datasets with many predictor variables. This study uses the potential of R and ArcGIS and applies cluster (CA) and linear discriminant analysis (LDA) on log-ratio transformed spatial data. CA on major and trace element data allows to group ignimbrites according to their geochemical characteristics into rhyolitic and a dacitic "end-members" and differentiates characteristic trace element signatures with respect to Eu anomaly, depletion of MREEs and variable enrichment in LREE. To highlight these distinct compositional signatures, we applied LDA to selected ignimbrites for which comprehensive data sets were available. The most important predictors for discriminating ignimbrites are La (LREE), Yb (HREE), Eu, Al2O3, K2O, P2O5, MgO, FeOt and TiO2. However, other REEs such as Gd, Pr, Tm, Sm and Er also contribute to the discrimination functions. Significant compositional differences were found between the older (>14 Ma) large-volume plateau-forming ignimbrites in northernmost Chile and southern Peru and the younger (< 10 Ma) Altiplano-Puna-Volcanic-Complex ignimbrites that are of similar volumes. Older ignimbrites are less depleted in HREEs and less radiogenic in Sr isotopes, indicating smaller crustal contributions during evolution in thinner and thermally less evolved crust. These compositional variations indicate a relation to crustal thickening with a "transition" from plagioclase to amphibole and garnet residual mineralogy between 13 to 9 Ma. We correlate compositional and volumetric variations to the N-S passage of the Juan-Fernandéz ridge and crustal shortening and thickening during the past 26 Ma. The value of GIS and multivariate statistics in comparison to traditional geochemical parameters are highlighted working with large datasets with many predictors in a spatial and temporal context. Algorithms implemented in R allow taking advantage of an n-dimensional space and, thus, of subtle compositional differences contained in the data, while space-time patterns can be analyzed easily in GIS.

Social network and census tract-level influences on substance use among emerging adult males: An activity spaces approach

PubMed Central

Gibson, Crystal; Perley, Lauren; Bailey, Jonathan; Barbour, Russell; Kershaw, Trace

2015-01-01

Social network and area level characteristics have been linked to substance use. We used snowball sampling to recruit 90 predominantly African American emerging adult men who provided typical locations visited (n=510). We used generalized estimating equations to examine social network and area level predictors of substance use. Lower social network quality was associated with days of marijuana use (B=-0.0037, p<0.0001) and problem alcohol use (B=-0.0050, p=0.0181). The influence of area characteristics on substance use differed between risky and non-risky spaces. Peer and area influences are important for substance use among men, and may differ for high and low risk places. PMID:26176810

Progress in Guidance and Control Research for Space Access and Hypersonic Vehicles (Preprint)

DTIC Science & Technology

2006-09-01

affect range capabilities. In 2003 an integrated adaptive guidance control and trajectory re- shaping algorithm was flight demonstrated using in-flight...21] which tied for the best scores as well as a Linear Quadratic Regulator[22], Predictor - Corrector [23], and Shuttle-like entry[24] guidance method...Accurate knowledge of mass, center- of-gravity and moments of inertia improves the perfor- mance of not only IAG& C algorithms but also model based

Image Processing Research

DTIC Science & Technology

1975-09-30

systems a linear model results in an object f being mappad into an image _ by a point spread function matrix H. Thus with noise j +Hf +n (1) The simplest... linear models for imaging systems are given by space invariant point spread functions (SIPSF) in which case H is block circulant. If the linear model is...Ij,...,k-IM1 is a set of two dimensional indices each distinct and prior to k. Modeling Procedare: To derive the linear predictor (block LP of figure

PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context

PubMed Central

Zhou, Jiyun; Xu, Ruifeng; He, Yulan; Lu, Qin; Wang, Hongpeng; Kong, Bing

2016-01-01

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community. PMID:27282833

Can we do better than the grid survey: Optimal synoptic surveys in presence of variable uncertainty and decorrelation scales

NASA Astrophysics Data System (ADS)

Frolov, Sergey; Garau, Bartolame; Bellingham, James

2014-08-01

Regular grid ("lawnmower") survey is a classical strategy for synoptic sampling of the ocean. Is it possible to achieve a more effective use of available resources if one takes into account a priori knowledge about variability in magnitudes of uncertainty and decorrelation scales? In this article, we develop and compare the performance of several path-planning algorithms: optimized "lawnmower," a graph-search algorithm (A*), and a fully nonlinear genetic algorithm. We use the machinery of the best linear unbiased estimator (BLUE) to quantify the ability of a vehicle fleet to synoptically map distribution of phytoplankton off the central California coast. We used satellite and in situ data to specify covariance information required by the BLUE estimator. Computational experiments showed that two types of sampling strategies are possible: a suboptimal space-filling design (produced by the "lawnmower" and the A* algorithms) and an optimal uncertainty-aware design (produced by the genetic algorithm). Unlike the space-filling designs that attempted to cover the entire survey area, the optimal design focused on revisiting areas of high uncertainty. Results of the multivehicle experiments showed that fleet performance predictors, such as cumulative speed or the weight of the fleet, predicted the performance of a homogeneous fleet well; however, these were poor predictors for comparing the performance of different platforms.

Anthropogenic resource subsidies determine space use by Australian arid zone dingoes: an improved resource selection modelling approach.

PubMed

Newsome, Thomas M; Ballard, Guy-Anthony; Dickman, Christopher R; Fleming, Peter J S; Howden, Chris

2013-01-01

Dingoes (Canis lupus dingo) were introduced to Australia and became feral at least 4,000 years ago. We hypothesized that dingoes, being of domestic origin, would be adaptable to anthropogenic resource subsidies and that their space use would be affected by the dispersion of those resources. We tested this by analyzing Resource Selection Functions (RSFs) developed from GPS fixes (locations) of dingoes in arid central Australia. Using Generalized Linear Mixed-effect Models (GLMMs), we investigated resource relationships for dingoes that had access to abundant food near mine facilities, and for those that did not. From these models, we predicted the probability of dingo occurrence in relation to anthropogenic resource subsidies and other habitat characteristics over ∼ 18,000 km(2). Very small standard errors and subsequent pervasively high P-values of results will become more important as the size of data sets, such as our GPS tracking logs, increases. Therefore, we also investigated methods to minimize the effects of serial and spatio-temporal correlation among samples and unbalanced study designs. Using GLMMs, we accounted for some of the correlation structure of GPS animal tracking data; however, parameter standard errors remained very small and all predictors were highly significant. Consequently, we developed an alternative approach that allowed us to review effect sizes at different spatial scales and determine which predictors were sufficiently ecologically meaningful to include in final RSF models. We determined that the most important predictor for dingo occurrence around mine sites was distance to the refuse facility. Away from mine sites, close proximity to human-provided watering points was predictive of dingo dispersion as were other landscape factors including palaeochannels, rocky rises and elevated drainage depressions. Our models demonstrate that anthropogenically supplemented food and water can alter dingo-resource relationships. The spatial distribution of such resources is therefore critical for the conservation and management of dingoes and other top predators.

Urban agriculture and Anopheles habitats in Dar es Salaam, Tanzania.

PubMed

Dongus, Stefan; Nyika, Dickson; Kannady, Khadija; Mtasiwa, Deo; Mshinda, Hassan; Gosoniu, Laura; Drescher, Axel W; Fillinger, Ulrike; Tanner, Marcel; Killeen, Gerry F; Castro, Marcia C

2009-05-01

A cross-sectional survey of agricultural areas, combined with routinely monitored mosquito larval information, was conducted in urban Dar es Salaam, Tanzania, to investigate how agricultural and geographical features may influence the presence of Anopheles larvae. Data were integrated into a geographical information systems framework, and predictors of the presence of Anopheles larvae in farming areas were assessed using multivariate logistic regression with independent random effects. It was found that more than 5% of the study area (total size 16.8 km2) was used for farming in backyard gardens and larger open spaces. The proportion of habitats containing Anopheles larvae was 1.7 times higher in agricultural areas compared to other areas (95% confidence interval = 1.56-1.92). Significant geographic predictors of the presence of Anopheles larvae in gardens included location in lowland areas, proximity to river, and relatively impermeable soils. Agriculture-related predictors comprised specific seedbed types, mid-sized gardens, irrigation by wells, as well as cultivation of sugar cane or leafy vegetables. Negative predictors included small garden size, irrigation by tap water, rainfed production and cultivation of leguminous crops or fruit trees. Although there was an increased chance of finding Anopheles larvae in agricultural sites, it was found that breeding sites originated by urban agriculture account for less than a fifth of all breeding sites of malaria vectors in Dar es Salaam. It is suggested that strategies comprising an integrated malaria control effort in malaria-endemic African cities include participatory involvement of farmers by planting shade trees near larval habitats.

Student Moon Observations and Spatial-Scientific Reasoning

NASA Astrophysics Data System (ADS)

Cole, Merryn; Wilhelm, Jennifer; Yang, Hongwei

2015-07-01

Relationships between sixth grade students' moon journaling and students' spatial-scientific reasoning after implementation of an Earth/Space unit were examined. Teachers used the project-based Realistic Explorations in Astronomical Learning curriculum. We used a regression model to analyze the relationship between the students' Lunar Phases Concept Inventory (LPCI) post-test score variables and several predictors, including moon journal score, number of moon journal entries, student gender, teacher experience, and pre-test score. The model shows that students who performed better on moon journals, both in terms of overall score and number of entries, tended to score higher on the LPCI. For every 1 point increase in the overall moon journal score, participants scored 0.18 points (out of 20) or nearly 1% point higher on the LPCI post-test when holding constant the effects of the other two predictors. Similarly, students who increased their scores by 1 point in the overall moon journal score scored approximately 1% higher in the Periodic Patterns (PP) and Geometric Spatial Visualization (GSV) domains of the LPCI. Also, student gender and teacher experience were shown to be significant predictors of post-GSV scores on the LPCI in addition to the pre-test scores, overall moon journal score, and number of entries that were also significant predictors on the LPCI overall score and the PP domain. This study is unique in the purposeful link created between student moon observations and spatial skills. The use of moon journals distinguishes this study further by fostering scientific observation along with skills from across science, technology, engineering, and mathematics disciplines.

Pulmonary Dead Space Fraction and Extubation Success in Children After Cardiac Surgery.

PubMed

Devor, Renee L; Kang, Paul; Wellnitz, Chasity; Nigro, John J; Velez, Daniel A; Willis, Brigham C

2018-04-01

1) Determine the correlation between pulmonary dead space fraction and extubation success in postoperative pediatric cardiac patients; and 2) document the natural history of pulmonary dead space fractions, dynamic compliance, and airway resistance during the first 72 hours postoperatively in postoperative pediatric cardiac patients. A retrospective chart review. Cardiac ICU in a quaternary care free-standing children's hospital. Twenty-nine with balanced single ventricle physiology, 61 with two ventricle physiology. None. We collected data for all pediatric patients undergoing congenital cardiac surgery over a 14-month period during the first 72 hours postoperatively as well as prior to extubation. Overall, patients with successful extubations had lower preextubation dead space fractions and shorter lengths of stay. Single ventricle patients had higher initial postoperative and preextubation dead space fractions. Two-ventricle physiology patients had higher extubation failure rates if the preextubation dead space fraction was greater than 0.5, whereas single ventricle patients had similar extubation failure rates whether preextubation dead space fractions were less than or equal to 0.5 or greater than 0.5. Additionally, increasing initial dead space fraction values predicted prolonged mechanical ventilation times. Airway resistance and dynamic compliance were similar between those with successful extubations and those who failed. Initial postoperative dead space fraction correlates with the length of mechanical ventilation in two ventricle patients but not in single ventricle patients. Lower preextubation dead space fractions are a strong predictor of successful extubation in two ventricle patients after cardiac surgery, but may not be as useful in single ventricle patients.

Shedding light on El Farol

NASA Astrophysics Data System (ADS)

Challet, Damien; Marsili, M.; Ottino, Gabriele

2004-02-01

We mathematize El Farol bar problem and transform it into a workable model. We find general conditions on the predictor space under which the convergence of the average attendance to the resource level does not require any intelligence on the side of the agents. Secondly, specializing to a particular ensemble of continuous strategies yields a model similar to the Minority Game. Statistical physics of disordered systems allows us to derive a complete understanding of the complex behavior of this model, on the basis of its phase diagram.

Robust Regression Procedures for Predictor Variable Outliers.

DTIC Science & Technology

1982-03-01

space of probability dis- tributions. Then the influence function of the estimator is defined to be the derivative of the functional evaluated at the...measure of the impact of an outlier x0 on the estimator . . . . . .0 10 T(F) is the " influence function " which is defined to be T(F) - lirT(F")-T(F...positive and negative directions. An em- pirical influence function can be defined in a similar fashion simply by replacing F with F in eqn. (3.4).n

The Utility of Testing Noncognitive Aptitudes as Additional Predictors of Graduation from U.S. Air Force Air Traffic Controller Training

DTIC Science & Technology

2015-11-13

Control The ability to resist or delay an impulse, drive, or temptation to act General Mood Happiness The ability to feel satisfied with one’s life...Neurostat Analytical Solutions as a consultant to the USAF School of Aero- space Medicine on numerous research projects. He currently researches suicide ...testing inter- ventions for suicidal military personnel at Fort Carson, Colorado. Dr. Bryan is a nationally-recognized expert on military suicide , and

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Roszman, L.; Cooley, J.

1992-01-01

Numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series are presented. This approach makes it possible to extract dynamical invariants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), given a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Roszman, L.; Cooley, J.

1993-01-01

This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

When the going gets tough: behavioural type-dependent space use in the sleepy lizard changes as the season dries

PubMed Central

Spiegel, Orr; Leu, Stephan T.; Sih, Andrew; Godfrey, Stephanie S.; Bull, C. Michael

2015-01-01

Understanding space use remains a major challenge for animal ecology, with implications for species interactions, disease spread, and conservation. Behavioural type (BT) may shape the space use of individuals within animal populations. Bolder or more aggressive individuals tend to be more exploratory and disperse further. Yet, to date we have limited knowledge on how space use other than dispersal depends on BT. To address this question we studied BT-dependent space-use patterns of sleepy lizards (Tiliqua rugosa) in southern Australia. We combined high-resolution global positioning system (GPS) tracking of 72 free-ranging lizards with repeated behavioural assays, and with a survey of the spatial distributions of their food and refuge resources. Bayesian generalized linear mixed models (GLMM) showed that lizards responded to the spatial distribution of resources at the neighbourhood scale and to the intensity of space use by other conspecifics (showing apparent conspecific avoidance). BT (especially aggressiveness) affected space use by lizards and their response to ecological and social factors, in a seasonally dependent manner. Many of these effects and interactions were stronger later in the season when food became scarce and environmental conditions got tougher. For example, refuge and food availability became more important later in the season and unaggressive lizards were more responsive to these predictors. These findings highlight a commonly overlooked source of heterogeneity in animal space use and improve our mechanistic understanding of processes leading to behaviourally driven disease dynamics and social structure. PMID:26609082

Structured functional additive regression in reproducing kernel Hilbert spaces.

PubMed

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2014-06-01

Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application.

The Application of a Three-Dimensional Printed Product to Fill the Space After Organ Removal.

PubMed

Weng, Jui-Yu; Wang, Che-Chuna; Chen, Pei-Jar; Lim, Sher-Wei; Kuo, Jinn-Rung

2017-11-01

Maintaining body integrity, especially in Asian societies, is an independent predictor of organ donation. Herein, we report the case of an 18-year-old man who suffered a traumatic brain injury with ensuing brain death caused by a car accident. According to the family's wishes, we used a 3-dimensional printer to create simulated heart, kidneys, and liver to fill the spaces after the patient's organs were removed. This is the first case report to introduce this new clinical application of 3-dimensional printed products during transplantation surgery. This new clinical application may have supportive psychological effects on the family and caregivers; however, given the varied responses to our procedure, this ethical issue is worth discussing. Copyright © 2017 Elsevier Inc. All rights reserved.

NASA-Ames workload research program

NASA Technical Reports Server (NTRS)

Hart, Sandra

1988-01-01

Research has been underway for several years to develop valid and reliable measures and predictors of workload as a function of operator state, task requirements, and system resources. Although the initial focus of this research was on aeronautics, the underlying principles and methodologies are equally applicable to space, and provide a set of tools that NASA and its contractors can use to evaluate design alternatives from the perspective of the astronauts. Objectives and approach of the research program are described, as well as the resources used in conducting research and the conceptual framework around which the program evolved. Next, standardized tasks are described, in addition to predictive models and assessment techniques and their application to the space program. Finally, some of the operational applications of these tasks and measures are reviewed.

Modeling to predict pilot performance during CDTI-based in-trail following experiments

NASA Technical Reports Server (NTRS)

Sorensen, J. A.; Goka, T.

1984-01-01

A mathematical model was developed of the flight system with the pilot using a cockpit display of traffic information (CDTI) to establish and maintain in-trail spacing behind a lead aircraft during approach. Both in-trail and vertical dynamics were included. The nominal spacing was based on one of three criteria (Constant Time Predictor; Constant Time Delay; or Acceleration Cue). This model was used to simulate digitally the dynamics of a string of multiple following aircraft, including response to initial position errors. The simulation was used to predict the outcome of a series of in-trail following experiments, including pilot performance in maintaining correct longitudinal spacing and vertical position. The experiments were run in the NASA Ames Research Center multi-cab cockpit simulator facility. The experimental results were then used to evaluate the model and its prediction accuracy. Model parameters were adjusted, so that modeled performance matched experimental results. Lessons learned in this modeling and prediction study are summarized.

Winners and losers in the competition for space in tropical forest canopies.

PubMed

Kellner, James R; Asner, Gregory P

2014-05-01

Trees compete for space in the canopy, but where and how individuals or their component parts win or lose is poorly understood. We developed a stochastic model of three-dimensional dynamics in canopies using a hierarchical Bayesian framework, and analysed 267,533 positive height changes from 1.25 m pixels using data from airborne LiDAR within 43 ha on the windward flank of Mauna Kea. Model selection indicates a strong resident's advantage, with 97.9% of positions in the canopy retained by their occupants over 2 years. The remaining 2.1% were lost to a neighbouring contender. Absolute height was a poor predictor of success, but short stature greatly raised the risk of being overtopped. Growth in the canopy was exponentially distributed with a scaling parameter of 0.518. These findings show how size and spatial proximity influence the outcome of competition for space, and provide a general framework for the analysis of canopy dynamics. © 2014 John Wiley & Sons Ltd/CNRS.

Understanding and controlling regime switching in molecular diffusion

NASA Astrophysics Data System (ADS)

Hallerberg, S.; de Wijn, A. S.

2014-12-01

Diffusion can be strongly affected by ballistic flights (long jumps) as well as long-lived sticking trajectories (long sticks). Using statistical inference techniques in the spirit of Granger causality, we investigate the appearance of long jumps and sticks in molecular-dynamics simulations of diffusion in a prototype system, a benzene molecule on a graphite substrate. We find that specific fluctuations in certain, but not all, internal degrees of freedom of the molecule can be linked to either long jumps or sticks. Furthermore, by changing the prevalence of these predictors with an outside influence, the diffusion of the molecule can be controlled. The approach presented in this proof of concept study is very generic and can be applied to larger and more complex molecules. Additionally, the predictor variables can be chosen in a general way so as to be accessible in experiments, making the method feasible for control of diffusion in applications. Our results also demonstrate that data-mining techniques can be used to investigate the phase-space structure of high-dimensional nonlinear dynamical systems.

Novel risk predictor for thrombus deposition in abdominal aortic aneurysms

NASA Astrophysics Data System (ADS)

Nestola, M. G. C.; Gizzi, A.; Cherubini, C.; Filippi, S.; Succi, S.

2015-10-01

The identification of the basic mechanisms responsible for cardiovascular diseases stands as one of the most challenging problems in modern medical research including various mechanisms which encompass a broad spectrum of space and time scales. Major implications for clinical practice and pre-emptive medicine rely on the onset and development of intraluminal thrombus in which effective clinical therapies require synthetic risk predictors/indicators capable of informing real-time decision-making protocols. In the present contribution, two novel hemodynamics synthetic indicators, based on a three-band decomposition (TBD) of the shear stress signal, are introduced. Extensive fluid-structure computer simulations of patient-specific scenarios confirm the enhanced risk-prediction capabilities of the TBD indicators. In particular, they permit a quantitative and accurate localization of the most likely thrombus deposition in realistic aortic geometries, where previous indicators would predict healthy operation. The proposed methodology is also shown to provide additional information and discrimination criteria on other factors of major clinical relevance, such as the size of the aneurysm.

Effect of surgical decompression of spinal metastases in acute treatment - Predictors of neurological outcome.

PubMed

Hohenberger, Christoph; Schmidt, Corinna; Höhne, Julius; Brawanski, Alexander; Zeman, Florian; Schebesch, Karl-Michael

2018-06-01

Space-occupying spinal metastases (SM), commonly diagnosed because of acute neurological deterioration, consequently lead to immediate decompression with tumor removal or debulking. In this study, we analyzed a series of patients with surgically treated spinal metastases and explicitly sought to determine individual predictors of functional outcome. 94 patients (26 women, 68 men; mean age 64.0 years) with spinal metastases, who had been surgically treated at our department, were included retrospectively. We reviewed the pre- and postoperative charts, surgical reports, radiographic data for demographics, duration of symptoms, histopathology, stage of systemic disease, co-morbidities, radiographic extension, surgical strategy, neurological performance (Frankel Grade Classification), and the Karnofsky Performance Index (KPI). Emergency surgery within <24 h after discharge had been conducted in 33% of patients. Prostate carcinoma (29.5%) and breast carcinoma (11.6%) were the most common histopathologies. Median KPI was 60% at admission that had significantly improved at discharge (KPI 70%; p = 0.01). The rate of complications without revision was 4.3%, the revision rate 4.2%. From admission to discharge, pain had been significantly reduced (p = 0.019) and motor deficits significantly improved (p = 0.003). KPI had been significantly improved during in-hospital treatment (median 60 vs 70, p = 0.010). In the multivariable analysis, predictors of poor outcome (KPI < 70) were male sex, multiple metastases, and pre-existing bowel and bladder dysfunction. Median follow up was 2 months. In our series, surgery for spinal metastases (laminectomy, tumor removal, and mass reduction) significantly reduced pain as well as sensory and motor deficits. We identified male sex, multiple metastases, and pre-existing bowel and bladder dysfunction as predictors of negative outcome. Copyright © 2018 Elsevier Ltd. All rights reserved.

Pleural Fluid Adenosine Deaminase (ADA) Predicts Survival in Patients with Malignant Pleural Effusion.

PubMed

Terra, Ricardo Mingarini; Antonangelo, Leila; Mariani, Alessandro Wasum; de Oliveira, Ricardo Lopes Moraes; Teixeira, Lisete Ribeiro; Pego-Fernandes, Paulo Manuel

2016-08-01

Systemic and local inflammations have been described as relevant prognostic factors in patients with cancer. However, parameters that stand for immune activity in the pleural space have not been tested as predictors of survival in patients with malignant pleural effusion. The objective of this study was to evaluate pleural lymphocytes and Adenosine Deaminase (ADA) as predictors of survival in patients with recurrent malignant pleural effusion. Retrospective cohort study includes patients who underwent pleurodesis for malignant pleural effusion in a tertiary center. Pleural fluid protein concentration, lactate dehydrogenase, glucose, oncotic cytology, cell count, and ADA were collected before pleurodesis and analyzed. Survival analysis was performed considering pleurodesis as time origin, and death as the event. Backwards stepwise Cox regression was used to find predictors of survival. 156 patients (out of 196 potentially eligible) were included in this study. Most were female (72 %) and breast cancer was the most common underlying malignancy (53 %). Pleural fluid ADA level was stratified as low (<15 U/L), normal (15 ≤ ADA < 40), and high (≥40). Low and high ADA levels were associated with worse survival when compared to normal ADA (logrank: 0.0024). In multivariable analysis, abnormal ADA (<15 or ADA ≥ 40) and underlying malignancies different from lymphoma, lung, or breast cancer were associated with worse survival. Pleural fluid cell count and lymphocytes number and percentage did not correlate with survival. Pleural fluid Adenosine Deaminase levels (<15 or ≥40 U/L) and neoplasms other than lung, breast, or lymphoma are independent predictors of worse survival in patients with malignant pleural effusion who undergo pleurodesis.

Posterior white matter disease distribution as a predictor of amyloid angiopathy

PubMed Central

Thanprasertsuk, Sekh; Martinez-Ramirez, Sergi; Pontes-Neto, Octavio Marques; Ni, Jun; Ayres, Alison; Reed, Anne; Swords, Kyleen; Gurol, M. Edip; Greenberg, Steven M.

2014-01-01

Objectives: We sought to examine whether a posterior distribution of white matter hyperintensities (WMH) is an independent predictor of pathologically confirmed cerebral amyloid angiopathy (CAA) and whether it is associated with MRI markers of CAA, in patients without lobar intracerebral hemorrhage. Methods: We developed a quantitative method to measure anteroposterior (AP) distribution of WMH. A retrospective cohort of patients without intracerebral hemorrhage and with pathologic evaluation of CAA was examined to determine whether posterior WMH distribution was an independent predictor of CAA (n = 59). The relationship of AP distributions of WMH to strictly lobar microbleeds (MBs) (n = 259) and location of dilated perivascular spaces (DPVS) (n = 85) was examined in a separate cohort of patients evaluated in a memory clinic. Results: A more posterior WMH distribution was found to be an independent predictor of pathologic evidence of CAA (p = 0.001, odds ratio [95% confidence interval] = 1.19 [1.07–1.32]), even in the subgroup without lobar MBs (p = 0.016, odds ratio [95% confidence interval] = 1.18 [1.03–1.36]). In the memory clinic cohort, strictly lobar MBs were independently associated with more posterior WMH distribution (p = 0.009). AP distribution of WMH was also associated with location of DPVS (p = 0.001), in that patients with predominant DPVS in the white matter over the basal ganglia harbored a more posterior WMH distribution. Conclusions: Our results suggest that AP distribution of WMH may represent an additional marker of CAA, irrespective of the presence of lobar hemorrhages. Classification of evidence: This study provides Class III evidence that there is a significant association between the AP distribution of WMH on MRI with the presence of pathologically confirmed CAA pathology. PMID:25063759

The Influence of Urbanism and Information Consumption on Political Dimensions of Social Capital: Exploratory Study of the Localities Adjacent to the Core City from Brașov Metropolitan Area, Romania.

PubMed

Rezeanu, Cătălina-Ionela; Briciu, Arabela; Briciu, Victor; Repanovici, Angela; Coman, Claudiu

2016-01-01

The last two decades have seen a growing trend towards the research of voting behavior in post-communist countries. Urban sociology theorists state that not only space structures influence political participation, but also space structures are changing under the influence of global, local, and individual factors. The growing role played by information in the globalised world has accelerated the paradigm shift in urban sociology: from central place model (based on urban-rural distinction and on monocentric metropolitan areas) to network society (based on space of flows and polycentric metropolitan areas). However, recent studies have mainly focused on countries with solid democracies, rather than on former communist countries. The present study aims to analyze the extent to which a new emerging spatial structure can be envisaged within a metropolitan area of Romania and its consequences for the political dimensions of social capital. The Transilvania University Ethics Commission approved this study (S1 Aprouval). The research is based upon individual and aggregate empirical data, collected from the areas adjacent to the core city in Brașov metropolitan area. Individual data has been collected during October 2012, using the oral survey technique (S1 Survey), based on a standardized questionnaire (stratified simple random sample, N = 600). The National Institute of Statistics and the Electoral Register provided the aggregate data per locality. Unvaried and multivariate analyses (hierarchical regression method) were conducted based on these data. Some dimensions of urbanism, identified as predictors of the political dimensions of social capital, suggest that the area under analysis has a predominantly monocentric character, where the rural-urban distinction continues to remain relevant. There are also arguments favoring the dissolution of the rural-urban distinction and the emergence of polycentric spatial structures. The presence of some influences related to the information consumption on all six indicators of the political dimensions of social capital under analysis suggests the occurrence of emerging forms of a space of flows. The identified effects of social problems associated with transport infrastructure and of migration experience on the political dimensions of social capital, also support the emergence of space of flows. We recommend that, in the urban studies in former communist countries, conceptualization of urbanism as predictor of the political dimensions of social capital should consider both the material dimensions of space, as well as the dimensions of information consumption and migration experience.

The Influence of Urbanism and Information Consumption on Political Dimensions of Social Capital: Exploratory Study of the Localities Adjacent to the Core City from Brașov Metropolitan Area, Romania

PubMed Central

Rezeanu, Cătălina-Ionela; Briciu, Arabela; Briciu, Victor; Repanovici, Angela; Coman, Claudiu

2016-01-01

Background The last two decades have seen a growing trend towards the research of voting behavior in post-communist countries. Urban sociology theorists state that not only space structures influence political participation, but also space structures are changing under the influence of global, local, and individual factors. The growing role played by information in the globalised world has accelerated the paradigm shift in urban sociology: from central place model (based on urban-rural distinction and on monocentric metropolitan areas) to network society (based on space of flows and polycentric metropolitan areas). However, recent studies have mainly focused on countries with solid democracies, rather than on former communist countries. The present study aims to analyze the extent to which a new emerging spatial structure can be envisaged within a metropolitan area of Romania and its consequences for the political dimensions of social capital. Methods The Transilvania University Ethics Commission approved this study (S1 Aprouval). The research is based upon individual and aggregate empirical data, collected from the areas adjacent to the core city in Brașov metropolitan area. Individual data has been collected during October 2012, using the oral survey technique (S1 Survey), based on a standardized questionnaire (stratified simple random sample, N = 600). The National Institute of Statistics and the Electoral Register provided the aggregate data per locality. Unvaried and multivariate analyses (hierarchical regression method) were conducted based on these data. Results Some dimensions of urbanism, identified as predictors of the political dimensions of social capital, suggest that the area under analysis has a predominantly monocentric character, where the rural-urban distinction continues to remain relevant. There are also arguments favoring the dissolution of the rural-urban distinction and the emergence of polycentric spatial structures. The presence of some influences related to the information consumption on all six indicators of the political dimensions of social capital under analysis suggests the occurrence of emerging forms of a space of flows. The identified effects of social problems associated with transport infrastructure and of migration experience on the political dimensions of social capital, also support the emergence of space of flows. Conclusions We recommend that, in the urban studies in former communist countries, conceptualization of urbanism as predictor of the political dimensions of social capital should consider both the material dimensions of space, as well as the dimensions of information consumption and migration experience. PMID:26807882

Association between Activity Space Exposure to Food Establishments and Individual Risk of Overweight

PubMed Central

Kestens, Yan; Lebel, Alexandre; Chaix, Basile; Clary, Christelle; Daniel, Mark; Pampalon, Robert; Theriault, Marius; p Subramanian, S. V.

2012-01-01

Objective Environmental exposure to food sources may underpin area level differences in individual risk for overweight. Place of residence is generally used to assess neighbourhood exposure. Yet, because people are mobile, multiple exposures should be accounted for to assess the relation between food environments and overweight. Unfortunately, mobility data is often missing from health surveys. We hereby test the feasibility of linking travel survey data with food listings to derive food store exposure predictors of overweight among health survey participants. Methods Food environment exposure measures accounting for non-residential activity places (activity spaces) were computed and modelled in Montreal and Quebec City, Canada, using travel surveys and food store listings. Models were then used to predict activity space food exposures for 5,578 participants of the Canadian Community Health Survey. These food exposure estimates, accounting for daily mobility, were used to model self-reported overweight in a multilevel framework. Median Odd Ratios were used to assess the proportion of between-neighborhood variance explained by such food exposure predictors. Results Estimates of food environment exposure accounting for both residential and non-residential destinations were significantly and more strongly associated with overweight than residential-only measures of exposure for men. For women, residential exposures were more strongly associated with overweight than non-residential exposures. In Montreal, adjusted models showed men in the highest quartile of exposure to food stores were at lesser risk of being overweight considering exposure to restaurants (OR = 0.36 [0.21–0.62]), fast food outlets (0.48 [0.30–0.79]), or corner stores (0.52 [0.35–0.78]). Conversely, men experiencing the highest proportion of restaurants being fast-food outlets were at higher risk of being overweight (2.07 [1.25–3.42]). Women experiencing higher residential exposures were at lower risk of overweight. Conclusion Using residential neighbourhood food exposure measures may underestimate true exposure and observed associations. Using mobility data offers potential for deriving activity space exposure estimates in epidemiological models. PMID:22936974

Development of a time-trend model for analyzing and predicting case-pattern of Lassa fever epidemics in Liberia, 2013-2017.

PubMed

Olugasa, Babasola O; Odigie, Eugene A; Lawani, Mike; Ojo, Johnson F

2015-01-01

The objective was to develop a case-pattern model for Lassa fever (LF) among humans and derive predictors of time-trend point distribution of LF cases in Liberia in view of the prevailing under-reporting and public health challenge posed by the disease in the country. A retrospective 5 years data of LF distribution countrywide among humans were used to train a time-trend model of the disease in Liberia. A time-trend quadratic model was selected due to its goodness-of-fit (R2 = 0.89, and P < 0.05) and best performance compared to linear and exponential models. Parameter predictors were run on least square method to predict LF cases for a prospective 5 years period, covering 2013-2017. The two-stage predictive model of LF case-pattern between 2013 and 2017 was characterized by a prospective decline within the South-coast County of Grand Bassa over the forecast period and an upward case-trend within the Northern County of Nimba. Case specific exponential increase was predicted for the first 2 years (2013-2014) with a geometric increase over the next 3 years (2015-2017) in Nimba County. This paper describes a translational application of the space-time distribution pattern of LF epidemics, 2008-2012 reported in Liberia, on which a predictive model was developed. We proposed a computationally feasible two-stage space-time permutation approach to estimate the time-trend parameters and conduct predictive inference on LF in Liberia.

Ecological niche transferability using invasive species as a case study.

PubMed

Fernández, Miguel; Hamilton, Healy

2015-01-01

Species distribution modeling is widely applied to predict invasive species distributions and species range shifts under climate change. Accurate predictions depend upon meeting the assumption that ecological niches are conserved, i.e., spatially or temporally transferable. Here we present a multi-taxon comparative analysis of niche conservatism using biological invasion events well documented in natural history museum collections. Our goal is to assess spatial transferability of the climatic niche of a range of noxious terrestrial invasive species using two complementary approaches. First we compare species' native versus invasive ranges in environmental space using two distinct methods, Principal Components Analysis and Mahalanobis distance. Second we compare species' native versus invaded ranges in geographic space as estimated using the species distribution modeling technique Maxent and the comparative index Hellinger's I. We find that species exhibit a range of responses, from almost complete transferability, in which the invaded niches completely overlap with the native niches, to a complete dissociation between native and invaded ranges. Intermediate responses included expansion of dimension attributable to either temperature or precipitation derived variables, as well as niche expansion in multiple dimensions. We conclude that the ecological niche in the native range is generally a poor predictor of invaded range and, by analogy, the ecological niche may be a poor predictor of range shifts under climate change. We suggest that assessing dimensions of niche transferability prior to standard species distribution modeling may improve the understanding of species' dynamics in the invaded range.

Determinants of Short Interbirth Interval among Reproductive Age Mothers in Arba Minch District, Ethiopia

PubMed Central

Hailu, Desta; Gulte, Teklemariam

2016-01-01

Background. One of the key strategies to reduce fertility and promote the health status of mothers and their children is adhering to optimal birth spacing. However, women still have shorter birth intervals and studies addressing their determinants were scarce. The objective of this study, therefore, was to assess determinants of birth interval among women who had at least two consecutive live births. Methods. Case control study was conducted from February to April 2014. Cases were women with short birth intervals (<3 years), whereas controls were women having history of optimal birth intervals (3 to 5 years). Bivariate and multivariable analyses were performed. Result. Having no formal education (AOR = 2.36, 95% CL: [1.23–4.52]), duration of breast feeding for less than 24 months (AOR: 66.03, 95% CI; [34.60–126]), preceding child being female (AOR: 5.73, 95% CI; [3.18–10.310]), modern contraceptive use (AOR: 2.79, 95% CI: [1.58–4.940]), and poor wealth index (AOR: 4.89, 95% CI; [1.81–13.25]) of respondents were independent predictors of short birth interval. Conclusion. In equalities in education, duration of breast feeding, sex of the preceding child, contraceptive method use, and wealth index were markers of unequal distribution of inter birth intervals. Thus, to optimize birth spacing, strategies of providing information, education and communication targeting predictor variables should be improved. PMID:27239553

Linear associations between clinically assessed upper motor neuron disease and diffusion tensor imaging metrics in amyotrophic lateral sclerosis.

PubMed

Woo, John H; Wang, Sumei; Melhem, Elias R; Gee, James C; Cucchiara, Andrew; McCluskey, Leo; Elman, Lauren

2014-01-01

To assess the relationship between clinically assessed Upper Motor Neuron (UMN) disease in Amyotrophic Lateral Sclerosis (ALS) and local diffusion alterations measured in the brain corticospinal tract (CST) by a tractography-driven template-space region-of-interest (ROI) analysis of Diffusion Tensor Imaging (DTI). This cross-sectional study included 34 patients with ALS, on whom DTI was performed. Clinical measures were separately obtained including the Penn UMN Score, a summary metric based upon standard clinical methods. After normalizing all DTI data to a population-specific template, tractography was performed to determine a region-of-interest (ROI) outlining the CST, in which average Mean Diffusivity (MD) and Fractional Anisotropy (FA) were estimated. Linear regression analyses were used to investigate associations of DTI metrics (MD, FA) with clinical measures (Penn UMN Score, ALSFRS-R, duration-of-disease), along with age, sex, handedness, and El Escorial category as covariates. For MD, the regression model was significant (p = 0.02), and the only significant predictors were the Penn UMN Score (p = 0.005) and age (p = 0.03). The FA regression model was also significant (p = 0.02); the only significant predictor was the Penn UMN Score (p = 0.003). Measured by the template-space ROI method, both MD and FA were linearly associated with the Penn UMN Score, supporting the hypothesis that DTI alterations reflect UMN pathology as assessed by the clinical examination.

2006 Status of the Momentum eXchange Electrodynamic Re-Boost (MXER) Tether Development

NASA Technical Reports Server (NTRS)

Bonometti, Joseph A.; Sorensen, Kirk F.; Dankanich, John W.; Frame, Kyle L.

2006-01-01

The MXER Tether technology development is a high-payoff/high-risk investment area within the NASA In-Space Propulsion Technology (ISPT) Program. The ISPT program is managed by the NASA Headquarters Science Mission Directorate and implemented by the Marshall Space Flight Center in Huntsville, Alabama. The MXER concept was identified and competitively ranked within NASA's comprehensive Integrated In-Space Transportation Plan (IISTP); an agency-wide technology assessment activity. The objective of the MXER tether project within ISPT is to advance the technological maturation level for the MXER system, and its subsystems, as well as other space and terrestrial tether applications. Recent hardware efforts have focused on the manufacturability of space-survivable high-strength tether material and coatings, high-current electrodynamic tether, lightweight catch mechanism, high-accuracy propagator/predictor code, and efficient electron collection/current generation. Significant technical progress has been achieved with modest ISPT funding to the extent that MXER has evolved to a well-characterized system with greater capability as the design has been matured. Synergistic efforts in high-current electrodynamic tethers and efficient electron collection/current generation have been made possible through SBIR and STTR support. The entire development endeavor was orchestrated as a collaborative team effort across multiple individual contracts and has established a solid technology resource base, which permits a wide variety of future space cable/tether applications to be realized.

Interval Predictor Models with a Formal Characterization of Uncertainty and Reliability

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2014-01-01

This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an interval valued function of the model's inputs, render a formal description of both the uncertainty in the model's parameters and of the spread in the predicted output. Uncertainty is prescribed as a hyper-rectangular set in the space of model's parameters. The propagation of this set through the empirical model yields a range of outputs of minimal spread containing all (or, depending on the formulation, most) of the observations. Optimization-based strategies for calculating IPMs and eliminating the effects of outliers are proposed. Outliers are identified by evaluating the extent by which they degrade the tightness of the prediction. This evaluation can be carried out while the IPM is calculated. When the data satisfies mild stochastic assumptions, and the optimization program used for calculating the IPM is convex (or, when its solution coincides with the solution to an auxiliary convex program), the model's reliability (that is, the probability that a future observation would be within the predicted range of outputs) can be bounded rigorously by a non-asymptotic formula.

When the going gets tough: behavioural type-dependent space use in the sleepy lizard changes as the season dries.

PubMed

Spiegel, Orr; Leu, Stephan T; Sih, Andrew; Godfrey, Stephanie S; Bull, C Michael

2015-11-22

Understanding space use remains a major challenge for animal ecology, with implications for species interactions, disease spread, and conservation. Behavioural type (BT) may shape the space use of individuals within animal populations. Bolder or more aggressive individuals tend to be more exploratory and disperse further. Yet, to date we have limited knowledge on how space use other than dispersal depends on BT. To address this question we studied BT-dependent space-use patterns of sleepy lizards (Tiliqua rugosa) in southern Australia. We combined high-resolution global positioning system (GPS) tracking of 72 free-ranging lizards with repeated behavioural assays, and with a survey of the spatial distributions of their food and refuge resources. Bayesian generalized linear mixed models (GLMM) showed that lizards responded to the spatial distribution of resources at the neighbourhood scale and to the intensity of space use by other conspecifics (showing apparent conspecific avoidance). BT (especially aggressiveness) affected space use by lizards and their response to ecological and social factors, in a seasonally dependent manner. Many of these effects and interactions were stronger later in the season when food became scarce and environmental conditions got tougher. For example, refuge and food availability became more important later in the season and unaggressive lizards were more responsive to these predictors. These findings highlight a commonly overlooked source of heterogeneity in animal space use and improve our mechanistic understanding of processes leading to behaviourally driven disease dynamics and social structure. © 2015 The Author(s).

Structured functional additive regression in reproducing kernel Hilbert spaces

PubMed Central

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2013-01-01

Summary Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application. PMID:25013362

Simulating visibility under reduced acuity and contrast sensitivity.

PubMed

Thompson, William B; Legge, Gordon E; Kersten, Daniel J; Shakespeare, Robert A; Lei, Quan

2017-04-01

Architects and lighting designers have difficulty designing spaces that are accessible to those with low vision, since the complex nature of most architectural spaces requires a site-specific analysis of the visibility of mobility hazards and key landmarks needed for navigation. We describe a method that can be utilized in the architectural design process for simulating the effects of reduced acuity and contrast on visibility. The key contribution is the development of a way to parameterize the simulation using standard clinical measures of acuity and contrast sensitivity. While these measures are known to be imperfect predictors of visual function, they provide a way of characterizing general levels of visual performance that is familiar to both those working in low vision and our target end-users in the architectural and lighting-design communities. We validate the simulation using a letter-recognition task.

Simulating Visibility Under Reduced Acuity and Contrast Sensitivity

PubMed Central

Thompson, William B.; Legge, Gordon E.; Kersten, Daniel J.; Shakespeare, Robert A.; Lei, Quan

2017-01-01

Architects and lighting designers have difficulty designing spaces that are accessible to those with low vision, since the complex nature of most architectural spaces requires a site-specific analysis of the visibility of mobility hazards and key landmarks needed for navigation. We describe a method that can be utilized in the architectural design process for simulating the effects of reduced acuity and contrast on visibility. The key contribution is the development of a way to parameterize the simulation using standard clinical measures of acuity and contrast sensitivity. While these measures are known to be imperfect predictors of visual function, they provide a way of characterizing general levels of visual performance that is familiar to both those working in low vision and our target end-users in the architectural and lighting design communities. We validate the simulation using a letter recognition task. PMID:28375328

Growth-Mortality Relationships in Piñon Pine (Pinus edulis) during Severe Droughts of the Past Century: Shifting Processes in Space and Time

PubMed Central

Macalady, Alison K.; Bugmann, Harald

2014-01-01

The processes leading to drought-associated tree mortality are poorly understood, particularly long-term predisposing factors, memory effects, and variability in mortality processes and thresholds in space and time. We use tree rings from four sites to investigate Pinus edulis mortality during two drought periods in the southwestern USA. We draw on recent sampling and archived collections to (1) analyze P. edulis growth patterns and mortality during the 1950s and 2000s droughts; (2) determine the influence of climate and competition on growth in trees that died and survived; and (3) derive regression models of growth-mortality risk and evaluate their performance across space and time. Recent growth was 53% higher in surviving vs. dying trees, with some sites exhibiting decades-long growth divergences associated with previous drought. Differential growth response to climate partly explained growth differences between live and dead trees, with responses wet/cool conditions most influencing eventual tree status. Competition constrained tree growth, and reduced trees’ ability to respond to favorable climate. The best predictors in growth-mortality models included long-term (15–30 year) average growth rate combined with a metric of growth variability and the number of abrupt growth increases over 15 and 10 years, respectively. The most parsimonious models had high discriminatory power (ROC>0.84) and correctly classified ∼70% of trees, suggesting that aspects of tree growth, especially over decades, can be powerful predictors of widespread drought-associated die-off. However, model discrimination varied across sites and drought events. Weaker growth-mortality relationships and higher growth at lower survival probabilities for some sites during the 2000s event suggest a shift in mortality processes from longer-term growth-related constraints to shorter-term processes, such as rapid metabolic decline even in vigorous trees due to acute drought stress, and/or increases in the attack rate of both chronically stressed and more vigorous trees by bark beetles. PMID:24786646

Growth-mortality relationships in piñon pine (Pinus edulis) during severe droughts of the past century: shifting processes in space and time.

PubMed

Macalady, Alison K; Bugmann, Harald

2014-01-01

The processes leading to drought-associated tree mortality are poorly understood, particularly long-term predisposing factors, memory effects, and variability in mortality processes and thresholds in space and time. We use tree rings from four sites to investigate Pinus edulis mortality during two drought periods in the southwestern USA. We draw on recent sampling and archived collections to (1) analyze P. edulis growth patterns and mortality during the 1950s and 2000s droughts; (2) determine the influence of climate and competition on growth in trees that died and survived; and (3) derive regression models of growth-mortality risk and evaluate their performance across space and time. Recent growth was 53% higher in surviving vs. dying trees, with some sites exhibiting decades-long growth divergences associated with previous drought. Differential growth response to climate partly explained growth differences between live and dead trees, with responses wet/cool conditions most influencing eventual tree status. Competition constrained tree growth, and reduced trees' ability to respond to favorable climate. The best predictors in growth-mortality models included long-term (15-30 year) average growth rate combined with a metric of growth variability and the number of abrupt growth increases over 15 and 10 years, respectively. The most parsimonious models had high discriminatory power (ROC>0.84) and correctly classified ∼ 70% of trees, suggesting that aspects of tree growth, especially over decades, can be powerful predictors of widespread drought-associated die-off. However, model discrimination varied across sites and drought events. Weaker growth-mortality relationships and higher growth at lower survival probabilities for some sites during the 2000s event suggest a shift in mortality processes from longer-term growth-related constraints to shorter-term processes, such as rapid metabolic decline even in vigorous trees due to acute drought stress, and/or increases in the attack rate of both chronically stressed and more vigorous trees by bark beetles.

Quantitative Model to Predict Melts on the Ol-Opx Saturation Boundary during Mantle Melting: The Role of H2O

NASA Astrophysics Data System (ADS)

Andrews, A. L.; Grove, T. L.

2014-12-01

Two quantitative, empirical models are presented that predict mantle melt compositions in equilibrium with olivine (ol) + orthopyroxene (opx) ± spinel (sp) as a function of variable pressure and H2O content. The models consist of multiple linear regressions calibrated using new data from H2O-undersaturated primitive and depleted mantle lherzolite melting experiments as well as experimental literature data. The models investigate the roles of H2O, Pressure, 1-Mg# (1-[XMg/(XMg+XFe)]), NaK# ((Na2O+K2O)/(Na2O+K2O+CaO)), TiO2, and Cr2O3 on mantle melt compositions. Melts are represented by the pseudoternary endmembers Clinopyroxene (Cpx), Olivine (Ol), Plagioclase (Plag), and Quartz (Qz) of Tormey et al. (1987). Model A returns predictive equations for the four endmembers with identical predictor variables, whereas Model B chooses predictor variables for the four compositional endmember equations and temperature independently. We employ the use of Akaike Information Criteria (Akaike, 1974) to determine the best predictor variables from initial variables chosen through thermodynamic reasoning and by previous models. In both Models A and B, the coefficients for H2O show that increasing H2O drives the melt to more Qz normative space, as the Qz component increases by +0.012(3) per 1 wt.% H2O. The other endmember components decrease and are all three times less affected by H2O (Ol: -0.004(2); Cpx: -0.004(2); Plag: -0.004(3)). Consistent with previous models and experimental data, increasing pressure moves melt compositions to more Ol normative space at the expense of the Qz component. The models presented quantitatively determine the influence of H2O, Pressure, 1-Mg#, NaK#, TiO2, and Cr2O3 on mantle melts in equilibrium with ol+opx±sp; the equations presented can be used to predict melts of known mantle source compositions saturated in ol+opx±sp. References Tormey, Grove, & Bryan (1987), doi: 10.1007/BF00375227. Akaike (1974), doi: 10.1109/TAC.1974.1100705.

Predicting thermal reference conditions for USA streams and rivers

USGS Publications Warehouse

Hill, Ryan A.; Hawkins, Charles P.; Carlisle, Daren M.

2013-01-01

Temperature is a primary driver of the structure and function of stream ecosystems. However, the lack of stream temperature (ST) data for the vast majority of streams and rivers severely compromises our ability to describe patterns of thermal variation among streams, test hypotheses regarding the effects of temperature on macroecological patterns, and assess the effects of altered STs on ecological resources. Our goal was to develop empirical models that could: 1) quantify the effects of stream and watershed alteration (SWA) on STs, and 2) accurately and precisely predict natural (i.e., reference condition) STs in conterminous USA streams and rivers. We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual ST. To build reference-condition models (RCMs), we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. We first created a set of dirty models (DMs) that related STs to both natural factors (e.g., climate, watershed area, topography) and measures of SWA, i.e., reservoirs, urbanization, and agriculture. The 3 models performed well (r2 = 0.84–0.94, residual mean square error [RMSE] = 1.2–2.0°C). For each DM, we used partial dependence plots to identify SWA thresholds below which response in ST was minimal. We then used data from only the sites with upstream SWA below these thresholds to build RCMs with only natural factors as predictors (r2 = 0.87–0.95, RMSE = 1.1–1.9°C). Use of only reference-quality sites caused RCMs to suffer modest loss of predictor space and spatial coverage, but this loss was associated with parts of ST response curves that were flat and, therefore, not responsive to further variation in predictor space. We then compared predictions made with the RCMs to predictions made with the DMs with SWA set to 0. For most DMs, setting SWAs to 0 resulted in biased estimates of thermal reference condition.

Real-time demonstration hardware for enhanced DPCM video compression algorithm

NASA Technical Reports Server (NTRS)

Bizon, Thomas P.; Whyte, Wayne A., Jr.; Marcopoli, Vincent R.

1992-01-01

The lack of available wideband digital links as well as the complexity of implementation of bandwidth efficient digital video CODECs (encoder/decoder) has worked to keep the cost of digital television transmission too high to compete with analog methods. Terrestrial and satellite video service providers, however, are now recognizing the potential gains that digital video compression offers and are proposing to incorporate compression systems to increase the number of available program channels. NASA is similarly recognizing the benefits of and trend toward digital video compression techniques for transmission of high quality video from space and therefore, has developed a digital television bandwidth compression algorithm to process standard National Television Systems Committee (NTSC) composite color television signals. The algorithm is based on differential pulse code modulation (DPCM), but additionally utilizes a non-adaptive predictor, non-uniform quantizer and multilevel Huffman coder to reduce the data rate substantially below that achievable with straight DPCM. The non-adaptive predictor and multilevel Huffman coder combine to set this technique apart from other DPCM encoding algorithms. All processing is done on a intra-field basis to prevent motion degradation and minimize hardware complexity. Computer simulations have shown the algorithm will produce broadcast quality reconstructed video at an average transmission rate of 1.8 bits/pixel. Hardware implementation of the DPCM circuit, non-adaptive predictor and non-uniform quantizer has been completed, providing realtime demonstration of the image quality at full video rates. Video sampling/reconstruction circuits have also been constructed to accomplish the analog video processing necessary for the real-time demonstration. Performance results for the completed hardware compare favorably with simulation results. Hardware implementation of the multilevel Huffman encoder/decoder is currently under development along with implementation of a buffer control algorithm to accommodate the variable data rate output of the multilevel Huffman encoder. A video CODEC of this type could be used to compress NTSC color television signals where high quality reconstruction is desirable (e.g., Space Station video transmission, transmission direct-to-the-home via direct broadcast satellite systems or cable television distribution to system headends and direct-to-the-home).

Management of post-traumatic retained hemothorax: a prospective, observational, multicenter AAST study.

PubMed

DuBose, Joseph; Inaba, Kenji; Demetriades, Demetrios; Scalea, Thomas M; O'Connor, James; Menaker, Jay; Morales, Carlos; Konstantinidis, Agathoklis; Shiflett, Anthony; Copwood, Ben

2012-01-01

The natural history and optimal management of retained hemothorax (RH) after chest tube placement is unknown. The intent of our study was to determine practice patterns used and identify independent predictors of the need for thoracotomy. An American Association for the Surgery of Trauma multicenter prospective observational trial was conducted, enrolling patients with placement of chest tube within 24 hours of trauma admission and RH on subsequent computed tomography of the chest. Demographics, interventions, and outcomes were analyzed. Logistic regression analysis was used to identify the independent predictors of successful intervention for each of the management choices chosen and complications. RH was identified in 328 patients from 20 centers. Video-assisted thoracoscopy (VATS) was the most commonly used initial procedure in 33.5%, but 26.5% required two and 5.4% required three procedures to clear RH or subsequent empyema. Thoracotomy was ultimately required in 20.4%. The strongest independent predictor of successful observation was estimated volume of RH ≤300 cc (odds ratio [OR], 3.7 [2.0-7.0]; p < 0.001). Independent predictors of successful VATS as definitive treatment were absence of an associated diaphragm injury (OR, 4.7 [1.6-13.7]; p = 0.005), use of periprocedural antibiotics for thoracostomy placement (OR, 3.3 [1.2-9.0]; p = 0.023), and volume of RH ≤900 cc (OR, 3.9 [1.4-13.2]; p = 0.03). No relationship between timing of VATS and success rate was identified. Independent predictors of the need for thoracotomy included diaphragm injury (OR, 4.9 [2.4-9.9]; p < 0.001), RH >900 cc (OR, 3.2 [1.4-7.5]; p = 0.007), and failure to give periprocedural antibiotics for initial chest tube placement (OR 2.3 [1.2-4.6]; p = 0.015). The overall empyema and pneumonia rates for RH patients were 26.8% and 19.5%, respectively. RH in trauma is associated with high rates of empyema and pneumonia. VATS can be performed with high success rates, although optimal timing is unknown. Approximately, 25% of patients require at least two procedures to effectively clear RH or subsequent pleural space infections and 20.4% require thoracotomy.

Telerobotic Surgery: An Intelligent Systems Approach to Mitigate the Adverse Effects of Communication Delay. Chapter 4

NASA Technical Reports Server (NTRS)

Cardullo, Frank M.; Lewis, Harold W., III; Panfilov, Peter B.

2007-01-01

An extremely innovative approach has been presented, which is to have the surgeon operate through a simulator running in real-time enhanced with an intelligent controller component to enhance the safety and efficiency of a remotely conducted operation. The use of a simulator enables the surgeon to operate in a virtual environment free from the impediments of telecommunication delay. The simulator functions as a predictor and periodically the simulator state is corrected with truth data. Three major research areas must be explored in order to ensure achieving the objectives. They are: simulator as predictor, image processing, and intelligent control. Each is equally necessary for success of the project and each of these involves a significant intelligent component in it. These are diverse, interdisciplinary areas of investigation, thereby requiring a highly coordinated effort by all the members of our team, to ensure an integrated system. The following is a brief discussion of those areas. Simulator as a predictor: The delays encountered in remote robotic surgery will be greater than any encountered in human-machine systems analysis, with the possible exception of remote operations in space. Therefore, novel compensation techniques will be developed. Included will be the development of the real-time simulator, which is at the heart of our approach. The simulator will present real-time, stereoscopic images and artificial haptic stimuli to the surgeon. Image processing: Because of the delay and the possibility of insufficient bandwidth a high level of novel image processing is necessary. This image processing will include several innovative aspects, including image interpretation, video to graphical conversion, texture extraction, geometric processing, image compression and image generation at the surgeon station. Intelligent control: Since the approach we propose is in a sense predictor based, albeit a very sophisticated predictor, a controller, which not only optimizes end effector trajectory but also avoids error, is essential. We propose to investigate two different approaches to the controller design. One approach employs an optimal controller based on modern control theory; the other one involves soft computing techniques, i.e. fuzzy logic, neural networks, genetic algorithms and hybrids of these.

Multi-Disciplinary Knowledge Synthesis for Human Health Assessment on Earth and in Space

NASA Astrophysics Data System (ADS)

Christakos, G.

We discuss methodological developments in multi-disciplinary knowledge synthesis (KS) of human health assessment. A theoretical KS framework can provide the rational means for the assimilation of various information bases (general, site-specific etc.) that are relevant to the life system of interest. KS-based techniques produce a realistic representation of the system, provide a rigorous assessment of the uncertainty sources, and generate informative health state predictions across space-time. The underlying epistemic cognition methodology is based on teleologic criteria and stochastic logic principles. The mathematics of KS involves a powerful and versatile spatiotemporal random field model that accounts rigorously for the uncertainty features of the life system and imposes no restriction on the shape of the probability distributions or the form of the predictors. KS theory is instrumental in understanding natural heterogeneities, assessing crucial human exposure correlations and laws of physical change, and explaining toxicokinetic mechanisms and dependencies in a spatiotemporal life system domain. It is hoped that a better understanding of KS fundamentals would generate multi-disciplinary models that are useful for the maintenance of human health on Earth and in Space.

The symmetry and mass of halo Coronal Mass Ejections (CMEs) as quantitative predictors for severe space weather at Earth.

NASA Astrophysics Data System (ADS)

Fuselier, S.; Allegrini, F.; Bzowski, M.; Dayeh, M. A.; Desai, M. I.; Funsten, H. O.; Galli, A.; Heirtzler, D.; Janzen, P. H.; Kubiak, M. A.; Kucharek, H.; Lewis, W. S.; Livadiotis, G.; McComas, D. J.; Moebius, E.; Petrinec, S. M.; Quinn, M. S.; Schwadron, N.; Sokol, J. M.; Trattner, K. J.

2014-12-01

The Bureau of Meteorology's Space Weather Service operates an alert service for severe space weather events. The service relies on a statistical model which ingests observations of M and X class solar flares at or shortly after the time of the flare to predict the occurrence and severity of terrestrial impacts with a lead time of 1 to 4 days. This model has been operational since 2012 and caters to the needs of critical infrastructure groups in the Australian region. This paper reports on improvements to the forecast model by including SOHO LASCO coronagraph observations of Coronal Mass Ejections (CMEs). The coronagraphs are analysed to determine the Earthward direction parameter and the integrated intensity as a measure of the CME mass. Both of these parameters can help to predict whether a CME will be geo-effective. This work aims to increase the accuracy of the model predictions and lower the rate of false positives, as well as providing an estimate of the expected level of geomagnetic storm intensity.

The symmetry and mass of halo Coronal Mass Ejections (CMEs) as quantitative predictors for severe space weather at Earth.

NASA Astrophysics Data System (ADS)

Freeland, L. E.; Terkildsen, M. B.

2015-12-01

The Bureau of Meteorology's Space Weather Service operates an alert service for severe space weather events. The service relies on a statistical model which ingests observations of M and X class solar flares at or shortly after the time of the flare to predict the occurrence and severity of terrestrial impacts with a lead time of 1 to 4 days. This model has been operational since 2012 and caters to the needs of critical infrastructure groups in the Australian region. This paper reports on improvements to the forecast model by including SOHO LASCO coronagraph observations of Coronal Mass Ejections (CMEs). The coronagraphs are analysed to determine the Earthward direction parameter and the integrated intensity as a measure of the CME mass. Both of these parameters can help to predict whether a CME will be geo-effective. This work aims to increase the accuracy of the model predictions and lower the rate of false positives, as well as providing an estimate of the expected level of geomagnetic storm intensity.

Using genetic algorithms to achieve an automatic and global optimization of analogue methods for statistical downscaling of precipitation

NASA Astrophysics Data System (ADS)

Horton, Pascal; Weingartner, Rolf; Obled, Charles; Jaboyedoff, Michel

2017-04-01

Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circulation, are likely to result in similar local or regional weather conditions. These methods consist of sampling a certain number of past situations, based on different synoptic-scale meteorological variables (predictors), in order to construct a probabilistic prediction for a local weather variable of interest (predictand). They are often used for daily precipitation prediction, either in the context of real-time forecasting, reconstruction of past weather conditions, or future climate impact studies. The relationship between predictors and predictands is defined by several parameters (predictor variable, spatial and temporal windows used for the comparison, analogy criteria, and number of analogues), which are often calibrated by means of a semi-automatic sequential procedure that has strong limitations. AMs may include several subsampling levels (e.g. first sorting a set of analogs in terms of circulation, then restricting to those with similar moisture status). The parameter space of the AMs can be very complex, with substantial co-dependencies between the parameters. Thus, global optimization techniques are likely to be necessary for calibrating most AM variants, as they can optimize all parameters of all analogy levels simultaneously. Genetic algorithms (GAs) were found to be successful in finding optimal values of AM parameters. They allow taking into account parameters inter-dependencies, and selecting objectively some parameters that were manually selected beforehand (such as the pressure levels and the temporal windows of the predictor variables), and thus obviate the need of assessing a high number of combinations. The performance scores of the optimized methods increased compared to reference methods, and this even to a greater extent for days with high precipitation totals. The resulting parameters were found to be relevant and spatially coherent. Moreover, they were obtained automatically and objectively, which reduces efforts invested in exploration attempts when adapting the method to a new region or for a new predictand. In addition, the approach allowed for new degrees of freedom, such as a weighting between the pressure levels, and non overlapping spatial windows. Genetic algorithms were then used further in order to automatically select predictor variables and analogy criteria. This resulted in interesting outputs, providing new predictor-criterion combinations. However, some limitations of the approach were encountered, and the need of the expert input is likely to remain necessary. Nevertheless, letting GAs exploring a dataset for the best predictor for a predictand of interest is certainly a useful tool, particularly when applied for a new predictand or a new region under different climatic characteristics.

Predictors of Behavior and Performance in Extreme Environments: The Antarctic Space Analogue Program

NASA Technical Reports Server (NTRS)

Palinkas, Lawrence A.; Gunderson, E K. Eric; Holland, A. W.; Miller, Christopher; Johnson, Jeffrey C.

2000-01-01

To determine which, if any, characteristics should be incorporated into a select-in approach to screening personnel for long-duration spaceflight, we examined the influence of crewmember social/ demographic characteristics, personality traits, interpersonal needs, and characteristics of station physical environments on performance measures in 657 American men who spent an austral winter in Antarctica between 1963 and 1974. During screening, subjects completed a Personal History Questionnaire which obtained information on social and demographic characteristics, the Deep Freeze Opinion Survey which assessed 5 different personality traits, and the Fundamental Interpersonal Relations Orientation-Behavior (FIRO-B) Scale which measured 6 dimensions of interpersonal needs. Station environment included measures of crew size and severity of physical environment. Performance was assessed on the basis of combined peer-supervisor evaluations of overall performance, peer nominations of fellow crewmembers who made ideal winter-over candidates, and self-reported depressive symptoms. Social/demographic characteristics, personality traits, interpersonal needs, and characteristics of station environments collectively accounted for 9-17% of the variance in performance measures. The following characteristics were significant independent predictors of more than one performance measure: military service, low levels of neuroticism, extraversion and conscientiousness, and a low desire for affection from others. These results represent an important first step in the development of select-in criteria for personnel on long-duration missions in space and other extreme environments. These criteria must take into consideration the characteristics of the environment and the limitations they place on meeting needs for interpersonal relations and task performance, as well as the characteristics of the individuals and groups who live and work in these environments.

Dementia incidence and predictors in cerebral amyloid angiopathy patients without intracerebral hemorrhage.

PubMed

Xiong, Li; Boulouis, Gregoire; Charidimou, Andreas; Roongpiboonsopit, Duangnapa; Jessel, Michael J; Pasi, Marco; Reijmer, Yael D; Fotiadis, Panagiotis; Ayres, Alison; Merrill, Emily; Schwab, Kristin; Blacker, Deborah; Gurol, M Edip; Greenberg, Steven M; Viswanathan, Anand

2018-02-01

Cerebral amyloid angiopathy (CAA) is a common cause of cognitive impairment in older individuals. This study aimed to investigate predictors of dementia in CAA patients without intracerebral hemorrhage (ICH). A total of 158 non-demented patients from the Stroke Service or the Memory Clinic who met the modified Boston Criteria for probable CAA were included. At baseline, neuroimaging markers, including lobar microbleeds (cerebral microbleeds (CMBs)), white matter hyperintensities (WMH), cortical superficial siderosis (cSS), magnetic resonance imaging (MRI)-visible centrum semiovale perivascular spaces (CSO-PVS), lacunes, and medial temporal atrophy (MTA) were assessed. The overall burden of small vessel disease (SVD) for CAA was calculated by a cumulative score based on CMB number, WMH severity, cSS presence and extent and CSO-PVS severity. The estimated cumulative dementia incidence at 1 year was 14% (95% confidence interval (CI): 5%-23%), and 5 years 73% (95% CI: 55%, 84%). Age (hazard ratio (HR) 1.05 per year, 95% CI: 1.01-1.08, p = 0.007), presence of MCI status (HR 3.40, 95% CI: 1.97-6.92, p < 0.001), MTA (HR 1.71 per point, 95% CI: 1.26-2.32, p = 0.001), and SVD score (HR 1.23 per point, 95% CI: 1.20-1.48, p = 0.030) at baseline were independent predictors for dementia conversion in these patients. Cognitive deterioration of CAA patients appears attributable to cumulative changes, from both vasculopathic and neurodegenerative lesions.

The prognostic impact of in-hospital worsening of renal function in patients with acute coronary syndrome.

PubMed

AlFaleh, Hussam F; Alsuwaida, Abdulkareem O; Ullah, Anhar; Hersi, Ahmad; AlHabib, Khalid F; AlNemer, Khalid; AlSaif, Shukri; Taraben, Amir; Kashour, Tarek; Balghith, Mohammed A; Ahmed, Waqar H

2013-08-10

Renal impairment is strongly linked to adverse cardiovascular (CV) events. Baseline renal dysfunction is a strong predictor of CV mortality and morbidity in patients admitted with acute coronary syndrome (ACS). However, the prognostic importance of worsening renal function (WRF) in these patients is not well characterized. ACS patients enrolled in the SPACE (Saudi Project for Assessment of Coronary Events) registry who had baseline and pre-discharge serum creatinine data available were eligible for this study. WRF was defined as a 25% reduction from admission estimated glomerular filtration rate (eGFR) within 7 days of hospitalization. Baseline demographics, clinical presentation, therapies, and in-hospital outcomes were compared. Of the 3583 ACS patients, WRF occurred in 225 patients (6.3%), who were older, had more cardiovascular risk factors, were more likely to be female, have past vascular disease, and presented with more non-ST-segment elevation myocardial infarction than patients without WRF (39.5% vs. 32.8%; p=0.042). WRF was associated with an increased risk of in-hospital death, heart failure, cardiogenic shock, and stroke. After adjusting for potential confounders, WRF was an independent predictor of in-hospital death (adjusted odd ratio 28.02, 95% CI 13.2-60.28, p<0.0001). WRF was more predictive of mortality than baseline eGFR. These results indicate that WRF is a powerful predictor for in-hospital mortality and CV complications in ACS patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Intelligent Predictor of Energy Expenditure with the Use of Patch-Type Sensor Module

PubMed Central

Li, Meina; Kwak, Keun-Chang; Kim, Youn-Tae

2012-01-01

This paper is concerned with an intelligent predictor of energy expenditure (EE) using a developed patch-type sensor module for wireless monitoring of heart rate (HR) and movement index (MI). For this purpose, an intelligent predictor is designed by an advanced linguistic model (LM) with interval prediction based on fuzzy granulation that can be realized by context-based fuzzy c-means (CFCM) clustering. The system components consist of a sensor board, the rubber case, and the communication module with built-in analysis algorithm. This sensor is patched onto the user's chest to obtain physiological data in indoor and outdoor environments. The prediction performance was demonstrated by root mean square error (RMSE). The prediction performance was obtained as the number of contexts and clusters increased from 2 to 6, respectively. Thirty participants were recruited from Chosun University to take part in this study. The data sets were recorded during normal walking, brisk walking, slow running, and jogging in an outdoor environment and treadmill running in an indoor environment, respectively. We randomly divided the data set into training (60%) and test data set (40%) in the normalized space during 10 iterations. The training data set is used for model construction, while the test set is used for model validation. The experimental results revealed that the prediction error on treadmill running simulation was improved by about 51% and 12% in comparison to conventional LM for training and checking data set, respectively. PMID:23202166

Enhancing performance of next generation FSO communication systems using soft computing-based predictions.

PubMed

Kazaura, Kamugisha; Omae, Kazunori; Suzuki, Toshiji; Matsumoto, Mitsuji; Mutafungwa, Edward; Korhonen, Timo O; Murakami, Tadaaki; Takahashi, Koichi; Matsumoto, Hideki; Wakamori, Kazuhiko; Arimoto, Yoshinori

2006-06-12

The deterioration and deformation of a free-space optical beam wave-front as it propagates through the atmosphere can reduce the link availability and may introduce burst errors thus degrading the performance of the system. We investigate the suitability of utilizing soft-computing (SC) based tools for improving performance of free-space optical (FSO) communications systems. The SC based tools are used for the prediction of key parameters of a FSO communications system. Measured data collected from an experimental FSO communication system is used as training and testing data for a proposed multi-layer neural network predictor (MNNP) used to predict future parameter values. The predicted parameters are essential for reducing transmission errors by improving the antenna's accuracy of tracking data beams. This is particularly essential for periods considered to be of strong atmospheric turbulence. The parameter values predicted using the proposed tool show acceptable conformity with original measurements.

Extrinsic local regression on manifold-valued data

PubMed Central

Lin, Lizhen; St Thomas, Brian; Zhu, Hongtu; Dunson, David B.

2017-01-01

We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging and many other areas. Our approach embeds the manifold where the responses lie onto a higher dimensional Euclidean space, obtains a local regression estimate in that space, and then projects this estimate back onto the image of the manifold. Outside the regression setting both intrinsic and extrinsic approaches have been proposed for modeling i.i.d manifold-valued data. However, to our knowledge our work is the first to take an extrinsic approach to the regression problem. The proposed extrinsic regression framework is general, computationally efficient and theoretically appealing. Asymptotic distributions and convergence rates of the extrinsic regression estimates are derived and a large class of examples are considered indicating the wide applicability of our approach. PMID:29225385

Guidance and Control Algorithms for the Mars Entry, Descent and Landing Systems Analysis

NASA Technical Reports Server (NTRS)

Davis, Jody L.; CwyerCianciolo, Alicia M.; Powell, Richard W.; Shidner, Jeremy D.; Garcia-Llama, Eduardo

2010-01-01

The purpose of the Mars Entry, Descent and Landing Systems Analysis (EDL-SA) study was to identify feasible technologies that will enable human exploration of Mars, specifically to deliver large payloads to the Martian surface. This paper focuses on the methods used to guide and control two of the contending technologies, a mid- lift-to-drag (L/D) rigid aeroshell and a hypersonic inflatable aerodynamic decelerator (HIAD), through the entry portion of the trajectory. The Program to Optimize Simulated Trajectories II (POST2) is used to simulate and analyze the trajectories of the contending technologies and guidance and control algorithms. Three guidance algorithms are discussed in this paper: EDL theoretical guidance, Numerical Predictor-Corrector (NPC) guidance and Analytical Predictor-Corrector (APC) guidance. EDL-SA also considered two forms of control: bank angle control, similar to that used by Apollo and the Space Shuttle, and a center-of-gravity (CG) offset control. This paper presents the performance comparison of these guidance algorithms and summarizes the results as they impact the technology recommendations for future study.

Simulator Study of Indoor Annoyance Caused by Shaped Sonic Boom Stimuli With and Without Rattle Augmentation

NASA Technical Reports Server (NTRS)

Rathsam, Jonathan; Loubeau, Alexandra; Klos, Jacob

2013-01-01

The National Aeronautics and Space Administration's High Speed Project is developing a predictive capability for annoyance caused by shaped sonic booms transmitted indoors. The predictive capability is intended for use by aircraft designers as well as by aircraft noise regulators who are considering lifting the current prohibition on overland civil supersonic flight. The goal of the current study is to use an indoor simulator to validate two models developed using headphone tests for annoyance caused by sonic booms with and without rattle augmentation. The predictors in the proposed models include Moore and Glasberg's Stationary Loudness Level, the time derivative of Moore and Glasberg's time-varying short-term Loudness Level, and the difference between two weighted sound exposure levels, CSEL-ASEL. The indoor simulator provides a more realistic listening environment than headphones due to lowfrequency sound reproduction down to 6 Hz, which also causes perceptible tactile vibration. The results of this study show that a model consisting of {PL + (CSEL-ASEL)} is a reliable predictor of annoyance caused by shaped sonic booms alone, rattle sounds alone, and shaped sonic booms and rattle sounds together.

Geographic analysis of vaccine uptake in a cluster-randomized controlled trial in Hue, Vietnam.

PubMed

Ali, Mohammad; Thiem, Vu Dinh; Park, Jin-Kyung; Ochiai, Rion Leon; Canh, Do Gia; Danovaro-Holliday, M Carolina; Kaljee, Linda M; Clemens, John D; Acosta, Camilo J

2007-09-01

This paper identifies spatial patterns and predictors of vaccine uptake in a cluster-randomized controlled trial in Hue, Vietnam. Data for this study result from the integration of demographic surveillance, vaccine record, and geographic data of the study area. A multi-level cross-classified (non-hierarchical) model was used for analyzing the non-nested nature of individual's ecological data. Vaccine uptake was unevenly distributed in space and there was spatial variability among predictors of vaccine uptake. Vaccine uptake was higher among students with younger, male, or not literate family heads. Students from households with higher per-capita income were less likely to participate in the trial. Residency south of the river or further from a hospital/polyclinic was associated with higher vaccine uptake. Younger students were more likely to be vaccinated than older students in high- or low-risk areas, but not in the entire study area. The findings are important for the management of vaccine campaigns during a trial and for interpretation of disease patterns during vaccine-efficacy evaluation.

Forecasting Lightning at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

NASA Technical Reports Server (NTRS)

Lambert, Winfred; Wheeler, Mark; Roeder, William

2005-01-01

The Applied Meteorology Unit (AMU) developed a set of statistical forecast equations that provide a probability of lightning occurrence on Kennedy Space Center (KSC) I Cape Canaveral Air Force Station (CCAFS) for the day during the warm season (May September). The 45th Weather Squadron (45 WS) forecasters at CCAFS in Florida include a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts, which are briefed at 1100 UTC (0700 EDT). This information is used for general scheduling of operations at CCAFS and KSC. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts for the KSC/CCAFS area during Shuttle flight operations. Much of the current lightning probability forecast at both groups is based on a subjective analysis of model and observational data. The objective tool currently available is the Neumann-Pfeffer Thunderstorm Index (NPTI, Neumann 1971), developed specifically for the KSCICCAFS area over 30 years ago. However, recent studies have shown that 1-day persistence provides a better forecast than the NPTI, indicating that the NPTI needed to be upgraded or replaced. Because they require a tool that provides a reliable estimate of the daily thunderstorm probability forecast, the 45 WS forecasters requested that the AMU develop a new lightning probability forecast tool using recent data and more sophisticated techniques now possible through more computing power than that available over 30 years ago. The equation development incorporated results from two research projects that investigated causes of lightning occurrence near KSCICCAFS and over the Florida peninsula. One proved that logistic regression outperformed the linear regression method used in NPTI, even when the same predictors were used. The other study found relationships between large scale flow regimes and spatial lightning distributions over Florida. Lightning, probabilities based on these flow regimes were used as candidate predictors in the equation development. Fifteen years (1 989-2003) of warm season data were used to develop the forecast equations. The data sources included a local network of cloud-to-ground lightning sensors called the Cloud-to-Ground Lightning Surveillance System (CGLSS), 1200 UTC Florida synoptic soundings, and the 1000 UTC CCAFS sounding. Data from CGLSS were used to determine lightning occurrence for each day. The 1200 UTC soundings were used to calculate the synoptic-scale flow regimes and the 1000 UTC soundings were used to calculate local stability parameters, which were used as candidate predictors of lightning occurrence. Five logistic regression forecast equations were created through careful selection and elimination of the candidate predictors. The resulting equations contain five to six predictors each. Results from four performance tests indicated that the equations showed an increase in skill over several standard forecasting methods, good reliability, an ability to distinguish between non-lightning and lightning days, and good accuracy measures and skill scores. Given the overall good performance the 45 WS requested that the equations be transitioned to operations and added to the current set of tools used to determine the daily lightning probability of occurrence.

Estimating dead-space fraction for secondary analyses of ARDS clinical trials

PubMed Central

Beitler, Jeremy R.; Thompson, B. Taylor; Matthay, Michael A.; Talmor, Daniel; Liu, Kathleen D.; Zhuo, Hanjing; Hayden, Douglas; Spragg, Roger G.; Malhotra, Atul

2015-01-01

Objective Pulmonary dead-space fraction is one of few lung-specific independent predictors of mortality from acute respiratory distress syndrome (ARDS). However, it is not measured routinely in clinical trials and thus altogether ignored in secondary analyses that shape future research directions and clinical practice. This study sought to validate an estimate of dead-space fraction for use in secondary analyses of clinical trials. Design Analysis of patient-level data pooled from ARDS clinical trials. Four approaches to estimate dead-space fraction were evaluated: three required estimating metabolic rate; one estimated dead-space fraction directly. Setting U.S. academic teaching hospitals. Patients Data from 210 patients across three clinical trials were used to compare performance of estimating equations with measured dead-space fraction. A second cohort of 3,135 patients from six clinical trials without measured dead-space fraction was used to confirm whether estimates independently predicted mortality. Interventions None. Measurements and Main Results Dead-space fraction estimated using the unadjusted Harris-Benedict equation for energy expenditure was unbiased (mean ± SD Harris-Benedict 0.59 ± 0.13; measured 0.60 ± 0.12). This estimate predicted measured dead-space fraction to within ± 0.10 in 70% of patients and ± 0.20 in 95% of patients. Measured dead-space fraction independently predicted mortality (OR 1.36 per 0.05 increase in dead-space fraction, 95% CI 1.10–1.68; p < .01). The Harris-Benedict estimate closely approximated this association with mortality in the same cohort (OR 1.55, 95% CI 1.21–1.98; p < .01) and remained independently predictive of death in the larger ARDSNet cohort. Other estimates predicted measured dead-space fraction or its association with mortality less well. Conclusions Dead-space fraction should be measured in future ARDS clinical trials to facilitate incorporation into secondary analyses. For analyses where dead-space fraction was not measured, the Harris-Benedict estimate can be used to estimate dead-space fraction and adjust for its association with mortality. PMID:25738857

Towards malaria risk prediction in Afghanistan using remote sensing.

PubMed

Adimi, Farida; Soebiyanto, Radina P; Safi, Najibullah; Kiang, Richard

2010-05-13

Malaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistan's diverse landscape and terrain contributes to the heterogeneous malaria prevalence across the country. Understanding the role of environmental variables on malaria transmission can further the effort for malaria control programme. Provincial malaria epidemiological data (2004-2007) collected by the health posts in 23 provinces were used in conjunction with space-borne observations from NASA satellites. Specifically, the environmental variables, including precipitation, temperature and vegetation index measured by the Tropical Rainfall Measuring Mission and the Moderate Resolution Imaging Spectoradiometer, were used. Regression techniques were employed to model malaria cases as a function of environmental predictors. The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation. Vegetation index, in general, is the strongest predictor, reflecting the fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time series are modelled well, with provincial average R2 of 0.845. Although the R2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases. The provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a cost-effective surveillance system that includes forecasting, early warning and detection. The predictive and early warning capabilities shown in this paper support this strategy.

Extraction and development of inset models in support of groundwater age calculations for glacial aquifers

USGS Publications Warehouse

Feinstein, Daniel T.; Kauffman, Leon J.; Haserodt, Megan J.; Clark, Brian R.; Juckem, Paul F.

2018-06-22

The U.S. Geological Survey developed a regional model of Lake Michigan Basin (LMB). This report describes the construction of five MODFLOW inset models extracted from the LMB regional model and their application using the particle-tracking code MODPATH to simulate the groundwater age distribution of discharge to wells pumping from glacial deposits. The five study areas of the inset model correspond to 8-digit hydrologic unit code (HUC8) basins. Two of the basins are tributary to Lake Michigan from the east, two are tributary to the lake from the west, and one is just west of the western boundary of the Lake Michigan topographic basin. The inset models inherited many of the inputs to the parent LMB model, including the hydrostratigraphy and layering scheme, the hydraulic conductivity assigned to bedrock layers, recharge distribution, and water use in the form of pumping rates from glacial and bedrock wells. The construction of the inset models entailed modifying some inputs, most notably the grid spacing (reduced from cells 5,000 feet on a side in the parent LMB model to 500 feet on a side in the inset models). The refined grid spacing allowed for more precise location of pumped wells and more detailed simulation of groundwater/surface-water interactions. The glacial hydraulic conductivity values, the top bedrock surface elevation, and the surface-water network input to the inset models also were modified. The inset models are solved using the MODFLOW–NWT code, which allows for more robust handling of conditions in unconfined aquifers than previous versions of MODFLOW. Comparison of the MODFLOW inset models reveals that they incorporate a range of hydrogeologic conditions relative to the glacial part of the flow system, demonstrated by visualization and analysis of model inputs and outputs and reflected in the range of ages generated by MODPATH for existing and hypothetical glacial wells. Certain inputs and outputs are judged to be candidate predictors that, if treated statistically, may be capable of explaining much of the variance in the simulated age metrics. One example of a predictor that model results indicate strongly affects simulated age is the depth of the well open interval below the simulated water table. The strength of this example variable as an overall predictor of groundwater age and its relation to other predictors can be statistically tested through the metamodeling process. In this way the inset models are designed to serve as a training area for metamodels that estimate groundwater age in glacial wells, which in turn will contribute to ongoing studies, under the direction of the U.S. Geological Survey National Water Quality Assessment, of contaminant susceptibility of shallow groundwater across the glacial aquifer system.

An approach to solving large reliability models

NASA Technical Reports Server (NTRS)

Boyd, Mark A.; Veeraraghavan, Malathi; Dugan, Joanne Bechta; Trivedi, Kishor S.

1988-01-01

This paper describes a unified approach to the problem of solving large realistic reliability models. The methodology integrates behavioral decomposition, state trunction, and efficient sparse matrix-based numerical methods. The use of fault trees, together with ancillary information regarding dependencies to automatically generate the underlying Markov model state space is proposed. The effectiveness of this approach is illustrated by modeling a state-of-the-art flight control system and a multiprocessor system. Nonexponential distributions for times to failure of components are assumed in the latter example. The modeling tool used for most of this analysis is HARP (the Hybrid Automated Reliability Predictor).

An Update on Mortality in the U.S. Astronaut Corps: 1959-2009

NASA Technical Reports Server (NTRS)

Amirian, E.; Clark, April; Halm, Melissa; Hartnett, Heather

2009-01-01

Although it has now been over 50 years since mankind first ventured into space, the long-term health impacts of human space flight remain largely unknown. Identifying factors that affect survival and prognosis among those who participate in space flight is vitally important, as the era of commercial space flight approaches and NASA prepares for missions to Mars. The Longitudinal Study of Astronaut Health is a prospective study designed to examine trends in astronaut morbidity and mortality. The purpose of this analysis was to describe and explore predictors of overall and cause-specific mortality among individuals selected for the U.S. astronaut corps. All U.S. astronauts (n=321), regardless of flight status, were included in this analysis. Death certificate searches were conducted to ascertain vital status and cause of death through April 2009. Data were collected from medical records and lifestyle questionnaires. Multivariable Cox regression modeling was used to calculate the mortality hazard associated with embarking on space flight, adjusted for sex, race, and age at selection. Between 1959 and 2009, there were 39 (12.1%) deaths. Of these deaths, 18 (42.2%) were due to occupational accidents; 7 (17.9%) were due to other accidents; 6 (15.4%) were attributable to cancer; 6 (15.4%) resulted from cardiovascular/circulatory diseases; and 2 (5.1%) were from other causes. Participation in space flight did not significantly increase mortality hazard over time (adjusted hazard ratio=0.57; 95% confidence interval=0.26-1.26. Because our results are based on a small sample size, future research that includes payload specialists, other space flight participants, and international crew members is warranted to maximize statistical power.

Lacunar Infarcts, but Not Perivascular Spaces, Are Predictors of Cognitive Decline in Cerebral Small-Vessel Disease

PubMed Central

Trippier, Sarah; Lawrence, Andrew J.; Lambert, Christian; Zeestraten, Eva; Williams, Owen A.; Patel, Bhavini; Morris, Robin G.; Barrick, Thomas R.; MacKinnon, Andrew D.; Markus, Hugh S.

2018-01-01

Background and Purpose— Cerebral small-vessel disease is a major cause of cognitive impairment. Perivascular spaces (PvS) occur in small-vessel disease, but their relationship to cognitive impairment remains uncertain. One reason may be difficulty in distinguishing between lacunes and PvS. We determined the relationship between baseline PvS score and PvS volume with change in cognition over a 5-year follow-up. We compared this to the relationship between baseline lacune count and total lacune volume with cognition. In addition, we examined change in PvS volume over time. Methods— Data from the prospective SCANS study (St Georges Cognition and Neuroimaging in Stroke) of patients with symptomatic lacunar stroke and confluent leukoaraiosis were used (n=121). Multimodal magnetic resonance imaging was performed annually for 3 years and neuropsychological testing annually for 5 years. Lacunes were manually identified and distinguished from PvS. PvS were rated using a validated visual rating scale, and PvS volumes calculated using T1-weighted images. Linear mixed-effect models were used to determine the impact of PvS and lacunes on cognition. Results— Baseline PvS scores or volumes showed no association with cognitive indices. No change was detectable in PvS volumes over the 3 years. In contrast, baseline lacunes associated with all cognitive indices and predicted cognitive decline over the 5-year follow-up. Conclusions— Although a feature of small-vessel disease, PvS are not a predictor of cognitive decline, in contrast to lacunes. This study highlights the importance of carefully differentiating between lacunes and PvS in studies investigating vascular cognitive impairment. PMID:29438074

[Factors predicting sensory profile of 4 to 18 month old infants].

PubMed

Pedrosa, Carina; Caçola, Priscila; Carvalhal, Maria Isabel Martins Mourão

2015-01-01

To identify environment factors predicting sensory profile of infants between 4 and 18 months old. This cross-sectional study evaluated 97 infants (40 females e 57 males), with a mean age of 1.05±0.32 years with the Test of Sensory Functions in Infants (TSFI) and also asked 97 parents and 11 kindergarten teachers of seven daycare centers to answer the Affordances in the Home Environment for Motor Development- Infant Scale (AHEMD-IS). The AHEMD-IS is a questionnaire that characterizes the opportunities in the home environment for infants between 3 and 18 months of age. We tested the association between affordances and the sensory profile of infants. Significant variables were entered into a regression model to determine predictors of sensory profile. The majority of infants (66%) had a normal sensory profile and 34% were at risk or deficit. Affordances in the home were classified as adequate and they were good in the studied daycare centers. The results of the regression revealed that only daily hours in daycare center and daycare outside space influenced the sensory profile of infants, in particular the Ocular-Motor Control component. The sensory profile of infants was between normal and at risk. While the family home offered adequate affordances for motor development, the daycare centers of the infants involved demonstrated a good quantity and quality of affordances. Overall, we conclude that daily hours in the daycare center and daycare outside space were predictors of the sensory profile, particular on Ocular-Motor Control component. Copyright © 2015 Associação de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

Mechanical response tissue analyzer for estimating bone strength

NASA Technical Reports Server (NTRS)

Arnaud, Sara B.; Steele, Charles; Mauriello, Anthony

1991-01-01

One of the major concerns for extended space flight is weakness of the long bones of the legs, composed primarily of cortical bone, that functions to provide mechanical support. The strength of cortical bone is due to its complex structure, described simplistically as cylinders of parallel osteons composed of layers of mineralized collagen. The reduced mechanical stresses during space flight or immobilization of bone on Earth reduces the mineral content, and changes the components of its matrix and structure so that its strength is reduced. Currently, the established clinical measures of bone strength are indirect. The measures are based on determinations of mineral density by means of radiography, photon absorptiometry, and quantitative computer tomography. While the mineral content of bone is essential to its strength, there is growing awareness of the limitations of the measurement as the sole predictor of fracture risk in metabolic bone diseases, especially limitations of the measurement as the sole predictor of fracture risk in metabolic bone diseases, especially osteoporosis. Other experimental methods in clinical trials that more directly evaluate the physical properties of bone, and do not require exposure to radiation, include ultrasound, acoustic emission, and low-frequency mechanical vibration. The last method can be considered a direct measure of the functional capacity of a long bone since it quantifies the mechanical response to a stimulus delivered directly to the bone. A low frequency vibration induces a response (impedance) curve with a minimum at the resonant frequency, that a few investigators use for the evaluation of the bone. An alternative approach, the method under consideration, is to use the response curve as the basis for determination of the bone bending stiffness EI (E is the intrinsic material property and I is the cross-sectional moment of inertia) and mass, fundamental mechanical properties of bone.

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity.

PubMed

Kolossa, Antonio; Kopp, Bruno

2016-01-01

The aim of this study was to analyze how measurement error affects the validity of modeling studies in computational neuroscience. A synthetic validity test was created using simulated P300 event-related potentials as an example. The model space comprised four computational models of single-trial P300 amplitude fluctuations which differed in terms of complexity and dependency. The single-trial fluctuation of simulated P300 amplitudes was computed on the basis of one of the models, at various levels of measurement error and at various numbers of data points. Bayesian model selection was performed based on exceedance probabilities. At very low numbers of data points, the least complex model generally outperformed the data-generating model. Invalid model identification also occurred at low levels of data quality and under low numbers of data points if the winning model's predictors were closely correlated with the predictors from the data-generating model. Given sufficient data quality and numbers of data points, the data-generating model could be correctly identified, even against models which were very similar to the data-generating model. Thus, a number of variables affects the validity of computational modeling studies, and data quality and numbers of data points are among the main factors relevant to the issue. Further, the nature of the model space (i.e., model complexity, model dependency) should not be neglected. This study provided quantitative results which show the importance of ensuring the validity of computational modeling via adequately prepared studies. The accomplishment of synthetic validity tests is recommended for future applications. Beyond that, we propose to render the demonstration of sufficient validity via adequate simulations mandatory to computational modeling studies.

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

NASA Astrophysics Data System (ADS)

Lima, Carlos H. R.; AghaKouchak, Amir

2017-12-01

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

Intimate Partner Aggression and Marital Satisfaction: A Cross-Lagged Panel Analysis.

PubMed

Hammett, Julia F; Lavner, Justin A; Karney, Benjamin R; Bradbury, Thomas N

2017-12-01

Intimate partner aggression is common in dissatisfied relationships, yet it remains unclear whether intimate partner aggression is a correlate of relationship satisfaction, whether it predicts or follows from relationship satisfaction over time, or whether longitudinal associations are in fact bidirectional in nature. The present study evaluates these perspectives by examining self-reports of aggressive behaviors in relation to corresponding self-reports of relationship satisfaction among a sample of 431 low-income, ethnically diverse (76% Hispanic, 12% African American, 12% Caucasian) newlywed couples. Using a cross-lagged panel analysis, we examined associations between aggression and satisfaction across four time points, spaced by 9-month intervals, during the first 2.5 years of marriage. Cross-sectionally, less satisfied couples reported higher levels of intimate partner aggression. Longitudinally, aggression was a more consistent predictor of satisfaction than vice versa, though neither pathway was particularly robust: Intimate partner aggression was a significant predictor of relationship satisfaction at 4 of the 12 tested lags, whereas relationship satisfaction was a significant predictor of intimate partner aggression at only one of 12 lags. Because all effects were relatively weak and inconsistent, more specificity is needed to clarify circumstances under which aggression does and does not predict satisfaction, including whether the predictive power of the aggression-to-satisfaction association varies based on the severity of aggression or other individual (e.g., personality) or external (e.g., stress and environmental context) factors. Together, results indicate that dissatisfied couples are more likely to engage in intimate partner aggression, but being dissatisfied is unlikely to increase the level of aggression a couple engages in over time.

The use of space and high altitude aerial photography to classify forest land and to detect forest disturbances

NASA Technical Reports Server (NTRS)

Aldrich, R. C.; Greentree, W. J.; Heller, R. C.; Norick, N. X.

1970-01-01

In October 1969, an investigation was begun near Atlanta, Georgia, to explore the possibilities of developing predictors for forest land and stand condition classifications using space photography. It has been found that forest area can be predicted with reasonable accuracy on space photographs using ocular techniques. Infrared color film is the best single multiband sensor for this purpose. Using the Apollo 9 infrared color photographs taken in March 1969 photointerpreters were able to predict forest area for small units consistently within 5 to 10 percent of ground truth. Approximately 5,000 density data points were recorded for 14 scan lines selected at random from five study blocks. The mean densities and standard deviations were computed for 13 separate land use classes. The results indicate that forest area cannot be separated from other land uses with a high degree of accuracy using optical film density alone. If, however, densities derived by introducing red, green, and blue cutoff filters in the optical system of the microdensitometer are combined with their differences and their ratios in regression analysis techniques, there is a good possibility of discriminating forest from all other classes.

Neighbourhood access to open spaces and the physical activity of residents: a national study.

PubMed

Witten, Karen; Hiscock, Rosemary; Pearce, Jamie; Blakely, Tony

2008-09-01

Increasing population levels of physical activity is high on the health agenda in many countries. There is some evidence that neighbourhood access to public open space can increase physical activity by providing easier and more direct access to opportunities for exercise. This national study examines the relationship between travel time access to parks and beaches, BMI and physical activity in New Zealand neighbourhoods. Access to parks and beaches, measured in minutes taken by a car, was calculated for 38,350 neighbourhoods nationally using Geographic Information Systems. Multilevel regression analyses were used to establish the significance of access to these recreational amenities as a predictor of BMI, and levels of physical activity and sedentary behaviour in the 12,529 participants, living in 1178 neighbourhoods, of the New Zealand Health Survey 2002/3. Neighbourhood access to parks was not associated with BMI, sedentary behaviour or physical activity, after controlling for individual-level socio-economic variables, and neighbourhood-level deprivation and urban/rural status. There was some evidence of a relationship between beach access and BMI and physical activity in the expected direction. This study found little evidence of an association between locational access to open spaces and physical activity.

Motor competence and characteristics within the preschool environment.

PubMed

True, Larissa; Pfeiffer, Karin Allor; Dowda, Marsha; Williams, Harriet G; Brown, William H; O'Neill, Jennifer R; Pate, Russell R

2017-08-01

Environmental characteristics within preschools that influence children's motor competence are largely unknown. The purpose of the present study was to examine the contribution of various preschool environmental characteristics to children's locomotor, object control, and total gross motor scores. Cross-sectional, observational study of 3-5 year-old children (n=229) from 22 preschools in South Carolina. The Children's Activity and Movement in Preschool Study (CHAMPS) Motor Skills Protocol assessed MC. Preschool directors provided information regarding policies and practices. The research team measured playgrounds and classrooms, and the Early Childhood Environment Rating Scale-Revised assessed preschool quality. Time spent in open space and electronic media use were also assessed using direct observation. The aforementioned variables predicted children's object control, locomotor, and total gross motor scores. Classroom size/child ratio, teacher education, playground size, electronic media use, and trips to outside organizations emerged as significant predictors of locomotor score and total motor score. The object control model was non-significant. Preschools may be able to promote motor competence by allowing children more time in open spaces, structured activity experiences, and by expanding existing outdoor playground space whenever possible. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Survey of On-Orbit Sleep Quality: Short-Duration Flyers

NASA Technical Reports Server (NTRS)

Locke, J.; Leveton, L.; Keeton, K.; Whitmire, A.; Patterson, H.; Faulk, J.

2010-01-01

The NASA Human Research Program (HRP) Behavioral Health and Performance Element (BHP), in conjunction with the NASA Space Medicine Division, is currently completing the largest systematic, subjective assessment of shuttle astronauts sleep behaviors and sleep quality on Earth, during training periods, and during space flight missions. Since July 2009, a total of 66 astronauts have completed a secure online survey regarding specific sleep strategies, crew policies, and mitigation effectiveness. In addition to the survey, each astronaut participant met individually with trained BHP and SD representatives for a structured, follow-up interview. Data are currently being assessed and the study s principal investigator will be providing some preliminary findings at the Investigators Workshop. Additional analyses will be conducted in the following months to examine predictors of optimal sleep in space, and to evaluate the differences in countermeasure effectiveness between groups based on their sleep experience on the ground and on orbit. A revised survey for a subsequent investigation on the experiences of long-duration flyers will be developed in the Spring and implemented in the Summer of 2010. Findings from both of these investigations will inform countermeasure strategies for astronauts, medical operations, and habitat designers for future exploration missions, as well as upcoming shuttle and ISS missions.

Objective Lightning Forecasting at Kennedy Space Center/Cape Canaveral Air Force Station using Cloud-to-Ground Lightning Surveillance System Data

NASA Technical Reports Server (NTRS)

Lambert, Winifred; Wheeler, Mark

2004-01-01

The 45th Weather Squadron (45 WS) forecasters at Cape Canaveral Air Force Station (CCAFS) in Florida include a probability of thunderstorm occurrence in their daily morning briefings. This information is used by personnel involved in determining the possibility of violating Launch Commit Criteria, evaluating Flight Rules for the Space Shuttle, and daily planning for ground operation activities on Kennedy Space Center (KSC)/CCAFS. Much of the current lightning probability forecast is based on a subjective analysis of model and observational data. The forecasters requested that a lightning probability forecast tool based on statistical analysis of historical warm-season (May - September) data be developed in order to increase the objectivity of the daily thunderstorm probability forecast. The tool is a set of statistical lightning forecast equations that provide a lightning occurrence probability for the day by 1100 UTC (0700 EDT) during the warm season. This study used 15 years (1989-2003) of warm season data to develop the objective forecast equations. The local CCAFS 1000 UTC sounding was used to calculate stability parameters for equation predictors. The Cloud-to-Ground Lightning Surveillance System (CGLSS) data were used to determine lightning occurrence for each day. The CGLSS data have been found to be more reliable indicators of lightning in the area than surface observations through local informal analyses. This work was based on the results from two earlier research projects. Everitt (1999) used surface observations and rawinsonde data to develop logistic regression equations that forecast the daily thunderstorm probability at CCAFS. The Everitt (1999) equations showed an improvement in skill over the Neumann-Pfeffer thunderstorm index (Neumann 1971), which uses multiple linear regression, and also persistence and climatology forecasts. Lericos et al. (2002) developed lightning distributions over the Florida peninsula based on specific flow regimes. The flow regimes were inferred from the average wind direction in the 1000-700 mb layer at Miami (MIA), Tampa (TBW), and Jacksonville (JAX), Florida, and the lightning data were from the National Lightning Detection Network. The results suggested that the daily flow regime may be an important predictor of lightning occurrence on KSC/CCAFS.

Neural source dynamics of brain responses to continuous stimuli: Speech processing from acoustics to comprehension.

PubMed

Brodbeck, Christian; Presacco, Alessandro; Simon, Jonathan Z

2018-05-15

Human experience often involves continuous sensory information that unfolds over time. This is true in particular for speech comprehension, where continuous acoustic signals are processed over seconds or even minutes. We show that brain responses to such continuous stimuli can be investigated in detail, for magnetoencephalography (MEG) data, by combining linear kernel estimation with minimum norm source localization. Previous research has shown that the requirement to average data over many trials can be overcome by modeling the brain response as a linear convolution of the stimulus and a kernel, or response function, and estimating a kernel that predicts the response from the stimulus. However, such analysis has been typically restricted to sensor space. Here we demonstrate that this analysis can also be performed in neural source space. We first computed distributed minimum norm current source estimates for continuous MEG recordings, and then computed response functions for the current estimate at each source element, using the boosting algorithm with cross-validation. Permutation tests can then assess the significance of individual predictor variables, as well as features of the corresponding spatio-temporal response functions. We demonstrate the viability of this technique by computing spatio-temporal response functions for speech stimuli, using predictor variables reflecting acoustic, lexical and semantic processing. Results indicate that processes related to comprehension of continuous speech can be differentiated anatomically as well as temporally: acoustic information engaged auditory cortex at short latencies, followed by responses over the central sulcus and inferior frontal gyrus, possibly related to somatosensory/motor cortex involvement in speech perception; lexical frequency was associated with a left-lateralized response in auditory cortex and subsequent bilateral frontal activity; and semantic composition was associated with bilateral temporal and frontal brain activity. We conclude that this technique can be used to study the neural processing of continuous stimuli in time and anatomical space with the millisecond temporal resolution of MEG. This suggests new avenues for analyzing neural processing of naturalistic stimuli, without the necessity of averaging over artificially short or truncated stimuli. Copyright © 2018 Elsevier Inc. All rights reserved.

Cougar space use and movements in the wildland-urban landscape of western Washington

USGS Publications Warehouse

Kertson, B.N.; Spencer, R.D.; Marzluff, J.M.; Hepinstall-Cymerman, Jeffrey; Grue, C.E.

2011-01-01

The wildland-urban interface lies at the confluence of human-dominated and wild landscapes, creating a number of management and conservation challenges. Because wildlife ecology, behavior, and evolution at this interface are shaped by both natural and human phenomena, this requires greater understanding of how diverse factors affect ecosystem and population processes. We illustrate the challenge of understanding and managing a frequent and often undesired inhabitant of the wildland-urban landscape, the cougar (Puma concolor). In wildland and residential areas of western Washington State, USA, we captured and radiotracked 27 cougars to model space use and understand the role of landscape features in interactions (sightings, encounters, and depredations) between cougars and humans. Resource utilization functions (RUFs) identified cougar use of areas with features that were probably attractive to prey, influential on prey vulnerability, and associated with limited or no residential development. Early-successional forest (+), conifer forest (+), distance to road (-), residential density (-), and elevation (-) were significant positive and negative predictors of use for the population, whereas use of other landscape features was highly variable. Space use and movement rates in wildland and residential areas were similar because cougars used wildland-like forest patches, reserves, and corridors in residential portions of their home range. The population RUF was a good predictor of confirmed cougar interactions, with 72% of confirmed reports occurring in the 50% of the landscape predicted to be medium-high and high cougar use areas. We believe that there is a threshold residential density at which the level of development modifies the habitat but maintains enough wildland characteristics to encourage moderate levels of cougar use and maximize the probability of interaction. Wildlife managers trying to reduce interactions between cougars and people should incorporate information on spatial ecology and landscape characteristics to identify areas with the highest overlap of human and cougar use to focus management, education, and landscape planning. Resource utilization functions provide a proactive tool to guide these activities for improved coexistence with wildlife using both wildland and residential portions of the landscape. ??2011 by the Ecological Society of America.

DVD-COOP: Innovative Conjunction Prediction Using Voronoi-filter based on the Dynamic Voronoi Diagram of 3D Spheres

NASA Astrophysics Data System (ADS)

Cha, J.; Ryu, J.; Lee, M.; Song, C.; Cho, Y.; Schumacher, P.; Mah, M.; Kim, D.

Conjunction prediction is one of the critical operations in space situational awareness (SSA). For geospace objects, common algorithms for conjunction prediction are usually based on all-pairwise check, spatial hash, or kd-tree. Computational load is usually reduced through some filters. However, there exists a good chance of missing potential collisions between space objects. We present a novel algorithm which both guarantees no missing conjunction and is efficient to answer to a variety of spatial queries including pairwise conjunction prediction. The algorithm takes only O(k log N) time for N objects in the worst case to answer conjunctions where k is a constant which is linear to prediction time length. The proposed algorithm, named DVD-COOP (Dynamic Voronoi Diagram-based Conjunctive Orbital Object Predictor), is based on the dynamic Voronoi diagram of moving spherical balls in 3D space. The algorithm has a preprocessing which consists of two steps: The construction of an initial Voronoi diagram (taking O(N) time on average) and the construction of a priority queue for the events of topology changes in the Voronoi diagram (taking O(N log N) time in the worst case). The scalability of the proposed algorithm is also discussed. We hope that the proposed Voronoi-approach will change the computational paradigm in spatial reasoning among space objects.

Six-degree-of-freedom guidance and control-entry analysis of the HL-20

NASA Technical Reports Server (NTRS)

Powell, Richard W.

1993-01-01

The ability of the HL-20 lifting body to fly has been evaluated for an automated entry from atmospheric interface to landing. This evaluation was required to demonstrate that not only successful touchdown conditions would be possible for this low lift-to-drag-ratio vehicle, but also the vehicle would not exceed its design dynamic pressure limit of 400 psf during entry. This dynamic pressure constraint limit, coupled with limited available pitch-control authority at low supersonic speeds, restricts the available maneuvering capability for the HL-20 to acquire the runway. One result of this analysis was that this restrictive maneuvering capability does not allow the use of a model-following atmospheric entry-guidance algorithm, such as that used by the Space Shuttle, but instead requires a more adaptable guidance algorithm. Therefore, for this analysis, a predictor-corrector guidance algorithm was developed that would provide successful touchdown conditions while not violating the dynamic pressure constraint. A flight-control system was designed and incorporated, along with the predictor-corrector guidance algorithm, into a six-DOF simulation. which showed that the HL-20 remained controllable and could reach the landing site and execute a successful landing under all off-nominal conditions simulated.

Antibody side chain conformations are position-dependent.

PubMed

Leem, Jinwoo; Georges, Guy; Shi, Jiye; Deane, Charlotte M

2018-04-01

Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone-dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody-specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position-dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ 1 and χ1+2 accuracy (78.7% and 64.8%) compared to three leading backbone-dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain-side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears. © 2018 Wiley Periodicals, Inc.

Reproductive decisions in the lives of West Bank Palestinian women: Dimensions and contradictions.

PubMed

Pell, Stephanie

2017-02-01

Palestinian women have one of the highest fertility rates in the world, averaging 4.38 births per woman. However, Palestinian fertility patterns are distinct from those of other developing nations, in that high fertility rates coexist alongside high levels of education and low levels of infant mortality - both of which have been established elsewhere as predictors of low total fertility rates. This study explores the dimensions and context of the contradictions between fertility predictors and rates, isolating main factors that shape Palestinian reproductive behaviour. Furthermore, while this study addresses factors that influence the high fertility in the Palestinian Territories, it also addresses factors that contribute to the steady decline of this trend. In-depth interviews were conducted with Palestinian women in urban refugee communities and key informant interviews with experts on Palestinian reproductive health. The findings indicate that five factors shape women's reproductive behaviour: (1) the fear of losing one's children in the ongoing conflict; (2) socio-economic factors including poverty and density of space; (3) the marital relationship; (4) religious values; and (5) generational differences. These results highlight the influence of socio-political conditions on reproductive behaviour and the significance of women's agency in manoeuvring their fertility outcomes.

Landscape scale measures of steelhead (Oncorhynchus mykiss) bioenergetic growth rate potential in Lake Michigan and comparison with angler catch rates

USGS Publications Warehouse

Hook, T.O.; Rutherford, E.S.; Brines, Shannon J.; Geddes, C.A.; Mason, D.M.; Schwab, D.J.; Fleischer, G.W.

2004-01-01

The relative quality of a habitat can influence fish consumption, growth, mortality, and production. In order to quantify habitat quality, several authors have combined bioenergetic and foraging models to generate spatially explicit estimates of fish growth rate potential (GRP). However, the capacity of GRP to reflect the spatial distributions of fishes over large areas has not been fully evaluated. We generated landscape scale estimates of steelhead (Oncorhynchus mykiss) GRP throughout Lake Michigan for 1994-1996, and used these estimates to test the hypotheses that GRP is a good predictor of spatial patterns of steelhead catch rates. We used surface temperatures (measured with AVHRR satellite imagery) and acoustically measured steelhead prey densities (alewife, Alosa pseudoharengus) as inputs for the GRP model. Our analyses demonstrate that potential steelhead growth rates in Lake Michigan are highly variable in both space and time. Steelhead GRP tended to increase with latitude, and mean GRP was much higher during September 1995, compared to 1994 and 1996. In addition, our study suggests that landscape scale measures of GRP are not good predictors of steelhead catch rates throughout Lake Michigan, but may provide an index of interannual variation in system-wide habitat quality.

Three-dimensional time dependent computation of turbulent flow

NASA Technical Reports Server (NTRS)

Kwak, D.; Reynolds, W. C.; Ferziger, J. H.

1975-01-01

The three-dimensional, primitive equations of motion are solved numerically for the case of isotropic box turbulence and the distortion of homogeneous turbulence by irrotational plane strain at large Reynolds numbers. A Gaussian filter is applied to governing equations to define the large scale field. This gives rise to additional second order computed scale stresses (Leonard stresses). The residual stresses are simulated through an eddy viscosity. Uniform grids are used, with a fourth order differencing scheme in space and a second order Adams-Bashforth predictor for explicit time stepping. The results are compared to the experiments and statistical information extracted from the computer generated data.

Abort Region Determinator (ARD) module feasibility report. Mission planning, mission analysis and software formulation

NASA Technical Reports Server (NTRS)

Draeger, B. G.; Joyner, J. A.

1976-01-01

A detailed performance evaluation of the Abort Region Determinator (ARD) module design was provided in support of OFT-1 ascent and OFT-1 intact launch aborts. The evaluation method used compared ARD results against results obtained using the full-up Space Vehicle Dynamic Simulations program under the same conditions. Results were presented for each of the three major ARD math models: (1) the ascent numerical integrator; (2) the mass model, and (3) the second stage predictor as well as the total ARD module. These results demonstrate that the baselined ARD module meets all design objectives for mission control center orbital flight test launch/abort support.

New Predictive Filters for Compensating the Transport Delay on a Flight Simulator

NASA Technical Reports Server (NTRS)

Guo, Liwen; Cardullo, Frank M.; Houck, Jacob A.; Kelly, Lon C.; Wolters, Thomas E.

2004-01-01

The problems of transport delay in a flight simulator, such as its sources and effects, are reviewed. Then their effects on a pilot-in-the-loop control system are investigated with simulations. Three current prominent delay compensators the lead/lag filter, McFarland filter, and the Sobiski/Cardullo filter were analyzed and compared. This paper introduces two novel delay compensation techniques an adaptive predictor using the Kalman estimator and a state space predictive filter using a reference aerodynamic model. Applications of these two new compensators on recorded data from the NASA Langley Research Center Visual Motion Simulator show that they achieve better compensation over the current ones.

A meshless method using radial basis functions for numerical solution of the two-dimensional KdV-Burgers equation

NASA Astrophysics Data System (ADS)

Zabihi, F.; Saffarian, M.

2016-07-01

The aim of this article is to obtain the numerical solution of the two-dimensional KdV-Burgers equation. We construct the solution by using a different approach, that is based on using collocation points. The solution is based on using the thin plate splines radial basis function, which builds an approximated solution with discretizing the time and the space to small steps. We use a predictor-corrector scheme to avoid solving the nonlinear system. The results of numerical experiments are compared with analytical solutions to confirm the accuracy and efficiency of the presented scheme.

Determination of the chemical parameters and manufacturer of divins from their broadband transmission spectra

NASA Astrophysics Data System (ADS)

Khodasevich, M. A.; Sinitsyn, G. V.; Skorbanova, E. A.; Rogovaya, M. V.; Kambur, E. I.; Aseev, V. A.

2016-06-01

Analysis of multiparametric data on transmission spectra of 24 divins (Moldovan cognacs) in the 190-2600 nm range allows identification of outliers and their removal from a sample under study in the following consideration. The principal component analysis and classification tree with a single-rank predictor constructed in the 2D space of principal components allow classification of divin manufacturers. It is shown that the accuracy of syringaldehyde, ethyl acetate, vanillin, and gallic acid concentrations in divins calculated with the regression to latent structures depends on the sample volume and is 3, 6, 16, and 20%, respectively, which is acceptable for the application.

A latent class distance association model for cross-classified data with a categorical response variable.

PubMed

Vera, José Fernando; de Rooij, Mark; Heiser, Willem J

2014-11-01

In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. © 2014 The British Psychological Society.

Driving Competence in Mild Dementia with Lewy Bodies: In Search of Cognitive Predictors Using Driving Simulation

PubMed Central

Yamin, Stephanie; Stinchcombe, Arne; Gagnon, Sylvain

2015-01-01

Driving is a multifactorial behaviour drawing on multiple cognitive, sensory, and physical systems. Dementia is a progressive and degenerative neurological condition that impacts the cognitive processes necessary for safe driving. While a number of studies have examined driving among individuals with Alzheimer's disease, less is known about the impact of Dementia with Lewy Bodies (DLB) on driving safety. The present study compared simulated driving performance of 15 older drivers with mild DLB with that of 21 neurologically healthy control drivers. DLB drivers showed poorer performance on all indicators of simulated driving including an increased number of collisions in the simulator and poorer composite indicators of overall driving performance. A measure of global cognitive function (i.e., the Mini Mental State Exam) was found to be related to the overall driving performance. In addition, measures of attention (i.e., Useful Field of View, UFOV) and space processing (Visual Object and Space Perception, VOSP, Test) correlated significantly with a rater's assessment of driving performance. PMID:26713169

MIT research in telerobotics

NASA Technical Reports Server (NTRS)

Sheridan, T. B.

1987-01-01

Ongoing MIT research in telerobotics (vehicles capable of some autonomous sensing and manipulating, having some remote supervisory control by people) and teleoperation (vehicles for sensing and manipulating which are fully controlled remotely by people) is discussed. The current efforts mix human and artificial intelligence/control. The idea of adjustable impedance at either end of pure master-slave teleoperation, and simultaneous coordinated control of teleoperator/telerobotic systems which have more than six degrees of freedom (e.g., a combined vehicle and arm, each with five or six DOF) are discussed. A new cable-controlled parallel link arm which offers many advantages over conventional arms for space is briefly described. Predictor displays to compensate for time delay in teleoperator loops, the use of state estimation to help human control decisions in space, and ongoing research in supervisory command language are covered. Finally, efforts to build a human flyable real-time dynamic computer-graphic telerobot simulator are described. These projects represent most, but not all, of the telerobotics research in our laboratory, supported by JPL, NASA Ames and NOAA.

Regionally Implicit Discontinuous Galerkin Methods for Solving the Relativistic Vlasov-Maxwell System Submitted to Iowa State University

NASA Astrophysics Data System (ADS)

Guthrey, Pierson Tyler

The relativistic Vlasov-Maxwell system (RVM) models the behavior of collisionless plasma, where electrons and ions interact via the electromagnetic fields they generate. In the RVM system, electrons could accelerate to significant fractions of the speed of light. An idea that is actively being pursued by several research groups around the globe is to accelerate electrons to relativistic speeds by hitting a plasma with an intense laser beam. As the laser beam passes through the plasma it creates plasma wakes, much like a ship passing through water, which can trap electrons and push them to relativistic speeds. Such setups are known as laser wakefield accelerators, and have the potential to yield particle accelerators that are significantly smaller than those currently in use. Ultimately, the goal of such research is to harness the resulting electron beams to generate electromagnetic waves that can be used in medical imaging applications. High-order accurate numerical discretizations of kinetic Vlasov plasma models are very effective at yielding low-noise plasma simulations, but are computationally expensive to solve because of the high dimensionality. In addition to the general difficulties inherent to numerically simulating Vlasov models, the relativistic Vlasov-Maxwell system has unique challenges not present in the non-relativistic case. One such issue is that operator splitting of the phase gradient leads to potential instabilities, thus we require an alternative to operator splitting of the phase. The goal of the current work is to develop a new class of high-order accurate numerical methods for solving kinetic Vlasov models of plasma. The main discretization in configuration space is handled via a high-order finite element method called the discontinuous Galerkin method (DG). One difficulty is that standard explicit time-stepping methods for DG suffer from time-step restrictions that are significantly worse than what a simple Courant-Friedrichs-Lewy (CFL) argument requires. The maximum stable time-step scales inversely with the highest degree in the DG polynomial approximation space and becomes progressively smaller with each added spatial dimension. In this work, we overcome this difficulty by introducing a novel time-stepping strategy: the regionally-implicit discontinuous Galerkin (RIDG) method. The RIDG is method is based on an extension of the Lax-Wendroff DG (LxW-DG) method, which previously had been shown to be equivalent (for linear constant coefficient problems) to a predictor-corrector approach, where the prediction is computed by a space-time DG method (STDG). The corrector is an explicit method that uses the space-time reconstructed solution from the predictor step. In this work, we modify the predictor to include not just local information, but also neighboring information. With this modification, we show that the stability is greatly enhanced; we show that we can remove the polynomial degree dependence of the maximum time-step and show vastly improved time-steps in multiple spatial dimensions. Upon the development of the general RIDG method, we apply it to the non-relativistic 1D1V Vlasov-Poisson equations and the relativistic 1D2V Vlasov-Maxwell equations. For each we validate the high-order method on several test cases. In the final test case, we demonstrate the ability of the method to simulate the acceleration of electrons to relativistic speeds in a simplified test case.

Meta-Analyses of Predictors of Hope in Adolescents.

PubMed

Yarcheski, Adela; Mahon, Noreen E

2016-03-01

The purposes of this study were to identify predictors of hope in the literature reviewed, to use meta-analysis to determine the mean effect size (ES) across studies between each predictor and hope, and to examine four moderators on each predictor-hope relationship. Using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for the literature reviewed, 77 published studies or doctoral dissertations completed between 1990 and 2012 met the inclusion criteria. Eleven predictors of hope were identified and each predictor in relation to hope was subjected to meta-analysis. Five predictors (positive affect, life satisfaction, optimism, self-esteem, and social support) of hope had large mean ESs, 1 predictor (depression) had a medium ES, 4 predictors (negative affect, stress, academic achievement, and violence) had small ESs, and 1 predictor (gender) had a trivial ES. Findings are interpreted for the 11 predictors in relation to hope. Limitations and conclusions are addressed; future studies are recommended. © The Author(s) 2014.

Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds.

PubMed

Potts, Jonathan R; Mokross, Karl; Stouffer, Philip C; Lewis, Mark A

2014-12-01

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal-habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities.

Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds

PubMed Central

Potts, Jonathan R; Mokross, Karl; Stouffer, Philip C; Lewis, Mark A

2014-01-01

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal–habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities. PMID:25558353

Predictors of negotiated NIH indirect rates at US institutions.

PubMed

Johnston, S Claiborne; Desmond-Hellmann, Susan; Hauser, Stewart; Vermillion, Eric; Mia, Nilo

2015-01-01

The United States (US) Department of Health and Human Services and the Office of Naval Research negotiate institutional rates for payments of overhead costs associated with administration and space usage, commonly known as indirect rates. Such payments account for a large proportion of spending by the National Institutes of Health (NIH). Little has been published about differences in rates and their predictors. Negotiated indirect rates for on-campus research grants were requested from the Council on Governmental Relations for the 100 institutions with greatest NIH funding in 2010. NIH funding, cost of living (ACCRA Index for 2008), public vs. private status, negotiating governmental organization (Department of Health and Human Services or Office of Naval Research), US Census Region, and year were assessed as predictors of institutional indirect rates using generalized estimating equations with all variables included in the model. Overall, 72 institutions participated, with 207 reported indirect rates for the years 2006, 2008, and 2010. Indirect rates ranged from 36.3% to 78%, with an average of 54.5%. Mean rates increased from 53.6% in 2006 to 55.4% in 2010 (p<0.001). In multivariable models, private institutions had 6.2% (95% CI 3.7%-8.7%; p<0.001) higher indirect rates than public institutions. Rates in the Northeast were highest (Midwest 4.0% lower; West 4.9% lower; South 5.2% lower). Greater NIH funding (p = 0.025) and cost of living (p = 0.034) also predicted indirect rates while negotiating governmental organization did not (p = 0.414). Negotiated indirect rates for governmental research grants to academic centers vary widely. Although the association between indirect rates and cost of living may be justified, the cause of variation in rates by region, public-private status, and NIH funding levels is unclear.

Abortion providers, stigma and professional quality of life.

PubMed

Martin, Lisa A; Debbink, Michelle; Hassinger, Jane; Youatt, Emily; Harris, Lisa H

2014-12-01

The Providers Share Workshop (PSW) provides abortion providers safe space to discuss their work experiences. Our objectives were to assess changes in abortion stigma over time and explore how stigma is related to aspects of professional quality of life, including compassion satisfaction, burnout and compassion fatigue for providers participating in the workshops. Seventy-nine providers were recruited to the PSW study. Surveys were completed prior to, immediately following and 1 year after the workshops. The outcome measures were the Abortion Provider Stigma Survey and the Professional Quality of Life (ProQOL) survey. Baseline ProQOL scores were compared to published averages using t tests. Changes in abortion stigma and aspects of professional quality of life were assessed by fitting a two-level random-effects model with repeated measures at level 1 (period-level) and static measures (e.g., demographic data) at level 2 (person-level). Potential covariates included age, parenting status, education, organizational tenure, job type and clinic type (stand-alone vs. hospital-based clinics). Compared to other healthcare workers, abortion providers reported higher compassion satisfaction (t=2.65, p=.009) and lower burnout (t=5.13, p<.0001). Repeated-measures analysis revealed statistically significant decreases in stigma over time. Regression analysis identified abortion stigma as a significant predictor of lower compassion satisfaction, higher burnout and higher compassion fatigue. Participants in PSW reported a reduction in abortion stigma over time. Further, stigma is an important predictor of compassion satisfaction, burnout and compassion fatigue, suggesting that interventions aimed at supporting the abortion providing workforce should likely assess abortion stigma. Stigma is an important predictor of compassion satisfaction, burnout and compassion fatigue among abortion care providers. Therefore, strengthening human resources for abortion care requires stigma reduction efforts. Participants in the PSWs show reductions in stigma over time. Copyright © 2014 Elsevier Inc. All rights reserved.

Challenges of DHS and MIS to capture the entire pattern of malaria parasite risk and intervention effects in countries with different ecological zones: the case of Cameroon.

PubMed

Massoda Tonye, Salomon G; Kouambeng, Celestin; Wounang, Romain; Vounatsou, Penelope

2018-04-06

In 2011, the demographic and health survey (DHS) in Cameroon was combined with the multiple indicator cluster survey. Malaria parasitological data were collected, but the survey period did not overlap with the high malaria transmission season. A malaria indicator survey (MIS) was also conducted during the same year, within the malaria peak transmission season. This study compares estimates of the geographical distribution of malaria parasite risk and of the effects of interventions obtained from the DHS and MIS survey data. Bayesian geostatistical models were applied on DHS and MIS data to obtain georeferenced estimates of the malaria parasite prevalence and to assess the effects of interventions. Climatic predictors were retrieved from satellite sources. Geostatistical variable selection was used to identify the most important climatic predictors and indicators of malaria interventions. The overall observed malaria parasite risk among children was 33 and 30% in the DHS and MIS data, respectively. Both datasets identified the Normalized Difference Vegetation Index and the altitude as important predictors of the geographical distribution of the disease. However, MIS selected additional climatic factors as important disease predictors. The magnitude of the estimated malaria parasite risk at national level was similar in both surveys. Nevertheless, DHS estimates lower risk in the North and Coastal areas. MIS did not find any important intervention effects, although DHS revealed that the proportion of population with an insecticide-treated nets access in their household was statistically important. An important negative relationship between malaria parasitaemia and socioeconomic factors, such as the level of mother's education, place of residence and the household welfare were captured by both surveys. Timing of the malaria survey influences estimates of the geographical distribution of disease risk, especially in settings with seasonal transmission. In countries with different ecological zones and thus different seasonal patterns, a single survey may not be able to identify all high risk areas. A continuous MIS or a combination of MIS, health information system data and data from sentinel sites may be able to capture the disease risk distribution in space across different seasons.

Adaptive lyapunov control and artificial neural networks for spacecraft relative maneuvering using atmospheric differential drag

NASA Astrophysics Data System (ADS)

Perez Chaparro, David Andres

At low Earth orbits, a differential in the drag acceleration between spacecraft can be used to control their relative motion. This drag differential allows for a propellant-free alternative to thrusters for performing relative maneuvers in these orbits. The interest in autonomous propellant-less maneuvering comes from the desire to reduce the costs of spacecraft formations. Formation maneuvering opens up a wide variety of new applications for spacecraft missions, such as on-orbit maintenance and refueling. In this work atmospheric differential drag based nonlinear controllers are presented that can be used for virtually any planar relative maneuver of two spacecraft, provided that there is enough atmospheric density and that the spacecraft can change their ballistic coefficients by sufficient amounts to generate the necessary differential accelerations. The control techniques are successfully tested using high fidelity Satellite Tool Kit simulations for re-phase, fly-around, and rendezvous maneuvers, proving the feasibility of the proposed approach for a real flight. Furthermore, the atmospheric density varies in time and in space as the spacecraft travel along their orbits. The ability to accurately forecast the density allows for accurate onboard orbit propagation and for creating realistic guidance trajectories for maneuvers that rely on the differential drag. In this work a localized density predictor based on artificial neural networks is also presented. The predictor uses density measurements or estimates along the past orbits and can use a set of proxies for solar and geomagnetic activities to predict the value of the density along the future orbits of the spacecraft. The performance of the localized predictor is studied for different neural network structures, testing periods of high and low solar and geomagnetic activities and different prediction windows. Comparison with previously developed methods show substantial benefits in using neural networks, both in prediction accuracy and in the potential for spacecraft onboard implementation. The controllers and the predictor are designed for onboard implementation, and provide spacecraft with the tools necessary for performing propellant-less formation maneuvers using differential drag.

Prediction of Coronal Mass Ejections From Vector Magnetograms: Quantitative Measures as Predictors

NASA Technical Reports Server (NTRS)

Falconer, D. A.; Moore, R. L.; Gary, G. A.; Rose, M. Franklin (Technical Monitor)

2001-01-01

We derived two quantitative measures of an active region's global nonpotentiality from the region's vector magnetogram, 1) the net current (I(sub N)), and 2) the length of strong-shear, strong-field main neutral line (Lss), and used these two measures in a pilot study of the CME productivity of 4 active regions. We compared the global nonpotentiality measures to the active regions' CME productivity determined from GOES and Yohkoh/SXT observations. We found that two of the active regions were highly globally nonpotential and were CME productive, while the other two active regions had little global nonpotentiality and produced no CMEs. At the Fall 2000 AGU, we reported on an expanded study (12 active regions and 17 magnetograms) in which we evaluated four quantitative global measures of an active region's magnetic field and compared these measures with the CME productivity. The four global measures (all derived from MSFC vector magnetograms) included our two previous measures (I(sub N) and L(sub ss)) as well as two new ones, the total magnetic flux (PHI) (a measure of an active region's size), and the normalized twist (alpha (bar)= muIN/PHI). We found that the three quantitative measures of global nonpotentiality (I(sub N), L(sub ss), alpha (bar)) were all well correlated (greater than 99% confidence level) with an active region's CME productivity within plus or minus 2 days of the day of the magnetogram. We will now report on our findings of how good our quantitative measures are as predictors of active-region CME productivity, using only CMEs that occurred after the magnetogram. We report the preliminary skill test of these quantitative measures as predictors. We compare the CME prediction success of our quantitative measures to the CME prediction success based on an active region's past CME productivity. We examine the cases of the handful of false positive and false negatives to look for improvements to our predictors. This work is funded by NSF through the Space Weather Program and by NASA through the Solar Physics Supporting Research and Technology Program.

What are the most crucial soil factors for predicting the distribution of alpine plant species?

NASA Astrophysics Data System (ADS)

Buri, A.; Pinto-Figueroa, E.; Yashiro, E.; Guisan, A.

2017-12-01

Nowadays the use of species distribution models (SDM) is common to predict in space and time the distribution of organisms living in the critical zone. The realized environmental niche concept behind the development of SDM imply that many environmental factors must be accounted for simultaneously to predict species distributions. Climatic and topographic factors are often primary included, whereas soil factors are frequently neglected, mainly due to the paucity of soil information available spatially and temporally. Furthermore, among existing studies, most included soil pH only, or few other soil parameters. In this study we aimed at identifying what are the most crucial soil factors for explaining alpine plant distributions and, among those identified, which ones further improve the predictive power of plant SDMs. To test the relative importance of the soil factors, we performed plant SDMs using as predictors 52 measured soil properties of various types such as organic/inorganic compounds, chemical/physical properties, water related variables, mineral composition or grain size distribution. We added them separately to a standard set of topo-climatic predictors (temperature, slope, solar radiation and topographic position). We used ensemble forecasting techniques combining together several predictive algorithms to model the distribution of 116 plant species over 250 sites in the Swiss Alps. We recorded the variable importance for each model and compared the quality of the models including different soil proprieties (one at a time) as predictors to models having only topo-climatic variables as predictors. Results show that 46% of the soil proprieties tested become the second most important variable, after air temperature, to explain spatial distribution of alpine plants species. Moreover, we also assessed that addition of certain soil factors, such as bulk soil water density, could improve over 80% the quality of some plant species models. We confirm that soil pH remains one of the most important soil factor for predicting plant species distributions, closely followed by water, organic and inorganic carbon related properties. Finally, we were able to extract three main categories of important soil properties for plant species distributions: grain size distribution, acidity and water in the soil.

Prediction of Coronal Mass Ejections from Vector Magnetograms: Quantitative Measures as Predictors

NASA Astrophysics Data System (ADS)

Falconer, D. A.; Moore, R. L.; Gary, G. A.

2001-05-01

In a pilot study of 4 active regions (Falconer, D.A. 2001, JGR, in press), we derived two quantitative measures of an active region's global nonpotentiality from the region's vector magnetogram, 1) the net current (IN), and 2) the length of the strong-shear, strong-field main neutral line (LSS), and used these two measures of the CME productivity of the active regions. We compared the global nonpotentiality measures to the active regions' CME productivity determined from GOES and Yohkoh/SXT observations. We found that two of the active regions were highly globally nonpotential and were CME productive, while the other two active regions had little global nonpotentiality and produced no CMEs. At the Fall 2000 AGU (Falconer, Moore, & Gary, 2000, EOS 81, 48 F998), we reported on an expanded study (12 active regions and 17 magnetograms) in which we evaluated four quantitative global measures of an active region's magnetic field and compared these measures with the CME productivity. The four global measures (all derived from MSFC vector magnetograms) included our two previous measures (IN and LSS) as well as two new ones, the total magnetic flux (Φ ) (a measure of an active region's size), and the normalized twist (α =μ IN/Φ ). We found that the three measures of global nonpotentiality (IN, LSS, α ) were all well correlated (>99% confidence level) with an active region's CME productivity within (2 days of the day of the magnetogram. We will now report on our findings of how good our quantitative measures are as predictors of active-region CME productivity, using only CMEs that occurred after the magnetogram. We report the preliminary skill test of these quantitative measures as predictors. We compare the CME prediction success of our quantitative measures to the CME prediction success based on an active region's past CME productivity. We examine the cases of the handful of false positive and false negatives to look for improvements to our predictors. This work is funded by NSF through the Space Weather Program and by NASA through the Solar Physics Supporting Research and Technology Program.

What Bed Size Does a Patient Need? The Relationship Between Body Mass Index and Space Required to Turn in Bed.

PubMed

Wiggermann, Neal; Smith, Kathryn; Kumpar, Dee

A bed that is too small to allow patients to turn from supine to side lying increases the difficulty of mobilizing patients, which can increase risk of musculoskeletal injury to caregivers, increase risk of pressure injuries to patients, and reduce patient comfort. Currently, no guidance is available for what patient sizes are accommodated by the standard 91cm (36 in.)-wide hospital bed, and no studies have evaluated the relationship between anthropometric attributes and space required to turn in bed. The purpose of this research was to determine how much space individuals occupy when turning from supine to side lying as predicted by their anthropometry (i.e., body dimensions) to establish guidance on selecting the appropriate bed size. Forty-seven adult participants (24 female) with body mass index (BMI) from 20 to 76 kg/m participated in a laboratory study. Body dimensions were measured, and the envelope of space required to turn was determined using motion capture. Linear regressions estimated the relationship between anthropometric attributes and space occupied when turning. BMI was strongly correlated (R = .88) with the space required to turn. Based on the linear regressions, individuals with BMI up to 35 kg/m could turn left and right within 91 cm and individuals with BMI up to 45 kg/m could turn one direction within 91 cm. BMI is a good predictor of the space required to turn from supine to lateral. Nurses should consider placing patients that are unable to laterally reposition themselves on a wider bed when BMI is greater than 35 kg/m and should consider placing all patients greater than 45 kg/m on a wider bed regardless of mobility. Hospital administrators can use historical demographic information about the BMI of their patient populations to plan facility-level equipment procurement for equipment that accommodates their patients.

What Bed Size Does a Patient Need? The Relationship Between Body Mass Index and Space Required to Turn in Bed

PubMed Central

Wiggermann, Neal; Smith, Kathryn; Kumpar, Dee

2017-01-01

Background A bed that is too small to allow patients to turn from supine to side lying increases the difficulty of mobilizing patients, which can increase risk of musculoskeletal injury to caregivers, increase risk of pressure injuries to patients, and reduce patient comfort. Currently, no guidance is available for what patient sizes are accommodated by the standard 91cm (36 in.)-wide hospital bed, and no studies have evaluated the relationship between anthropometric attributes and space required to turn in bed. Objective The purpose of this research was to determine how much space individuals occupy when turning from supine to side lying as predicted by their anthropometry (i.e., body dimensions) to establish guidance on selecting the appropriate bed size. Methods Forty-seven adult participants (24 female) with body mass index (BMI) from 20 to 76 kg/m2 participated in a laboratory study. Body dimensions were measured, and the envelope of space required to turn was determined using motion capture. Linear regressions estimated the relationship between anthropometric attributes and space occupied when turning. Results BMI was strongly correlated (R2 = .88) with the space required to turn. Based on the linear regressions, individuals with BMI up to 35 kg/m2 could turn left and right within 91 cm and individuals with BMI up to 45 kg/m2 could turn one direction within 91 cm. Discussion BMI is a good predictor of the space required to turn from supine to lateral. Nurses should consider placing patients that are unable to laterally reposition themselves on a wider bed when BMI is greater than 35 kg/m2 and should consider placing all patients greater than 45 kg/m2 on a wider bed regardless of mobility. Hospital administrators can use historical demographic information about the BMI of their patient populations to plan facility-level equipment procurement for equipment that accommodates their patients. PMID:28968285

Community measures of low-fat milk consumption: comparing store shelves with households.

PubMed

Fisher, B D; Strogatz, D S

1999-02-01

This study examined the relationship between the proportion of milk in food stores that is low-fat and consumption of low-fat milk in the community. Data were gathered from 503 stores across 53 New York State zip codes. In 19 zip codes, a telephone survey measured household low-fat milk use. Census data were obtained to examine sociodemographic predictors of the percentage of low-fat milk in stores. The proportion of low-fat milk in stores was directly related to low-fat milk consumption in households and to the median income and urban level of the zip code. These results support using food store shelf-space observations to estimate low-fat milk consumption.

Annoyance to Noise Produced by a Distributed Electric Propulsion High-Lift System

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.; Palumbo, Daniel L.; Rathsam, Jonathan; Christian, Andrew; Rafaelof, Menachem

2017-01-01

A psychoacoustic test was performed using simulated sounds from a distributed electric propulsion aircraft concept to help understand factors associated with human annoyance. A design space spanning the number of high-lift leading edge propellers and their relative operating speeds, inclusive of time varying effects associated with motor controller error and atmospheric turbulence, was considered. It was found that the mean annoyance response varies in a statistically significant manner with the number of propellers and with the inclusion of time varying effects, but does not differ significantly with the relative RPM between propellers. An annoyance model was developed, inclusive of confidence intervals, using the noise metrics of loudness, roughness, and tonality as predictors.

Developing empirical lightning cessation forecast guidance for the Kennedy Space Center

NASA Astrophysics Data System (ADS)

Stano, Geoffrey T.

The Kennedy Space Center in east Central Florida is one of the few locations in the country that issues lightning advisories. These forecasts are vital to the daily operations of the Space Center and take on even greater significance during launch operations. The U.S. Air Force's 45th Weather Squadron (45WS), who provides forecasts for the Space Center, has a good record of forecasting the initiation of lightning near their locations of special concern. However, the remaining problem is knowing when to cancel a lightning advisory. Without specific scientific guidelines detailing cessation activity, the Weather Squadron must keep advisories in place longer than necessary to ensure the safety of personnel and equipment. This unnecessary advisory time costs the Space Center millions of dollars in lost manpower each year. This research presents storm and environmental characteristics associated with lightning cessation that then are utilized to create lightning cessation guidelines for isolated thunderstorms for use by the 45WS during the warm season months of May through September. The research uses data from the Lightning Detection and Ranging (LDAR) network at the Kennedy Space Center, which can observe intra-cloud and portions of cloud-to-ground lightning strikes. Supporting data from the Cloud-to-Ground Lightning Surveillance System (CGLSS), radar observations from the Melbourne WSR-88D, and Cape Canaveral morning radiosonde launches also are included. Characteristics of 116 thunderstorms comprising our dataset are presented. Most of these characteristics are based on LDAR-derived spark and flash data and have not been described previously. In particular, the first lightning activity is quantified as either cloud-to-ground (CG) or intra-cloud (IC). Only 10% of the storms in this research are found to initiate with a CG strike. Conversely, only 16% of the storms end with a CG strike. Another characteristic is the average horizontal extent of all the flashes comprising a storm. Our average is 12-14 km, while the greatest flash extends 26 km. Comparisons between the starting altitude of the median and last flashes of a storm are analyzed, with only 37% of the storms having a higher last flash initiating altitude. Additional observations are made of the total lightning flash rate, percentage of CG to IC lightning, trends of individual flash initiation altitudes versus the average initiation altitude, the average inter-flash time distribution, and time series of inter-flash times. Five schemes to forecast lightning cessation are developed and evaluated. 100 of the 116 storms were randomly selected as the dependent sample, while the remaining 16 storms were used for verification. The schemes included a correlation and regression tree analysis, multiple linear regression, trends of storm duration, trend of the altitude of the greatest reflectivity to the time of the final flash, and a percentile scheme. Surprisingly, the percentile method was found to be the most effective technique and the simplest. The inclusion of real time storm parameters is found to have little effect on the results, suggesting that different forecast predictors, such as microphysical data from polarimetric radar, will be necessary to produce improved skill. When the percentile method used a confidence level of 99.5%, it successfully maintained lightning advisories for all 16 independent storms on which the schemes were tested. Since the computed wait time was 25 min, compared to the 45WS' most conservative and accurate wait time of 30 min, the percentile method saves 5 min for each advisory. This 5 min of savings safely shortens the Weather Squadron's advisories and saves money. Additionally, these results are the first to evaluate the 30/30 rule that is used commonly. The success of the percentile method is surprising since it out performs more complex procedures involving correlation and regression tree analysis and regression schemes. These more sophisticated statistical analyses were expected to perform better since they include more predictors in the forecasts. However, with the predictors available to us, this was not the case. While not the expected result, the percentile method succeeds in creating a safe and expedited forecast.

Technique for ranking potential predictor layers for use in remote sensing analysis

Treesearch

Andrew Lister; Mike Hoppus; Rachel Riemann

2004-01-01

Spatial modeling using GIS-based predictor layers often requires that extraneous predictors be culled before conducting analysis. In some cases, using extraneous predictor layers might improve model accuracy but at the expense of increasing complexity and interpretability. In other cases, using extraneous layers can dilute the relationship between predictors and target...

Floristic units and their predictors unveiled in part of the Atlantic Forest hotspot: implications for conservation planning.

PubMed

Saiter, Felipe Z; Eisenlohr, Pedro V; França, Glauco S; Stehmann, João R; Thomas, William W; De Oliveira-Filho, Ary T

2015-01-01

We submitted tree species occurrence and geoclimatic data from 59 sites in a river basin in the Atlantic Forest of southeastern Brazil to ordination, ANOVA, and cluster analyses with the goals of investigating the causes of phytogeographic patterns and determining whether the six recognized subregions represent distinct floristic units. We found that both climate and space were significantly (p ≤ 0.05) important in the explanation of phytogeographic patterns. Floristic variations follow thermal gradients linked to elevation in both coastal and inland subregions. A gradient of precipitation seasonality was found to be related to floristic variation up to 100 km inland from the ocean. The temperature of the warmest quarter and the precipitation during the coldest quarter were the main predictors. The subregions Sandy Coastal Plain, Coastal Lowland, Coastal Highland, and Central Depression were recognized as distinct floristic units. Significant differences were not found between the Inland Highland and the Espinhaço Range, indicating that these subregions should compose a single floristic unit encompassing all interior highlands. Because of their ecological peculiarities, the ferric outcrops within the Espinhaço Range may constitute a special unit. The floristic units proposed here will provide important information for wiser conservation planning in the Atlantic Forest hotspot.

Challenges in Achieving Trajectory-Based Operations

NASA Technical Reports Server (NTRS)

Cate, Karen Tung

2012-01-01

In the past few years much of the global ATM research community has proposed advanced systems based on Trajectory-Based Operations (TBO). The concept of TBO uses four-dimensional aircraft trajectories as the base information for managing safety and capacity. Both the US and European advanced ATM programs call for the sharing of trajectory data across different decision support tools for successful operations. However, the actual integration of TBO systems presents many challenges. Trajectory predictors are built to meet the specific needs of a particular system and are not always compatible with others. Two case studies are presented which examine the challenges of introducing a new concept into two legacy systems in regards to their trajectory prediction software. The first case describes the issues with integrating a new decision support tool with a legacy operational system which overlap in domain space. These tools perform similar functions but are driven by different requirements. The difference in the resulting trajectories can lead to conflicting advisories. The second case looks at integrating this same new tool with a legacy system originally developed as an integrated system, but diverged many years ago. Both cases illustrate how the lack of common architecture concepts for the trajectory predictors added cost and complexity to the integration efforts.

High resolution spatio-temporal mapping of NO2 pollution for estimating personal exposures of the Dutch population

NASA Astrophysics Data System (ADS)

Soenario, Ivan; Helbich, Marco; Schmitz, Oliver; Strak, Maciek; Hoek, Gerard; Karssenberg, Derek

2017-04-01

Air pollution has been associated with adverse health effects (e.g., cardiovascular and respiration diseases) in the urban environments. Therefore, the assessment of people's exposure to air pollution is central in epidemiological studies. The estimation of exposures on an individual level can be done by combining location information across space and over time with spatio-temporal data on air pollution concentrations. When detailed information on peoples' space-time paths (e.g. commuting patterns calculated by means of spatial routing algorithms or tracked through GPS) and peoples' major activity locations (e.g. home location, work location) are available, it is possible to calculate more precise personal exposure levels depending on peoples' individual space-time mobility patterns. This requires air pollution values not only at a high level of spatial accuracy and high temporal granularity but such data also needs to be available on a nation-wide scale. As current data is seriously limited in this respect, we introduce a novel data set of NO2 levels across the Netherlands. The provided NO2 concentrations are accessible on hourly timestamps on a 5 meter grid cell resolution for weekdays and weekends, and each month of the year. We modeled a single Land Use Regression model using a five year average of NO2 data from the Dutch NO2 measurement network consisting of N=46 sampling locations distributed over the country. Predictor variables for this model were selected in a data-driven manner using an Elastic Net and Best Subset Selection procedure from 70 candidate predictors including traffic, industry, infrastructure and population-based variables. Subsequently, to model NO2 for each time scale (hour, week, month), the LUR coefficients were fitted using the NO2 data, aggregated per time scale. Model validation was grounded on independent data collected in an ad hoc measurement campaign. Our results show a considerable difference in urban concentrations between weekdays and weekend-days. We observe a diurnal variation in concentrations particularly during weekdays related to traffic intensity and considerable differences in concentrations between seasons. Considerable spatial variation occurs both within cities and urban areas where concentrations on roads are high and decrease rapidly with distance to roads. Both on-road and far-from-road concentrations are consistently higher in urban areas than in rural areas.

Rationalising predictors of child sexual exploitation and sex-trading.

PubMed

Klatt, Thimna; Cavner, Della; Egan, Vincent

2014-02-01

Although there is evidence for specific risk factors leading to child sexual exploitation and prostitution, these influences overlap and have rarely been examined concurrently. The present study examined case files for 175 young persons who attended a voluntary organization in Leicester, United Kingdom, which supports people who are sexually exploited or at risk of sexual exploitation. Based on the case files, the presence or absence of known risk factors for becoming a sex worker was coded. Data were analyzed using t-test, logistic regression, and smallest space analysis. Users of the voluntary organization's services who had been sexually exploited exhibited a significantly greater number of risk factors than service users who had not been victims of sexual exploitation. The logistic regression produced a significant model fit. However, of the 14 potential predictors--many of which were associated with each other--only four variables significantly predicted actual sexual exploitation: running away, poverty, drug and/or alcohol use, and having friends or family members in prostitution. Surprisingly, running away was found to significantly decrease the odds of becoming involved in sexual exploitation. Smallest space analysis of the data revealed 5 clusters of risk factors. Two of the clusters, which reflected a desperation and need construct and immature or out-of-control lifestyles, were significantly associated with sexual exploitation. Our research suggests that some risk factors (e.g. physical and emotional abuse, early delinquency, and homelessness) for becoming involved in sexual exploitation are common but are part of the problematic milieu of the individuals affected and not directly associated with sex trading itself. Our results also indicate that it is important to engage with the families and associates of young persons at risk of becoming (or remaining) a sex worker if one wants to reduce the numbers of persons who engage in this activity. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effect of a governmentally-led physical activity program on motor skills in young children attending child care centers: a cluster randomized controlled trial.

PubMed

Bonvin, Antoine; Barral, Jérôme; Kakebeeke, Tanja H; Kriemler, Susi; Longchamp, Anouk; Schindler, Christian; Marques-Vidal, Pedro; Puder, Jardena J

2013-07-08

To assess the effect of a governmentally-led center based child care physical activity program (Youp'là Bouge) on child motor skills. We conducted a single blinded cluster randomized controlled trial in 58 Swiss child care centers. Centers were randomly selected and 1:1 assigned to a control or intervention group. The intervention lasted from September 2009 to June 2010 and included training of the educators, adaptation of the child care built environment, parental involvement and daily physical activity. Motor skill was the primary outcome and body mass index (BMI), physical activity and quality of life secondary outcomes. The intervention implementation was also assessed. At baseline, 648 children present on the motor test day were included (age 3.3 ± 0.6, BMI 16.3 ± 1.3 kg/m2, 13.2% overweight, 49% girls) and 313 received the intervention. Relative to children in the control group (n = 201), children in the intervention group (n = 187) showed no significant increase in motor skills (delta of mean change (95% confidence interval: -0.2 (-0.8 to 0.3), p = 0.43) or in any of the secondary outcomes. Not all child care centers implemented all the intervention components. Within the intervention group, several predictors were positively associated with trial outcomes: (1) free-access to a movement space and parental information session for motor skills (2) highly motivated and trained educators for BMI (3) free-access to a movement space and purchase of mobile equipment for physical activity (all p < 0.05). This "real-life" physical activity program in child care centers confirms the complexity of implementing an intervention outside a study setting and identified potentially relevant predictors that could improve future programs. Clinical trials.gov NCT00967460.

Anterior delayed gadolinium-enhanced MRI of cartilage values predict joint failure after periacetabular osteotomy.

PubMed

Kim, Sang Do; Jessel, Rebecca; Zurakowski, David; Millis, Michael B; Kim, Young-Jo

2012-12-01

Several available compositional MRIs seem to detect early osteoarthritis before radiographic appearance. Delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) has been most frequently used in clinical studies and reportedly predicts premature joint failure in patients undergoing Bernese periacetabular osteotomies (PAOs). We asked, given regional variations in biochemical composition in dysplastic hips, whether the dGEMRIC index of the anterior joint would better predict premature joint failure after PAOs than the coronal dGEMRIC index as previously reported. We retrospectively reviewed 43 hips in 41 patients who underwent Bernese PAO for hip dysplasia. Thirty-seven hips had preserved joints after PAOs and six were deemed premature failures based on pain, joint space narrowing, or subsequent THA. We used dGEMRIC to determine regional variations in biochemical composition. Preoperative demographic and clinical outcome score, radiographic measures of osteoarthritis and severity of dysplasia, and dGEMRIC indexes from different hip regions were analyzed in a multivariable regression analysis to determine the best predictor of premature joint failure. Minimum followup was 24 months (mean, 32 months; range, 24-46 months). The two cohorts were similar in age and sex distribution. Severity of dysplasia was similar as measured by lateral center-edge, anterior center-edge, and Tönnis angles. Preoperative pain, joint space width, Tönnis grade, and coronal and sagittal dGEMRIC indexes differed between groups. The dGEMRIC index in the anterior weightbearing region of the hip was lower in the prematurely failed group and was the best predictor. Success of PAO depends on the amount of preoperative osteoarthritis. These degenerative changes are seen most commonly in the anterior joint. The dGEMRIC index of the anterior joint may better predict premature joint failure than radiographic measures of hip osteoarthritis and coronal dGEMRIC index. Level II, prognostic study. See Instructions for Authors for a complete description of levels of evidence.

Evaluating methods for estimating space-time paths of individuals in calculating long-term personal exposure to air pollution

NASA Astrophysics Data System (ADS)

Schmitz, Oliver; Soenario, Ivan; Vaartjes, Ilonca; Strak, Maciek; Hoek, Gerard; Brunekreef, Bert; Dijst, Martin; Karssenberg, Derek

2016-04-01

Air pollution is one of the major concerns for human health. Associations between air pollution and health are often calculated using long-term (i.e. years to decades) information on personal exposure for each individual in a cohort. Personal exposure is the air pollution aggregated along the space-time path visited by an individual. As air pollution may vary considerably in space and time, for instance due to motorised traffic, the estimation of the spatio-temporal location of a persons' space-time path is important to identify the personal exposure. However, long term exposure is mostly calculated using the air pollution concentration at the x, y location of someone's home which does not consider that individuals are mobile (commuting, recreation, relocation). This assumption is often made as it is a major challenge to estimate space-time paths for all individuals in large cohorts, mostly because limited information on mobility of individuals is available. We address this issue by evaluating multiple approaches for the calculation of space-time paths, thereby estimating the personal exposure along these space-time paths with hyper resolution air pollution maps at national scale. This allows us to evaluate the effect of the space-time path and resulting personal exposure. Air pollution (e.g. NO2, PM10) was mapped for the entire Netherlands at a resolution of 5×5 m2 using the land use regression models developed in the European Study of Cohorts for Air Pollution Effects (ESCAPE, http://escapeproject.eu/) and the open source software PCRaster (http://www.pcraster.eu). The models use predictor variables like population density, land use, and traffic related data sets, and are able to model spatial variation and within-city variability of annual average concentration values. We approximated space-time paths for all individuals in a cohort using various aggregations, including those representing space-time paths as the outline of a persons' home or associated parcel of land, the 4 digit postal code area or neighbourhood of a persons' home, circular areas around the home, and spatial probability distributions of space-time paths during commuting. Personal exposure was estimated by averaging concentrations over these space-time paths, for each individual in a cohort. Preliminary results show considerable differences of a persons' exposure using these various approaches of space-time path aggregation, presumably because air pollution shows large variation over short distances.

Predictors of Psychiatric Disorders in Combat Veterans

DTIC Science & Technology

2013-05-07

Naval Health Research Center Predictors of Psychiatric Disorders in Combat Veterans Stephanie Booth-Kewley Emily A. Schmied Robin M...ARTICLE Open Access Predictors of psychiatric disorders in combat veterans Stephanie Booth-Kewley1*, Emily A Schmied1, Robyn M Highfill-McRoy1, Gerald E...examined predictors of actual mental health diagnoses. The objective of this longitudinal investigation was to identify predictors of psychiatric disorders

Psychometric and demographic predictors of the perceived risk of terrorist threats and the willingness to pay for terrorism risk management programs.

PubMed

Mumpower, Jeryl L; Shi, Liu; Stoutenborough, James W; Vedlitz, Arnold

2013-10-01

A 2009 national telephone survey of 924 U.S. adults assessed perceptions of terrorism and homeland security issues. Respondents rated severity of effects, level of understanding, number affected, and likelihood of four terrorist threats: poisoned water supply; explosion of a small nuclear device in a major U.S. city; an airplane attack similar to 9/11; and explosion of a bomb in a building, train, subway, or highway. Respondents rated perceived risk and willingness to pay (WTP) for dealing with each threat. Demographic, attitudinal, and party affiliation data were collected. Respondents rated bomb as highest in perceived risk but gave the highest WTP ratings to nuclear device. For both perceived risk and WTP, psychometric variables were far stronger predictors than were demographic ones. OLS regression analyses using both types of variables to predict perceived risk found only two significant demographic predictors for any threat--Democrat (a negative predictor for bomb) and white male (a significant positive predictor for airline attack). In contrast, among psychometric variables, severity, number affected, and likelihood were predictors of all four threats and level of understanding was a predictor for one. For WTP, education was a negative predictor for three threats; no other demographic variables were significant predictors for any threat. Among psychometric variables, perceived risk and number affected were positive predictors of WTP for all four threats; severity and likelihood were predictors for three; level of understanding was a significant predictor for two. © 2013 Society for Risk Analysis.

Predictors of Parent-Teacher Agreement in Youth with Autism Spectrum Disorder and Their Typically Developing Siblings.

PubMed

Stratis, Elizabeth A; Lecavalier, Luc

2017-08-01

This study evaluated the magnitude of informant agreement and predictors of agreement on behavior and emotional problems and autism symptoms in 403 children with autism and their typically developing siblings. Parent-teacher agreement was investigated on the Child Behavior Checklist (CBCL) and Social Responsiveness Scale (SRS). Agreement between parents and teachers fell in the low to moderate range. Multiple demographic and clinical variables were considered as predictors, and only some measures of parent broad autism traits were associated with informant agreement. Parent report on the SRS was a positive predictor of agreement, while teacher report was a negative predictor. Parent report on the CBCL emerged as a positive predictor of agreement, while teacher report emerged as a negative predictor.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Predictors of Burnout Among Nurses in Taiwan.

PubMed

Lee, Huan-Fang; Yen, Miaofen; Fetzer, Susan; Chien, Tsair Wei

2015-08-01

Nurse burnout is a crucial issue for health care professionals and impacts nurse turnover and nursing shortages. Individual and situational factors are related to nurse burnout with predictors of burnout differing among cultures and health care systems. The predictors of nurse burnout in Asia, particularly Taiwan, are unknown. The purpose of this study was to investigate the predictors of burnout among a national sample of nurses in Taiwan. A secondary data analysis of a nationwide database investigated the predictors of burnout among 1,846 nurses in Taiwan. Hierarchical regression analysis determined the relationship between predictors and burnout. Predictors of Taiwanese nurse burnout were age, physical/psychological symptoms, job satisfaction, work engagement, and work environment. The most significant predictors were physical/psychological symptoms and work engagement. The variables explained 35, 39, and 18 % of the emotional exhaustion, personal accomplishment, and depersonalization variance for 54 % of the total variance of burnout. Individual characteristics and nurse self-awareness, especially work, engagement can impact Taiwanese nurses' burnout. Nurse burnout predictors provide administrators with information to develop strategies including education programs and support services to reduce nurse burnout.

SHORT-TERM SOLAR FLARE PREDICTION USING MULTIRESOLUTION PREDICTORS

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

Yu Daren; Huang Xin; Hu Qinghua

2010-01-20

Multiresolution predictors of solar flares are constructed by a wavelet transform and sequential feature extraction method. Three predictors-the maximum horizontal gradient, the length of neutral line, and the number of singular points-are extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. A maximal overlap discrete wavelet transform is used to decompose the sequence of predictors into four frequency bands. In each band, four sequential features-the maximum, the mean, the standard deviation, and the root mean square-are extracted. The multiresolution predictors in the low-frequency band reflect trends in the evolution of newly emerging fluxes. The multiresolution predictors in the high-frequencymore » band reflect the changing rates in emerging flux regions. The variation of emerging fluxes is decoupled by wavelet transform in different frequency bands. The information amount of these multiresolution predictors is evaluated by the information gain ratio. It is found that the multiresolution predictors in the lowest and highest frequency bands contain the most information. Based on these predictors, a C4.5 decision tree algorithm is used to build the short-term solar flare prediction model. It is found that the performance of the short-term solar flare prediction model based on the multiresolution predictors is greatly improved.« less

Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study).

PubMed

Perchoux, Camille; Kestens, Yan; Thomas, Frédérique; Van Hulst, Andraea; Thierry, Benoit; Chaix, Basile

2014-10-01

Prior epidemiological studies have mainly focused on local residential neighborhoods to assess environmental exposures. However, individual spatial behavior may modify residential neighborhood influences, with weaker health effects expected for mobile populations. By examining individual patterns of daily mobility and associated socio-demographic profiles and transportation modes, this article seeks to develop innovative methods to account for daily mobility in health studies. We used data from the RECORD Cohort Study collected in 2011-2012 in the Paris metropolitan area, France. A sample of 2062 individuals was investigated. Participants' perceived residential neighborhood boundaries and regular activity locations were geocoded using the VERITAS application. Twenty-four indicators were created to qualify individual space-time patterns, using spatial analysis methods and a geographic information system. Three domains of indicators were considered: lifestyle indicators, indicators related to the geometry of the activity space, and indicators related to the importance of the residential neighborhood in the overall activity space. Principal component analysis was used to identify main dimensions of spatial behavior. Multilevel linear regression was used to determine which individual characteristics were associated with each spatial behavior dimension. The factor analysis generated five dimensions of spatial behavior: importance of the residential neighborhood in the activity space, volume of activities, and size, eccentricity, and specialization of the activity space. Age, socioeconomic status, and location of the household in the region were the main predictors of daily mobility patterns. Activity spaces of small sizes centered on the residential neighborhood and implying a large volume of activities were associated with walking and/or biking as a transportation mode. Examination of patterns of spatial behavior by individual socio-demographic characteristics and in relation to transportation modes is useful to identify populations with specific mobility/accessibility needs and has implications for investigating transportation-related physical activity and assessing environmental exposures and their effects on health. Copyright © 2014 Elsevier Ltd. All rights reserved.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Clinical predictors of challenging atrioventricular node ablation procedure for rate control in patients with atrial fibrillation.

PubMed

Polin, Baptiste; Behar, Nathalie; Galand, Vincent; Auffret, Vincent; Behaghel, Albin; Pavin, Dominique; Daubert, Jean-Claude; Mabo, Philippe; Leclercq, Christophe; Martins, Raphael P

2017-10-15

Atrioventricular node (AVN) ablation is usually a simple procedure but may sometimes be challenging. We aimed at identifying pre-procedural clinical predictors of challenging AVN ablation. Patients referred for AVN ablation from 2009 to 2015 were retrospectively included. Baseline clinical data, procedural variables and outcomes of AVN ablation were collected. A "challenging procedure" was defined 1) total radiofrequency delivery to get persistent AVN block≥400s, 2) need for left-sided arterial approach or 3) failure to obtain AVN ablation. 200 patients were included (71±10years). A total of 37 (18.5%) patients had "challenging" procedures (including 9 failures, 4.5%), while 163 (81.5%) had "non-challenging" ablations. In multivariable analysis, male sex (Odds ratio (OR)=4.66, 95% confidence interval (CI): 1.74-12.46), body mass index (BMI, OR=1.08 per 1kg/m 2 , 95%CI 1.01-1.16), operator experience (OR=0.40, 95%CI 0.17-0.94), and moderate-to-severe tricuspid regurgitation (TR, OR=3.65, 95%CI 1.63-8.15) were significant predictors of "challenging" ablations. The proportion as a function of number of predictors was analyzed (from 0 to 4, including male sex, operator inexperience, a BMI>23.5kg/m 2 and moderate-to-severe TR). There was a gradual increase in the risk of "challenging" procedure with the number of predictors by patient (No predictor: 0%; 1 predictor: 6.3%; 2 predictors: 16.5%; 3 predictors: 32.5%; 4 predictors: 77.8%). Operator experience, male sex, higher BMI and the degree of TR were independent predictors of "challenging" AVN ablation procedure. The risk increases with the number of predictors by patient. Copyright © 2017. Published by Elsevier B.V.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Measures for Predictors of Innovation Adoption

PubMed Central

Chor, Ka Ho Brian; Wisdom, Jennifer P.; Olin, Su-Chin Serene; Hoagwood, Kimberly E.; Horwitz, Sarah M.

2014-01-01

Building on a narrative synthesis of adoption theories by Wisdom et al. (2013), this review identifies 118 measures associated with the 27 adoption predictors in the synthesis. The distribution of measures is uneven across the predictors and predictors vary in modifiability. Multiple dimensions and definitions of predictors further complicate measurement efforts. For state policymakers and researchers, more effective and integrated measurement can advance the adoption of complex innovations such as evidence-based practices. PMID:24740175

CryoTran user's manual, version 1.0

NASA Technical Reports Server (NTRS)

Cowgill, Glenn R.; Chato, David J.; Saad, Ehab

1989-01-01

The development of cryogenic fluid management systems for space operation is a major portion of the efforts of the Cryogenic Fluids Technology Office (CFTO) at the NASA Lewis Research Center. Analytical models are a necessary part of experimental programs which are used to verify the results of experiments and are also used as a predictor for parametric studies. The CryoTran computer program is a bridge to obtain analytical results. The object of CryoTran is to coordinate these separate analyses into an integrated framework with a user-friendly interface and a common cryogenic property database. CryoTran is an integrated software system designed to help solve a diverse set of problems involving cryogenic fluid storage and transfer in both ground and low-g environments.

Community measures of low-fat milk consumption: comparing store shelves with households.

PubMed Central

Fisher, B D; Strogatz, D S

1999-01-01

OBJECTIVES: This study examined the relationship between the proportion of milk in food stores that is low-fat and consumption of low-fat milk in the community. METHODS: Data were gathered from 503 stores across 53 New York State zip codes. In 19 zip codes, a telephone survey measured household low-fat milk use. Census data were obtained to examine sociodemographic predictors of the percentage of low-fat milk in stores. RESULTS: The proportion of low-fat milk in stores was directly related to low-fat milk consumption in households and to the median income and urban level of the zip code. CONCLUSIONS: These results support using food store shelf-space observations to estimate low-fat milk consumption. PMID:9949755

Predictor-based multivariable closed-loop system identification of the EXTRAP T2R reversed field pinch external plasma response

NASA Astrophysics Data System (ADS)

Olofsson, K. Erik J.; Brunsell, Per R.; Rojas, Cristian R.; Drake, James R.; Hjalmarsson, Håkan

2011-08-01

The usage of computationally feasible overparametrized and nonregularized system identification signal processing methods is assessed for automated determination of the full reversed-field pinch external plasma response spectrum for the experiment EXTRAP T2R. No assumptions on the geometry of eigenmodes are imposed. The attempted approach consists of high-order autoregressive exogenous estimation followed by Markov block coefficient construction and Hankel matrix singular value decomposition. It is seen that the obtained 'black-box' state-space models indeed can be compared with the commonplace ideal magnetohydrodynamics (MHD) resistive thin-shell model in cylindrical geometry. It is possible to directly map the most unstable autodetected empirical system pole to the corresponding theoretical resistive shell MHD eigenmode.

Cox Regression Models with Functional Covariates for Survival Data.

PubMed

Gellar, Jonathan E; Colantuoni, Elizabeth; Needham, Dale M; Crainiceanu, Ciprian M

2015-06-01

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.

On neural networks in identification and control of dynamic systems

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Hyland, David C.

1993-01-01

This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

Evaluating segmentation error without ground truth.

PubMed

Kohlberger, Timo; Singh, Vivek; Alvino, Chris; Bahlmann, Claus; Grady, Leo

2012-01-01

The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems. In this paper we present a generic learning approach based on a novel space of segmentation features, which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation. We show the regressor to be much stronger a predictor of these error metrics than the responses of probabilistic boosting classifiers trained on the segmentation boundary. The presented approach not only allows us to build reliable confidence measures and fidelity checks, but also to rank several segmentation hypotheses against each other during online usage of the segmentation algorithm in clinical practice.

Maxillary hypoplasia in the cleft patient: contribution of orthodontic dental space closure to orthognathic surgery.

PubMed

Lee, Justine C; Slack, Ginger C; Walker, Ryann; Graves, Lindsay; Yen, Sandra; Woo, Jessica; Ambaram, Rishal; Martz, Martin G; Kawamoto, Henry K; Bradley, James P

2014-02-01

Cleft lip and palate surgery in the developing child is known to be associated with maxillary hypoplasia. However, the effects of nonsurgical manipulations on maxillary growth have not been well investigated. The authors present the contribution of orthodontic dental space closure with canine substitution to maxillary hypoplasia and the need for orthognathic surgery. Cleft lip/palate and cleft palate patients older than 15 years of age were reviewed for dental anomalies, orthodontic canine substitution, and Le Fort I advancement. Skeletal relationships of the maxilla to the skull base (SNA), mandible (ANB), and facial height were determined on lateral cephalograms. Logistic regression analyses were performed to estimate odds ratios. Ninety-five patients were reviewed (mean age, 18.1 years). In 65 patients with congenitally missing teeth, 55 percent with patent dental spaces required Le Fort I advancement. In contrast, 89 percent who underwent canine substitution required Le Fort I advancement (p = 0.004). Canine substitution is associated with a statistically significant increase in maxillary retrognathia when compared with dental space preservation on lateral cephalograms (mean SNA, 75.2 and 79.0, respectively; p = 0.006). Adjusting for missing dentition, logistic regression analyses demonstrated that canine substitution is an independent predictor for orthognathic surgery (OR, 6.47) and maxillary retrusion defined by SNA < 78 (OR, 8.100). The coordination of orthodontia and surgery is essential to cleft care. The authors report a strong association between orthodontic cleft closure using canine substitution with maxillary hypoplasia and subsequent Le Fort I advancement, and suggest systematic criteria for management of cleft-related dental agenesis. Therapeutic, III.

Predicting risk in space: Genetic markers for differential vulnerability to sleep restriction

NASA Astrophysics Data System (ADS)

Goel, Namni; Dinges, David F.

2012-08-01

Several laboratories have found large, highly reliable individual differences in the magnitude of cognitive performance, fatigue and sleepiness, and sleep homeostatic vulnerability to acute total sleep deprivation and to chronic sleep restriction in healthy adults. Such individual differences in neurobehavioral performance are also observed in space flight as a result of sleep loss. The reasons for these stable phenotypic differential vulnerabilities are unknown: such differences are not yet accounted for by demographic factors, IQ or sleep need, and moreover, psychometric scales do not predict those individuals cognitively vulnerable to sleep loss. The stable, trait-like (phenotypic) inter-individual differences observed in response to sleep loss—with intraclass correlation coefficients accounting for 58-92% of the variance in neurobehavioral measures—point to an underlying genetic component. To this end, we utilized multi-day highly controlled laboratory studies to investigate the role of various common candidate gene variants—each independently—in relation to cumulative neurobehavioral and sleep homeostatic responses to sleep restriction. These data suggest that common genetic variations (polymorphisms) involved in sleep-wake, circadian, and cognitive regulation may serve as markers for prediction of inter-individual differences in sleep homeostatic and neurobehavioral vulnerability to sleep restriction in healthy adults. Identification of genetic predictors of differential vulnerability to sleep restriction—as determined from candidate gene studies—will help identify astronauts most in need of fatigue countermeasures in space flight and inform medical standards for obtaining adequate sleep in space. This review summarizes individual differences in neurobehavioral vulnerability to sleep deprivation and ongoing genetic efforts to identify markers of such differences.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

USGS Publications Warehouse

Zimmermann, N.E.; Edwards, T.C.; Moisen, Gretchen G.; Frescino, T.S.; Blackard, J.A.

2007-01-01

1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. ?? 2007 The Authors.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

PubMed Central

ZIMMERMANN, N E; EDWARDS, T C; MOISEN, G G; FRESCINO, T S; BLACKARD, J A

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. PMID:18642470

Influence of age, sex, and education on the Visual Object and Space Perception Battery (VOSP) in a healthy normal elderly population.

PubMed

Herrera-Guzmán, I; Peña-Casanova, J; Lara, J P; Gudayol-Ferré, E; Böhm, P

2004-08-01

The assessment of visual perception and cognition forms an important part of any general cognitive evaluation. We have studied the possible influence of age, sex, and education on a normal elderly Spanish population (90 healthy subjects) in performance in visual perception tasks. To evaluate visual perception and cognition, we have used the subjects performance with The Visual Object and Space Perception Battery (VOSP). The test consists of 8 subtests: 4 measure visual object perception (Incomplete Letters, Silhouettes, Object Decision, and Progressive Silhouettes) while the other 4 measure visual space perception (Dot Counting, Position Discrimination, Number Location, and Cube Analysis). The statistical procedures employed were either simple or multiple linear regression analyses (subtests with normal distribution) and Mann-Whitney tests, followed by ANOVA with Scheffe correction (subtests without normal distribution). Age and sex were found to be significant modifying factors in the Silhouettes, Object Decision, Progressive Silhouettes, Position Discrimination, and Cube Analysis subtests. Educational level was found to be a significant predictor of function for the Silhouettes and Object Decision subtests. The results of the sample were adjusted in line with the differences observed. Our study also offers preliminary normative data for the administration of the VOSP to an elderly Spanish population. The results are discussed and compared with similar studies performed in different cultural backgrounds.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Examining educational attainment, prepregnancy smoking rate, and delay discounting as predictors of spontaneous quitting among pregnant smokers.

PubMed

White, Thomas J; Redner, Ryan; Skelly, Joan M; Higgins, Stephen T

2014-10-01

We investigated three potential predictors (educational attainment, prepregnancy smoking rate, and delay discounting [DD]) of spontaneous quitting among pregnant smokers. These predictors were examined alone and in combination with other potential predictors using study-intake assessments from controlled clinical trials examining the efficacy of financial incentives for smoking cessation and relapse prevention. Data from 349 pregnant women (231 continuing smokers and 118 spontaneous quitters) recruited from the greater Burlington, VT, area contributed to this secondary analysis, including psychiatric/sociodemographic characteristics, smoking characteristics, and performance on a computerized DD task. Educational attainment, smoking rate, and DD values were each significant predictors of spontaneous quitting in univariate analyses. A model examining those three predictors together retained educational attainment as a main effect and revealed a significant interaction of DD and smoking rate (i.e., DD was a significant predictor at lower but not higher smoking rates). A final model considering all potential predictors, included education, the interaction of DD and smoking rate, and five additional predictors (i.e., stress ratings, the belief that smoking during pregnancy will "greatly harm my baby," age of smoking initiation, marital status, and prior quit attempts during pregnancy). The study presented here contributes new knowledge on predictors of spontaneous quitting among pregnant smokers with substantive practical implications for reducing smoking during pregnancy. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

The development of a Kalman filter clock predictor

NASA Technical Reports Server (NTRS)

Davis, John A.; Greenhall, Charles A.; Boudjemaa, Redoane

2005-01-01

A Kalman filter based clock predictor is developed, and its performance evaluated using both simulated and real data. The clock predictor is shown to possess a neat to optimal Prediction Error Variance (PEV) when the underlying noise consists of one of the power law noise processes commonly encountered in time and frequency measurements. The predictor's performance is the presence of multiple noise processes is also examined. The relationship between the PEV obtained in the presence of multiple noise processes and those obtained for the individual component noise processes is examined. Comparisons are made with a simple linear clock predictor. The clock predictor is used to predict future values of the time offset between pairs of NPL's active hydrogen masers.

Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

PubMed

Korvigo, Ilia; Afanasyev, Andrey; Romashchenko, Nikolay; Skoblov, Mikhail

2018-01-01

Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that even relatively simple modern neural networks can significantly improve both prediction accuracy and coverage. We provide open-access to our finest model via the web-site: http://score.generesearch.ru/services/badmut/.

Prevalence, Predictors, and Prevention of Motion Sickness in Zero-G Parabolic Flights.

PubMed

Golding, John F; Paillard, Aurore C; Normand, Hervé; Besnard, Stéphane; Denise, Pierre

2017-01-01

Zero-G parabolic flight reproduces the weightlessness of space for short periods. However, motion sickness may affect some fliers. The aim was to assess the extent of this problem and to find possible predictors and modifying factors. Airbus zero-G flights consist of 31 parabolas performed in blocks. Each parabola consisted of 20 s of 0 g sandwiched by 20 s of hypergravity of 1.5-1.8 g. The survey covered N = 246 person-flights (193 men, 53 women), ages (M ± SD) 36.0 ± 11.3 yr. An anonymous questionnaire included motion sickness rating (1 = OK to 6 = vomiting), Motion Sickness Susceptibility Questionnaire (MSSQ), antimotion sickness medication, prior zero-G experience, anxiety level, and other characteristics. Participants had lower MSSQ percentile scores (27.4 ± 28.0) than the population norm of 50. Motion sickness was experienced by 33% and 12% vomited. Less motion sickness was predicted by older age, greater prior zero-G flight experience, medication with scopolamine, lower MSSQ scores, but not gender or anxiety. Sickness ratings in fliers pretreated with scopolamine (1.81 ± 1.58) were lower than for nonmedicated fliers (2.93 ± 2.16), and incidence of vomiting in fliers using scopolamine treatment was reduced by half to a third. Possible confounding factors including age, sex, flight experience, and MSSQ could not account for this. Motion sickness affected one-third of zero-G fliers despite being intrinsically less motion sickness susceptible compared to the general population. Susceptible individuals probably try to avoid such a provocative environment. Risk factors for motion sickness included younger age and higher MSSQ scores. Protective factors included prior zero-G flight experience (habituation) and antimotion sickness medication.Golding JF, Paillard AC, Normand H, Besnard S, Denise P. Prevalence, predictors, and prevention of motion sickness in zero-G parabolic flights. Aerosp Med Hum Perform. 2017; 88(1):3-9.

Sequential colonization and diversification of Galapágos endemic land snail genus Bulimulus (Gastropoda, Stylommatophora).

PubMed

Parent, Christine E; Crespi, Bernard J

2006-11-01

Species richness on island or islandlike systems is a function of colonization, within-island speciation, and extinction. Here we evaluate the relative importance of the first two of these processes as a function of the biogeographical and ecological attributes of islands using the Galápagos endemic land snails of the genus Bulimulus, the most species-rich radiation of these islands. Species in this clade have colonized almost all major islands and are found in five of the six described vegetation zones. We use molecular phylogenetics (based on COI and ITS 1 sequence data) to infer the diversification patterns of extant species of Bulimulus, and multiple regression to investigate the causes of variation among islands in species richness. Maximum-likelihood, Bayesian, and maximum-parsimony analyses yield well-resolved trees with similar topologies. The phylogeny obtained supports the progression rule hypothesis, with species found on older emerged islands connecting at deeper nodes. For all but two island species assemblages we find support for only one or two colonization events, indicating that within-island speciation has an important role in the formation of species on these islands. Even though speciation through colonization is not common, island insularity (distance to nearest major island) is a significant predictor of species richness resulting from interisland colonization alone. However, island insularity has no effect on the overall bulimulid species richness per island. Habitat diversity (measured as plant species diversity), island elevation, and island area, all of which are indirect measures of niche space, are strong predictors of overall bulimulid land snail species richness. Island age is also an important independent predictor of overall species richness, with older islands harboring more species than younger islands. Taken together, our results demonstrate that the diversification of Galápagos bulimulid land snails has been driven by a combination of geographic factors (island age, size, and location), which affect colonization patterns, and ecological factors, such as plant species diversity, that foster within-island speciation.

Physicians’ accuracy and interrator reliability for the diagnosis of unstable meniscal tears in patients having osteoarthritis of the knee

PubMed Central

Dervin, Geoffrey F.; Stiell, Ian G.; Wells, George A.; Rody, Kelly; Grabowski, Jenny

2001-01-01

Objective To determine clinicians’ accuracy and reliability for the clinical diagnosis of unstable meniscus tears in patients with symptomatic osteoarthritis of the knee. Design A prospective cohort study. Setting A single tertiary care centre. Patients One hundred and fifty-two patients with symptomatic osteoarthritis of the knee refractory to conservative medical treatment were selected for prospective evaluation of arthroscopic débridement. Intervention Arthroscopic débridement of the knee, including meniscal tear and chondral flap resection, without abrasion arthroplasty. Outcome measures A standardized assessment protocol was administered to each patient by 2 independent observers. Arthroscopic determination of unstable meniscal tears was recorded by 1 observer who reviewed a video recording and was blinded to preoperative data. Those variables that had the highest interobserver agreement and the strongest association with meniscal tear by univariate methods were entered into logistic regression to model the best prediction of resectable tears. Results There were 92 meniscal tears (77 medial, 15 lateral). Interobserver agreement between clinical fellows and treating surgeons was poor to fair (κ < 0.4) for all clinical variables except radiographic measures, which were good. Fellows and surgeons predicted unstable meniscal tear preoperatively with equivalent accuracy of 60%. Logistic regression modelling revealed that a history of swelling and a ballottable effusion were negative predictors. A positive McMurray test was the only positive predictor of unstable meniscal tear. “Mechanical” symptoms were not reliable predictors in this prospective study. The model was 69% accurate for all patients and 76% for those with advanced medial compartment osteoarthritis defined by a joint space height of 2 mm or less. Conclusions This study underscored the difficulty in using clinical variables to predict unstable medial meniscal tears in patients with pre-existing osteoarthritis of the knee. The lack of interobserver agreement must be overcome to ensure that the findings can be generalized to other physician observers. PMID:11504260

Terrain-based Predictive Modeling of a Functional Riparian Corridor in a Coastal Northern California Watershed

NASA Astrophysics Data System (ADS)

Robinson, T.; Davis, J. D.

2016-12-01

Riparian corridors and their associated geomorphic landforms (e.g., channels, floodplains, and terraces) and vegetation communities (e.g., forests and wetlands) have been significantly degraded in California, prompting an expansion of efforts to delineate riparian corridors and identify priorities for conservation via deed restrictions and easements. Current techniques to delineate riparian corridors for these purposes include fixed-width buffers based on stream centerlines and digitization of woody vegetation from aerial photos. Although efficient, these delineation methods do not accurately capture the extent of ecologically functional riparian corridors and result in riparian habitat being excluded from conservation efforts while non-riparian is included. From a physical perspective, ecologically functional riparian corridors have widths that vary with topography and ample space for dynamic fluvial geomorphic processes that create and maintain river morphology and vegetation and sustain ecological interactions that extend from the stream channel laterally into upland ecosystems and up- and downstream ecosystems in longitudinal directions. New terrain-based spatial analysis techniques and high-resolution digital terrain data show promise in delineating ecologically functional riparian corridors. In this study, we compare the efficacy of three terrain-based predictors of riparian corridors that have emerged in the literature—elevation above channel, flow accumulation, and distance from channel. The results of each terrain predictor are compared with field-based indicators of the riparian corridor of an alluvial reach of Mark West Creek in Sonoma County, California (a mediterranean climate). Indicators include soil type, fluvial geomorphic landforms, and vegetation. A one-meter digital terrain model from LiDAR (Light Detection and Ranging) supplied by a NASA ROSES grant is used as the base terrain data for spatial analysis. We discuss in detail the use of regional curves of hydraulic geometry in the calculation of the elevation above channel predictor because it offers the advantage of efficiency while carrying significant potential for error.

The probability forecast evaluation of hazard and storm wind over the territories of Russia and Europe

NASA Astrophysics Data System (ADS)

Perekhodtseva, E. V.

2012-04-01

The results of the probability forecast methods of summer storm and hazard wind over territories of Russia and Europe are submitted at this paper. These methods use the hydrodynamic-statistical model of these phenomena. The statistical model was developed for the recognition of the situation involving these phenomena. For this perhaps the samples of the values of atmospheric parameters (n=40) for the presence and for the absence of these phenomena of storm and hazard wind were accumulated. The compressing of the predictors space without the information losses was obtained by special algorithm (k=7<19m/s, the values of 65%24m/s, the values of 75%29m/s or the area of the tornado and strong squalls. The evaluation of this probability forecast was provided by criterion of Brayer. The estimation was successful and was equal for the European part of Russia B=0,37. The application of the probability forecast of storm and hazard winds allows to mitigate the economic losses when the errors of the first and second kinds of storm wind categorical forecast are not so small. A lot of examples of the storm wind probability forecast are submitted at this report.

Patient Characteristics and Patient Behavior as Predictors of Outcome in Cognitive Therapy and Exposure Therapy for Hypochondriasis.

PubMed

Richtberg, Samantha; Jakob, Marion; Höfling, Volkmar; Weck, Florian

2017-06-01

Psychotherapy for hypochondriasis has greatly improved over the last decades and cognitive-behavioral treatments are most promising. However, research on predictors of treatment outcome for hypochondriasis is rare. Possible predictors of treatment outcome in cognitive therapy (CT) and exposure therapy (ET) for hypochondriasis were investigated. Characteristics and behaviors of 75 patients were considered as possible predictors: sociodemographic variables (sex, age, and cohabitation); psychopathology (pretreatment hypochondriacal symptoms, comorbid mental disorders, and levels of depression, anxiety, and somatic symptoms); and patient in-session interpersonal behavior. Severity of pretreatment hypochondriacal symptoms, comorbid mental disorders, and patient in-session interpersonal behavior were significant predictors in multiple hierarchical regression analyses. Interactions between the predictors and the treatment (CT or ET) were not found. In-session interpersonal behavior is an important predictor of outcome. Furthermore, there are no specific contraindications to treating hypochondriasis with CT or ET. © 2016 Wiley Periodicals, Inc.

Quantile Regression in the Study of Developmental Sciences

ERIC Educational Resources Information Center

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…

Situational and Intrapersonal Predictors of School and Life Satisfaction of Elementary School Students

ERIC Educational Resources Information Center

Drost, Amy Linden

2012-01-01

This study examined predictors of school and life satisfaction of fifth-grade students. Two situational predictor variables (school climate and school stress) and two intrapersonal predictor variables (locus of control and academic self-concept) were examined. It was hypothesized that positive school climate, low levels of school stress, internal…

Branch classification: A new mechanism for improving branch predictor performance

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

Chang, P.Y.; Hao, E.; Patt, Y.

There is wide agreement that one of the most significant impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Speculative execution is one solution to the branch problem, but speculative work is discarded if a branch is mispredicted. For it to be effective, speculative work is discarded if a branch is mispredicted. For it to be effective, speculative execution requires a very accurate branch predictor; 95% accuracy is not good enough. This paper proposes branch classification, a methodology for building more accurate branch predictors. Branch classification allows anmore » individual branch instruction to be associated with the branch predictor best suited to predict its direction. Using this approach, a hybrid branch predictor can be constructed such that each component branch predictor predicts those branches for which it is best suited. To demonstrate the usefulness of branch classification, an example classification scheme is given and a new hybrid predictor is built based on this scheme which achieves a higher prediction accuracy than any branch predictor previously reported in the literature.« less

Sex-specific predictors of inpatient rehabilitation outcomes after traumatic brain injury

PubMed Central

Chan, Vincy; Mollayeva, Tatyana; Ottenbacher, Kenneth J.; Colantonio, Angela

2016-01-01

Objective To identify sex-specific predictors of inpatient rehabilitation outcomes among patients with a traumatic brain injury (TBI) from a population based perspective. Design Retrospective cohort study Setting Ontario, Canada Participants Patients in inpatient rehabilitation for a TBI within one year of acute care discharge between 2008/09 and 2011/12 (N=1,730, 70% male, 30% female). Interventions None Main Outcome Measures Inpatient rehabilitation length of stay, total Functional Independence Measure (FIM™) score, and motor and cognitive FIM™ ratings at discharge. Results Sex, as a covariate in multivariable linear regression models, was not a significant predictor of rehabilitation outcomes. While many of the predictors examined were similar across males and females, sex-specific multivariable models identified some predictors of rehabilitation outcome that are specific for males and females; mechanism of injury (p<.0001) was a significant predictor of functional outcome only among females while comorbidities (p<.0001) was a significant predictor for males only. Conclusions Predictors of outcomes after inpatient rehabilitation differed by sex, providing evidence for a sex-specific approach in planning and resource allocation for inpatient rehabilitation services for patients with TBI. PMID:26836952

Sex-Specific Predictors of Inpatient Rehabilitation Outcomes After Traumatic Brain Injury.

PubMed

Chan, Vincy; Mollayeva, Tatyana; Ottenbacher, Kenneth J; Colantonio, Angela

2016-05-01

To identify sex-specific predictors of inpatient rehabilitation outcomes among patients with a traumatic brain injury (TBI) from a population-based perspective. Retrospective cohort study. Inpatient rehabilitation. Patients in inpatient rehabilitation for a TBI within 1 year of acute care discharge between 2008/2009 and 2011/2012 (N=1730, 70% men, 30% women). None. Inpatient rehabilitation length of stay, total FIM score, and motor and cognitive FIM ratings at discharge. Sex, as a covariate in multivariable linear regression models, was not a significant predictor of rehabilitation outcomes. Although many of the predictors examined were similar across men and women, sex-specific multivariable models identified some predictors of rehabilitation outcome that are specific for men and women; mechanism of injury (P<.0001) was a significant predictor of functional outcome only among women, whereas comorbidities (P<.0001) was a significant predictor for men only. Predictors of outcomes after inpatient rehabilitation differed by sex, providing evidence for a sex-specific approach in planning and resource allocation for inpatient rehabilitation services for patients with TBI. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

NASA Astrophysics Data System (ADS)

Florios, Kostas; Kontogiannis, Ioannis; Park, Sung-Hong; Guerra, Jordan A.; Benvenuto, Federico; Bloomfield, D. Shaun; Georgoulis, Manolis K.

2018-02-01

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 - 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude {>} M1 and {>} C1 within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02), and Heidke skill score HSS=0.49(0.01) for {>} M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01), and HSS=0.59(0.01) for {>} C1 flare prediction with probability threshold 35%.

Parent involvement and science achievement: A latent growth curve analysis

NASA Astrophysics Data System (ADS)

Johnson, Ursula Yvette

This study examined science achievement growth across elementary and middle school and parent school involvement using the Early Childhood Longitudinal Study - Kindergarten Class of 1998--1999 (ECLS-K). The ECLS-K is a nationally representative kindergarten cohort of students from public and private schools who attended full-day or half-day kindergarten class in 1998--1999. The present study's sample (N = 8,070) was based on students that had a sampling weight available from the public-use data file. Students were assessed in science achievement at third, fifth, and eighth grades and parents of the students were surveyed at the same time points. Analyses using latent growth curve modeling with time invariant and varying covariates in an SEM framework revealed a positive relationship between science achievement and parent involvement at eighth grade. Furthermore, there were gender and racial/ethnic differences in parents' school involvement as a predictor of science achievement. Findings indicated that students with lower initial science achievement scores had a faster rate of growth across time. The achievement gap between low and high achievers in earth, space and life sciences lessened from elementary to middle school. Parents' involvement with school usually tapers off after elementary school, but due to parent school involvement being a significant predictor of eighth grade science achievement, later school involvement may need to be supported and better implemented in secondary schooling.

Predictors for Recurrence of Chronic Subdural Hematoma.

PubMed

Hammer, Alexander; Tregubow, Alexander; Kerry, Ghassan; Schrey, Michael; Hammer, Christian; Steiner, Hans-Herbert

2017-01-01

This prospective study was designed to analyze the dependence of different factors on the recurrence rate of chronic subdural hematoma (cSDH) after surgical treatment. Seventy-three consecutive patients, who were surgically treated at our department due to cSDH between 2009 and 2012, were included. The following parameters were analyzed: patient age and gender, occurrence of trauma, time between trauma and admission, neurological symptoms, presence of minor diseases, intake of anticoagulation medication. We classified the results of diagnostic imaging and determined the space-consuming effect via the cerebral midline shift. In addition, we scrutinized intraoperative findings and the dependence of the position of subdural drainage on the recurrence rate of cSDH. In our patient group, cSDH recurrence was significantly associated with aphasia (p=0.008). Moreover an increased cSDH recurrence rate was observed in the patient group that had a separated manifestation of the cSDH in the preoperative diagnostic imaging (p=0.048) and received no drainage implant (p=0.016). Homogeneous isodense cSDH was associated with no apparent recurrence (p=0.037). Within the scope of this study, we detected aphasia and separated cSDH as predictors of cSDH recurrence. Homogeneous isodense cSDH seems to be a good prognostic sign regarding the risk of recurrence development. Furthermore, our data clearly emphasize the importance of surgically applied drainage implants to prevent a recurrence of cSDH.

An evolution-based DNA-binding residue predictor using a dynamic query-driven learning scheme.

PubMed

Chai, H; Zhang, J; Yang, G; Ma, Z

2016-11-15

DNA-binding proteins play a pivotal role in various biological activities. Identification of DNA-binding residues (DBRs) is of great importance for understanding the mechanism of gene regulations and chromatin remodeling. Most traditional computational methods usually construct their predictors on static non-redundant datasets. They excluded many homologous DNA-binding proteins so as to guarantee the generalization capability of their models. However, those ignored samples may potentially provide useful clues when studying protein-DNA interactions, which have not obtained enough attention. In view of this, we propose a novel method, namely DQPred-DBR, to fill the gap of DBR predictions. First, a large-scale extensible sample pool was compiled. Second, evolution-based features in the form of a relative position specific score matrix and covariant evolutionary conservation descriptors were used to encode the feature space. Third, a dynamic query-driven learning scheme was designed to make more use of proteins with known structure and functions. In comparison with a traditional static model, the introduction of dynamic models could obviously improve the prediction performance. Experimental results from the benchmark and independent datasets proved that our DQPred-DBR had promising generalization capability. It was capable of producing decent predictions and outperforms many state-of-the-art methods. For the convenience of academic use, our proposed method was also implemented as a web server at .

Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences.

PubMed

Chen, Peng; Li, Jinyan; Wong, Limsoon; Kuwahara, Hiroyuki; Huang, Jianhua Z; Gao, Xin

2013-08-01

Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. Copyright © 2013 Wiley Periodicals, Inc.

Impact of correlation of predictors on discrimination of risk models in development and external populations.

PubMed

Kundu, Suman; Mazumdar, Madhu; Ferket, Bart

2017-04-19

The area under the ROC curve (AUC) of risk models is known to be influenced by differences in case-mix and effect size of predictors. The impact of heterogeneity in correlation among predictors has however been under investigated. We sought to evaluate how correlation among predictors affects the AUC in development and external populations. We simulated hypothetical populations using two different methods based on means, standard deviations, and correlation of two continuous predictors. In the first approach, the distribution and correlation of predictors were assumed for the total population. In the second approach, these parameters were modeled conditional on disease status. In both approaches, multivariable logistic regression models were fitted to predict disease risk in individuals. Each risk model developed in a population was validated in the remaining populations to investigate external validity. For both approaches, we observed that the magnitude of the AUC in the development and external populations depends on the correlation among predictors. Lower AUCs were estimated in scenarios of both strong positive and negative correlation, depending on the direction of predictor effects and the simulation method. However, when adjusted effect sizes of predictors were specified in the opposite directions, increasingly negative correlation consistently improved the AUC. AUCs in external validation populations were higher or lower than in the derivation cohort, even in the presence of similar predictor effects. Discrimination of risk prediction models should be assessed in various external populations with different correlation structures to make better inferences about model generalizability.

Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah

Treesearch

N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species...

The Cognition of Multiaircraft Control (MAC): Cognitive Ability Predictors, Working Memory, Interference, and Attention Control in Radio Communication

DTIC Science & Technology

2015-03-26

THE COGNITION OF MULTIAIRCRAFT CONTROL (MAC): COGNITIVE ABILITY PREDICTORS, WORKING MEMORY ...COGNITIVE ABILITY PREDICTORS, WORKING MEMORY , INTERFERENCE, AND ATTENTION CONTROL IN RADIO COMMUNICATION THESIS Presented to the Faculty...UNLIMITED. AFIT-ENV-MS-15-M-205 THE COGNITION OF MULTIAIRCRAFT CONTROL (MAC): COGNITIVE ABILITY PREDICTORS, WORKING MEMORY , INTERFERENCE

A generalized conditional heteroscedastic model for temperature downscaling

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2014-11-01

This study describes a method for deriving the time varying second order moment, or heteroscedasticity, of local daily temperature and its association to large Coupled Canadian General Circulation Models predictors. This is carried out by applying a multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) approach to construct the conditional variance-covariance structure between General Circulation Models (GCMs) predictors and maximum and minimum temperature time series during 1980-2000. Two MGARCH specifications namely diagonal VECH and dynamic conditional correlation (DCC) are applied and 25 GCM predictors were selected for a bivariate temperature heteroscedastic modeling. It is observed that the conditional covariance between predictors and temperature is not very strong and mostly depends on the interaction between the random process governing temporal variation of predictors and predictants. The DCC model reveals a time varying conditional correlation between GCM predictors and temperature time series. No remarkable increasing or decreasing change is observed for correlation coefficients between GCM predictors and observed temperature during 1980-2000 while weak winter-summer seasonality is clear for both conditional covariance and correlation. Furthermore, the stationarity and nonlinearity Kwiatkowski-Phillips-Schmidt-Shin (KPSS) and Brock-Dechert-Scheinkman (BDS) tests showed that GCM predictors, temperature and their conditional correlation time series are nonlinear but stationary during 1980-2000 according to BDS and KPSS test results. However, the degree of nonlinearity of temperature time series is higher than most of the GCM predictors.

Molecular and Clinical Predictors of Aggressive Prostate Cancer

DTIC Science & Technology

2007-09-01

AD_________________ Award Number: W81XWH-05-1-0562 TITLE: Molecular and Clinical Predictors of...DATES COVERED (From - To) 1 SEP 2006 - 31 AUG 2007 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Molecular And Clinical Predictors Of Aggressive...course. We are evaluating molecular and clinical predictors at diagnosis to distinguish lethal and indolent prostate cancer. In a related project, we

Multicollinearity and Regression Analysis

NASA Astrophysics Data System (ADS)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

An explicit solution to the exoatmospheric powered flight guidance and trajectory optimization problem for rocket propelled vehicles

NASA Technical Reports Server (NTRS)

Jaggers, R. F.

1977-01-01

A derivation of an explicit solution to the two point boundary-value problem of exoatmospheric guidance and trajectory optimization is presented. Fixed initial conditions and continuous burn, multistage thrusting are assumed. Any number of end conditions from one to six (throttling is required in the case of six) can be satisfied in an explicit and practically optimal manner. The explicit equations converge for off nominal conditions such as engine failure, abort, target switch, etc. The self starting, predictor/corrector solution involves no Newton-Rhapson iterations, numerical integration, or first guess values, and converges rapidly if physically possible. A form of this algorithm has been chosen for onboard guidance, as well as real time and preflight ground targeting and trajectory shaping for the NASA Space Shuttle Program.

Orbital Debris Impact Damage to Reusable Launch Vehicles

NASA Technical Reports Server (NTRS)

Robinson, Jennifer H.

1998-01-01

In an effort by the National Aeronautics and Space Administration (NASA), hypervelocity impact tests were performed on thermal protection systems (TPS) applied on the external surfaces of reusable launch vehicles (RLV) to determine the potential damage from orbital debris impacts. Three TPS types were tested, bonded to composite structures representing RLV fuel tank walls. The three heat shield materials tested were Alumina-Enhanced Thermal Barrier-12 (AETB-12), Flexible Reusable Surface Insulation (FRSI), and Advanced Flexible Reusable Surface Insulation (AFRSI). Using this test data, predictor equations were developed for the entry hole diameters in the three TPS materials, with correlation coefficients ranging from 0.69 to 0.86. Possible methods are proposed for approximating damage occurring at expected orbital impact velocities higher than tested, with references to other published work.

Surface wind convergence as a short-term predictor of cloud-to-ground lightning at Kennedy Space Center: A four-year summary and evaluation

NASA Technical Reports Server (NTRS)

Watson, Andrew I.; Holle, Ronald L.; Lopez, Raul E.; Nicholson, James R.

1991-01-01

Since 1986, USAF forecasters at NASA-Kennedy have had available a surface wind convergence technique for use during periods of convective development. In Florida during the summer, most of the thunderstorm development is forced by boundary layer processes. The basic premise is that the life cycle of convection is reflected in the surface wind field beneath these storms. Therefore the monitoring of the local surface divergence and/or convergence fields can be used to determine timing, location, longevity, and the lightning hazards which accompany these thunderstorms. This study evaluates four years of monitoring thunderstorm development using surface wind convergence, particularly the average over the area. Cloud-to-ground (CG) lightning is related in time and space with surface convergence for 346 days during the summers of 1987 through 1990 over the expanded wind network at KSC. The relationships are subdivided according to low level wind flow and midlevel moisture patterns. Results show a one in three chance of CG lightning when a convergence event is identified. However, when there is no convergence, the chance of CG lightning is negligible.

Well-balanced high-order centered schemes on unstructured meshes for shallow water equations with fixed and mobile bed

NASA Astrophysics Data System (ADS)

Canestrelli, Alberto; Dumbser, Michael; Siviglia, Annunziato; Toro, Eleuterio F.

2010-03-01

In this paper, we study the numerical approximation of the two-dimensional morphodynamic model governed by the shallow water equations and bed-load transport following a coupled solution strategy. The resulting system of governing equations contains non-conservative products and it is solved simultaneously within each time step. The numerical solution is obtained using a new high-order accurate centered scheme of the finite volume type on unstructured meshes, which is an extension of the one-dimensional PRICE-C scheme recently proposed in Canestrelli et al. (2009) [5]. The resulting first-order accurate centered method is then extended to high order of accuracy in space via a high order WENO reconstruction technique and in time via a local continuous space-time Galerkin predictor method. The scheme is applied to the shallow water equations and the well-balanced properties of the method are investigated. Finally, we apply the new scheme to different test cases with both fixed and movable bed. An attractive future of the proposed method is that it is particularly suitable for engineering applications since it allows practitioners to adopt the most suitable sediment transport formula which better fits the field data.

Recent advances in high-order WENO finite volume methods for compressible multiphase flows

NASA Astrophysics Data System (ADS)

Dumbser, Michael

2013-10-01

We present two new families of better than second order accurate Godunov-type finite volume methods for the solution of nonlinear hyperbolic partial differential equations with nonconservative products. One family is based on a high order Arbitrary-Lagrangian-Eulerian (ALE) formulation on moving meshes, which allows to resolve the material contact wave in a very sharp way when the mesh is moved at the speed of the material interface. The other family of methods is based on a high order Adaptive Mesh Refinement (AMR) strategy, where the mesh can be strongly refined in the vicinity of the material interface. Both classes of schemes have several building blocks in common, in particular: a high order WENO reconstruction operator to obtain high order of accuracy in space; the use of an element-local space-time Galerkin predictor step which evolves the reconstruction polynomials in time and that allows to reach high order of accuracy in time in one single step; the use of a path-conservative approach to treat the nonconservative terms of the PDE. We show applications of both methods to the Baer-Nunziato model for compressible multiphase flows.

Reliability and dimensionality of judgments of visually textured materials.

PubMed

Cho, R Y; Yang, V; Hallett, P E

2000-05-01

We extended perceptual studies of the Brodatz set of textured materials. In the experiments, texture perception for different texture sets, viewing distances, or lighting intensities was examined. Subjects compared one pair of textures at a time. The main task was to rapidly rate all of the texture pairs on a number scale for their overall dissimilarities first and then for their dissimilarities according to six specified attributes (e.g., texture contrast). The implied dimensionality of perceptual texture space was usually at least four, rather than three. All six attributes proved to be useful predictors of overall dissimilarity, especially coarseness and regularity. The novel attribute texture lightness, an assessment of mean surface reflectance, was important when viewing conditions were wide-ranging. We were impressed by the general validity of texture judgments across subject, texture set, and comfortable viewing distances or lighting intensities. The attributes are nonorthogonal directions in four-dimensional perceptual space and are probably not narrow linear axes. In a supplementary experiment, we studied a completely different task: identifying textures from a distance. The dimensionality for this more refined task is similar to that for rating judgments, so our findings may have general application.

Living environment and mobility of older adults.

PubMed

Cress, M Elaine; Orini, Stefania; Kinsler, Laura

2011-01-01

Older adults often elect to move into smaller living environments. Smaller living space and the addition of services provided by a retirement community (RC) may make living easier for the individual, but it may also reduce the amount of daily physical activity and ultimately reduce functional ability. With home size as an independent variable, the primary purpose of this study was to evaluate daily physical activity and physical function of community dwellers (CD; n = 31) as compared to residents of an RC (n = 30). In this cross-sectional study design, assessments included: the Continuous Scale Physical Functional Performance - 10 test, with a possible range of 0-100, higher scores reflecting better function; Step Activity Monitor (StepWatch 3.1); a physical activity questionnaire, the area of the home (in square meters). Groups were compared by one-way ANOVA. A general linear regression model was used to predict the number of steps per day at home. The level of significance was p < 0.05. Of the 61 volunteers (mean age: 79 ± 6.3 years; range: 65-94 years), the RC living space (68 ± 37.7 m(2)) was 62% smaller than the CD living space (182.8 ± 77.9 m(2); p = 0.001). After correcting for age, the RC took fewer total steps per day excluding exercise (p = 0.03) and had lower function (p = 0.005) than the CD. On average, RC residents take 3,000 steps less per day and have approximately 60% of the living space of a CD. Home size and physical function were primary predictors of the number of steps taken at home, as found using a general linear regression analysis. Copyright © 2010 S. Karger AG, Basel.

Quantitative Rapid Assessment of Leukoaraiosis in CT : Comparison to Gold Standard MRI.

PubMed

Hanning, Uta; Sporns, Peter Bernhard; Schmidt, Rene; Niederstadt, Thomas; Minnerup, Jens; Bier, Georg; Knecht, Stefan; Kemmling, André

2017-10-20

The severity of white matter lesions (WML) is a risk factor of hemorrhage and predictor of clinical outcome after ischemic stroke; however, in contrast to magnetic resonance imaging (MRI) reliable quantification for this surrogate marker is limited for computed tomography (CT), the leading stroke imaging technique. We aimed to present and evaluate a CT-based automated rater-independent method for quantification of microangiopathic white matter changes. Patients with suspected minor stroke (National Institutes of Health Stroke scale, NIHSS < 4) were screened for the analysis of non-contrast computerized tomography (NCCT) at admission and compared to follow-up MRI. The MRI-based WML volume and visual Fazekas scores were assessed as the gold standard reference. We employed a recently published probabilistic brain segmentation algorithm for CT images to determine the tissue-specific density of WM space. All voxel-wise densities were quantified in WM space and weighted according to partial probabilistic WM content. The resulting mean weighted density of WM space in NCCT, the surrogate of WML, was correlated with reference to MRI-based WML parameters. The process of CT-based tissue-specific segmentation was reliable in 79 cases with varying severity of microangiopathy. Voxel-wise weighted density within WM spaces showed a noticeable correlation (r = -0.65) with MRI-based WML volume. Particularly in patients with moderate or severe lesion load according to the visual Fazekas score the algorithm provided reliable prediction of MRI-based WML volume. Automated observer-independent quantification of voxel-wise WM density in CT significantly correlates with microangiopathic WM disease in gold standard MRI. This rapid surrogate of white matter lesion load in CT may support objective WML assessment and therapeutic decision-making during acute stroke triage.

Risk of Visual Impairment and Intracranial Hypertension After Space Flight: Evaluation of the Role of Polymorphism of Enzymes Involved in One-Carbon Metabolism

NASA Technical Reports Server (NTRS)

Smith, S. M.; Gregory, J. F.; Zeisel, G. H.; Gibson, C. R.; Mader, T. H.; Kinchen, J.; Ueland, P.; Ploutz-Snyder, R.; Heer, M.; Zwart, S. R.

2016-01-01

Data from the Nutritional Status Assessment protocol provided biochemical evidence that the one-carbon metabolic pathway may be altered in individuals experiencing vision-related issues during and after space flight (1, 2). Briefly, serum concentrations of homocysteine, cystathionine, 2-methylcitric acid, and methylmalonic acid were significantly (P<0.001) higher (25-45%) in astronauts with ophthalmic changes than in those without such changes (1). These differences existed before, during, and after flight. Serum folate was lower (P<0.01) during flight in individuals with ophthalmic changes. Preflight serum concentrations of cystathionine and 2-methylcitric acid, and mean in-flight serum folate, were significantly (P<0.05) correlated with postflight changes in refraction (1). A follow-up study was conducted to evaluate a small number of known polymorphisms of enzymes in the one-carbon pathway, and to evaluate how these relate to vision and other medical aspects of the eye. Specifically, we investigated 5 polymorphisms in MTRR, MTHFR, SHMT, and CBS genes and their association with ophthalmic changes after flight in 49 astronauts. The number of G alleles of MTRR 66 and C alleles of SHMT1 1420 both contributed to the odds of visual disturbances (3). Block regression showed that B-vitamin status at landing and genetics were significant predictors for many of the ophthalmic outcomes studied (3). In conclusion, we document an association between MTRR 66 and SHMT1 1420 polymorphisms and space flightinduced vision changes. These data document that individuals with an altered 1-carbon metabolic pathway may be predisposed to anatomic and/or physiologic changes that render them susceptible to ophthalmic damage during space flight.

Predicting PDZ domain mediated protein interactions from structure

PubMed Central

2013-01-01

Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. PMID:23336252

Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

PubMed

Schummers, Laura; Himes, Katherine P; Bodnar, Lisa M; Hutcheon, Jennifer A

2016-09-21

Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r 2 ) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

Forest Type and Above Ground Biomass Estimation Based on Sentinel-2A and WorldView-2 Data Evaluation of Predictor nd Data Suitability

NASA Astrophysics Data System (ADS)

Fritz, Andreas; Enßle, Fabian; Zhang, Xiaoli; Koch, Barbara

2016-08-01

The present study analyses the two earth observation sensors regarding their capability of modelling forest above ground biomass and forest density. Our research is carried out at two different demonstration sites. The first is located in south-western Germany (region Karlsruhe) and the second is located in southern China in Jiangle County (Province Fujian). A set of spectral and spatial predictors are computed from both, Sentinel-2A and WorldView-2 data. Window sizes in the range of 3*3 pixels to 21*21 pixels are computed in order to cover the full range of the canopy sizes of mature forest stands. Textural predictors of first and second order (grey-level-co-occurrence matrix) are calculated and are further used within a feature selection procedure. Additionally common spectral predictors from WorldView-2 and Sentinel-2A data such as all relevant spectral bands and NDVI are integrated in the analyses. To examine the most important predictors, a predictor selection algorithm is applied to the data, whereas the entire predictor set of more than 1000 predictors is used to find most important ones. Out of the original set only the most important predictors are then further analysed. Predictor selection is done with the Boruta package in R (Kursa and Rudnicki (2010)), whereas regression is computed with random forest. Prior the classification and regression a tuning of parameters is done by a repetitive model selection (100 runs), based on the .632 bootstrapping. Both are implemented in the caret R pack- age (Kuhn et al. (2016)). To account for the variability in the data set 100 independent runs are performed. Within each run 80 percent of the data is used for training and the 20 percent are used for an independent validation. With the subset of original predictors mapping of above ground biomass is performed.

Ethnic distribution of ECG predictors of atrial fibrillation and its impact on understanding the ethnic distribution of ischemic stroke in the Atherosclerosis Risk in Communities (ARIC) study.

PubMed

Soliman, Elsayed Z; Prineas, Ronald J; Case, L Douglas; Zhang, Zhu-ming; Goff, David C

2009-04-01

The paradox of the reported low prevalence of atrial fibrillation (AF) in blacks compared with whites despite higher stroke rates in the former could be related to limitations in the current methods used to diagnose AF in population-based studies. Hence, this study aimed to use the ethnic distribution of ECG predictors of AF as measures of AF propensity in different ethnic groups. The distribution of baseline measures of P-wave terminal force, P-wave duration, P-wave area, and PR duration (referred to as AF predictors) were compared by ethnicity in 15 429 participants (27% black) from the Atherosclerosis Risk in Communities (ARIC) study by unpaired t test, chi(2), and logistic-regression analysis, as appropriate. Cox proportional-hazards analysis was used to separately examine the association of AF predictors with incident AF and ischemic stroke. Whereas AF was significantly less common in blacks compared with whites (0.24% vs 0.95%, P<0.0001), similar to what has been reported in previous studies, blacks had significantly higher and more abnormal values of AF predictors (P<0.0001 for all comparisons). Black ethnicity was significantly associated with abnormal AF predictors compared with whites; odds ratios for different AF predictors ranged from 2.1 to 3.1. AF predictors were significantly and independently associated with AF and ischemic stroke with no significant interaction between ethnicity and AF predictors, findings that further justify using AF predictors as an earlier indicator of future risk of AF and stroke. There is a disconnect between the ethnic distribution of AF predictors and the ethnic distribution of AF, probably because the former, unlike the latter, do not suffer from low sensitivity. These results raise the possibility that blacks might actually have a higher prevalence of AF that might have been missed by previous studies owing to limited methodology, a difference that could partially explain the greater stroke risk in blacks.

PID-controller with predictor and auto-tuning algorithm: study of efficiency for thermal plants

NASA Astrophysics Data System (ADS)

Kuzishchin, V. F.; Merzlikina, E. I.; Hoang, Van Va

2017-09-01

The problem of efficiency estimation of an automatic control system (ACS) with a Smith predictor and PID-algorithm for thermal plants is considered. In order to use the predictor, it is proposed to include an auto-tuning module (ATC) into the controller; the module calculates parameters for a second-order plant module with a time delay. The study was conducted using programmable logical controllers (PLC), one of which performed control, ATC, and predictor functions. A simulation model was used as a control plant, and there were two variants of the model: one of them was built on the basis of a separate PLC, and the other was a physical model of a thermal plant in the form of an electrical heater. Analysis of the efficiency of the ACS with the predictor was carried out for several variants of the second order plant model with time delay, and the analysis was performed on the basis of the comparison of transient processes in the system when the set point was changed and when a disturbance influenced the control plant. The recommendations are given on correction of the PID-algorithm parameters when the predictor is used by means of using the correcting coefficient k for the PID parameters. It is shown that, when the set point is changed, the use of the predictor is effective taking into account the parameters correction with k = 2. When the disturbances influence the plant, the use of the predictor is doubtful, because the transient process is too long. The reason for this is that, in the neighborhood of the zero frequency, the amplitude-frequency characteristic (AFC) of the system with the predictor has an ascent in comparison with the AFC of the system without the predictor.

[Influential factors on psychosocial health of the migrant workers in Guangzhou].

PubMed

Lin, Qiu-hong; Liu, Yi-min; Zhou, Jing-dong; Cao, Nai-qiong; Fang, Yuan-yu

2012-03-01

To study the influential factors on psychosocial health of the migrant workers in Guangzhou. The Symptom Checklist 90 (SCL-90) and Eysenck Personality Questionnaire (EPQ) were used to investigate 518 migrant workers in Guangzhou. The rate of migrant workers with psychosocial problems was 36.5%. The scores of SCL-90 and positive rates in migrant workers with the different personality types had significant difference (P < 0.01). The results of binary logistic regression analysis indicated that the working years, drinking, sex, P scores, E scores and N scores of EPQ were main predictors of the poor physical fitness status. The vocations, working years, P scores and N scores of EPQ were strong predictors of the somatization. he vocations, P scores and N scores of EPQ were strong predictors of the obsessive compulsive symptom. The smoking, P scores and N scores of EPQ were strong predictors of the interpersonal sensitivity. The working years, P scores of EPQ were strong predictors of the depression. P scores of EPQ was strong predictors of the anxiety. P scores, E scores and N scores of EPQ were strong predictors of the hostility. The working years, smoking, P scores, E scores and N scores of EPQ were strong predictors of the phobic anxiety. The working years, P scores of EPQ were strong predictors of the paranoid ideation. The working years, P scores and N scores of EPQ were strong predictors of the psychosis. The level of mental health of the migrant workers was significantly associated with the personality. The results of present study indicated that different vocation, sex, working years, smoking and drinking might interfere with the psychological states. The migrant workers with the personality of psychoticism, neuroticism and introversion may have unhealthy mental reaction.

Predictors of Entering a Hearing Aid Evaluation Period: A Prospective Study in Older Hearing-Help Seekers

PubMed Central

Deeg, Dorly J.H.; Versfeld, Niek J.; Heymans, Martijn W.; Naylor, Graham; Kramer, Sophia E.

2017-01-01

This study aimed to determine the predictors of entering a hearing aid evaluation period (HAEP) using a prospective design drawing on the health belief model and the transtheoretical model. In total, 377 older persons who presented with hearing problems to an Ear, Nose, and Throat specialist (n = 110) or a hearing aid dispenser (n = 267) filled in a baseline questionnaire. After 4 months, it was determined via a telephone interview whether or not participants had decided to enter a HAEP. Multivariable logistic regression analyses were applied to determine which baseline variables predicted HAEP status. A priori, candidate predictors were divided into ‘likely’ and ‘novel’ predictors based on the literature. The following variables turned out to be significant predictors: more expected hearing aid benefits, greater social pressure, and greater self-reported hearing disability. In addition, greater hearing loss severity and stigma were predictors in women but not in men. Of note, the predictive effect of self-reported hearing disability was modified by readiness such that with higher readiness, the positive predictive effect became stronger. None of the ‘novel’ predictors added significant predictive value. The results support the notion that predictors of hearing aid uptake are also predictive of entering a HAEP. This study shows that some of these predictors appear to be gender specific or are dependent on a person’s readiness for change. After assuring the external validity of the predictors, an important next step would be to develop prediction rules for use in clinical practice, so that older persons’ hearing help-seeking journey can be facilitated. PMID:29237333

Uncertainties of statistical downscaling from predictor selection: Equifinality and transferability

NASA Astrophysics Data System (ADS)

Fu, Guobin; Charles, Stephen P.; Chiew, Francis H. S.; Ekström, Marie; Potter, Nick J.

2018-05-01

The nonhomogeneous hidden Markov model (NHMM) statistical downscaling model, 38 catchments in southeast Australia and 19 general circulation models (GCMs) were used in this study to demonstrate statistical downscaling uncertainties caused by equifinality to and transferability. That is to say, there could be multiple sets of predictors that give similar daily rainfall simulation results for both calibration and validation periods, but project different amounts (or even directions of change) of rainfall changing in the future. Results indicated that two sets of predictors (Set 1 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and specific humidity at 700 hPa and Set 2 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and dewpoint temperature depression at 850 hPa) as inputs to the NHMM produced satisfactory results of seasonal rainfall in comparison with observations. For example, during the model calibration period, the relative errors across the 38 catchments ranged from 0.48 to 1.76% with a mean value of 1.09% for the predictor Set 1, and from 0.22 to 2.24% with a mean value of 1.16% for the predictor Set 2. However, the changes of future rainfall from NHMM projections based on 19 GCMs produced projections with a different sign for these two different sets of predictors: Set 1 predictors project an increase of future rainfall with magnitudes depending on future time periods and emission scenarios, but Set 2 predictors project a decline of future rainfall. Such divergent projections may present a significant challenge for applications of statistical downscaling as well as climate change impact studies, and could potentially imply caveats in many existing studies in the literature.

Factors in the development of proportional reasoning strategies by concrete operational college students

NASA Astrophysics Data System (ADS)

Roth, Wolff-Michael; Milkent, Marlene M.

This study was designed as a test for two neo-Piagetian theories. More specifically, this research examined the relationships between the development of proportional reasoning strategies and three cognitive variables from Pascual-Leone's and Case's neo-Piagetian theories. A priori hypotheses linked the number of problems students worked until they induced a proportional reasoning strategy to the variables of M-space, degree of field dependence, and short-term storage space. The subjects consisted of students enrolled in Physical Science I, a science course for nonscience majors at the University of Southern Mississippi. Of the 34 subjects in the study, 23 were classified as concrete operational on the basis of eight ratio tasks. Problems corresponding to five developmental levels of proportional reasoning (according to Piagetian and neo-Piagetian theory), were presented by a microcomputer to the 23 subjects who had been classified as concrete operational. After a maximum of 6 hours of treatment, 17 of the 23 subjects had induced ratio schemata at the upper formal level (IIIB), while the remaining subjects used lower formal level (IIIA) schemata. The data analyses showed that neither M-space and degree of field-dependence, either alone or in combination, nor short-term storage predicted the number of problems students need to do until they induce an appropriate problem-solving strategy. However, there were significant differences in the short-term storage space of those subjects who mastered ratio problems at the highest level and those who did not. Also, the subjects' degree of field-dependence was not a predictor of either the ability to transfer problem-solving strategies to a new setting or the reuse of inappropriate strategies. The results of this study also suggest that short-term storage space is a variable with high correlations to a number of aspects of learning such as transfer and choice of strategy after feedback.

The behaviour of random forest permutation-based variable importance measures under predictor correlation.

PubMed

Nicodemus, Kristin K; Malley, James D; Strobl, Carolin; Ziegler, Andreas

2010-02-27

Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results. In the case when both predictor correlation was present and predictors were associated with the outcome (HA), the unconditional RF VIM attributed a higher share of importance to correlated predictors, while under the null hypothesis that no predictors are associated with the outcome (H0) the unconditional RF VIM was unbiased. Conditional VIMs showed a decrease in VIM values for correlated predictors versus the unconditional VIMs under HA and was unbiased under H0. Scaled VIMs were clearly biased under HA and H0. Unconditional unscaled VIMs are a computationally tractable choice for large datasets and are unbiased under the null hypothesis. Whether the observed increased VIMs for correlated predictors may be considered a "bias" - because they do not directly reflect the coefficients in the generating model - or if it is a beneficial attribute of these VIMs is dependent on the application. For example, in genetic association studies, where correlation between markers may help to localize the functionally relevant variant, the increased importance of correlated predictors may be an advantage. On the other hand, we show examples where this increased importance may result in spurious signals.

Development of a work environment rating scale for kindergarten teachers.

PubMed

Wong, Yau-ho P

2015-08-01

Kindergarten education in Hong Kong serves children aged 32-68 months. However, there is no extant scale that measures kindergarten teachers' perceived work environment, an important influence on their well-being. To develop a new instrument, the Teachers' Perceived Work Environment (TPWE) scale, and to assess whether kindergarten teachers with higher TPWE ratings had higher scores for job satisfaction, self-esteem and mental health. A 25-item rating scale was developed and used with a sample of in-service kindergarten teachers. Their perceived work environment was represented by five factors (ergonomics, staffing, teaching space, work hours and social space). These teachers also completed three well-being inventories: the Job Satisfaction Survey, the Rosenberg Self-Esteem Inventory and the General Health Questionnaire-12. In a second stage, a new sample of in-service kindergarten teachers was used to cross-validate the findings from the earlier assessment. In the first sample of 141 teachers and the second of 125, social space, staffing and work hours were associated with job satisfaction, while ergonomics was a significant negative predictor of mental health complaints. The TPWE exhibited satisfactory reliability and validity. Some factors were differentially associated with specific types of well-being. The results may inform future studies of the working conditions of kindergarten teachers. © The Author 2015. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Sampling algorithms for validation of supervised learning models for Ising-like systems

NASA Astrophysics Data System (ADS)

Portman, Nataliya; Tamblyn, Isaac

2017-12-01

In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).

In vivo perfusion assessment of an anastomosis surgery on porcine intestinal model (Conference Presentation)

NASA Astrophysics Data System (ADS)

Le, Hanh N. D.; Opferman, Justin; Decker, Ryan; Cheon, Gyeong W.; Kim, Peter C. W.; Kang, Jin U.; Krieger, Axel

2016-04-01

Anastomosis, the connection of two structures, is a critical procedure for reconstructive surgery with over 1 million cases/year for visceral indication alone. However, complication rates such as strictures and leakage affect up to 19% of cases for colorectal anastomoses and up to 30% for visceral transplantation anastomoses. Local ischemia plays a critical role in anastomotic complications, making blood perfusion an important indicator for tissue health and predictor for healing following anastomosis. In this work, we apply a real time multispectral imaging technique to monitor impact on tissue perfusion due to varying interrupted suture spacing and suture tensions. Multispectral tissue images at 470, 540, 560, 580, 670 and 760 nm are analyzed in conjunction with an empirical model based on diffuse reflectance process to quantify the hemoglobin oxygen saturation within the suture site. The investigated tissues for anastomoses include porcine small (jejunum and ileum) and large (transverse colon) intestines. Two experiments using interrupted suturing with suture spacing of 1, 2, and 3 mm and tension levels from 0 N to 2.5 N are conducted. Tissue perfusion at 5, 10, 20 and 30 min after suturing are recorded and compared with the initial normal state. The result indicates the contrast between healthy and ischemic tissue areas and assists the determination of suturing spacing and tension. Therefore, the assessment of tissue perfusion will permit the development and intra-surgical monitoring of an optimal suture protocol during anastomosis with less complications and improved functional outcome.

Locus of Control & Motivation Strategies for Learning Questionnaire: Predictors of Student Success on the ATI Comprehensive Predictor Exam & NCLEX-RN Examination

ERIC Educational Resources Information Center

Carpenter, Jane H.

2011-01-01

The two purposes of this study were to determine whether locus of control (LOC) was predictive of how a student would perform on the ATI Comprehensive Predictor Exam and the NCLEX-RN, and if the Motivated Strategies for Learning Questionnaire (MSLQ) provided information that would help determine predictors of success on these two exams. The study…

Smoothed Particle Hydrodynamics: Applications Within DSTO

DTIC Science & Technology

2006-10-01

Most SPH codes use either an improved Euler method (a mid-point predictor - corrector method) [50] or a leapfrog predictor - corrector algorithm for...in the next section we used the predictor - corrector leapfrog algorithm for time stepping. If we write the set of equations describing the change in... predictor - corrector or leapfrog method is used when solving the equations. Monaghan has also noted [53] that, with a correctly chosen time step, total

Work-home interface stress: an important predictor of emotional exhaustion 15 years into a medical career.

PubMed

Hertzberg, Tuva Kolstad; Rø, Karin Isaksson; Vaglum, Per Jørgen Wiggen; Moum, Torbjørn; Røvik, Jan Ole; Gude, Tore; Ekeberg, Øivind; Tyssen, Reidar

2016-01-01

The importance of work-home interface stress can vary throughout a medical career and between genders. We studied changes in work-home interface stress over 5 yr, and their prediction of emotional exhaustion (main dimension of burn-out), controlled for other variables. A nationwide doctor cohort (NORDOC; n=293) completed questionnaires at 10 and 15 yr after graduation. Changes over the period were examined and predictors of emotional exhaustion analyzed using linear regression. Levels of work-home interface stress declined, whereas emotional exhaustion stayed on the same level. Lack of reduction in work-home interface stress was an independent predictor of emotional exhaustion in year 15 (β=-0.21, p=0.001). Additional independent predictors were reduction in support from colleagues (β=0.11, p=0.04) and emotional exhaustion at baseline (β=0.62, p<0.001). Collegial support was a more important predictor for men than for women. In separate analyses, significant adjusted predictors were lack of reduction in work-home interface stress among women, and reduction of collegial support and lack of reduction in working hours among men. Thus, change in work-home interface stress is a key independent predictor of emotional exhaustion among doctors 15 yr after graduation. Some gender differences in predictors of emotional exhaustion were found.

Predictors of BMI Vary along the BMI Range of German Adults – Results of the German National Nutrition Survey II

PubMed Central

Moon, Kilson; Krems, Carolin; Heuer, Thorsten; Roth, Alexander; Hoffmann, Ingrid

2017-01-01

Objective The objective of the study was to identify predictors of BMI in German adults by considering the BMI distribution and to determine whether the association between BMI and its predictors varies along the BMI distribution. Methods The sample included 9,214 adults aged 18–80 years from the German National Nutrition Survey II (NVS II). Quantile regression analyses were conducted to examine the association between BMI and the following predictors: age, sports activities, socio-economic status (SES), healthy eating index-NVS II (HEI-NVS II), dietary knowledge, sleeping duration and energy intake as well as status of smoking, partner relationship and self-reported health. Results Age, SES, self-reported health status, sports activities and energy intake were the strongest predictors of BMI. The important outcome of this study is that the association between BMI and its predictors varies along the BMI distribution. Especially, energy intake, health status and SES were marginally associated with BMI in normal-weight subjects; this relationships became stronger in the range of overweight, and were strongest in the range of obesity. Conclusions Predictors of BMI and the strength of these associations vary across the BMI distribution in German adults. Consequently, to identify predictors of BMI, the entire BMI distribution should be considered. PMID:28219069

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

PubMed

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

Body image flexibility: A predictor and moderator of outcome in transdiagnostic outpatient eating disorder treatment.

PubMed

Pellizzer, Mia L; Waller, Glenn; Wade, Tracey D

2018-04-01

Predictors of attrition and predictors and moderators of outcome were explored in a transdiagnostic sample of patients who received ten-session cognitive behavioral therapy (CBT-T) for nonunderweight eating disorders. Body image flexibility, a protective positive body image construct, was hypothesized to be a significant moderator. Data from two case series were combined to form a sample of 78 participants who received CBT-T. Baseline measures of body image, negative affect, personality, and motivation (readiness to change and self-efficacy) were included as potential predictors. Global eating disorder psychopathology at each assessment point (baseline, mid- and post-treatment, 1- and 3-month follow-up) was the outcome variable. Predictors of attrition were assessed using logistic regression, and multilevel modeling was applied for predictors and moderators of outcome. Body image flexibility emerged as the strongest predictor and moderator of global eating disorder psychopathology, followed by body image avoidance. Body checking, negative affect, personality beliefs, and self-efficacy were significant predictors of global eating disorder psychopathology. Higher body image flexibility predicted lower global eating disorder psychopathology at every assessment point. Further research is required to replicate findings and explore the benefit of focusing on positive body image in treatment. © 2018 Wiley Periodicals, Inc.

Frontline registered nurse job satisfaction and predictors over three decades: a meta-analysis from 1980 to 2009.

PubMed

Saber, Deborah A

2014-01-01

Frontline registered nurses' job satisfaction is important because it is tied to retention, organizational commitment, workforce safety, patient safety, and cost savings. The purpose of this study was to comprehensively, quantitatively examine the largest, moderate, and smallest predictors of frontline registered nurse job satisfaction from 1980 to 2009. A non-a priori meta-analysis was used to analyze studies that met inclusion. Sixty-two studies and 27 job satisfaction predictors met inclusion for analysis. The largest effect sizes were found for task requirements (r = .61), empowerment (r = .55), and control (r = .52), and moderate effect sizes were found for 10 predictors. Fail-safe N indicates high reliability. Heterogeneity between studies was present in all of the 27 predictor analyses. The largest predictors of job satisfaction for the frontline registered nurse may be different than previously thought. Supporting past research, autonomy and stress were found to be moderate predictors of satisfaction. Heterogeneity indicates study differences or moderator influence in studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Quantifying pediatric neuro-oncology risk factors: development of the neurological predictor scale.

PubMed

Micklewright, Jackie L; King, Tricia Z; Morris, Robin D; Krawiecki, Nicolas

2008-04-01

Pediatric neuro-oncology researchers face methodological challenges associated with quantifying the influence of tumor and treatment-related risk factors on child outcomes. The Neurological Predictor Scale was developed to serve as a cumulative index of a child's exposure to risk factors. The clinical utility of the Neurological Predictor Scale was explored in a sample of 25 children with heterogeneous brain tumors. Consistent with expectation, a series of regression analyses demonstrated that the Neurological Predictor Scale significantly predicted composite intellectual functioning (r(2) = 0.21, p < .05), short-term memory (r(2) = 0.16, p = .05), and abstract visual reasoning abilities (r(2) = 0.28, p < .05). With the exception of chemotherapy, the Neurological Predictor Scale accounted for a significant amount of the variance in child intellectual functioning above and beyond individually examined variables. The Neurological Predictor Scale can be used to quickly quantify the cumulative risk factors associated with pediatric brain tumor diagnoses.

The Association of Clinic-Based Mobility Tasks and Measures of Community Performance and Risk.

PubMed

Callisaya, Michele L; Verghese, Joe

2018-01-10

Gait speed is recognized as an important predictor of adverse outcomes in older people. However, it is unknown whether other more complex mobility tasks are better predictors of such outcomes. To examine a range of clinic-based mobility tests and determine which were most strongly associated with measures of community performance and risk (CP&R). Cross-sectional study. Central Control Mobility and Aging Study, Westchester County, New York. Aged ≥65 years (n = 424). Clinic-based mobility measures included gait speed measured during normal and dual-task conditions, the Floor Maze Immediate and Delay tasks, and stair ascending and descending. CP&R measures were self-reported by the use of standardized questionnaires and classified into measures of performance (distance walked, travel outside one's home [life space], activities of daily living, and participation in cognitive leisure activities) or risk (balance confidence, fear of falling, and past falls). Linear and logistic regression were used to examine associations between the clinic-based mobility measures and CP&R measures adjusting for covariates. The mean age of the sample was 77.8 (SD 6.4) years, and 55.2% (n = 234) were female. In final models, faster normal walking speed was most strongly associated with 5 of the 7 community measures (greater distance walked, greater life space, better activities of daily living function, higher balance confidence, and less fear of falling; all P < .05). More complex tasks (walking while talking and maze immediate) were associated with cognitive leisure activity (P < .05), and ascending stairs was the only measure associated with a history of falls (P < .05). Normal walking speed is a simple and inexpensive clinic-based mobility test that is associated with a wide range of CP&R measures. In addition, poorer performance ascending stairs may assist in identifying those at risk of falls. Poorer performance in more complex mobility tasks (walking while talking and maze immediate) may suggest inability to participate in cognitive leisure activities. III. Copyright © 2018 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Resource stoichiometry and availability modulate species richness and biomass of tropical litter macro-invertebrates.

PubMed

Jochum, Malte; Barnes, Andrew D; Weigelt, Patrick; Ott, David; Rembold, Katja; Farajallah, Achmad; Brose, Ulrich

2017-09-01

High biodiversity and biomass of soil communities are crucial for litter decomposition in terrestrial ecosystems such as tropical forests. However, the leaf litter that these communities consume is of particularly poor quality as indicated by elemental stoichiometry. The impact of resource quantity, quality and other habitat parameters on species richness and biomass of consumer communities is often studied in isolation, although much can be learned from simultaneously studying both community characteristics. Using a dataset of 780 macro-invertebrate consumer species across 32 sites in tropical lowland rain forest and agricultural systems on Sumatra, Indonesia, we investigated the effects of basal resource stoichiometry (C:X ratios of N, P, K, Ca, Mg, Na, S in local leaf litter), litter mass (basal resource quantity and habitat space), plant species richness (surrogate for litter habitat heterogeneity), and soil pH (acidity) on consumer species richness and biomass across different consumer groups (i.e. 3 feeding guilds and 10 selected taxonomic groups). In order to distinguish the most important predictors of consumer species richness and biomass, we applied a standardised model averaging approach investigating the effects of basal resource stoichiometry, litter mass, plant species richness and soil pH on both consumer community characteristics. This standardised approach enabled us to identify differences and similarities in the magnitude and importance of such effects on consumer species richness and biomass. Across consumer groups, we found litter mass to be the most important predictor of both species richness and biomass. Resource stoichiometry had a more pronounced impact on consumer species richness than on their biomass. As expected, taxonomic groups differed in which resource and habitat parameters (basal resource stoichiometry, litter mass, plant species richness and pH) were most important for modulating their community characteristics. The importance of litter mass for both species richness and biomass indicates that these tropical consumers strongly depend on habitat space and resource availability. Our study supports previous theoretical work indicating that consumer species richness is jointly influenced by resource availability and the balanced supply of multiple chemical elements in their resources. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Changes in fertility patterns can improve child survival in Southeast Asia.

PubMed

Greenspan, A

1993-12-01

This analysis of 1988 Philippine Demographic Survey data provides information on the direct and indirect effects of several major determinants of childhood mortality in the Philippines. Data are compared to rates in Indonesia and Thailand. The odds of infant mortality in the Philippines are reduced by 39% by spacing children more than two years apart. This finding is significant because infant mortality rates have not declined over the past 20 years. Child survival is related to the number of children in the family, the spacing of the children, the mother's age and education, and the risks of malnutrition and infection. Directs effects on child survival are related to infant survival status of the preceding child and the length of the preceding birth interval, while key indirect or background variables are maternal age and education, birth order, and place of residence. The two-stage causation model is tested with data on 13,716 ever married women aged 15-49 years and 20,015 index children born between January 1977 and February 1987. Results in the Philippine confirm that maternal age, birth order, mortality of the previous child, and maternal education are directly related to birth interval, while mortality of the previous child, birth order, and maternal educational status are directly related to infant mortality. Thailand, Indonesia, and the Philippines all show similar explanatory factors that directly influence infant mortality. The survival status of the preceding child is the most important predictor in all three countries and is particularly strong in Thailand. This factor acts through the limited time interval for rejuvenation of mother's body, nutritional deficiencies, and transmission of infectious disease among siblings. The conclusion is that poor environmental conditions increase vulnerability to illness and death. There are 133% greater odds of having a short birth interval among young urban women than among older rural women. There is a 29% increase in odds for second parity births compared to third or higher order parities. Maternal education is a strong predictor of infant survival only in the Philippines and Indonesia. Adolescent pregnancy is a risk only in Indonesia. Socioeconomic factors are not as important as birth interval, birth order, and maternal education in determining survival status.

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins.

PubMed

Liu, Yu-Cheng; Yang, Meng-Han; Lin, Win-Li; Huang, Chien-Kang; Oyang, Yen-Jen

2009-12-03

Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research. In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode. Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Contemporary rates and predictors of fast progression of chronic kidney disease in adults with and without diabetes mellitus.

PubMed

Go, Alan S; Yang, Jingrong; Tan, Thida C; Cabrera, Claudia S; Stefansson, Bergur V; Greasley, Peter J; Ordonez, Juan D

2018-06-22

Chronic kidney disease (CKD) is highly prevalent but identification of patients at high risk for fast CKD progression before reaching end-stage renal disease in the short-term has been challenging. Whether factors associated with fast progression vary by diabetes status is also not well understood. We examined a large community-based cohort of adults with CKD to identify predictors of fast progression during the first 2 years of follow-up in the presence or absence of diabetes mellitus. Within a large integrated healthcare delivery system in northern California, we identified adults with estimated glomerular filtration rate (eGFR) 30-59 ml/min/1.73 m 2 by CKD-EPI equation between 2008 and 2010 who had no previous dialysis or renal transplant, who had outpatient serum creatinine values spaced 10-14 months apart and who did not initiate renal replacement therapy, die or disenroll during the first 2 years of follow-up. Through 2012, we calculated the annual rate of change in eGFR and classified patients as fast progressors if they lost > 4 ml/min/1.73 m 2 per year. We used multivariable logistic regression to identify patient characteristics that were independently associated with fast CKD progression stratified by diabetes status. We identified 36,195 eligible adults with eGFR 30-59 ml/min/1.73 m 2 and mean age 73 years, 55% women, 11% black, 12% Asian/Pacific Islander and 36% with diabetes mellitus. During 24-month follow-up, fast progression of CKD occurred in 23.0% of patients with diabetes vs. 15.3% of patients without diabetes. Multivariable predictors of fast CKD progression that were similar by diabetes status included proteinuria, age ≥ 80 years, heart failure, anemia and higher systolic blood pressure. Age 70-79 years, prior ischemic stroke, current or former smoking and lower HDL cholesterol level were also predictive in patients without diabetes, while age 18-49 years was additionally predictive in those with diabetes. In a large, contemporary population of adults with eGFR 30-59 ml/min/1.73 m 2 , accelerated progression of kidney dysfunction within 2 years affected ~ 1 in 4 patients with diabetes and ~ 1 in 7 without diabetes. Regardless of diabetes status, the strongest independent predictors of fast CKD progression included proteinuria, elevated systolic blood pressure, heart failure and anemia.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

Investigating Predictors of Listening Comprehension in Third-, Seventh-, and Tenth-Grade Students: A Dominance Analysis Approach

PubMed Central

Tighe, Elizabeth L.; Spencer, Mercedes; Schatschneider, Christopher

2015-01-01

This study rank ordered the contributive importance of several predictors of listening comprehension for third, seventh, and tenth graders. Principal components analyses revealed that a three-factor solution with fluency, reasoning, and working memory components provided the best fit across grade levels. Dominance analyses indicated that fluency and reasoning were the strongest predictors of third grade listening comprehension. Reasoning emerged as the strongest predictor of seventh and tenth grade listening comprehension. These findings suggest a shift in the contributive importance of predictors to listening comprehension across development (i.e., grade levels). The implications of our findings for educators and researchers are discussed. PMID:26877573

Prediction of Ripple Properties in Shelf Seas. Mark 2 Predictor for Time Evolution

DTIC Science & Technology

2005-12-01

respectively. It is seen in Figure 15 that the time -evolving ripple predictor manages to predict many of the features seen in the data: the growth from a...UNCLASSIFIED Prediction of Ripple Properties in Shelf Seas Mark 2 Predictor for Time Evolution Final Technical Report Prepared for US Office of Naval...distribution is unlimited j~j HR Wallingford UNCLASSIFIED Prediction of Ripple Properties in Shelf Seas Mark 2 Predictor for Time Evolution

Physically demanding situations as predictors of disability pensioning with soft tissue rheumatism among persons 30-39 years old in Norway, 1981-90.

PubMed

Holte, Hilde H; Tambs, Kristian; Bjerkedal, Tor

2002-08-01

Physically demanding work is a predictor of disability pensioning with musculoskeletal diseases. Being a parent is probably also physically demanding. Having manual work and being a parent will be analyzed as possible predictors of becoming a disability pensioner with soft tissue rheumatism (DPSTR) after controlling for level of education, employment, number of hours worked, income, age, sex, and marital status. In this prospective study based on census data of persons 30-39 years old in 1980, predictors of becoming DPSTR during the followup period 1981-90 were identified by logistic regression analysis. Manual work was a predictor for becoming DPSTR for both men and women, while being a parent was neither a risk factor nor a protective factor for becoming DPSTR. Being employed was a predictor of becoming DPSTR for married women, but a protective factor for unmarried women and all men. Low level of education and being married or divorced were predictors of becoming DPSTR for both men and women. Working part time and having low income were predictors of becoming DPSTR among men. Physically demanding employment, but not a physically demanding private life, predicts becoming DPSTR. This may reflect that factors concerning a patient's private life are not taken into account when evaluating whether or not a disability pension should be granted, at least not for patients with uncertain medical conditions.

Work-home interface stress: an important predictor of emotional exhaustion 15 years into a medical career

PubMed Central

HERTZBERG, Tuva Kolstad; RØ, Karin Isaksson; VAGLUM, Per Jørgen Wiggen; MOUM, Torbjørn; RØVIK, Jan Ole; GUDE, Tore; EKEBERG, Øivind; TYSSEN, Reidar

2015-01-01

The importance of work-home interface stress can vary throughout a medical career and between genders. We studied changes in work-home interface stress over 5 yr, and their prediction of emotional exhaustion (main dimension of burn-out), controlled for other variables. A nationwide doctor cohort (NORDOC; n=293) completed questionnaires at 10 and 15 yr after graduation. Changes over the period were examined and predictors of emotional exhaustion analyzed using linear regression. Levels of work-home interface stress declined, whereas emotional exhaustion stayed on the same level. Lack of reduction in work-home interface stress was an independent predictor of emotional exhaustion in year 15 (β=−0.21, p=0.001). Additional independent predictors were reduction in support from colleagues (β=0.11, p=0.04) and emotional exhaustion at baseline (β=0.62, p<0.001). Collegial support was a more important predictor for men than for women. In separate analyses, significant adjusted predictors were lack of reduction in work-home interface stress among women, and reduction of collegial support and lack of reduction in working hours among men. Thus, change in work-home interface stress is a key independent predictor of emotional exhaustion among doctors 15 yr after graduation. Some gender differences in predictors of emotional exhaustion were found. PMID:26538002

Predictors of utilisation of dental care services in a nationally representative sample of adults.

PubMed

Guiney, H; Woods, N; Whelton, H; Morgan, K

2011-12-01

The objective of this study was to identify the predictors of utilisation of dental care services in Ireland. The 2007 Irish Survey of Lifestyle, Attitudes and Nutrition is a cross-sectional study, conducted in 2006/2007 (n = 10,364), by interviews at home to a representative sample of adults aged 18 years or over. Multivariate logistic regression was used to investigate the influence of socioeconomic, predisposing and enabling factors on the odds of males and females having a dental visit in the past year. The significant predictors of visiting the dentist in the past year were for males: having 3rd level education, employment status, earning 50,000 euros or more, location of residence, use of a car, brushing frequently, and dentition status. For females, the predictors were being between 25-34 or 55-64 years-old, education level, earning 50,000 euros or more, location of residence, use of a car, brushing frequently and dentition status. Predictors of the use of dental services vary by gender. Predictors common to both genders were education level, higher income, location of residence, use of a car, brushing frequently and dentition status. Many of the predictors of dental visiting in the past year are also related to social inequalities in health. These predictors may be useful markers of impact for policies designed to address inequalities in access to oral health services.

Predictors of the On-Road Driving Assessment After Traumatic Brain Injury: Comparing Cognitive Tests, Injury Factors, and Demographics.

PubMed

McKay, Adam; Liew, Carine; Schönberger, Michael; Ross, Pamela; Ponsford, Jennie

(1) To examine the relations between performance on cognitive tests and on-road driving assessment in a sample of persons with traumatic brain injury (TBI). (2) To compare cognitive predictors of the on-road assessment with demographic and injury-related predictors. Ninety-nine people with mild-severe TBI who completed an on-road driving assessment in an Australian rehabilitation setting. Retrospective case series. Wechsler Test of Adult Reading or National Adult Reading Test-Revised; 4 subtests from the Wechsler Adult Intelligence Scale-III; Rey Auditory Verbal Leaning Test; Rey Complex Figure Test; Trail Making Test; demographic factors (age, sex, years licensed); and injury-related factors (duration of posttraumatic amnesia; time postinjury). Participants who failed the driving assessment did worse on measures of attention, visual memory, and executive processing; however, cognitive tests were weak correlates (r values <0.3) and poor predictors of the driving assessment. Posttraumatic amnesia duration mediated by time postinjury was the strongest predictor of the driving assessment-that is, participants with more severe TBIs had later driving assessments and were more likely to fail. Cognitive tests are not reliable predictors of the on-road driving assessment outcome. Traumatic brain injury severity may be a better predictor of on-road driving; however, further research is needed to identify the best predictors of driving behavior after TBI.

Predictors of BMI Vary along the BMI Range of German Adults - Results of the German National Nutrition Survey II.

PubMed

Moon, Kilson; Krems, Carolin; Heuer, Thorsten; Roth, Alexander; Hoffmann, Ingrid

2017-01-01

The objective of the study was to identify predictors of BMI in German adults by considering the BMI distribution and to determine whether the association between BMI and its predictors varies along the BMI distribution. The sample included 9,214 adults aged 18-80 years from the German National Nutrition Survey II (NVS II). Quantile regression analyses were conducted to examine the association between BMI and the following predictors: age, sports activities, socio-economic status (SES), healthy eating index-NVS II (HEI-NVS II), dietary knowledge, sleeping duration and energy intake as well as status of smoking, partner relationship and self-reported health. Age, SES, self-reported health status, sports activities and energy intake were the strongest predictors of BMI. The important outcome of this study is that the association between BMI and its predictors varies along the BMI distribution. Especially, energy intake, health status and SES were marginally associated with BMI in normal-weight subjects; this relationships became stronger in the range of overweight, and were strongest in the range of obesity. Predictors of BMI and the strength of these associations vary across the BMI distribution in German adults. Consequently, to identify predictors of BMI, the entire BMI distribution should be considered. © 2017 The Author(s) Published by S. Karger GmbH, Freiburg.

Integrated Strategy Improves the Prediction Accuracy of miRNA in Large Dataset

PubMed Central

Lipps, David; Devineni, Sree

2016-01-01

MiRNAs are short non-coding RNAs of about 22 nucleotides, which play critical roles in gene expression regulation. The biogenesis of miRNAs is largely determined by the sequence and structural features of their parental RNA molecules. Based on these features, multiple computational tools have been developed to predict if RNA transcripts contain miRNAs or not. Although being very successful, these predictors started to face multiple challenges in recent years. Many predictors were optimized using datasets of hundreds of miRNA samples. The sizes of these datasets are much smaller than the number of known miRNAs. Consequently, the prediction accuracy of these predictors in large dataset becomes unknown and needs to be re-tested. In addition, many predictors were optimized for either high sensitivity or high specificity. These optimization strategies may bring in serious limitations in applications. Moreover, to meet continuously raised expectations on these computational tools, improving the prediction accuracy becomes extremely important. In this study, a meta-predictor mirMeta was developed by integrating a set of non-linear transformations with meta-strategy. More specifically, the outputs of five individual predictors were first preprocessed using non-linear transformations, and then fed into an artificial neural network to make the meta-prediction. The prediction accuracy of meta-predictor was validated using both multi-fold cross-validation and independent dataset. The final accuracy of meta-predictor in newly-designed large dataset is improved by 7% to 93%. The meta-predictor is also proved to be less dependent on datasets, as well as has refined balance between sensitivity and specificity. This study has two folds of importance: First, it shows that the combination of non-linear transformations and artificial neural networks improves the prediction accuracy of individual predictors. Second, a new miRNA predictor with significantly improved prediction accuracy is developed for the community for identifying novel miRNAs and the complete set of miRNAs. Source code is available at: https://github.com/xueLab/mirMeta PMID:28002428

Ethnic Distribution of ECG Predictors of Atrial Fibrillation and Its Impact on Understanding the Ethnic Distribution of Ischemic Stroke in the Atherosclerosis Risk in Communities (ARIC) Study

PubMed Central

Soliman, Elsayed Z.; Prineas, Ronald J.; Case, L. Douglas; Zhang, Zhu-ming; Goff, David C.

2009-01-01

Background and Purpose The paradox of the reported low prevalence of atrial fibrillation (AF) in blacks compared with whites despite higher stroke rates in the former could be related to limitations in the current methods used to diagnose AF in population-based studies. Hence, this study aimed to use the ethnic distribution of ECG predictors of AF as measures of AF propensity in different ethnic groups. Methods The distribution of baseline measures of P-wave terminal force, P-wave duration, P-wave area, and PR duration (referred to as AF predictors) were compared by ethnicity in 15 429 participants (27% black) from the Atherosclerosis Risk in Communities (ARIC) study by unpaired t test, χ2, and logistic-regression analysis, as appropriate. Cox proportional-hazards analysis was used to separately examine the association of AF predictors with incident AF and ischemic stroke. Results Whereas AF was significantly less common in blacks compared with whites (0.24% vs 0.95%, P<0.0001), similar to what has been reported in previous studies, blacks had significantly higher and more abnormal values of AF predictors (P<0.0001 for all comparisons). Black ethnicity was significantly associated with abnormal AF predictors compared with whites; odds ratios for different AF predictors ranged from 2.1 to 3.1. AF predictors were significantly and independently associated with AF and ischemic stroke with no significant interaction between ethnicity and AF predictors, findings that further justify using AF predictors as an earlier indicator of future risk of AF and stroke. Conclusions There is a disconnect between the ethnic distribution of AF predictors and the ethnic distribution of AF, probably because the former, unlike the latter, do not suffer from low sensitivity. These results raise the possibility that blacks might actually have a higher prevalence of AF that might have been missed by previous studies owing to limited methodology, a difference that could partially explain the greater stroke risk in blacks. PMID:19213946

Skin color parameters and Fitzpatrick phototypes in estimating the risk of skin cancer: A case-control study in the Polish population.

PubMed

Sitek, Aneta; Rosset, Iwona; Żądzińska, Elżbieta; Kasielska-Trojan, Anna; Neskoromna-Jędrzejczak, Aneta; Antoszewski, Bogusław

2016-04-01

Light skin pigmentation is a known risk factor for skin cancer. Skin color parameters and Fitzpatrick phototypes were evaluated in terms of their usefulness in predicting the risk of skin cancer. A case-control study involved 133 individuals with skin cancer (100 with basal cell carcinoma, 21 with squamous cell carcinoma, 12 with melanoma) and 156 healthy individuals. All of them had skin phototype determined and spectrophotometric skin color measurements were done on the inner surfaces of their arms and on the buttock. Using those data, prediction models were built and subjected to 17-fold stratified cross-validation. A model, based on skin phototypes, was characterized by area under the receiver operating characteristic curve = 0.576 and exhibited a lower predictive power than the models, which were mostly based on spectrophotometric variables describing pigmentation levels. The best predictors of skin cancer were R coordinate of RGB color space (area under the receiver operating characteristic curve 0.687) and melanin index (area under the receiver operating characteristic curve 0.683) for skin on the buttock. A small number of patients were studied. Models were not externally validated. Skin color parameters are more accurate predictors of skin cancer occurrence than skin phototypes. Spectrophotometry is a quick, easy, and affordable method offering relatively good predictive power. Copyright © 2015 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2016-08-01

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

Quantification of Covariance in Tropical Cyclone Activity across Teleconnected Basins

NASA Astrophysics Data System (ADS)

Tolwinski-Ward, S. E.; Wang, D.

2015-12-01

Rigorous statistical quantification of natural hazard covariance across regions has important implications for risk management, and is also of fundamental scientific interest. We present a multivariate Bayesian Poisson regression model for inferring the covariance in tropical cyclone (TC) counts across multiple ocean basins and across Saffir-Simpson intensity categories. Such covariability results from the influence of large-scale modes of climate variability on local environments that can alternately suppress or enhance TC genesis and intensification, and our model also simultaneously quantifies the covariance of TC counts with various climatic modes in order to deduce the source of inter-basin TC covariability. The model explicitly treats the time-dependent uncertainty in observed maximum sustained wind data, and hence the nominal intensity category of each TC. Differences in annual TC counts as measured by different agencies are also formally addressed. The probabilistic output of the model can be probed for probabilistic answers to such questions as: - Does the relationship between different categories of TCs differ statistically by basin? - Which climatic predictors have significant relationships with TC activity in each basin? - Are the relationships between counts in different basins conditionally independent given the climatic predictors, or are there other factors at play affecting inter-basin covariability? - How can a portfolio of insured property be optimized across space to minimize risk? Although we present results of our model applied to TCs, the framework is generalizable to covariance estimation between multivariate counts of natural hazards across regions and/or across peril types.

Separate but correlated: The latent structure of space and mathematics across development.

PubMed

Mix, Kelly S; Levine, Susan C; Cheng, Yi-Ling; Young, Chris; Hambrick, D Zachary; Ping, Raedy; Konstantopoulos, Spyros

2016-09-01

The relations among various spatial and mathematics skills were assessed in a cross-sectional study of 854 children from kindergarten, third, and sixth grades (i.e., 5 to 13 years of age). Children completed a battery of spatial mathematics tests and their scores were submitted to exploratory factor analyses both within and across domains. In the within domain analyses, all of the measures formed single factors at each age, suggesting consistent, unitary structures across this age range. Yet, as in previous work, the 2 domains were highly correlated, both in terms of overall composite score and pairwise comparisons of individual tasks. When both spatial and mathematics scores were submitted to the same factor analysis, the 2 domain specific factors again emerged, but there also were significant cross-domain factor loadings that varied with age. Multivariate regressions replicated the factor analysis and further revealed that mental rotation was the best predictor of mathematical performance in kindergarten, and visual-spatial working memory was the best predictor of mathematical performance in sixth grade. The mathematical tasks that predicted the most variance in spatial skill were place value (K, 3rd, 6th), word problems (3rd, 6th), calculation (K), fraction concepts (3rd), and algebra (6th). Thus, although spatial skill and mathematics each have strong internal structures, they also share significant overlap, and have particularly strong cross-domain relations for certain tasks. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Choice is good, but relevance is excellent: autonomy-enhancing and suppressing teacher behaviours predicting students' engagement in schoolwork.

PubMed

Assor, Avi; Kaplan, Haya; Roth, Guy

2002-06-01

This article examines two questions concerning teacher-behaviours that are characterised in Self-Determination Theory (Ryan & Deci, 2000) as autonomy-supportive or suppressive: (1) Can children differentiate among various types of autonomy-enhancing and suppressing teacher behaviours? (2) Which of those types of behaviour are particularly important in predicting feelings toward and engagement in schoolwork? It was hypothesised that teacher behaviours that help students to understand the relevance of schoolwork for their personal interests and goals are particularly important predictors of engagement in schoolwork. Israeli students in grades 3-5 (N = 498) and in grades 6-8 (N = 364) completed questionnaires assessing the variables of interest. Smallest Space Analyses indicated that both children and early adolescents can differentiate among three types of autonomy enhancing teacher behaviours - fostering relevance, allowing criticism, and providing choice - and three types of autonomy suppressing teacher behaviours - suppressing criticism, intruding, and forcing unmeaningful acts. Regression analyses supported the hypothesis concerning the importance of teacher behaviours that clarify the personal relevance of schoolwork. Among the autonomy-suppressing behaviours, 'Criticism-suppression' was the best predictor of feelings and engagement. The findings underscore the active and empathic nature of teachers' role in supporting students' autonomy, and suggest that autonomy-support is important not only for early adolescents but also for children. Discussion of potential determinants of the relative importance of various autonomy-affecting teacher actions suggests that provision of choice should not always be viewed as a major indicator of autonomy support.

Towards measuring the semantic capacity of a physical medium demonstrated with elementary cellular automata.

PubMed

Dittrich, Peter

2018-02-01

The organic code concept and its operationalization by molecular codes have been introduced to study the semiotic nature of living systems. This contribution develops further the idea that the semantic capacity of a physical medium can be measured by assessing its ability to implement a code as a contingent mapping. For demonstration and evaluation, the approach is applied to a formal medium: elementary cellular automata (ECA). The semantic capacity is measured by counting the number of ways codes can be implemented. Additionally, a link to information theory is established by taking multivariate mutual information for quantifying contingency. It is shown how ECAs differ in their semantic capacities, how this is related to various ECA classifications, and how this depends on how a meaning is defined. Interestingly, if the meaning should persist for a certain while, the highest semantic capacity is found in CAs with apparently simple behavior, i.e., the fixed-point and two-cycle class. Synergy as a predictor for a CA's ability to implement codes can only be used if context implementing codes are common. For large context spaces with sparse coding contexts synergy is a weak predictor. Concluding, the approach presented here can distinguish CA-like systems with respect to their ability to implement contingent mappings. Applying this to physical systems appears straight forward and might lead to a novel physical property indicating how suitable a physical medium is to implement a semiotic system. Copyright © 2017 Elsevier B.V. All rights reserved.

The Developmental Costs and Benefits of Children’s Involvement in Interparental Conflict

PubMed Central

Davies, Patrick T.; Coe, Jesse L.; Martin, Meredith J.; Sturge-Apple, Melissa L.; Cummings, E. Mark

2015-01-01

Building on empirical documentation of children’s involvement in interparental conflicts as a weak predictor of psychopathology, we tested the hypothesis that involvement in conflict more consistently serves as a moderator of associations between children’s emotional reactivity to interparental conflict and their psychological problems. In Study 1, 263 early adolescents (M age = 12.62 years), mothers, and fathers completed surveys of family and child functioning at two measurement occasions spaced two years apart. In Study 2, 243 preschool children (M age = 4.60 years) participated in a multi-method (i.e., observations, structured interview, surveys) measurement battery to assess family functioning, children’s reactivity to interparental conflict, and their psychological adjustment. Across both studies, latent difference score (LDS) analyses revealed that involvement moderated associations between emotional reactivity and children’s increases in psychological (i.e., internalizing and externalizing) problems. Children’s emotional reactivity to interparental conflict was a significantly stronger predictor of their psychological maladjustment when they were highly involved in the conflicts. In addition, the developmental benefits and costs of involvement varied as a function of emotional reactivity. Involvement in interparental conflict predicted increases in psychological problems for children experiencing high emotional reactivity and decreases in psychological problems when they exhibited low emotional reactivity. We interpret the results in the context of the new formulation of emotional security theory (e.g. Davies & Martin, 2013) and family systems models of children’s parentification (e.g., Byng-Hall, 2002). PMID:26053147

Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.

2001-01-01

This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

The developmental costs and benefits of children's involvement in interparental conflict.

PubMed

Davies, Patrick T; Coe, Jesse L; Martin, Meredith J; Sturge-Apple, Melissa L; Cummings, E Mark

2015-08-01

Building on empirical documentation of children's involvement in interparental conflicts as a weak predictor of psychopathology, we tested the hypothesis that involvement in conflict more consistently serves as a moderator of associations between children's emotional reactivity to interparental conflict and their psychological problems. In Study 1, 263 early adolescents (M age = 12.62 years), mothers, and fathers completed surveys of family and child functioning at 2 measurement occasions spaced 2 years apart. In Study 2, 243 preschool children (M age = 4.60 years) participated in a multimethod (i.e., observations, structured interview, surveys) measurement battery to assess family functioning, children's reactivity to interparental conflict, and their psychological adjustment. Across both studies, latent difference score analyses revealed that involvement moderated associations between emotional reactivity and children's increases in psychological (i.e., internalizing and externalizing) problems. Children's emotional reactivity to interparental conflict was a significantly stronger predictor of their psychological maladjustment when they were highly involved in the conflicts. In addition, the developmental benefits and costs of involvement varied as a function of emotional reactivity. Involvement in interparental conflict predicted increases in psychological problems for children experiencing high emotional reactivity and decreases in psychological problems when they exhibited low emotional reactivity. We interpret the results in the context of the new formulation of emotional security theory (e.g., Davies & Martin, 2013) and family systems models of children's parentification (e.g., Byng-Hall, 2002). (c) 2015 APA, all rights reserved).

Constraints on Cumulus Parameterization from Simulations of Observed MJO Events

NASA Technical Reports Server (NTRS)

Del Genio, Anthony; Wu, Jingbo; Wolf, Audrey B.; Chen, Yonghua; Yao, Mao-Sung; Kim, Daehyun

2015-01-01

Two recent activities offer an opportunity to test general circulation model (GCM) convection and its interaction with large-scale dynamics for observed Madden-Julian oscillation (MJO) events. This study evaluates the sensitivity of the Goddard Institute for Space Studies (GISS) GCM to entrainment, rain evaporation, downdrafts, and cold pools. Single Column Model versions that restrict weakly entraining convection produce the most realistic dependence of convection depth on column water vapor (CWV) during the Atmospheric Radiation Measurement MJO Investigation Experiment at Gan Island. Differences among models are primarily at intermediate CWV where the transition from shallow to deeper convection occurs. GCM 20-day hindcasts during the Year of Tropical Convection that best capture the shallow–deep transition also produce strong MJOs, with significant predictability compared to Tropical Rainfall Measuring Mission data. The dry anomaly east of the disturbance on hindcast day 1 is a good predictor of MJO onset and evolution. Initial CWV there is near the shallow–deep transition point, implicating premature onset of deep convection as a predictor of a poor MJO simulation. Convection weakly moistens the dry region in good MJO simulations in the first week; weakening of large-scale subsidence over this time may also affect MJO onset. Longwave radiation anomalies are weakest in the worst model version, consistent with previous analyses of cloud/moisture greenhouse enhancement as the primary MJO energy source. The authors’ results suggest that both cloud-/moisture-radiative interactions and convection–moisture sensitivity are required to produce a successful MJO simulation.

Topographic and Bioclimatic Determinants of the Occurrence of Forest and Grassland in Tropical Montane Forest-Grassland Mosaics of the Western Ghats, India

PubMed Central

Das, Arundhati; Nagendra, Harini; Anand, Madhur; Bunyan, Milind

2015-01-01

The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation. PMID:26121353

Topographic and Bioclimatic Determinants of the Occurrence of Forest and Grassland in Tropical Montane Forest-Grassland Mosaics of the Western Ghats, India.

PubMed

Das, Arundhati; Nagendra, Harini; Anand, Madhur; Bunyan, Milind

2015-01-01

The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000 m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300 m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation.

A static predictor of seismic demand on frames based on a post-elastic deflected shape

USGS Publications Warehouse

Mori, Y.; Yamanaka, T.; Luco, N.; Cornell, C.A.

2006-01-01

Predictors of seismic structural demands (such as inter-storey drift angles) that are less time-consuming than nonlinear dynamic analysis have proven useful for structural performance assessment and for design. Luco and Cornell previously proposed a simple predictor that extends the idea of modal superposition (of the first two modes) with the square-root-of-sum-of-squares (SRSS) rule by taking a first-mode inelastic spectral displacement into account. This predictor achieved a significant improvement over simply using the response of an elastic oscillator; however, it cannot capture well large displacements caused by local yielding. A possible improvement of Luco's predictor is discussed in this paper, where it is proposed to consider three enhancements: (i) a post-elastic first-mode shape approximated by the deflected shape from a nonlinear static pushover analysis (NSPA) at the step corresponding to the maximum drift of an equivalent inelastic single-degree-of-freedom (SDOF) system, (ii) a trilinear backbone curve for the SDOF system, and (iii) the elastic third-mode response for long-period buildings. Numerical examples demonstrate that the proposed predictor is less biased and results in less dispersion than Luco's original predictor. Copyright ?? 2006 John Wiley & Sons, Ltd.

A Systematic Review of Predictors of, and Reasons for, Adherence to Online Psychological Interventions.

PubMed

Beatty, Lisa; Binnion, Claire

2016-12-01

A key issue regarding the provision of psychological therapy in a self-guided online format is low rates of adherence. The aim of this systematic review was to assess both quantitative and qualitative data on the predictors of adherence, as well as participant reported reasons for adhering or not adhering to online psychological interventions. Database searches of PsycINFO, Medline, and CINAHL identified 1721 potentially relevant articles published between 1 January 2000 and 25 November 2015. A further 34 potentially relevant articles were retrieved from reference lists. Articles that reported predictors of, or reasons for, adherence to an online psychological intervention were included. A total of 36 studies met the inclusion criteria. Predictors assessed included demographic, psychological, characteristics of presenting problem, and intervention/computer-related predictors. Evidence suggested that female gender, higher treatment expectancy, sufficient time, and personalized intervention content each predicted higher adherence. Age, baseline symptom severity, and control group allocation had mixed findings. The majority of assessed variables however, did not predict adherence. Few clear predictors of adherence emerged overall, and most results were either mixed or too preliminary to draw conclusions. More research of predictors associated with adherence to online interventions is warranted.

Predictors of the Longevity Difference: A 25-Year Follow-Up.

ERIC Educational Resources Information Center

Palmore, Erdman B.

1982-01-01

Studied predictors of longevity among 252 panelists in a 25-year longitudinal study of aging. The strongest independent predictors for men were health self-rating, work satisfaction, and performance intelligence; for women they were health satisfaction, and physical function rating. (Author)

Employee Turnover: An Empirical and Methodological Assessment.

ERIC Educational Resources Information Center

Muchinsky, Paul M.; Tuttle, Mark L.

1979-01-01

Reviews research on the prediction of employee turnover. Groups predictor variables into five general categories: attitudinal (job satisfaction), biodata, work-related, personal, and test-score predictors. Consistent relationships between common predictor variables and turnover were found for four categories. Eight methodological problems/issues…

Predictors of Severe Disease in Melioidosis Patients in Kuala Lumpur, Malaysia

PubMed Central

Mohd Roslani, Ardita Dewi Roslani; Tay, Sun Tee; Puthucheary, Savithri D.; Rukumani, Devi V.; Sam, I-Ching

2014-01-01

The predictors of severe disease or death were determined for 85 melioidosis patients in Kuala Lumpur, Malaysia. Most of the patients were male, > 40 years old, and diabetic. Severe disease or death occurred in 28 (32.9%) cases. Lower lymphocyte counts and positive blood cultures were significant independent predictors of severe disease, but age, presentations with pneumonia, inappropriate empirical antibiotics, or flagellin types of the infecting isolates were not. Knowledge of local predictors of severe disease is useful for clinical management. PMID:25246695

Using the Random Nearest Neighbor Data Mining Method to Extract Maximum Information Content from Weather Forecasts from Multiple Predictors of Weather and One Predictand (Low-Level Turbulence)

DTIC Science & Technology

2014-10-30

Force Weather Agency (AFWA) WRF 15-km atmospheric model forecast data and low-level turbulence. Archives of historical model data forecast predictors...Relationships between WRF model predictors and PIREPS were developed using the new data mining methodology. The new methodology was inspired...convection. Predictors of turbulence were collected from the AFWA WRF 15km model, and corresponding PIREPS (the predictand) were collected between 2013

Predictors of temporary and permanent work disability in patients with inflammatory bowel disease: results of the swiss inflammatory bowel disease cohort study.

PubMed

Siebert, Uwe; Wurm, Johannes; Gothe, Raffaella Matteucci; Arvandi, Marjan; Vavricka, Stephan R; von Känel, Roland; Begré, Stefan; Sulz, Michael C; Meyenberger, Christa; Sagmeister, Markus

2013-01-01

Inflammatory bowel disease can decrease the quality of life and induce work disability. We sought to (1) identify and quantify the predictors of disease-specific work disability in patients with inflammatory bowel disease and (2) assess the suitability of using cross-sectional data to predict future outcomes, using the Swiss Inflammatory Bowel Disease Cohort Study data. A total of 1187 patients were enrolled and followed up for an average of 13 months. Predictors included patient and disease characteristics and drug utilization. Potential predictors were identified through an expert panel and published literature. We estimated adjusted effect estimates with 95% confidence intervals using logistic and zero-inflated Poisson regression. Overall, 699 (58.9%) experienced Crohn's disease and 488 (41.1%) had ulcerative colitis. Most important predictors for temporary work disability in patients with Crohn's disease included gender, disease duration, disease activity, C-reactive protein level, smoking, depressive symptoms, fistulas, extraintestinal manifestations, and the use of immunosuppressants/steroids. Temporary work disability in patients with ulcerative colitis was associated with age, disease duration, disease activity, and the use of steroids/antibiotics. In all patients, disease activity emerged as the only predictor of permanent work disability. Comparing data at enrollment versus follow-up yielded substantial differences regarding disability and predictors, with follow-up data showing greater predictor effects. We identified predictors of work disability in patients with Crohn's disease and ulcerative colitis. Our findings can help in forecasting these disease courses and guide the choice of appropriate measures to prevent adverse outcomes. Comparing cross-sectional and longitudinal data showed that the conduction of cohort studies is inevitable for the examination of disability.

Assessment of the uncertainty and predictive power of large-scale predictors for nonlinear precipitation downscaling in the European Arctic (Invited)

NASA Astrophysics Data System (ADS)

Sauter, T.

2013-12-01

Despite the extensive research on downscaling methods there is still little consensus about the choice of useful atmospheric predictor variables. Besides the general decision of a proper statistical downscaling model, the selection of an informative predictor set is crucial for the accuracy and stability of the resulting downscaled time series. These requirements must be fullfilled by both the atmospheric variables and the predictor domains in terms of geographical location and spatial extend, to which in general not much attention is paid. However, only a limited number of studies is interested in the predictive capability of the predictor domain size or shape, and the question to what extent variability of neighboring grid points influence local-scale events. In this study we emphasized the spatial relationships between observed daily precipitation and selected number of atmospheric variables for the European Arctic. Several nonlinear regression models are used to link the large-scale predictors obtained from reanalysed Weather Research and Forecast model runs to the local-scale observed precipitation. Inferences on the sources of uncertainty are then drawn from variance based sensitivity measures, which also permit to capture interaction effects between individual predictors. The information is further used to develop more parsimonious downscaling models with only small decreases in accuracy. Individual predictors (without interactions) account for almost 2/3 of the total output variance, while the remaining fraction is solely due to interactions. Neglecting predictor interactions in the screening process will lead to some loss of information. Hence, linear screening methods are insufficient as they neither account for interactions nor for non-additivity as given by many nonlinear prediction algorithms.

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison.

PubMed

Vervloet, Marlies; Van den Noortgate, Wim; Ceulemans, Eva

2018-02-12

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights' estimates unstable (i.e., the "bouncing beta" problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.

Average is Boring: How Similarity Kills a Meme's Success

NASA Astrophysics Data System (ADS)

Coscia, Michele

2014-09-01

Every day we are exposed to different ideas, or memes, competing with each other for our attention. Previous research explained popularity and persistence heterogeneity of memes by assuming them in competition for limited attention resources, distributed in a heterogeneous social network. Little has been said about what characteristics make a specific meme more likely to be successful. We propose a similarity-based explanation: memes with higher similarity to other memes have a significant disadvantage in their potential popularity. We employ a meme similarity measure based on semantic text analysis and computer vision to prove that a meme is more likely to be successful and to thrive if its characteristics make it unique. Our results show that indeed successful memes are located in the periphery of the meme similarity space and that our similarity measure is a promising predictor of a meme success.

Planning in subsumption architectures

NASA Technical Reports Server (NTRS)

Chalfant, Eugene C.

1994-01-01

A subsumption planner using a parallel distributed computational paradigm based on the subsumption architecture for control of real-world capable robots is described. Virtual sensor state space is used as a planning tool to visualize the robot's anticipated effect on its environment. Decision sequences are generated based on the environmental situation expected at the time the robot must commit to a decision. Between decision points, the robot performs in a preprogrammed manner. A rudimentary, domain-specific partial world model contains enough information to extrapolate the end results of the rote behavior between decision points. A collective network of predictors operates in parallel with the reactive network forming a recurrrent network which generates plans as a hierarchy. Details of a plan segment are generated only when its execution is imminent. The use of the subsumption planner is demonstrated by a simple maze navigation problem.

A State-Space Approach to Optimal Level-Crossing Prediction for Linear Gaussian Processes

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2009-01-01

In many complex engineered systems, the ability to give an alarm prior to impending critical events is of great importance. These critical events may have varying degrees of severity, and in fact they may occur during normal system operation. In this article, we investigate approximations to theoretically optimal methods of designing alarm systems for the prediction of level-crossings by a zero-mean stationary linear dynamic system driven by Gaussian noise. An optimal alarm system is designed to elicit the fewest false alarms for a fixed detection probability. This work introduces the use of Kalman filtering in tandem with the optimal level-crossing problem. It is shown that there is a negligible loss in overall accuracy when using approximations to the theoretically optimal predictor, at the advantage of greatly reduced computational complexity. I

Average is boring: how similarity kills a meme's success.

PubMed

Coscia, Michele

2014-09-26

Every day we are exposed to different ideas, or memes, competing with each other for our attention. Previous research explained popularity and persistence heterogeneity of memes by assuming them in competition for limited attention resources, distributed in a heterogeneous social network. Little has been said about what characteristics make a specific meme more likely to be successful. We propose a similarity-based explanation: memes with higher similarity to other memes have a significant disadvantage in their potential popularity. We employ a meme similarity measure based on semantic text analysis and computer vision to prove that a meme is more likely to be successful and to thrive if its characteristics make it unique. Our results show that indeed successful memes are located in the periphery of the meme similarity space and that our similarity measure is a promising predictor of a meme success.

Neighborhood Sociodemographic Predictors of Serious Emotional Disturbance (SED) in Schools: Demonstrating a Small Area Estimation Method in the National Comorbidity Survey (NCS-A) Adolescent Supplement

PubMed Central

Alegría, Margarita; Kessler, Ronald C.; McLaughlin, Katie A.; Gruber, Michael J.; Sampson, Nancy A.; Zaslavsky, Alan M.

2014-01-01

We evaluate the precision of a model estimating school prevalence of SED using a small area estimation method based on readily-available predictors from area-level census block data and school principal questionnaires. Adolescents at 314 schools participated in the National Comorbidity Supplement, a national survey of DSM-IV disorders among adolescents. A multilevel model indicated that predictors accounted for under half of the variance in school-level SED and even less when considering block-group predictors or principal report alone. While Census measures and principal questionnaires are significant predictors of individual-level SED, associations are too weak to generate precise school-level predictions of SED prevalence. PMID:24740174

Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders: Evidence Report

NASA Technical Reports Server (NTRS)

Slack, Kelley J.; Williams, Thomas J.; Schneiderman, Jason S.; Whitmire, Alexandra M.; Picano, James J.; Leveton, Lauren B.; Schmidt, Lacey L.; Shea, Camille

2016-01-01

In April 2010, President Obama declared a space pioneering goal for the United States in general and NASA in particular. "Fifty years after the creation of NASA, our goal is no longer just a destination to reach. Our goal is the capacity for people to work and learn and operate and live safely beyond the Earth for extended periods of time, ultimately in ways that are more sustainable and even indefinite." Thus NASA's Strategic Objective 1.1 emerged as "expand human presence into the solar system and to the surface of Mars to advance exploration, science, innovation, benefits to humanity, and international collaboration" (NASA 2015b). Any space flight, be it of long or short duration, occurs in an extreme environment that has unique stressors. Even with excellent selection methods, the potential for behavioral problems among space flight crews remain a threat to mission success. Assessment of factors that are related to behavioral health can help minimize the chances of distress and, thus, reduce the likelihood of adverse cognitive or behavioral conditions and psychiatric disorders arising within a crew. Similarly, countermeasures that focus on prevention and treatment can mitigate the cognitive or behavioral conditions that, should they arise, would impact mission success. Given the general consensus that longer duration, isolation, and confined missions have a greater risk for behavioral health ensuring crew behavioral health over the long term is essential. Risk, which within the context of this report is assessed with respect to behavioral health and performance, is addressed to deter development of cognitive and behavioral degradations or psychiatric conditions in space flight and analog populations, and to monitor, detect, and treat early risk factors, predictors and other contributing factors. Based on space flight and analog evidence, the average incidence rate of an adverse behavioral health event occurring during a space mission is relatively low for the current conditions. While mood and anxiety disturbances have occurred, no behavioral emergencies have been reported to date in space flight. Anecdotal and empirical evidence indicate that the likelihood of an adverse cognitive or behavioral condition or psychiatric disorder occurring greatly increases with the length of a mission. Further, while cognitive, behavioral, or psychiatric conditions might not immediately and directly threaten mission success, such conditions can, and do, adversely impact individual and crew health, welfare, and performance.

Mean phase predictor for maximum a posteriori demodulator

NASA Technical Reports Server (NTRS)

Altes, Richard A. (Inventor)

1996-01-01

A system and method for optimal maximum a posteriori (MAP) demodulation using a novel mean phase predictor. The mean phase predictor conducts cumulative averaging over multiple blocks of phase samples to provide accurate prior mean phases, to be input into a MAP phase estimator.

A Preliminary Examination of Negative Life Events and Sexual Assault Victimization as Predictors of Psychological Functioning in Female College Students: Does One Matter More Than the Other?

PubMed

Chang, Edward C; Lee, Jerin; Morris, Lily E; Lucas, Abigael G; Chang, Olivia D; Hirsch, Jameson K

2017-07-01

The present study examined negative life events (NLEs) and sexual assault victimization as predictors of positive and negative psychological functioning in a sample of 151 female college students. Results obtained from conducting regression analyses indicated several notable patterns. NLEs, compared with sexual assault victimization, were a stronger negative predictor of positive functioning based on indices related to subjective well-being (e.g., life satisfaction, positive affect). Alternatively, sexual assault victimization, compared with NLEs, was a stronger positive predictor of negative functioning based on indices related to posttraumatic stress disorder symptoms (e.g., anxiety) and related conditions (e.g., alcohol use). Furthermore, both NLEs and sexual assault victimization were found to be positive predictors of negative functioning based on indices related to suicide risk (e.g., depressive symptoms, suicidal behaviors). Overall, our findings indicate that both NLEs and sexual assault victimization represent important and distinct predictors of psychological functioning in female college students.

Predictor symbology in computer-generated pictorial displays

NASA Technical Reports Server (NTRS)

Grunwald, A. J.

1981-01-01

The display under investigation, is a tunnel display for the four-dimensional commercial aircraft approach-to-landing under instrument flight rules. It is investigated whether more complex predictive information such as a three-dimensional perspective vehicle symbol, predicting the future vehicle position as well as future vehicle attitude angles, contributes to a better system response, and suitable predictor laws for the predictor motions, are formulated. Methods for utilizing the predictor symbol in controlling the forward velocity of the aircraft in four-dimensional approaches, are investigated. The simulator tests show, that the complex perspective vehicle symbol yields improved damping in the lateral response as compared to a flat two-dimensional predictor cross, but yields generally larger vertical deviations. Methods of using the predictor symbol in controlling the forward velocity of the vehicle are shown to be effective. The tunnel display with superimposed perspective vehicle symbol yields very satisfactory results and pilot acceptance in the lateral control but is found to be unsatisfactory in the vertical control, as a result of too large vertical path-angle deviations.

Predicting Reading and Spelling Difficulties in Transparent and Opaque Orthographies: A Comparison between Scandinavian and U.S./Australian Children

PubMed Central

Furnes, Bjarte; Samuelsson, Stefan

2010-01-01

In this study, predictors of reading and spelling difficulties among children learning more transparent (Norwegian/Swedish) and less transparent (English) orthographies were examined longitudinally from preschool through Grade 2 using parallel versions of tests. A series of logistic regression analysis indicated three main findings. First, phonological awareness as a predictor of reading difficulties in the Scandinavian sample was time-limited to Grade 1, but remained as a significant predictor in the English-speaking sample. Second, phonological awareness predicted spelling difficulties similarly across orthographies. Third, preschool and kindergarten RAN was a significant predictor of reading and spelling difficulties at both Grades 1 and 2 across orthographies. The authors conclude that phonological awareness diminishes as a predictor of reading difficulties in transparent orthographies after the first years of schooling, that RAN is a better long term predictor of reading difficulties, and that phonological awareness is associated with spelling difficulties similarly in transparent and opaque orthographies. PMID:20440743

Academic and Demographic Predictors of NCLEX-RN Pass Rates in First- and Second-Degree Accelerated BSN Programs.

PubMed

Kaddoura, Mahmoud A; Flint, Elizabeth P; Van Dyke, Olga; Yang, Qing; Chiang, Li-Chi

Relatively few studies have addressed predictors of first-attempt outcomes (pass-fail) on the National Council Licensure Examination-Registered Nurses (NCLEX-RN) for accelerated BSN programs. The purpose of this study was to compare potential predictors of NCLEX outcomes in graduates of first-degree accelerated (FDA; n=62) and second-degree accelerated (SDA; n=173) BSN programs sharing a common nursing curriculum. In this retrospective study, bivariate analyses and multiple logistic regression assessed significance of selected demographic and academic characteristics as predictors of NCLEX-RN outcomes. FDA graduates were more likely than SDA graduates to fail the NCLEX-RN (P=.0013). FDA graduates were more likely to speak English as a second or additional language (P<.0001), have lower end-of-program GPA and HESI Exit Exam scores (both P<.0001), and have a higher proportions of grades ≤ C (P=.0023). All four variables were significant predictors of NCLEX-RN outcomes within both FDA and SDA programs. The only significant predictors in adjusted logistic regression of NCLEX-RN outcome for the pooled FDA+SDA graduate sample were proportion of grades ≤ C (a predictor of NCLEX-RN failure) and HESI Exit Exam score (a predictor of passing NCLEX-RN). Grades of C or lower on any course may indicate inadequate mastery of critical NCLEX-RN content and increased risk of NCLEX-RN failure. Copyright © 2016 Elsevier Inc. All rights reserved.

Predictors of women's exercise maintenance after cardiac rehabilitation.

PubMed

Moore, Shirley M; Dolansky, Mary A; Ruland, Cornelia M; Pashkow, Fredric J; Blackburn, Gordon G

2003-01-01

Less than 50% of persons who participate in cardiac rehabilitation (CR) programs maintain an exercise regimen for as long as 6 months after completion. This study was conducted to identify factors that predict women's exercise following completion of a CR program. In this prospective, descriptive study, a convenience sample of 60 women were recruited at completion of a phase II CR program. Exercise was measured using a heart rate wristwatch monitor over 3 months. Predictor variables collected at the time of the subjects' enrollment were age, body mass index, cardiac functional status, comorbidity, muscle or joint pain, motivation, mood state, social support, self-efficacy, perceived benefits or barriers, and prior exercise. Of women, 25% did not exercise at all following completion of a CR program and only 48% of the subjects were exercising at 3 months. Different predictors were found of the various dimensions of exercise maintenance. Predictors of exercise frequency were comorbidity and instrumental social support. Instrumental social support was the only predictor of exercise persistence. Comorbidity was the only predictor of exercise intensity. The only predictor of the total amount of exercise was benefits or barriers. Interventions aimed at increasing women's exercise should focus on increasing their problem-solving abilities to reduce barriers to exercise and increase social support by family and friends. Because comorbidity was a significant predictor of exercise, women should be encouraged to use exercise techniques that reduce impact on muscles and joints (eg, swimming) or exercising for short periods several times a day.

Development of a sugar-binding residue prediction system from protein sequences using support vector machine.

PubMed

Banno, Masaki; Komiyama, Yusuke; Cao, Wei; Oku, Yuya; Ueki, Kokoro; Sumikoshi, Kazuya; Nakamura, Shugo; Terada, Tohru; Shimizu, Kentaro

2017-02-01

Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (https://doi.org/10.5281/zenodo.61513). Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predictors of Sustainability of Social Programs

ERIC Educational Resources Information Center

Savaya, Riki; Spiro, Shimon E.

2012-01-01

This article presents the findings of a large scale study that tested a comprehensive model of predictors of three manifestations of sustainability: continuation, institutionalization, and duration. Based on the literature the predictors were arrayed in four groups: variables pertaining to the project, the auspice organization, the community, and…

Community College Faculty Recruitment: Predictors of Applicant Attraction to Faculty Positions.

ERIC Educational Resources Information Center

Winter, Paul A.; Kjorlien, Chad L.

2000-01-01

Utilizes MBA students' biographical data and reactions to simulated position ads for community college business faculty positions to identify predictors of applicant decisions. Reveals four significant predictors of participants' ratings of simulated positions: applicant's current job satisfaction, spouse's contribution to household income,…

Predicting Intentional Communication in Preverbal Preschoolers with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Sandbank, Micheal; Woynaroski, Tiffany; Watson, Linda R.; Gardner, Elizabeth; Keçeli Kaysili, Bahar; Yoder, Paul

2017-01-01

Intentional communication has previously been identified as a value-added predictor of expressive language in preverbal preschoolers with autism spectrum disorder. In the present study, we sought to identify value-added predictors of intentional communication. Of five theoretically-motivated putative predictors of intentional communication…

Predictors of Transience among Homeless Emerging Adults

ERIC Educational Resources Information Center

Ferguson, Kristin M.; Bender, Kimberly; Thompson, Sanna J.

2014-01-01

This study identified predictors of transience among homeless emerging adults in three cities. A total of 601 homeless emerging adults from Los Angeles, Austin, and Denver were recruited using purposive sampling. Ordinary least squares regression results revealed that significant predictors of greater transience include White ethnicity, high…

Estimating the Classification Efficiency of a Test Battery.

ERIC Educational Resources Information Center

De Corte, Wilfried

2000-01-01

Shows how a theorem proven by H. Brogden (1951, 1959) can be used to estimate the allocation average (a predictor based classification of a test battery) assuming that the predictor intercorrelations and validities are known and that the predictor variables have a joint multivariate normal distribution. (SLD)

Most Likely to Succeed: Exploring Predictor Variables for the Counselor Preparation Comprehensive Examination

ERIC Educational Resources Information Center

Hartwig, Elizabeth Kjellstrand; Van Overschelde, James P.

2016-01-01

The authors investigated predictor variables for the Counselor Preparation Comprehensive Examination (CPCE) to examine whether academic variables, demographic variables, and test version were associated with graduate counseling students' CPCE scores. Multiple regression analyses revealed all 3 variables were statistically significant predictors of…

Effects of Internship Predictors on Successful Field Experience.

ERIC Educational Resources Information Center

Beard, Fred; Morton, Linda

1999-01-01

Finds that a majority of advertising and public-relations interns found their internships successful. Indicates that successful internships depend on predictors given the least attention by school programs: quality of supervision was the most important single predictor variable, followed in importance by organizational practices/policies, positive…

Relations among Socioeconomic Status, Age, and Predictors of Phonological Awareness

ERIC Educational Resources Information Center

McDowell, Kimberly D.; Lonigan, Christopher J.; Goldstein, Howard

2007-01-01

Purpose: This study simultaneously examined predictors of phonological awareness within the framework of 2 theories: the phonological distinctness hypothesis and the lexical restructuring model. Additionally, age as a moderator of the relations between predictor variables and phonological awareness was examined. Method: This cross-sectional…

Predictors of Anxiety and Depression in Taiwanese Secondary Students.

ERIC Educational Resources Information Center

Hong, Zuway-R; Veach, Patricia McCarthy; Lawrenz, Frances

This study investigated significant predictors of anxiety and depression in Taiwanese secondary students and the different functions of these predictors. Surveys were completed by 1,672 senior high school students in Taiwan. As part of a larger study, these students completed the Secondary Student Questionnaire (SSQ), an instrument developed by…

Predictors of Academic Achievement for Elementary Teacher Education Students in Turkey

ERIC Educational Resources Information Center

Buyukozturk, Sener

2004-01-01

Studies examining the important predictors of academic achievement of elementary teacher education students help us to understand the predictors of student achievement. These studies (House, 2000b; Ting & Bryant, 2001; Zheng, Saunders, Shelley, & Whalen, 2002)focus on the relationship between academic achievement and a number of cognitive…

Religiosity and Authoritarianism as Predictors of Attitude toward the Disabled: A Regression Analysis.

ERIC Educational Resources Information Center

Tunick, Roy H.; And Others

1979-01-01

This study identifies predictors and correlates of attitudes toward the disabled. Authoritarianism, church attendance, religious orthodoxy, age, and education were significantly related to these attitudes of people in a Rocky Mountain Community. Significant predictors of the criterion were authoritarianism, religiosity, and age. Recommendations…

Alternative predictors in chaotic time series

NASA Astrophysics Data System (ADS)

Alves, P. R. L.; Duarte, L. G. S.; da Mota, L. A. C. P.

2017-06-01

In the scheme of reconstruction, non-polynomial predictors improve the forecast from chaotic time series. The algebraic manipulation in the Maple environment is the basis for obtaining of accurate predictors. Beyond the different times of prediction, the optional arguments of the computational routines optimize the running and the analysis of global mappings.

Beyond ORF: Student-Level Predictors of Reading Achievement

ERIC Educational Resources Information Center

Canto, Angela I.; Proctor, Briley E.

2013-01-01

This study explored student-level predictors of reading achievement among third grade regular education students. Predictors included student demographics (sex and socioeconomic status (SES), using free and reduced lunch as proxy for SES), direct observations of reading skills (oral reading fluency (ORF) and word decoding skill (nonsense word…

How Binary Skills Obscure the Transition from Non-Mastery to Mastery

ERIC Educational Resources Information Center

Karelitz, Tzur M.

2008-01-01

What is the nature of latent predictors that facilitate diagnostic classification? Rupp and Templin (this issue) suggest that these predictors should be multidimensional, categorical variables that can be combined in various ways. Diagnostic Classification Models (DCM) typically use multiple categorical predictors to classify respondents into…

Identifying Predictors of Social Functioning in College Students: A Meta-Analysis

ERIC Educational Resources Information Center

Beard, Jennifer Blair

2011-01-01

This meta-analysis draws studies from the literature on college student persistence, need theories, and positive psychology in investigating the strongest predictors of social functioning in college students in the United States and Canada. The predictor categories included background characteristics, measures of personality, mental health…

Exploring Statistics Anxiety: Contrasting Mathematical, Academic Performance and Trait Psychological Predictors

ERIC Educational Resources Information Center

Bourne, Victoria J.

2018-01-01

Statistics anxiety is experienced by a large number of psychology students, and previous research has examined a range of potential correlates, including academic performance, mathematical ability and psychological predictors. These varying predictors are often considered separately, although there may be shared variance between them. In the…

Probabilistic streamflow forecasting for hydroelectricity production: A comparison of two non-parametric system identification algorithms

NASA Astrophysics Data System (ADS)

Pande, Saket; Sharma, Ashish

2014-05-01

This study is motivated by the need to robustly specify, identify, and forecast runoff generation processes for hydroelectricity production. It atleast requires the identification of significant predictors of runoff generation and the influence of each such significant predictor on runoff response. To this end, we compare two non-parametric algorithms of predictor subset selection. One is based on information theory that assesses predictor significance (and hence selection) based on Partial Information (PI) rationale of Sharma and Mehrotra (2014). The other algorithm is based on a frequentist approach that uses bounds on probability of error concept of Pande (2005), assesses all possible predictor subsets on-the-go and converges to a predictor subset in an computationally efficient manner. Both the algorithms approximate the underlying system by locally constant functions and select predictor subsets corresponding to these functions. The performance of the two algorithms is compared on a set of synthetic case studies as well as a real world case study of inflow forecasting. References: Sharma, A., and R. Mehrotra (2014), An information theoretic alternative to model a natural system using observational information alone, Water Resources Research, 49, doi:10.1002/2013WR013845. Pande, S. (2005), Generalized local learning in water resource management, PhD dissertation, Utah State University, UT-USA, 148p.

A Systematic Review and Meta-Analysis of Predictors of Expressive-Language Outcomes Among Late Talkers.

PubMed

Fisher, Evelyn L

2017-10-17

The purpose of this study was to explore the literature on predictors of outcomes among late talkers using systematic review and meta-analysis methods. We sought to answer the question: What factors predict preschool-age expressive-language outcomes among late-talking toddlers? We entered carefully selected search terms into the following electronic databases: Communication & Mass Media Complete, ERIC, Medline, PsycEXTRA, Psychological and Behavioral Sciences, and PsycINFO. We conducted a separate, random-effects model meta-analysis for each individual predictor that was used in a minimum of 5 studies. We also tested potential moderators of the relationship between predictors and outcomes using metaregression and subgroup analysis. Last, we conducted publication-bias and sensitivity analyses. We identified 20 samples, comprising 2,134 children, in a systematic review. According to the results of the meta-analyses, significant predictors of expressive-language outcomes included toddlerhood expressive-vocabulary size, receptive language, and socioeconomic status. Nonsignificant predictors included phrase speech, gender, and family history. To our knowledge this is the first synthesis of the literature on predictors of outcomes among late talkers using meta-analysis. Our findings clarify the contributions of several constructs to outcomes and highlight the importance of early receptive language to expressive-language development. https://doi.org/10.23641/asha.5313454.

Predictors of Complications in Patients Receiving Head and Neck Free Flap Reconstructive Procedures.

PubMed

Eskander, Antoine; Kang, Stephen; Tweel, Ben; Sitapara, Jigar; Old, Matthew; Ozer, Enver; Agrawal, Amit; Carrau, Ricardo; Rocco, James W; Teknos, Theodoros N

2018-05-01

Objective To (1) determine the overall complication rate, wound healing, and wound infection complications and (2) identify preoperative, intraoperative, and postoperative predictors of these complications. Study Design Case series with chart review. Setting Tertiary academic cancer hospital. Subjects and Methods All head and neck free flap patients at The Ohio State University (2006-2012) were assessed. Multivariable logistic regression assessed the impact of patient factors, flap and wound factors, and intraoperative factors on the aforementioned quality metric outcomes. Results Of the 515 patients identified, 54% had a complication predicted by longer operating room (OR) time, higher comorbidity index, and oral cavity and pharyngeal tumor sites. Predictors of wound-healing complications (15%) were longer OR time, volume of crystalloid given intraoperatively, and oral cavity and pharyngeal tumor sites. Predictors of wound infection (12%) were younger age, diabetes mellitus, and malnutrition. Conclusions Wound healing and infectious complications account for most complications in patients with head and neck cancer undergoing free flap reconstruction. Clean contaminated wounds are a significant predictor of wound complications. Advanced OR time, advanced age, and comorbidity status, including diabetes mellitus and malnutrition, are other important predictors. Crystalloid administration is also an important predictor of wound-healing complications, and this warrants further study.

Objective Lightning Probability Forecasting for Kennedy Space Center and Cape Canaveral Air Force Station, Phase III

NASA Technical Reports Server (NTRS)

Crawford, Winifred C.

2010-01-01

The AMU created new logistic regression equations in an effort to increase the skill of the Objective Lightning Forecast Tool developed in Phase II (Lambert 2007). One equation was created for each of five sub-seasons based on the daily lightning climatology instead of by month as was done in Phase II. The assumption was that these equations would capture the physical attributes that contribute to thunderstorm formation more so than monthly equations. However, the SS values in Section 5.3.2 showed that the Phase III equations had worse skill than the Phase II equations and, therefore, will not be transitioned into operations. The current Objective Lightning Forecast Tool developed in Phase II will continue to be used operationally in MIDDS. Three warm seasons were added to the Phase II dataset to increase the POR from 17 to 20 years (1989-2008), and data for October were included since the daily climatology showed lightning occurrence extending into that month. None of the three methods tested to determine the start of the subseason in each individual year were able to discern the start dates with consistent accuracy. Therefore, the start dates were determined by the daily climatology shown in Figure 10 and were the same in every year. The procedures used to create the predictors and develop the equations were identical to those in Phase II. The equations were made up of one to three predictors. TI and the flow regime probabilities were the top predictors followed by 1-day persistence, then VT and Ll. Each equation outperformed four other forecast methods by 7-57% using the verification dataset, but the new equations were outperformed by the Phase II equations in every sub-season. The reason for the degradation may be due to the fact that the same sub-season start dates were used in every year. It is likely there was overlap of sub-season days at the beginning and end of each defined sub-season in each individual year, which could very well affect equation performance.

Predictors of natively unfolded proteins: unanimous consensus score to detect a twilight zone between order and disorder in generic datasets.

PubMed

Deiana, Antonio; Giansanti, Andrea

2010-04-21

Natively unfolded proteins lack a well defined three dimensional structure but have important biological functions, suggesting a re-assignment of the structure-function paradigm. To assess that a given protein is natively unfolded requires laborious experimental investigations, then reliable sequence-only methods for predicting whether a sequence corresponds to a folded or to an unfolded protein are of interest in fundamental and applicative studies. Many proteins have amino acidic compositions compatible both with the folded and unfolded status, and belong to a twilight zone between order and disorder. This makes difficult a dichotomic classification of protein sequences into folded and natively unfolded ones. In this work we propose an operational method to identify proteins belonging to the twilight zone by combining into a consensus score good performing single predictors of folding. In this methodological paper dichotomic folding indexes are considered: hydrophobicity-charge, mean packing, mean pairwise energy, Poodle-W and a new global index, that is called here gVSL2, based on the local disorder predictor VSL2. The performance of these indexes is evaluated on different datasets, in particular on a new dataset composed by 2369 folded and 81 natively unfolded proteins. Poodle-W, gVSL2 and mean pairwise energy have good performance and stability in all the datasets considered and are combined into a strictly unanimous combination score SSU, that leaves proteins unclassified when the consensus of all combined indexes is not reached. The unclassified proteins: i) belong to an overlap region in the vector space of amino acidic compositions occupied by both folded and unfolded proteins; ii) are composed by approximately the same number of order-promoting and disorder-promoting amino acids; iii) have a mean flexibility intermediate between that of folded and that of unfolded proteins. Our results show that proteins unclassified by SSU belong to a twilight zone. Proteins left unclassified by the consensus score SSU have physical properties intermediate between those of folded and those of natively unfolded proteins and their structural properties and evolutionary history are worth to be investigated.

Predictors of natively unfolded proteins: unanimous consensus score to detect a twilight zone between order and disorder in generic datasets

PubMed Central

2010-01-01

Background Natively unfolded proteins lack a well defined three dimensional structure but have important biological functions, suggesting a re-assignment of the structure-function paradigm. To assess that a given protein is natively unfolded requires laborious experimental investigations, then reliable sequence-only methods for predicting whether a sequence corresponds to a folded or to an unfolded protein are of interest in fundamental and applicative studies. Many proteins have amino acidic compositions compatible both with the folded and unfolded status, and belong to a twilight zone between order and disorder. This makes difficult a dichotomic classification of protein sequences into folded and natively unfolded ones. In this work we propose an operational method to identify proteins belonging to the twilight zone by combining into a consensus score good performing single predictors of folding. Results In this methodological paper dichotomic folding indexes are considered: hydrophobicity-charge, mean packing, mean pairwise energy, Poodle-W and a new global index, that is called here gVSL2, based on the local disorder predictor VSL2. The performance of these indexes is evaluated on different datasets, in particular on a new dataset composed by 2369 folded and 81 natively unfolded proteins. Poodle-W, gVSL2 and mean pairwise energy have good performance and stability in all the datasets considered and are combined into a strictly unanimous combination score SSU, that leaves proteins unclassified when the consensus of all combined indexes is not reached. The unclassified proteins: i) belong to an overlap region in the vector space of amino acidic compositions occupied by both folded and unfolded proteins; ii) are composed by approximately the same number of order-promoting and disorder-promoting amino acids; iii) have a mean flexibility intermediate between that of folded and that of unfolded proteins. Conclusions Our results show that proteins unclassified by SSU belong to a twilight zone. Proteins left unclassified by the consensus score SSU have physical properties intermediate between those of folded and those of natively unfolded proteins and their structural properties and evolutionary history are worth to be investigated. PMID:20409339

Levels and predictors of airborne and internal exposure to chromium and nickel among welders--results of the WELDOX study.

PubMed

Weiss, Tobias; Pesch, Beate; Lotz, Anne; Gutwinski, Eleonore; Van Gelder, Rainer; Punkenburg, Ewald; Kendzia, Benjamin; Gawrych, Katarzyna; Lehnert, Martin; Heinze, Evelyn; Hartwig, Andrea; Käfferlein, Heiko U; Hahn, Jens-Uwe; Brüning, Thomas

2013-03-01

The objective of this analysis was to investigate levels and determinants of exposure to airborne and urinary chromium (Cr, CrU) and nickel (Ni, NiU) among 241 welders. Respirable and inhalable welding fume was collected during a shift, and the metal content was determined using inductively coupled plasma mass spectrometry. In post-shift urine, CrU and NiU were measured by means of graphite furnace atom absorption spectrometry, with resulting concentrations varying across a wide range. Due to a large fraction below the limits of quantitation we applied multiple imputations to the log-transformed exposure variables for the analysis of the data. Respirable Cr and Ni were about half of the concentrations of inhalable Cr and Ni, respectively. CrU and NiU were determined with medians of 1.2 μg/L (interquartile range <1.00; 3.61) and 2.9 μg/L (interquartile range <1.50; 5.97). Furthermore, Cr and Ni correlated in respirable welding fume (r=0.79, 95% CI 0.74-0.85) and urine (r=0.55, 95% CI 0.44-0.65). Regression models identified exposure-modulating variables in form of multiplicative factors and revealed slightly better model fits for Cr (R(2) respirable Cr 48%, CrU 55%) than for Ni (R(2) respirable Ni 42%, NiU 38%). The air concentrations were mainly predicted by the metal content in electrodes or base material in addition to the welding technique. Respirable Cr and Ni were good predictors for CrU and NiU, respectively. Exposure was higher when welding was performed in confined spaces or with inefficient ventilation, and lower in urine when respirators were used. In conclusion, statistical modelling allowed the evaluation of determinants of internal and external exposure to Cr and Ni in welders. Welding parameters were stronger predictors than workplace conditions. Airborne exposure was lowest inside respirators with supply of purified air. Copyright © 2012 Elsevier GmbH. All rights reserved.

Determinants of severe anemia among laboring mothers in Mekelle city public hospitals, Tigray region, Ethiopia

PubMed Central

Alemayehu, Mussie; Mitiku, Mengistu; Goba, Gelila K.

2017-01-01

Introduction Anemia is a global public health problem that has affected a significant number of pregnant mothers worldwide. The World Health Organization has estimated the prevalence of anemia in pregnant women at 18% and 56% in developed and developing countries, respectively. This study aimed to identify factors associated with severe anemia among laboring women in Mekelle city public hospitals, Tigray, Ethiopia. Methods This unmatched case–control study involved 264 (88 = cases and 176 = controls) pregnant women who were recruited when they came for delivery service in Mekelle city public hospitals. The data was collected from July to August, 2016. In this study, a systematic sampling technique was used inselecting controls, but the cases were enrolled until the required sample size was reached. Bivariate and multivariate analyses were conducted to find predictors of severe anemia. Statistically significant predictors of severe anemia were identified at P-value <0.05 and 95% confidence interval. Results A total of 264 pregnant women who came for delivery services were enrolled in this study. The major predicting variables for the occurrence of severe anemia among laboring women were residency (AOR = 3.28, 95% CI: 1.26–8.48), number of pregnancies (AOR = 2.46, 95% CI: 1.14–5.29), iron folate supplementation (AOR = 3.29, 95% CI: 1.27–8.49), dietary diversification score (AOR = 3.23, 95% CI: 1.19–8.71) and duration of menstrual cycle (AOR = 2.37, 95% CI: 1.10–5.10). The variable ‘blood loss during pregnancy’ (AOR = 6.63 95% CI: 2.96–14.86) was identified as a strong predictor of the outcome variable, severe anemia. Conclusion This study identified determinants of severe anemia among laboring women in Mekelle city public hospitals, Northern Ethiopia. To reduce anemia, strengthening health education provision related to the importance of birth spacing and consuming diversified and iron-enriched food should be considered. Moreover, screening of pregnant women for state of anemia during their visits to health facilities, as well as de-worming for intestinal parasites infection are needed. PMID:29099850

Determinants of severe anemia among laboring mothers in Mekelle city public hospitals, Tigray region, Ethiopia.

PubMed

Ebuy, Yirga; Alemayehu, Mussie; Mitiku, Mengistu; Goba, Gelila K

2017-01-01

Anemia is a global public health problem that has affected a significant number of pregnant mothers worldwide. The World Health Organization has estimated the prevalence of anemia in pregnant women at 18% and 56% in developed and developing countries, respectively. This study aimed to identify factors associated with severe anemia among laboring women in Mekelle city public hospitals, Tigray, Ethiopia. This unmatched case-control study involved 264 (88 = cases and 176 = controls) pregnant women who were recruited when they came for delivery service in Mekelle city public hospitals. The data was collected from July to August, 2016. In this study, a systematic sampling technique was used inselecting controls, but the cases were enrolled until the required sample size was reached. Bivariate and multivariate analyses were conducted to find predictors of severe anemia. Statistically significant predictors of severe anemia were identified at P-value <0.05 and 95% confidence interval. A total of 264 pregnant women who came for delivery services were enrolled in this study. The major predicting variables for the occurrence of severe anemia among laboring women were residency (AOR = 3.28, 95% CI: 1.26-8.48), number of pregnancies (AOR = 2.46, 95% CI: 1.14-5.29), iron folate supplementation (AOR = 3.29, 95% CI: 1.27-8.49), dietary diversification score (AOR = 3.23, 95% CI: 1.19-8.71) and duration of menstrual cycle (AOR = 2.37, 95% CI: 1.10-5.10). The variable 'blood loss during pregnancy' (AOR = 6.63 95% CI: 2.96-14.86) was identified as a strong predictor of the outcome variable, severe anemia. This study identified determinants of severe anemia among laboring women in Mekelle city public hospitals, Northern Ethiopia. To reduce anemia, strengthening health education provision related to the importance of birth spacing and consuming diversified and iron-enriched food should be considered. Moreover, screening of pregnant women for state of anemia during their visits to health facilities, as well as de-worming for intestinal parasites infection are needed.

Spatiotemporal exposure modeling of ambient erythemal ultraviolet radiation.

PubMed

VoPham, Trang; Hart, Jaime E; Bertrand, Kimberly A; Sun, Zhibin; Tamimi, Rulla M; Laden, Francine

2016-11-24

Ultraviolet B (UV-B) radiation plays a multifaceted role in human health, inducing DNA damage and representing the primary source of vitamin D for most humans; however, current U.S. UV exposure models are limited in spatial, temporal, and/or spectral resolution. Area-to-point (ATP) residual kriging is a geostatistical method that can be used to create a spatiotemporal exposure model by downscaling from an area- to point-level spatial resolution using fine-scale ancillary data. A stratified ATP residual kriging approach was used to predict average July noon-time erythemal UV (UV Ery ) (mW/m 2 ) biennially from 1998 to 2012 by downscaling National Aeronautics and Space Administration (NASA) Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) gridded remote sensing images to a 1 km spatial resolution. Ancillary data were incorporated in random intercept linear mixed-effects regression models. Modeling was performed separately within nine U.S. regions to satisfy stationarity and account for locally varying associations between UV Ery and predictors. Cross-validation was used to compare ATP residual kriging models and NASA grids to UV-B Monitoring and Research Program (UVMRP) measurements (gold standard). Predictors included in the final regional models included surface albedo, aerosol optical depth (AOD), cloud cover, dew point, elevation, latitude, ozone, surface incoming shortwave flux, sulfur dioxide (SO 2 ), year, and interactions between year and surface albedo, AOD, cloud cover, dew point, elevation, latitude, and SO 2 . ATP residual kriging models more accurately estimated UV Ery at UVMRP monitoring stations on average compared to NASA grids across the contiguous U.S. (average mean absolute error [MAE] for ATP, NASA: 15.8, 20.3; average root mean square error [RMSE]: 21.3, 25.5). ATP residual kriging was associated with positive percent relative improvements in MAE (0.6-31.5%) and RMSE (3.6-29.4%) across all regions compared to NASA grids. ATP residual kriging incorporating fine-scale spatial predictors can provide more accurate, high-resolution UV Ery estimates compared to using NASA grids and can be used in epidemiologic studies examining the health effects of ambient UV.

Long-term personality data collection in support of spaceflight and analogue research.

PubMed

Musson, David M; Helmreich, Robert L

2005-06-01

This is a review of past and present research into personality and performance at the University of Texas (UT) Human Factors Research Project. Specifically, personality trait data collected from astronauts, pilots, Antarctic personnel, and other groups over a 15-yr period is discussed with particular emphasis on research in space and space analogue environments. The UT Human Factors Research Project conducts studies in personality and group dynamics in aviation, space, and medicine. Current studies include personality determinants of professional cultures, team effectiveness in both medicine and aviation, and personality predictors of long-term astronaut performance. The Project also studies the design and effectiveness of behavioral strategies used to minimize error and maximize team performance in safety-critical work settings. A multi-year personality and performance dataset presents many opportunities for research, including long-term and follow-up studies of human performance, analyses of trends in recruiting and attrition, and the ability to adapt research design to operational changes and methodological advances. Special problems posed by such long-duration projects include issues of confidentiality and security, as well as practical limitations imposed by current peer-review and short-term funding practices. Practical considerations for ongoing dataset management include consistency of assessment instruments over time, variations in data acquisition from one year to the next, and dealing with changes in theory and practice that occur over the life of the project. A fundamental change in how research into human performance is funded would be required to ensure the ongoing development of such long-duration research databases.

The dependency of timbre on fundamental frequency

NASA Astrophysics Data System (ADS)

Marozeau, Jeremy; de Cheveigné, Alain; McAdams, Stephen; Winsberg, Suzanne

2003-11-01

The dependency of the timbre of musical sounds on their fundamental frequency (F0) was examined in three experiments. In experiment I subjects compared the timbres of stimuli produced by a set of 12 musical instruments with equal F0, duration, and loudness. There were three sessions, each at a different F0. In experiment II the same stimuli were rearranged in pairs, each with the same difference in F0, and subjects had to ignore the constant difference in pitch. In experiment III, instruments were paired both with and without an F0 difference within the same session, and subjects had to ignore the variable differences in pitch. Experiment I yielded dissimilarity matrices that were similar at different F0's, suggesting that instruments kept their relative positions within timbre space. Experiment II found that subjects were able to ignore the salient pitch difference while rating timbre dissimilarity. Dissimilarity matrices were symmetrical, suggesting further that the absolute displacement of the set of instruments within timbre space was small. Experiment III extended this result to the case where the pitch difference varied from trial to trial. Multidimensional scaling (MDS) of dissimilarity scores produced solutions (timbre spaces) that varied little across conditions and experiments. MDS solutions were used to test the validity of signal-based predictors of timbre, and in particular their stability as a function of F0. Taken together, the results suggest that timbre differences are perceived independently from differences of pitch, at least for F0 differences smaller than an octave. Timbre differences can be measured between stimuli with different F0's.

Moving overlapping grids with adaptive mesh refinement for high-speed reactive and non-reactive flow

NASA Astrophysics Data System (ADS)

Henshaw, William D.; Schwendeman, Donald W.

2006-08-01

We consider the solution of the reactive and non-reactive Euler equations on two-dimensional domains that evolve in time. The domains are discretized using moving overlapping grids. In a typical grid construction, boundary-fitted grids are used to represent moving boundaries, and these grids overlap with stationary background Cartesian grids. Block-structured adaptive mesh refinement (AMR) is used to resolve fine-scale features in the flow such as shocks and detonations. Refinement grids are added to base-level grids according to an estimate of the error, and these refinement grids move with their corresponding base-level grids. The numerical approximation of the governing equations takes place in the parameter space of each component grid which is defined by a mapping from (fixed) parameter space to (moving) physical space. The mapped equations are solved numerically using a second-order extension of Godunov's method. The stiff source term in the reactive case is handled using a Runge-Kutta error-control scheme. We consider cases when the boundaries move according to a prescribed function of time and when the boundaries of embedded bodies move according to the surface stress exerted by the fluid. In the latter case, the Newton-Euler equations describe the motion of the center of mass of the each body and the rotation about it, and these equations are integrated numerically using a second-order predictor-corrector scheme. Numerical boundary conditions at slip walls are described, and numerical results are presented for both reactive and non-reactive flows that demonstrate the use and accuracy of the numerical approach.

Predictors of dyadic planning: Perspectives of prostate cancer survivors and their partners.

PubMed

Keller, Jan; Wiedemann, Amelie U; Hohl, Diana Hilda; Scholz, Urte; Burkert, Silke; Schrader, Mark; Knoll, Nina

2017-02-01

Extending individual planning of health behaviour change to the level of the dyad, dyadic planning refers to a target person and a planning partner jointly planning the target person's health behaviour change. To date, predictors of dyadic planning have not been systematically investigated. Integrating cognitive predictors of individual planning with four established predictor domains of social support provision, we propose a framework of predictors of dyadic planning. Including target persons' and partners' perspectives, we examine these predictor domains in the context of prostate cancer patients' rehabilitative pelvic floor exercise (PFE) following radical prostatectomy. Longitudinal data from 175 patients and their partners were analysed in a study with four post-surgery assessments across 6 months. PFE-related dyadic planning was assessed from both partners together with indicators from four predictor domains: context, target person, partner, and relationship factors. Individual planning and social support served as covariates. Findings from two-level models nesting repeated assessments in individuals showed that context (patients' incontinence), target person (i.e., positive affect and self-efficacy), and relationship factors (i.e., relationship satisfaction) were uniquely associated with dyadic planning, whereas partner factors (i.e., positive and negative affects) were not. Factors predicting patients' and partners' accounts of dyadic planning differed. Resembling prior findings on antecedents of support provision in this context, partner factors did not prevail as unique predictors of dyadic planning, whereas indicators from all other predictor domains did. To establish predictive direction, future work should use lagged predictions with shorter intermeasurement intervals. Statement of contribution What is already known on this subject? Dyadic planning has been shown to be linked to health behaviour change. However, its role in behaviour regulation frameworks is not well investigated, especially regarding factors that might be predictive of dyadic planning. What does this study add? A framework of predictors of dyadic planning in the health behaviour change process is presented. The framework is investigated accounting for both planning partners' perspectives. Context, target person, and relationship factors were related to dyadic planning. © 2016 The British Psychological Society.

Recovery from aphasia after hemicraniectomy for infarction of the speech-dominant hemisphere.

PubMed

Kastrau, Frank; Wolter, Marcus; Huber, Walter; Block, Frank

2005-04-01

The space-occupying effect of cerebral edema limits survival chances of patients with severe ischemic stroke. Besides conventional therapies to reduce intracranial pressure, hemicraniectomy can be considered as a therapeutic option after space-occupying cerebral infarction. There is controversy regarding the use of this method in patients with infarction of the speech-dominant hemisphere. In 14 patients with infarction of the dominant hemisphere and subsequent treatment with hemicraniectomy, recovery from aphasic symptoms was evaluated retrospectively. A group of patients who were treated between 1994 and 2003 in our aphasia ward was selected for the study. In all patients, a psychometric quantification was accomplished applying the Aachen Aphasia Test at least twice within a mean observation period of 470 days. A significant improvement of the statistical parameters representing different aspects of aphasia was observed in 13 of 14 patients. Also, an increase of the ability to communicate was evident in 13 patients. Young age at the time of stroke and early poststroke decompressive surgery were identified as main predictors for recovery from aphasia. A significant improvement of aphasic symptoms can be observed in a preselected group of patients after a massive stroke of the speech-dominant hemisphere treated by consecutive hemicraniectomy. Therefore, decompressive surgery can be considered for the treatment of this kind of stroke.

Customer satisfaction in medical service encounters -- a comparison between obstetrics and gynecology patients and general medical patients.

PubMed

Chang, Ching-Sheng; Weng, Hui-Ching; Chang, Hsin-Hsin; Hsu, Tsuen-Ho

2006-03-01

This study is concerned with the "service encounter", and seeks to describe, by use of the Service Encounter Evaluation Model, how the processes involved in the service encounter affect customer satisfaction. Its findings have implications for management practice and research directions, and recommendations are made. With the implementation of a national health insurance scheme, an ever-prospering economy and continually improving educational levels in Taiwan, demand among citizens for good health and medical care is ever increasing. Obstetrics and gynecology patients often differ greatly from general patients, in terms of their moods and emotions. This research involved an empirical study, whose subjects were 590 customers of general clinics and 339 customers of gynecology clinics, in various medical centers in southern Taiwan. By factor analysis, the study established four influencing factors, which were "Medical professionals", "Nursing professionals", "Service personnel" and "Space and facilities". Using the Linear Structural Relation Model (LISREL), it found that medical professionals, nursing professionals, service personnel and space and facilities were effective predictors of medical treatment satisfaction. We also found that the greatest positive impact on overall medical treatment satisfaction resulted from rises in satisfaction with medical professionals, but that the least impact was achieved in relation to service personnel in the general and gynecology clinics.

Clinical and Radiographic Outcomes of Meniscus Surgery and Future Targets for Biologic Intervention: A review of data from the MOON Group

PubMed Central

Westermann, Robert W.; Jones, Morgan; Wasserstein, David; Spindler, Kurt P.

2017-01-01

Meniscus injury and treatment occurred with the majority of anterior cruciate ligament reconstructions (ACLR) in the multicenter orthopaedic outcomes (MOON) cohort. We describe the patient reported outcomes, radiographic outcomes and predictors of pain from meniscus injuries and treatment in the setting of ACLR. Patient reported outcomes improve significantly following meniscus repair with ACLR, but differences exist based on the meniscus injury laterally (medial or lateral). Patients undergoing medial meniscus repair have worse patient-reported outcomes and more pain compared to those with uninjured menisci. However, lateral meniscal tears can be repaired with similar outcomes as uninjured menisci. Medial meniscal treatment (meniscectomy or repair) results in a significant loss of joint space at 2 years compared to uninjured menisci. Menisci treated with excision had a greater degree of joint space loss compared to those treated with repair. Clinically significant knee pain is more common following injuries to the medial meniscus and increased in patients who undergo early re-operation after initial ACLR. Future research efforts aimed at improving outcomes after combined ACLR and meniscus treatment should focus on optimizing biologic and mechanical environments that promote healing of medial meniscal tears sustained during ACL injury. PMID:28282214

Can we use Earth Observations to improve monthly water level forecasts?

NASA Astrophysics Data System (ADS)

Slater, L. J.; Villarini, G.

2017-12-01

Dynamical-statistical hydrologic forecasting approaches benefit from different strengths in comparison with traditional hydrologic forecasting systems: they are computationally efficient, can integrate and `learn' from a broad selection of input data (e.g., General Circulation Model (GCM) forecasts, Earth Observation time series, teleconnection patterns), and can take advantage of recent progress in machine learning (e.g. multi-model blending, post-processing and ensembling techniques). Recent efforts to develop a dynamical-statistical ensemble approach for forecasting seasonal streamflow using both GCM forecasts and changing land cover have shown promising results over the U.S. Midwest. Here, we use climate forecasts from several GCMs of the North American Multi Model Ensemble (NMME) alongside 15-minute stage time series from the National River Flow Archive (NRFA) and land cover classes extracted from the European Space Agency's Climate Change Initiative 300 m annual Global Land Cover time series. With these data, we conduct systematic long-range probabilistic forecasting of monthly water levels in UK catchments over timescales ranging from one to twelve months ahead. We evaluate the improvement in model fit and model forecasting skill that comes from using land cover classes as predictors in the models. This work opens up new possibilities for combining Earth Observation time series with GCM forecasts to predict a variety of hazards from space using data science techniques.

Space resource utilisation: a novel indicator to quantify species competitive ability for light.

PubMed

Zhang, Pengfei; Zhou, Xiaolong; Li, Junyong; Guo, Zhi; Du, Guozhen

2015-11-23

Species richness and productivity are two fundamental aspects of ecosystems. As a result, the relationship between species richness and productivity has been widely studied. A series of fertilisation experiments in an alpine meadow on the Tibetan Plateau were performed to study the relationship between species richness and productivity. In this paper, we present a novel indicator, i.e., space resource utilisation (SRU), which is calculated by a volume formula (V(i)  =  h(i) · S(i); h(i) = plant height of species i, S(i) = quadrat area × percent cover of species i). SRU more fully reflected species competitive ability for light in both horizontal and vertical dimensions compared with plant height and cover. We used this novel indicator to investigate the effects of SRU on the changes in species richness and productivity following fertilisation. We found that the SRU of the community was correlated with increasing productivity and decreasing species richness following fertilisation and was a better predictor of species richness than productivity. The changes in SRU following fertilisation vary among species. These results demonstrate that SRU can be a more useful tool in explaining plant biodiversity loss and predicting the fate of different species than each of height, cover and productivity.

The Impact of Simulated Microgravity on the Growth of Different Genotypes of the Model Legume Plant Medicago truncatula

NASA Astrophysics Data System (ADS)

Lionheart, Gemma; Vandenbrink, Joshua P.; Hoeksema, Jason D.; Kiss, John Z.

2018-05-01

Simulated microgravity has been a useful tool to help understand plant development in altered gravity conditions. Thirty-one genotypes of the legume plant Medicago truncatula were grown in either simulated microgravity on a rotating clinostat, or in a static, vertical environment. Twenty morphological features were measured and compared between these two gravity treatments. Within-species genotypic variation was a significant predictor of the phenotypic response to gravity treatment in 100% of the measured morphological and growth features. In addition, there was a genotype-environment interaction (G × E) for 45% of the response variables, including shoot relative growth rate (p < 0.0005), median number of roots (p ˜ 0.02), and root dry mass (p < 0.005). Our studies demonstrate that genotype does play a significant role in M. truncatula morphology and affects the response of plants to the gravity treatment, influencing both the magnitude and direction of the gravity response. These findings are discussed in the context of improving future studies in plant space biology by controlling for genotypic differences. Thus, manipulation of genotype effects, in combination with M. truncatula's symbiotic relationships with bacteria and fungi, will be important for optimizing legumes for cultivation on long-term space missions.

Bayesian block-diagonal variable selection and model averaging

PubMed Central

Papaspiliopoulos, O.; Rossell, D.

2018-01-01

Summary We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also provides a novel and efficient solution to the frequentist best subset selection problem for block-diagonal designs. Posterior probabilities for any number of models are obtained by evaluating a single one-dimensional integral, and other quantities of interest such as variable inclusion probabilities and model-averaged regression estimates are obtained by an adaptive, deterministic one-dimensional numerical integration. The overall computational cost scales linearly with the number of blocks, which can be processed in parallel, and exponentially with the block size, rendering it most adequate in situations where predictors are organized in many moderately-sized blocks. For general designs, we approximate the Gram matrix by a block-diagonal matrix using spectral clustering and propose an iterative algorithm that capitalizes on the block-diagonal algorithms to explore efficiently the model space. All methods proposed in this paper are implemented in the R library mombf. PMID:29861501

Space resource utilisation: a novel indicator to quantify species competitive ability for light

PubMed Central

Zhang, Pengfei; Zhou, Xiaolong; Li, Junyong; Guo, Zhi; Du, Guozhen

2015-01-01

Species richness and productivity are two fundamental aspects of ecosystems. As a result, the relationship between species richness and productivity has been widely studied. A series of fertilisation experiments in an alpine meadow on the Tibetan Plateau were performed to study the relationship between species richness and productivity. In this paper, we present a novel indicator, i.e., space resource utilisation (SRU), which is calculated by a volume formula (Vi  =  hi · Si; hi = plant height of species i, Si = quadrat area × percent cover of species i). SRU more fully reflected species competitive ability for light in both horizontal and vertical dimensions compared with plant height and cover. We used this novel indicator to investigate the effects of SRU on the changes in species richness and productivity following fertilisation. We found that the SRU of the community was correlated with increasing productivity and decreasing species richness following fertilisation and was a better predictor of species richness than productivity. The changes in SRU following fertilisation vary among species. These results demonstrate that SRU can be a more useful tool in explaining plant biodiversity loss and predicting the fate of different species than each of height, cover and productivity. PMID:26593068

Continuation of studies on thermoregulation of fish and turtles in thermally stressed habitats. Summary progress report, 1 October 1977-30 September 1980

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

Spotila, J.R.

1980-05-01

Biophysical-behavioral-ecological models have been completed to explain the behavioral thermoregulation of largemouth bass (Micropterus salmoides) and turtles (Chrysemys scripta). Steady state and time dependent mathematical models accurately predict the body temperatures of largemouth bass. Field experiments using multichannel radio transmitters have provided temperatures of several body compartments of free ranging bass in their natural habitat. Initial studies have been completed to describe the behavioral thermoregulation of bass in a reactor cooling reservoir. Energy budgets, fundamental climate spaces, and realized climate spaces have been completed for the turtle, C. scripta. We have described the behavioral thermoregulation of C. scripta in Parmore » Pond, S.C. and have measured its movements, home ranges and population levels in heated and unheated arms of the reservoir. Operative environmental temperature is a good predictor of the basking behavior of this turtle. A new synthesis explained the evolution of thermoregulatory strategies among animals. Laboratory experiments clarified the effects of movement, diving and temperature on the blood flow of alligators. Other experiments defined the role of boundary layers in controlling the evaporation of water from the surfaces of turtles and alligators in still and moving air. Nutritional status may be an important factor affecting the thermoregulatory behavior of turtles.« less

The use of regression tree analysis for predicting the functional outcome following traumatic spinal cord injury.

PubMed

Facchinello, Yann; Beauséjour, Marie; Richard-Denis, Andreane; Thompson, Cynthia; Mac-Thiong, Jean-Marc

2017-10-25

Predicting the long-term functional outcome following traumatic spinal cord injury is needed to adapt medical strategies and to plan an optimized rehabilitation. This study investigates the use of regression tree for the development of predictive models based on acute clinical and demographic predictors. This prospective study was performed on 172 patients hospitalized following traumatic spinal cord injury. Functional outcome was quantified using the Spinal Cord Independence Measure collected within the first-year post injury. Age, delay prior to surgery and Injury Severity Score were considered as continuous predictors while energy of injury, trauma mechanisms, neurological level of injury, injury severity, occurrence of early spasticity, urinary tract infection, pressure ulcer and pneumonia were coded as categorical inputs. A simplified model was built using only injury severity, neurological level, energy and age as predictor and was compared to a more complex model considering all 11 predictors mentioned above The models built using 4 and 11 predictors were found to explain 51.4% and 62.3% of the variance of the Spinal Cord Independence Measure total score after validation, respectively. The severity of the neurological deficit at admission was found to be the most important predictor. Other important predictors were the Injury Severity Score, age, neurological level and delay prior to surgery. Regression trees offer promising performances for predicting the functional outcome after a traumatic spinal cord injury. It could help to determine the number and type of predictors leading to a prediction model of the functional outcome that can be used clinically in the future.

Predictors of CPAP compliance in different clinical settings: primary care versus sleep unit.

PubMed

Nadal, Núria; de Batlle, Jordi; Barbé, Ferran; Marsal, Josep Ramon; Sánchez-de-la-Torre, Alicia; Tarraubella, Nuria; Lavega, Merce; Sánchez-de-la-Torre, Manuel

2018-03-01

Good adherence to continuous positive airway pressure (CPAP) treatment improves the patient's quality of life and decreases the risk of cardiovascular disease. Previous studies that have analyzed the adherence to CPAP were performed in a sleep unit (SU) setting. The involvement of primary care (PC) in the management of obstructive sleep apnea (OSA) patients receiving CPAP treatment could introduce factors related to the adherence to treatment. The objective was to compare the baseline predictors of CPAP compliance in SU and PC settings. OSA patients treated with CPAP were followed for 6 months in SU or PC setting. We included baseline clinical and anthropometrical variables, the Epworth Sleep Scale (ESS) score, the quality of life index, and the Charlson index. A logistic regression was performed for each group to determine the CPAP compliance predictors. Discrimination and calibration were performed using the area under the curve and Hosmer-Lemeshow tests. We included 191 patients: 91 in the PC group and 100 in the SU group. In 74.9% of the patients, the compliance was ≥ 4 h per day, with 80% compliance in the SU setting and 69.2% compliance in the PC setting (p = 0.087). The predictors of CPAP compliance were different between SU and PC settings. Body mass index, ESS, and CPAP pressure were predictors in the SU setting, and ESS, gender, and waist circumference were predictors in the PC setting. The predictors of adequate CPAP compliance vary between SU and PC settings. Detecting compliance predictors could help in the planning of early interventions to improve CPAP adherence.

Comparing species distribution models constructed with different subsets of environmental predictors

USGS Publications Warehouse

Bucklin, David N.; Basille, Mathieu; Benscoter, Allison M.; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.; Speroterra, Carolina; Watling, James I.

2014-01-01

Our results indicate that additional predictors have relatively minor effects on the accuracy of climate-based species distribution models and minor to moderate effects on spatial predictions. We suggest that implementing species distribution models with only climate predictors may provide an effective and efficient approach for initial assessments of environmental suitability.

Predictors of College Retention and Performance between Regular and Special Admissions

ERIC Educational Resources Information Center

Kim, Johyun

2015-01-01

This predictive correlational research study examined the effect of cognitive, demographic, and socioeconomic variables as predictors of regular and special admission students' first-year GPA and retention among a sample of 7,045 students. Findings indicated high school GPA and ACT scores were the two most effective predictors of regular and…

Predictors of Bullying and Victimization in Childhood and Adolescence: A Meta-Analytic Investigation

ERIC Educational Resources Information Center

Cook, Clayton R.; Williams, Kirk R.; Guerra, Nancy G.; Kim, Tia E.; Sadek, Shelly

2010-01-01

Research on the predictors of 3 bully status groups (bullies, victims, and bully victims) for school-age children and adolescents was synthesized using meta-analytic procedures. The primary purpose was to determine the relative strength of individual and contextual predictors to identify targets for prevention and intervention. Age and how…

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…

An Inexpensive Predictor of Student Performance on Licensure Examinations.

ERIC Educational Resources Information Center

Hyde, R. M.; And Others

1987-01-01

The construction of a comprehensive final examination over the basic medical sciences is described. Performance on the exam was a better predictor of NBME-I scores than GPA in basic science or MCAT scores and a better predictor of NBME-II scores than preclinical course performance and MCAT scores. (Author/RH)

The CPI Subscales as Predictors of Parental Coping with Childhood Leukemia.

ERIC Educational Resources Information Center

Kupst, Mary Jo; Schulman, Jerome L.

1981-01-01

Determined the role of the California Psychological Inventory (CPI) in prediction of parental coping with leukemia. None of the standard CPI subscales was a significant predictor of coping. Coping with the specific situation may be a better predictor of later coping with a similar situation than more global assessments. (Author)

Comparison of molecular breeding values based on within- and across-breed training in beef cattle

USDA-ARS?s Scientific Manuscript database

Background Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized ...

Predictors of Self-Regulated Learning in Malaysian Smart Schools

ERIC Educational Resources Information Center

Yen, Ng Lee; Bakar, Kamariah Abu; Roslan, Samsilah; Luan, Wong Su; Abd Rahman, Petri Zabariah Mega

2005-01-01

This study sought to uncover the predictors of self-regulated learning in Malaysian smart schools. The sample consisted of 409 students, from six randomly chosen smart schools. A quantitative correlational research design was employed and the data were collected through survey method. Six factors were examined in relation to the predictors of…

Examination of Predictors and Moderators for Self-Help Treatments of Binge-Eating Disorder

ERIC Educational Resources Information Center

Masheb, Robin M.; Grilo, Carlos M.

2008-01-01

Predictors and moderators of outcomes were examined in 75 overweight patients with binge-eating disorder (BED) who participated in a randomized clinical trial of guided self-help treatments. Age variables, psychiatric and personality disorder comorbidity, and clinical characteristics were tested as predictors and moderators of treatment outcomes.…

Teacher Self-Efficacy as a Long-Term Predictor of Instructional Quality in the Classroom

ERIC Educational Resources Information Center

Künsting, Josef; Neuber, Victoria; Lipowsky, Frank

2016-01-01

In this longitudinal study, we examined teachers' self-efficacy as a long-term predictor of their mastery goal orientation and three dimensions of instructional quality: supportive classroom climate, effective classroom management, and cognitive activation. Mastery goal orientation was also analyzed as a predictor of instructional quality.…

Predictors of White and Minority Student Success at a Private Women's College

ERIC Educational Resources Information Center

Kirby, Elizabeth; White, Samantha; Aruguete, Mara

2007-01-01

This study examines predictors of academic success in a private women's college. We investigated academic factors (e.g., high school grade point average, standardized test scores) and socioeconomic status (e.g., parents' occupation, need for financial aid) as possible predictors of academic success among White and non-White students. Data was…

Changes in Situational and Dispositional Factors as Predictors of Job Satisfaction

ERIC Educational Resources Information Center

Keller, Anita C.; Semmer, Norbert K.

2013-01-01

Arguably, job satisfaction is one of the most important variables with regard to work. When explaining job satisfaction, research usually focuses on predictor variables in terms of levels but neglects growth rates. Therefore it remains unclear how potential predictors evolve over time and how their development affects job satisfaction. Using…

Personality Typologies as a Predictor of Being a Successful Elementary School Principal

ERIC Educational Resources Information Center

Mendiburu, John G.

2010-01-01

Purpose: The purpose of this study was to examine personality typologies as a predictor of being a successful elementary school principal. Methodology: A post-hoc analysis design was used to describe the personality typologies as a predictor of being a successful elementary school principal. Eighteen principals were selected to participate in…

Dysfunctional Career Thoughts and Attitudes as Predictors of Vocational Identity among Young Adults with Attention Deficit Hyperactivity Disorder

ERIC Educational Resources Information Center

Dipeolu, Abiola; Sniatecki, Jessica L.; Storlie, Cassandra A.; Hargrave, Stephanie

2013-01-01

This study examined dysfunctional career thoughts and attitudes as predictors of vocational identity among high school students with Attention Deficit Hyperactivity Disorder (ADHD). Regression analysis results indicated that dysfunctional career thoughts and attitudes were significant predictors of vocational identity, accounting for 42% of the…

Adolescent Mothers and Depression: Predictors of Resilience and Risk through the Toddler Years

ERIC Educational Resources Information Center

Eshbaugh, Elaine M.

2006-01-01

This study investigated predictors of depression in 278 African-American, 206 European-American, and 122 Hispanic teen mothers approximately 36 months after the birth while controlling for depression 14 months after the birth. Predictor variables were age, ethnicity, mastery, knowledge of development, and parental distress. Younger teens were not…

Symptom, Family, and Service Predictors of Children's Psychiatric Rehospitalization within One Year of Discharge.

ERIC Educational Resources Information Center

Blader, Joseph C.

2004-01-01

Objective: To investigate predictors of readmission to inpatient psychiatric treatment for children aged 5 to 12 discharged from acute-care hospitalization. Method: One hundred nine children were followed for 1 year after discharge from inpatient care. Time to rehospitalization was the outcome of interest. Predictors of readmission, examined via…

Health Conditions and Perceived Quality of Life in Retirement.

ERIC Educational Resources Information Center

Dorfman, Lorraine T.

1995-01-01

Investigates the effects of specific health conditions on perceived quality of life for retirees (n=451). Pulmonary disease was a predictor of dissatisfaction for both sexes. Pulmonary disease and heart attack were the strongest predictors of dissatisfaction with health for men, followed closely by stroke. Arthritis was the strongest predictor of…

Salient Predictors of School Dropout among Secondary Students with Learning Disabilities

ERIC Educational Resources Information Center

Doren, Bonnie; Murray, Christopher; Gau, Jeff M.

2014-01-01

The purpose of this study was to identify the unique contributions of a comprehensive set of predictors and the most salient predictors of school dropout among a nationally representative sample of students with learning disabilities (LD). A comprehensive set of theoretically and empirically relevant factors was selected for examination. Analyses…

"Turn that Thing off!" Parent and Adolescent Predictors of Proactive Media Monitoring

ERIC Educational Resources Information Center

Padilla-Walker, Laura M.; Coyne, Sarah M.

2011-01-01

Though much research has focused on the positive outcomes of parents' monitoring of adolescents' media use, few studies have examined predictors of parents' media monitoring. Accordingly, the current study was designed to assess both parent and child predictors of proactive media monitoring during adolescence. Participants consisted of 478…

Predictors of Student Commitment at Two-Year and Four-Year Institutions

ERIC Educational Resources Information Center

Strauss, Linda C.; Volkwein, J. Fredericks

2004-01-01

The research presented in this article examines the predictors of institutional commitment of first-year students at 28 two-year and 23 four-year public institutions. Previous research has demonstrated that institutional commitment is a strong predictor of college students' intent to persist, and ultimately student persistence itself (Braxton,…

A Meta-Analysis of the Predictors of Delinquency among Girls.

ERIC Educational Resources Information Center

Hubbard, Dana Jones; Pratt, Travis C.

2002-01-01

Presents the results of a meta-analysis of the predictors of female delinquency. Finds that most of the strong predictors of female delinquency are the same as those for males, including history of antisocial behavior, antisocial attitudes, antisocial peers, and antisocial personality. School and family relationships and a history of…

Predictors of Sleep Quantity and Quality in College Students

ERIC Educational Resources Information Center

Davidson, Eric S.

2012-01-01

Whereas sleep is often thought of as a common health issue among college students, few, if any, researchers have comprehensively evaluated correlates and predictors of sleep quality and quantity within this population. Most often, studies of this type are used by researchers to assess particular categories of correlates and predictors (e.g.,…

Pre-Veterinary Medical Grade Point Averages as Predictors of Academic Success in Veterinary College.

ERIC Educational Resources Information Center

Julius, Marcia F.; Kaiser, Herbert E.

1978-01-01

A five-year longitudinal study was designed to find the best predictors of academic success in veterinary school at Kansas State University and to set up a multiple regression formula to be used in selecting students. The preveterinary grade point average was found to be the best predictor. (JMD)

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

ERIC Educational Resources Information Center

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

Spatial and Numerical Predictors of Measurement Performance: The Moderating Effects of Community Income and Gender

ERIC Educational Resources Information Center

Casey, Beth M.; Dearing, Eric; Vasilyeva, Marina; Ganley, Colleen M.; Tine, Michele

2011-01-01

Spatial reasoning and numerical predictors of measurement performance were investigated in 4th graders from low-income and affluent communities. Predictors of 2 subtypes of measurement performance (spatial-conceptual and formula based) were assessed while controlling for verbal and spatial working memory. Consistent with prior findings, students…

Measuring Teacher Quality: Continuing the Search for Policy-Relevant Predictors of Student Achievement

ERIC Educational Resources Information Center

Knoeppel, Robert C.; Logan, Joyce P.; Keiser, Clare M.

2005-01-01

The purpose of this study was to investigate the potential viability of the variable certification by the National Board for Professional Teaching Standards (NBPTS) as a policy-relevant predictor of student achievement. Because research has identified the teacher as the most important school-related predictor of student achievement, more research…

Prevalence and Predictors of Change in Adult-Child Primary Caregivers

ERIC Educational Resources Information Center

Szinovacz, Maximiliane E.; Davey, Adam

2013-01-01

Family caregiving research is increasingly contextual and dynamic, but few studies have examined prevalence and predictors of change in primary caregivers, those with the most frequent contact with healthcare professionals. We identified prevalence and predictors of 2-year change in primary adult-child caregivers. Data pooled from the 1992-2000…

Physical Activity and Perceived Self-Efficacy in Older Adults.

ERIC Educational Resources Information Center

Langan, Mary E.; Marotta, Sylvia A.

2000-01-01

The purpose of this study was to examine predictors of self-efficacy in older adults, with physical activity, age, and sex as the predictor variables. Regression analyses revealed physical activity to be the only statistically significant predictor of self-efficacy. These findings may be of interest to counselors who work with older people.…

Predictors of Service Utilization among Youth Diagnosed with Mood Disorders

ERIC Educational Resources Information Center

Mendenhall, Amy N.

2012-01-01

In this study, I investigated patterns and predictors of service utilization for children with mood disorders. The Behavioral Model for Health Care Utilization was used as an organizing framework for identifying predictors of the number and quality of services utilized. Hierarchical regression was used in secondary data analyses of the…

Sufficient Dimension Reduction for Longitudinally Measured Predictors

PubMed Central

Pfeiffer, Ruth M.; Forzani, Liliana; Bura, Efstathia

2013-01-01

We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker product structure, that accommodates the longitudinal nature of the predictors, we develop first moment sufficient dimension reduction techniques to replace the original markers with linear transformations that contain sufficient information for the regression of the predictors on the outcome. These linear combinations can then be combined into a score that has better predictive performance than the score built under a general model that ignores the longitudinal structure of the data. Our methods can be applied to either continuous or categorical outcome measures. In simulations we focus on binary outcomes and show that our method outperforms existing alternatives using the AUC, the area under the receiver-operator characteristics (ROC) curve, as a summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. PMID:22161635

More Precise Estimation of Lower-Level Interaction Effects in Multilevel Models.

PubMed

Loeys, Tom; Josephy, Haeike; Dewitte, Marieke

2018-01-01

In hierarchical data, the effect of a lower-level predictor on a lower-level outcome may often be confounded by an (un)measured upper-level factor. When such confounding is left unaddressed, the effect of the lower-level predictor is estimated with bias. Separating this effect into a within- and between-component removes such bias in a linear random intercept model under a specific set of assumptions for the confounder. When the effect of the lower-level predictor is additionally moderated by another lower-level predictor, an interaction between both lower-level predictors is included into the model. To address unmeasured upper-level confounding, this interaction term ought to be decomposed into a within- and between-component as well. This can be achieved by first multiplying both predictors and centering that product term next, or vice versa. We show that while both approaches, on average, yield the same estimates of the interaction effect in linear models, the former decomposition is much more precise and robust against misspecification of the effects of cross-level and upper-level terms, compared to the latter.

The Cognitive Predictors of Computational Skill with Whole versus Rational Numbers: An Exploratory Study.

PubMed

Seethaler, Pamela M; Fuchs, Lynn S; Star, Jon R; Bryant, Joan

2011-10-01

The purpose of the present study was to explore the 3(rd)-grade cognitive predictors of 5th-grade computational skill with rational numbers and how those are similar to and different from the cognitive predictors of whole-number computational skill. Students (n = 688) were assessed on incoming whole-number calculation skill, language, nonverbal reasoning, concept formation, processing speed, and working memory in the fall of 3(rd) grade. Students were followed longitudinally and assessed on calculation skill with whole numbers and with rational numbers in the spring of 5(th) grade. The unique predictors of skill with whole-number computation were incoming whole-number calculation skill, nonverbal reasoning, concept formation, and working memory (numerical executive control). In addition to these cognitive abilities, language emerged as a unique predictor of rational-number computational skill.

The Cognitive Predictors of Computational Skill with Whole versus Rational Numbers: An Exploratory Study

PubMed Central

Seethaler, Pamela M.; Fuchs, Lynn S.; Star, Jon R.; Bryant, Joan

2011-01-01

The purpose of the present study was to explore the 3rd-grade cognitive predictors of 5th-grade computational skill with rational numbers and how those are similar to and different from the cognitive predictors of whole-number computational skill. Students (n = 688) were assessed on incoming whole-number calculation skill, language, nonverbal reasoning, concept formation, processing speed, and working memory in the fall of 3rd grade. Students were followed longitudinally and assessed on calculation skill with whole numbers and with rational numbers in the spring of 5th grade. The unique predictors of skill with whole-number computation were incoming whole-number calculation skill, nonverbal reasoning, concept formation, and working memory (numerical executive control). In addition to these cognitive abilities, language emerged as a unique predictor of rational-number computational skill. PMID:21966180

A threshold-based fixed predictor for JPEG-LS image compression

NASA Astrophysics Data System (ADS)

Deng, Lihua; Huang, Zhenghua; Yao, Shoukui

2018-03-01

In JPEG-LS, fixed predictor based on median edge detector (MED) only detect horizontal and vertical edges, and thus produces large prediction errors in the locality of diagonal edges. In this paper, we propose a threshold-based edge detection scheme for the fixed predictor. The proposed scheme can detect not only the horizontal and vertical edges, but also diagonal edges. For some certain thresholds, the proposed scheme can be simplified to other existing schemes. So, it can also be regarded as the integration of these existing schemes. For a suitable threshold, the accuracy of horizontal and vertical edges detection is higher than the existing median edge detection in JPEG-LS. Thus, the proposed fixed predictor outperforms the existing JPEG-LS predictors for all images tested, while the complexity of the overall algorithm is maintained at a similar level.

A change in fault-plane orientation between foreshocks and aftershocks of the Galway Lake earthquake, ML = 5.2, 1975, Mojave desert, California

USGS Publications Warehouse

Fuis, G.S.; Lindh, A.G.

1979-01-01

A marked change is observed in P/SV amplitude ratios, measured at station TPC, from foreshocks to aftershocks of the Galway Lake earthquake. This change is interpreted to be the result of a change in fault-plane orientation occurring between foreshocks and aftershocks. The Galway Lake earthquake, ML= 5.2, occurred on June 1, 1975. The first-motion fault-plane solutions for the main shock and most foreshocks and aftershocks indicate chiefly right-lateral strike-slip on NNW-striking planes that dip steeply, 70-90??, to the WSW. The main event was preceded by nine located foreshocks, ranging in magnitude from 1.9 to 3.4, over a period of 12 weeks, starting on March 9, 1975. All of the foreshocks form a tight cluster approximately 1 km in diameter. This cluster includes the main shock. Aftershocks are distributed over a 6-km-long fault zone, but only those that occurred inside the foreshock cluster are used in this study. Seismograms recorded at TPC (?? = 61 km), PEC (?? = 93 km), and CSP (?? = 83 km) are the data used here. The seismograms recorded at TPC show very consistent P/SV amplitude ratios for foreshocks. For aftershocks the P/SV ratios are scattered, but generally quite different from foreshock ratios. Most of the scatter for the aftershocks is confined to the two days following the main shock. Thereafter, however, the P/SV ratios are consistently half as large as for foreshocks. More subtle (and questionable) changes in the P/SV ratios are observed at PEC and CSP. Using theoretical P/SV amplitude ratios, one can reproduce the observations at TPC, PEC and CSP by invoking a 5-12?? counterclockwise change in fault strike between foreshocks and aftershocks. This interpretation is not unique, but it fits the data better than invoking, for example, changes in dip or slip angle. First-motion data cannot resolve this small change, but they permit it. Attenuation changes would appear to be ruled out by the fact that changes in the amplitude ratios, PTPC/PPEC and ptpc/pcsp, are observed, and these changes accompany the changes in P/SV. Observations for the Galway Lake earthquake are similar to observations for the Oroville, California, earthquake (ML = 5.7) of August 1, 1975, and the Brianes Hills, California, earthquake (ML = 4.3) of January 8, 1977 (Lindh et al., Science Vol. 201, pp. 56-59). A change in fault-plane orientation between foreshocks and aftershocks may be understandable in terms of early en-echelon cracking (foreshocks) giving way to shear on the main fault plane (main shock plus aftershocks). Recent laboratory data (Byerlee et al., Tectonophysics, Vol. 44, pp. 161-171) tend to support this view. ?? 1979.

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

PubMed

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

Predict the Medicare Functional Classification Level (K-level) using the Amputee Mobility Predictor in people with unilateral transfemoral and transtibial amputation: A pilot study.

PubMed

Dillon, Michael P; Major, Matthew J; Kaluf, Brian; Balasanov, Yuri; Fatone, Stefania

2018-04-01

While Amputee Mobility Predictor scores differ between Medicare Functional Classification Levels (K-level), this does not demonstrate that the Amputee Mobility Predictor can accurately predict K-level. To determine how accurately K-level could be predicted using the Amputee Mobility Predictor in combination with patient characteristics for persons with transtibial and transfemoral amputation. Prediction. A cumulative odds ordinal logistic regression was built to determine the effect that the Amputee Mobility Predictor, in combination with patient characteristics, had on the odds of being assigned to a particular K-level in 198 people with transtibial or transfemoral amputation. For people assigned to the K2 or K3 level by their clinician, the Amputee Mobility Predictor predicted the clinician-assigned K-level more than 80% of the time. For people assigned to the K1 or K4 level by their clinician, the prediction of clinician-assigned K-level was less accurate. The odds of being in a higher K-level improved with younger age and transfemoral amputation. Ordinal logistic regression can be used to predict the odds of being assigned to a particular K-level using the Amputee Mobility Predictor and patient characteristics. This pilot study highlighted critical method design issues, such as potential predictor variables and sample size requirements for future prospective research. Clinical relevance This pilot study demonstrated that the odds of being assigned a particular K-level could be predicted using the Amputee Mobility Predictor score and patient characteristics. While the model seemed sufficiently accurate to predict clinician assignment to the K2 or K3 level, further work is needed in larger and more representative samples, particularly for people with low (K1) and high (K4) levels of mobility, to be confident in the model's predictive value prior to use in clinical practice.

Comparison of Immature Platelet Count to Established Predictors of Platelet Reactivity During Thienopyridine Therapy.

PubMed

Stratz, Christian; Bömicke, Timo; Younas, Iris; Kittel, Anja; Amann, Michael; Valina, Christian M; Nührenberg, Thomas; Trenk, Dietmar; Neumann, Franz-Josef; Hochholzer, Willibald

2016-07-19

Previous data suggest that reticulated platelets significantly affect antiplatelet response to thienopyridines. It is unknown whether parameters describing reticulated platelets can predict antiplatelet response to thienopyridines. The authors sought to determine the extent to which parameters describing reticulated platelets can predict antiplatelet response to thienopyridine loading compared with established predictors. This study randomized 300 patients undergoing elective coronary stenting to loading with clopidogrel 600 mg, prasugrel 30 mg, or prasugrel 60 mg. Adenosine diphosphate (ADP)-induced platelet reactivity was assessed by impedance aggregometry before loading (intrinsic platelet reactivity) and again on day 1 after loading. Multiple parameters of reticulated platelets were assessed by automated whole blood flow cytometry: absolute immature platelet count (IPC), immature platelet fraction, and highly fluorescent immature platelet fraction. Each parameter of reticulated platelets correlated significantly with ADP-induced platelet reactivity (p < 0.01 for all 3 parameters). In a multivariable model including all 3 parameters, only IPC remained a significant predictor of platelet reactivity (p < 0.001). In models adjusting each of the 3 parameters for known predictors of on-treatment platelet reactivity including cytochrome P450 2C19 (CYP2C19) polymorphisms, age, body mass index, diabetes, and intrinsic platelet reactivity, only IPC prevailed as an independent predictor (p = 0.001). In this model, IPC was the strongest predictor of on-treatment platelet reactivity followed by intrinsic platelet reactivity. IPC is the strongest independent platelet count-derived predictor of antiplatelet response to thienopyridine treatment. Given its easy availability, together with its even stronger association with on-treatment platelet reactivity compared with known predictors, including the CYP2C19*2 polymorphism, IPC may become the preferred predictor of antiplatelet response to thienopyridine treatment. (Impact of Extent of Clopidogrel-Induced Platelet Inhibition During Elective Stent Implantation on Clinical Event Rate-Advanced Loading Strategies [ExcelsiorLOAD]; DRKS00006102). Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Predictors of early change in bulimia nervosa after a brief psychoeducational therapy.

PubMed

Fernàndez-Aranda, Fernando; Álvarez-Moya, Eva M; Martínez-Viana, Cristina; Sànchez, Isabel; Granero, Roser; Penelo, Eva; Forcano, Laura; Peñas-Lledó, Eva

2009-06-01

We aimed to examine baseline predictors of treatment response in bulimic patients. 241 seeking-treatment females with bulimia nervosa completed an exhaustive assessment and were referred to a six-session psychoeducational group. Regression analyses of treatment response were performed. Childhood obesity, lower frequency of eating symptomatology, lower body mass index, older age, and lower family's and patient's concern about the disorder were predictors of poor abstinence. Suicidal ideation, alcohol abuse, higher maximum BMI, higher novelty seeking and lower baseline purging frequency predicted dropouts. Predictors of early symptom changes and dropouts were similar to those identified in longer CBT interventions.

One-leg balance is an important predictor of injurious falls in older persons.

PubMed

Vellas, B J; Wayne, S J; Romero, L; Baumgartner, R N; Rubenstein, L Z; Garry, P J

1997-06-01

To test the hypothesis that one-leg balance is a significant predictor of falls and injurious falls. Analysis of data from a longitudinal cohort study. Healthy, community-living volunteers older than age 60 enrolled in the Albuquerque Falls Study and followed for 3 years (N = 316; mean age 73 years). Falls and injurious falls detected via reports every other month. Baseline measures of demographics, history, physical examination, Iowa Self Assessment Inventory, balance and gait assessment, and one-leg balance (ability to stand unassisted for 5 seconds on one leg). At baseline, 84.5% of subjects could perform one-leg balance. (Impairment was associated with older age and gait abnormalities.) Over the 3-year follow-up, 71% experienced a fall and 22% an injurious fall. The only independent significant predictor of all falls using logistic regression was age greater than 73. However, impaired one-leg balance was the only significant independent predictor of injurious falls (relative risk: 2.13; 95% CI: 1.04, 4.34; P = .03). One-leg balance appears to be a significant and easy-to-administer predictor of injurious falls, but not of all falls. In our study, it was the strongest individual predictor. However, no single factor seems to be accurate enough to be relied on as a sole predictor of fall risk or fall injury risk because so many diverse factors are involved in falling.

Predictors of Older Adults’ Technology Use and Its Relationship to Depressive Symptoms and Well-being

PubMed Central

Mooney, Christopher J.; Douthit, Kathryn Z.; Lynch, Martin F.

2014-01-01

Objective. To extend the empirical evidence regarding the predictors of older adults’ use of information and communications technology (ICT) and to further examine its relationship to depressive symptoms and well-being. Method. This cross-sectional study utilized a sample of community-dwelling older adults from the National Health and Aging Trends Study (N = 6,443). Structural equation modeling was used to estimate the effects of predictor variables on ICT use and the effects of use on depressive symptoms and well-being. Tests of moderation by demographic characteristics and level of ICT use were also performed. Results. Socioeconomic status (SES), age, and cognitive function accounted for approximately 60% of the variance in ICT use. SES was a stronger predictor for Blacks/African Americans, whereas cognitive function was a stronger predictor for Whites. ICT use was unrelated to depressive symptoms or well-being. However, it acted as a moderator, such that limitations in activities of daily living (ADLs) was a stronger predictor of depressive symptoms for high ICT users, whereas ill-health was a stronger predictor for non/limited users. Discussion. Findings do not support the claim that ICT use directly enhances mental health or well-being among older adults although it may protect against depressive symptoms for individuals coping with health conditions other than ADL impairments. PMID:24304556

US characteristics for the prediction of neoplasm in gallbladder polyps 10 mm or larger.

PubMed

Kim, Jin Sil; Lee, Jeong Kyong; Kim, Yookyung; Lee, Sang Min

2016-04-01

To evaluate the characteristics of gallbladder polyps 10 mm or larger to predict a neoplasm in US examinations. Fifty-three patients with gallbladder polyps ≥ 10 mm with follow-up images or pathologic diagnosis were included in the retrospective study. All images and reports were reviewed to determine the imaging characteristics of gallbladder polyps. Univariate and multivariate analyses were used to evaluate predictors for a neoplastic polyp. A neoplastic polyp was verified in 12 of 53 patients and the mean size was 13.9 mm. The univariate analysis revealed that adjacent gallbladder wall thickening, larger size (≥15 mm), older age (≥57 years), absence of hyperechoic foci in a polyp, CT visibility, sessile shape, a solitary polyp, and an irregular surface were significant predictors for a neoplastic polyp. In the multivariate analysis, larger size (≥15 mm) was a significant predictor for a neoplastic polyp. A polyp size ≥15 mm was the strongest predictor for a neoplastic polyp with US. The hyperechoic foci in a polyp and CT visibility would be useful indicators for the differentiation of a neoplastic polyp, in addition to the established predictors. • A polyp size ≥15 mm is the strongest predictor for a neoplastic polyp with US. • Hyperechoic foci in a polyp and CT visibility are new predictors. • The rate of malignancy is low in polyps even 10 mm or larger (15.1 %).

Job embeddedness factors as a predictor of turnover intention among infection control nurses in Korea.

PubMed

Choi, Jeong Sil; Kim, Kyung Mi

2015-11-01

Job embeddedness indicates the degree to which an employee of an organization intends to remain in his or her position at that organization. This study examined how job embeddedness affects infection control nurses' turnover intention along with general characteristics, job satisfaction, and perceived job alternatives. We collected data from a total of 133 infection control nurses using self-reporting questionnaire methods. All questions were answered on a 5-point Likert scale. The average turnover intention was 3.01 ± 0.72 (100-point conversion, 60.2%), and average job satisfaction was 3.48 ± 0.79 (100-point conversion, 69.6%). The average perceived availability of job alternatives was 3.02 ± 0.78 (100-point conversion, 60.4%), and average job embeddedness was 3.33 ± 0.57 (100-point conversion, 66.6%). Predictors of turnover intention were monthly income, perceived availability of job alternatives, and job embeddedness. Job embeddedness among predictors has high explanatory power as a predictor of infection control nurses' turnover intention. Through this study we identified predictors of turnover intention and found that job embeddedness among predictors has high explanatory power as a predictor of infection control nurses' turnover intention. Strategies to enhance infection control nurses' job embeddedness are needed. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Applying the reasoned action approach to understanding health protection and health risk behaviors.

PubMed

Conner, Mark; McEachan, Rosemary; Lawton, Rebecca; Gardner, Peter

2017-12-01

The Reasoned Action Approach (RAA) developed out of the Theory of Reasoned Action and Theory of Planned Behavior but has not yet been widely applied to understanding health behaviors. The present research employed the RAA in a prospective design to test predictions of intention and action for groups of protection and risk behaviors separately in the same sample. To test the RAA for health protection and risk behaviors. Measures of RAA components plus past behavior were taken in relation to eight protection and six risk behaviors in 385 adults. Self-reported behavior was assessed one month later. Multi-level modelling showed instrumental attitude, experiential attitude, descriptive norms, capacity and past behavior were significant positive predictors of intentions to engage in protection or risk behaviors. Injunctive norms were only significant predictors of intention in protection behaviors. Autonomy was a significant positive predictor of intentions in protection behaviors and a negative predictor in risk behaviors (the latter relationship became non-significant when controlling for past behavior). Multi-level modelling showed that intention, capacity, and past behavior were significant positive predictors of action for both protection and risk behaviors. Experiential attitude and descriptive norm were additional significant positive predictors of risk behaviors. The RAA has utility in predicting both protection and risk health behaviors although the power of predictors may vary across these types of health behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

PubMed

Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M

2011-07-25

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

Predictors and Moderators of Treatment Outcome in the Pediatric Obsessive Compulsive Treatment Study (POTS I)

ERIC Educational Resources Information Center

Garcia, Abbe Marrs; Sapyta, Jeffrey J.; Moore, Phoebe S.; Freeman, Jennifer B.; Franklin, Martin E.; March, John S.; Foa, Edna B.

2010-01-01

Objective: To identify predictors and moderators of outcome in the first Pediatric OCD Treatment Study (POTS I) among youth (N = 112) randomly assigned to sertraline, cognitive behavioral therapy (CBT), both sertraline and CBT (COMB), or a pill placebo. Method: Potential baseline predictors and moderators were identified by literature review. The…

Base-Rate Neglect as a Function of Base Rates in Probabilistic Contingency Learning

ERIC Educational Resources Information Center

Kutzner, Florian; Freytag, Peter; Vogel, Tobias; Fiedler, Klaus

2008-01-01

When humans predict criterion events based on probabilistic predictors, they often lend excessive weight to the predictor and insufficient weight to the base rate of the criterion event. In an operant analysis, using a matching-to-sample paradigm, Goodie and Fantino (1996) showed that humans exhibit base-rate neglect when predictors are associated…

Parent and Peer Predictors of Physical Dating Violence Perpetration in Early Adolescence: Tests of Moderation and Gender Differences

ERIC Educational Resources Information Center

Miller, Shari; Gorman-Smith, Deborah; Sullivan, Terri; Orpinas, Pamela; Simon, Thomas R.

2009-01-01

This study examined parenting and peer predictors of physical dating violence perpetration during early adolescence and tested moderation among these predictors and gender. Participants were 2,824 ethnically diverse sixth-grade students with a recent boyfriend/girlfriend who was part of a multisite, longitudinal investigation of the development…

Predictors of Complicated Grief: A Systematic Review of Empirical Studies

ERIC Educational Resources Information Center

Lobb, Elizabeth A.; Kristjanson, Linda J.; Aoun, Samar M.; Monterosso, Leanne; Halkett, Georgia K. B.; Davies, Anna

2010-01-01

A systematic review of the literature on predictors of complicated grief (CG) was undertaken with the aim of clarifying the current knowledge and to inform future planning and work in CG following bereavement. Predictors of CG prior to the death include previous loss, exposure to trauma, a previous psychiatric history, attachment style, and the…

Predictor sort sampling and one-sided confidence bounds on quantiles

Treesearch

Steve Verrill; Victoria L. Herian; David W. Green

2002-01-01

Predictor sort experiments attempt to make use of the correlation between a predictor that can be measured prior to the start of an experiment and the response variable that we are investigating. Properly designed and analyzed, they can reduce necessary sample sizes, increase statistical power, and reduce the lengths of confidence intervals. However, if the non- random...

Emotional Intelligence and Personality as Predictors of Psychological Well-Being

ERIC Educational Resources Information Center

James, Colin; Bore, Miles; Zito, Susanna

2012-01-01

Research studies have reported elevated rates of psychological distress (e.g., depression) in practicing lawyers yet little research has examined predictors of such problems in law students. Specific personality traits have been shown to be predictors of a range of psychological problems. We administered a battery of tests to a cohort of 1st-year…

Investigation of Remedial Education Course Scores as a Predictor of Introduction-Level Course Performances

ERIC Educational Resources Information Center

Ulmer, Ward; Means, Darris R.; Cawthon, Tony W.; Kristensen, Sheryl A.

2016-01-01

This study explores whether performance in remedial English and remedial math is a predictor of success in a college-level introduction English or college-level math class; and whether demographic variables increase the likelihood of remedial English and remedial math as a predictor of success in a college-level introduction English or…

Who Is Retained in School, and When? Survival Analysis of Predictors of Grade Retention in Luxembourgish Secondary School

ERIC Educational Resources Information Center

Klapproth, Florian; Schaltz, Paule

2015-01-01

Based on a large longitudinal sample (N?=?9031) of Luxembourgish secondary school students, this study examined whether variables reflecting the sociodemographic background of the students (gender, nationality and socioeconomic status) as well as the school track proved to be predictors of grade retention. These possible predictors of grade…

Development of a Situational Judgment Test as a Predictor of College Student Performance

ERIC Educational Resources Information Center

Matošková, Jana; Kovárík, Martin

2017-01-01

It has been suggested that tacit knowledge may be a good predictor of performance in college. The purpose of this study was to investigate the extent to which a situational judgment test developed to measure tacit knowledge correlates with predictors and indicators of college performance. This situational judgment test includes eight situations…

Transfer of Training from Predictor to Conventional Displays. Interim Report.

ERIC Educational Resources Information Center

Wulfeck, J. W.

Use of a predictor display has been shown to virtually transform the difficulty of a variety of complex, manual control pursuit tracking tasks to the level of those having relatively simple control requirements. With 15-minutes practice, naive operators are able to perform some complex tasks with a predictor display at accuracy levels previously…

Effect of a Predictor Instrument on Learning to Land a Simulated Jet Trainer. Final Report.

ERIC Educational Resources Information Center

Smith, Russell L.; And Others

The study investigates the potential utility of a predictor instrument in the training of manual control operators in aircraft simulators. Various predictor display design configurations were presented to subjects during training trials on an aircraft approach to landing task. Subsequently, subjects were tested on trials devoid of the predictor…

How Does One Assess the Accuracy of Academic Success Predictors? ROC Analysis Applied to University Entrance Factors

ERIC Educational Resources Information Center

Vivo, Juana-Maria; Franco, Manuel

2008-01-01

This article attempts to present a novel application of a method of measuring accuracy for academic success predictors that could be used as a standard. This procedure is known as the receiver operating characteristic (ROC) curve, which comes from statistical decision techniques. The statistical prediction techniques provide predictor models and…

Predictors of Success on the National Council Licensure Examination for Registered Nurses among Transfer BSN Students

ERIC Educational Resources Information Center

Fortier, Mary E.

2010-01-01

This quantitative research study (N=175) examined predictors of first time success on the National Council Licensure Examination for Registered Nurses (NCLEX-RN) among transfer students in a baccalaureate degree program (BSN). The predictors were chosen after an extensive literature review yielded few studies related to this population. Benner's…

In Pursuit of the Elusive Elixir: Predictors of First Grade Reading.

ERIC Educational Resources Information Center

Porter, Robin

Multivariate sets of predictor variables including both cognitive and social variables, different types of preschool experiences, and family environment variables were used to predict the first-grade reading achievement of 144 first-grade boys and girls. Measures for the predictor variables had been taken at school entry and at the end of the…

Predictors, Including Blood, Urine, Anthropometry, and Nutritional Indices, of All-Cause Mortality among Institutionalized Individuals with Intellectual Disability

ERIC Educational Resources Information Center

Ohwada, Hiroko; Nakayama, Takeo; Tomono, Yuji; Yamanaka, Keiko

2013-01-01

As the life expectancy of people with intellectual disability (ID) increases, it is becoming necessary to understand factors affecting survival. However, predictors that are typically assessed among healthy people have not been examined. Predictors of all-cause mortality, including blood, urine, anthropometry, and nutritional indices, were…

Negative Sibling Interaction Patterns as Predictors of Later Adjustment Problems in Adolescent and Young Adult Males.

ERIC Educational Resources Information Center

Bank, Lew; And Others

1996-01-01

This study investigated sibling interaction patterns in middle childhood as predictors of adjustment outcomes in males during adolescence and early adulthood using a social interactional perspective. It was theorized that negative interaction during middle childhood with siblings and parents would be the most powerful predictor of adjustment in…

Oral Language, Sex and Socio-Economic Status as Predictors of Reading Achievement.

ERIC Educational Resources Information Center

Ebert, Dorothy Jo Williamson

This study was designed to discover the degree of relationship between a number of predictor variables and reading achievement for 65 black second grade students in two Austin, Texas, schools. The seven predictor variables used were: oral language performance as measured by the Gloria and David Beginning English, Series 20, Test 6 (GDBE); an…

Childhood Predictors of Young Adult Social Functioning in 22q11.2 Deletion Syndrome

ERIC Educational Resources Information Center

Wagner, Kayla E.; Kates, Wendy R.; Fremont, Wanda; Antshel, Kevin M.

2017-01-01

The primary objectives of the current prospective longitudinal study were to (a) describe social functioning outcomes and (b) identify childhood predictors of social functioning in young adults with (22q11.2DS). Childhood predictors of young adult social functioning were examined. Family environment and parental stress in adolescence were…

Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples.

PubMed

Elloumi, Fathi; Hu, Zhiyuan; Li, Yan; Parker, Joel S; Gulley, Margaret L; Amos, Keith D; Troester, Melissa A

2011-06-30

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

Predictors of persistent pain after total knee arthroplasty: a systematic review and meta-analysis.

PubMed

Lewis, G N; Rice, D A; McNair, P J; Kluger, M

2015-04-01

Several studies have identified clinical, psychosocial, patient characteristic, and perioperative variables that are associated with persistent postsurgical pain; however, the relative effect of these variables has yet to be quantified. The aim of the study was to provide a systematic review and meta-analysis of predictor variables associated with persistent pain after total knee arthroplasty (TKA). Included studies were required to measure predictor variables prior to or at the time of surgery, include a pain outcome measure at least 3 months post-TKA, and include a statistical analysis of the effect of the predictor variable(s) on the outcome measure. Counts were undertaken of the number of times each predictor was analysed and the number of times it was found to have a significant relationship with persistent pain. Separate meta-analyses were performed to determine the effect size of each predictor on persistent pain. Outcomes from studies implementing uni- and multivariable statistical models were analysed separately. Thirty-two studies involving almost 30 000 patients were included in the review. Preoperative pain was the predictor that most commonly demonstrated a significant relationship with persistent pain across uni- and multivariable analyses. In the meta-analyses of data from univariate models, the largest effect sizes were found for: other pain sites, catastrophizing, and depression. For data from multivariate models, significant effects were evident for: catastrophizing, preoperative pain, mental health, and comorbidities. Catastrophizing, mental health, preoperative knee pain, and pain at other sites are the strongest independent predictors of persistent pain after TKA. © The Author 2014. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Predictors of multidisciplinary treatment outcome in fibromyalgia:a systematic review.

PubMed

de Rooij, Aleid; Roorda, Leo D; Otten, René H J; van der Leeden, Marike; Dekker, Joost; Steultjens, Martijn P M

2013-03-01

To identify outcome predictors for multidisciplinary treatment in patients with chronic widespread pain (CWP) or fibromyalgia (FM). A systematic literature search in PubMed, PsycINFO, CINAHL, Cochrane Library, EMBASE and Pedro. Selection criteria included: age over 18; diagnosis CWP or FM; multidisciplinary treatment; longitudinal study design; original research report. Outcome domains: pain, physical functioning, emotional functioning, global treatment effect and 'others'. Methodological quality of the selected articles was assessed and a qualitative data synthesis was performed to identify the level of evidence. Fourteen studies (all with FM patients) fulfilled the selection criteria. Six were of high quality. Poorer outcome (pain, moderate evidence; physical functioning and quality of life, weak evidence) was predicted by depression. Similarly, poorer outcome was predicted by the disturbance and pain profile of the Minnesota Multiphasic Personality Inventory (MMPI), strong beliefs in fate and high disability (weak evidence). A better outcome was predicted by a worse baseline status, the dysfunctional and the adaptive copers profile of the Multidimensional Pain Inventory (MPI), and high levels of pain (weak evidence). Some predictors were related to specific multidisciplinary treatment (weak evidence). Inconclusive evidence was found for other demographic and clinical factors, cognitive and emotional factors, symptoms and physical functioning as predictors of outcome. It was found that a higher level of depression was a predictor of poor outcome in FM (moderate evidence). In addition, it was found that the baseline status, specific patient profiles, belief in fate, disability, and pain were predictors of the outcome of multidisciplinary treatment. Our results highlight the lack of high quality studies for evaluating predictors of the outcome of multidisciplinary treatment in FM. Further research on predictors of multidisciplinary treatment outcome is needed.

Simple Decision-Analytic Functions of the AUC for Ruling Out a Risk Prediction Model and an Added Predictor.

PubMed

Baker, Stuart G

2018-02-01

When using risk prediction models, an important consideration is weighing performance against the cost (monetary and harms) of ascertaining predictors. The minimum test tradeoff (MTT) for ruling out a model is the minimum number of all-predictor ascertainments per correct prediction to yield a positive overall expected utility. The MTT for ruling out an added predictor is the minimum number of added-predictor ascertainments per correct prediction to yield a positive overall expected utility. An approximation to the MTT for ruling out a model is 1/[P (H(AUC model )], where H(AUC) = AUC - {½ (1-AUC)} ½ , AUC is the area under the receiver operating characteristic (ROC) curve, and P is the probability of the predicted event in the target population. An approximation to the MTT for ruling out an added predictor is 1 /[P {(H(AUC Model:2 ) - H(AUC Model:1 )], where Model 2 includes an added predictor relative to Model 1. The latter approximation requires the Tangent Condition that the true positive rate at the point on the ROC curve with a slope of 1 is larger for Model 2 than Model 1. These approximations are suitable for back-of-the-envelope calculations. For example, in a study predicting the risk of invasive breast cancer, Model 2 adds to the predictors in Model 1 a set of 7 single nucleotide polymorphisms (SNPs). Based on the AUCs and the Tangent Condition, an MTT of 7200 was computed, which indicates that 7200 sets of SNPs are needed for every correct prediction of breast cancer to yield a positive overall expected utility. If ascertaining the SNPs costs $500, this MTT suggests that SNP ascertainment is not likely worthwhile for this risk prediction.

Predictors of nurse manager stress: a dominance analysis of potential work environment stressors.

PubMed

Kath, Lisa M; Stichler, Jaynelle F; Ehrhart, Mark G; Sievers, Andree

2013-11-01

Nurse managers have important but stressful jobs. Clinical or bedside nurse predictors of stress have been studied more frequently, but less has been done on work environment predictors for those in this first-line leadership role. Understanding the relative importance of those work environment predictors could be used to help identify the most fruitful areas for intervention, potentially improving recruitment and retention for nurse managers. Using Role Stress Theory and the Job Demands-Resources Theory, a model was tested examining the relative importance of five potential predictors of nurse manager stress (i.e., stressors). The work environment stressors included role ambiguity, role overload, role conflict, organizational constraints, and interpersonal conflict. A quantitative, cross-sectional survey study was conducted with a convenience sample of 36 hospitals in the Southwestern United States. All nurse managers working in these 36 hospitals were invited to participate. Of the 636 nurse managers invited, 480 responded, for a response rate of 75.5%. Questionnaires were distributed during nursing leadership meetings and were returned in person (in sealed envelopes) or by mail. Because work environment stressors were correlated, dominance analysis was conducted to examine which stressors were the most important predictors of nurse manager stress. Role overload was the most important predictor of stress, with an average of 13% increase in variance explained. The second- and third-most important predictors were organizational constraints and role conflict, with an average of 7% and 6% increase in variance explained, respectively. Because other research has shown deleterious effects of nurse manager stress, organizational leaders are encouraged to help nurse managers reduce their actual and/or perceived role overload and organizational constraints. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cardiopulmonary exercise testing and prognosis in heart failure due to systolic left ventricular dysfunction: a validation study of the European Society of Cardiology Guidelines and Recommendations (2008) and further developments.

PubMed

Corrà, Ugo; Giordano, Andrea; Mezzani, Alessandro; Gnemmi, Marco; Pistono, Massimo; Caruso, Roberto; Giannuzzi, Pantaleo

2012-02-01

The study aims were to validate the cardiopulmonary exercise testing (CPET) parameters recommended by the European Society of Cardiology 2008 Guidelines for risk assessment in heart failure (HF) (ESC-predictors) and to verify the predictive role of 11 supplementary CPET (S-predictors) parameters. We followed 749 HF patients for cardiovascular death and urgent heart transplantation for 3 years: 139 (19%) patients had cardiac events. ESC-predictors - peak oxygen consumption (VO(2)), slope of minute ventilation vs carbon dioxide production (VE/VCO(2)) and exertional oscillatory ventilation - were all related to outcome at univariate and multivariable analysis. The ESC/2008 prototype based on ESC-predictors presented a Harrell's C concordance index of 0.725, with a likely χ2 of 98.31. S-predictors - predicted peak VO(2), peak oxygen pulse, peak respiratory exchange ratio, peak circulatory power, peak VE/VCO(2), VE/VCO(2) slope normalized by peak VO(2), VO(2) efficiency slope, ventilatory anaerobic threshold detection, peak end-tidal CO(2) partial pressure, peak heart rate, and peak systolic arterial blood pressure (SBP) - were all linked to outcome at univariate analysis. When individually added to the ESC/2008 prototype, only peak SBP and peak O(2) pulse significantly improved the model discrimination ability: the ESC + peak SBP prototype had a Harrell's C index 0.750 and reached the highest likely χ2 (127.16, p < 0.0001). We evaluated the longest list of CPET prognostic parameters yet studied in HF: ESC-predictors were independent predictors of cardiovascular events, and the ESC prototype showed a convincing predictive capacity, whereas none of 11 S-predictors enhanced the prognostic performance, except peak SBP.

MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles.

PubMed

Sharma, Ronesh; Bayarjargal, Maitsetseg; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok

2018-01-21

Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task. In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores are combined to predict final MoRF propensity score for a given residue. The first score reflects the characteristics of a query residue to be part of MoRF region based on the composition and similarity of assumed MoRF and flank regions. The second score reflects the characteristics of a query residue to be part of MoRF region based on the properties of flanks associated around the given residue in the query protein sequence. The propensity scores are processed and common averaging is applied to generate the final prediction score of MoRFpred-plus. Performance of the proposed predictor is compared with available MoRF predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training and test sets used to evaluate the mentioned predictors, the proposed predictor outperforms these predictors and generates lower false positive rate. In addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it can be used as input to other computational tools. https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download. Copyright © 2017 Elsevier Ltd. All rights reserved.

Body mass index and buttock circumference are independent predictors of disintegration failure in extracorporeal shock wave lithotripsy for ureteral calculi.

PubMed

Yang, Teng-Kai; Yang, Hung-Ju; Lee, Liang-Min; Liao, Chun-Hou

2013-07-01

Effective stone disintegration by extracorporeal shockwave lithotripsy (ESWL) may depend on patient- and stone-related factors. We investigated predictors of disintegration failure in ESWL for a solitary ureteral calculus. From July 2008 to May 2010, 203 patients who underwent ESWL for a solitary ureteral calculus were enrolled. Clinical and radiologic data were collected, and factors related to ESWL failure were analyzed. Fifty-two patients (25.6%) showed ESWL failure, with a mean follow-up of 41 days. Forty patients (19.7%) required retreatment, including 12 who underwent repeat ESWL and 28 who underwent curative ureteroscopy. Patients with ESWL failure had significantly higher body weight, body mass index (BMI), and buttock circumference (BC) than patients for whom ESWL was successful. Univariate analysis showed that stone burden (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.03-1.06) and BC (OR, 1.06; 95% CI, 1.01-1.11) were predictors of ESWL failure, while BMI was a potential predictor with borderline significance (OR, 1.09; 95% CI, 0.99-1.20). Multivariate analysis showed that stone burden (OR, 1.04; 95% CI, 1.03-1.06) was a significant predictor for all patients. On stratifying patients according to the level of ureteral calculi, BC was found to be an independent predictor (OR, 1.35; 95% CI, 1.02-1.80) for ESWL failure for middle/lower ureteral calculi and BMI (OR, 1.47; 95% CI, 1.13-1.91) for upper ureteral calculi. Stone burden is the main predictor of ESWL failure for all patients with ureteral calculi. BC and BMI are independent predictors for ESWL failure for middle/lower and upper ureteral calculi, respectively. Copyright © 2012. Published by Elsevier B.V.

A functional role of Rv1738 in Mycobacterium tuberculosis persistence suggested by racemic protein crystallography.

PubMed

Bunker, Richard D; Mandal, Kalyaneswar; Bashiri, Ghader; Chaston, Jessica J; Pentelute, Bradley L; Lott, J Shaun; Kent, Stephen B H; Baker, Edward N

2015-04-07

Protein 3D structure can be a powerful predictor of function, but it often faces a critical roadblock at the crystallization step. Rv1738, a protein from Mycobacterium tuberculosis that is strongly implicated in the onset of nonreplicating persistence, and thereby latent tuberculosis, resisted extensive attempts at crystallization. Chemical synthesis of the L- and D-enantiomeric forms of Rv1738 enabled facile crystallization of the D/L-racemic mixture. The structure was solved by an ab initio approach that took advantage of the quantized phases characteristic of diffraction by centrosymmetric crystals. The structure, containing L- and D-dimers in a centrosymmetric space group, revealed unexpected homology with bacterial hibernation-promoting factors that bind to ribosomes and suppress translation. This suggests that the functional role of Rv1738 is to contribute to the shutdown of ribosomal protein synthesis during the onset of nonreplicating persistence of M. tuberculosis.

A sampling strategy for promoting and assessing medical student retention of physical examination skills.

PubMed

Williams, Reed G; Klamen, Debra L; Mayer, David; Valaski, Maureen; Roberts, Nicole K

2007-10-01

Skill acquisition and maintenance requires spaced deliberate practice. Assessing medical students' physical examination performance ability is resource intensive. The authors assessed the nature and size of physical examination performance samples necessary to accurately estimate total physical examination skill. Physical examination assessment data were analyzed from second year students at the University of Illinois College of Medicine at Chicago in 2002, 2003, and 2004 (N = 548). Scores on subgroups of physical exam maneuvers were compared with scores on the total physical exam, to identify sound predictors of total test performance. Five exam subcomponents were sufficiently correlated to overall test performance and provided adequate sensitivity and specificity to serve as a means to prompt continued student review and rehearsal of physical examination technical skills. Selection and administration of samples of the total physical exam provide a resource-saving approach for promoting and estimating overall physical examination skills retention.

Image statistics underlying natural texture selectivity of neurons in macaque V4

PubMed Central

Okazawa, Gouki; Tajima, Satohiro; Komatsu, Hidehiko

2015-01-01

Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. This yielded parameterized models for individual texture-selective neurons. The models provided parsimonious but powerful predictors for each neuron’s preferred textures using a sparse combination of image statistics. As a whole population, the neuronal tuning was distributed in a way suitable for categorizing textures and quantitatively predicts human ability to discriminate textures. Together, we suggest that the collective representation of visual image statistics in V4 plays a key role in organizing the natural texture perception. PMID:25535362

Comparison of Hyperthermal Ground Laboratory Atomic Oxygen Erosion Yields With Those in Low Earth Orbit

NASA Technical Reports Server (NTRS)

Banks, Bruce A.; Dill, Grace C.; Loftus, Ryan J.; deGroh, Kim K.; Miller, Sharon K.

2013-01-01

The atomic oxygen erosion yields of 26 materials (all polymers except for pyrolytic graphite) were measured in two directed hyperthermal radio frequency (RF) plasma ashers operating at 30 or 35 kHz with air. The hyperthermal asher results were compared with thermal energy asher results and low Earth orbital (LEO) results from the Materials International Space Station Experiment 2 and 7 (MISSE 2 and 7) flight experiments. The hyperthermal testing was conducted to a significant portion of the atomic oxygen fluence similar polymers were exposed to during the MISSE 2 and 7 missions. Comparison of the hyperthermal asher prediction of LEO erosion yields with thermal energy asher erosion yields indicates that except for the fluorocarbon polymers of PTFE and FEP, the hyperthermal energy ashers are a much more reliable predictor of LEO erosion yield than thermal energy asher testing, by a factor of four.

Numerical study of the SSME nozzle flow fields during transient operations: A comparison of the animated results with test

NASA Technical Reports Server (NTRS)

Wang, Ten-See; Dumas, Catherine

1993-01-01

A computational fluid dynamics (CFD) model has been applied to study the transient flow phenomena of the nozzle and exhaust plume of the Space Shuttle Main Engine (SSME), fired at sea level. The CFD model is a time accurate, pressure based, reactive flow solver. A six-species hydrogen/oxygen equilibrium chemistry is used to describe the chemical-thermodynamics. An adaptive upwinding scheme is employed for the spatial discretization, and a predictor, multiple corrector method is used for the temporal solution. Both engine start-up and shut-down processes were simulated. The elapse time is approximately five seconds for both cases. The computed results were animated and compared with the test. The images for the animation were created with PLOT3D and FAST and then animated with ABEKAS. The hysteresis effects, and the issues of free-shock separation, restricted-shock separation and the end-effects were addressed.

A probabilistic framework to infer brain functional connectivity from anatomical connections.

PubMed

Deligianni, Fani; Varoquaux, Gael; Thirion, Bertrand; Robinson, Emma; Sharp, David J; Edwards, A David; Rueckert, Daniel

2011-01-01

We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.

Average is Boring: How Similarity Kills a Meme's Success

PubMed Central

Coscia, Michele

2014-01-01

Every day we are exposed to different ideas, or memes, competing with each other for our attention. Previous research explained popularity and persistence heterogeneity of memes by assuming them in competition for limited attention resources, distributed in a heterogeneous social network. Little has been said about what characteristics make a specific meme more likely to be successful. We propose a similarity-based explanation: memes with higher similarity to other memes have a significant disadvantage in their potential popularity. We employ a meme similarity measure based on semantic text analysis and computer vision to prove that a meme is more likely to be successful and to thrive if its characteristics make it unique. Our results show that indeed successful memes are located in the periphery of the meme similarity space and that our similarity measure is a promising predictor of a meme success. PMID:25257730

The Business Case for Automated Software Engineering

NASA Technical Reports Server (NTRS)

Menzies, Tim; Elrawas, Oussama; Hihn, Jairus M.; Feather, Martin S.; Madachy, Ray; Boehm, Barry

2007-01-01

Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the 'local tuning' problem. Normally. predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable. This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR 1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR 1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems. STARI found one project where ASE were essential for minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.

Microbial specialists in below-grade foundation walls in Scandinavia.

PubMed

Nunez, M; Hammer, H

2014-10-01

Below-grade foundation walls are often exposed to excessive moisture by water infiltration, condensation, leakage, or lack of ventilation. Microbial growth in these structures depends largely on environmental factors, elapsed time, and the type of building materials and construction setup. The ecological preferences of Actinomycetes (Actinobacteria) and the molds Ascotricha chartarum, Myxotrichum chartarum (Ascomycota), Geomyces pannorum, and Monocillium sp. (Hyphomycetes) have been addressed based on analyses of 1764 samples collected in below-grade spaces during the period of 2001-2012. Our results show a significant correlation between these taxa and moist foundation walls as ecological niches. Substrate preference was the strongest predictor of taxa distribution within the wall, but the taxa's physiological needs, together with gradients of abiotic factors within the wall structure, also played a role. Our study describes for the first time how the wall environment affects microbial growth. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Therapeutic Effects of Caloric Stimulation and Optokinetic Stimulation on Hemispatial Neglect

PubMed Central

Moon, SY; Lee, BH

2006-01-01

Hemispatial neglect refers to a cognitive disorder in which patients with unilateral brain injury cannot recognize or respond to stimuli located in the contralesional hemispace. Hemispatial neglect in stroke patients is an important predictor for poor functional outcome. Therefore, there is a need for effective treatment for this condition. A number of interventions for hemispatial neglect have been proposed, although an approach resulting in persistent improvement is not available. Of these interventions, our review is focused on caloric stimulation and optokinetic stimulation. These lateralized or direction-specific stimulations of peripheral sensory systems can temporarily improve hemispatial neglect. According to recent functional MRI and PET studies, this improvement might result from the partial (re)activation of a distributed, multisensory vestibular network in the lesioned hemisphere, which is a part of a system that codes ego-centered space. However, much remain unknown regarding exact signal timing and directional selectivity of the network. PMID:20396481

Odontological approach to sexual dimorphism in southeastern France.

PubMed

Lladeres, Emilie; Saliba-Serre, Bérengère; Sastre, Julien; Foti, Bruno; Tardivo, Delphine; Adalian, Pascal

2013-01-01

The aim of this study was to establish a prediction formula to allow for the determination of sex among the southeastern French population using dental measurements. The sample consisted of 105 individuals (57 males and 48 females, aged between 18 and 25 years). Dental measurements were calculated using Euclidean distances, in three-dimensional space, from point coordinates obtained by a Microscribe. A multiple logistic regression analysis was performed to establish the prediction formula. Among 12 selected dental distances, a stepwise logistic regression analysis highlighted the two most significant discriminate predictors of sex: one located at the mandible and the other at the maxilla. A cutpoint was proposed to prediction of true sex. The prediction formula was then tested on a validation sample (20 males and 34 females, aged between 18 and 62 years and with a history of orthodontics or restorative care) to evaluate the accuracy of the method. © 2012 American Academy of Forensic Sciences.

Shaping up: a geometric morphometric approach to assemblage ecomorphology.

PubMed

Bower, L M; Piller, K R

2015-09-01

This study adopts an ecomorphological approach to test the utility of body shape as a predictor of niche relationships among a stream fish assemblage of the Tickfaw River (Lake Pontchartrain Basin) in southeastern Louisiana, U.S.A. To examine the potential influence of evolutionary constraints, analyses were performed with and without the influence of phylogeny. Fish assemblages were sampled throughout the year, and ecological data (habitat and tropic guild) and body shape (geometric morphometric) data were collected for each fish specimen. Multivariate analyses were performed to examine relationships and differences between body shape and ecological data. Results indicate that a relationship exists between body shape and trophic guild as well as flow regime, but no significant correlation between body shape and substratum was found. Body shape was a reliable indicator of position within assemblage niche space. © 2015 The Fisheries Society of the British Isles.

The impact of conventional surface data upon VAS regression retrievals in the lower troposphere

NASA Technical Reports Server (NTRS)

Lee, T. H.; Chesters, D.; Mostek, A.

1983-01-01

Surface temperature and dewpoint reports are added to the infrared radiances from the VISSR Atmospheric Sounder (VAS) in order to improve the retrieval of temperature and moisture profiles in the lower troposphere. The conventional (airways) surface data are combined with the twelve VAS channels as additional predictors in a ridge regression retrieval scheme, with the aim of using all available data to make high resolution space-time interpolations of the radiosonde network. For one day of VAS observations, retrievals using only VAS radiances are compared with retrievals using VAS radiances plus surface data. Temperature retrieval accuracy evaluated at coincident radiosonde sites shows a significant impact within the boundary layer. Dewpoint retrieval accuracy shows a broader improvement within the lowest tropospheric layers. The most dramatic impact of surface data is observed in the improved relative spatial and temporal continuity of low-level fields retrieved over the Midwestern United States.

English Proficiency and Peer Interethnic Relations as Predictors of Math Achievement among Latino and Asian Immigrant Students

PubMed Central

Barrett, Alice N.; Barile, John P.; Malm, Esther K.; Weaver, Scott R.

2013-01-01

Studies show math achievement to be the best predictor of entering post-secondary education. However, less is known about the predictors of math achievement, particularly among immigrant youth. This study examined English proficiency and peer interethnic relations as predictors of mathematics achievement among Latino and Asian high school students, postulating an interaction between the predictors and mediation by academic motivation. A multilevel moderated-mediation model was used to analyze data from a national sample of 2,113 non-native English speaking Latino and Asian students attending high school in the U.S. We found that higher academic motivation mediated the relationship between English proficiency during their sophomore year and gains in senior math achievement scores for both Asian and Latino students. For Latino students however, this indirect path was only significant for students whose perceptions of positive peer interethnic relations at school were average or above average. PMID:22959129